Content:
Presentation type:
NP – Nonlinear Processes in Geosciences

EGU23-2654 | Orals | MAL20 | Lewis Fry Richardson Medal Lecture

The Role of Theory and  Data in Model Building:  from Richardson to   machine learning 

Angelo Vulpiani

The talk is devoted to a discussion of different  typologies of models:

 I-  Oversimplified models;

 II- Models by analogy;

  III- Large scale models;

IV- Models from data. 

In the class  I  there is the celebrated   Lorenz model; the  Lotka-Volterra  system  is in the class  II, and it is at the origin of biomathematics.

Among the  models in the class III  we have the effective equations used, e.g., in meteorology and engineering,  where only "relevant variables" are taken into account.

In the class  IV we find the most interesting (and difficult) problem:the building  of models just from datawithout a reference theoretical framework.

How to cite: Vulpiani, A.: The Role of Theory and  Data in Model Building:  from Richardson to   machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2654, https://doi.org/10.5194/egusphere-egu23-2654, 2023.

NP0 – Inter- and Transdisciplinary Sessions

EGU23-107 | ECS | Orals | ITS1.10/NP0.1

Spatio-temporal variations in environmental DNA within heavily urbanized streams in Berlin, Germany 

Maria Warter, Michael T. Monaghan, Ann-Marie Ring, Jan Christopher, Hanna L. Kissener, Elisabeth Funke, Chris Soulsby, and Dörthe Tetzlaff

Understanding urban ecohydrological interactions is crucial for the assessment of ecosystem responses to climate change and anthropogenic influences, especially in heavily urbanized environments. Urban water bodies can enhance local biodiversity, with urban blue water infrastructure providing valuable ecosystem services that contribute to healthier and more sustainable environments. Because the urban water cycle is less resilient to extreme climate events, there is a need to better understand how biological flow paths interact with climate and hydrological dynamics. To that end, synoptic sampling of environmental DNA (eDNA) was carried out on four major rivers in Berlin, Germany (Spree, Erpe, Wuhle, Panke) on a weekly basis over the course of one year. In conjunction with climate and hydrological data, the spatial and temporal variations in planktonic microbial communities were assessed in order to identify the differences in ecohydrological interactions among urban streams. Preliminary results indicate that while the rivers Wuhle and Erpe harbour similar bacterial communities, the more urbanized rivers Panke and Spree each had a different taxonomic composition. All rivers show a clear seasonal signal, although with varying intensity and directions of change. To further disaggregate the seasonal ecological changes, we determined the relative influence of climate as well as water chemistry, land use and stream flow conditions on bacterial community composition. In future, the integration of eDNA with other ecohydrological tracers such as stable water isotopes will provide even more insights into the ecological and hydrological functioning of urban environments. Such a combination of ecohydrological tracers has wider implications not only for future urban planning but for mitigating the negative effects of climate change in urban environments and assessing the resilience of urban water bodies to future extreme events.

How to cite: Warter, M., Monaghan, M. T., Ring, A.-M., Christopher, J., Kissener, H. L., Funke, E., Soulsby, C., and Tetzlaff, D.: Spatio-temporal variations in environmental DNA within heavily urbanized streams in Berlin, Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-107, https://doi.org/10.5194/egusphere-egu23-107, 2023.

Building-integrated photovoltaic technology (BIPV) has been proven as an effective way to increase renewable energy in the urban environment. Without occupying any land resources, this technology has great potentials for achieving low carbon in the economically developed cities. Due to the lack of modelling tools, the impact of BIPV window in the street canyon is not well understood. To fill the gap, we developed a new parameterization scheme for BIPV window, and incorporated it into building energy simulations coupled with a single-layer urban canyon model. Model evaluation suggests that the coupled model is able to reasonably capture the diurnal profiles of BIPV window temperature and power generation, building cooling load, and outdoor microclimate. Canyon aspect ratio, window coverage, façade orientation, and power generation efficiency are found to be the most critical factors in maximizing the power generation of BIPV windows. Simulation results of an office floor in three Chinese cities under different climate backgrounds show that Beijing has the greatest solar potential in south orientation for power generation, which is 1.5 times the power generation in Shenzhen and Nanjing. Compared to clear window, BIPV window has positive benefits when window coverage is greater than 60% in open canyon. With lighting energy saving and power generation, BIPV window consistently has positive benefits than wall materials. The benefit of BIPV windows is larger in Beijing, followed by Shenzhen and Nanjing. Under future climate forcing of year 2050, the net electricity benefit of BIPV window will be larger than 15%. Findings in this study provide guidance for BIPV application in the built environment, and cast light on the construction of sustainable and low-carbon neighborhoods.

How to cite: yang, J., Chen, L., and Zheng, X.: Quantifying the BIPV window benefit in urban environment under climate change: a comparison of three Chinese cities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-970, https://doi.org/10.5194/egusphere-egu23-970, 2023.

Knowledge creation is essential to encourage regional development especially for areas in regional transition. The positive impact of university-enterprise cooperation on the process has been extensively discussed in previous studies, but a comprehensive picture of the mechanism has not been fully described, such as how academic researches promote practical projects. Modeling this complex nature has been dealt and required by both academia and planner. Toward this target, we propose an empirically founded agent-based model to demonstrate the collaborative network. It uses the literature and patents created in each project as a basis for measuring regional knowledge production and innovation. Based on the analysis of the observed data, a conceptual model of network-based university-enterprise cooperation was constructed with the help of NetLogo. It will simulate the whole process from academic research to industrial practice to explore the driving mechanism of universities for regional knowledge production and innovation. The collaborative network of practice projects is constructed from the partnership data of every project recorded within the REVIERa platform, where each node in the network was classified into fields of research. Knowledge was quantified by metrics such as the quantity and quality of literature. Based on the various characteristics of nodes, networks and their path dependencies, the birth of innovative projects will be simulated and the impact of interdisciplinary on regional transformation will be quantified in the model. The model is based on the real world data and corroborated with it to capture the mechanism and characteristics of this complex process, showing its value to boost the scientific regional planning in the future.

How to cite: Feng, W. and Li, B.: A Network-Based Model for Simulating Regional Transformation Driven by University-Enterprise Collaboration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1091, https://doi.org/10.5194/egusphere-egu23-1091, 2023.

EGU23-1770 | ECS | Posters on site | ITS1.10/NP0.1

Influential factors of intercity patient mobility and its network structure in China 

Jiaqi Ding, Yang Chao, Yueyao Wang, Pengfei Li, Fulin Wang, Yuhao Kang, Haoyang Wang, Ze Liang, Jiawei Zhang, Peien Han, Zheng Wang, Erxuan Chu, Shuangcheng Li, and Luxia Zhang

Intercity patient mobility reflects the geographic mismatch between healthcare resources and the population, and has rarely been studied with big data at large spatial scales. In this study, we investigated the patterns of intercity patient mobility and factors influencing this behavior based on over 4 million hospitalization records of patients with chronic kidney disease in China. To provide practical policy recommendations, a role identification framework informed by complex network theory was proposed considering the strength and distribution of connections of cities in mobility networks. Such a mobility network features multiscale community structure with “universal administrative constraints and a few boundary breaches”. We discovered that cross-module visits which accounted for only 20 % of total visits, accounted for >50 % of the total travel distance. The explainable machine learning modeling results revealed that distance has a power-law-like effect on flow volume, and high-quality healthcare resources are an important driving factor. This study provides not only a methodological reference for patient mobility studies but also valuable insights into public health policies.

How to cite: Ding, J., Chao, Y., Wang, Y., Li, P., Wang, F., Kang, Y., Wang, H., Liang, Z., Zhang, J., Han, P., Wang, Z., Chu, E., Li, S., and Zhang, L.: Influential factors of intercity patient mobility and its network structure in China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1770, https://doi.org/10.5194/egusphere-egu23-1770, 2023.

EGU23-1780 | Orals | ITS1.10/NP0.1 | Highlight

Persistent urban heat 

Dan Li, Linying Wang, and Ting Sun

While there have been many studies on the diurnal and seasonal variations of urban heat islands (UHIs), the day-to-day variability of UHI remains largely unknown, despite being the key to explaining interactions between UHIs and extreme heat events or heatwaves. In this study, we aim to understand and quantify the persistence of urban/rural temperatures. Autocorrelation and spectral analyses are conducted on urban and rural temperatures simulated by a global land model to quantify the urban-rural difference of temperature persistence. A surface energy balance model is then derived to explain the simulation results, elucidating the key biophysical processes that contribute to the stronger persistence of urban surface temperature in certain areas.

How to cite: Li, D., Wang, L., and Sun, T.: Persistent urban heat, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1780, https://doi.org/10.5194/egusphere-egu23-1780, 2023.

EGU23-1969 | ECS | Orals | ITS1.10/NP0.1

Estimating installed cooling capacities on city scale 

Florian Barth, Simon Schüppler, Kathrin Menberg, and Philipp Blum

Heating and cooling of buildings is one of the largest final energy uses and largest sources of greenhouse gas emissions. To reduce the impact of heating and cooling on our climate, more efficient strategies are needed. Coupling and centralizing the production of heat and cold in combination with underground seasonal thermal storage (UTES) can significantly reduce CO2 emissions and costs. To plan and implement such strategies for heating and cooling, information on sources and sinks of heat and cold is essential for local authorities. However, spatial information on the cooling sector is rare and difficult to obtain. Often, the theoretical cooling demand of specific buildings and building types is modeled, but not met by air-conditioning equipment in reality. On the other hand, large-scale cooling demand models, which focus on entire countries, may use data from different countries as proxy or are not applicable below kilometer-scale.

In this study, we present a method to identify air-conditioning equipment on the rooftops of buildings and quantify their cooling capacity. Thus, air-cooled and hybrid evaporative condensers, cooling towers and packaged rooftop units are detected on aerial images. Using manufacturer data, regression analyses are created to estimate the cooling capacity based on the size of the units and the number of condenser fans. The unit locations and all required parameters are obtained by convolutional neural network-based pixel classification models, which are easily executable within a geographical information system (GIS) framework. The approach is successfully evaluated by testing the capability of the detection models and comparing our estimated cooling capacities to the actual installed cooling capacities of air-conditioners for different locations. The detection performance strongly depends on the resolution of the used aerial images. At a resolution of 8 cm/pixel, the model detects 93% of the units and the pixel classification overestimates the relevant parameters for the regression by 0.7%. Using the regression analyses, the overall capacity in the evaluated areas is overestimated by 7-21%. To demonstrate the capability of our approach, we map the cooling capacity of air-conditioners in parts of Manhattan. In the Manhattan financial district alone, a cooling capacity of over 2 GW is estimated, which is equivalent to 1.3% of the summer peak load demand of the energy grid of the entire state of New York.

The presented approach is a fast and easy to conduct method that requires little input data. It can detect individual air conditioners over large areas. The obtained information can support the creation of cooling cadastres and can serve as supplement or validation for other cooling demand models, such as building stock models, or example to include additional building types, such as industrial buildings.

How to cite: Barth, F., Schüppler, S., Menberg, K., and Blum, P.: Estimating installed cooling capacities on city scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1969, https://doi.org/10.5194/egusphere-egu23-1969, 2023.

Lead (Pb) exposure to residents of impacted communities depends on the environmental concentrations of lead that is potentially bioaccessible. Such concentrations are the result of a complex array of interactive factors that influence one another through direct or indirect linkages. Current models to predict the health impact of Pb exposure often do not consider the complexity from a system perspective. Therefore, there exists a great need to develop a holistic modeling strategy to simulate the risk to Pb exposure and resulting blood Pb concentrations based on bioaccessible Pb concentrations in the environment and how socioeconomic status, policy/ scholarly intervention, and collective community behaviors influence that concentration. Our study attempts to develop a grey system integrated system dynamics simulative modeling framework to simulate general Pb bioaccessibility in the environments and how it transmits from the soil, the water, the house, and the general environment to human bodies. The study aims to predicting risk of Pb exposure in the long run in a community/neighborhood, especially the risk to vulnerable populations, such as young children and the aged population. The model also aims to identify the most effective ways to curb human exposure to bioaccessible Pb. This is the first stage of a multi-stage research activity. In this stage, the study focuses on developing a theoretical and empirical modeling framework of the simulative model, and the data structure. In this study, we take a macro perspective to treat a neighborhood/community, a city, or a designated area as an integrated and dynamic system in that it is composed of many interrelated, feedback-linked components. Each component exists and acts because of its interaction with other system components, both observable and hidden. The integrated mutual interaction and multiple components collectively determine how the system will change and evolve in the future, manifested as the change and evolution of the various system components. Bioaccessible Pb in the neighborhood’s environment is our key system component. The grey system and system dynamic simulative model attempts to analyze the potential interactive co-variations among different system components, or the changing and/or evolving trend a system component demonstrates over time. By establishing the interactive feedback loops that connect all the observable system components with sufficient data, the model will be able to simulate the dynamics of the system to predict its behavior and manifestation in the future. Since the simulative model is built upon the interactive feedback loops among all system components, the model will produce simulated results for all observable system components as well. Our goals in later stages are to predict the potential damage to human bodies that will be the basis for Pb reduction and removal related policies at the macro management levels.

How to cite: Yu, D.: A system dynamics simulative modeling framework to assess bioaccessibility of lead and facilitate lead reduction in the urban environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2461, https://doi.org/10.5194/egusphere-egu23-2461, 2023.

EGU23-2970 | Posters on site | ITS1.10/NP0.1

SuPy and SUEWS urban land surface modelling: new developments and capabilities 

Ting Sun, Sue Grimmond, Oskar Backlin, Lewis Bluun, Robin Hogan, Meg Stretton, and Xiaoxiong Xie

Accurate and agile modelling of weather, climate, hydrology and air quality in cities is essential for delivering integrated urban services. SUEWS (Surface Urban Energy and Water balance Scheme) allows simulation of urban–atmospheric interactions by quantifying the energy, water and carbon fluxes.  SuPy (SUEWS in Python) provides the SUEWS computation kernel, a Python-based data stack that streamlines pre-processing, computation and post-processing to facilitate common urban climate modelling. This paper documents the recent developments in both SuPy and SUEWS, and the background principles of their interface, F2PY (Fortran to Python) configuration and Python front-end implementation. SuPy is deployed via PyPI (Python Package Index) allowing an automated workflow for cross-platform compilation on all mainstream operating systems (Windows, Linux and macOS). The online tutorials, using Jupyter Notebooks, allow users to become familiar with SuPy. A brief overview of other complementary SUEWS developments will be given, and include within canopy layer profiles of temperature, humidity, wind, and radiation that are supporting a wide range of applications; and database developments for obtaining model parameters.

How to cite: Sun, T., Grimmond, S., Backlin, O., Bluun, L., Hogan, R., Stretton, M., and Xie, X.: SuPy and SUEWS urban land surface modelling: new developments and capabilities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2970, https://doi.org/10.5194/egusphere-egu23-2970, 2023.

EGU23-3148 | ECS | Orals | ITS1.10/NP0.1

Blue Green Infrastructure in a future climate: can we reduce combined sewer overflows? 

Giovan Battista Cavadini and Lauren Cook

In response to climate change and growing ecological threats, many cities are planning to increase the resilience of urban drainage systems, including the reduction of combined sewer overflows (CSOs) - one of the leading causes of surface water pollution. Blue green infrastructure (BGI) are growing in popularity to do so, and recent studies have made progress to evaluate the potential of BGI to eliminate CSOs. However, current research tends to consider a limited number of individual BGI elements and scenarios, often overlooking different combinations (e.g., bioretention basins combined with green roofs) and uncertainty in a future climate. The aim of this research is to evaluate the ability of a range of blue green infrastructure combinations to reduce CSOs under multiple future climate scenarios.
A hydrological simulation model, EPA SWMM, is used to simulate the performance of a 95-hectare combined sewer system near Zurich, Switzerland. Four types of BGI are evaluated, including bioretention basins, porous pavements, green roofs, and stormwater ponds. The potential surface availability for each BGI element was quantified using GIS and LiDAR data, yet scenarios include a range of different implantation rates for each type. Combinations of BGI element types are generated by combining different implementation surfaces to the share of the BGI type (e.g., 20% of the available surface with the same share of bioretention basins, porous pavements and green roofs, etc.).
Bioretention basins are assumed to be implemented on pervious surfaces (i.e., gardens, traffic islands), porous pavements on impervious surfaces (i.e., sidewalks, cycling lanes) and green roofs on flat roof buildings. Observed rainfall data (1990-2019) are used to simulated the baseline conditions, while more than five bias-corrected future rainfall timeseries (2070-2099) from EURO-CORDEX regional climate models (RCP 8.5) are used to represent a worst-case future climate. CSO Volume, duration and frequency are used to characterize system-wide CSO events across the seven outfalls.
Preliminary results show that in a current climate, bioretention basins are most effective at reducing CSO volume, followed by porous pavements and green roofs. BGI do not relevantly reduce the duration and number of CSO events. In one future scenario, future precipitation is concentrated into shorter duration events, which consistently leads to shorter, higher intensity CSO events at a frequency similar to the historical record. Overall, the only scenario that can avoid an increase in future CSO volume is an extensive implementation of bioretention basins. Porous pavement and green roofs are less effective in a future climate because they can store limited amounts of water compared to bioretention basins. As rainfall intensities increase, the ability to retain large amounts of water will be the most effective. These results point to strategies with higher storage capacities to account for high-intensity rainfall events that are expected in the future. Future work will evaluate additional BGI elements, including urban ponds, and a more comprehensive set of BGI scenarios, future climate scenarios, and case studies, enabling a definition of guidelines and BGI design requirements at an urban scale for Switzerland.

How to cite: Cavadini, G. B. and Cook, L.: Blue Green Infrastructure in a future climate: can we reduce combined sewer overflows?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3148, https://doi.org/10.5194/egusphere-egu23-3148, 2023.

Urbanization has been shown to significantly increase the frequency and intensity of extreme weather events, i.e., extreme precipitation events, and heatwave events. Actually, the occurrence of compound extreme events, such as sequential flood-heatwave (SFH) events, can lead to more severe impacts than singular extreme events. However, the impact of urbanization on these compound extreme events is not well understood. In this study, we examine urbanization effects on the growth of SFH events in the Guangdong-Hong Kong-Macao Greater Bay Area from 1961 to 2017. We classify stations into urban and rural types based on dynamic land use data and define SFH events using the criteria from previous studies. We find that the frequency of SFH events in urban stations increased from 0.218 events per year before the 1990s to 1.401 events per year after the 1990s, while the frequency of SFH events in rural stations increased from 0.250 events per year to 0.920 events per year. The urban impact of 0.131 events per decade also shows that urbanization can promote the occurrence of SFH events. Our analysis also indicates that urbanization promotes the growth of SFH events mainly by increasing the frequency of heatwave (HW) events. These findings highlight the need for further research on the effects of urbanization on compound extreme events and the development of effective management strategies to reduce their risks.

How to cite: Liao, W. and Zheng, K.: Urbanization impacts on sequential flood-heatwave events in the Guangdong-Hong Kong-Macao Greater Bay Area , China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3807, https://doi.org/10.5194/egusphere-egu23-3807, 2023.

EGU23-3877 | ECS | Posters virtual | ITS1.10/NP0.1

Urban canopy parameterization of the non-local building effects with variable building height 

Jiachen Lu, Negin Nazarian, Melissa Hart, Scott Krayenhoff, and Alberto Martilli

Variability of building height induces flow heterogeneity and directly controls the depth of the roughness sub-layer, the strength of mutual sheltering, and the overlapping of urban canopy flow, which poses challenges for accurate modeling. Large-eddy simulations over 96 building arrays with varying density, height variability (standard deviation of building height), and horizontal arrangements were conducted to reveal the impact on the urban flow. Results demonstrate a strong non-local building effect on the flow due to height variability, where flow around high buildings possesses high wind speed, dispersive momentum flux, and other distinctive flow patterns, whereas around low buildings, the flow pattern is less unique. The complex flow behavior is beyond the capacity of the current multi-layer urban canopy model (MLUCM) where turbulent constants and drag effects were considered in a simplified way. The increased height variability and urban density also blur the interface of urban canopy, further making MLUCM estimates model constants heavily based on a clear urban canopy inappropriate. Based on the original model, we comprehensively tested potential contributing factors such as the estimation of displacement height, height-dependent drag coefficients, and the extended roughness sublayer. The modified model provides a better overall agreement with the LES results, especially above the mean building height where the prediction of the extended urban canopy layer is largely improved. 

How to cite: Lu, J., Nazarian, N., Hart, M., Krayenhoff, S., and Martilli, A.: Urban canopy parameterization of the non-local building effects with variable building height, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3877, https://doi.org/10.5194/egusphere-egu23-3877, 2023.

Buildings are common components in the urban environment whose 3D information is fundamental for urban hydrometeorological modeling and planning applications. In order to monitor building footprint and height across large areas on a regular basis, recent earth observation research has witnessed promising progress in mapping such information from publicly available satellite imagery by statistical methods using regression between multi-source remotely sensed data and target variables. However, most of them often involve tedious feature preprocessing, which constrains their capability to establish a comprehensive representation of an ever-changing and multi-scale urban system efficiently.

Considering this bottleneck, this work develops a deep-learning-based (DL) Python package-SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel Imagery) to estimate 3D building information at various scales. SHAFTS provides Convolutional Neural Networks (CNN) with the Multi-Branch Multi-Head (MBMH) structure to automatically learn representative features shared by building height and footprint mapping tasks from multi-modal Sentinel imagery and additional background DEM information. Besides, to leverage the power of big data infrastructures, SHAFTS offers essential functionality including automatically collecting potential reference datasets by web scraping and filtering appropriate input imagery from Google Earth Engine, which can effectively ease model upgrading and deployment for large-scale mapping.

To evaluate the patch-level prediction skills and city-level spatial transferability of developed models, this work performs diagnostic performance comparisons in 46 cities worldwide by using conventional machine-learning-based (ML) models and CNN with the Multi-Branch Single-Head (MBSH) structure as benchmarks. Patch-level results show that DL models successfully produce more discriminative feature representation and improve the coefficient of determination of building height and footprint prediction over ML models by 0.27-0.63, 0.11-0.49, respectively. Moreover, stratified error assessment reveals that DL models effectively mitigate severe systematic underestimation of ML models in the high-value domain. Additionally, within the DL family, comparison in spatial transferability demonstrates that the MBMH structure improves the accuracy of CNN and reduces the uncertainty of building height predictions in the high-value domain at the refined scale. Therefore, multi-task learning can be considered as a possible solution for improving the generalization ability of models for 3D building information mapping.

How to cite: Li, R., Sun, T., Tian, F., and Ni, G.: SHAFTS – A deep-learning-based Python package for Simultaneous extraction of building Height And Footprint from Sentinel Imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4192, https://doi.org/10.5194/egusphere-egu23-4192, 2023.

EGU23-4807 | Posters on site | ITS1.10/NP0.1

Effects of roof greening and surface heating on street-canyon flows 

Ju-Hwan Rho, Da-Som Mun, Jang-Woon Wang, and Jae-Jin Kim

Extreme high-temperature phenomena in urban areas are recognized as natural disasters. Asphalt roads and high-rise buildings are concentrated in megacities, and buildings and roads may contribute to extremely high air temperatures, especially in the summer season. For the improvement of the thermal environment in urban areas, green spaces are being created on building roofs. In this study, we investigated the effects of roof greening on street-canyon flows in the presence of roof and ground heating using a computational fluid dynamics (CFD) model. For validation, we compared the simulated street-canyon flows to the measured ones in a wind tunnel experiment. The CFD model used in this study reproduced the wind speeds, turbulent kinetic energies, and air temperatures in a street canyon in the presence of building roof and ground heating.

How to cite: Rho, J.-H., Mun, D.-S., Wang, J.-W., and Kim, J.-J.: Effects of roof greening and surface heating on street-canyon flows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4807, https://doi.org/10.5194/egusphere-egu23-4807, 2023.

While road transport is a cornerstone of modern civilization bringing profound positive impacts to the economy and human well-being, it is also associated with several undesirable and unsustainable outcomes including urban air pollution, climate change, noise and congestion. Hence, sustainable road transport has been given significant attention in the past two decades. Digital twins of urban transport are promising digital assets to evaluate and improve the sustainability level of the transport systems.  Digital twins provides a testbed whereby the impacts of the current and future policies and strategies can be modelled and analysed in a digital environment, helping ensure that tax money spent delivers the expected results. However, the desperate shortage of spatiotemporal road data is the major challenge in establishing data flow between the digital and physical twins of road transport. Vehicle telematics data, typically collected from GPS-connected, can provide an excellent source of intormation with which to address the spatial and temporal aspects of transport data. This presentation will highlight how telematics data can be used within road transport digital twins.

In this study, we develop digital twins of road transport for Tyseley Environmental Enterprise District (TEED) a small area of east Birmingham, UK, using the newly-developed approach of GeoSpatial and Temporal Mapping of Urban Mobility (GeoSTMUM). GeoSTMUM uses vehicle telematics (location and time) data to estimate several road transport characteristics such as the average speed of traffic flow, travel time, etc., with high spatial and temporal resolutions of 15m and 2h, respectively. It also allows for evaluation of the average vehicle dynamic status as the speed-time-acceleration profile of the roads. Vehicle telematics data for this study were collected for the years 2016 and 2018 through the WM-AIR projct (www.wm-air.org.uk). We then use real-world fleet composition and exhaustive emission measurements to translate the vehicle dynamics status into the real-urban fuel consumption and CO2 and NOx emission factors. Results highlight the importance on fleet renovation, in terms of vehicle propulsion systems (EURO class, fuel type, etc.) upon real-urban emissions and fuel consumption. The presentation will end with example future use cases of telematics data within digital twins.            

How to cite: Ghaffarpasand, O. and Pope, F.: Using vehicle telematics data within a digital twin of urban transport systems; a case study in the West Midlands, UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5315, https://doi.org/10.5194/egusphere-egu23-5315, 2023.

EGU23-6583 | Orals | ITS1.10/NP0.1 | Highlight

A spatial regression model to measure the urban population exposure to extreme heat 

Emanuele Massaro, Luca Caporaso, Matteo Piccardo, Rossano Schifanella, Hannes Taubenböck, Alessandro Cescatti, and Gregory Duveiller

Temperatures are rising and the frequency of heat waves is increasing due to anthropogenic climate change. At the same time, the population in urban areas is rapidly growing. As a result, an ever-larger part of humankind will be exposed to even greater heat stress from heat waves in urban areas in the future. In this research, we focus on studying the determinants of land surface temperature (LST) gradients in urban environments. We implement a spatial regression model that is able to predict with high accuracy (R2 > 0.9 in the test phase of k-fold cross-validation) the LST of urban environments across 200 cities based on land surface properties like vegetation, built-up areas, and distance to water bodies, without any additional climate information. We show that, on average, by increasing the overall urban vegetation by 3%, it would be possible to reduce by 50% the exposure of the urban population that lives in the warmest areas of the cities for the average of the three summer months, achieving a reduction of 1 K in LST. By coupling the model information with the population layer, we show that an 11% increase in urban vegetation is necessary in order to obtain a reduction of 1 K in the most populated areas, where at least 50% of the population live. We finally discuss the challenges and the limitations of greening interventions in the context of available surfaces in urban areas.

How to cite: Massaro, E., Caporaso, L., Piccardo, M., Schifanella, R., Taubenböck, H., Cescatti, A., and Duveiller, G.: A spatial regression model to measure the urban population exposure to extreme heat, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6583, https://doi.org/10.5194/egusphere-egu23-6583, 2023.

EGU23-6611 | ECS | Orals | ITS1.10/NP0.1

Flood Impact to Urban Transport Networks Considering the Flooding Propagation 

Yimeng Liu, Saini Yang, Richard Dawson, Alistair Ford, and Jiting Tang

The urban transport network is threatened by urbanisation and climate change-enhanced urban flood, leading to substantial impacts on economic activities, social well-being and the environment. By taking the flood propagation process into consideration, we developed a flood-impact-assessment method to comprehensively assessed the economic impacts of traffic disruption in terms of time delay, fuel consumption and pollutant emission. The flood is simulated with CADDIES-2D flood model and the traffic flow is simulated with a microscopic model (SUMO). We applied this method to Beijing and quantified the economic damage of various flood scenarios. Comparing the baseline traffic scenario with those of three flooded scenarios yields the impacts of floods on traffic. The study revealed three key findings: (a) a rain occurring at 7 a.m. induces four times more cost than the baseline scenario, while rain of the same intensity and duration occurring at 8 a.m. or 9 a.m. lead to a traffic cost increase for 37.33% and 13.21% respectively. (b) The central and southern parts of Beijing suffer more from flooding and should be given priority for adaptation planning. (c) There is no significant spatial correlation between flood depth and traffic cost increase on a census block level. The proposed framework has the potential to assist decision-makers in prioritizing flood mitigation investments and therefore increase the resilience of transport networks to flooding impacts.  

How to cite: Liu, Y., Yang, S., Dawson, R., Ford, A., and Tang, J.: Flood Impact to Urban Transport Networks Considering the Flooding Propagation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6611, https://doi.org/10.5194/egusphere-egu23-6611, 2023.

EGU23-9249 | ECS | Orals | ITS1.10/NP0.1 | Highlight

Europe-wide road traffic noise modelling using a harmonized methodological framework (CNOSSOS-EU) 

Youchen Shen, Kees de Hoogh, Oliver Schmitz, John Gulliver, Derek Karssenberg, Roel Vermeulen, and Gerard Hoek

Road traffic is usually the most pervasive source of noise in an urban environment. Epidemiological studies conducted at the regional or national scale have shown associations of road traffic noise with sleep disturbance, cardiovascular diseases, and mental health problems. Strategic noise mapping (European Noise Directive) only covers populations living in large urban areas. The limited coverage of harmonised noise exposure data at a pan-European scale prevents us from studying the effect of road traffic noise on health in larger populations across Europe. Therefore, this study aims to develop models capturing within-city, intra-city and national variations in road traffic noise exposures across Europe to facilitate pan-European multi-cohort health studies. To estimate noise, we used a simplified version of CNOSSOS-EU (Common NOise aSSessment MethOdS) noise modelling framework.   

The CNOSSOS-EU model requires a range of input data, including a detailed road network, traffic intensity, traffic speed, and land use data including building footprints. We used OpenStreetMap (OSM) to define the road network and buildings. Because traffic intensity is not provided in OSM, we estimated Europe-wide annual average daily traffic (AADT) counts using random forest trained by observations collected in Austria, Switzerland, Germany, France, Italy, and the United Kingdom. Three random forest models were built separately for 1) motorway and trunk roads, 2) primary roads, and 3) secondary, tertiary, residential and unclassified roads defined in OpenStreetMap (OSM). Predictor variables included road length, sizes of residential areas, and population within different circular buffer (ranging from 100m to 200km). The models were validated using 5-fold cross-validation. The 5-fold root mean square errors of AADTs were 19646, 6589, 4005, 3824 and 3210 for highway (motorway and trunk roads), primary, secondary, tertiary, and residential roads. The traffic speed was approximated by the speed limit from OSM, and the missing speed limit data was replaced by the legal country-specific speed limit separated by inside and outside built-up areas, depending on the road type. Building height was approximated by using a morphological operation on the AW3D30 digital surface model (DSM). The road traffic noise was estimated at noisiest building façades (i.e., with shortest Euclidean distance to nearby roads within 100m with the highest AADT) using CNOSSOS-EU. The modelled noise level of LAeq16 with these input data ranged from 52.17 dB to 72.54 dB for points in the test city of Bristol in the United Kingdom. In conclusion, we developed the input data required for noise modelling, especially traffic intensity, at a European scale. Modelled noise will be used in Europe-wide studies of health effects of noise. We will also compare our Europe-wide noise estimates with national noise model estimates in the Netherlands and Switzerland.

How to cite: Shen, Y., de Hoogh, K., Schmitz, O., Gulliver, J., Karssenberg, D., Vermeulen, R., and Hoek, G.: Europe-wide road traffic noise modelling using a harmonized methodological framework (CNOSSOS-EU), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9249, https://doi.org/10.5194/egusphere-egu23-9249, 2023.

It is predicted that over 60% of the urban population in sub-Saharan Africa (SSA) lives in slums, and this number is increasing in the coming years. However, issues on urban poverty and slum persistence in SSA cities are ignored and suffered from a paucity of robust evidence for a longtime. As reliable data on where are locations of urban deprived areas and slums, and on how these areas have evolved remain scarce, the scale of urban deprivation and challenges related to slums in SSA cities are underestimated.

This study explores to which extent urban morphology and accessibility of social services could explain urban poverty and slum locations, by using geospatial and socio-economic data, as well as machine learning techniques. Taking four African countries including Nigeria, Kenya, Ghana, and Malawi as examples, we mapped slum locations and demonstrate that urban building morphological variables only can explain up to over 78% of slum locations. Our results further showed a declining trend in slum growth in old towns that are compacted in space. However, slums are not representing the most deprived urban area, while outskirts of megacities, middle-sized and small cities showed the least economic well-being, demonstrated by lower GDP and wealth index value; poor road and water access services. Our proposed slum and urban poverty mapping methods and results will be accessible and instrumental for scientists, local communities, policy-makers, and city planners, which will accelerate the process of finding solutions for tackling poverty, better managing public health and infrastructure in developing countries.

How to cite: Li, C., Yu, L., and Hong, J.: Monitoring slum and urban deprived area in sub-Saharan Africa using geospatial and socio-economic data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10872, https://doi.org/10.5194/egusphere-egu23-10872, 2023.

EGU23-12606 | ECS | Posters on site | ITS1.10/NP0.1

Modelling the coevolution of London's population and railway system 

Isabella Capel-Timms, David Levinson, Sara Bonetti, and Gabriele Manoli

As cities continue to expand it has become crucial to describe their evolution in time and space. Building on analogies with biological systems, we propose a minimalist reaction-diffusion model coupled with economic constraints and an adaptive transport network, describing the co-evolution of population density with the transport system. Using a unique dataset, we reconstruct the evolution of London (UK) over 180 years and show that after an initial phase of diffusion limited growth, population has become less centralised and more suburban in response to economic needs and an expanding railway network. The coevolution of the rail system with a growing urban population has generated a transport network with hierarchical characteristics which have remained relatively constant over time. These results show that urbanisation patterns largely depend on the evolution of transport systems and population-transport feedbacks should be carefully considered when planning and retrofitting urban areas.

How to cite: Capel-Timms, I., Levinson, D., Bonetti, S., and Manoli, G.: Modelling the coevolution of London's population and railway system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12606, https://doi.org/10.5194/egusphere-egu23-12606, 2023.

The urban environment is made up of a complex and changing mosaic of territories. To what extent these spatial heterogeneities, between territories, determine the quality of life or social, economic or health inequalities? Within the framework of the ANR EGOUT project (egout.cnrs.fr), we assume that the spatial distribution and temporal evolution of tracers archived in the sediments of the Parisian sewerage networks can help deciphering the diversity of aboveground conditions and their temporal trajectory.

We compiled the geochemical results acquired before cleaning out operations on sediments accumulated in more than 100 silt traps (STs) that line the sewerage network of the City of Paris. These STs receive sediments that transit through the Parisian combined (wastewater and stormwater) sewer system. These analyses concern granulometry, metals, polychlorinated biphenyls (PCBs), but also 16 polycyclic aromatic hydrocarbons (PAHs). These regulatory analyses (which guide the nature of the treatment processes to be implemented) have been available since the year 2000, with cleaning out and thus measurement frequencies varying from one ST to another. They therefore allow addressing not only geochemical spatial disparities but also their temporal evolution.

In order to assign these results to the corresponding catchment areas for every ST, we first defined the catchment areas of each DT. The TIGRE 7 information system of the City of Paris was used to distinguish each sub-network draining the sediments to each ST. Two spatial scales of drainage (wet and dry weather), but also a sedimentary cascade system could be highlighted. The catchment areas of each ST were then defined by linking individual connections to individual addresses and cadastral parcels.

Here are the most striking results from the exploitation of existing data:

  • The Haussmannian buildings, which are present for the most part in the city Centre, are major sources of zinc emissions (Gromaire et al., 2001). This element is found in the sediments of DT draining areas with a high density of historical buildings.
  • Based on concentration ratios (Ayrault et al., 2008), PAHs mainly result from road traffic. The concentration of PAHs has been decreasing in ST sediments since 2000. This decrease could reflect the 59% decrease in the car traffic in Paris recorded between 2001 and 2018.
  • PAH levels and types differ from DT to another, as already noted by Rocher et al. (2004). These differences probably indicate local specificities in PAH production of each catchment area. Cross-referencing our data with other spatialized data related to potential PAH sources (road traffic, heating, etc.) should allow us to better understanding the factors that control their presence in sewer sediments.

How to cite: Asselin, C., Jacob, J., and Moilleron, R.: Controls on the spatial distribution and temporal variation of anthropogenic tracers in the sediments of the Paris sewerage system., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13043, https://doi.org/10.5194/egusphere-egu23-13043, 2023.

EGU23-13160 | ECS | Orals | ITS1.10/NP0.1

Automatic global building completeness assessment of OpenStreetMap using remote sensing data 

Laurens Jozef Nicolaas Oostwegel, Nicolas Garcia Ospina, Tara Evaz Zadeh, Simantini Shinde, and Danijel Schorlemmer

OpenStreetMap (OSM) is the largest crowd-sourced mapping effort to date, with an infrastructure network that is considered near-complete. The mapping activities started as any crowd-sourced information platform: the community expanded OSM anywhere there was a collective interest. Initial efforts were found around universities, hometowns of mappers and areas designated by organizations like the Humanitarian OSM Team (HOT). This resulted in a map that is of non-uniform completeness, with some areas having all building footprints in, while other areas remain incomplete or even untouched. Currently, with 530 million footprints, OSM identifies between a quarter and half of the total building footprints in the world, if we estimate that there are around 1-2 billion buildings in the world.

A global view on the completeness of buildings existing in OSM did not yet exist. Unlike other efforts, that only look at a subset of OSM building data (Biljecki & Ang 2020; Orden et al., 2020; Zhou et al., 2020), we have used the Global Human Settlement Layer (GHSL) to estimate completeness of the entire dataset. The remote sensing dataset is distributed onto a grid and in each tile of the grid, the built area of GHSL is compared to the total area of OSM building footprints. The computed ratio is measured against a completeness threshold that is calibrated using areas that were manually assessed.

Using information derived from remote sensing datasets can be problematic: GHSL does not only measure building footprints: it includes any human-built structures, including infrastructure and industrial areas. Next to that, due to circumstances like imperfect input data or failing algorithms, the dataset is not of the same quality as the crowd-sourced data in OSM in areas that are complete. False positives (i.e. rocky coasts) and false negatives (i.e. buildings missing in mountainous areas) exist in automatically generated data.

Even with these limitations, a comprehensive global completeness assessment is created. The assessment should not be used as ground truth, but rather as reflection on the OSM building dataset as is and as a guideline for priorities for the future. Statistics on regional completeness can be created and the quality of GHSL could be assessed on countries that are considered to be complete, such as France or the Netherlands.

How to cite: Oostwegel, L. J. N., Garcia Ospina, N., Evaz Zadeh, T., Shinde, S., and Schorlemmer, D.: Automatic global building completeness assessment of OpenStreetMap using remote sensing data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13160, https://doi.org/10.5194/egusphere-egu23-13160, 2023.

EGU23-13747 | ECS | Posters on site | ITS1.10/NP0.1

Development of a decision support system for regional planning and the assessment of brownfield sites: A case-study 

Ellis Hammond, Frederic Coulon, Stephen Hallett, Russell Thomas, Alistair Dick, Drew Hardy, and Darren Beriro

The complex nature of brownfield sites means that making decisions about their regeneration can be challenging and involve a wide range of stakeholders. To support these stakeholders, data-driven spatial decision support systems (DSSs) are often used. For these kinds of tools to be effective, it is crucial to have a comprehensive understanding of the problems and challenges faced by stakeholders. This research builds on previous large-scale stakeholder engagement (Hammond et al., 2023), critical review (Hammond et al., 2021), and detailed user requirements gathering. We present a framework for the development of a novel web-based DSS to support early-stage city region-scale brownfield planning and redevelopment. The DSS has four objectives: (1) improve the findability and visualisation of data, (2) support the assessment and understanding of ground risk posed by contamination and geotechnical instability, (3) provide better visualisation of data related to economic viability assessment, and (4) support evidence gathering for master planning through the modelling of land-use potential using a GIS multi-criteria method. We demonstrate the capabilities of the DSS framework through the implementation of a case-study focussing on the Liverpool City Region, a combined authority area in north-west England. Findings from user testing of the DSS and verification work are also presented.

How to cite: Hammond, E., Coulon, F., Hallett, S., Thomas, R., Dick, A., Hardy, D., and Beriro, D.: Development of a decision support system for regional planning and the assessment of brownfield sites: A case-study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13747, https://doi.org/10.5194/egusphere-egu23-13747, 2023.

EGU23-14487 | ECS | Orals | ITS1.10/NP0.1

Challenges for achieving clean air - The case of Barcelona (Spain) 

Daniel Rodriguez-Rey, Marc Guevara, Jan Mateu Armengol, Alvaro Criado, Santiago Enciso, Carles Tena, Jaime Benavides, Albert Soret, Oriol Jorba, and Carlos Pérez-Garcia Pando

Air pollution affects the economy, the environment, and public health. This is particularly relevant in dense urban areas due to their urban built, high traffic activity, and near-the-source population exposure. In the city of Barcelona, the 40 ug/m3 nitrogen dioxide NO2 annual limit value set up by the 2008/50/EC European Air Quality Directive (AQD) is systematically exceeded in traffic stations mainly due to the contribution of road transport. In the last Urban Mobility Plan (2019-2024), the city hall of Barcelona presented several traffic management strategies aiming to reduce on-road traffic emissions by both renewing and reducing the private motorized transport in the city. These measures include the application of tactical urban actions, green corridors and superblocks along with a Low Emission Zone, which together are expected to reduce the number of private vehicles circulating throughout the city by -25%. In parallel, the Port of Barcelona has recently announced a plan to electrify the docks and reduce emission from hotelling activities by -38%. To properly assess the impact of such measures, the AQD recommends the application of numerical models in combination with monitoring data. Following AQD recommendations, our study runs a coupled transport-emission model able to characterize traffic movement along the city and produce multiple scenarios that quantify the impact of the aforementioned measures on primary emissions. The resulting scenarios are then used to feed a multi-scale air quality modeling system to estimate NO2 concentration values at very high resolution (20m, hourly). To reduce the uncertainty typically associated with modeling results, the estimated values are corrected with a data-fusion methodology using observations from the official monitoring network and several measurement campaigns. Our results show that the implementation of all mobility restrictions and electrification of the Port will allow Barcelona to comply with the current legislated NO2 air quality standards at the traffic monitoring stations, with reductions up to -24.7% and -12 ug/m3. However, the resulting NO2 levels achieved at these locations would still fail to meet the new 2021 WHO guideline (10 ug/m3) and the recent proposal for a revision of the EU AQD (20 ug/m3). Also, despite the estimated NO2 reductions, several areas in the city would still be above the current legal limit of 40 ug/m3, including 16.7% of schools and 19.7% of hospitals and healthcare facilities. All in all, our results suggest the planned measures are steps in the right direction, yet still insufficient to ensure healthy AQ values across the entire city.

How to cite: Rodriguez-Rey, D., Guevara, M., Armengol, J. M., Criado, A., Enciso, S., Tena, C., Benavides, J., Soret, A., Jorba, O., and Pérez-Garcia Pando, C.: Challenges for achieving clean air - The case of Barcelona (Spain), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14487, https://doi.org/10.5194/egusphere-egu23-14487, 2023.

EGU23-15135 | Posters on site | ITS1.10/NP0.1

Monitoring Urban Areas for Climate Change Adaptation Using Remotely Sensed Indicators in the UrbanGreenEye Project 

Kathrin Wagner, Annett Frick, Benjamin Stoeckigt, Sascha Gey, Sebastian Lehmler, Nastasja Scholz, Franziska Loeffler, Viktoria Engnath, Stefan Heiland, Sebastian Schubert, and Mohamed Salim

In the context of climate change adaptation, sustainable urban development, and environmental justice, local civil services must meet a range of dynamic demands. To do so, municipalities require both qualitative and quantitative knowledge about the current state and the development of urban structures such as impervious surfaces and vegetated areas within their boundaries. However, obtaining this data through surveys is costly and time-consuming, and the frequency of these surveys is too low to capture changes consistently. As a result, data availability to support urban planning strategies often depends on financial priorities and is not consistently available to all local authorities in Germany. Remote sensing data, which has extensive spatial coverage and is regularly available, offers a more viable option for effective monitoring of urban structures. However, local authorities often lack knowledge about the benefits and limitations of using such data. The UrbanGreenEye project aims to bridge this gap by developing urban climate indicators based on Earth Observation data that meet the needs of local authorities. Drawing on the experience of nine partner municipalities, the project will demonstrate the use and implementation of these indicators in planning processes and strategies. It will also help create digital twins for urban planning applications and provide a free, regularly updated indicator-geodata foundation for Germany to support decision-making, particularly for climate change adaptation. The indicators will help identify locations experiencing high thermal and hydrological stress and quantify the relief provided by vegetated and pervious areas. Land surface temperature (LST) derived from satellite data from the US Landsat program will be used to monitor thermal stress, while the urban green volume and vegetation vitality indicators, derived from EU Copernicus Sentinel-2 satellite data, will contribute to thermal stress relief. The imperviousness indicator will also be derived from Sentinel-2 data using spectral models. Artificial intelligence algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) and attention-based transformer models, will be used to extract complex information from the urban surfaces, which require large amounts of reference data to capture necessary details. This reference data will be generated from high-resolution aerial images using CNN, supported by local ground truth data from municipal authorities and citizen science projects. It will then be upscaled and used as a reference for satellite-level models to provide nationwide consistent products. The satellite-based indicators will be validated for error ranges and at different spatial scales using the micro-scale climate model PALM-4U. Eventually, the indicators will be used to create a model for urban green volume deficiency to identify hot spots for adaptation measures and support planning strategies.

How to cite: Wagner, K., Frick, A., Stoeckigt, B., Gey, S., Lehmler, S., Scholz, N., Loeffler, F., Engnath, V., Heiland, S., Schubert, S., and Salim, M.: Monitoring Urban Areas for Climate Change Adaptation Using Remotely Sensed Indicators in the UrbanGreenEye Project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15135, https://doi.org/10.5194/egusphere-egu23-15135, 2023.

EGU23-15145 | ECS | Orals | ITS1.10/NP0.1

Urban meteorological forcing data for building energy simulations at a neighbourhood scale 

Zhihui Ren, Gerald Mills, and Francesco Pilla

Building energy use is one of the largest global demands,  accounting for 36% of final energy use and 39% of energy and process-related carbon dioxide (CO2) emissions. Green plans are the recommended planning technique for reducing the energy demand of buildings without changing the current built environment. This study investigates the effect of neighbourhood features on the energy performance of buildings. On the basis of building age and tree density, four typical Dublin city centre neighbourhoods are chosen to generate simulations. Surface Urban Energy and Water Balance Scheme (SUEWS) was used to generate the forcing climate data surrounding the neighbourhood, which was then fed into Integrated Environmental Solutions Virtual Environment (IES VE) as the meteorological data for conducting building energy simulations. The results showed that the fraction of trees plays an important role in wind speed in neighbourhoods. Incorporating the missing neighbourhood signature into the forcing data for building energy modelling improves the simulation's efficiency and precision. This study illustrates the importance of considering the local climate while simulating building energy efficiency.

How to cite: Ren, Z., Mills, G., and Pilla, F.: Urban meteorological forcing data for building energy simulations at a neighbourhood scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15145, https://doi.org/10.5194/egusphere-egu23-15145, 2023.

EGU23-15999 | ECS | Orals | ITS1.10/NP0.1

A socio-spatial analysis of air pollution exposure in the Greater Paris 

Taos Benoussaïd, Isabelle Coll, Hélène Charreire, and Arthur Elessa Etuman

According to the WHO, in 2019, 23% of global mortality was attributable to environmental risk factors with significant gradients related to social factors, at the origin of significant health inequalities in the cities. Air pollution is one main environmental risk in urban spaces, and must therefore also be understood by taking into account the issue of socio-spatial inequalities.

Realistic modelling of population exposure to air pollution in large cities requires taking into account air quality at the level of the individual, as well as individual spatial dynamics (mobility and realization of daily activities) that shape each person's risk of exposure. These requirements call for the development of interdisciplinary tools combining the representation of urban space, traffic simulation, emission calculation, advanced air quality models, and the consideration of behavioral and socio-economic dimensions in the modeling process.

We present here a socially and spatially differentiated modeling study of the factors and behaviors that build the exposure of individuals to air pollution in the Greater Paris. This study was carried out using an integrated urban modeling platform including the OLYMPUS emissions model and the CHIMERE chemistry-transport model. The OLYMPUS tool, developed at LISA, is an innovative emission model based on the activity of individuals, making it possible to simulate the socio-differentiated mobility of individuals, for the construction of a pollutant emission inventory adapted to a given urban area. The use of CHIMERE then makes it possible to cross air quality, individuals and mobility, and address the issue of individual exposure to air pollution in a dynamic and integrated manner.

We simulated the year 2009 in the Greater Paris region, and calculated the exposure of individuals taking into account their mobility and their social characteristics, activity and place of residence. Our results are interpreted with regard to the main scientific and societal questions that arise on the subject: Are some individuals more exposed than others? Are these inequalities in exposure linked to the places where people live? To mobility practices? Can they be dependent on socio-professional categories? Do they affect socially vulnerable populations in the same way?

Beyond access to an assessment of exposure inequalities in the current situation, this work makes it possible to support the reflection on the impact of public action on the reduction of environmental inequalities.

Acknowledgments

This research received funding from the French National Agency for Research (ANR-14-CE22-0013), the French Environment and Energy Management Agency (ADEME) and the Île-de-France region (DIM QI²). It was granted access to the HPC resources of TGCC under the allocation A0090107232 made by GENCI. We acknowledge AIRPARIF for data supply.

Références

Elessa Etuman, A., & Coll, I. (2018). OLYMPUS v1.0: Development of an integrated air pollutant and GHG urban emissions model-methodology and calibration over greater Paris. Geoscientific Model Development, 11(12), 5085–5111. https://doi.org/10.5194/gmd-11-5085-2018

Elessa Etuman A., Coll I., Makni I., Benoussaid T., Addressing the issue of exposure to primary pollution in urban areas: Application to Greater Paris, Atmospheric Environment, Volume 239, 117661, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2020.117661, 2020

How to cite: Benoussaïd, T., Coll, I., Charreire, H., and Elessa Etuman, A.: A socio-spatial analysis of air pollution exposure in the Greater Paris, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15999, https://doi.org/10.5194/egusphere-egu23-15999, 2023.

EGU23-16298 | ECS | Posters on site | ITS1.10/NP0.1

Building a LEZ scenario and its social and environmental impacts in the Greater Paris area 

Malo Costes, Arthur Elessa Etuman, Taos Benoussaïd, and Isabelle Coll

The urban environment has a large population and therefore a large number of polluting activities. Urban organization and the organization of road traffic are levers for controlling energy consumption, transport demand, air quality and the exposure of city dwellers, but these initiatives have social repercussions that do not impact all citizens in the same way, which may create a form of social and environmental injustice. Modeling of urban development scenarios should make it possible to investigate these questions. The challenges of such scenarios lie in the difficulty of modeling a given situation at the level of individuals by taking into account their socio-demographic characteristics, their places of life and their mobility behaviors.

In 2017, a Low Emission Zone (LEZ) has been implemented in the Paris Metropolis, including the city of Paris and the nearby municipalities. The objective is to reduce pollutant emissions and improve air quality in the territory. Recent studies have assessed the average health impact expected from such measures, but they did not consider socio-environmental outcomes. In our work, we thus decided to build a complete LEZ scenario considering the mobility of individuals differentiated by their geographic, demographic and socio-professional specificites.

The urban modeling platform that we use is centered on the OLYMPUS tool, which makes it possible to design mobility scenarios and the associated pollutant emissions by taking into account the urban form, the transport offer, the constraints of land use planning as well as than individual and socially differentiated mobility. With this tool we built different forms for the implementation of the Greater Paris LEZ, and we used the resulting emissions in the CHIMERE air quality model.

We present here the methodology implemented to transcribe the LEZ scenario in our platform as well as the first results obtained on the socio-differentiation of the LEZ constraints (mobility for individuals) and impacts (exposure on individuals).

 

Aknowledgments

This work was supported by the EUR-LIVE program at Université Paris Est Créteil (French National Research Agency fundings).

 

References

Elessa Etuman, A., & Coll, I. (2018). OLYMPUS v1.0: Development of an integrated air pollutant and GHG urban emissions model-methodology and calibration over greater Paris. Geoscientific Model Development, 11(12), 5085–5111. https://doi.org/10.5194/gmd-11-5085-2018

Elessa Etuman A., Coll I., Makni I., Benoussaid T., Addressing the issue of exposure to primary pollution in urban areas: Application to Greater Paris, Atmospheric Environment, Volume 239, 117661, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2020.117661, 2020

Host, Sabine, Cécile Honoré, Fabrice Joly, Adrien Saunal, Alain Le Tertre, et Sylvia Medina. « Implementation of Various Hypothetical Low Emission Zone Scenarios in Greater Paris: Assessment of Fine-Scale Reduction in Exposure and Expected Health Benefits ». Environmental Research 185 (juin 2020): 109405. https://doi.org/10.1016/j.envres.2020.109405.

How to cite: Costes, M., Elessa Etuman, A., Benoussaïd, T., and Coll, I.: Building a LEZ scenario and its social and environmental impacts in the Greater Paris area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16298, https://doi.org/10.5194/egusphere-egu23-16298, 2023.

EGU23-16910 | ECS | Orals | ITS1.10/NP0.1 | Highlight

Integrated Urban Planning Decision-Making Process Towards Water Neutral Solutions 

Pepe Puchol-Salort, Stanislava Boskovic, Barnaby Dobson, Vladimir Krivtsov, Eduardo Rico-Carranza, Maarten van Reeuwijk, Jennifer Whyte, and Ana Mijic

Urban water security levels will be threatened during the next few years due to new development pressures combined with the climate emergency and increasing population growth in cities. In the UK, London’s planning authorities have a target of more than half a million households for the next 10 years. This new housing will increase the current impacts on urban consumer demand, flood risk, and river water quality indicators. In our previous work, we developed a new concept for urban Water Neutrality (WN) inside an integrated urban planning sustainability framework called CityPlan to deal with water stress and urban complexity issues. This framework integrates the UK’s planning application process with systemic design solutions and evaluation, all being spatially represented in a GIS platform. With the new digital era, there is a constantly increasing number of spatial datasets that are openly available from different sources, but most of them are disaggregated and difficult to understand by key urban stakeholders such as Local Planning Authorities, housing developers, and water companies. Moreover, there are several Multi-Criteria Decision Support Tools (MCDST) that address water management challenges in the literature; but there is still little evidence of one that evaluates the impacts and opportunities to allocate water neutral urban developments.

In this work, we expand the CityPlan framework and present an innovative fully data-driven approach to test WN indicators at different urban scales. WaNetDST integrates GIS spatial data with a series of rules for development impact and offset opportunity based on the current properties of the urban land. This integration is linked to a new scoring system from expert advice that maps strategic areas for water neutral interventions and links the most impactful zones with others more prone to be intervened. The tool connects different urban scales with a series of case study areas: from city (i.e., London), to borough (i.e., Enfield), and to urban development scale (i.e., Meridian Water Development). In the end, WaNetDST visually compares the need for housing vs. green spaces and the trade-offs between new housing vs. retrofitting existing infrastructure, providing a series of maps that guide the planning decision-making process in an integrated way. The results from CityPlan might potentially change the decision-making process for LPAs and housing developers and open a new dialogue between boroughs inside the same city, providing a novel and automated system for WN trade-offs and linking data-driven design with future planning decisions

How to cite: Puchol-Salort, P., Boskovic, S., Dobson, B., Krivtsov, V., Rico-Carranza, E., van Reeuwijk, M., Whyte, J., and Mijic, A.: Integrated Urban Planning Decision-Making Process Towards Water Neutral Solutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16910, https://doi.org/10.5194/egusphere-egu23-16910, 2023.

EGU23-17338 | Orals | ITS1.10/NP0.1

Co-Creating Digital Twins for Planning of Water Resources and Housing Development 

Eduardo Rico Carranza, Ana Mijic, and Jennifer Whyte

The potential to deliver better, more efficient and sustainable cities has motivated recent research on Digital Twins (DTs) that seek to support planning decisions by displaying analytical evidence to inform collaborative design actions. Prior research identifies different types of DT tools and gives recommendations for their use, but it has not been grounded in engaged research that co-designs DTs with planning users from the context description and problem formulation stage. We report on a research project to co-create DTs with local government planners to visualize interactions between water resources and housing development. We describe co-creating two different DTs starting from the context and problem for water management and assess the steps against these three categories. While engaging with the field we built on prior studies to identify a set of categories relevant to co-creating and assessing DTs: context, governance, spatial and technical definition.  By recording the steps of the DT design process, and contrasting the results with the theoretical proposals, we develop a three-step framework for the co-creation of DTs. This step-by-step framework, illustrated by examples, provides a contribution to the literature on the co-design of DT, and we conclude by discussing implications for practitioners and areas for further research.  

How to cite: Rico Carranza, E., Mijic, A., and Whyte, J.: Co-Creating Digital Twins for Planning of Water Resources and Housing Development, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17338, https://doi.org/10.5194/egusphere-egu23-17338, 2023.

This study shows the unintended tradeoffs of water conservation strategies in the City of Las Vegas. We used the Weather and Research Forecasting model coupled with a multilayer Urban Canopy Model, and forced with the Local Climate Zones from WUDAPTv2, to carry out cloud-resolving simulations aiming to estimate the impacts of city-wide turf removal. Results show that removing the turf, which removes most of non-functional urban irrigation needs, significantly warms up surface temperature via surface energy rebalancing by reducing latent heat and increasing sensible and ground heat fluxes. A striking result is that the increase in sensible heat also increases boundary layer instability favoring more and longer lasting clouds and invigorating afternoon storms. The enhanced afternoon storms tend to cool the surface temperature, but the turf removal net warming impact remains. We also used the model to show how climate intervention scenarios based on cool roofing and pavement strategies can ameliorate the underlying turf removal consequences.

How to cite: Mejía Valencia, J. F., Henao, J., and Saher, R.: The effect of removal of all non-functional turf in Las Vegas: tradeoffs between water conservation, excessive heat, and storminess, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17481, https://doi.org/10.5194/egusphere-egu23-17481, 2023.

EGU23-768 | ECS | Orals | ITS1.11/NP0.2 | Highlight

Deep Learning and Universal Multifractal for Nowcasting Precipitation in Urban Geosciences 

Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia

Precipitation nowcasting (short-term forecasting ahead for 0 to 6 hours) is crucial for decision-making of weather-dependent industries in order to mitigate socio-economic impacts. Accurate and trustworthy precipitation nowcasting can serve as an early warning of massive floods, as well as a guide for water-related risk management. Although precipitation nowcasting is not a novel concept, it is challenging and complicated due to the extreme variability of precipitation. The traditional theory-driven numerical weather prediction (NWP) methods confront numerous obstacles, including an insufficient understanding of physical processes, enormous initial conditions impacts on predictions and requiring substantial computing resources. On the other hand, data-driven deep learning models establish a relationship between input and output data to predict future precipitation without taking into account the underlying physical processes. The framework of universal multifractal (UM) is also presented to describe the variability of precipitation nowcasting and compared to the radar observations. In this study, the convolutional long short-term memory (ConvLSTM) model is used to perform precipitation nowcasting over Metropolitan France. The study employs radar data collected every 5 minutes with a spatial resolution of 1km from Meteo-France. The preliminary results show that the structure of the field is reasonably forecast, as well as the somewhat moderate rain rates, but not the most intense ones. We discuss how to improve the methodology.

How to cite: Zhou, H., Schertzer, D., and Tchiguirinskaia, I.: Deep Learning and Universal Multifractal for Nowcasting Precipitation in Urban Geosciences, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-768, https://doi.org/10.5194/egusphere-egu23-768, 2023.

EGU23-820 | ECS | Posters on site | ITS1.11/NP0.2

Enhancing landscape connectivity by Nature-based Solutions for cities in a post-COVID world: a case study in Paris 

Yangzi Qiu, Ioulia Tchiguirinskaia, and Daniel Schertzer

During the lockdown of the COVID-19 pandemic, most countries imposed mobility restrictions such as physical distancing from others, “self-isolation”, and/or “quarantine”. Although these “isolation” measures had been effective in hindering the spread of the virus effectively, people’s mental health can be strongly affected by these “isolation” measures. Some researchers found that the mental health problem indeed increased during the lockdown. In order to reduce the negative effects of isolation on mental health, some studies suggested that people should be brought closer to nature spaces during the lockdown. In this regard, policymakers are paying more attention to Nature-based Solutions (NBS) (e.g. green roofs, gardens, and urban parks), which can potentially improve people's physical and mental health via interactions between people and nature.

In order to enhance the connectivity of the landscape to improve the ecosystem services and reduce health risks with the help of NBS in a post-COVID world, it is significant to consider the heterogeneity of the spatial distribution of green spaces. A number of studies have found that estimates of green space areas are scale-dependent, it is therefore important to investigate the intrinsic complexity of the heterogeneity of the green spaces across a range of scales. This could be achieved with the help of the universal multifractal (UM) framework (Schertzer and Lovejoy, 1987), a stochastic approach widely used to quantify the variability of geophysical fields across a range of scales. This study aims to improve the landscape connectivity of the green spaces in Paris across scales with the help of the UM cascade model.

To achieve the aim of this study, we first quantified the heterogeneous spatial distributions of green spaces of the selected areas by using the fractal dimension. Then, a distance analysis is performed for non-green spaces to green spaces, and a series of NBS scenarios are created based on integrating potential NBS into the current landscape by using the UM cascade model. Finally, the spatial distributions of the NBS combined with the original green spaces are quantified by the fractal dimension and distance analysis. The results indicate that NBS can effectively improve the connectivity of the landscape and has the potential to reduce the physical and mental risks caused by COVID-19. More specifically, this study proposes a scale-independent approach for enhancing the multiscale connectivity of the NBS network in urban areas and provides quantitative suggestions for cities in a post-COVID world.

How to cite: Qiu, Y., Tchiguirinskaia, I., and Schertzer, D.: Enhancing landscape connectivity by Nature-based Solutions for cities in a post-COVID world: a case study in Paris, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-820, https://doi.org/10.5194/egusphere-egu23-820, 2023.

EGU23-841 | ECS | Posters on site | ITS1.11/NP0.2

Multifractals, Climate Networks and the extreme variability of precipitation 

Adarsh Jojo Thomas, Jürgen Kurths, and Daniel Schertzer
Precipitation is a complex process that is extremely variable over a wide range of space-time scales. More specifically, it is strongly intermittent: the heaviest precipitation are increasingly concentrated on sparser and sparser fractions of the space-time domain. At the same time, precipitation is a key variable of urban geosciences. 
Multifractals have been developed to analyse and simulate across scales this multiscale intermittency, while the climate networks can detect and characterise event synchronisation. In contrast to multifractal analysis, climate networks are usually performed at a given scale, defined by the resolution of the data. In this communication, we present how to overcome this dichotomy and propose multiscale climate networks in the hope of reaching scales relevant to urban geosciences.
Specifically, we study theoretically and/or numerically the scale dependance of different centrality measures of climate networks determined at different scales by coarse graining the precipitation data, as is done for multifractal analysis. Among the preliminary results, we show how to modify some of the parameters of the climate networks to force scale invariance of their structure.

How to cite: Thomas, A. J., Kurths, J., and Schertzer, D.: Multifractals, Climate Networks and the extreme variability of precipitation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-841, https://doi.org/10.5194/egusphere-egu23-841, 2023.

EGU23-1989 * | ECS | Orals | ITS1.11/NP0.2 | Highlight

Tracking global urban green space trends 

Giacomo Falchetta and Ahmed T. Hammad

Urban green space - the presence of vegetation-covered area within cities' boundaries - is an increasingly relevant indicator for evaluating sustainable cities. This is because besides providing a set of local services such as mitigating the urban heat island effect and reducing the impact of extreme precipitation events, urban green space has been widely associated with increasing well-being of urban dwellers. Here we present a global analysis of recent trends in urban green space based on modelling of multi-spectral satellite imagery data to reproduce street-based vegetation presence indicators. We estimate local to continental trends over the 2016-2022 period and estimate global scale (considering a large set of the most populated cities) urban green space change statistical trends. We examine heterogeneities in the direction and magnitude of trends across cities and regions, while also analysing within-city inequalities. Our analysis provides an updated picture of urban green space across world cities and an open-source and open data-driven, spatially cross-validated approach to assess changes in near-real-time.

How to cite: Falchetta, G. and Hammad, A. T.: Tracking global urban green space trends, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1989, https://doi.org/10.5194/egusphere-egu23-1989, 2023.

EGU23-2994 | ECS | Orals | ITS1.11/NP0.2 | Highlight

Mapping dengue fever risk for a non-endemic high-density city in subtropical region 

Shi Yin, Junyi Hua, Chao Ren, Benoit Guénard, Runxi Wang, André Ibáñez Weemaels, Yuan Shi, Tsz-Cheung Lee, Hsiang-Yu Yuan, Marc Ka-chun Chong, and Linwei Tian

Dengue fever is a mosquito-borne disease caused by the dengue virus bringing huge health burdens in tropical regions. With global warming, rapid urbanization, and mosquito species introductions, the range of dengue fever is expected to expand to subtropical regions and increase potential health risks for local populations. To reduce dengue fever transmission, relevant risk map is one of the most effective tools for public health management. Though there is abundant literature about mapping the dengue fever risks in endemic regions, few studies in contrast have investigated dengue fever risks for non-endemic regions; hindering the development of preparedness planning.

In this study, the spatial hazard-exposure-vulnerability assessment framework proposed by the Intergovernmental Panel on Climate Change was applied in to detect the dengue fever risk in Hong Kong, which is a typical high-density city located within a subtropical region. Firstly, the spatial distribution of the habitat suitability for Aedes albopictus, a mosquito species common in Hong Kong and proxy for the potential dengue fever hazard, was predicted using MaxEnt models relying on the surveillance data and a list of variables related to urban morphology, landscape, land utilization, and local climate. Secondly, the bivariate local Moran’s I was measured to identify urban areas with both high dengue hazard and high human population exposure. Then, vulnerable groups among the human population were identified from the 2016 Hong Kong census data. Finally, dengue risks were assessed at the community scale by overlapping the results of hazard, exposure, and vulnerability analysis.

In the optimal MaxEnt model predicting the presence possibility of Aedes albopictus, the normalized difference vegetation index, frontal area index, and the aggregation index of public residential land ranked the top three among all predictors, with permutation importance of 31.8%, 22.8%, and 17% respectively. Three components were generated after principal component analysis on the vulnerable groups. Lastly, this approach allowed the identification of 17 high-risk spots within Hong Kong. In addition, the underlying factors behind each hot spot were investigated from the aspects of hazard, exposure, and vulnerability respectively, and specific suggestions for dengue prevention were proposed accordingly.

The findings provide a useful reference for developing local dengue fever risk prevention measures, with the proposed method easily exportable to other high-density cities within subtropical Asia and elsewhere.

This study was funded by the Health and Medical Research Fund of the Food and Health Bureau (No. 20190672).

How to cite: Yin, S., Hua, J., Ren, C., Guénard, B., Wang, R., Weemaels, A. I., Shi, Y., Lee, T.-C., Yuan, H.-Y., Chong, M. K., and Tian, L.: Mapping dengue fever risk for a non-endemic high-density city in subtropical region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2994, https://doi.org/10.5194/egusphere-egu23-2994, 2023.

This presentation focuses on the governing equations of incompressible and compressible flow in fractional time and multi-fractional space as developed recently by the authors in DOI: 10.1038/s41598-022-20911-3. Mathematical differentiation has found many applications in real-life problems in the last two decades, before which it was mainly utilized by mathematicians and theoretical physicists. The proposed fractional governing equations for fluid flow may be interpreted as the general forms of the classical Navier–Stokes equations; as they reduce to the classical ones when integer values are replaced with their fractional powers in space and time. Due to their nonlocal structure, proposed governing equations in factional time/space can reflect the initial conditions for long times, and the boundary conditions for long distances. Results of numerical applications are presented for flow due to a wall suddenly set into motion. It is found that the proposed equations have the potential to model both sub-diffusive and super-diffusive flow cases.

How to cite: Ercan, A. and Kavvas, M. L.: Navier–Stokes equations in Fractional Time and Multi‑Fractional Space, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6138, https://doi.org/10.5194/egusphere-egu23-6138, 2023.

Limited knowledge on current and future processes as well as data scarcity pose a major challenge when it comes to the evaluation of adaptation strategies towards flooding. Current simulation approaches often lack the flexibility do deal with the inherit dynamics of future development (land use, urban growth, re-development of slum areas, infrastructure construction, etc.) in coastal cities and the resulting changes in flood hazard, exposure and vulnerability whilst facing a lack of sufficient data. Therefore, we developed a modelling approach which is able to integrate future dynamics in the three risk components, hazard, exposure and vulnerability under the uncertainties arising from lacking data as well as limited knowledge. We used Mumbai, India as a first case study to combine Urban Structure Types with Bayesian Networks (BN) and to assess pluvial flooding. BN structures are defined by process understanding supported by existing models, literature and expert evaluations. The quantification of the BNs is done by using urban structure types as proxies for relevant parameters/nodes where data is not available, like the distribution and capacity drainage infrastructure and its condition or the degree of imperviousness of certain areas. This is justified by the assumption that the appearance and the processes in urban structure types are similar. However, the probabilistic definition of nodes in a BN allows to account for the variability within an urban structure type class. As a first step, the approach was set up for the hazard component of risk. Here first results of the simulation of pluvial flooding are shown and validated against flood hotspots reported by the government of Mumbai. The simulation approach reproduced the flooding hotspots, however it has a great sensitivity towards certain parameters, especially towards the digital elevation model and the condition of the drainage infrastructure. In a next step BNs for multi-hazard evaluation and vulnerability assessment will be developed and linked, i.e. fluvial and coastal flooding as well as social vulnerability. The integration of different risk components and the flexibility of the approach help to assess the effect of individual and combinations of soft and hard adaptation measures on future flood risk.

How to cite: Zwirglmaier, V. and Garschagen, M.: Towards an integrated assessment of future flooding in dynamic and data-scarce urban environments by linking Urban Structure Types with Bayesian Network modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6276, https://doi.org/10.5194/egusphere-egu23-6276, 2023.

EGU23-8235 * | Posters on site | ITS1.11/NP0.2 | Highlight

The influence of geographical factors on COVID-19 outbreak: A literature review 

Paola Coratza, Alessandro Ghinoi, Lucia Palandri, Elena Righi, Cristiana Rizzi, Mauro Soldati, and Vittoria Vandelli

A significant number of papers focusing on the relationships between COVID-19 diffusion and geographic factors is available in literature. The same applies to the use of geographic techniques (e.g., spatial tools and mapping) for the study of the pandemic. Although the literature on these topics is already abundant, a detailed and comprehensive review is still lacking.
In this context, the purpose of this paper is to fill the existing gap by presenting a literature review of geographical studies dealing with the COVID-19 pandemic. The review is aimed at: i) understanding the role of geographic/territorial determinants (e.g., geographic location of confirmed cases, climatic and environmental characteristics, urbanization) in the spread of COVID-19; ii) identifying common approaches, materials, and methods used in the study of the COVID-19 outbreak from a geographical perspective; iii) recognising possible research gaps to address future in-depth analyses.
To achieve these goals a literature review was made concerning the application of geographical approaches for the study of one or more geographical factors/variables, as well as socioeconomic factors in relation to the outbreak and diffusion of the COVID-19 pandemic. The main academic literature databases were inquired. More than 80 papers were reviewed and categorized according to different criteria, e.g., considered variables, investigated period, spatial and temporal resolution and applied methodologies.
This research is part of an interdisciplinary project (“DISCOV19”) funded by the University of Modena and Reggio Emilia and aiming at identifying the main vulnerability and risk factors related to COVID-19 outbreak and at formulating prevention and management schemes with a focus on the Province of Modena (Northern Italy). The investigation crosses different disciplines: i) public health epidemiology, investigating the contagion modalities and health and socio-demographic predisposing factors; ii) economic-statistical methodology, pointing out the structural characteristics of the networks that convey the contagion and the main social, technological and management vulnerabilities with respect to COVID-19 spread; iii) geography and geomorphology, for thematic mapping and spatial analysis of COVID-19 outbreak and understanding the role of environmental and physical-geographical factors on COVID-19 incidence. The review here presented fits into this context being one of the first outputs of the project implementation.

How to cite: Coratza, P., Ghinoi, A., Palandri, L., Righi, E., Rizzi, C., Soldati, M., and Vandelli, V.: The influence of geographical factors on COVID-19 outbreak: A literature review, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8235, https://doi.org/10.5194/egusphere-egu23-8235, 2023.

The improved ability of imaging sensors to capture very high resolution (VHR) remote sensing images has been boosted by recent enhancement in the data processing algorithms. This improvement raises the potential of providing precise scene understanding for many applications. For instance, the task of semantic segmentation is an important one in the context of 3D building modelling. In this work, the main objective is to show the potential of using images of a stereo pair as inputs to a neural network trained for semantic segmentation into different urban classes. Actually, instead of increasing artificially the amount of training data, the use of stereo pair images can be seen as a realistic data augmentation, where the model will be trained to see the same object from different acquisition angles. From an experimental point of view, the results show that the method achieves better performances and gives a greater ability to generalize compared to the use of a single view. 

The segmentation process is performed using an encoder-decoder network architecture, namely the U-net network which includes an EfficientNet for the encoder part and a RefineNet for the decoder stage. The model is trained on Pleiades images involving different sources of ground truth (OpenStreetMap, IGN databases and in-house LCLU AI4GEO hierarchical labelled data). Additionally to the spectral information, height information is also considered to enhance the segmentation accuracy. This latter information is obtained using digital surface model (DSM). Indeed, classes identifying urban areas (building class for example) can be more easily discerned according to their height information. 

Furthermore, since Pleiades images are used as inputs of the proposed model, some geometrical issues need to be handled. To remove this complexity, a simulated imaging geometry of a perfect instrument is designed with no optical distortion and no high attitude perturbations. Resulting geometry is commonly called perfect sensor geometry. Since then, to avoid problems of geometric offsets between different data sources (satellite images in perfect geometry), terrain geometry of DSM/DTM and various ground truth databases, several tools have been developed to allow conversion between different geometries. The ortho-rectification is a commonly used geometrical correction that aims at presenting images as if they had been captured from the vertical. Therefore, this correction requires the availability of a HR digital terrain model (DTM) and may result on some distortions. In particular, some area may be occluded and others may arise a spreading effect of buildings.

 To address this issue, and preserve the native image information of the perfect sensor geometry, one key contribution of this work is to map the DSM data and ground truth image into the perfect sensor geometry. By doing this geometric processing and object positioning during the training process, better overlays between different data sources (stereo pair images, DSM model and ground truth data) are ensured and geometrical distortions and offsets can be avoided. In addition, the inferences can be done directly on the perfect sensor geometry without having to go through terrain geometries which requires high resolved DSM/DTM models.

How to cite: Akodad, S. and Lassalle, P.: Automatic Land Cover Segmentation from Perfect Sensor Stereo Images with Height Information, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9983, https://doi.org/10.5194/egusphere-egu23-9983, 2023.

Malaria is a widespread, mosquito-borne, potentially lethal infectious disease that affects humans and other animals. Its prevention and treatment have been targeted in science and medicine for hundreds of years. During the 20th century it was widespread in the Middle East, including Cyprus, and was one of the most important health hazards worldwide. In 1967, the World Health Organisation (WHO) declared Cyprus as malaria-free, an impressive feat considering malaria had plagued the island since the Roman period (Demetrios, 2009). We conducted a critical analysis of anti-malarial campaigns on urban and rural districts in Cyprus, under British colonial rule (1878 – 1960), in the context of malarial disease knowledge in health surveillance and care policy. Under the HIGH-PASM (High-resolution palaeoclimate records and social vulnerability for the last Millennium) project, we present a methodology for constructing database tools relative to heterogeneously distributed historical sources. The aim of this research is to study the impact of the anti-malarial works on the Cypriot landscape as the social-political situation and the methods implemented did not follow the stringent protocols that exist today. Main issues are the complexity regarding British, Ottoman and French social and political roles, ground truth data extracted from historical sources - that need critical analysis, and the complex phenomena under scope. Primary sources are annual medical reports, written by British medical officers, published from 1913 to 1953. The main focus of these actions linked geography to healthcare issues by eliminating the newly identified malaria-vector by directly influencing the mosquito’s habitat, thus indirectly affecting the Cyprus landscape. The assertion, verification and evaluation of the before mentioned actions requires the medical reports to be contextually placed alongside secondary sources (for example correspondences, journal articles, conference proceedings, etc), which were produced or disseminated during this time period by different actors or groups of actors. We aim to apply methodologies used in digital humanities and conceptual modelling within geosciences to verify and understand spatial-temporal information that may be found within archival references. Raw data are extracted from a small corpus to produce meta-data (data sense ((Hui, 2015)) using existing cultural heritage vocabularies (CIDOC CRM base and extensions) relative to different fields and objects to model spatial-temporal events and align these data with authority databases using W3C (World Wide Web Consortium) semantic web standards (technologies). Given the British colonial role in the governance of the island, there is a lack of empirical evidence on the choices of techniques or actions employed. Thus, this meta-data conceptual modelling and raw data collection, as a data management approach, offers a syntactic, semantic and pragmatic understanding of archival sources. This methodology ultimately aims to study the impact on the landscape of the anti-malarial campaigns by bridging gaps in existing literature by the digitisation of physical reports and digitalisation of a healthcare system from the late 19th to 20th century.

How to cite: Danielkutty, L. E., Krummeich, R., Couillet, A., Charalambidou, I., and Eliot, E.: Constructing an event centred modelling process to produce spatial and temporal data for the critical study of the impact of British colonial rule (1878 – 1960) on the Cypriot landscape through their anti-malarial campaigns, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11346, https://doi.org/10.5194/egusphere-egu23-11346, 2023.

EGU23-11851 | ECS | Posters on site | ITS1.11/NP0.2

A feasibility study for a novel remediation and sustainable growth digital tool for the Environment Agency, England, UK 

Darren Beriro, Yolande Macklin, Jane Thrasher, David Griggs, and Angela Haslam

The remediation of brownfield is vital to sustainable place-making and levelling up across the country. It provides an improved local environment that can unlock regeneration and the social, economic and ecological revitalisation of communities. However the total benefits of remediation are not fully understood or utilised in decision making. As a result, sites can remain derelict for years and opportunities to optimise value from public and private investment are missed.

Jacobs and BGS undertook research for the Environment Agency in England to evaluate the feasibility of developing a tool, which included:

  • A virtual workshop using MURAL to enable digital interaction and collaboration to refine scope, define data requirements and map project stakeholders;
  • Primary benefit and user requirements research, including looking at the potential impact of a tool through the development of a Theory of Change model and focussed interviews with key stakeholders to understand user requirements.
  • Review of academic and grey literature;
  • Accelerated design sprint to frame the problem/opportunity, explore technology agonistic solutions for the tool and develop into a storyboard.
  • Develop a low fidelity prototype as a blueprint of how a tool might look.

The outcome of the work indicated there is both a need and demand for such a tool. It was also demonstrated to be technically feasible through the literature review and design sprint. Such a tool would have an extremely positive impact on the perceptions of brownfield, shifting it from a constraint to an opportunity. The presentation will provide a summary of the methods, an overview of the results and a demonstration of a prototype digital tool. Our disucssion will focus on the opportunities presented by using systems thinking combined with design thinking to influence the approach taken to planning and redeveloping brownfield sites.  

How to cite: Beriro, D., Macklin, Y., Thrasher, J., Griggs, D., and Haslam, A.: A feasibility study for a novel remediation and sustainable growth digital tool for the Environment Agency, England, UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11851, https://doi.org/10.5194/egusphere-egu23-11851, 2023.

EGU23-13059 | ECS | Orals | ITS1.11/NP0.2

Profiling households through a combined vulnerability and flood exposure index in Ho Chi Minh City, Vietnam 

Jiachang Tu, Andrea Reimuth, Antje Katzschner, Liang Emlyn Yang, and Matthias Garschagen

Understanding how the exposure and vulnerability to floods and other climate hazard varies between different groups of urban residents is an urgent prerequisite for guiding urban climate change adaptation policy and action. To be most effective, adaptation measures need to be designed specifically in relation the exposure and vulnerability profiles of different groups. Index approaches have since long been used to cluster households according to different levels of exposure and vulnerability. However, two main gaps remain in current research: First, while indices are typically based on either survey or statistical data, approaches transcending both levels of resolution through proxy variables are rare. Second, profiling is typically not linked to spatial categories such as urban morphology types.

The approach presented here contributes to bridging both gaps. We use original household survey data from Ho Chi Minh City to generate an exposure and vulnerability index for the city. We then test the validity of that index, which is based on detailed data, in comparison to an index which builds on rougher statistical data. In a third step, we test how well vulnerability and exposure profiles from the index map against urban morphology types which can be used in risk modeling in order to analyse in how far valid exposure and vulnerability profiles can be linked to such morphology types. Our results show an existing yet limited link between exposure and vulnerability profiles on the one side and urban morphology types on the other. The results are essential for advancing urban risk modeling in an integrated manner and rolling such modeling out to larger spatial areas.

How to cite: Tu, J., Reimuth, A., Katzschner, A., Yang, L. E., and Garschagen, M.: Profiling households through a combined vulnerability and flood exposure index in Ho Chi Minh City, Vietnam, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13059, https://doi.org/10.5194/egusphere-egu23-13059, 2023.

EGU23-13357 | Orals | ITS1.11/NP0.2

New trends in urban change detection: detecting 3D changes from bitemporal optical images 

Valerio Marsocci, Virginia Coletta, Roberta Ravanelli, Simone Scardapane, and Mattia Crespi

Keywords: Urban sustainability, Earth observation, 3D change detection, Deep Learning, Dataset

Nowadays, remote sensing products can provide useful and consistent information about urban areas and their morphological structures with different spatial and temporal resolutions, making it possible to perform long term spatiotemporal analyses of the historic development of the cities and in this way to monitor the evolution of their urbanization patterns, a goal strictly related to the United Nations (UN) Sustainable Development Goals (SDGs) concerning the sustainability of the cities (SDG 11 - Sustainable Cities and Communities).

In this context, Change Detection (CD) algorithms estimate the changes occurred at ground level and are employed in a wide range of applications, including the identification of urban changes. Most of the recently developed CD methodologies rely on deep learning architectures. Nevertheless, the CD algorithms currently available are mainly focused on generating two-dimensional (2D) change maps, where the planimetric extent of the areas affected by changes is identified without providing any information on the corresponding elevation (3D) variations. These algorithms can thus only identify planimetric changes such as appearing/disappearing buildings/trees, shrinking/expanding structures and are not able to satisfy the requirements of applications which need to detect and, most of all, to quantify the elevation variations occurred in the area of interest (AOI), such as the estimation of volumetric changes in urban areas.

It is therefore essential to develop CD algorithms capable of automatically generating an elevation (3D) CD map (a map containing the quantitative changes in elevation for the AOI) together with a standard 2D CD map, from the smallest possible amount of information. In this contribution, we will present the MultiTask Bitemporal Images Transformer (MTBIT) [1], a recently developed network, belonging to the family of vision Transformers and based on a semantic tokenizer, explicitly designed to solve the 2D and 3D CD tasks simultaneously from bitemporal optical images, and thus without the need to rely directly on elevation data during the inference phase. 

The MTBIT performances were evaluated in the urban area of Valladolid on the modified version of the 3DCD dataset [2], comparing this architecture with other networks designed to solve the 2D CD task. In particular, MTBIT reaches a metric accuracy equal to 6.46 m – the best performance among the compared architectures – with a limited number of parameters (13,1 M) [1].

The code and the 3DCD dataset are available at https://sites.google.com/uniroma1.it/3dchangedetection/home-page.

 

References

[1] Marsocci, V., Coletta, V., Ravanelli, R., Scardapane, S., and Crespi, M.: Inferring 3D change detection from bitemporal optical images. ISPRS Journal of Photogrammetry and Remote Sensing. 2023 - in press.

[2] Coletta, V., Marsocci, V., and Ravanelli, R.: 3DCD: A NEW DATASET FOR 2D AND 3D CHANGE DETECTION USING DEEP LEARNING TECHNIQUES, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 1349–1354, 2022.

How to cite: Marsocci, V., Coletta, V., Ravanelli, R., Scardapane, S., and Crespi, M.: New trends in urban change detection: detecting 3D changes from bitemporal optical images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13357, https://doi.org/10.5194/egusphere-egu23-13357, 2023.

EGU23-13960 | Orals | ITS1.11/NP0.2

Spatial-temporal modeling of COVID-19 areas in Italy and the role of urban settings 

Massimiliano Alvioli, Daniel Fowler, and Samsung Lim

COVID-19 severely affected Italy from the beginning of the pandemic. The number of cases can be analysed within statistical methods, to understand its spread over time, in conjunction with factors as meteorological and environmental conditions, socio-economic conditions and urban settings [1]. The role of spatial aggregation of data is seldom investigated in detail, as the information available to the public is often limited to administrative boundaries.

We investigated the number of infections stratified by spatial location and time, analyzing each of the 107 provinces of Italy during the two infection waves in 2020. Infections were greater in urban areas such as Rome, Milan and Naples. We further investigated the role of urban areas by considering specific spatial aggregations that explicitly included indicators of human presence [2].

We used the hhh4 endemic-epidemic model to study the spatial-temporal pattern of COVID-19 [3]. The model includes three components, representing autoregressive effects (transmission of disease within a single province), neighborhood effects (transmission between provinces) and endemic effects (sporadic events by unobserved sources of infection).  Covariates included daily temperature, humidity, employment rate and number of high-care hospital beds for each province. In addition, we considered specific covariates to account for urban indicators: population, population density, the proportion of urban area, average area of cities, and number of cities. Covariates were considered on both the autoregressive and neighbourhood components to determine the effect of transmission within and between provinces. To simulate the spread between provinces on the neighbourhood component, we considered the spatial adjacency between provinces, and considered decreasing importance with increasing distance.

Outputs from the model included the risk ratios (RRs) of the covariates, with resulting RR of 0.89 on the autoregressive component and RR of 0.83 on the neighbourhood component. An existing study found that higher temperatures were related to a decline in daily confirmed COVID-19 case counts with a corresponding RR of 0.80 [4].

We specifically looked at covariates related to urban settings, as an existing study showed positive correlation between population density and COVID-19 transmission rate [5]. Our results showed a RR of 1.23 (autoregressive component) and RR of 1.48 (neighbourhood component), suggesting that larger population density leads to more infections, and that movement of people across provinces could lead to a higher risk of COVID-19 cases.  Province area, average city area and number of cities were not statistically significant.

Eventually, we explicitly considered the role of urban settings by aggregating spatial-temporal data within individual urban areas [2], instead of administrative boundaries. As COVID-19 data itself were available at the province level, we distributed them to urban areas proportionally to the area occupied within each province; other data was actually aggregated within urban polygons. We argue that study of the spatial-temporal transmission of infection using urban areas may provide reliable results and help selecting characteristics in urban settings that may favour or prevent the spread of diseases.

[1] M. Agnoletti et al. DOI: 10.1016/j.landurbplan.2020

[2] M. Alvioli. DOI: 10.1016/j.landurbplan.2020.103906

[3] S. Meyer et al. DOI: 10.1214/14-AOAS743

[4] J. Liu et al. DOI: 10.1016/j.scitotenv.2020.138513

[5] K.T.L. Sy et al. DOI: 10.1371/journal.pone.024927

How to cite: Alvioli, M., Fowler, D., and Lim, S.: Spatial-temporal modeling of COVID-19 areas in Italy and the role of urban settings, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13960, https://doi.org/10.5194/egusphere-egu23-13960, 2023.

Expansion of built-up areas has consumed large areas of natural ecological patches in many cities around the world, affecting environmental and living quality of urban residents.  Managing urban landscape and urban trees has gained a special attention in Vietnam in recent years. The green space development plan for Hanoi to 2030 and a vision to 2050 has targeted to reach 62% of green spaces. However, there is lack of detailed green space development plan at the district and commune/ward levels. This study aims to assess urban green space area and quality in Hanoi at the commune/ward levels using remote sensing and population data. The study uses combined remote sensing data from Google Earth, Sentinel-2, and Normalized Difference Vegetation Index (NDVI) to analyze urban green quality and space for Hanoi. The urban green space will be combined with population data at commune/ward level to estimate urban tree cover per person. The research results can contribute to improve the credibility and scientifically of green space construction so that urban planning can adapt and serve the city and its residents and achieve green development.

How to cite: Reimuth, A., Nong, D. H., and Ngo, S. T.: Assessing urban green space area and quality using remote sensing and population data: A case study of Hanoi urban districts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14511, https://doi.org/10.5194/egusphere-egu23-14511, 2023.

EGU23-14618 | ECS | Posters on site | ITS1.11/NP0.2

Multifractal analysis of Cn2 scitillometer data and consequences for evapotranspiration estimates in urban areas 

Sitian Zhu, Auguste Gires, Cedo Maksimovic, Ioulia Tchiguirinskaia, and Daniel Schertzer

The cooling impact of green roofs is highlighted in the context of urbanisation and urban heat island (UHI) effect. And it is usually described and quantified by evapotranspiration (ET) processes. Understanding ET process is the key to optimize cooling effect. ET estimation can be achieved either directly (weighing lysimeters) or indirectly (e.g., Penman-Monteith equation). Micro-meteorological approaches have been developed in recent years. Among which scintillometer can evaluate ET by its measurement parameter Cn2 (which corresponds to the fluctuations of air refractive index n ) in combination with surface energy balance (SEB) and Monin-Obukhov similarity theory (MOST) . Hence, Cn2 improvement in Cn2 data would result in better ET estimation. But it is often overlooked and very little research has focused on it. In this project, the research area lies on the top of the Carnot and Bienvenüe buildings in Ecole des Ponts Paristech. Covering an area of ​​1 ha, it is a wavy and vegetated large green roof, known as the Blue Green Wave (BGW). Data from a large aperture scintillometer (LAS) with 10-minute time step during December 2019 and January 2020 on BGW is used in this study. Three estimates of Cn2(Cn2_UCn2, Cn2_PUCn2 and Cn2_Var) were analysed with structure function and universal multifractal model (UM). Such framework has been widely use to characterize geophysical fields extremely variable across wide range of space-time scales. There are two relevant parameters in an UM model, the mean codimension of intermittency C1≥0and multifractality index 0≤α≤2. α=0, indicates monofractal; α=2, indicates log-normal model. Data in UM framework is analysed by Trace Moment (TM) method and Double Trace Moment (DTM) method. All of estimates demonstrated scale invariance, which could be used for upscaling and downscaling. Cn2_Var performed well even during measurement malfunction, but UM analysis showed it was contradictory to the hypothesis of lognormality. It implies the way it calculates Cn2_Var need some revisions and an assessment of the scintillometer could be achieved by analysing Cn2. This research provides a complete grasp of the properties of Cn2 and sets the stage for its future application in precise ET estimates.

 

How to cite: Zhu, S., Gires, A., Maksimovic, C., Tchiguirinskaia, I., and Schertzer, D.: Multifractal analysis of Cn2 scitillometer data and consequences for evapotranspiration estimates in urban areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14618, https://doi.org/10.5194/egusphere-egu23-14618, 2023.

EGU23-14807 | Posters on site | ITS1.11/NP0.2

Monitoring land deformation through PSI technique for Einstein Telescope site characterization of Sos Enattos (Sardinia, Italy) 

Francesco Dessì, Maria Teresa Melis, Stefania Da Pelo, and Antonio Funedda

The Einstein Telescope (ET) is a proposed underground infrastructure to host a third-generation, gravitational-wave observatory. There are currently two candidate sites to host it: one of this is located in Sardinia region (Italy), in a favourable geological context, the other one in the Meuse-Rhine Euregion. Site-characterization studies are under way towards the site selection, which is expected for 2024. The scope work of this research is to evaluate the surface deformation of this site by integration of remote sensing techniques with geological and geophysical data. In this framework the PSI (Persistent Scattered Interferometry) technique with SAR data is the proposed approach for the analysis of a long time-series imagery. Although recent crustal movements in the study area are supposed to be very small (≃ - 0.5 mm/years from 2014 as measured by EUREF Permanent Network https://epnd.sgo-penc.hu), ESA Sentinel-1 data from Copernicus program, represents an effective tool to update this knowledge and monitor the phenomenon. A first analysis has been performed in the study area using the Snap2Stamps methodology. During the first assessment of this research, this methodology has been tested to a dataset of 94 images from Sentinel-1. The radar data (SLC, Single Looking complex) acquired from January 2021 to July 2022 for both descending and ascending orbits on an area of 250 sq km has been managed. The applied methodology requires a long-time for the processing in order to derive vertical velocities, and we considered the opportunity to use a cloud service. So, we exploited the possibility offered by SNAPPING service provided by Terradue (https://www.terradue.com/portal/), a cloud on-demand computing service for Sentinel-1 Multi-Temporal DInSAR processing, based on integrated SNAP and StaMPS chain. In this service, the dataset can be improved, considering a longer time of acquisition, exploiting the complete Sentinel revisiting time, starting from 2014.

The first results of this analysis have been calibrated with the existing GNSS measures provided by EUREF using the data of the Nuoro station. The ground vertical displacement calculations, composing data from both acquisition orbits, confirm the existing evaluations and extend the current information to the whole study area. Moreover, it will be possible to consider also future acquisition with a continuous monitoring process.

These results can be considered an important value for the proposed Italian site and the ET infrastructure realization.

 

How to cite: Dessì, F., Melis, M. T., Da Pelo, S., and Funedda, A.: Monitoring land deformation through PSI technique for Einstein Telescope site characterization of Sos Enattos (Sardinia, Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14807, https://doi.org/10.5194/egusphere-egu23-14807, 2023.

EGU23-16718 | ECS | Posters virtual | ITS1.11/NP0.2 | Highlight

Driving mechanisms of urbanization: Evidence from Geographical, Climatic, Social-economic and Nighttime Light data 

Siyi Huang, Lijun Yu, Danlu Cai, Jianfeng Zhu, Ze Liu, Zongke Zhang, Yueping Nie, and Klaus Fraedrich

Urbanization induced changes have attracted widespread attention. Key challenges arise from the inherent uncertainties in attribution models diagnosing the driving mechanisms and the interrelationships of the attributes given by the complexity of interactions within a city. Here, we investigate urbanization dynamics from nighttime light signals before analyzing their driving mechanisms from 2014 to 2020 on both provincial and regional scale and a flat versus mountainous urbanization comparison. Model uncertainties are discussed comparing the contribution results from Geodetector and the Gini importance from Random Forest analyses. The method is applied to Shaanxi Province, where flat urban land is located mainly in its center and mountainous urban land is situated in the North and South. The following results are noted: i) Employing the Geodetector based maximum contribution method for urban region extraction of night time light reveals a notable accuracy improvement in flat urban land compared with the closest area method. ii) Geographical factors attain high contribution for mountainous urban land of Shannan, while for flat urbanization land dynamics, economic factors and community factors prevail. iii) The most obvious driving mechanisms are economic factors which, associated with local urban development strategies, show highest contribution values in 2014 (2018) over the flat (mountainous) urban land of Guanzhong Plain (Northern Shaanxi Plateau or Shanbei region) linked with an early (late) development. iv) Population factors achieve high contribution values in the initially low populated urban land of the northern mountainous land initiating huge migration. v) The contributions resulting from Geodetector are in agreement with the Gini importance from Random Forest in agriculture, geographical and population factors (R2 > 0.5) but not in economy, community and climatic factors (R2 < 0.5). The dynamics of driving mechanisms for urbanization provides insights in connecting urban geographical expansion with multi-factors and thus to assist municipal governments and city stakeholders to design a city with geographical, climatic and social-economic changes and interactions in mind.

How to cite: Huang, S., Yu, L., Cai, D., Zhu, J., Liu, Z., Zhang, Z., Nie, Y., and Fraedrich, K.: Driving mechanisms of urbanization: Evidence from Geographical, Climatic, Social-economic and Nighttime Light data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16718, https://doi.org/10.5194/egusphere-egu23-16718, 2023.

EGU23-16766 | ECS | Orals | ITS1.11/NP0.2

Supervised versus semi-supervised urban functional area prediction: uncertainty, robustness and sensitivity 

Rui Deng, Yanning Guan, Danlu Cai, Tao Yang, Klaus Fraedrich, Chunyan Zhang, Jiakui Tang, Zhouwei Liao, Zhishou Wei, and Shan Guo

To characterize a community-scale urban functional area using geo-tagged data and available land-use information, several supervised and semi-supervised classification models are presented and evaluated in Hong Kong for comparing their uncertainty, robustness and sensitivity. The following results are noted: (i) As the training set size grows, models’ accuracies are improved, particularly for multi-layer perceptron (MLP) or random forest (RF). The graph convolutional network (GCN) (MLP or RF) model reveals top accuracy when the proportion of training samples is less (greater) than 10% of the total number of functional areas; (ii) With a large amount of training samples, MLP shows the highest prediction accuracy and good performances in cross-validation, but less stability on same training sets; (iii) With a small amount of training samples, GCN provides viable results, by incorporating the auxiliary information provided by the proposed semantic linkages, which is meaningful in real-world predictions; (iv) When the training samples are less than 10%, one should be cautious using MLP to test the optimal epoch for obtaining the best accuracy, due to its model overfitting problem. The above insights could support efficient and scalable urban functional area mapping, even with insufficient land-use information (e.g., covering only ~20% of Beijing in the case study).

How to cite: Deng, R., Guan, Y., Cai, D., Yang, T., Fraedrich, K., Zhang, C., Tang, J., Liao, Z., Wei, Z., and Guo, S.: Supervised versus semi-supervised urban functional area prediction: uncertainty, robustness and sensitivity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16766, https://doi.org/10.5194/egusphere-egu23-16766, 2023.

EGU23-257 | ECS | Posters on site | ITS2.1/NP0.4

Assessment of the Long-term Temporal Resilience of the Indian Terrestrial Ecosystems: Insights into the Country-scale Drivers 

Abhishek Chakraborty, Sekhar Muddu, and Lakshminarayana Rao

The knowledge of the long-term resilience of Indian terrestrial ecosystems is essential in the background of massive land-use conversion to croplands, intensification of irrigation, and the enhanced climate change signals over the past few decades. Previous assessments of Indian ecosystem resilience were limited by a smaller temporal span, lack of consideration for the sub-annual ecosystem transitions, and non-aridity-based stressors of the loss of resilience of ecosystems (Sharma and Goyal, 2017, Glob Chang Biol; Kumar and Sharma, 2023, J Environ Manage). This study aims towards a comprehensive understanding of the resilience of Indian terrestrial ecosystems through monthly scale assessment considering the driving role of the stressors in a standalone and compound manner.

The study utilizes ecosystem water use efficiency (WUE) as a state variable to assess the resilience of Indian ecosystems. WUE, produced from the FLUXCOM RS+METEO gross primary productivity (GPP) and evapotranspiration (ET) datasets at a monthly scale (WUEe=GPP/ET) from 1950 to 2010 (Jung et al., 2019, Sci Data; Tramontana et al., 2016, Biogeosciences), is a metric to quantify the strength of the coupling between terrestrial water and carbon cycles. Further lag-1 autocorrelation time series (AC(1)) is produced by evaluating the Kendall tau correlations for each pixel's residual component of the decomposed time series of WUE (excluding the impacts of trends and seasonal cycles). Such higher-order statistical assessments have been used earlier to quantify the loss of resilience (Smith et al., 2022, Nat Clim Change; Boulton et al., 2022, Nat Clim Change). We conduct the AC(1) analysis for resilience for India's six homogeneous meteorological regions, the eight major river basins, and the biome scale. We further consider the impacts of different forms of aridity on the loss of resilience: atmospheric aridity, hydrological aridity, and soil moisture aridity, individually and in a compound pattern. We also assess the loss of resilience at a seasonal scale (winter, summer, monsoon, post-monsoon) for the two major anthropogenic influences on Indian ecosystems: intensity of irrigation and groundwater fluctuations. This study attempts at a holistic understanding of the loss of resilience through its quantification and impacts of drivers, which could help the policymakers to identify the hotspots of loss of resilience and the significant perturbations to the resilience of Indian terrestrial ecosystems.

How to cite: Chakraborty, A., Muddu, S., and Rao, L.: Assessment of the Long-term Temporal Resilience of the Indian Terrestrial Ecosystems: Insights into the Country-scale Drivers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-257, https://doi.org/10.5194/egusphere-egu23-257, 2023.

EGU23-406 | ECS | Posters on site | ITS2.1/NP0.4

Stochastic data adapted AMOC box models 

Ruth Chapman, Peter Ashwin, and Richard Wood

The Atlantic Meridional overturning Circulation is responsible for the comparatively temperate climate found in Western Europe, and its previous collapse thought to have triggered glacial periods seen in the paleo data. This is a system that has multiple stable states- referred to as ‘on’ when the circulation is strong as in the current climate, and ‘off’ when it is much weaker. The AMOC has tipping points between these states. Tipping points occur when a rapid shift in dynamics happens in response to a relatively small change in a parameter. Making future projections of AMOC response to the climate is essential for avoiding any anthropogenic caused tipping, but it is computationally expensive to calculate the full hysteresis for different scenarios. This work looks at a conceptual five box model of the AMOC [1] which is easy to understand and cheap to implement. Previous work has considered bifurcation and rate-dependent tipping [2] of this model. This current work looks to estimate a realistic amount of noise from various GCM data sets and apply this to the model. We compare the covariance of the salinity data for a variety of CMIP6 models, and we compare the amount of noise covariance found in each data set, and how this can be input back into the box model. We perform some analysis to suggest where in the model the largest noise sources should be found.

[1] Wood, R. et.al. (2019), Climate Dynamics, 53(11), 6815-6834

[2] Alkhayuon, H. et.al. (2019), Proc. R. Soc. A, 475(2225)

How to cite: Chapman, R., Ashwin, P., and Wood, R.: Stochastic data adapted AMOC box models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-406, https://doi.org/10.5194/egusphere-egu23-406, 2023.

EGU23-687 | ECS | Orals | ITS2.1/NP0.4

Stochastic resonance, climate variability, and phase-tipping: The increasing risk of extinction in cyclic ecosystems 

Hassan Alkhayuon, Rebecca Tyson, and Sebastian Wieczorek

Global warming is expected to lead to increase in amplitude and autocorrelation in climate variability in most locations around the world. These changes could have a great and imminent impact on ecosystems. In this work, we demonstrate that changes in climate variability can drive cyclic predator-prey ecosystems to extinction via so-called phase tipping (P-tipping), a new type of instability that occurs only from certain phases of the predator-prey cycle. We coupled a simple mathematical model of climate variability to a self-oscillating paradigmatic predator-prey model. Most importantly, we combine realistic parameter values for the Canada lynx and snowshoe hare with actual climate data from the boreal forest to demonstrate that critically important species in the boreal forest have increased likelihood of extinction under predicted changes in climate variability. The cyclic populations of these species are most vulnerable during stages of the cycle when the predator population is near its maximum. We identify stochastic resonance as the underlying mechanism for the increased likelihood extinction.

How to cite: Alkhayuon, H., Tyson, R., and Wieczorek, S.: Stochastic resonance, climate variability, and phase-tipping: The increasing risk of extinction in cyclic ecosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-687, https://doi.org/10.5194/egusphere-egu23-687, 2023.

EGU23-1021 | ECS | Posters on site | ITS2.1/NP0.4

Impact of tropical cyclones on global ecosystems 

Chahan M. Kropf, Loïc Pellissier, Lisa Vaterlaus, Christopher Fairless, and David N. Bresch

Human societies rely on the existence of functioning global ecosystems, which are threatened by a combination of gradual changes and extreme events. Among the latter, natural hazards such as wildfires or floods can play a *functional* role for ecosystems, with plant and animal species requiring regular disturbance in their life-cycle in order to thrive, but beyond a threshold, the extreme events might cause ecosystem degradation.

Here we map and project the risk of tropical cyclones on coastal ecosystems worldwide, using the probabilistic risk model CLIMADA to describe the vulnerability of global terrestrial ecosystems to tropical cyclones. First, a baseline for the current climate conditions is used to determine whether ecosystems are resilient, dependent, or vulnerable to tropical cyclones. We show that most ecosystems in the tropics are at least resilient to lower-intensity storms, but only a few ecosystems are not vulnerable to high-intensity storms. Second, the changes in tropical cyclone frequency under the high-emission scenario RCP8.5 in 2050 are used to determine which ecosystems are at risk. We show that while the global increase in the frequency of strong storms is the most threatening effect, several ecosystems with a dependency relationship are also at risk of locally decreasing frequency of low to middle-intensity storms.

Our study paves the way for a better understanding of the functional and vital relationship between extreme weather events and ecosystems at a global scale, and how regime shifts under climate change might threaten them. This can prove useful to improve ecosystem management and design appropriate nature-based protection measures in a rapidly changing climate.  

How to cite: Kropf, C. M., Pellissier, L., Vaterlaus, L., Fairless, C., and Bresch, D. N.: Impact of tropical cyclones on global ecosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1021, https://doi.org/10.5194/egusphere-egu23-1021, 2023.

EGU23-1283 | Orals | ITS2.1/NP0.4

Drought mortality and resilience of savannas and forests in tropical Asia 

Simon Scheiter, Dushyant Kumar, Mirjam Pfeiffer, and Liam Langan

The projected increase of drought occurrence under future climates will affect terrestrial ecosystems, particularly by increasing drought-induced tree mortality. The capacity to simulate drought mortality in vegetation models is therefore essential to understand climate change impacts on ecosystem functions and services, as well as on functional diversity. Using the trait-based vegetation model aDGVM2, we assessed tree mortality under drought conditions in tropical Asia under future climate, and if vegetation is resilient to drought or if tipping point behavior occurs. We further assessed how drought impacts are related to pre-drought community composition and diversity. We conducted model simulations for multiple sites in tropical Asia, representing a biogeographic gradient ranging from savannas to tropical forests. Responses of vegetation attributes and mortality rates were simulated until 2099 under the RCP8.5 scenario. Repeated droughts of different length were modeled to test drought impacts and resilience. Finally, the diversity of pre-drought communities was constrained by removing different trait syndromes to test how community composition and diversity influence drought resistance and resilience. Model simulations showed substantial biomass dieback during drought which was attributed to increased mortality rates, primarily among tall and old trees. Drought response differed between current and elevated CO2 levels under RCP8.5, with higher biomass recovery under elevated CO2 due to fertilization effects. Pre-drought community composition influenced biomass dieback and mortality during drought, and the presence or absence of drought-adapted plants had the highest effect on drought impacts. Despite severe drought impacts, recovery of most vegetation attributes was possible after drought periods. We conclude that repeated droughts under future conditions will have vast impacts on vegetation attributes and mortality in tropical ecosystems. Conserving functional diversity in ecosystems buffers drought impacts. However, according to model results, vegetation is resilient, and irreversible transitions to alternative vegetation states do, for the investigated scenarios, not occur. Improved models representing lagged drought impacts, irreversible damage of individual plants, and the interactions between drought regimes, CO2 fertilization and trait diversity are required.

How to cite: Scheiter, S., Kumar, D., Pfeiffer, M., and Langan, L.: Drought mortality and resilience of savannas and forests in tropical Asia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1283, https://doi.org/10.5194/egusphere-egu23-1283, 2023.

A hybrid data assimilation algorithm is developed for complex dynamical systems with partial observations. The method starts with applying a spectral decomposition to the entire spatiotemporal fields, followed by creating a machine learning model that builds a nonlinear map between the coefficients of observed and unobserved state variables for each spectral mode. A cheap low-order nonlinear stochastic parameterized extended Kalman filter (SPEKF) model is employed as the forecast model in the ensemble Kalman filter to deal with each mode associated with the observed variables. The resulting ensemble members are then fed into the machine learning model to create an ensemble of the corresponding unobserved variables. In addition to the ensemble spread, the training residual in the machine learning-induced nonlinear map is further incorporated into the state estimation that advances the quantification of the posterior uncertainty. The hybrid data assimilation algorithm is applied to a precipitation quasi-geostrophic (PQG) model, which includes the effects of water vapor, clouds, and rainfall beyond the classical two-level QG model. The complicated nonlinearities in the PQG equations prevent traditional methods from building simple and accurate reduced-order forecast models. In contrast, the SPEKF model is skillful in recovering the intermittent observed states, and the machine learning model effectively estimates the chaotic unobserved signals. Utilizing the calibrated SPEKF and machine learning models under a moderate cloud fraction, the resulting hybrid data assimilation remains reasonably accurate when applied to other geophysical scenarios with nearly clear skies or relatively heavy rainfall, implying the robustness of the algorithm for extrapolation.

How to cite: Mou, C., Smith, L. M., and Chen, N.: Combining Stochastic Parameterized Reduced-Order Models with Machine Learning for Data Assimilation and Uncertainty Quantification with Partial Observations , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1335, https://doi.org/10.5194/egusphere-egu23-1335, 2023.

EGU23-2147 | ECS | Posters on site | ITS2.1/NP0.4

Bifurcations and Early-Warning Signs for SPDEs 

Paolo Bernuzzi and Christian Kuehn

Bistability is a key property of many systems arising in the nonlinear sciences. For example, it appears in many partial differential equations (PDEs). For scalar bistable reaction-diffusions PDEs, the bistable case even has taken on different names within communities such as Allee, Allen-Cahn, Chafee-Infante, Nagumo, Ginzburg-Landau, Schlögl, Stommel, just to name a few structurally similar bistable model names. One key mechanism, how bistability arises under parameter variation is a pitchfork bifurcation. In particular, taking the pitchfork bifurcation normal form for reaction-diffusion PDEs is yet another variant within the family of PDEs mentioned above. More generally, the study of this PDE class considering steady states and stability, related to bifurcations due to a parameter is well-understood for the deterministic case. For the stochastic PDE (SPDE) case, the situation is less well-understood and has been studied recently. We generalize and unify several recent results for SPDE bifurcations. Our generalisation is motivated directly by applications as we introduce in the equation a spatially heterogeneous term and relax the assumptions on the covariance operator that defines the noise. For this spatially heterogeneous SPDE, we prove a finite-time Lyapunov exponent bifurcation result. Furthermore, we extend the theory of early warning signs in our context and we explain the role of universal exponents between covariance operator warning signs and the lack of finite-time Lyapunov uniformity. Our results are accompanied and cross-validated by numerical simulations.

How to cite: Bernuzzi, P. and Kuehn, C.: Bifurcations and Early-Warning Signs for SPDEs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2147, https://doi.org/10.5194/egusphere-egu23-2147, 2023.

EGU23-2359 | ECS | Orals | ITS2.1/NP0.4

Uncertainties in critical slowing down indicators of observation-based fingerprints of the AMOC 

Maya Ben Yami, Niklas Boers, Vanessa Skiba, and Sebastian Bathiany

In recent years, sea-surface temperature (SST) and salinity-based indices have been used to detect critical slowing down (CSD) indicators for a possible collapse of the Atlantic Meridional Overturning Circulation (AMOC). However, these observational SST and salinity datasets have inherent uncertainties and biases which could influence the CSD analysis. Here we present an in-depth uncertainty analysis of AMOC CSD indicators. We first use uncertainties provided with the HadSST4 and HadCRUT5 datasets to generate uncertainty ensembles and estimate the uncertainty of SST-based AMOC fingerprints, and we then calculate stringent significance measures on the CSD indicators in the EN4.2.2, HadISST1 and HadCRUT5 datasets.

How to cite: Ben Yami, M., Boers, N., Skiba, V., and Bathiany, S.: Uncertainties in critical slowing down indicators of observation-based fingerprints of the AMOC, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2359, https://doi.org/10.5194/egusphere-egu23-2359, 2023.

EGU23-2840 | Posters on site | ITS2.1/NP0.4

Estimate of Critical Thresholds with Variance and Parabolic Approximations 

Alessandro Cotronei and Martin Rypdal

It is wide scientific consensus that tipping points, in the form of rapid, large and irreversible changes in features of the climate system, are a possible scenario consequent to anthropogenic climate change. In literature there are several ways to detect the so-called Early-Warning-Signals, indicators (as increasing variance) that these changes are close to our current state and that the climate state is about to shift. We propose two novel indicators based on variance and parabolic approximations that expand the current theory to detect these EWSs. We show that the methods can produce estimations for the critical thresholds for particular systems. We finally show that our indicators predict close thresholds for the loss of ice of the Greenland ice sheet.

How to cite: Cotronei, A. and Rypdal, M.: Estimate of Critical Thresholds with Variance and Parabolic Approximations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2840, https://doi.org/10.5194/egusphere-egu23-2840, 2023.

EGU23-3074 | ECS | Orals | ITS2.1/NP0.4 | Highlight

Overshooting the critical threshold for the Greenland ice sheet 

Nils Bochow, Anna Poltronieri, Martin Rypdal, Alexander Robinson, and Niklas Boers

Global sea level rise due to the melting of the Greenland ice sheet (GrIS) in response to anthropogenic global warming poses a severe threat to ecosystems and human society (IPCC, 2021). Modelling and paleoclimatic evidence suggest that rapidly increasing temperatures in the Arctic can trigger positive feedback mechanisms, and the GrIS is hypothesised to exhibit multiple stable states (Gregory et al., 2020). 
Consequently, critical transitions are expected when the global mean surface temperature crosses specific thresholds, and there is substantial hysteresis between the alternative stable states (Robinson et al., 2012). 
Here, we investigate the impact of different climate scenarios that overshoot temperature goals and then return to lower temperatures at different pace. Our results show that both the maximum GMT and the time span of overshooting given GMT targets are critical in determining GrIS stability. We find an abrupt loss of the ice sheet for a threshold temperature, preceded by several intermediate stable states. We show that even temporarily overshooting the temperature threshold may lead to catastrophic consequences in specific scenarios. On the other hand, overshoots might be tolerable if GMTs are subsequently reduced below 1.5°C GMT above pre-industrial levels within a few centuries. Even without a transition to a new ice sheet state the short-term global sea level rise can exceed several metres before returning to moderate GMTs.

Allan, R. P., Hawkins, E., Bellouin, N., & Collins, B. (2021). IPCC, 2021: Summary for Policymakers (V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, & B. Zhou, Eds.). Cambridge University Press. https://centaur.reading.ac.uk/101317/

Gregory, J. M., George, S. E., & Smith, R. S. (2020). Large and irreversible future decline of the Greenland ice sheet. The Cryosphere, 14(12), 4299–4322. https://doi.org/10.5194/tc-14-4299-2020

Robinson, A., Calov, R., & Ganopolski, A. (2012). Multistability and critical thresholds of the Greenland ice sheet. Nature Climate Change, 2(6), 429–432. https://doi.org/10.1038/nclimate1449

How to cite: Bochow, N., Poltronieri, A., Rypdal, M., Robinson, A., and Boers, N.: Overshooting the critical threshold for the Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3074, https://doi.org/10.5194/egusphere-egu23-3074, 2023.

EGU23-3246 | ECS | Posters on site | ITS2.1/NP0.4

Links between climate tipping elements: A story of ice, overturning and trade winds 

Swinda Falkena and Anna von der Heydt

Within the earth system several tipping elements exist. It is important to understand the links between these tipping elements, as a critical transition in one element could lead to tipping of another. Here, we study the links between some of these tipping elements in CMIP6 data. The starting point is the Atlantic Meridional Overturning Circulation (AMOC), whose collapse would have world-wide impacts and for which nearly all climate models show a decrease in the strength. In the Northern Hemisphere it would induce wide-spread cooling, impacting both sea-ice and the Greenland Ice Sheet (GIS). The corresponding changes in the global distribution of heat impact the atmospheric circulation. Where the response of the trade winds in the Atlantic is still relatively similar between models, this is not the case for the Pacific resulting in large uncertainty in the El Nino Southern Oscillation (ENSO) response.

To understand the effect of the AMOC on ENSO and other tipping elements, we consider the effect it has on the physical processes involved. For example, to study the effect of the AMOC on ENSO we consider its effect on the Pacific trade winds and other physically relevant variables. This aids in better understanding the consequences of an AMOC collapse and the potential for tipping cascades.

How to cite: Falkena, S. and von der Heydt, A.: Links between climate tipping elements: A story of ice, overturning and trade winds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3246, https://doi.org/10.5194/egusphere-egu23-3246, 2023.

EGU23-3291 | ECS | Orals | ITS2.1/NP0.4

The combined effect of global warming and AMOC collapse on the Amazon Forest 

Da Nian, Sebastian Bathiany, Maya Ben-Yami, Lana Blaschke, Marina Hirota, Regina Rodrigues, and Niklas Boers

The Amazon forest is at risk of dieback due to climate change, in particular decreasing mean annual precipitation (MAP) and increasing mean annual temperature (MAT). This study assesses the influence on South American vegetation under two possible future climate change scenarios: global warming, and global warming combined with an AMOC collapse. We consider MAT and MAP as control parameters and use their projected changes from climate model simulations with the Earth System Model HadGEM3. We then estimate the most probable states of vegetation based on empirical relationships between these parameters and tree cover. Our results suggest that an AMOC collapse would not contribute to further rainforest dieback over most of the Amazon basin. Instead, in parts of tropical South America, MAP increases and MAT decreases after AMOC collapse, which tends to stabilize the Amazon forest and hence delay the Amazon dieback compared to the default global warming scenario.

How to cite: Nian, D., Bathiany, S., Ben-Yami, M., Blaschke, L., Hirota, M., Rodrigues, R., and Boers, N.: The combined effect of global warming and AMOC collapse on the Amazon Forest, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3291, https://doi.org/10.5194/egusphere-egu23-3291, 2023.

EGU23-3328 | Orals | ITS2.1/NP0.4 | Highlight

Using self-organization to build climate-resilient ecosystems 

Johan Van de Koppel, Loreta Cornacchia, Roeland Van de Vijsel, and Daphne Van der Wal

Whether current-day ecosystems, often heavily modified by humans, can adapt to climate change is one of the most pressing scientific questions. Coastal ecosystems are at the forefront of climate impact, as salt marshes and intertidal flats may drown if these systems cannot follow sea level rise.

We developed a model to investigate how the emergence of complex creek networks during early salt marsh development affects the ability of marsh ecosystems to accumulate sediment, thereby compensating for sea level rise. This model is based on a scale-dependent feedback relation between vegetation growth and sedimentation, as plants locally block water flow, which then diverts to their surroundings. The model revealed that this self-organization process drives the emergence of a complex creek network of ever smaller creeks nested in between larger ones.

We used the model to analyze the importance of creek network complexity for the rate at which marshes accumulate sediment. The model highlights that in salt marshes, plant traits have a defining effect on the development of creek network complexity. Yet, it is the emergent effect of creek network complexity on sedimentation, rather than plant traits directly, that controlled sedimentation rates, determining the adaptive capacity of the marsh to sea level rise. Self-organized creek complexity proved a defining characteristic determining the resilience of this ecosystem to climate change.

We used our model to study whether restored coastal wetlands can be designed in such a way as to improve the adaptive capacity to sea level rise. We explored 14 realigned coastal wetlands and related the established, real-world creek network, being either entirely artificial dug-out channels or naturally formed creeks, to their potential, model-predicted sedimentation rate.

We observed that the developing channel networks in restored wetlands had much lower creek development and channel branching than natural systems, resulting in less efficient channel networks. Model simulations showed that if artificial creek networks deviated more from the creek pattern observed in natural ecosystems, or from the ones predicted from our model, they had lower sediment transport efficiency. Our findings suggest that if a more natural organization is followed when designing climate-proof coastal ecosystems, they are more resilient to climate change.

How to cite: Van de Koppel, J., Cornacchia, L., Van de Vijsel, R., and Van der Wal, D.: Using self-organization to build climate-resilient ecosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3328, https://doi.org/10.5194/egusphere-egu23-3328, 2023.

EGU23-3354 | ECS | Posters on site | ITS2.1/NP0.4

Tipping points in hydrology: attribution of regime shifts using historical climate simulations and dynamical system modeling 

Erwan Le Roux, Valentin Wendling, Gérémy Panthou, Paul-Alain Raynal, Abdramane Ba, Ibrahim Bouzou-Moussa, Jean-Martial Cohard, Jérome Demarty, Fabrice Gangneron, Manuela Grippa, Basile Hector, Pierre Hiernaux, Laurent Kergoat, Emmanuel Lawin, Thierry Lebel, Olivier Mora, Eric Mougin, Caroline Pierre, Jean-Louis Rajot, and Christophe Peugeot and the TipHyc Project
The Sahel (the semi-arid fringe south of the Sahara) experienced a severe drought in the 70s-90s. During this drought, an hydrological regime shift was observed for most watersheds in the Central Sahel: runoff has significantly increased despite the rainfall deficit. Did the drought cause this regime shift ? What if the drought did not happen ? To answer these questions, we introduce a simple dynamical model that represents feedbacks between soil, vegetation and runoff at the watershed scale and at the annual time step. This model is forced with annual rainfall and evaluated using long-term observations of runoff from selected watersheds. We find that the model forced with observed rainfall reproduces well the observed regime shift in runoff. For the attribution of the regime shift to the drought, we rely on two sets of historical rainfall simulations from CMIP6 global climate models: fully-coupled simulations that do not reproduce the drought, and atmosphere-only simulations (AMIP) that represent the drought. Our results show that a regime shift would have been unlikely without the drought. This approach will be extended to identify areas that are likely to experience an hydrological regime shift in the future.

How to cite: Le Roux, E., Wendling, V., Panthou, G., Raynal, P.-A., Ba, A., Bouzou-Moussa, I., Cohard, J.-M., Demarty, J., Gangneron, F., Grippa, M., Hector, B., Hiernaux, P., Kergoat, L., Lawin, E., Lebel, T., Mora, O., Mougin, E., Pierre, C., Rajot, J.-L., and Peugeot, C. and the TipHyc Project: Tipping points in hydrology: attribution of regime shifts using historical climate simulations and dynamical system modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3354, https://doi.org/10.5194/egusphere-egu23-3354, 2023.

EGU23-3612 | Posters on site | ITS2.1/NP0.4

Automatic characterisation of Dansgaard-Oeschger events in palaeoclimate ice records 

Susana Barbosa, Maria Eduarda Silva, Nuno Dias, and Denis-Didier Rousseau

Greenland ice core records display abrupt transitions, designated as Dansgaard-Oeschger (DO) events, characterised by episodes of rapid warming (typically decades) followed by a slower cooling. The identification of abrupt transitions is hindered by the typical low resolution and small size of paleoclimate records, and their significant temporal variability. Furthermore, the amplitude and duration of the DO events varies substantially along the last glacial period, which further hinders the objective identification of abrupt transitions from ice core records Automatic, purely data-driven methods, have the potential to foster the identification of abrupt transitions in palaeoclimate time series in an objective way, complementing the traditional identification of transitions by visual inspection of the time series.

In this study we apply an algorithmic time series method, the Matrix Profile approach, to the analysis of the NGRIP Greenland ice core record, focusing on:

- the ability of the method to retrieve in an automatic way abrupt transitions, by comparing the anomalies identified by the matrix profile method with the expert-based identification of DO events;

- the characterisation of DO events, by classifying DO events in terms of shape and identifying events with similar warming/cooling temporal pattern

The results for the NGRIP time series show that the matrix profile approach struggles to retrieve all the abrupt transitions that are identified by experts as DO events, the main limitation arising from the diversity in length of DO events and the method’s dependence on fixed-size sub-sequences within the time series. However, the matrix profile method is able to characterise the similarity of shape patterns between DO events in an objective and consistent way.

How to cite: Barbosa, S., Silva, M. E., Dias, N., and Rousseau, D.-D.: Automatic characterisation of Dansgaard-Oeschger events in palaeoclimate ice records, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3612, https://doi.org/10.5194/egusphere-egu23-3612, 2023.

A deterministic excitation (DE) paradigm is formulated, according to which the abrupt glacial-interglacial transitions occurred after the Mid-Pleistocene Transition correspond to the excitation by the orbital forcing, of nonlinear relaxation oscillations (ROs) internal to the climate system in the absence of any stochastic parameterization. Specific rules are derived from the DE paradigm: they parameterize internal climate feedbacks which, when activated by the crossing of certain tipping points, excite a RO. Such rules are then applied to the fluctuations of the glacial state simulated by a conceptual model subjected to realistic orbital forcing. The timing of the glacial terminations thus obtained in a reference simulation is found to be in good agreement with proxy records; besides, a sensitivity analysis insures the robustness of the timing. The role of noise in the glacial-interglacial transitions and the problems arising in the implementation of theories in which noise is crucial (such as stochastic resonance) are finally discussed. In conclusion, the DE paradigm provides the simplest possible dynamical systems characterization of the link between orbital forcing and glacial terminations implied by the Milankovitch hypothesis.

How to cite: Pierini, S.: The deterministic excitation paradigm, with application to the glacial-interglacial transitions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3864, https://doi.org/10.5194/egusphere-egu23-3864, 2023.

Greenland ice core records feature Dansgaard–Oeschger (DO) events; abrupt warming episodes followed by a gradual cooling phase during mid-glacial periods. Here, we analysis spontaneous self-sustained D-O type oscillations reproduced in three climate models: CCSM4, MPI-ESM and HadCM3. The three models show D-O type oscillatory behaviour in a remarkably similar, narrow window of atmospheric CO2 concentrations between approximately 185 to 230 parts per million (ppm). This CO2 range also compares particularly well with Marine Isotopic Stage 3 (MIS 3 - between 27.8 – 59.4 thousand of years BP, hereafter ka) atmospheric CO2 values (∼ 233-187.5 ppm), when D-O events occurred with most regularity. Outside this CO2 window of oscillatory behaviour, two different stable states are shown in the three models; warm high CO2 (strong AMOC) and cold low CO2 (weak AMOC) states. The weak state remains stable below the first critical tipping point near 185-195 ppm and the strong state remains stable above the second tipping point near 217-230 ppm. In all three models, the oscillatory experiments with higher CO2 show an increased built-up of stadial salinity in the upper ocean in the subtropics, especially in the eastern edge of the North Atlantic Current, compared with the ensemble mean: the tendency to re-invigorate the Atlantic Meridional Overturning Circulation (AMOC) is increased and so the system spend less time in the stadial phase. CO2 also affects North Atlantic and Arctic sea ice, determining interstadial and stadial duration. Similar sensitivity CO2 experiments performed with other climate models may help in further constraining the here-identified range of atmospheric CO2 (∼185-230 ppm) bounding this D-O sweet-spot. 

How to cite: Malmierca Vallet, I. and Sime, L. C.: Atmospheric CO2 impact on spontaneous Dansgaard–Oeschger type oscillations: oscillatory sweet-spot for three climate models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4149, https://doi.org/10.5194/egusphere-egu23-4149, 2023.

EGU23-4501 | ECS | Posters virtual | ITS2.1/NP0.4

Identifying topological tipping points in noise-driven chaotic dynamics using random templexes 

Gisela Daniela Charó, Michael Ghil, and Denisse Sciamarella

Random attractors are the time-evolving pullback attractors of stochastically perturbed, deterministically chaotic dynamical systems. These attractors have a structure that changes in time, and that has been characterized recently using BraMAH cell complexes and their homology groups (Chaos, 2021, doi:10.1063/5.0059461). A more complete description is obtained for their deterministic counterparts if the cell is endowed with a directed graph (digraph) that prescribes cell connections in terms of the flow direction. Such a topological description is given by a templex, which carries the information of the structure of the branched manifold, as well as information on the flow (Chaos, 2022, doi:10.1063/5.0092933). The present work (Chaos, 2023, arXiv:2212.14450 [nlin.CD]) introduces the stochastic version of a templex. Stochastic attractors in the pullback approach, like the LOrenz Random Attractor (LORA), include sharp transitions in their branched manifold. These sharp transitions can be suitably described using what we call here a random templex. In a random templex, there is one cell complex per snapshot of the random attractor and the cell complexes are such that changes can be followed in terms of how the generators of the homology groups, i.e., the “holes” of these complexes, evolve. The nodes of the digraph are the generators of the homology groups, and its directed edges indicate the correspondence between holes from one snapshot to the next. Topological tipping points can be identified with the creation, destruction, splitting or merging of holes, through a definition in terms of the nodes in the digraph.

How to cite: Charó, G. D., Ghil, M., and Sciamarella, D.: Identifying topological tipping points in noise-driven chaotic dynamics using random templexes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4501, https://doi.org/10.5194/egusphere-egu23-4501, 2023.

EGU23-5180 | Orals | ITS2.1/NP0.4 | Highlight

Emerging signals of a global drift in forest resilience under climate change 

Giovanni Forzieri, Vasilis Dakos, Nate G Mc Dowell, Ramdane Alkama, and Alessandro Cescatti

The persistence and functionality of forest ecosystems are highly dependent on their resilience to the ongoing rapid changes in climate conditions and in natural and anthropogenic pressures. Experimental evidences of a sudden increase in tree mortality across different biomes are rising concerns about the ongoing changes in forest resilience. However, how forest resilience, which is the capacity to withstand and recover from perturbations, is evolving in response to global changes is not yet explored. Here, we integrate satellite-based vegetation indices with machine learning to show how forest resilience, quantified in terms of critical slowing down indicators, has changed over the period 2000-2020. We show that tropical, arid and temperate forests are experiencing a significant decline in resilience, likely related to the increase in water limitations and climate variability. On the contrary, boreal forests show an increasing trend in resilience, likely for the benefits of climate warming and CO2 fertilization in cold biomes, which may outweigh the adverse effects of climate change. These patterns emerge consistently in both managed and intact forests corroborating the existence of common large-scale climate drivers. Reductions in resilience are statistically linked to abrupt declines in forest productivity, occurring in response to a slow drifting toward a critical resilience threshold. We estimate that about 22% of intact undisturbed forests, corresponding to 3.32 Pg C of GPP, have already reached such critical threshold and are experiencing a further degradation in resilience. Altogether, these signals reveal a widespread and increasing instability of global forests and should be accounted for in the design of land-based mitigation and adaption plans.

How to cite: Forzieri, G., Dakos, V., G Mc Dowell, N., Alkama, R., and Cescatti, A.: Emerging signals of a global drift in forest resilience under climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5180, https://doi.org/10.5194/egusphere-egu23-5180, 2023.

EGU23-5250 | ECS | Orals | ITS2.1/NP0.4

Detecting Critical Slowing Down under the influence of continuous-time Red Noise 

Andreas Morr and Niklas Boers

The observational research of tipping elements in the climate system relies largely on time series analysis via so-called Early Warning Signals. An upward trend in the estimated variance or lag-1 autocorrelation of the observable may be a sign for Critical Slowing Down (CSD), a phenomenon exhibited during the destabilization a system’s fixed point. This approach has been employed extensively both for assessing contemporary tipping risks [1] and understanding the dynamics in the advent of past abrupt climate change [2]. However, this inference of destabilization from statistical observations is in general only valid under certain model assumptions with regard to both the deterministic dynamics and the stochastic component (noise). While the assumption of additive white noise is the most canonical approach to representing unresolved dynamics, it has long been understood that certain variabilities in the climate system exhibit correlation and persistence [3]. In this case, trends in the above indicators should no longer be attributed solely to CSD, since they may also be rooted in possibly changing correlation characteristics of the driving noise. While there has been progress in the development of indicators for discrete-time models incorporating correlated noise [4], the task of assessing discrete-time data from continuous-time models has not received as much attention. We present a simple linearly restoring stochastic model with red noise as its driving force and discuss possible avenues of estimating system stability from time series data through the autocorrelation structure and power spectral density of the observable. We quantitatively compare these methods to conventional Early Warning Signals, highlighting the potential pitfalls of the latter in this setting.

 

[1] Boers, N. (2021). Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation. Nature Climate Change 11

[2] Rypdal, M. (2016). Early-Warning Signals for the Onsets of Greenland Interstadials and the Younger Dryas–Preboreal Transition, Journal of Climate, 29(11)

[3] Mann, M.E., Lees, J.M. (1996). Robust estimation of background noise and signal detection in climatic time series. Climatic Change 33

[4] Rodal, M., Krumscheid, S., Madan,G. , LaCasce, J.H., and Vercauteren, N. (2022). Dynamical stability indicator based on autoregressive moving-average models: Critical transitions and the Atlantic meridional overturning circulation, Chaos 32

How to cite: Morr, A. and Boers, N.: Detecting Critical Slowing Down under the influence of continuous-time Red Noise, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5250, https://doi.org/10.5194/egusphere-egu23-5250, 2023.

EGU23-5409 | Posters on site | ITS2.1/NP0.4

Timing the collapse of the Atlantic Meridional Overturning Circulation 

Peter Ditlevsen and Susanne Ditlevsen

Statistical Early warning signals (EWS) indicate an approach towards a tipping point. These are increased variance (loss of resilience) and increased autocorrelation (critical slow down). The early warning is based on the significance in a linear trend above random fluctuations in the measures. Here we suggest a more rigorous evaluation of the statistics assuming a linear change with time of a control parameter towards a critical value. We calculate explicitly the uncertainty of the EWS as a function of the length of the data window and the time scales involved. This enables us to not only detect a trend but also estimate the timing of the forthcoming collapse.

 

 

Ref: Ditlevsen & Ditlevsen: Warning of a forthcoming collapse of the Atlantic meridional overturning circulation, preprint

How to cite: Ditlevsen, P. and Ditlevsen, S.: Timing the collapse of the Atlantic Meridional Overturning Circulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5409, https://doi.org/10.5194/egusphere-egu23-5409, 2023.

Spatial and temporal aggregations are common when preparing remote sensing data for analysis. Aggregations often serve to enhance the underlying signal of interest while suppressing noise, and can improve estimations of mean states and long-term trends in data. However, aggregating means that the highest-resolution parts of a signal can no longer be resolved, and rapid or fine-scale fluctuations are removed, potentially biasing analyses that rely on these parts of the signal. Further, data aggregation often goes along with gap-filling, which can further dilute the signals of interest.

In this work, we examine the impact of spatial aggregation on estimates of vegetation resilience by comparing MODIS vegetation data sets at a range of spatial resolutions (native 250 m – 25 km). We first use synthetic data to investigate various de-seasoning and de-trending schemes and their responsiveness to gaps in the underlying data. Based on these insights, we calculate two estimates of vegetation resilience at the global scale and at multiple spatial resolutions to determine the optimal level of spatial aggregation for MODIS data, considering the tradeoffs between fine-scale (gappy, noisy) and aggregated (continuous, smooth) vegetation data in terms of resilience estimation. Our results provide best practices for the aggregation, deseasoning, detrending, and analysis of vegetation resilience at the global scale.

How to cite: Smith, T. and Boers, N.: How Low Can You Go? Implications of Spatial Aggregation for the Estimation of Ecosystem Resilience, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5449, https://doi.org/10.5194/egusphere-egu23-5449, 2023.

EGU23-5496 | ECS | Orals | ITS2.1/NP0.4

Multistability in a Coupled Ocean-AtmosphereReduced Order Model: Non-linear TemperatureEquations 

Oisin Hamilton, Jonathan Demaeyer, Stéphane Vannitsem, and Michel Crucifix

Reduced order quasi-geostrophic ocean-atmosphere coupled models provide a platform that preserve key atmosphere behaviours, while still being simple enough to allow for analysis of the system dynamics. These models produce typical atmospheric dynamical features like atmospheric blocking and other low-frequency variability, while having a low number of degrees of freedom. For this reason, these models are well suited to investigating tipping points or bifurcations in the Earth's climate due to their simplified but insightful dynamics.

In our present work we compare the dynamics of an ocean-atmosphere coupled model, previously implemented with linearised temperature equations (Vannitsem et al., 2015), but here we solve the equations including the non-linear Stefan-Boltzmann law in the radiative temperature term. When compared with the original version of the model with linearised temperature equations, the modified version of the model is found to produce multiple stable flows in the coupled ocean-atmosphere system. We find, for increasing atmospheric emissivity, there is an increase in the number of stable attractors, and these stable attractors present distinct flows in the ocean and atmosphere and distinct Lyapunov stability properties.

Vannitsem, S., Demaeyer, J., De Cruz, L., & Ghil, M. (2015). Low-frequency variability and heat transport in a low-order nonlinear coupled ocean–atmosphere model. Physica D: Nonlinear Phenomena, 309, 71-85.

How to cite: Hamilton, O., Demaeyer, J., Vannitsem, S., and Crucifix, M.: Multistability in a Coupled Ocean-AtmosphereReduced Order Model: Non-linear TemperatureEquations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5496, https://doi.org/10.5194/egusphere-egu23-5496, 2023.

EGU23-6420 | ECS | Posters on site | ITS2.1/NP0.4

Cloud-based quantification of Spatial Explicit Uncertainty of Remote Sensing-based Benthic Habitat Classification and its utilization in the context of Active Learning 

Spyridon Christofilakos, Avi Putri Pertiwi, Chengfa Benjamin Lee, and Dimosthenis Traganos

With the latest advances in cloud image processing, scientists and policy makers have found an effective and robust platform to process vast satellite data in order to be able to map the extent, monitor the condition and create effective protection policies for different ecosystems across the globe. Cloud-based techniques though lack information on the spatial explicit uncertainty of the mapping algorithms. In this study, we present a novel approach on the estimation of uncertainty in a benthic habitat classification context. We explore the benefits of such information in the context of better classification results through an ensemble classifier and the visualization of the uncertain areas in an attempt to provide better maps to the policy makers. 

The study area consists of Komodo and Wakatobi islands in Indonesia while reference and satellite data come from the Allen Coral Atlas(ACA) project sampling and a six-year PlanetScope composite, free of clouds and optical deep waters Our semi-automated algorithm is divided in three sectors. The first one prepares the data in the context of sampling a number of subsets of reference points according to ACA map products and runs the first classification based on the first subset. The second one aims to help the model re-train itself in a data driven way by accepting training points of the remaining subsets that have mediocre to low uncertainty scores. The uncertainty score is calculated based on probabilistic principles and the theory of Information. The last stage consists of three ensemble classifiers with the inputs of the classification of the second sector. The ensemble classifiers produce three different map products based on mode, max likelihood and simple weighted average values, respectively.

 According to the results, our workflow is able to minimize the noise of reference points, especially when they come from mapping products and non in-situ measurements. Furthermore, accuracy scores following retraining are better than the initial ones which verifies the hypothesis of removing training data with noise in an attempt to reduce the introduced bias in the classification model. Last but not least, the bi-product of classification uncertainty map can be utilized as a tool for better in-situ sampling planning and render a better understanding to policy makers regarding the validity of scientific reports such as change detection, satellite derived bathymetry and blue carbon accounting, among others.

How to cite: Christofilakos, S., Pertiwi, A. P., Lee, C. B., and Traganos, D.: Cloud-based quantification of Spatial Explicit Uncertainty of Remote Sensing-based Benthic Habitat Classification and its utilization in the context of Active Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6420, https://doi.org/10.5194/egusphere-egu23-6420, 2023.

A superrotating atmosphere, one in which the angular momentum of the atmosphere exceeds the solid body rotation of the planet occurs on Venus and Titan. However, it may have occurred on the Earth in the hot house climates of the Early Cenozoic and some climate models have transitioned abruptly to a superrotating state under the more extreme global warming scenarios. Applied to the Earth, the transition to superrotation causes the prevailing easterlies at the equator to become westerlies and accompanying large changes in global circulation patterns. Although current thinking is that this scenario is unlikely, it shares features of other global tipping points in that it is a low probability, high risk event.

Using an idealized general circulation model developed for exoplanet research here at Exeter, we simulate the transition from a normal to a superrotating atmospheric state. We look at the changes in typical early warning indicators of tipping which show critical slowing down as well as oscillatory behaviour close to the transition. Inspired by the studies of phase transitions we also look at the critical spatial modes and correlation lengths close to the transition.

How to cite: Williamson, M.: Early warnings of the transition to a superrotating atmospheric state, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6501, https://doi.org/10.5194/egusphere-egu23-6501, 2023.

EGU23-6885 | ECS | Posters on site | ITS2.1/NP0.4

Analysis of Early-Warning Signals for Arctic Summer Sea Ice Loss 

Anna Poltronieri, Nils Bochow, and Martin Rypdal

The rapid loss of Arctic Sea Ice (ASI) in the last decades is one of the most evident manifestations of anthropogenic climate change. A transition to an ice-free Arctic during summer would impact climate and ecosystems, both regionally and globally. The identification of Early-Warning Signals (EWSs) for the loss of the summer ASI could provide important insights into the state of the Arctic region.

We collect and analyze CMIP6 model runs that reach ASI-free conditions (area below 106 km2) in September. Despite the high inter-model spread, with the range for the date of an ice-free summer spanning around 100 years, the evolution of the summer ASI area right before reaching ice-free conditions is strikingly similar across the CMIP6 models.

When looking for EWSs for summer ASI loss, we observe a significant increase in the variance of the ASI area before reaching ice-free conditions. This behavior is detected in the majority of the models and also averaged over the ensemble. We find no increase in the 1-year-lag autocorrelation in model data, possibly due to the multiscale characteristics of climate variability, which can mask changes in serial correlations. However, in the satellite-inferred observations, increases in both variance and 1-year-lag autocorrelation have recently been revealed. 

How to cite: Poltronieri, A., Bochow, N., and Rypdal, M.: Analysis of Early-Warning Signals for Arctic Summer Sea Ice Loss, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6885, https://doi.org/10.5194/egusphere-egu23-6885, 2023.

EGU23-7787 | ECS | Posters on site | ITS2.1/NP0.4

Escape by jumps and diffusion by 𝛼-stable noise across the barrier in a double well potential 

Ignacio del Amo and Peter Ditlevsen

Inspired by the previous evidence that the DO events can be modelled as transitions driven by Lévy noise, we perform a detailed numerical study of the average transition rate in a double well potential for a Langevin equation driven by Lévy noise. The potential considered has the height and width of the potential barrier as free parameters, which allows to study their influence separately. The results show that there are two different behaviours depending on the noise intensity. For high noise intensity the transitions are dominated by gaussian diffusion and follow Kramer’s law. When noise intensity decreases the average transition time changes to the expected power law only dependent on the width on the potential and not on the height. Moreover, we find a scaling under which the transition time collapses for all heights and widths into a universal curve, only dependent on 𝛼.

How to cite: del Amo, I. and Ditlevsen, P.: Escape by jumps and diffusion by 𝛼-stable noise across the barrier in a double well potential, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7787, https://doi.org/10.5194/egusphere-egu23-7787, 2023.

EGU23-7885 | ECS | Posters on site | ITS2.1/NP0.4

Adaptive cycles of ecosystems under natural perturbation and human intervention 

Hannah Zoller, Borgþor Magnússon, Bjarni D. Sigurdsson, and Wolfgang zu Castell

In light of global changes and the need of a sustainable lifestyle, understanding the dynamics of ecological systems is steadily gaining in importance. However, with ecosystems being shaped by the complex interplay of physical, chemical, and biological processes, this remains a demanding endeavor. Addressing this challenge, we have developed a computational method to assess complex systems development, based on the abstract framework provided by Gunderson’s and Holling’s adaptive cycle metaphor [1]. The metaphor describes ecosystem development as alternating phases of stability and reorganization, being shaped by three systemic properties: the system’s potential available for future change, the connectedness among its internal variables and processes, and its resilience in the light of unpredicted perturbations. Resilience, in the sense of Gunderson and Holling, denotes the amount of disturbance that a system can absorb without changing its identity [2]. Our definitions of these three notions are based on a representation of the system as directed network of information transfer. While we consider the system’s potential and connectedness as information theoretical features of the network, we approach the system’s resilience via the spectral properties of the network’s Laplacian matrices.

In the present study, we follow this approach to provide holistic analyses of two ecosystems evolving through different successional stages. One of the systems, a vascular plant community on a volcanic island near Iceland, has been largely unspoiled since its formation and has therefore been exposed to natural perturbations, like droughts and breeding birds, only [3]. In contrast, we consider a plant community in the prairie-forest ecotone of Kansas, which has been subject to regular direct human interventions in the form of spring burns [4]. In both cases, our method reveals phases of system breakdown and reorganization, allows us to identify the corresponding drivers of change, and gives hints on the systemic role of single species in the maturation process [1,5].

The case studies illustrate the application of the R-package QtAC (Quantifying the adaptive cycle), which provides an easy access to our method [6].

 

[1] W. zu Castell, and H. Schrenk, Computing the adaptive cycle, Scientific Reports 2020(10):18175 (2020).

[2] L. H. Gunderson and C. S. Holling. Panarchy: understanding transformations in human and natural systems (Island, Washington, D.C., 2002).

[3] S. Fridriksson, Surtsey. Ecosystems formed (University of Iceland Press, 2005).

[4] Long-term studies of secondary succession and community assembly in the prairie-forest ecotone of eastern Kansas. https://foster.ku.edu/long-term-studies-secondary-succession-and-community-assembly-prairie-forest-ecotone-eastern-kansas. Accessed: 2019-05-19.

[5] H. Schrenk, B. Magnússon, B. D. Sigurdsson, and W. zu Castell, Systemic analysis of a developing plant community on the island of Surtsey, Ecology and Society 27(1):35 (2022).

[6] H. Schrenk, C. Garcia-Perez, N. Schreiber, and W. zu Castell, QtAC: an R-package for analyzing complex systems development in the framework of the adaptive cycle metaphor, Ecological Modelling 466:109860 (2022).

How to cite: Zoller, H., Magnússon, B., Sigurdsson, B. D., and zu Castell, W.: Adaptive cycles of ecosystems under natural perturbation and human intervention, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7885, https://doi.org/10.5194/egusphere-egu23-7885, 2023.

EGU23-7898 | ECS | Posters on site | ITS2.1/NP0.4

Dependence of Early Warning Signals on Time Scale Separation 

Kolja Kypke

The  two-dimensional stochastic FitzHugh-Nagumo (sFHN) model is a popular idealization of the dynamics of the temperature of Greenland during the Last Glacial Period as measured in the ice-core record. Specifically, the sFHN model is used to simulate the Dansgaard-Oeschger (D-O) events, which are sharp changes in temperature and the most prominent example of abrupt climate change in the paleoclimate. The theory of early warning signals (EWS) has been applied to D-O events, specifically the critical slowdown corresponding to an increase in variance and autocorrelation of the climate signal right before approaching a bifurcation point where the system changes state. There is a debate in the literature on the state of these in the record of D-O events, with studies demonstrating both the absence and existence of these EWS. A desirable element of the sFHN is that it is a fast-slow system with multiple timescales. For a very large time scale separation, a quasi-steady-state in the slow variable causes the system to act as a bistable potential, where EWS do not precede an abrupt change in state. On the other hand, for a smaller time scale separation, the system displays clear EWS. The subject of this study is the case of intermediate time scale separation and its effects on EWS, along with an exploration of the physical implications of the results. 

How to cite: Kypke, K.: Dependence of Early Warning Signals on Time Scale Separation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7898, https://doi.org/10.5194/egusphere-egu23-7898, 2023.

EGU23-7989 | ECS | Orals | ITS2.1/NP0.4

Model complexity and Arctic sea ice tipping points – a single column model approach 

Edmund Derby and Raymond Pierrehumbert

Some simple models of Arctic sea ice show bifurcations associated with the loss of sea ice under increased surface radiative forcing (Eisenman and Wettlaufer 2009). However, experiments using GCMs typically show a smooth loss of sea ice under increasing CO2. This mismatch adds to uncertainty on the existence of tipping point behaviour in the Arctic and the processes that stabilise or destabilise it from this behaviour.

Simple models exhibiting tipping points typically omit many features of the Arctic climate system. Their bifurcations usually arise from the ice-albedo feedback. The purpose of my work is to use a bottom-up hierarchical approach to investigate how additional features of Arctic climate not included in simple models affect the existence of bifurcations in the system.

I started with a base ice model (Eisenman and Wettlaufer 2009) and investigate the role of local ice-atmosphere feedbacks using a coupled atmospheric column model. This allowed me to analyse the impact of the following on the possible states for the model to exist in:

  • Changes to the atmospheric temperature profile – particularly the transition from a stable atmosphere with a strong temperature inversion to a less stable atmosphere as the Arctic warms.
  • Explicitly resolved changes in surface heat fluxes and downwelling longwave radiation.
  • Changes in low level Arctic clouds – particularly as the atmospheric structure changes.

I also explored the sensitivity of the model to changes and variation in atmospheric heat transport.

I will present results of this work and demonstrate how local atmospheric feedbacks affect the stability of tipping points in Arctic sea ice.

How to cite: Derby, E. and Pierrehumbert, R.: Model complexity and Arctic sea ice tipping points – a single column model approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7989, https://doi.org/10.5194/egusphere-egu23-7989, 2023.

EGU23-8099 | ECS | Posters virtual | ITS2.1/NP0.4

Simulating spontaneous AMOC collapses with a Rare Event Algorithm 

Matteo Cini, Giuseppe Zappa, Susanna Corti, and Francesco Ragone

 Understanding the stability of the Atlantic Meridional Overturning Circulation (AMOC) and its future development under anthropogenic forcing is of key importance for advancing climate science. Previous studies have explored the stability of the AMOC by applying external perturbations in climate models, such as freshwater hosing to the North Atlantic Ocean. However, if the system is close to losing stability, the tipping of the AMOC may also spontaneously occur via internal coupled atmosphere-ocean variability. Here, we address this hypothesis - using an innovative approach - by studying the nature of a spontaneous collapse of the AMOC in an intermediate complexity climate model (PlaSIM coupled to the LSG ocean) featuring - under pre-industrial conditions - an apparently stable state. Excluding all possible external forcing elements (for example green-house gasses increase, water hosing, radiative forcing anomalies), significant AMOC slowdowns and collapses can be treated as extreme events solely driven by the chaotic internal atmospheric variability.  Facing this problem, we look for extreme AMOC slowdowns by applying a Rare Event Algorithm (Ragone, Wouters and Bouchet, 2018), which - via a selective cloning of the most interesting model trajectories -  allows a faster exploration of the model phase space in the direction of an AMOC decrease.

After exploring the parameters of the rare event algorithm, we find a regime in which PLASIM/LSG shows an abrupt AMOC slowdown over a 20-years period to a substantially weakened state, which is unprecedented in the pre-industrial run. Stability analysis reveals that part of these slowdown states are actually collapsed, i.e. states around a much lower value of the AMOC that do not recover to previous values.

This approach also enables us to isolate the atmospheric processes driving the AMOC slowdown, from the climate response to the weakened AMOC state. Interestingly, we find that the climatic response to internally-induced AMOC slowdowns shows strong similarities with the responses to externally forced AMOC slowdowns in state-of-the-art climate models  for what concerns temperature, wind, and precipitation changes. Looking at the mechanisms causing the AMOC weakening, instead, we find that zonal wind stress over the North Atlantic is the main driver of the AMOC slowdown, via changes in Ekman transport that affect salinity and deep convection in the Labrador sea. In this climate model, the repeated occurrence of this circulation anomaly for a few decades is sufficient to drive  an AMOC collapse without possibility of recovery on multi-centennial time scales.

Overall, these results show that the methodology proposed here can be generally useful for other studies in Tipping Points since it introduces the possibility of collecting a large number of critical events that cannot be sampled using traditional approaches. 

 

How to cite: Cini, M., Zappa, G., Corti, S., and Ragone, F.: Simulating spontaneous AMOC collapses with a Rare Event Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8099, https://doi.org/10.5194/egusphere-egu23-8099, 2023.

EGU23-8105 | ECS | Orals | ITS2.1/NP0.4

When to Expect Rate-Induced Tipping in Natural and Human Systems 

Paul Ritchie, Hassan Alkhayuon, Peter Cox, and Sebastian Wieczorek

Over the last two decades, tipping points have become a hot topic due to the devastating consequences that they may have on natural and human systems. Tipping points are typically associated with a system bifurcation when external forcing crosses a critical level, causing an abrupt transition to an alternative, and often less desirable, state. However, the rate of change in forcing is arguably of even greater relevance in the human-dominated anthropocene, but is rarely examined as a potential sole mechanism for tipping points. Thus, I will introduce the related phenomenon of rate-induced tipping: an instability that occurs when external forcing varies across some critical rate, usually without crossing any bifurcations. First, I will explain when to expect rate-induced tipping. Then, using illustrating examples of differing complexity I will highlight universal and generic properties of rate-induced tipping in a range of natural and human systems.

How to cite: Ritchie, P., Alkhayuon, H., Cox, P., and Wieczorek, S.: When to Expect Rate-Induced Tipping in Natural and Human Systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8105, https://doi.org/10.5194/egusphere-egu23-8105, 2023.

EGU23-8187 | ECS | Posters on site | ITS2.1/NP0.4

Testing new indicators for ecological resilience in a dryland mountain ecosystem using a multidecadal NDVI time-series 

Angelique Vermeer, Ángeles Garcia Mayor, and Saskia Förster

In this work, the ecological resilience to drought of a dryland catchment in the Moroccan High Atlas Mountains was studied. A time-series of Landsat NDVI data between 1984 and 2019 was used to determine areas of overall greening and browning. The Breaks For Additive Seasonal and Trend (BFAST) change detection methodology was used to determine breakpoints and trends in the time-series. The breakpoints were classified using a newly developed typology based on the trend before and after the breakpoint. The improved typology that is introduced, considers the statistical significance of trends, and subdivides them in categories of abrupt changes that lead to an improvement of ecosystem functioning (positive breakpoints) and abrupt changes that lead to a deterioration of ecosystem functioning (negative breakpoints). The ecological resilience in the catchment was explored using the number, sign and typology of the breakpoints detected and their relation to the various land uses and climatic zones of the catchment. In general, a small amount of change in NDVI between 1984 and 2019 was observed in the whole catchment. However, there was a large spatial variability in the number and type of breakpoints, pointing to the additional information provided by these indicators, which will be discussed in our presentation.

How to cite: Vermeer, A., Garcia Mayor, Á., and Förster, S.: Testing new indicators for ecological resilience in a dryland mountain ecosystem using a multidecadal NDVI time-series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8187, https://doi.org/10.5194/egusphere-egu23-8187, 2023.

EGU23-8340 | Orals | ITS2.1/NP0.4

Probabilistic forecast of extreme heat waves using convolutional neural networks and rare event simulations 

Freddy Bouchet, George Milosevich, Francesco Ragone, Alessandro Lovo, Pierre Borgnat, and Patrice Abry

Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. Extreme heatwaves are, and likely will be in the future, among the deadliest weather events. Forecasting their occurrence probability a few days, weeks, or months in advance is a primary challenge for risk assessment and attribution, but also for fundamental studies about processes, dataset or model validation, and climate change studies.

       Because of a lack of data related to a too short historical record, the rarity of the events, and of the difficulty to obtain rare events in climate models, uncertainty quantification is extremely difficult for extreme events. We develop a methodology to tackle this problem by combining probabilistic machine learning using deep neural network and rare event simulations.

We will first demonstrate that deep neural networks can predict the probability of occurrence of long lasting 14-day heatwaves over France, up to 15 days ahead of time for fast dynamical drivers (500 hPa geopotential height fields), and at much longer lead times for slow physical drivers (soil moisture). This forecast is made seamlessly in time and space, for fast hemispheric and slow local drivers.

A key scientific message is that training deep neural networks for predicting extreme heatwaves occurs in a regime of drastic lack of data. We suggest that this is likely the case for most other applications of machine learning to large scale atmosphere and climate phenomena. We discuss perspectives for dealing with this lack of data issue, for instance using rare event simulations.

Rare event simulations are a very efficient tool to oversample drastically the statistics of rare events. Using a climate model, with this tool we obtain several orders of magnitude more extreme heat waves compared to a control run. We will discuss the coupling of machine learning approaches, for instance the analogue method, with rare event simulations, and discuss their efficiency and their future interest for climate simulations. 

How to cite: Bouchet, F., Milosevich, G., Ragone, F., Lovo, A., Borgnat, P., and Abry, P.: Probabilistic forecast of extreme heat waves using convolutional neural networks and rare event simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8340, https://doi.org/10.5194/egusphere-egu23-8340, 2023.

EGU23-8645 | ECS | Orals | ITS2.1/NP0.4

Is El Niño only due to the noise or it is a self-sustained phenomenon? 

Francesco Guardamagna, Henk Dijkstra, and Claudia Weiners

On average every 4 years, the sea-surface temperature in the Eastern Equatorial Pacific is a few degrees higher than normal. This phenomenon, which reaches its maximum usually around Christmas is known as El Niño. This event has a strong influence on the climate all around the globe through well-known tele-connections. The occurrence of El Niño is related to extreme weather events, that affect people and properties. For these reasons is important to better understand the behavior of this climatic phenomenon. The property of EL Niño we have focused on during our project is related to the following research question: Is El Niño only due to external noise, or it is a self-sustained phenomenon, which amplitude is amplified by the noise?

To answer to this question, we have applied a Machine Learning tool called Reservoir Computer. After the training procedure, through feedback connections, the Reservoir model can be transformed into an autonomous evolving system. Our results show that the autonomous evolving Reservoir can delete the noise from the training data. The signal produced in output by the autonomous evolving Reservoir reflects the patterns of the training data, without noise. This method can therefore be used to understand if the EL Niño oscillations is only due to random noise, that excites a steady state, or it is a periodic phenomenon, which amplitude is randomly increased by external noise. To understand its limitations, our approach has been first applied to data produced by different models, that simulate EL Niño (Jin Timmerman, Zebiak Cane and CESM). After these first experiments, performed in a controlled scenario, our method has been applied to real data, to see what the self-evolving Reservoir model can tell us about the real EL Niño phenomenon.

How to cite: Guardamagna, F., Dijkstra, H., and Weiners, C.: Is El Niño only due to the noise or it is a self-sustained phenomenon?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8645, https://doi.org/10.5194/egusphere-egu23-8645, 2023.

EGU23-8648 | ECS | Orals | ITS2.1/NP0.4

Computation of the AMOC collapse probability using a rare-event algorithm 

Valérian Jacques-Dumas, René M. van Westen, and Henk A. Dijkstra

The Atlantic Meridional Overturning Circulation (AMOC) transports warm, saline water towards the northern North Atlantic, contributing substantially to the meridional heat transport in the climate system. Measurements of the Atlantic freshwater divergence show that the AMOC may be in a bistable state and hence subject to collapsing under anthropogenic greenhouse gas forcing. We aim at computing the probability of such a transition, focusing on time scales up to the end of this century.  

Simulating trajectories in a climate model is very expensive. To minimize the amount of data required to compute the probability of such rare AMOC transitions, we use a rare-events algorithm called TAMS (Trajectory-Adaptive Multilevel Sampling), that encourages the transition without changing the statistics. In TAMS, N trajectories are simulated and evaluated with a score function; the poorest-performing trajectories are discarded, and the best ones are re-simulated.

The optimal score function is the committor function, defined as the probability that a trajectory reaches a zone A of the phase space before another zone B. To avoid the difficulties raised by its exact computation, we estimate it using a feedforward neural network. Because of the expense of simulating data in a climate model, we also minimize the amount of data needed to train the neural network by reusing data processed through TAMS.

As a first step, using simulated data from an idealized stochastic AMOC model, where forcing and white noise are applied via a surface freshwater flux, we compute the transition probabilities versus noise and forcing amplitudes. Then, we apply the same protocol to compute these transition probabilities in the much more sophisticated climate model FAMOUS. This model is a coarse resolution Atmosphere-Ocean General Circulation Model that has been shown to exhibit a collapse of the AMOC via hosing experiments. In this new setup, we compute once again the transition probabilities of the AMOC versus noise and forcing, where the forcing amplitude is a hosing flux, and the atmosphere dynamics plays the role of the noise.

How to cite: Jacques-Dumas, V., van Westen, R. M., and Dijkstra, H. A.: Computation of the AMOC collapse probability using a rare-event algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8648, https://doi.org/10.5194/egusphere-egu23-8648, 2023.

EGU23-9072 | ECS | Orals | ITS2.1/NP0.4

Loss of Amazon rainforest resilience confirmed from single-sensor satellite data 

Lana Blaschke, Da Nian, Sebastian Bathiany, Maya Ben-Yami, and Niklas Boers

The Amazon rainforest acts as a carbon sink and is one of the most bio-diverse ecosystems of our planet. As such, it is an important but vulnerable subsystem of the Earth System. Studies suggest that the region is bi-stable with respect to mean annual precipitation. Thus, it is considered a Tipping Element of the Earth System.

In this work, we investigate several statistics which, according to dynamical system theory, change in the advent of a Tipping Point. To assess the state of the Amazon rainforest, various remotely sensed vegetation indices (VIs) exist. Multiple single-sensor VIs are considered and analyzed if they show reasonable behavior. The results reveal an ongoing loss of resilience in several parts of the Amazon rainforest. 

 

How to cite: Blaschke, L., Nian, D., Bathiany, S., Ben-Yami, M., and Boers, N.: Loss of Amazon rainforest resilience confirmed from single-sensor satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9072, https://doi.org/10.5194/egusphere-egu23-9072, 2023.

EGU23-9078 | ECS | Posters on site | ITS2.1/NP0.4

The contribution of stochastic vegetation dynamics to overall model uncertainty of the global carbon sink 

Lucia Sophie Layritz, Prabha Neupane, and Anja Rammig

The terrestrial carbon sink plays a central role in the global carbon cycle, providing a strong negative feedback on anthropogenic climate change. However, it is also one of the more uncertain elements when simulating past and future carbon dynamics, mainly due to the challenge of modeling biological and ecological complexity across scales. One possible strategy, taken by the dynamic vegetation model LPJ-GUESS, is to use stochastic processes to describe key ecological processes, whose mechanistic modeling is still challenging (e.g. tree mortality, establishment, seed dispersal and disturbance).

Such introduced randomness can propagate through the model in various ways and may result in a final model output that is probabilistic in nature as well. Internal stochasticity can thus be seen as an additional source of model uncertainty, which so far has rarely been investigated systematically.

We perform global simulations of terrestrial carbon dynamics with LPJ-GUESS and quantify the resulting stochastic uncertainty. We find that stochasticity-induced uncertainty is a relevant share of overall uncertainty, comparable in magnitude to scenario uncertainty in some instances. When introducing stochastic processes into Earth system models, the resulting additional uncertainty should therefore be something to always be aware of.

How to cite: Layritz, L. S., Neupane, P., and Rammig, A.: The contribution of stochastic vegetation dynamics to overall model uncertainty of the global carbon sink, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9078, https://doi.org/10.5194/egusphere-egu23-9078, 2023.

It is an ongoing debate whether the abrupt climate changes during the last glacial interval, the so-called Dansgaard-Oeschger (DO) events, are solely due to stochastic fluctuations or a result of bifurcations in the structural stability of the climate. This raises the question whether they are predictable, and thus whether early warning signals for the abrupt transitions from Greenland stadial to interstadial periods could be observed.

Here, we propose a new method to analyze the DO events between 60 ka before present and the Holocene, where we look at the ensemble of oxygen isotope ratio (δ¹⁸O) measurements from three different Greenland ice cores. For each rapid transition from a Greenland stadial to interstadial period, the three time series are normalized and scaled individually. The goal is to determine whether early warning signals in the further detrended ensemble are observable and thus to contribute to the ongoing debate whether past abrupt climate change has been purely noise-induced or a result of changed stability in the climate system.

 

How to cite: Hummel, C.: Predictability of Dansgaard-Oeschger events in the Greenland ice core ensemble, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9117, https://doi.org/10.5194/egusphere-egu23-9117, 2023.

EGU23-9121 | Orals | ITS2.1/NP0.4

Learning Stochastic Dynamics with Probabilistic Neural Networks to study Zonal Jets 

Ira Shokar, Peter Haynes, and Rich Kerswell

In this study, we present a deep learning approach to deriving a reduced-order model of stochastically forced atmospheric zonal jets. The approach provides a four orders of magnitude speed-up in simulating the jets, over numerical integration, together with a lower-degrees-of-freedom latent representation of the system- used to yield insight into the underlying dynamics.

We consider the behaviour of zonal jets on a beta plane as represented by a two-dimensional model driven by stochastic forcing, which parameterises the turbulence due to baroclinic instability. This idealised model gives a useful analogue for week-to-week variations in the large-scale dynamics of the tropospheric midlatitude jet - the driver of European weather. We establish that the time evolution of the jets depends both on the nonlinear two-way interaction between the mean flow and the eddies and, crucially, the time history of the stochastic forcing. As a result, the current state or recent history of the system does not predict the forward evolution but instead determines a distribution of possible time evolutions.

To model the flow, we utilise methods in manifold learning to learn a transformation to a latent representation of the system and then use a probabilistic neural network to model the stochastic latent dynamics. We verify the neural network’s performance by comparing the statistical and spectral properties of an ensemble from the neural network, obtained via sampling in the latent space, with an ensemble of numerical integrations, with different realisations of the stochastic forcing- with identical initial conditions. To study jet variability, we use ensembles of trajectories in both the latent and observation space to quantify to what extent different system states are driven by deterministic or stochastic dynamics.

 

How to cite: Shokar, I., Haynes, P., and Kerswell, R.: Learning Stochastic Dynamics with Probabilistic Neural Networks to study Zonal Jets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9121, https://doi.org/10.5194/egusphere-egu23-9121, 2023.

EGU23-9297 | Orals | ITS2.1/NP0.4

Indicators of tropical forest resilience in vegetation models 

Sebastian Bathiany, Da Nian, and Niklas Boers

The resilience of tropical forests against climate change and deforestation is vital for biodiversity and carbon drawdown. This resilience is hard to measure directly, but is suspected to be decreasing. There is particular concern that the Amazon rainforest may be approaching a “tipping point” where the large-scale loss of species and carbon pools amplifies substantially. Candidate mechanisms for such threshold effects often involve positive feedbacks and span a large range of scales. For example, individual trees can die from hydraulic failure when soil moisture decreaseses, forest fires can mediate a regional transition to a savanna state, and by synchronising remote regions, the moisture recycling feedback could cause a continental-scale forest dieback. Conceptual dynamical systems suggest that the loss of resilience that accompanies such transitions can be measured by statistical indicators like increasing autocorrelation. Satellite observations of vegetation indices related to greenness and biomass seem to support these theoretical expectations.

Here we analyse dynamic global vegetation models (DGVMs) from CMIP6, as well as idealised simulations with LPJ, in order to bridge the complexity gap between conceptual models and the real world. First, we assess how resilience of terrestrial carbon pools in the tropics depends on mean annual rainfall (MAP). We find that this relationship differs between models, and can also differ substantially from the observed positive relationship, depending on how the models capture carbon pool dynamics on the grid-cell level. Second, we show that changes in resilience do not necessarily require any atmosphere-vegetation feedbacks, fire feedback or ecological interactions, suggesting that observed relationships may capture physiological effects in individual trees rather than the stability of the entire forest. Third, we also find that the coexistence of vegetation types affects vegetation resilience in DGVMs. In particular, plant types with faster dynamics can replace slower ones (e.g., grass replacing trees), leading to decreased autocorrelation but not necessarily larger sensitivity to MAP. We conclude that suitable indicators of tropical vegetation resilience should be determined by (i) using DGVMs to understand better what mechanisms are at play, and (ii) using observations to rule out certain model approaches (e.g. area-averaged versus individual-based models).

How to cite: Bathiany, S., Nian, D., and Boers, N.: Indicators of tropical forest resilience in vegetation models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9297, https://doi.org/10.5194/egusphere-egu23-9297, 2023.

EGU23-9335 | ECS | Orals | ITS2.1/NP0.4

Emulating internal and external components of global temperature variability with a stochastic energy balance model and Bayesian approach 

Maybritt Schillinger, Beatrice Ellerhoff, Robert Scheichl, and Kira Rehfeld

To characterize Earth’s temperature variability, it is necessary to better understand underlying mechanisms and contributions from internal and externally forced components. Here, we utilize a stochastic two-box energy balance model to emulate internal and forced global mean surface temperature (GMST) variability [1]. As target data for the emulation, we employ observations and 20 last millennium simulations from climate models of intermediate to high complexity. We infer the parameters of the stochastic EBM using the target data and a Bayesian approach, as implemented with a Markov Chain Monte Carlo algorithm in our “ClimBayes” software package [2]. This yields the best estimates of the EBM’s forced and forced + internal response. Applying spectral analysis, we contrast timescale-dependent variances of the EBM’s forced and forced + internal variance with that of the GMST target. Our findings show that the simple two-box stochastic EBM reproduces the characteristics of simulated global temperature fluctuations, even from comprehensive climate models. Minor deviations occur mainly at interannual timescales and are related to the simplistic representation of internal variability in the EBM. Furthermore, the relative contribution of internal dynamics increases with model complexity and decreases with timescale. Altogether, we demonstrate that the combined use of simple stochastic climate models and Bayesian inference provides a valuable tool to emulate climate variability across timescales.

[1] M. Schillinger, B. Ellerhoff, R. Scheichl, and K. Rehfeld: “Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic energy balance framework,” Chaos,  https://doi.org/10.1063/5.0106123 (2022). 

[2] M. Schillinger, B. Ellerhoff, R. Scheichl, and K. Rehfeld, “The ClimBayes package in R,” Zenodo, V. 0.1.1, https://doi.org/10.5281/zenodo.7317984 (2022). 

How to cite: Schillinger, M., Ellerhoff, B., Scheichl, R., and Rehfeld, K.: Emulating internal and external components of global temperature variability with a stochastic energy balance model and Bayesian approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9335, https://doi.org/10.5194/egusphere-egu23-9335, 2023.

EGU23-9441 | Posters on site | ITS2.1/NP0.4

Evaluating the risk of tipping cascades through the strength of the bipolar seesaw 

Marisa Montoya, Laura C. Jackson, Jorge Alvarez-Solas, and Alexander Robinson

The potential for the coupling between tipping elements leading to the occurrence of tipping cascades is of deep concern. One major tipping cascade that is often invoked results from coupling between the Greenland ice sheet, the Atlantic meridional overturning circulation (AMOC) and the Antarctic Ice Sheet (AIS). Melting of Greenland could contribute to a weakening of the AMOC, which would then result in a decrease in the northward heat transport in the Atlantic Ocean, causing warming of the Southern Ocean around Antarctica. This idea is supported by the evidence provided by ice-core records and models of different complexity suggesting that, during the last glacial period, the Southern Ocean acted as a heat reservoir which dampened and integrated in time the North Atlantic abrupt climatic variations through the bipolar seesaw. However, it has been argued instead that the heat reservoir to the Atlantic meridional heat transport involved does not lie in the Southern Ocean but north of the Antarctic Circumpolar Current, and transmitted via the atmosphere to the interior of Antarctica. Determining the ultimate heat reservoir in the sense of the strength of the Southern Ocean heat reservoir is critical to evaluate the risk of a tipping cascade.  Here we will investigate how model resolution affects the strength of the bipolar seesaw and the ultimate heat reservoir involved in this mechanism by using two different model horizontal resolution versions (0.25 and 1 degree, respectively) of the HadGEM3-GC3-1 model in simulations with a reduced AMOC in response to freshwater forcing in the North Atlantic.

How to cite: Montoya, M., Jackson, L. C., Alvarez-Solas, J., and Robinson, A.: Evaluating the risk of tipping cascades through the strength of the bipolar seesaw, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9441, https://doi.org/10.5194/egusphere-egu23-9441, 2023.

EGU23-9554 | ECS | Posters on site | ITS2.1/NP0.4

An extension of SURFER to study tipping cascades on multiple time scales 

Victor Couplet, Marina Martínez Montero, and Michel Crucifix

Tipping cascades are series of tipping events in the Earth system where transitions in one subsystem can trigger further transitions in other subsystems. In previous work, we demonstrated that the near-linear relationship predicted by GCMs between global temperature and cumulative greenhouse gas emissions for the next century can break up at millennial time scales due to cascades involving slower tipping elements such as the ice sheets. This means that we must consider tipping cascades also from a long-term perspective. Subsequently, we need fast models that encode the relevant physical processes and that we can calibrate on more comprehensive models. In this context, we present an extension of the SURFER model (Martínez Montero et al. 2022) that incorporates sediments and weathering feedbacks in the carbon cycle submodel (Archer et al. 2009), and an additional set of coupled tipping elements. This model may be used both as a surrogate for more computationally expensive models, for example in the context of decision-making problems, and as an exploratory tool to investigate the climate response's sensitivity to specific processes on long-time scales.

Archer, D. et al. (2009). “Atmospheric Lifetime of Fossil Fuel Carbon Dioxide”.en. In : Annual Review of Earth and Planetary Sciences 37.1, p. 117-134. DOI : 10.1146/annurev.earth.031208.100206.

Martínez Montero, M. et al. (2022). “SURFER v2.0 : a flexible and simple model linking anthropogenic CO2 emissions and solar radiation modification to ocean acidification and sea level rise”. en. In : Geoscientific Model Development 15.21, p. 8059-8084. DOI : 10.5194/gmd-15-8059-2022.

How to cite: Couplet, V., Martínez Montero, M., and Crucifix, M.: An extension of SURFER to study tipping cascades on multiple time scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9554, https://doi.org/10.5194/egusphere-egu23-9554, 2023.

In response to abruptly increasing atmospheric CO2 concentrations, general circulation model experiments typically evidence a rapid reduction or full collapse of the Atlantic Meridional Overturning Circulation (AMOC) from its current, strongly overturning state, into one characterized by weak overturning and reduced northward oceanic heat transport. This tipping point is frequently discussed in the context of present and past global climate changes. Less understood, however, is the evolution of the circulation towards a new equilibrium state, which occurs over many centuries or millennia following the initial AMOC response. To revisit this problem, we have performed multi-millennial simulations of the Community Earth System Model version 1 (CESM1) in a low-resolution configuration (T31 gx3v7), appropriate for paleoclimate studies. We consider a pre-industrial control (284.7ppm) simulation, as well as abrupt 2x, 4x, 8x, 16x, and 0.5x pre-industrial control atmospheric CO2 concentrations whereby atmospheric concentrations are increased at the start of integration and held constant for the duration of the experiment. In all global warming scenarios, we observe a rapid collapse to the AMOC within the first 250 years, attributed mechanistically to the complex interplay between surface salinity and temperature which inhibits deep-water formation in the sub-polar North Atlantic. Then, in our abrupt doubling and quadrupling of atmospheric CO2 experiments we observe a recovery to the circulation after some 1,000 years, and 3,500 years, respectively. After initially collapsing, our 8xCO2 experiment remains in this weakened state even after 10,000 years of integration have been performed, potentially indicating that a new equilibrium may have been met in this very warm climate.

 

We have further observed other intriguing bifurcations which arise stochastically in the forced system. First, in our abrupt 4xCO2 experiment, with the AMOC in a collapsed state we observe a spontaneous activation of the Pacific Meridional Overturning Circulation (PMOC) some 2,500 years following the initial forcing. The circulation persists for 1,000 years and has a notable effect on climate in the North Pacific region, for instance raising surface temperatures through the associated increase in Pacific Ocean northward heat transport. At 3,500 years the circulation collapses concomitantly with an AMOC recovery in the experiment, demonstrating a AMOC/PMOC seesaw. Secondly, in our abrupt global cooling experiment, we observe a spontaneous collapse of the AMOC after 2,000 years, which precedes a recovery over the next 1,500 years, before a secondary, rapid collapse to the circulation at 3,500 years. The behavior resembles a Dansgaard-Oeschger Event. Overall, our results highlight the rich quasi-equilibrium dynamical behavior of the Global Meridional Overturning Circulation in past climates for which atmospheric CO2 concentrations were markedly different.

How to cite: Curtis, P. E. and Fedorov, A.: The Tipping Points of the Atlantic Meridional Overturning Circulation in Warm and Cold Climates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9907, https://doi.org/10.5194/egusphere-egu23-9907, 2023.

Supervised machine learning (ML) models rely on labels in the training data to learn the patterns of interest. In Earth science applications, these labels are usually collected by humans either as labels annotated on imagery (such as land cover class) or as in situ measurements (such as soil moisture). Both annotations and in situ measurements contain uncertainties resulting from factors such as class misinterpretation and device error. These training data uncertainties propagate through the ML model training and result in uncertainties in the model outputs. Therefore, it is essential to quantify these uncertainties and incorporate them in the model [1].

In this research, we will present results of inputting semantic segmentation label uncertainties into the model training and show that it improves model performance. The experiment is run using the LandCoverNet training dataset which contains global land cover labels based on time-series of Sentinel-2 multispectral imagery [2]. These labels are human annotations derived using a consensus algorithm based on the input labels from three independent annotators. The training dataset contains the consensus label and consensus score, and we treat the latter as a measure of uncertainty for each labeled pixel in the data. Our model architecture is a Convolutional Neural Network (CNN) trained on a subset of LandCoverNet with the rest of the dataset used for validation. We compare the results of this experiment with the same model trained on the dataset without the uncertainty information and show the improvement in the accuracy of the model.

 

[1] Elmes, A., Alemohammad, H., Avery, R., Caylor, K., Eastman, J., Fishgold, L., Friedl, M., Jain, M., Kohli, D., Laso Bayas, J., Lunga, D., McCarty, J., Pontius, R., Reinmann, A., Rogan, J., Song, L., Stoynova, H., Ye, S., Yi, Z.-F., Estes, L. (2020). Accounting for Training Data Error in Machine Learning Applied to Earth Observations. Remote Sensing, 12(6), 1034. https://doi.org/10.3390/rs12061034

[2] Alemohammad, H., Booth, K. (2020). LandCoverNet: A global benchmark land cover classification training dataset. NeurIPS 2020 Workshop on AI for Earth Sciences. http://arxiv.org/abs/2012.03111

How to cite: Alemohammad, H.: Incorporating Training Data Uncertainty in Machine Learning Models for Satellite Imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10528, https://doi.org/10.5194/egusphere-egu23-10528, 2023.

EGU23-10972 | Orals | ITS2.1/NP0.4

Uncertainty quantification for the retrieval of cloud properties with deep neural networks for TROPOMI / Sentinel-5 Precursor 

Fabian Romahn, Diego Loyola, Adrian Doicu, Víctor Molina García, Ronny Lutz, and Athina Argyrouli

Due to their fast computational performance, neural networks (NNs) are nowadays commonly used in the context of remote sensing. The issue of performance is especially important in the context of big data and operational processing. Classical retrieval algorithms often use a radiative transfer model (RTM) as forward model with which an optimization algorithm can then solve the inverse problem of inferring the quantities of interest from the measured spectra. However, these RTMs are usually computationally very expensive and therefore replacing them by a NN is desirable to increase performance. But the application of NNs is not straightforward and there are at least two main approaches:

1. NNs used as forward model, where a NN approximates the radiative transfer model and can thus replace it in the inversion algorithm

2. NNs for solving the inverse problem, where a NN is trained to infer the atmospheric parameters from the measurement directly

The first approach is more straightforward to apply. However, the inversion algorithm still faces many challenges, as the spectral fitting problem is generally ill-posed. Therefore, local minima are possible and the results often depend on the selection of the a-priori values for the retrieval parameters.

For the second case, some of these issues can be avoided: no a-priori values are necessary, and as the training of the NN is performed globally, i.e. for many training samples at once, this approach is potentially less affected by local minima. However, due to the black-box nature of a NN, no indication about the quality of the results is available. In order to address this issue, novel methods like Bayesian neural networks (BNNs) or invertible neural networks (INNs) have been presented in recent years. This allows the characterization of the retrieved values by an estimate of uncertainty describing a range of values that are probable to produce the observed measurement. We apply and evaluate these new BNN and INN methods for the retrieval of cloud properties from TROPOMI in order to demonstrate their potential as operational algorithms for current (Sentinel-5P) and future (Sentinel-4 and Sentinel-5) Copernicus atmospheric composition missions.

How to cite: Romahn, F., Loyola, D., Doicu, A., Molina García, V., Lutz, R., and Argyrouli, A.: Uncertainty quantification for the retrieval of cloud properties with deep neural networks for TROPOMI / Sentinel-5 Precursor, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10972, https://doi.org/10.5194/egusphere-egu23-10972, 2023.

EGU23-11666 | Orals | ITS2.1/NP0.4 | Highlight

Prevalence and drivers of abrupt shifts in global drylands: gathering dynamical evidences of aridity thresholds 

Miguel Berdugo, Manuel Delgado-Baquerizo, Emilio Guirado, Juan J. Gaitan, Camille Fournier, Thomas W. Crowther, and Vasilis Dakos

Drylands occupy 45% emerged lands on Earth, are home of more than 2 billion people and are extremely vulnerable to climate change. Aridity increases is expected to influence the structure and functioning of drylands in a non-linear fashion. Yet, the prevalence and drivers of these abrupt changes in ecosystem structure and function remain poorly studied. We especially lack investigations of the changes of dynamical properties of these systems and how these dynamical properties relate to aridity. Those are key to understand the real menace of experiencing abrupt shifts with aridity increases in the near future.

Here we used remote sensing tools to acquire dynamical trajectories of normalized vegetation indices (NDVI, surrogates of plant fractional cover) for more than 50,000 dryland sites. With this information we conducted analysis using machine learning processes to examine the relationship of aridity with some key dynamical properties of dryland ecosystems, including several aspects of resilience (ability to withstand fluctuations without changing the functioning of ecosystems), dynamical drivers of productivity, complexity of dynamical trajectories and abruptness of productivity changes.

By doing so we provide a comprehensive assessment of aridity thresholds on dynamical properties of dryland productivity that show clear vulnerability of certain zones of the Earth exhibiting critical aridity thresholds previously identified through space. In particular, we show accumulation of abrupt shifts on aridity values characteristic of transition areas from semiarid to arid ecosystems. Furthermore, these values exhibit also nonlinear shifts in resilience of ecosystems and on the identity of key dynamical drivers. Our work paves the way to expand the incidence of aridity threshold from spatial to temporal implications, and highlights the necessity of developing strategies to protect and monitor especially vulnerable areas affecting more than one fifth of emerged lands.

How to cite: Berdugo, M., Delgado-Baquerizo, M., Guirado, E., Gaitan, J. J., Fournier, C., Crowther, T. W., and Dakos, V.: Prevalence and drivers of abrupt shifts in global drylands: gathering dynamical evidences of aridity thresholds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11666, https://doi.org/10.5194/egusphere-egu23-11666, 2023.

EGU23-11696 | ECS | Posters on site | ITS2.1/NP0.4

A New Strategy for Training Deep Learning Ensembles 

Tobias Schanz and David Greenberg

Quantifying the error of predictions in earth system models is just as important as the quality of the predictions themselves. While machine learning methods become better by the day in emulating weather and climate forecasting systems, they are rarely used operationally. Two reasons for this are poor handling of extreme events and a lack of uncertainty quantification. The poor handling of extreme events can mainly be attributed to loss functions emphasizing accurate prediction of mean outcomes. Since extreme events are not frequent in climate and weather applications, capturing them accurately is not a natural consequence of minimizing such a loss. Uncertainty quantification for numerical weather prediction usually proceeds through creating an ensemble of predictions. The machine learning domain has adapted this to some extent, creating machine learning ensembles, with multiple architectures trained on the same data or the same architecture trained on altered datasets. Nevertheless, few approaches currently exist for tuning a deep learning ensemble. 

We introduce a new approach using a generative neural network, similar to those employed in adversarial learning, but we replace the discriminator with a new loss function. This gives us the control over the statistical properties the generator should learn and increases the stability of the training process immensely. By generating a prediction ensemble during training, we can tune ensemble properties such as variance or skewness in addition to the mean. Early results of this approach will be demonstrated using simple 1D experiments, showing the advantage over classically trained neural networks. Especially the task of predicting extremes and the added value of ensemble predictions will be highlighted. Additionally, predictions of a Lorenz-96 system are demonstrated to show the skill in forecasting chaotic systems.

How to cite: Schanz, T. and Greenberg, D.: A New Strategy for Training Deep Learning Ensembles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11696, https://doi.org/10.5194/egusphere-egu23-11696, 2023.

EGU23-11899 | ECS | Orals | ITS2.1/NP0.4

Glacial abrupt climate change as a multi-scalephenomenon resulting from monostable excitabledynamics 

Keno Riechers, Georg Gottwald, and Niklas Boers

During past glacial intervals the high northern latitude’s climate was punctuated by abrupt warming events which were accompanied by a sudden loss of sea ice, a reinvigoration of the Atlantic Meridional Overturning Circulation (AMOC), and cooling of the Nordic Seas. Despite being considered the archetype of past abrupt climatic change, to date there is no consensus about the physical mechanism behind these so-called Dansgaard-Oeschger events and the subsequent milder interstadial phase. Here, we propose an excitable model system to explain the DO cycles, in which interstadials are regarded as noise-induced state space excursions. Our model comprises the mutual multi-scale interactions between four dynamical variables representing Arctic atmospheric temperatures, Nordic Seas’ temperatures and sea ice cover, and AMOC. Crucially, the model’s atmosphere-ocean heat flux is moderated by the sea ice variable, which in turn is subject to large perturbations dynamically generated by fast evolving intermittent noise. If supercritical, these perturbations trigger interstadial-like state space excursions seizing all four model variables. As a physical source for such a driving noise process we propose convective events in the ocean or atmospheric blocking events. The key characteristics of DO cycles are reproduced by our model with remarkable resemblance to the proxy record; in particular, their shape, return time, as well as the dependence of the interstadial and stadial durations on the background temperatures are reproduced accurately. In contrast to the prevailing understanding that the DO variability showcases bistability in the underlying dynamics, we conclude that multi-scale, monostable excitable dynamics provides a promising alternative candidate to explain the millennial-scale climate variability associated with the DO events.

How to cite: Riechers, K., Gottwald, G., and Boers, N.: Glacial abrupt climate change as a multi-scalephenomenon resulting from monostable excitabledynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11899, https://doi.org/10.5194/egusphere-egu23-11899, 2023.

EGU23-12102 | ECS | Posters on site | ITS2.1/NP0.4 | Highlight

Revealing global drivers of recent losses in vegetation resilience 

Camille Fournier de Lauriere, Kathi Runge, Gabriel Smith, Vasilis Dakos, Sonia Kéfi, Thomas Crowther, and Miguel Berdugo
  • Context: Changes in ecosystem resilience have been recently studied on various scales using remote sensing data, revealing various regions exhibiting decreasing resilience. However, the drivers of these changes have not been identified yet. Our study aims at filling this gap by exploring the factors that have caused the resilience of ecosystems to change during the last two decades.
  • Methods: We investigate changes in vegetation resilience at the planetary scale, by quantifying two complementary aspects of resilience, namely sensitivity and autocorrelation, which are respectively associated with resistance and recovery abilities of ecosystems. We use a machine learning approach to identify the main environmental, climatic, and anthropogenic drivers of changes in resilience between two periods (the period 2000-2010 vs that of 2010-2020).
  • Results: We find that in 26% of ecosystems worldwide, vegetation exhibits signs of resilience loss, and that the changes in climate conditions as well as the ecosystem’s intrinsic properties (aridity, elevation, anthropization) affect the way vegetation resilience has changed over time. Different biomes (forest, grasslands, and savannas) exhibit similar responses to their changing environment. Regions experiencing intense warming (>0.2ºC/decade) have shown a major loss in vegetation resilience. Decreasing productivity is associated with reduced resilience, and interacts with warming, exacerbating resilience loss of degraded lands. This shows that global warming and human activities are major drivers of losses in vegetation resilience across vegetation types.
  • Conclusions: We reveal a decline in the capacity of a number of ecosystems to withstand perturbations, which should be accounted for in the management of vulnerable areas. Our results raise concerns about the persistence of ecosystems due to projected warming and expected intensification of human activities.

How to cite: Fournier de Lauriere, C., Runge, K., Smith, G., Dakos, V., Kéfi, S., Crowther, T., and Berdugo, M.: Revealing global drivers of recent losses in vegetation resilience, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12102, https://doi.org/10.5194/egusphere-egu23-12102, 2023.

EGU23-12900 | Orals | ITS2.1/NP0.4 | Highlight

Tipping Points: A challenge for climate change projections 

Thomas Stocker

Multiple equilibria are found in all members of the hierarchy of climate models, ranging from simple planetary energy balance models to fully coupled general circulation models. They arise from the physical and biogeochemical coupling of different climate system components, and hence they are a general feature of planetary dynamics. Transitions from one equilibrium to another can be triggered by a temporary perturbation of the system which crosses a tipping point. Greenland ice cores and many other paleoclimate archives have abundantly demonstrated that the Earth System had limited stability during the last ice ages and that tipping has occurred in the past. A particularly dynamic period was the transition from the last ice age to the present. We present recent model simulations that reconcile different paleoceanographic indicators and so permit the quantitative reconstruction of the transient changes of the Atlantic meridional overturning circulation. This circulation may also tip in the future depending on the level and rate of increases in greenhouse gas concentrations. However, reducing the uncertainties where such tipping points lie and how close the climate system is to them, requires much better resolved climate models.

The tipping of regional systems has come into recent focus because the impacts on humans and ecosystems may be substantial. Among them are the various monsoon systems, parts of the Antarctic ice sheet, shifts in the statistics of extreme climate and weather events, the extent of the Amazon rain forest, or the grassland distribution in Eastern Africa, and hence biodiversity. Such changes would all have regional consequences that are not yet reflected in current climate change projections.

Therefore, regional tipping needs to be assessed systematically by the scientific community using a new generation of climate models at kilometer-scale resolution. A cross-working group IPCC Special Report on “Climate Tipping Points and Consequences for Habitability and Resources” in its forthcoming 7th assessment cycle would help strengthening a consensus on this topic and trigger the much needed advances in scientific understanding to more comprehensively inform adaptation and mitigation strategies.

 

How to cite: Stocker, T.: Tipping Points: A challenge for climate change projections, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12900, https://doi.org/10.5194/egusphere-egu23-12900, 2023.

EGU23-12923 | ECS | Orals | ITS2.1/NP0.4

Spatial Early Warning Signals for Rapidly Forced Systems 

Joe Clarke, Peter Cox, Paul Ritchie, and Chris Huntingford

Climate Change is forcing Earth System tipping elements rapidly, in some cases this forcing occurs on a similar timescale to the intrinsic timescale of the tipping element itself. This poses challenges for our ability to get good early warning signals for these tipping elements, as typical approaches require a clear timescale separation between the assumed slow forcing and the timescale of the system. We demonstrate that by calculating early warning signals ‘over space’ instead of ‘over time’ better early warning signals can be obtained for faster forcing. We compare the relative merits of these two ways of calculating early warning signals.

How to cite: Clarke, J., Cox, P., Ritchie, P., and Huntingford, C.: Spatial Early Warning Signals for Rapidly Forced Systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12923, https://doi.org/10.5194/egusphere-egu23-12923, 2023.

EGU23-12928 | ECS | Posters on site | ITS2.1/NP0.4

Improving and understanding probabilistic precipitation forecasts using machine learning 

Hannah Brown, Stephen Haddad, Aaron Hopkinson, Nigel Roberts, Steven Ramsdale, and Peter Killick

Uncertainty in numerical weather prediction (NWP) arises due to the initial state not being fully known and physical processes not being perfectly represented within the models. Precipitation is challenging to predict because it is non-linear with complex drivers from the atmosphere and so varies quickly even on a local scale. This means even advanced NWP models struggle to predict precipitation with the correct intensity at the right time or location. This study aims to explore whether machine learning (ML) can rediagnose precipitation rates based on vertical profiles of temperature, humidity and wind, thus replicating the precipitation calculated by cloud and precipitation parametrization schemes that are used in NWP models to represent the unresolved microphysical processes. A small but high-quality dataset comprised of days with widespread precipitation has been curated for developing an initial model, with in depth exploratory data analysis carried out to understand any trends in the model input data and assess the need for feature engineering. Vertical profiles of atmospheric variables (temperature, humidity, wind) taken from 6-hour forecasts of the Met Office Unified Model global ensemble (MOGREPS-G) provide input features for the ML model, and the target variable (or truth) is instantaneous precipitation intensity measured by the UK radar network at a 1km resolution. The two data sources are aligned onto the same grid by calculating the fractions of the MOGREPS-G ~20km cell containing radar precipitation in five precipitation intensity bands, with bounds informed by domain experts.

Each MOGREPS-G ensemble member is used to generate a ML prediction of the fractional precipitation coverage that exceeds each intensity threshold, then an ensemble average of these fractions is calculated for each intensity threshold. These values can be considered as ML generated ensemble probabilities. They can then be compared with the true fractional coverage from radar, as well as precipitation probabilities from MOGREPS-G to identify similarities and differences in their behaviour. Explainable AI techniques are applied to better understand the decisions made by the ML model when creating predictions.  The aim is to understand the potential of using ML for improving precipitation forecasts, either through complementing NWP outputs with ML outputs, or by using ML as a tool for improving the understanding of the drivers of errors in NWP precipitation forecasts. Initial results look promising and a number of avenues for further development have been identified following consultation with domain experts.

How to cite: Brown, H., Haddad, S., Hopkinson, A., Roberts, N., Ramsdale, S., and Killick, P.: Improving and understanding probabilistic precipitation forecasts using machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12928, https://doi.org/10.5194/egusphere-egu23-12928, 2023.

EGU23-12987 | ECS | Posters on site | ITS2.1/NP0.4

Rethinking tipping points in spatial ecosystems 

Swarnendu Banerjee, Mara Baudena, Paul Carter, Robbin Bastiaansen, Arjen Doelman, and Max Rietkerk

The theory of alternative stable states and tipping points has garnered a lot of attention in recent years. However, typically the ecosystem models that predict tipping behaviors do not resolve space explicitly. Ecosystems being inherently spatial, it is important to understand the implication of incorporating spatial processes in theoretical models and their applicability to real world. In this talk, I will illustrate several pattern formation phenomena that may arise when incorporating spatial dynamics in models exhibiting alternative stable state. For this, we use simple mathematical models of savannas to study the behavior of these spatial ecosystems in the face of environmental change. Model analyses presented here challenge the simplistic notion of tipping and lay down a way forward regarding studying ecosystem response to global change.

How to cite: Banerjee, S., Baudena, M., Carter, P., Bastiaansen, R., Doelman, A., and Rietkerk, M.: Rethinking tipping points in spatial ecosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12987, https://doi.org/10.5194/egusphere-egu23-12987, 2023.

EGU23-13893 | ECS | Orals | ITS2.1/NP0.4

Effects of different uncertainties on optimal policies 

Marina Martinez Montero, Michel Crucifix, Nuria Brede, Nicola Botta, and Victor Couplet

Decisions are usually taken sequentially in climate change policy: every certain amount of years, new agreements and promises are made about greenhouse gas emission reduction etc. In the intersection of decision theory and climate science, sequential decision problems can be formulated and solved, to find optimal sequences of policies and support policy makers with some advice.

There are, however, many uncertainties affecting the outcome of these optimisations. Since these decision problems tend to be very simple in comparison with the complexity of the real world, knowing how different uncertainties affect optimal policies might be more important than what the optimal policy comes out to be. In this work, we explore how some uncertainties affect optimal policies and the possible trajectories associated with those optimal policies. 
  
For this aim we formulate a sequential decision problem with a single "global" policy maker. The decision problem starts with the world state in 2020 and decisions are taken every 10 years till 2100. The policy maker has options regarding CO2 emissions reduction, geoengineering in the form of solar radiation modification and carbon dioxide removal.

We simulate the effects of the decisions on the world’s state with SURFER. SURFER is a simple and fast model featuring a carbon cycle responsive to positive and negative emissions, it allows for geoengineering and accounts for sea level rise from ice sheets (containing tipping points) and from ocean expansion and glacier melt. SURFER has been shown to reproduce the globally averaged behavior of earth system models and models of intermediate complexity from decades to millennia. As opposed to some optimal decision problems in the context of climate change which use integrated assessment models of the climate and the economy, here, with the aim of transparency and simplicity, we consider only a climate model. 

We define a modular and transparent cost function that contains what the policy maker cares about. This function is a linear sum of costs associated with: green transition, geoengineering use and risks, temperature and ocean acidification damages and long term sea level rise commitments.

Using this decision problem we investigate how different kinds of uncertainties affect the sequence of optimal policies obtained and the optimal trajectories associated with those optimal policies. We consider three different kinds of uncertainties: uncertainties in the priorities of the decision maker (i.e., in the reward, cost or utility function), uncertainties on some physical parameters (in particular, climate sensitivity and ice sheet tipping points) and political uncertainty (policymaker’s decisions may not be implemented). 

How to cite: Martinez Montero, M., Crucifix, M., Brede, N., Botta, N., and Couplet, V.: Effects of different uncertainties on optimal policies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13893, https://doi.org/10.5194/egusphere-egu23-13893, 2023.

EGU23-14342 | ECS | Posters on site | ITS2.1/NP0.4

Investigating the dynamics of cusp bifurcations: A conceptual model for glacial-interglacial cycles 

Jade Ajagun-Brauns and Peter Ditlevsen

An investigation into the dynamics of a two-parameter family of non-linear differential equations inspired by MacAyeal (1979) reveals the utility of simple conceptual models in understanding climate response to forcing. A slow-fast model is used to explain the non-linear response of the climate to insolation forcing after the Mid-Pleistocene Transition (MPT) which produces the saw-toothed glacial cycles in the paleoclimate record. Global ice volume is taken to be a function of two independently varying parameters, the solar insolation and ‘alpha’, a secondary control parameter. The pleated cusp geometry of the model, due to the addition of the second control allows the system to exhibit both smooth changes and sudden discontinuous transitions from one stable solution to another, producing the gradual increase and sudden decrease in global ice volume observed in the paleoclimate record.  The control parameter alpha is suggested to be related to internal dynamics of the climate system, proposed to be a measure of glacial-oceanic interaction, which varies due to glacial isostatic adjustments of the bedrock.  The transition in period of glacial cycles at the MPT is suggested to occur as a result of northern hemisphere glaciers exceeding a critical threshold, which allows alpha to become larger, causing the asymmetric, higher amplitude glacial cycles with quasi-period of 100kyr of the late Pleistocene.

 

Reference

R. MacAyeal, ‘A Catastrophe Model of the Paleoclimate Record’ , Journal of Glaciology , Volume 24 , Issue 90 , 1979 , pp. 245 – 257.

 

How to cite: Ajagun-Brauns, J. and Ditlevsen, P.: Investigating the dynamics of cusp bifurcations: A conceptual model for glacial-interglacial cycles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14342, https://doi.org/10.5194/egusphere-egu23-14342, 2023.

EGU23-14486 | ECS | Posters on site | ITS2.1/NP0.4

Transition indicators on a flowline ice sheet model 

Daniel Moreno-Parada, Jan Swierczek-Jereczek, Marisa Montoya, Jorge Alvarez-Solas, and Alexander Robinson

Marine ice-sheet behaviour and grounding line stability have been fundamental objects of study in the last two decades. In particular, the ice sheet-shelf transition deserves special attention as it determines the outflow of ice from the grounded region and, together with accumulation, governs the global mass balance. Yet, the dynamics of ice flow are strongly coupled to the climate system via surface mass balance, frontal ablation and atmospheric temperature among others. The interplay of such variables combined with the bed geometry determine the equilibrium position of a glacier terminus, which can display bistability due to the marine ice-sheet instability. These variables further define the boundary conditions of an ice-sheet model and are given by the particular climate scenario. However, a realistic representation of the climate must be described as a stochastic process (short-term variability i.e., “noise”) interacting with long-term deterministic dynamics. The response of a multi-stable system to noisy forcing can be used to predict abrupt transitions by means of so-called transition indicators. That is, a direct application of classical slowdown theory to capture the essence of shifts at tipping points. In the present work, we apply some of these indicators to a 1-D flowline model to study whether a glacier collapse can be predicted by critical slowdown theory. A key challenge with transition indicators is to determine when the system can be expected to tip given that a critical slowdown begins to occur. We explore this issue through a large ensemble of simulations.

How to cite: Moreno-Parada, D., Swierczek-Jereczek, J., Montoya, M., Alvarez-Solas, J., and Robinson, A.: Transition indicators on a flowline ice sheet model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14486, https://doi.org/10.5194/egusphere-egu23-14486, 2023.

EGU23-14678 | ECS | Posters virtual | ITS2.1/NP0.4

Minimal Modelling of Internal Macroeconomic Variability 

Daniel Ohara and Michael Ghil

In the climate sciences, highly simplified nonlinear models are useful tools for understanding and discussing tipping points. However, the economic models used to study their coupling to the economy, as in Integrated Assessment Models (IAMs), are typically linear and represent an inertia-free economy in equilibrium. This representation is challenged by persistent unemployment, recessions, and changing economic institutions. 

Therefore, we investigate the non-equilibrium dynamics of the economy and the corresponding tipping from equilibrium to so-called endogenous business cycles. To this end, we build a basic Solow-type equilibrium growth model that incorporates, in a highly simplified manner, frictions and delay in the labor system. When the delay exceeds a critical value of 3.4 days, business cycles with periodic unemployment and recessions arise in our minimal business cycle (MinBC) model. Given a dynamic investment mode, the MinBC's cyclic economy responds to external forcing asymmetrically throughout the cycle. Advanced time series analysis methods are applied to macroeconomic data sets to evaluate the realism of the model's response, with encouraging results.

Our study is a step towards understanding the evolution of the sources of internal economic variability. Such an understanding is needed to represent the extent of coupling between the earth system and the economy.

How to cite: Ohara, D. and Ghil, M.: Minimal Modelling of Internal Macroeconomic Variability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14678, https://doi.org/10.5194/egusphere-egu23-14678, 2023.

EGU23-16103 | ECS | Posters on site | ITS2.1/NP0.4

Fractal Dimension of nonattracting chaotic sets 

Raphael Roemer and Peter Ashwin

The fractal dimension of a nonattracting chaotic set provides information about its geometric complexity and can often be of practical use. For example in the case of a chaotic saddle on a (fractal) basin boundary between two basins of attraction where the boundary is the stable set of the chaotic saddle. Then, the fractal dimension of the saddle and of the boundary provide information about the impact of small changes to the initial conditions on the future behaviour of the system, when the system is in a state close to the boundary.
This information is highly relevant in the context of climate tipping phenomena.
Building on Edward Ott’s and David Sweet’s work from 2000, we will discuss how to rigorously construct a measure on a chaotic repellor which leads to the estimation of its fractal dimension. Further, we discuss the fractal dimension of its stable and unstable set.

How to cite: Roemer, R. and Ashwin, P.: Fractal Dimension of nonattracting chaotic sets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16103, https://doi.org/10.5194/egusphere-egu23-16103, 2023.

It is thought that tropical forests can exist as an alternative stable state to savanna. Therefore, perturbation by climate change or human impact may lead to crossing of a tipping point beyond which there is rapid large-scale forest dieback that is not easily reversed. Modelling studies of alternative stable tree cover states have either relied on mean-field assumptions or not included the spatiotemporal dynamics of fire, making it hard to compare their output to spatial data. In this talk, we analyse a microscopic model of tropical forest and fire and show how dynamics of forest area are linked to its emergent spatial structure. We find that the relation between forest perimeter and area determines the nonlinearity in forest growth while forest perimeter weighted by adjacent grassland area determines the nonlinearity in forest loss. Together with the linear changes, which are independent of spatial structure, these two effects lead to an emergent relation between forest area change and forest area, defining a single-variable ordinary differential equation. Such a relation between pattern and dynamics enables empiricists to assess forest stability and resilience directly from a single spatial observation of a tropical forest-grassland landscape.

How to cite: Wuyts, B. and Sieber, J.: Emergent structure, dynamics and abrupt transitions in a cellular automaton of tropical forest and fire, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16946, https://doi.org/10.5194/egusphere-egu23-16946, 2023.

Discovering the underlying dynamics of complex systems from data is an important practical topic. Constrained optimization algorithms are widely utilized and lead to many successes. Yet, such purely data-driven methods may bring about incorrect physics in the presence of random noise and cannot easily handle the situation with incomplete data. In this paper, a new iterative learning algorithm for complex turbulent systems with partial observations is developed that alternates between identifying model structures, recovering unobserved variables, and estimating parameters. First, a causality-based learning approach is utilized for the sparse identification of model structures, which takes into account certain physics knowledge that is pre-learned from data. It has unique advantages in coping with indirect coupling between features and is robust to the stochastic noise. A practical algorithm is designed to facilitate the causal inference for high-dimensional systems. Next, a systematic nonlinear stochastic parameterization is built to characterize the time evolution of the unobserved variables. Closed analytic formula via an efficient nonlinear data assimilation is exploited to sample the trajectories of the unobserved variables, which are then treated as synthetic observations to advance a rapid parameter estimation. Furthermore, the localization of the state variable dependence and the physics constraints are incorporated into the learning procedure, which mitigate the curse of dimensionality and prevent the finite time blow-up issue. Numerical experiments show that the new algorithm succeeds in identifying the model structure and providing suitable stochastic parameterizations for many complex nonlinear systems with chaotic dynamics, spatiotemporal multiscale structures, intermittency, and extreme events.

How to cite: Zhang, Y. and Chen, N.: A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16981, https://doi.org/10.5194/egusphere-egu23-16981, 2023.

EGU23-17031 | Posters on site | ITS2.1/NP0.4

Probabilistic Machine Learning of the Natural Variability of Climate 

Balasubramanya Nadiga

Because of natural or internal variability, the behavior of processes ranging from unresolved small-scale physical and dynamical processes to the response of the climate system at the largest scales is probabilistic rather than denterministic. Indeed, it is also the case that while climate models are skilful at predicting the response of the climate system to external forcing, they are less skilful when it comes to predicting natural variability. A variety of probabilistic machine learning techniques ranging from Reservoir Computing to Generative Adversarial Networks to Bayesian Neural Networks are considered in the context of modeling natural variability. At the large scales, these models are seen to improve upon the Linear Inverse Modeling (LIM) approach which has itself been sometimes thought of as capturing the bulk of the predictable component of natural variability. 

How to cite: Nadiga, B.: Probabilistic Machine Learning of the Natural Variability of Climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17031, https://doi.org/10.5194/egusphere-egu23-17031, 2023.

NP1 – Mathematics of Planet Earth

EGU23-920 | Posters on site | NP1.1 | Highlight

An alternative approach to the ocean eddy parameterization roblem 

Igor Shevchenko

It is typical for low-resolution ocean simulations to miss not only small- but also large-scale patterns of the flow dynamics compared with their high-resolution analogues. It is usually attributed to the inability of coarse-grid models to properly reproduce the effects of the unresolved small-scale dynamics on the resolved large scales. In part, the reason for that is that coarse-grid models fail to at least keep the coarse-grid solution within the region of phase space occupied by the reference solution  (the high-resolution solution projected onto the coarse grid). 

In this presentation we discuss two methods to solve this problem: (1) computation of the image point in the phase space restricted to the region of the reference flow dynamics, and (2) reconstruction of a dynamical system from the available reference solution data. The proposed methods show encouraging results for both low- and high-dimensional phase spaces.

One of the important and general conclusions that can be drawn from our results is that not only mesoscale eddy parameterisation is possible in principle but also it can be highly accurate (up to reproducing individual vortices). This conclusion provides great optimism for the ongoing parameterisation studies.

How to cite: Shevchenko, I.: An alternative approach to the ocean eddy parameterization roblem, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-920, https://doi.org/10.5194/egusphere-egu23-920, 2023.

EGU23-1680 | ECS | Posters on site | NP1.1 | Highlight

From Sea Level Rise to COVID-19: Extending a Bayesian Hierarchical Model to unfamiliar problems with the 4D-Modeller framework 

John M. Aiken, Xueqing Yin, Samantha Royston, Yann Ziegler, and Jonathan L. Bamber

The recently completed European Research Council project “Global Mass” (www.globalmass.eu) aimed to reconcile the global sea-level budget as measured through a variety of satellite and in-situ data sources using a space-time Bayesian Hierarchical Model (BHM). The BHM uses Gaussian latent processes to estimate the contribution and uncertainty of different physical processes such as land hydrology, ocean thermal expansion, and glacier melt, to ongoing sea-level rise. Each process has a unique spatial and temporal length scale, which can be provided as a prior or inferred from the observations within the model. The BHM can separate the physical process sources represented in the data, model the stationarity of these processes, and estimate their uncertainty globally. A particular strength of the BHM is its ability to estimate and separate the different processes, from data with disparate spatial and temporal sampling and for observations that are influenced by multiple processes. This is often termed the source separation problem and we utilize novel statistical methods to solve for this and for dimensional reduction to allow the problem to be computationally tractable. We use the Integrated Nested Laplace Approximation (INLA) framework to approximate the observation layer and for the inference itself due to its accuracy and computational speed. The BHM has the potential to address a wider class of spatio-temporal inference problems and here we introduce the model structure (named 4D-modeller) and apply it to new classes of problem to extend its versatility. We apply it to COVID-19 transmittability in England and hydrology uncertainties related to hydropower reservoirs in Norway: problems that span social and physical sciences.  

How to cite: Aiken, J. M., Yin, X., Royston, S., Ziegler, Y., and Bamber, J. L.: From Sea Level Rise to COVID-19: Extending a Bayesian Hierarchical Model to unfamiliar problems with the 4D-Modeller framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1680, https://doi.org/10.5194/egusphere-egu23-1680, 2023.

EGU23-3217 | Orals | NP1.1 | Highlight

The velocity of climate change revisited: Smooth velocity field and ecological relevance 

Jérôme Kasparian, Iaroslav Gaponenko, Laure Moinat, Guillaume Rohat, Stéphane Goyette, and Patrycja Paruch

Describing climate change in terms of spatial velocity is essential to assess the ability for ecosystems or individual species to migrate at a sufficient pace to keep environmental conditions allowing their survival. While climate models provide a temporal evolution of a number of  variables at each point of their computational grid, Loarie et al. introduced a velocity of climate change, defined as the ratio of the temporal derivative to the spatial gradient of temperature, or any other variable such as precipitations [1]. This amounts to assume that isotherms shift along the temperature gradient. Although intuitive, this idea is mathematically correct only for straight isotherms parallel to each other [2]. Whenever this condition is not met, e.g., due to complex topography or coastlines, the gradient-based velocity field will display artefacts in the form of local convergence or divergence that are likely to bias the analysis.

We show that these artefacts can be fixed by defining a much more regular velocity field. This alternative approach to the velocity of climate change determines the direction of the velocity vector by minimising the local vorticity rather than by the gradient. From a fundamental point of view, the resulting smoother velocity field allow an analysis at finer temporal and spatial scales. It also allows to define the climate trajectory of a given origin point. Our approach also provides tools to estimate the stability of climate trajectories depending on the behaviour of their "return" trajectory obtained by reversing time [3].  

From an ecological point of view, we discuss preliminary results on the relevance of each definition of the velocity of climate change, based on comparisons of the obtained climate trajectories with ecological trajectories from observational data relative to species distribution areas.

References

1. S. R. Loarie et al., Nature 462, 1052 (2009)

2. J. Rey, G. Rohat, M. Perroud, S. Goyette, J. Kasparian, Env. Res. Lett. 15, 034027 (2020)

3. I. Gaponenko, G. Rohat, S. Goyette, P. Paruch, J. Kasparian, Sci. Rep., 12, 2997, (2022)

How to cite: Kasparian, J., Gaponenko, I., Moinat, L., Rohat, G., Goyette, S., and Paruch, P.: The velocity of climate change revisited: Smooth velocity field and ecological relevance, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3217, https://doi.org/10.5194/egusphere-egu23-3217, 2023.

EGU23-3509 | ECS | Orals | NP1.1

Cascading Transitions in the Weak and Strong Coupling Limit 

Sacha Sinet, Christian Kuehn, Robbin Bastiaansen, Anna S. von der Heydt, and Henk A. Dijkstra

Many components of the Earth system are thought to be prone to dangerous transitions, presenting a big challenge for human societies. Known as tipping elements, those form an intricate network of interacting subsystems, creating the possibility of cascading critical transitions. The presence of those interacting tipping events makes it hard to predict the outcome of climate change. In this research, we investigate those phenomena above the usual approach of linearly interacting bistable components.

We propose to study generic nonlinear systems under generic nonlinear interaction. As a first step, we focus on unilaterally coupled components, where a leading and a following subsystem are naturally identified. Using singular perturbation methods, we show how the stability landscape can be approached semi-analytically when considering the weak and strong coupling limit. With only limited knowledge about the system's structure, this method applies to a wide class of interacting systems and allows for approaching steady states with a controlled error. This provides information on important structural features of the bifurcation diagram such as the presence of steady branches, their stability, and bifurcations. Finally, we illustrate our results using climate relevant conceptual models.

How to cite: Sinet, S., Kuehn, C., Bastiaansen, R., von der Heydt, A. S., and Dijkstra, H. A.: Cascading Transitions in the Weak and Strong Coupling Limit, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3509, https://doi.org/10.5194/egusphere-egu23-3509, 2023.

Large and/or long-lived convective clusters are associated with extreme weather, drive the global circulation by forcing atmospheric waves, and affect the energy budget of the atmosphere by modulating outgoing longwave radiation in their vicinity. The majority of tropical clusters follow scale-free occurrence frequency distributions for cluster sizes and the rainfall integrated over a cluster (intensity). The relationships between intensity and area, and circumference and area also follow scaling laws. The exponents of all of these four scaling laws follow when we assume that precipitation clusters inherit their properties from the geometry of the integrated column water vapor field. Specifically, the column water vapor field would have to be a self-affine surface with a roughness exponent H=0.4. Coincidentally, H=0.4 is the prediction of the Kardar-Parisi-Zhang universality class in two dimensions.

I analyze the statistics of precipitation clusters and the column water vapor field in observations (using data from CMORPH and ERA5) and thirteen one-year global simulations performed with the ICON model at a horizontal resolution of 10 km. The simulations differ for example in their forcing (RCE or realistic forcing), in their rotation (no rotation, real rotation, constant Coriolis parameter), in their sea surface temperatures (SSTs; realistic and with land, zonal mean with land, constant without land, latitudinal gradient without land) etc. They are designed to test how robust the scaling laws of precipitation and column water vapor are.

What changes drastically between the simulations is the probability density distribution of points in the phase space of column water vapor and tropospheric bulk temperature. This distribution occupies a very narrow space in the RCE simulations, but a very wide space in the realistic simulation with land. The critical column water vapor, where precipitation starts to occur, is approximately a linear function of temperature. It turns out that the column water vapor axes and the temperatures axes can be rescaled so that the onset curves of all simulations collapse onto one line (approximately). The results show that there is a good match with the observed scaling in most simulations, with the control simulation (realistic SSTs and land) showing the closest match. I speculate what the results may imply for interpreting observed scalings based on the Kardar-Parisi-Zhang equation.

How to cite: Stephan, C.: Testing the robustness of precipitation cluster scalings with an ensemble of aquaplanet simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3901, https://doi.org/10.5194/egusphere-egu23-3901, 2023.

The climate system can be regarded as a non-equilibrium dynamical system that relaxes toward a steady state under the continuous input of solar radiation and dissipative mechanisms over a multitude of temporal and spatial scales. The steady state is not necessarily unique. A useful tool to describe the possible steady states of the climate system is the bifurcation diagram, where the long-term behaviour of a state variable (like surface air temperature) is plotted as a function of force intensity. This diagram reveals the regions of multi-stability, the position of B-tipping (bifurcation points at critical forcing values giving rise to an abrupt and irreversible climate change), the range of stability of each attractor and the intensity of climate variability needed to induce transitions between states (N-tipping).

The construction of the bifurcation diagram requires to run long simulations from a huge ensemble of initial conditions until convergence to a steady state is attained (standard method). This procedure has prohibitive computational costs in general circulation models of the climate that include deep ocean dynamics relaxing on timescales of the order of thousand years, or other feedback mechanisms with even longer time scales, like continental ice or carbon cycle.

Using a coupled setup of the MIT general circulation model, we propose two techniques that require lower computational costs and show complementary advantages. We test them in a numerical setup that includes deep ocean dynamics and we compare the resulting bifurcation diagram with the one obtained with the standard method. The first technique is based on the introduction of random fluctuations in the forcing and allows one to explore a large part of the phase space. The second, based on the estimate of internal variability and relaxation time, is more precise in finding B-tipping.

How to cite: Brunetti, M. and Ragon, C.: Steady states in complex climate models and different methods for the construction of the bifurcation diagram, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6502, https://doi.org/10.5194/egusphere-egu23-6502, 2023.

Ocean models relying on geopotential (Z) vertical coordinates suffer from spurious diapycnal mixing created by advection due to the misalignment of isopycnal and grid-layer surfaces. Given the delicateness of diapycnal mixing in ocean models, several studies have been performed to determine its impact, mainly by means of global analyses. Here we present a local analysis of spurious diapycnal mixing based on tracer variance decay. We apply the discrete variance decay (DVD) method proposed by Klingbeil et al. (2014) to diagnose numerical mixing created by several third-order advection schemes used in FESOM (Finite volumE Sea ice Ocean Model). The analysis is applied for an idealized channel flow test setup with Z* vertical coordinates and a linear equation of state. This ensures numerical DVD to be entirely diapycnal enabling identification of its spatial distribution. Further modification of the DVD method is proposed which allows for splitting of total diapycnal mixing into individual contributions from advection and diffusion. The new modifications are then used to compare spurious diapycnal mixing due to advection and explicit horizontal diffusion with parameterized physical diapycnal mixing due to vertical diffusion.

How to cite: Banerjee, T., Danilov, S., and Klingbeil, K.: Diagnosing spurious diapycnal mixing and its spatial distribution due to advection in Z-coordinate ocean models using discrete variance decay, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6887, https://doi.org/10.5194/egusphere-egu23-6887, 2023.

EGU23-7813 | ECS | Orals | NP1.1

Drivers and predictability of extreme summer Arctic sea ice reduction with rare event simulation methods 

Jerome Sauer, Francesco Ragone, François Massonnet, Jonathan Demaeyer, and Giuseppe Zappa

Various studies have identified possible drivers of extreme Arctic sea ice reduction, as observed in the summers of 2007 and 2012, including preconditioning, the oceanic heat transport and the synoptic-scale to large-scale atmospheric circulation. However, a quantitative statistical assessment of these drivers and a better understanding of the seasonal predictability of these events are hindered by the poor statistics of extremes in observations and in numerical simulations with computationally expensive climate models. Recent studies have addressed the problem of sampling extreme events in climate models by using rare event algorithms, computational techniques developed in statistical physics to increase the sampling efficiency of rare events in numerical models. In this work, we study the statistics of summer seasons with extremely low pan-Arctic sea ice area under pre-industrial greenhouse gas conditions, applying a rare event algorithm to the intermediate complexity coupled climate model PlaSim. Using the rare event algorithm, we oversample dynamical trajectories leading to events with extremely low summer and September mean pan-Arctic sea ice area. Compared to standard simulations of the same computational cost, we increase the sample size of the extremes by several orders of magnitude, which allows to perform statistically robust composite analyses of dynamical quantities conditional on these events. In addition, we have access to ultra-rare events with return times of up to 105 years. We exploit the improved statistics of summers with extremely low pan-Arctic sea ice area to study precursors of these events, including a surface energy budget analysis to disentangle the oceanic and atmospheric forcing on the sea ice. Particularly, we investigate the linkage between the extremes in summer Arctic sea ice area and the preceding states of the Arctic Oscillation and of the Arctic Dipole Anomaly pattern, as well as between the extremes and the preconditioning in the sea ice-ocean system during the onset of the melt season.

How to cite: Sauer, J., Ragone, F., Massonnet, F., Demaeyer, J., and Zappa, G.: Drivers and predictability of extreme summer Arctic sea ice reduction with rare event simulation methods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7813, https://doi.org/10.5194/egusphere-egu23-7813, 2023.

EGU23-8133 | ECS | Orals | NP1.1

On flow decomposition in realistic ocean models 

Silvano Rosenau, Manita Chouksey, and Carsten Eden

Oceanic flow comprises of a fast and a slow evolving component. Decomposing the flow field into these components is necessary to understand processes like mesoscale eddy dissipation and spontaneous wave emission. These processes are potentially important wave sources and lead to an energy transfer between the slow and the fast component. The first order approach is to decompose in geostrophic and non-geostrophic components. Since a part of the non-geostrophic component evolves slowly due to nonlinear interactions between both component, this approach is not precise enough to quantify energy transfers. To obtain higher accuracy in decomposing the flow field, more precise methods are required, such as optimal balance or nonlinear normal mode decomposition. However, their application is limited to idealized model settings that neither include topography nor a varying Coriolis parameter. Here, we modified the optimal balance method with a time averaging procedure, such that it is applicable in more realistic ocean models. We compared the new modified method with existing methods in a shallow water model and in a non-hydrostatic model. For longer time averaging periods, the modified optimal balance method converges against the original method.

How to cite: Rosenau, S., Chouksey, M., and Eden, C.: On flow decomposition in realistic ocean models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8133, https://doi.org/10.5194/egusphere-egu23-8133, 2023.

EGU23-8616 | ECS | Orals | NP1.1

A new calibration method for the stochastic rotating shallow water model 

Oana Lang, Dan Crisan, and Alexander Lobbe

In recent years, the applications of stochastic partial differential equations to geophysical fluid dynamics has increased massively, as there are several complex dynamic models which can be represented using systems of SPDEs. An important problem to be adressed in this context is the correct noise calibration such that the resulting stochastic model efficiently incorporates the a priori unrepresented sub-scale geophysical processes. In this talk I will present a new method of stochastic calibration which can be applied to a class of stochastic fluid dynamics models. I will focus on an application specifically tailored for the stochastic rotating shallow water model. 

How to cite: Lang, O., Crisan, D., and Lobbe, A.: A new calibration method for the stochastic rotating shallow water model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8616, https://doi.org/10.5194/egusphere-egu23-8616, 2023.

EGU23-9433 | Orals | NP1.1

Progess in non-Markovian (and Fractional) StochasticClimate Modelling: A GLE-based perspective 

Nicholas Wynn Watkins, Raphael Calel, Sandra Chapman, Aleksei Chechkin, Ian Ford, Rainer Klages, and David Stainforth

The mathematical stochastic energy balance models (SEBMs) pioneered by Hasselmann and Mitchell  have long been known to climate scientists to be important aids to gaining both qualitative insight and quantitative information about global mean temperatures. SEBMs are now much more widely visible, after the award of the 2021 Physics Nobel Prize to Hasselmann,  Manabe and Parisi. The earliest univariate SEBMs were, however, built around the simplest linear and Markovian stochastic process, enabling Hasselmann and his successors to exploit their equivalence to the Langevin equation of 1908. Multivariate SEBMs have now been extensively studied  but this presentation focuses on the continuing value of univariate SEBMs, especially when coupled to economic models, or when used to study longer-ranged memory than the exponential type seen in Hasselmann's Markovian case.

I will highlight how we and others are now going beyond the first SEBMs to incorporate more general temporal dependence, motivated by increasing evidence of non-Markovian, and in particular long-ranged, memory in the climate system. This effort has brought new and interesting challenges, both in mathematical methods and physical interpretation. I will highlight our recent paper [Calel et al, Nature Communications, 2021] on using a Markovian Hasselmann-type EBM to study the economic impacts of climate change and variability and our other ongoing work on generalisations (in particular fractional ones) of Hasselmann SEBMs.

This presentation updates our preprints [Watkins et al, arXiv; Watkins et al, in preparation for submission to Chaos] to show how the overdamped generalised Langevin equation can be mapped onto an SEBM that generalises Lovejoy et al's FEBE and I will give a progress report on this work. I will also briefly discuss  the relation of such non-Markovian SEBMs to fluctuation-dissipation relations.

How to cite: Watkins, N. W., Calel, R., Chapman, S., Chechkin, A., Ford, I., Klages, R., and Stainforth, D.: Progess in non-Markovian (and Fractional) StochasticClimate Modelling: A GLE-based perspective, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9433, https://doi.org/10.5194/egusphere-egu23-9433, 2023.

EGU23-9628 | ECS | Orals | NP1.1 | Highlight

A Resource Dependent Competition Model 

Robert Garvey and Andrew Fowler

There have been five major mass extinction events and a number of smaller extinction events throughout geological time. Each of these events characterises a widespread decrease in species diversity. The largest of these was the End-Permian extinction which saw about 90% of species go extinct. Extinction may be caused by a variety of factors such as asteroid impacts, CO2 driven ocean acidification, large igneous provinces, global warming/cooling, and oceanic anoxic events. All of these factors cause stress on the environment.

The ability of a species to avoid extinction is dependent on its environmental tolerances, i.e., the ability of a species to tolerate, or survive, changes in environmental conditions.

In population biology one way in which species may become extinct is through competition. The classical theory of competitive exclusion does not consider the type of interaction between species. We create a new mathematical model of competition between species in which the maximum population of a species is dependent on the availability of resources (or food supply) and competition is in the form of competition for these resources. We find this model always leads to stable coexistence. Another way in which populations can go extinct is through extreme oscillations in predator-prey systems; we explain how this can occur and illustrate this with a specific realistic predator-prey model that we then couple to our competition model.

How to cite: Garvey, R. and Fowler, A.: A Resource Dependent Competition Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9628, https://doi.org/10.5194/egusphere-egu23-9628, 2023.

EGU23-9798 | Orals | NP1.1

Linear response for stochastic models of geophysical fluid dynamics with medium complexity 

Jochen Broecker, Giulia Carigi, and Tobias Kuna

An important question of climate science is the effect of a changing climate on the long term statistical properties of the atmosphere and ocean dynamics. Mathematically speaking, the question is whether and how statistical quantities of the dynamics (e.g. correlations, averages, variabilities etc) react to changes in the external forcing of the system.

A (stochastic or deterministic) dynamical system is said to exhibit linear response if the statistical quantities describing the long term behaviour of the system depend differentiably on the relevant parameter (i.e. the forcing), and therefore a small change in the forcing will result in a small and proportional change of the statistical quantity. A methodology to establish response theory for a class of nonlinear stochastic partial differential equations has recently been provided in [1]. This contribution will discuss the ``ingredients'' necessary for this methodology on an intuitive level. In particular, the required mathematical properties of the system are related to their physical counterparts. The results are applied to stochastic single-layer and two-layer quasi-geostrophic models which are popular in the geosciences to study atmosphere and ocean dynamics.

[1] G. Carigi, T. Kuna and J. Bröcker, Linear and fractional response for nonlinear dissipative SPDEs, arXiv, doi = 10.48550/ARXIV.2210.12129, 2022.

How to cite: Broecker, J., Carigi, G., and Kuna, T.: Linear response for stochastic models of geophysical fluid dynamics with medium complexity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9798, https://doi.org/10.5194/egusphere-egu23-9798, 2023.

EGU23-9799 | Orals | NP1.1 | Highlight

Sample Path Large Deviations for Climate, Ocean, and Atmosphere 

Tobias Grafke

Rare and extreme events are notoriously hard to handle in any complex stochastic system: They are simultaneously too rare to be reliably observable in numerics or experiment, but at the same time too important to be ignored if they have a large impact. This is a particular complication in climate science, atmosphere and ocean dynamics that deals with a large number of strongly coupled degrees of freedom. Often these rare events come in the form of a stochastically induced transition between different viable macrostates. Examples include atmospheric jets, oceanic currents, etc, that correspond to large coherent structures which are long live-lived, but might ultimately disappear. In this talk, I discuss rare events algorithms based on instanton calculus and large deviation theory that are capable of computing probabilities of such transitions happening, as well as their most likely pathway of occurrence.

How to cite: Grafke, T.: Sample Path Large Deviations for Climate, Ocean, and Atmosphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9799, https://doi.org/10.5194/egusphere-egu23-9799, 2023.

EGU23-9865 | ECS | Posters on site | NP1.1

Seasonal evolution of the Arctic sea ice thickness distribution 

Srikanth Toppaladoddi, Woosok Moon, and John Wettlaufer

The Thorndike et al., (J. Geophys. Res. 80, 4501, 1975) theory of the ice thickness distribution, g(h), treats the dynamic and thermodynamic aggregate properties of the ice pack in a novel and physically self-consistent manner. Therefore, it has provided the conceptual basis of the treatment of sea-ice thickness categories in climate models. The approach, however, is not mathematically closed due to the treatment of mechanical deformation using the redistribution function ψ, the authors noting "The present theory suffers from a burdensome and arbitrary redistribution function ψ.''  Toppaladoddi and Wettlaufer (Phys. Rev. Lett. 115, 148501, 2015) showed how ψ can be written in terms of g(h), thereby solving the mathematical closure problem and writing the theory in terms of a Fokker-Planck equation, which they solved analytically to quantitatively reproduce the observed winter g(h). Here, we extend this approach to include open water by formulating a new boundary condition for their Fokker-Planck equation, which is then coupled to the observationally consistent sea-ice growth model of Semtner (J. Phys. Oceanogr. 6(3), 379, 1976) to study the seasonal evolution of g(h). We find that as the ice thins, g(h) transitions from a single- to a double-peaked distribution, which is in agreement with observations. To understand the cause of this transition, we construct a simpler description of the system using the equivalent Langevin equation formulation and solve the resulting stochastic ordinary differential equation numerically. Finally, we solve the Fokker-Planck equation for g(h) under different climatological conditions to study the evolution of the open-water fraction.

How to cite: Toppaladoddi, S., Moon, W., and Wettlaufer, J.: Seasonal evolution of the Arctic sea ice thickness distribution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9865, https://doi.org/10.5194/egusphere-egu23-9865, 2023.

Stirring and mixing plays a central role in the oceans and atmosphere, where the large-scale circulation is characterized by strong anisotropy. When the tracer evolution has no effect on the inertia of the velocity field, i.e., the tracer is passive, the governing evolution equation for the tracer is linear no matter how complicated the advecting velocity field is. Exploiting the linearity of the problem, we present a general approach for computing analytical solutions to the governing tracer equation for prescribed, time-evolving velocity fields. We apply it to analyze the evolution of a passive tracer in the case the advecting velocity field is a form of renewing flow, a prototype of chaotic advection, with stronger transport along a preferred axis. We consider both the freely decaying case and the case with a source of scalar variance (equilibrated), and discuss the possibility to generalize this approach for reacting tracers (biogeochemistry) and more complicated time-varying velocity fields.

How to cite: Jimenez-Urias, M. A. and Haine, T.: A mathematical investigation of stirring and mixing of passive tracers by an anisotropic flow field characterized by chaotic advection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10911, https://doi.org/10.5194/egusphere-egu23-10911, 2023.

EGU23-11488 | Posters on site | NP1.1 | Highlight

A Critical Analysis of Optimal Fingerprinting Methods for Climate Change through the Lens of Linear Response Theory 

Valerio Lucarini and Mickaël D. Chekroun

Detection and attribution studies have played a major role in shaping contemporary climate science and have provided key motivations supporting global climate policy negotiations. The goal of such studies is to associate observed climatic patterns of climate change with acting forcings - both anthropogenic and natural ones - with the goal of making statements on the acting drivers of climate change. The statistical inference is usually performed using regression methods referred to as optimal fingerprinting. We show here how a fairly general formulation of linear response theory relevant for nonequilibrium systems provides the physical and mathematical foundations behind the optimal fingerprinting approach for the climate change detection and attribution problem. Our angle allows one to clearly frame assumptions, strengths and potential pitfalls of the method.

 

 

How to cite: Lucarini, V. and Chekroun, M. D.: A Critical Analysis of Optimal Fingerprinting Methods for Climate Change through the Lens of Linear Response Theory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11488, https://doi.org/10.5194/egusphere-egu23-11488, 2023.

EGU23-14148 | Orals | NP1.1 | Highlight

Ensemble Design: Sensitivity Beyond Initial Values 

David A. Stainforth

Climate change is a complex, multidisciplinary problem which relates our physical understanding of the consequences of greenhouse gas emissions with economic and socio-political actions to mitigate and adapt to those consequences. An important role that the mathematics of climate change can play involves utilising and developing understanding of nonlinear systems in such a way as to guide the design of ensembles of Global Climate and Earth System Models (ESMs), as well as integrated assessment and economic models. To this end it is informative to view these computer models as high-dimensional nonlinear systems and ask what we can learn about ensemble design from somewhat related, low-dimensional nonlinear systems.

 

This talk will discuss what it means to make a prediction of climate change within a computer model as well as how we can design ensembles to reflect our uncertainty in the real-world, physical climate system. The Lorenz ’84/Stommel ’61 (L84-S61) system will be introduced as a valuable tool for studying issues of ensemble design and will be used to illustrate key sources of uncertainty and sensitivity.

 

First amongst these senstitivities is initial value sensitivity of the sort explored in a variety of single model large ensembles (see session CL4.10/NH11/OS4) - these are known as micro-initial-condition ensembles. However, the results of such ensembles can themselves be dependent on large scale features of the starting conditions - so-called macro-initial-condition uncertainty. Lastly, the sensitivity of ensemble results to model structure and parameter value selection is crucial. How can we identify how close to the target system a model has to be to make useful probabilistic forecasts at different lead times? This question raises the prospect that climate predictions could be vulnerable to the “hawkmoth effect” - the potential for probabilistic forecasts based on initial condition ensembles to be highly sensitive to the finest details of model formulation.

 

Here the different types of initial value and model parameter sensitivities will be illustrated with the L84-S61 system. Based on these, a series of design questions will be raised - questions which suitably-designed ensembles of low-dimensional systems could help us understand and answer, and which could be extremely valuable in improving the design of ensembles of GCMs and ESMs.

How to cite: Stainforth, D. A.: Ensemble Design: Sensitivity Beyond Initial Values, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14148, https://doi.org/10.5194/egusphere-egu23-14148, 2023.

EGU23-15021 | ECS | Orals | NP1.1 | Highlight

Computing precipitations with a vertical radiative-convective model with no adjustable parameters, using the maximum entropy production hypothesis. 

Quentin Pikeroen, Didier Paillard, Bérengère Dubrulle, and Karine Watrin

The state-of-the-art General Circulation Models or Earth System Models are based on conservation equations like the conservation of mass, momentum (Navier-Stokes), energy, and water... These equations are written in the form of partial derivative equations and are resolved on a grid whose spatial increment is a few tens or hundreds of kilometers and whose time increment is a few minutes. This means that phenomena acting below the numerical resolution are not computed. But because of the nonlinearity of equations, large scales are not independent of small scales, therefore the cutoff in resolution induces errors. For example, the linear relation between energy fluxes and temperature gradients (Fourier law) is not true for a grid of this size. To overcome this issue, it is usual to add new equations in order to close the conservation equations. In these new equations, new parameters are added and are generally tuned to fit observations. Though they are all based on the same physics, every climate model has a different set of "closure equations" and tuned parameters, leading to different results. For instance, while model comparisons are satisfying when looking at temperatures, results may differ significantly between two models when looking at precipitations.

Now, I am going to present an alternative way of resolving the climate system using zero tunable parameters. To achieve this, a paradigm change is needed. Partial derivative equations are no longer used, and variables are resolved with an optimization problem: maximizing a function under constraints (of conservations). The maximized function is the entropy production due to energy transfers and depends on temperatures only. Because solving the optimization problem isn't straightforward, the climate system is for now reduced to a vertical atmosphere, with only vertical energy fluxes. Such kind of model is sometimes called "radiative-convective" model and can be compared to tropical atmospheric observations because horizontal fluxes are less important there. The constraints imposed are the conservation of energy, the conservation of mass, and the conservation of water. Surprisingly, adding this last constraint to the model enables us to predict precipitations of about 1.2 m/year, in the good order of magnitude of average tropical precipitations. Theoretically, this means that precipitations depend mostly on the radiative transfer in the atmosphere.

The maximization of entropy production is probably not a generic "law of Nature" and might not apply to any out-of-equilibrium system. Here, we choose not to enter the debate whether it should be true for the climate or not, but only to show that this procedure can be a useful and efficient tool to close equations without introducing any tunable parameters, even when applied to precipitations. Though the optimization problem may rapidly become intractable, we can still envision building a more complete model of the atmospheric water cycle on these premises.

How to cite: Pikeroen, Q., Paillard, D., Dubrulle, B., and Watrin, K.: Computing precipitations with a vertical radiative-convective model with no adjustable parameters, using the maximum entropy production hypothesis., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15021, https://doi.org/10.5194/egusphere-egu23-15021, 2023.

Many systems in nature are characterized by the coexistence of different stable states for a given set of environmental parameters and external forcing. Examples for such behavior can be found in different fields of Earth system, e.g. ecosystems and climate dynamics. As a consequence of the coexistence of a multitude of stable states, the final state of the system depends strongly on the initial condition.  The set of initial conditions which all converge to the same stable state is called the basin of attraction. In addition, the dynamics of complex systems is often characterized by the different time scales on which certain processes act. We show that the interplay of these different time scales is important particularly for the case of rate-induced tipping. This tipping phenomenon occurs when the rate of change of an internal parameter or an external forcing is varying on a different timescale as the intrinsic timescale of the system.  The system can track its original stable state under such time-dependent forcing as long as the rate of environmental change is slow. If this rate is larger than a critical rate the system will tip and obey a rather different dynamical behavior, either by approaching a different stable state or by visiting temporarily different parts of the state space.  We study the role of the relative size of the basins of attraction and the location of their boundaries in rate-induced tipping and demonstrate that the decision whether a trajectory tips or tracks the original stable state depends crucially on the changes in the basins of attraction, in particular their size and, more importantly on their boundaries, that also “move” in state space under a time-dependent variation of intrinsic parameters/external forcing.  This dependence is discussed for the two cases of smooth basin boundaries made up by the stable manifolds of saddle points and fractal basin boundaries where chaotic saddles embedded in the boundary influence the tipping of trajectories. 

How to cite: Feudel, U.: The role of multiple time scales for rate-induced tipping phenomena, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15095, https://doi.org/10.5194/egusphere-egu23-15095, 2023.

EGU23-15627 | ECS | Orals | NP1.1

Towards a subgrid momentum closure via stochastic backscatter and its linkages with the Gent-McWilliams parametrization 

Ekaterina Bagaeva, Stephan Juricke, Sergey Danilov, and Marcel Oliver

Parametrizing physical processes in the ocean is a universal approach to overcome resolution limitations across different scales. Parametrizations represent the mean effect of processes occurring on the scales less than the grid scale (i.e. on the subgrid) on the resolved mean flow through parametric equations. In this work, a viscous momentum closure, equipped with a backscatter operator that returns excessively dissipated energy back to the system, is used to parametrize mesoscale range processes on eddy-permitting mesh resolutions.

The part of the variability that is not represented by the deterministic backscatter operator is modelled stochastically. We propose a stochastic field component, based on the patterns of variability extracted from the output of model simulations with different grid resolutions.

As a continuation of this work, we propose an interaction of the backscatter parametrization with the Gent-McWilliams parametrization which is generally applied for coarser grids corresponding to non-eddy resolving resolutions. This connection is relevant to link kinetic and potential energy backscatter.

The implementations are tested on two intermediate complexity setups of the global ocean model FESOM2: a doubly-periodic channel and a double-gyre box model. In this contribution, we present an increase of eddy activity and show that a greater complexity of setup enhances response to the implementation.  

Keywords: mesoscale eddies, parametrization, backscatter, stochastic parametrization, GM parametrization.

How to cite: Bagaeva, E., Juricke, S., Danilov, S., and Oliver, M.: Towards a subgrid momentum closure via stochastic backscatter and its linkages with the Gent-McWilliams parametrization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15627, https://doi.org/10.5194/egusphere-egu23-15627, 2023.

EGU23-15700 | ECS | Posters on site | NP1.1

On the numerical dependence of balance state in geophysical flows 

Manita Chouksey, Carsten Eden, Gökce Tuba Masur, and Marcel Oliver

Balance flows dictate the evolution and dynamics of geophysical flows, such as the atmosphere and ocean, that are central to the Earth's climate. Here, balance geophysical flows are balanced using two different methods and compared in simulations of the single-layer shallow water model with two different numerical model codes and two different initial conditions over a range of different parameters. Both methods: nonlinear higher order balancing and optimal balance, add to the linear geostrophic mode, the linear wave mode contributions. The resulting approximately balanced states are characterized by very small residual wave emission during time evolution of the flow. Overall, the performance of both methods is comparable. Cross-balancing suggests that both methods find approximately the same balanced states. The results contradict previous studies claiming significant spontaneous wave emission from balanced flow. Further, the results clearly show that the notion of balance in numerical models of geophysical flows is ultimately related to the particular discretization.

How to cite: Chouksey, M., Eden, C., Masur, G. T., and Oliver, M.: On the numerical dependence of balance state in geophysical flows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15700, https://doi.org/10.5194/egusphere-egu23-15700, 2023.

EGU23-16258 | Orals | NP1.1

Analysis of proxy response and sensitivities in a coupled general circulation model 

Francesco Ragone, Robbin Bastiaansen, Valerio Lembo, and Valerio Lucarini

In the analysis and interpretation of climate data, both from model simulations and observations, it is often of interest to establish relations between the responses of different observables to a global forcing. This problem in its generality is relevant in the context of the identification of emergent constraints for the climate system, detection and attribution studies, and the analysis of proxy data. Recently it has been discussed how in linear response theory it is possible to build proxy response operators, that allow to use the response of one observable to a forcing to predict the response of another observable. The spectral properties of the proxy response functions determine then the properties of statistical predictability at different time scales for the pair of observables. The skill and feasibility of this approach for complex climate data has however not been fully tested yet. In this work we analyse the properties of proxy response in experiments with the coupled general circulation model MPI-ESM v.1.2. We consider ensemble simulations of abrupt CO2 doubling and 1% per year CO2 increase scenarios. We study the response of different atmospheric and oceanic variables, and we compute proxy response functions for different pairs of observables. We analyse the predictive power for the different cases, and interpret differences in skills in terms of causal relations among observables. We also study the relation between statistical variability and long term sensitivity, and we discuss differences between ensemble and internal variability in unforced and forced states. We then link our results to the discussion on the interpretation of emergent constraints in climate change simulations.

How to cite: Ragone, F., Bastiaansen, R., Lembo, V., and Lucarini, V.: Analysis of proxy response and sensitivities in a coupled general circulation model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16258, https://doi.org/10.5194/egusphere-egu23-16258, 2023.

We study methods that aim to reduce the dimension of a finite dimensional solution space, in which the solution corresponding to a certain parametrized Optimal Control Problems governed by environmental models, e.g. Quasi-Geostrophic flow, is sought. The parameter is modeled as a random variable to incorporate possible uncertainty, for example in parametric measurements. For such a reduction to be useful, it should be guaranteed, for every possible parameter value, that it results in an acceleration of the solution process while maintaining an accurate approximate solution. In order to do this, conditions are formulated, and under those conditions, several versions of a specific reduction method known as Proper Orthogonal Decomposition are implemented. We consider examples and show that a simplification of the general state of the art reduction method performs equally well.

How to cite: Carere, G.: Reduced Basis Methods for Optimal Control Problems with Random Inputs in Environmental Science, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16389, https://doi.org/10.5194/egusphere-egu23-16389, 2023.

EGU23-16458 | ECS | Orals | NP1.1

Limits of large deviation theory in predicting transition paths of climate tipping events 

Reyk Börner, Ryan Deeley, Calvin Nesbitt, Raphael Römer, Tobias Grafke, Ulrike Feudel, and Valerio Lucarini

Following Hasselmann’s ansatz, the climate system may be viewed as a multistable dynamical system internally driven by noise. Its long-term evolution will then feature noise-induced critical transitions between the competing attracting states. In the weak-noise limit, large deviation theory allows predicting the transition rate and most probable transition path of these tipping events. However, the limit of zero noise is never obtained in reality. In this work we show that, even for weak finite noise, sample transition paths may disagree with the large deviation prediction – the minimum action path, or instanton – if multiple timescales are at play. We illustrate this behavior in selected box models of the bistable Atlantic Meridional Overturning Circulation (AMOC), where different restoring times of temperature and salinity induce a fast-slow characteristic. While the minimum action path generally crosses the basin boundary at a saddle point, we demonstrate cases in which ensembles of sample transition paths cross far away from the saddle. We discuss the conditions for saddle avoidance and relate this to the flatness of the quasipotential, a central object of large deviation theory. We further probe the vicinity of the weak-noise limit by applying a pathspace method that generates transition samples for arbitrarily weak noise. Our results highlight that predictions by large deviation theory must be treated cautiously in multiscale dynamical systems.

How to cite: Börner, R., Deeley, R., Nesbitt, C., Römer, R., Grafke, T., Feudel, U., and Lucarini, V.: Limits of large deviation theory in predicting transition paths of climate tipping events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16458, https://doi.org/10.5194/egusphere-egu23-16458, 2023.

We discuss a derivation of the analytic solution of the wave equations in complex structures perturbed by local defects, long waveguides, and various sources. After obtaining the exact analytic form of the solution, the numerical implementation becomes more or less straightforward. The corresponding real-time simulations will be demonstrated. Another important point is that the analytic solutions do not have disadvantages associated with the noise of reflections from the artificial boundaries of the model and other drawbacks inherent in purely numerical simulations. The solution is based on integral and algebraic transforms, including the active use of special functions. Even for linear waves that propagate in inhomogeneous structures, the solution is very complex. This fact probably makes the process of obtaining exact analytic solutions for nonlinear waves practically hopeless.

How to cite: Kutsenko, A.: Analytic solution of the wave equation in complex structures with defects, waveguides, sources, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16558, https://doi.org/10.5194/egusphere-egu23-16558, 2023.

EGU23-382 | ECS | Orals | NP1.2

Latent Dirichlet Allocation: a new machine learning tool to evaluate CMIP6 climate models atmospheric circulation and extremes 

Nemo Malhomme, Bérengère Podvin, Davide Faranda, and Lionel Mathelin

Climate models aim at representing as closely as possible the statistical properties of the climate components, including the extreme events. This is a fundamental requirement to correctly project changes in their dynamics due to anthropogenic forcing. In order to evaluate how closely models match observations, we need algorithms capable of selecting, processing and evaluating relevant dynamical features of the climate components. This has to be reiterated efficiently for large datasets such as those issued from the Coupled Model Intercomparison Project 6 (CMIP6). In this work, we use Latent Dirichlet Allocation (LDA), a statistical learning method initially designed for natural language processing, to extract synoptic patterns from sea-level pressure data and evaluate how close the dynamics of CMIP6 climate models are to the state-of-the-art reanalyses datasets such as ERA5 or NCEPv2, in general as well as in the case of extremes.

LDA allows for learning a basis of decomposition of maps into objects called "motifs". Applying it to sea-level pressure data, reanalysis or simulation, robustly yields motifs that are known relevant synoptic objects, i.e. cyclones or anticyclones. Furthermore, LDA provides their weight in each of the maps of the dataset, their most probable geographical position and their possible changes due to internal variability or external forcings. LDA decomposition is efficient and sparse, most of the information of a given sea-level pressure map is contained in few motifs, making it possible to decompose any map in a limited number of easy-to-interpret synoptic objects. This allows for a variety of new angles for statistical analysis.

We look at the dominant motifs and their distributions either on entire datasets or conditionally to particular extreme events, such as cold or heat waves, and compare results between reanalysis data and historical simulations. This enables us to assess which models can or cannot reproduce statistical properties of the observations, and whether or not there are properties that no model yet demonstrates. We find that models can capture the statistical synoptic composition of sea-level pressure data in general, but that some drawbacks still exist in the modelling of extreme events. LDA can also be applied separately to each dataset, and the two resulting synoptic bases can be compared. We find the sets of motifs from reanalysis and historical simulations are very similar, even if different spatial resolutions are used.

How to cite: Malhomme, N., Podvin, B., Faranda, D., and Mathelin, L.: Latent Dirichlet Allocation: a new machine learning tool to evaluate CMIP6 climate models atmospheric circulation and extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-382, https://doi.org/10.5194/egusphere-egu23-382, 2023.

EGU23-483 | ECS | Posters virtual | NP1.2

Characteristics of Medicanes using ERA-5 reanalysis 

Jesús Gutiérrez-Fernández, Mario Marcello Miglietta, Juan Jesús González-Alemán, and Miguel Ángel Gaertner

Several Medicanes, which have been previously analyzed in the literature, have been studied using ERA-5 reanalyses to identify the environment in which they develop and possibly distinguish tropical-like cyclones from warm seclusions. Initially, the cyclone phase space was analyzed to identify changes in the environmental characteristics. Subsequently, the temporal evolution of several parameters was considered, including sea surface fluxes, CAPE, coupling index, potential intensity, baroclinicity.

Although the results are not consistent for all cyclones, some general characteristics can be identified: cyclones develop in areas of moderate-to-high baroclinicity associated with intense jet streams, while in the mature stage the environment becomes less baroclinic. A general reduction in the horizontal extent of the cyclone can be observed as the cyclones begin to show a shallow warm core. In this phase a progressive reduction of the CAPE can be observed in proximity of the cyclone center. Finally, the wind speed appears strongly underestimated compared to the observations, raising some concerns about the applicability of ERA-5 for the detection of wind features.

How to cite: Gutiérrez-Fernández, J., Miglietta, M. M., González-Alemán, J. J., and Gaertner, M. Á.: Characteristics of Medicanes using ERA-5 reanalysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-483, https://doi.org/10.5194/egusphere-egu23-483, 2023.

EGU23-1388 | ECS | Orals | NP1.2

Extreme events in multiscale systems: theory and applications 

Tommaso Alberti

Many geophysical systems show emergent phenomena and extreme events at different scales, with signatures of chaos at large scales and an apparently random behavior at small scales. Despite the intrinsic morphological and/or physical difference between geophysical extremes, they all originate as temporary deviations from the typical trajectories of the large scale geophysical flows, resulting in dynamical patterns and structures. This motivated to bring together statistics (extreme value theory) and dynamics (dynamical system theory) to provide a new definition of extremes as rare recurrences in the phase space of physical systems. This means to explore the instantaneous properties of the geometrical object hosting the frequency and probability of all physical states attainable by the system, namely the so-called attractor, to inform us on the predictability, persistence and synchronization of physical states.

 

Here we present a recently proposed formalism to explore the active number of degrees of freedom and the predictability horizon of multiscale complex systems showing non-hyperbolic chaos, randomness, state-dependent persistence and predictability. We briefly discuss the newly introduced framework in comparison with classical approaches, based on generalized fractal dimensions, Lyapunov exponents, and Renyi entropies. Finally, we demonstrate the suitability of this novel formalism to trace the instantaneous scale-dependent and state-dependent features of climate and geophysical extremes, pointing out how the predictability horizon, the persistence and synchronization of geophysical systems’ states is a matter of scales.

How to cite: Alberti, T.: Extreme events in multiscale systems: theory and applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1388, https://doi.org/10.5194/egusphere-egu23-1388, 2023.

EGU23-1555 | ECS | Posters on site | NP1.2

Drought impact-based forecasting: Trade-offs between indicators and impacts 

Anastasiya Shyrokaya, Giuliano Di Baldassarre, Hannah Cloke, Gabriele Messori, Florian Pappenberger, and Ilias Pechlivanidis

Despite the progress in seasonal drought forecasting, it remains challenging to identify suitable drought indices for accurately predicting the impacts of a future drought event. In this study, we identified relationships across Europe between the forecasting skill of various drought indices and the estimated drought impacts. We calculated the indices over various accumulation periods, and assessed the forecasting skill of indices computed based on various seasonal prediction systems. An evaluation was performed by computing the same indices from the ERA5 reanalysis data and comparing them across various verification metrics. We further conducted a literature review of the studies assessing the performance of the indices in terms of estimating drought impacts across Europe. We finally performed a trade-off analysis and mapped the drought indices based on their drought forecasting and drought impact estimating skills.

Overall, this analysis is a step forward to detect the most suitable drought indices for predicting drought impacts across Europe. Here, not only we present a new approach for evaluating the relationship between drought indices and impacts, we also resolve the dilemma of choosing the indices to be incorporated in the impact functions. Such scientific advancements are setting significant contributions to the emerging field of operational impact-based forecasting and operational drought early warning services.

How to cite: Shyrokaya, A., Di Baldassarre, G., Cloke, H., Messori, G., Pappenberger, F., and Pechlivanidis, I.: Drought impact-based forecasting: Trade-offs between indicators and impacts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1555, https://doi.org/10.5194/egusphere-egu23-1555, 2023.

EGU23-2146 | ECS | Posters on site | NP1.2

Contrasting Deep and Shallow Arctic Warming Events on the Intraseasonal Time Scale in Boreal Winter 

Juncong Li, Xiaodan Chen, Yuanyuan Guo, and Zhiping Wen

The vertical structure of Arctic warming is of great importance and attracts increasing attention. This study defines two types of Arctic warming events (viz., deep versus shallow) according to their temperature profiles averaged over the Barents-Kara Seas (BKS), and thereupon compares their characteristics and examines their difference in generation through thermodynamic diagnoses. The deep Arctic warming event—characterized by significant bottom-heavy warming extending from the surface into the middle-to-upper troposphere—emanates from the east of Greenland and then moves downstream towards the BKS primarily through zonal temperature advection. The peak day of deep warming event lags that of the precipitation and resultant diabatic heating over Southeast Greenland by about four days, suggesting that the middle-to-high tropospheric BKS warming is likely triggered by the enhanced upstream convection at the North Atlantic high latitudes. In contrast, the shallow warming event—manifested by warming confined within the lower troposphere—is preceded by the meridional advection of warm air from inland Eurasia. These anomalous southerlies over Eurasian lands during shallow warming events are related to the eastward extension of deepened Icelandic Low. Whereas during deep warming events, the in-situ reinforcement of Icelandic Low favors abundant moisture transport interplaying with the Southeast Greenland terrain, leading to intense precipitation and latent heat release there. Both deep and shallow warming events are accompanied by Eurasian cooling, but the corresponding cooling of deep warming event is profoundly stronger. Further, intraseasonal deep Arctic warming events could explain nearly half of the winter-mean change in warm Arctic-cold Eurasia anomaly.

How to cite: Li, J., Chen, X., Guo, Y., and Wen, Z.: Contrasting Deep and Shallow Arctic Warming Events on the Intraseasonal Time Scale in Boreal Winter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2146, https://doi.org/10.5194/egusphere-egu23-2146, 2023.

EGU23-2242 | ECS | Posters on site | NP1.2

A Quantile Generalised Additive Approach for Compound Climate Extremes: Pan-Atlantic Extremes as a Case Study 

Leonardo Olivetti, Gabriele Messori, and Shaobo Jin

We present an application of quantile generalised additive models (QGAMs) to study the rela-
tionship between spatially compounding climate extremes - namely extremes that occur (near-)
simultaneously in geographically remote regions. We take as example wintertime cold spells
in North America and co-occurring wet or windy surface weather extremes in Western Europe,
which we collectively term Pan-Atlantic compound extremes. QGAMS are largely novel in cli-
mate science applications and present three key advantages over conventional statistical models
of weather extremes:


1. they do not require a direct identification and parametrisation of the extremes themselves,
since they model all quantiles of the distributions of interest;
2. they do not require any a priori knowledge of the functional relationship between the predic-
tors and the dependent variable;
3. they make use of all information available, and not only of a small number of extreme values.


Here, we use QGAMs to both characterise the co-occurrence statistics and investigate possible
dynamical drivers of the Pan-Atlantic compound extremes. We find that recent cold spells in
North America are a useful predictor of upcoming near-surface extremes in Western Europe,
and that QGAMs can predict those extremes more accurately than conventional peak-over-
threshold models.

How to cite: Olivetti, L., Messori, G., and Jin, S.: A Quantile Generalised Additive Approach for Compound Climate Extremes: Pan-Atlantic Extremes as a Case Study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2242, https://doi.org/10.5194/egusphere-egu23-2242, 2023.

EGU23-2644 | ECS | Posters on site | NP1.2

Investigating the typicality of the dynamics leading to extreme temperatures in the IPSL-CM6A-LR model 

Robin Noyelle, Pascal Yiou, and Davide Faranda

Understanding the physical mechanisms leading to extremes of quantities of interest in dynamical systems remains a challenge. Under mild hypothesis, the application of the theory of large deviations to dynamical systems predicts the convergence of trajectories leading to extremes towards a typical, i.e. most probable, one called the instanton. In this paper, we use a 2000 years long simulation of the IPSL-CM6A-LR model under a stationary pre-industrial climate to test this prediction. We investigate the convergence properties of trajectories leading to extreme temperatures at four locations in Europe for several variables. We show the convergence of trajectories for most physical variables, with some geographical and temporal discrepancies. Our results are coherent with the most probable path prediction and suggest that the instanton dynamics leading to extremes is a relevant feature of climate models.

How to cite: Noyelle, R., Yiou, P., and Faranda, D.: Investigating the typicality of the dynamics leading to extreme temperatures in the IPSL-CM6A-LR model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2644, https://doi.org/10.5194/egusphere-egu23-2644, 2023.

EGU23-3300 | Posters on site | NP1.2

Simulating the West Pacific Heatwave of 2021 with Analog Importance Samping 

Flavio Pons, Pascal Yiou, and Aglae Jezequel

During the summer of 2021, the North American Pacific Northwest was affected by an extreme heatwave that broke previous temperature records by several degrees and lasted almost two months after the initial peak. The event caused severe impacts on human life and ecosystems, and was associated with the superposition of concurrent extreme drivers, whose effects were amplified by climate change. We evaluate whether this record-breaking heatwave could be anticipated prior to 2021, and how climate change affects North American Pacific Northwest worst case heatwave scenarios. We use a stochastic weather generator  with empirical importance sampling. The generator simulates temperature sequences with realistic statistics using circulation analogues, chosen with an importance sampling based on the daily maximum temperature over the region that recorded the most extreme impacts. We show how some of the large-scale drivers of the event can be obtained form the circulation analogues, even if such information is not directly given to the stochastic weather generator.

How to cite: Pons, F., Yiou, P., and Jezequel, A.: Simulating the West Pacific Heatwave of 2021 with Analog Importance Samping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3300, https://doi.org/10.5194/egusphere-egu23-3300, 2023.

EGU23-4216 | ECS | Orals | NP1.2

Large-scale perspective on the extreme near-surface winds in the central North Atlantic 

Aleksa Stanković, Rodrigo Caballero, and Gabriele Messori

This study investigates winter cyclones that cause extreme 10 m winds in the central North Atlantic region (30o to 60latitude, -50o to -10o longitude) in the ERA5 dataset. We employ a bottom-up approach consisting of selection of the extreme 10 m wind events and analysis of the cyclones that caused the extremes.

The 10 m wind extremes were ranked using the Klawa and Ulbrich (2003) destructiveness index, which takes into account wind exceedances over the local 98th percentiles. The top 1% of destructive events were chosen for further analysis. Cyclones were associated with the extreme winds by finding the closest sea-level pressure lows at the times of maximum wind speeds.

By analyzing various meteorological fields associated with the temporal evolution of the selected cyclones, we find an important role of interactions with other pre-existing cyclones that create suitable conditions for the development of the subsequent extreme windstorms.  

How to cite: Stanković, A., Caballero, R., and Messori, G.: Large-scale perspective on the extreme near-surface winds in the central North Atlantic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4216, https://doi.org/10.5194/egusphere-egu23-4216, 2023.

EGU23-5515 | ECS | Orals | NP1.2

Estimating non-linear persistence for impact assessment in European forests 

Tristan Williams, Miguel D. Mahecha, and Gustau Camps-Valls

Persistence is an important characteristic of many complex systems in nature and of the Earth system in particular. Relating this statistical concept to physical properties of ecosystems is rather elusive, but reflects how long the system remains at a certain state before changing to a different one and is measured via the memory and dependence of values on past states [1]. Characterizing persistence in the terrestrial biosphere is very relevant to understand intrinsic properties of the system such as legacy effects of extreme climate events [2]. Such memory effects are often highly non-linear and therefore challenging to detect in observational records and poorly represented in Earth system models. This study estimates long and short-term non-linear persistence in eddy-covariance flux measurements and remote sensing products in European forests and the corresponding hydro-meteorological data. Characterizing persistence in the data allows us to make inferences on the interaction between Drought-Heat events, forest dynamics, and ecosystem resilience [3]. The comparison of in-situ and Earth Observation (EO) data allows us to infer how meaningful EO data are for monitoring complex dynamics in ecosystems.

For short-term, spatio-temporal persistence, we use echo state networks using the technique suggested in [4] as an explainable AI (XAI) technique. In this context, the persistence of the system can be estimated by the model's response when the input fades abruptly. For the characterization of long-term persistence, we introduce a novel kernel extension of the well-established Detrended Fluctuation Analysis (DFA) [5], a method widely used in atmospheric sciences [1]. The DFA method is a scaling analysis that provides a simple quantitative parameter (the scaling exponent) to represent the correlation properties of a signal and a characteristic time of the event of interest. Unlike DFA, the proposed kernel DFA method can handle non-linear time-scales interactions. 

Estimating the non-linear persistence of forests and climate data allows us to relate characteristic times, crossover points between different scaling exponents, and short-term memory parameters with the duration and intensity of the events, as well as an indicator of change in the vegetation response to hydro-climatic conditions.

 

[1] Salcedo-Sanz, S., et al. “Persistence in complex systems”. Physics Reports 957, 1-73, (2022).

[2] Bastos, Ana, et al. “Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity." Science advances 6.24 (2020)

[3] Scheffer, M., Carpenter, S. R., Dakos, V. & van Nes, E. H. Generic indicators of ecological resilience: inferring the chance of a critical transition. Annu. Rev. Ecol. Evol. Syst. 46, 145–167 (2015).

[4] Barredo Arrieta, A., Gil-Lopez, S., Laña, I. et al. On the post-hoc explainability of deep echo state networks for time series forecasting, image and video classification. Neural Comput & Applic 34, 10257–10277 (2022).

[5] Peng, C‐K., et al. "Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series." Chaos: an interdisciplinary journal of nonlinear science 5.1 (1995): 82-87.

How to cite: Williams, T., Mahecha, M. D., and Camps-Valls, G.: Estimating non-linear persistence for impact assessment in European forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5515, https://doi.org/10.5194/egusphere-egu23-5515, 2023.

Atmospheric blocking can be described as a large-scale stationary or quasi-stationary circulation anomaly that blocks the mean westerlies. Blocking often triggers extreme temperature events like heat waves or cold spells. However, dynamical processes leading to the formation, maintenance, and decay mechanisms of blocking are still not well understood.

Moist processes have recently been proven to play a significant role in the formation and maintenance of blocking. However, it is unclear if moist processes generate special properties in the blocking life cycle that cannot be represented by dry dynamics or if they are just there to inject extra energy into the atmospheric disturbances. The following is the question we address in the present study: Is a dry dynamical model with climatology close to the observations capable of representing blocking characteristics correctly? The methodology relies on numerical experiments made with the new IPSL dynamical core called DYNAMICO, which enables high spatial resolutions. DYNAMICO is used here to analyze a long-term simulation in which the model forcing is designed to obtain a realistic climatology for a given season (perpetual winter in the present case). Blocking statistics like frequency of occurrence and duration are provided using two blocking detection algorithms and compared to the re-analysis dataset (ERA5). A focus is made on blocking onsets in the Euro-Atlantic sector. To highlight the differences in the processes leading to blocking onsets, backward Lagrangian trajectories seeded in the blocking regions are systematically computed and analyzed. Additional long-term simulations of the same dry model with the increased horizontal resolution are also analyzed following the same approach.

How to cite: Deshmukh, V., Rivière, G., and Fromang, S.: How are atmospheric blockings represented in a dry general circulation model with wave energy just as powerful as in the observations?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5671, https://doi.org/10.5194/egusphere-egu23-5671, 2023.

EGU23-6131 | ECS | Orals | NP1.2

Stochastic weather generator and deep learning approach for predicting and sampling extreme European heatwaves 

George Miloshevich, Dario Lucente, Freddy Bouchet, and Pascal Yiou

Sampling rare events such as extreme heatwaves whose return period is larger than the length of available observations requires developing and benchmarking new  simulation methods. There is growing interest in applying deep learning alongside already existing statistical approaches to better generate and predict rare events. Our goal is to benchmark Stochastic Weather Generator (SWG) [1] based on analogs of circulation, soil moisture and temperature as a tool for sampling tails of distribution as well as forecasting heatwaves in France and Scandinavia using data from General Circulation Model (GCM). Analog method has been successfully implemented in rare event algorithms for low dimensional climate models [2].

SWG is implemented using a Markov chain with hidden states (.e.g. geopotential height at 500 hPa) with Euclidean metric. When applying such methods to climate data two challenges emerge: a large number of degrees of freedom and the difficulty of including slow drivers such as soil moisture alongside circulation patterns. Consequently, we are going to discuss ways of adjusting the distance metric of the analog Markov chain and dimensionality reduction techniques such as EOFs and variational auto encoder. By choosing the correct combination of weighted variables in the Euclidean metric and using analogs of only 100 years and generating long synthetic sequences we are able to correctly estimate return times of order 7000 years, which is validated based on a 7200 year long control run. The teleconnection patterns generated thus also look reliable compared to the control run.

Next we compare SWG forecasts of heatwaves with a direct supervised approach based on a Convolutional Neural Network (CNN). Both CNN and SWG are trained and validated on exactly the same GCM runs which allows us to conclude that CNN performs better in both regions. One could consider SWG as a baseline approach for CNN for this task.

[1] Yiou, P. and Jézéquel, A., https://doi.org/10.5194/gmd-13-763-2020, 2020

[2] D. Lucente at al. https://10.1088/1742-5468/ac7aa7, 2022

[3] DP Kingma, M Welling - https://doi.org/10.48550/arXiv.1312.6114, 2013

[4] G. Miloshevich, at al - https://doi.org/10.48550/arXiv.2208.00971, 2022

How to cite: Miloshevich, G., Lucente, D., Bouchet, F., and Yiou, P.: Stochastic weather generator and deep learning approach for predicting and sampling extreme European heatwaves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6131, https://doi.org/10.5194/egusphere-egu23-6131, 2023.

EGU23-6507 | ECS | Posters on site | NP1.2

Simulation of future extreme rainfall events over Belgium with a focus on the Vesdre valley using the regional climate model MAR. 

Josip Brajkovic, Hans Van De Vyver, Sébastien Doutreloup, Nicolas Ghilain, and Xavier Fettweis

The rainfall in July 2021 that hit West Germany, Netherlands and Belgium was of unprecedented intensity. To assess the probability of such events ocuring in a near and far future (until 2100), the regional climate model MAR has been used to make simulations at a resolution of 7,5 km. To this end, the regional climate model MAR is linked to a set of Earth System models (ESMs) with 4 IPCC SSP scenarios over a domain that includes Belgium and Luxemburg. The analysis focused on the valley of the Vesdre which in Belgium was the most impacted by flooding in terms of damage to human infrastructures.

For some specific climatic conditions, MAR simulates events of similar intensity to those of the 2021-floods over the next 5 decades. To assess the statistic significance of the results, a Peaks Over Threshold analysis (POT) has been applied to MAR outputs for precipitation events of 1,2,3,4 and 5-days. Quantiles associated with high return periods have been calculated for the historical period of simulation (1980-2010) and for the 2011-2040, 2041-2070 an 2071-2100 periods. This shows that the frequency of such events in the periods 2011-2040 and 2041-2070 is likely to increase if climatic conditions are wet enough. For global warming levels above 3 to 4 °C, conditions appear too dry for such events to occur.

How to cite: Brajkovic, J., Van De Vyver, H., Doutreloup, S., Ghilain, N., and Fettweis, X.: Simulation of future extreme rainfall events over Belgium with a focus on the Vesdre valley using the regional climate model MAR., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6507, https://doi.org/10.5194/egusphere-egu23-6507, 2023.

EGU23-6732 | Posters on site | NP1.2

Deep learning techniques applied to an attribution study for heatwaves in the Iberian Peninsula 

Pablo G. Zaninelli, David Barriopedro-Cepero, Marie Drouard, José Manuel Garrido-Pérez, Jorge Pérez-Aracil, Dušan Fister, Ricardo García-Herrera, Sancho Salcedo-Sanz, and M. Carmen Alvarez-Castro

Extreme event attribution quantifies the influence of climate change on a particular extreme event (EE). Understanding the extent to which climate change is responsible for particular EE is of paramount importance because of the vulnerability of society and ecosystems to these events, especially when it comes to heatwaves that have become more frequent and intense in many parts of the world in recent decades. This led the scientific community to focus its efforts on attribution analysis and the implementation of new techniques for its study. Attribution studies of temperature EE using machine learning (ML) methods are scarce in the specialized literature. Most attribution studies perform statistical comparison between the probability of occurrence of an event today with its probability in the pre-industrial past, making it possible to determine how much more likely that event is due to climate change and how much severe it could be. However, some limitations of these classical methodologies are the difficulty in understanding the links between the physical processes responsible for the occurrence of extreme events and anthropogenic forcing and the impossibility of detecting new trends associated with this forcing. The CLImate INTelligent (CLINT) project aims, among its objectives, to design ML algorithms to improve classical attribution methodologies in some of the aforementioned limitations for three european hot-spots located in Spain, Italy and Netherlands. In this framework, this work presents a preliminary attribution analysis for summer heatwaves focused in Iberian Peninsula and based on deep learning tools such as anomaly detection with autoencoders. The autoencoder is an unsupervised method that comprises two neural networks, one to encode information and the other to decode it. The autoencoder is fed with pre-industrial realizations integrated in the framework of the Coupled Model Intercomparison Project in its sixth version (CMIP6) in such a way that it allows detecting variabilities and trends that are present in the historical run and not in the pre-industrial one. In addition, the influence of climate change for a particular temperature EE could be associated with the AE anomaly for this EE.

How to cite: Zaninelli, P. G., Barriopedro-Cepero, D., Drouard, M., Garrido-Pérez, J. M., Pérez-Aracil, J., Fister, D., García-Herrera, R., Salcedo-Sanz, S., and Alvarez-Castro, M. C.: Deep learning techniques applied to an attribution study for heatwaves in the Iberian Peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6732, https://doi.org/10.5194/egusphere-egu23-6732, 2023.

EGU23-7087 | Posters on site | NP1.2

A Temperature-Duration-Curve model for the real-time estimation of extreme river water temperatures at ungauged sites 

Taha Ouarda, Christian Charron, and André St-Hilaire

Water temperature is an important environmental variable that has impacts on the physical, chemical, and biological processes in streamflows. Extreme river water temperatures affect the spawning, development and survival of several fish species, and are considered as important indicators of the health of a river and essential variables in all habitat models. Unfortunately, river water temperature data is characterised by its limited availability: measurement sites are often scarce, and records are regularly very short when available. It is hence crucial to develop regional thermal data estimation models for ungauged and partially gauged locations. Very few studies in the literature focused on the estimation of extreme water temperatures at sites where thermal data are limited or inexistent. A Temperature-Duration-Curve (TDC) model is proposed in this work to provide real-time estimates of river water temperature at ungauged locations during extreme events. The TDCs are estimated at the ungauged locations using a Generalised Additive Model and are then used to provide continuous estimates of river water temperature at these sites based on a spatial interpolation model. The model is developed based on a data base of 126 river thermal stations from Canada. The performance of the method is compared to a simpler approach and results indicate that the developed TDC model is robust and useful in practice.

How to cite: Ouarda, T., Charron, C., and St-Hilaire, A.: A Temperature-Duration-Curve model for the real-time estimation of extreme river water temperatures at ungauged sites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7087, https://doi.org/10.5194/egusphere-egu23-7087, 2023.

EGU23-7124 | ECS | Orals | NP1.2

Influence of the Atlantic Multidecadal Variability and of Soil Moisture on Extreme Heatwaves in Europe 

Valeria Mascolo, Clément Le Priol, Fabio d'Andrea, and Freddy Bouchet

Nowadays heat waves are a growing issue, causing detrimental effects on society, people’s health and environment in several parts of the world. Slow drivers such as spring soil moisture and sea surface temperature are known to impact the probability of occurrence of heatwaves in many areas of the globe. However, their influence remains still little understood and studied. Even fewer has been said on the cross effect and relative impact of both factors. 

Our work aims at analysing and comparing the effects of spring soil moisture deficit in Europe and sea surface temperature decadal variability in the North Atlantic (AMV) on the occurrence of typical and more extreme European heat waves. To do that, we use the outputs from three climate models, namely IPSL-CM6A-LR, EC-Earth3 and CNRM-CM6-1, in which North Atlantic sea surface temperatures are nudged to the observed AMV anomalies.

At a methodological level, previous studies mainly focused on typical heat waves. Our work goes beyond that and proposes a new methodology to study events with larger return times. By introducing return time maps we can study rare heatwaves with return time from 10 to 50 years. We find that the temperature and duration of typical and extreme heatwaves are influenced by the AMV and soil moisture. In general, the changes induced by typical AMV or soil moisture anomalies are of comparable amplitude. In many areas of Europe, the influence of AMV and soil moisture over duration or temperature of extreme heatwaves increases when the return time is longer and is statistically significant even for return times of 50 years. In general, the three models give consistent results. 

With positive AMV phase or low soil moisture, the temperature and duration of extreme heatwaves are changed according to regional patterns. As might be expected, positive AMV phase or low soil moisture often induce hotter and longer typical and extreme heatwaves. However, counter-intuitively, they also induce cooler and shorter heatwaves over part of Northern-Eastern Europe. For more extreme events, the impact of the AMV and soil moisture increases, according to rather similar regional patterns. However, the regions with decreased temperature or duration impact extend in size.

In this work, we have improved the study of extreme heat waves and better understood their slow drivers. Studying those drivers is important to enhance heat wave predictability. To move further in this direction, we need to improve the statistics of the events. In this context, developing and using new tools such as rare event simulations might be the right path to follow.

How to cite: Mascolo, V., Le Priol, C., d'Andrea, F., and Bouchet, F.: Influence of the Atlantic Multidecadal Variability and of Soil Moisture on Extreme Heatwaves in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7124, https://doi.org/10.5194/egusphere-egu23-7124, 2023.

EGU23-7697 | ECS | Posters on site | NP1.2

Diagnosing atmospheric persistence for heatwaves and in extended range forecasts 

Emma Holmberg, Gabriele Messori, Rodrigo Caballero, Steffen Tietsche, and Davide Faranda

Extreme events can cause severe disruption to society on many levels, and the ability to forecast these events represents a significant step towards the ability to reduce their impacts. Anomalously persistent atmospheric configurations are typically regarded to be strongly linked with temperature extremes in Europe, however, traditional methods of analysing atmospheric persistence lack a mathematically well-grounded definition. Furthermore, we are not aware of a metric which allows for quantification of instantaneous atmospheric persistence for forecasts for either an individual ensemble member or a deterministic forecast. We aim to help refine the definition of atmospheric persistence by presenting a mathematically well-grounded definition of persistence, which can potentially also be applied in a forecasting environment. We examine the link between the extremal index, an indicator for atmospheric persistence based on dynamical systems theory, and warm temperature extremes in several regions of Europe. We then consider the applicability of this technique to forecast data, in particular ECMWF extended range reforecast data, discussing its potential value as an additional forecast evaluation metric.

How to cite: Holmberg, E., Messori, G., Caballero, R., Tietsche, S., and Faranda, D.: Diagnosing atmospheric persistence for heatwaves and in extended range forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7697, https://doi.org/10.5194/egusphere-egu23-7697, 2023.

EGU23-7908 | ECS | Orals | NP1.2

Dynamical pathways for pan-Atlantic compound cold and windy extremes 

Jacopo Riboldi, Josh Dorrington, Richard Leeding, Antonio Segalini, and Gabriele Messori

North American cold spells tend to co-occur with extreme wind and precipitation events over Europe, but the physical mechanisms behind such “pan-Atlantic” compound extremes have not been fully clarified yet. Rather than proposing a single mechanism, we discuss how cold spells over a single North American region can be connected with wind extremes over different European regions through separate, physically consistent dynamical pathways. The first one involves the propagation of a Rossby wave train from the Pacific Ocean, and is associated with windstorms over north-western Europe in the 5-10 days after the cold spell peak. The second one is associated with a high-latitude anticyclone over the North Atlantic and an equatorward-shifted jet, leading to windstorms over south-western Europe already in the days preceding the cold spell peak.

The same dynamical pathways can be independently retrieved from a cluster analysis based on the temporal evolution of the North Atlantic circulation in the days preceding North American cold spells. Such an analysis highlights significantly different stratospheric circulation patterns between the two pathways, with cold spells of the second pathway tied to a weaker than usual stratospheric polar vortex, and an enhanced occurrence of sudden stratospheric warmings.

How to cite: Riboldi, J., Dorrington, J., Leeding, R., Segalini, A., and Messori, G.: Dynamical pathways for pan-Atlantic compound cold and windy extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7908, https://doi.org/10.5194/egusphere-egu23-7908, 2023.

EGU23-7954 | ECS | Orals | NP1.2

On the Response of North Atlantic Extratropical Cyclones to North America Cold Air Outbreaks 

Richard Leeding, Gabriele Messori, and Jacopo Riboldi

We examine the characteristics of North Atlantic extratropical cyclones in ERA5 data during cold air outbreaks over continental North America. Previous research has established a statistical link between occurrences of North American cold air outbreaks and an increased frequency of extreme wet and windy conditions over Europe. The theoretical understanding of cyclogenesis suggests that greater numbers of extratropical cyclones will be generated in the North Atlantic, resulting from an enhanced temperature difference between the North American continent and the Gulf Stream during cold air outbreaks. Our analysis finds that counts of extratropical cyclones in the North  Atlantic storm track are no greater, or even less than climatology during periods with cold air outbreaks. We instead find anomalous jet stream activity associated with the cold air outbreaks. The jet stream acts to focus extratropical cyclones to a specific region of the North  Atlantic, depending on the regional extent of the cold air outbreak, resulting in significantly higher extratropical cyclone counts for that specific region. The regions found to be experiencing higher counts of extratropical cyclones align with previously established geographical dependencies between co-occurrences of North American cold air outbreaks and wet and windy extremes over Europe. We also find that cold air outbreaks associated with an anomalously strengthened jet result in a general increase in the strength of the extratropical cyclones reaching Europe, whilst a more equatorward-displaced jet, with lower maximum speed, results in more persistent extratropical cyclones over southern Europe. 

How to cite: Leeding, R., Messori, G., and Riboldi, J.: On the Response of North Atlantic Extratropical Cyclones to North America Cold Air Outbreaks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7954, https://doi.org/10.5194/egusphere-egu23-7954, 2023.

EGU23-8138 | ECS | Orals | NP1.2

Return levels of extreme European windstorms, their dependency on the NAO, and potential future risks 

Matthew Priestley, David Stephenson, and Adam Scaife

European windstorms experience considerable interannual variability, which makes the quantification of extreme return periods challenging. Estimating 200-year return levels is also complicated by having only ~60 years of comprehensive observational data. Such estimations of return periods are often performed using ‘catastrophe models’, which use complex calibration and tuning processes.  We have developed a reliable statistical model to estimate extreme windstorm gust speed return levels from only a multi-year sample of windstorm footprints without the need for the complexities associated with catastrophe models.

 

We have also been able to include variations of the NAO in our estimates, allowing for the generation of NAO-dependent return levels. Positive phases of the NAO result in larger return levels across the northwest of Europe. Additionally, the NAO is shown to be especially important for modulating low return period gusts, with the most extreme gusts occurring due to further stochastic processes. Using plausible future states of the NAO we also show that return levels have the potential to increase significantly in the next 100 years to rise well above historical uncertainty levels.

How to cite: Priestley, M., Stephenson, D., and Scaife, A.: Return levels of extreme European windstorms, their dependency on the NAO, and potential future risks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8138, https://doi.org/10.5194/egusphere-egu23-8138, 2023.

EGU23-8224 | ECS | Orals | NP1.2

Severe windstorm projections for Europe 

Nicholas James Leach, Gabriele Messori, Alex Crawford, Ryota Wada, Sally Woodhouse, and Claire Burke

Extreme windstorms are of considerable interest due to their potential to cause significant socio-economic damages over very large areas of land. As a result, understanding how climate change may affect the characteristics of the most severe storms is an important question for adaptation planing. However, projections of how the hazard associated with windstorms will change in the future are highly uncertain.

Here, we use an efficient statistical approach that characterises individual windstorms in terms of their intensity and exposure to estimate the present-day risk from such storms. We then use a methodology used widely in detection and attribution of climate change to assess how such characteristics may change into the future. Using windstorms simulated by a diverse set of high-resolution regional climate model projections for Europe, the EURO-CORDEX ensemble, we provide projections of risk over a range of future climate scenarios. Finally, we explore how the variety of driving and regional models influence the associated uncertainties, and how considering the performance and independence of the models can improve the robustness of the projections.

How to cite: Leach, N. J., Messori, G., Crawford, A., Wada, R., Woodhouse, S., and Burke, C.: Severe windstorm projections for Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8224, https://doi.org/10.5194/egusphere-egu23-8224, 2023.

EGU23-8244 | ECS | Posters on site | NP1.2

Predictability of blocking and zonal flow regimes  in a reduced-order land atmosphere coupled model 

Anupama K Xavier, Jonathan Demaeyer, and Stéphane Vannitsem

Low-frequency variability (LFV) encompasses atmospheric and climate processes on time scales from a few weeks to decades.​ This includes atmospheric blockings, heat waves, cold spells, and at longer time scales long-term oscillations like the MJO, the NAO, ENSO….. Better understanding of LFV, could contribute to improved long term forecasts​. Identifying and evaluating LFVs in GCMs is computationally expensive, so in this study an idealised low order coupled model is used. They are climate models ‘stripped to the bone’,  which links theoretical understanding to the complexity of more realistic models, made by key ingredients and approximations​; which hence helps us to study a particular phenomenon by tweaking the parameters affecting them with less computational cost​. 

The Quasi Geostrophic land atmosphere coupled model is a python implementation of mid-latitude atmospheric model​ with two layer quasi geostrophic channel atmosphere on beta -plane​ coupled to a simple land portion.  The system exhibits blocking conditions at different time scales depending on the incoming solar radiation and also experiences transitions from blocking to zonal flow after applying different sets of parameters to the model. The predictability and persistence of these regimes is investigated by calculating the local lyapunov exponents at the specified transition points and around them.  The findings are discussed in the perspective of the current literature on the predictability of blocking.

How to cite: K Xavier, A., Demaeyer, J., and Vannitsem, S.: Predictability of blocking and zonal flow regimes  in a reduced-order land atmosphere coupled model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8244, https://doi.org/10.5194/egusphere-egu23-8244, 2023.

EGU23-9105 | Orals | NP1.2

Effect of anthropogenic climate change on explosive cyclogenesis cases in Europe 

Mireia Ginesta, Emmanouil Flaounas, Pascal Yiou, and Davide Faranda

Mid-latitude storms are essential features of atmospheric variability in the cold season. The subsequent damages are caused by high wind speeds and heavy precipitation. Among such events, explosive cyclones can lead to extreme impacts when they make landfall. Climate change is affecting the underlying characteristics of such types of extremes. Being able to understand the way it modifies their dynamics is of great importance. In this work, we assess the influence of anthropogenic climate change on observed explosive cyclones in an Extreme Event Attribution framework using a large ensemble dataset. We evaluate three storms that hit different parts of Europe: Xynthia in February 2010, Alex in October 2020, and Eunice in January 2022. 

We use three ensembles of 35 members of the Community Earth System Model (CESM). We compare two periods of 6-hourly data: present-day climate [1991-2001] and future climate [RCP8.5 scenario, 2091–2101]. We find analogues of the trajectories of the three storms before their highest intensity in both periods. We do that by tracking all cyclones in the dataset and selecting the cyclone tracks that have the minimum Euclidean distance in km from the trajectories of Xynthia, Alex, and Eunice. We explore the characteristics of the analogues of the trajectories in both periods such as frequency of explosive cyclogenesis and intensity to evaluate whether the dynamics of the storms have been affected by climate change. We further compare the analogues in terms of precipitation and low-level wind in the regions of impact.

How to cite: Ginesta, M., Flaounas, E., Yiou, P., and Faranda, D.: Effect of anthropogenic climate change on explosive cyclogenesis cases in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9105, https://doi.org/10.5194/egusphere-egu23-9105, 2023.

EGU23-9123 | ECS | Posters on site | NP1.2

Modeling vegetation response to climate in Africa at fine resolution: EarthNet2023, a deep learning dataset and challenge. 

Claire Robin, Christian Requena-Mesa, Vitus Benson, Lazaro Alonso, Jeran Poehls, Nuno Carvalhais, and Markus Reichstein

Droughts are a major disaster in Africa, threatening livelihoods through their influence on crop yields but also by impacting and weakening ecosystems. Modeling the vegetation state can help anticipate and reduce the impact of droughts by predicting the vegetation response over time. Forecasting the state of vegetation is challenging: it depends on complex interactions between the plants and different environmental drivers, which can result in both instantaneous and time-lagged responses, as well as spatial effects. Furthermore, modeling these interactions at the fine resolution of landscape scale can only rely on remote sensing observations, as in-situ measurements are not global and weather models have a coarse grid. With the increasing availability of remote sensing data, deep learning methods are a promising avenue for these spatiotemporal tasks. Here, we introduce both a dataset and a baseline deep neural network, modeling the vegetation response to climate at landscape scale in Africa.

EarthNet2021 [1] introduced leveraging self-supervised learning for satellite imagery forecasting based on coarse-scale weather in Europe. Here, we introduce EarthNet2023 with a more narrow focus on drought impacts in Africa. It contains over 45,000 Spatio-temporal minicubes (each 1.28x1.28km) at representative locations over the whole African continent. Alongside Sentinel-2 reflectance, ERA5 weather, and topography, it also contains Sentinel-1 backscatter, soil properties, and a long-term Normalized Difference Vegetation Index (NDVI) climatology based on Landsat. The latter allows evaluating models on vegetation anomalies, thereby including modeling of drought impacts. EarthNet2023 is intended as an open benchmark challenge, allowing multiple research groups to develop their approaches to drought impact modeling in Africa. 

As a baseline for EarthNet2023, we train a  Convolutional Long Short-Term Memory (ConvLSTM) deep learning model. Previous work has shown it is suitable for spatiotemporal satellite imagery forecasting [2, 3, 4]. The ConvLSTM baseline captures the seasonal evolution of NDVI over a wide range of vegetation types. General spatial patterns are well-captured as well as a first indication of skill during weather extremes is seen, although the accuracy of the predictions is inconsistent, and the confidence in the model is therefore too low. This suggests, with further development, deep learning approaches are promising for modeling vegetation evolution in Africa, potentially even up to the degree to support anticipatory action with drought impact modeling.

 

[1] Requena-Mesa, C., Benson, V., Reichstein, M., Runge, J., & Denzler, J. (2021). EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task. In CVPR 2021 (pp. 1132-1142).

[2] Diaconu, C. A., Saha, S., Günnemann, S., & Zhu, X. X. (2022). Understanding the Role of Weather Data for Earth Surface Forecasting Using a ConvLSTM-Based Model. In CVPR 2022 (pp. 1362-1371).

[3] Kladny, K. R. W., Milanta, M., Mraz, O., Hufkens, K., & Stocker, B. D. (2022). Deep learning for satellite image forecasting of vegetation greenness. bioRxiv.

[4] Robin, C., Requena-Mesa, C., Benson, V., Alonso, L., Poehls, J., Carvalhais, N., & Reichstein, M. (2022). Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs. In Tackling Climate Change with Machine Learning: workshop at NeurIPS 2022. 

How to cite: Robin, C., Requena-Mesa, C., Benson, V., Alonso, L., Poehls, J., Carvalhais, N., and Reichstein, M.: Modeling vegetation response to climate in Africa at fine resolution: EarthNet2023, a deep learning dataset and challenge., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9123, https://doi.org/10.5194/egusphere-egu23-9123, 2023.

Loss and damage (L&D) has been on the international agenda for over 20 years, and recently gained significant headway at UNFCCC COP27. L&D has been a controversial aspect of the international climate negotiations. This is largely due to L&D being connected to responsibility and compensation for the impacts of climate change on vulnerable communities. Researchers and practitioners are beginning to ask how they can help with L&D while many remain unsure about what this may mean.

Loss and Damage (L&D) is associated with the adverse effects of climate change, including the effects that are related to extreme weather events, such as intense typhons, but also occur in slow events, such as at sea level rise. The paper sets out to synthesise three specific challenges to L&D: lack of a coherent definition of L&D, gaps in measuring disproportionate effects of loss and damage on people, including the non economic consequences of L&D events, who it affects, how and why, and on what scale, and finally, absence of coherent understanding of climate governance instruments to influence L&D in ways that do not undermine existing adaptation and development efforts.

How to cite: Boyd, E.: Recasting the disproportionate impacts of climate extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9492, https://doi.org/10.5194/egusphere-egu23-9492, 2023.

EGU23-9945 | Posters on site | NP1.2

Oceanic Maintenance of Atmospheric Blocking 

Jamie Mathews

In recent years the understanding of atmospheric blocking has changed from solely a dry phenomena to one that includes moist processes. The primary source of that moisture, the ocean, has, until recently, been neglected as a driver of this basin scale structure. Here, the connection between atmospheric blocking over the North Atlantic and the diabatic influence of the Gulf Stream was investigated using potential vorticity diagnostics. In line with previous research, the reliance atmospheric blocking has on latent heat fluxes over the Gulf Stream and its extension, for induction and maintenance, was shown to be significant. It was shown that not only is it more likely for a North Atlantic block to occur after significant surface latent heat fluxes over the Gulf Stream and its extension, but the resulting block is likely to be anchored on the western flank of the Atlantic, making it more stationary and hence, more impactful. Additionally, blocks that have a longer duration were highly associated with surface latent heat fluxes over the western boundary current, while shorter blocks were not, indicating a positive feedback from the oceanic mesoscale phenomena onto this basin scale structure. Finally, the frequency of the block was seen to correspond to the amount of surplus heat content in the western boundary currents prior to the blocking event which, in the North Atlantic, had leading order dependence on the heat transport via the Gulf Stream.

How to cite: Mathews, J.: Oceanic Maintenance of Atmospheric Blocking, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9945, https://doi.org/10.5194/egusphere-egu23-9945, 2023.

EGU23-11943 | Orals | NP1.2 | Highlight

Simulating worst case  heatwaves during the Paris 2024 Olympics 

Pascal Yiou, Camille Cadiou, Davide Faranda, Aglaé Jézéquel, Nemo Malhomme, George Miloshevich, Robin Noyelle, Flavio Pons, Yoann Robin, and Mathieu Vrac

The Summer Olympic Games in 2024 will take place during the apex of the temperature seasonal cycle in the Paris Area. The midlatitudes of the Northern hemisphere have witnessed a few intense heatwaves since the 2003 epitome event. Those heatwaves have had environmental and health impacts, which often came as surprises. In this paper, we search for the most extreme heatwaves in Ile-de-France that are physically plausible, under climate change scenarios, for the decades around 2024. We apply a rare event algorithm on CMIP6 data to evaluate the range of such extremes. We find that the 2003 record can be exceeded by more than 4°C in Ile-de-France before 2050, with a combination of prevailing anticyclonic conditions and cut-off lows. This study intends to build awareness on those unprecedented events, against which our societies are ill-prepared. Those results could be extended to other areas of the world.

How to cite: Yiou, P., Cadiou, C., Faranda, D., Jézéquel, A., Malhomme, N., Miloshevich, G., Noyelle, R., Pons, F., Robin, Y., and Vrac, M.: Simulating worst case  heatwaves during the Paris 2024 Olympics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11943, https://doi.org/10.5194/egusphere-egu23-11943, 2023.

EGU23-12059 | ECS | Posters virtual | NP1.2

Present and projected humid heat exposure and precipitation extremes in Turkey 

Berkay Donmez, Kutay Donmez, Cemre Yuruk Sonuc, and Yurdanur Unal

Regional intensification of precipitation extremes and the emergence of humid heat stress conducive to periling vulnerable populations suggest the need for further nation-specific risk assessments. Here, we conduct the first analysis of present and projected population exposure to extreme wet-bulb temperature (Tw) values in Turkey and concurrently use the generalized extreme value (GEV) theory to model extreme precipitation based on multiple intensity, duration, and frequency metrics. Using simulations dynamically downscaled to 0.11-degree resolution via the COSMO-CLM model, we provide a nationwide picture of the trends in these metrics and derive the number of people exposed to Tw extremes based on the population estimates in the Shared Socioeconomic Pathways (SSPs) under the high-emission RCP 8.5 scenario. As part of the GEV analysis, our main goal is to show how precipitation extremes in Turkey evolve and transform due to the changing climate not only in stationary but also in non-stationary climate settings. Our results convey a detailed understanding of the potentially dangerous conditions across climatologically different regions of Turkey and are relevant for decision-makers.

How to cite: Donmez, B., Donmez, K., Yuruk Sonuc, C., and Unal, Y.: Present and projected humid heat exposure and precipitation extremes in Turkey, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12059, https://doi.org/10.5194/egusphere-egu23-12059, 2023.

EGU23-12095 | ECS | Posters on site | NP1.2

Weakened impact of the Atlantic equatorial mode of variability on the future Guinea Coast extreme rainfall indices 

Koffi Worou, Thierry Fichefet, and Hugues Goosse

The Atlantic equatorial mode (AEM) is an interannual oceanic internal mode of variability which impacts the tropical circulation during its active phases in the boreal summer. A positive phase of the AEM is characterized by above-normal sea surface temperature anomalies in the eastern equatorial Atlantic which lead to positive rainfall anomalies over the Guinea Coast, a region located in the southern part of West Africa. The AEM appears as the leading oceanic driver of the Guinea Coast rainfall (GCR) during the monsoon season, and the AEM-GCR relation during the last century is stationary.  Moreover, extreme rainfall events over the Guinea Coast are also enhanced by the AEM-positive phases.  Therefore, there is a need to study how the relationship between the AEM and extreme rainfall indices would change under future global warming. The present work assesses this relationship between the AEM and the Guinea Coast extreme rainfall indices in the historical simulations performed by 24 General Circulation Models (GCMs) participating in the sixth phase of the Coupled Models Intercomparison Project (CMIP6). Results indicate that the extreme rainfall responses to the AEM under present-day climate conditions are qualitatively well reproduced by the GCMs in the 1995-2014 period, although there are substantial biases in their magnitudes.  For the future changes, we consider the CMIP6 Shared Socio-economic pathway 5-8.5 (SSP5-8.5) simulations and three different periods: the near-term (2021-2040), the mid-term (2041-2060) and the long-term (2080-2099).  Relative to the present-day period, our results indicate an overall gradual increase with time in the mean and variability of the different extreme indices for the Guinea Coast. However, the future influence of the AEM on the extreme rainfall indices decreases with time, which is in line with the projected decrease in the future AEM variability.

How to cite: Worou, K., Fichefet, T., and Goosse, H.: Weakened impact of the Atlantic equatorial mode of variability on the future Guinea Coast extreme rainfall indices, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12095, https://doi.org/10.5194/egusphere-egu23-12095, 2023.

EGU23-12130 | ECS | Orals | NP1.2 | Highlight

A probabilistic assessment of extreme weather event impacts on crop yield in Germany 

Federico Stainoh, Julia Moemken, and Joaquim Pinto

The impacts of extreme weather on the agricultural sector are a global concern in a changing climate. In recent years, single and compound weather extremes have increased in frequency, intensity and duration and are expected to worsen in the upcoming decades. Therefore, it is necessary to have a better understanding of extreme weather-related crop yield shock to ensure food security in a growing worldwide population. In this study, we employed a logistic regression model to quantify the risk of major crop yield shocks associated with heat stress, extreme precipitation and frosts. We used reported sub-national level data from Germany and a percentile-based threshold to define yield shock. Climate extreme drivers were based on statistical thresholds over daily maximum temperature, minimum temperature and precipitation. In addition to this,  we investigated how the seasonal meteorological pre-conditions of temperature and precipitation can modulate extreme weather-related yield shock.

How to cite: Stainoh, F., Moemken, J., and Pinto, J.: A probabilistic assessment of extreme weather event impacts on crop yield in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12130, https://doi.org/10.5194/egusphere-egu23-12130, 2023.

EGU23-13507 | Orals | NP1.2

Using Artificial Intelligence to Reconstruct Missing Climate Data In Extreme Events Datasets 

Étienne Plésiat, Robert Dunn, Markus Donat, Colin Morice, Thomas Ludwig, Hannes Thiemann, and Christopher Kadow

Evaluating the trends of extreme indices (EI) is crucial to detect and attribute extreme events (EE) and establish adaptation and mitigation strategies to the current and future climate conditions. However, the observational climate data used for the calculation of these indices often contains many missing values and leads to incomplete and inaccurate EI. This problem is even greater as we go back in time due to the scarcity of the older measurements.

To tackle this problem, interpolation techniques such as the kriging method are often used to fill in the gaps. However, it has been shown that such techniques are inadequate to reconstruct specific climatic patterns [1]. Deep-learning based technologies give the possibility to surpass standard statistical methods by learning complex patterns and features in climate data.

In this work, we are using an inpainting technique based on a U-Net neural network made of partial convolutional layers and a loss function designed to produce semantically meaningful predictions [1]. Models are trained using vast amounts of climate model data and can be used to reconstruct large and irregular regions of missing data with few computational resources.

The efficiency of the method is well demonstrated through its application to the HadEX3 dataset [2]. This dataset contains gridded land surface EI, among which the TX90p index that measures the monthly (or annual) frequency of warm days (defined as a percentage of days where daily maximum temperature is above the 90th percentile). As for other EI, there is a lack of TX90p values in many regions of the world, even in recent years. It is particularly true when looking at an intermediate product of HadEX3 where the station-based indices have been combined without interpolation. This is illustrated by the left map of the figure where the gray pixels correspond to missing values. By training our model using data from the CMIP6 archive, we have been able to reconstruct the missing TX90p values for all the time steps of HadEX3 (see right map in the figure) and detect EE that were not included in the original dataset. The reconstructed dataset is being prepared for the community in the framework of the H2020 CLINT project [3] for further detection and attribution studies.

[1] Kadow C. et al., Nat. Geosci., 13, 408-413 (2020)
[2] Dunn R.J.H. et al., J. Geophys. Res. Atmos., 125, 1 (2020)
[3] https://climateintelligence.eu/

How to cite: Plésiat, É., Dunn, R., Donat, M., Morice, C., Ludwig, T., Thiemann, H., and Kadow, C.: Using Artificial Intelligence to Reconstruct Missing Climate Data In Extreme Events Datasets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13507, https://doi.org/10.5194/egusphere-egu23-13507, 2023.

EGU23-14675 | ECS | Posters on site | NP1.2

Indirect and direct impacts of Typhoon In-Fa (2021) on heavy precipitation in inland and coastal areas of China: Synoptic-scale environments and return period analysis 

Liangyi Wang, Xihui Gu, Louise J. Slaster, Yangchen Lai, Xiang Zhang, Dongdong Kong, Jianyu Liu, and Jianfeng Li

Typhoon In-Fa in 2021 produced an indirect heavy precipitation event (HPE) in central China well over a thousand kilometers away from its center, as well as a direct HPE in eastern China near its eyewall, inner and outer spiral rainbands. Both indirect and direct HPEs of Typhoon In-Fa caused severe impacts on the society. However, the synoptic-scale environments and the impacts of return period estimations of these HPE events remain poorly understood. Here, we first evaluated the spatio-temporal evolution of the two HPEs indirectly and directly induced by Typhoon In-Fa, then examined the synoptic patterns during Typhoon In-Fa for both HPEs in central and eastern China, and finally analyzed how the Typhoon In-Fa-induced HPEs affected local return period estimations of precipitation extremes. Our results show that the remote HPE over central China ~2,200 km ahead of Typhoon In-Fa was a typical predecessor rain event (PRE). A low-level southeasterly jet conveyed abundant moisture from the vicinity of Typhoon In-Fa to central China. Abundant moisture experienced strong convergence and was forced ascent, which caused frontogenesis on the windward slope due to the impacts of orographic forcing, thereby the occurrence of PRE in central China. The PRE occurred beneath the equatorward entrance of the upper-level westerly jet. Meanwhile, Typhoon In-Fa and the PRE favored divergently and adiabatically driving outflow in the upper level, and thus intensified the upper-level westerly jet. In eastern China, the HPE occurred in areas situated less than 200 km from Typhoon In-Fa’s center and left of Typhoon In-Fa’s propagation. The persistent HPE was primarily due to the long duration and slow movement of Typhoon In-Fa. On the one hand, favorable thermodynamic and dynamic conditions, and abundant atmospheric moisture favored the maintenance of Typhoon In-Fa intensity. On the other hand, a saddle-shaped pressure field in the north of eastern China and peripheral weak steering flow impeded Typhoon In-Fa’s movement northward. From the perspective of hydrological impacts, indirect and direct HPEs induced by Typhoon In-Fa led to decreases in return period estimates of HPEs (especially in central China), indicating that such extreme HPEs might increase the failure risk of engineering operations. These results suggest that anomalous HPEs remotely triggered by TCs require improved early warnings, and that more attention should be paid to such HPEs when estimating the design values of hydraulic infrastructure.

How to cite: Wang, L., Gu, X., Slaster, L. J., Lai, Y., Zhang, X., Kong, D., Liu, J., and Li, J.: Indirect and direct impacts of Typhoon In-Fa (2021) on heavy precipitation in inland and coastal areas of China: Synoptic-scale environments and return period analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14675, https://doi.org/10.5194/egusphere-egu23-14675, 2023.

EGU23-14838 | Orals | NP1.2

Freva for ClimXtreme: an aid to get the bigger picture in analysis of extremes 

Etor E. Lucio-Eceiza, Christopher Kadow, Martin Bergemann, Andrej Fast, Hannes Thiemann, and Thomas Ludwig

 

The number of damaging events caused by natural disasters is increasing because of climate change. Projects of public interest such as ClimXtreme (Climate Change and Extreme Events [1, 2]), aim to improve our knowledge of extreme events, the influence of environmental changes and their societal impacts.

ClimXtreme focuses on an integral evaluation through a three-pronged approach, namely: the physical processes behind the extremes, the statistical assessment of them, and their impact. The success of such a project depends on a coordinate effort from many interdisciplinary groups down to the management of computational and data storage resources. The ever-growing amount of available data at the researcher’s disposal is a two-sided blade that craves for greater resources to host, access, and evaluate them efficiently through High Performance Computing (HPC) infrastructures. Additionally, these last years the community is demanding an easier reproducibility of evaluation workflows and data FAIRness [3]. Frameworks like Freva (Free Evaluation System Framework [4, 5]) offer an efficient solution to handle customizable evaluation systems of large research projects, institutes or universities in the Earth system community [6-8] over the HPC environment and in a centralized manner. Mainly written on python, Freva offers:

  • A centralized access. Freva can be accessed via command line interface, via web, and via python module (e.g. for jupyter notebooks) offering similar features.
  • A standardized data search. Freva allows for a quick and intuitive incorporation and search of several datasets stored centrally.
  • Flexible analysis. Freva provides a common interface for user defined data evaluation routines to plug them in to the system irrespective of the programming language. These plugins are able to search from and integrate own results back to Freva. This environment enables an ecosystem of plugins that fosters the interchange of results and ideas between researchers, and facilitates the portability to any other research project that uses a Freva instance.
  • Transparent and reproducible results. Every analysis run through Freva (including parameter configuration and plugin version information) is stored in a central database and can be consulted, shared, modified and re-run by anyone within the project. Freva optimizes the usage of computational and storage resources and paves the way of traceability in line with FAIR data principles.

Hosted at the DKRZ, ClimXtreme’s Freva instance (XCES [7]) offers quick access to more than 9 million datafiles of models (e.g. CMIP, CORDEX), observations (stations, gridded) and evaluation outputs. The ClimXtreme community has been actively contributing with plugins to XCES, its biggest asset, with close to 20 plugins of different disciplines at the disposal of everyone within the project, and more than 20,000 analysis run through the system. At present, any researcher can focus on a past, present or future period and a geographical region and run a series of evaluations ranging from coocurrence probabilities of extreme events, their impact on crops to wind tracking algorithms among many others. Freva facilitates comprehensive and exhaustive analysis of extreme events in an easy way.

 

References:

[1] https://www.fona.de/de/massnahmen/foerdermassnahmen/climxtreme.php

[2] https://www.climxtreme.net/index.php/en/

[3] https://www.go-fair.org/fair-principles/

[4] http://doi.org/10.5334/jors.253

[5] https://github.com/FREVA-CLINT/freva-deployment

[6] freva.met.fu-berlin.de

[7] https://www.xces.dkrz.de/

[8] www-regiklim.dkrz.de

 

How to cite: Lucio-Eceiza, E. E., Kadow, C., Bergemann, M., Fast, A., Thiemann, H., and Ludwig, T.: Freva for ClimXtreme: an aid to get the bigger picture in analysis of extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14838, https://doi.org/10.5194/egusphere-egu23-14838, 2023.

EGU23-14879 | ECS | Posters on site | NP1.2 | Highlight

Assessing recent trends in globally co-occurring hot, dry and wet events under climate change 

Bianca Biess, Lukas Gudmundsson, and Sonia I. Seneviratne

The spring-to-summer seasons in recent years were characterized by co-occurring hot, dry, and wet extremes around the globe, leading to questions about the contribution of human-induced global warming to the changing likelihoods of such extreme years.  Here we investigate recent trends in the fraction of global (and regional) land-area that is affected by hot days, wet days and dry months. Observed trends are put into context of Earth System Model (ESM) ensemble simulations accounting for present day and pre-industrial climate conditions in a detection and attribution setting. The analysis is applied to the global land area as well as to the regions defined in the sixth IPCC assessment report. Results show that on a global scale as well as on a regional level, observed trends of co-occurring hot, dry and wet events cannot be explained by internal climate variability, but are only captured by model simulations that account for anthropogenic changes in the composition of the atmosphere. Thus, the results show that recent global trends in spatially co-occuring hot and dry extremes are very likely linked to anthropogenic climate change.

How to cite: Biess, B., Gudmundsson, L., and Seneviratne, S. I.: Assessing recent trends in globally co-occurring hot, dry and wet events under climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14879, https://doi.org/10.5194/egusphere-egu23-14879, 2023.

EGU23-15813 | ECS | Posters on site | NP1.2

Delayed Effects of ENSO and Indian Ocean Dipole on the ensuing-summer extreme precipitation over Yangtze River Valley 

Yucong Lin, Silvio Gualdi, and Enrico Scoccimarro

Yangtze River Valley (YRV) locates in Southeast China, is home to about a third of the population in China. Summer extreme precipitation in Yangtze River can lead to extensive social problems and loss of lives. Understanding the characteristics of extreme precipitation and identifying the possible driving factors can increase our ability to plan for, manage and respond to related extreme events over the YRV. This study applies ERA5 data during the period of 1950~2021 to examine the possible influence of ENSO and the sea surface temperature (SST) variability over the Indian Ocean domain on the interannual variability of the extreme precipitation over the YRV. The related physical processes that link the summer Yangtze River extreme precipitation, ENSO and Indian Ocean Dipole (IOD) are investigated.

Using composites analysis and Pearson correlation method, we found that both ENSO and IOD have delayed effects on summer extreme precipitation over the YRV, warm ENSO events and positive IOD phases are in favor of increased extreme precipitation in the subsequent summers, and vice versa. The anomalous anticyclone over the western Pacific Ocean (WNPAC) is the key factor in altering the inter-annual variability of extreme precipitation over the YRV. By comparing the extreme precipitation composites with different ENSO-IOD coupling events, we found that the signals of enhanced extreme precipitation are significant when El Niño occurs with a positive phase of IOD in the previous winter. The results based on the large circulation patterns also support that IOD plays an essential role in modulating the WNPAC. Our research highlights the need for a fundamental exploration into air-sea interactions over the tropical Pacific associate to ENSO-IOD coupling modes, our understanding in learning the impacts of these modes of variability on precipitation extremes over the YRV will contribute to improve the predictability of extreme events over this region.

Keywords:

Yangtze River Valley, extreme precipitation, ENSO, IOD, western North Pacific anomalous anticyclone 

How to cite: Lin, Y., Gualdi, S., and Scoccimarro, E.: Delayed Effects of ENSO and Indian Ocean Dipole on the ensuing-summer extreme precipitation over Yangtze River Valley, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15813, https://doi.org/10.5194/egusphere-egu23-15813, 2023.

The climate and weather over Europe and Asia are strongly influenced by the large-scale atmospheric circulation over the North Atlantic area. During the winter of 2009/10, the usually separate Atlantic and African jets merged into one zonal jet, resulting in unusually cold and wet conditions in Eurasian regions. During this winter the jet was unusually persistent, with characteristics more typical of the Pacific jet stream, which is a mixed thermally-eddy driven jet, suggesting the jet underwent a rare dynamical regime change.  Such a merging was only observed to occur for a whole winter during winters of 1968-69 and 1969-70. In this study, we apply GKTL rare event algorithm to produce an ensemble of PlaSim model runs of similar winter flow conditions, to study such merged jet (mixed thermally-eddy driven jet) transition and its dynamics. We try to understand how the initial conditions during the beginning of the winter could affect the jet to be in a persistent merged state. It is seen that there is a larger probability to continue in a merged jet state if there is a merged jet state at the beginning of winter. Similarly, there is a larger probability to continue in an eddy-driven jet state if there is an eddy-driven jet state at the beginning of winter. On comparing the ensemble of merged jet winter trajectories with the ensemble of eddy-driven jet winter trajectories there is a significant weakening of eddy heat fluxes over the west and central North Atlantic region. Also, the typically poleward-directed eddy momentum fluxes are significantly weaker during the winter merged jet state with small increases in the subtropics over the eastern North Atlantic due to the equatorward shift of the eddies.

How to cite: Suresan, S. and Harnik, N.: Computing and analyzing persistent merged jet state in climate model using rare event algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16917, https://doi.org/10.5194/egusphere-egu23-16917, 2023.

NP2 – Dynamical Systems Approaches to Problems in the Geosciences

EGU23-408 | ECS | Posters on site | CL2.2

El Niño Southern Oscillation influence over the Orinoco low-level jet variability 

Alejandro Builes, Johanna Yepes, and Hernán D. Salas

We studied the most active season of the Orinoco Low-Level jet (OLLJ), December-January-February (DJF), during the El Niño-Southern Oscillation canonical phases, El Niño and La Niña. In particular, we studied the occurrence days of the jet in each month, wind speed, moisture transport and precipitation over northern south America. In terms of the occurrence of the OLLJ, during El Niño in January, the jet exhibits its highest reduction with changes up to 24% in the eastern Colombian plains. On the contrary, during La Niña, the jet exhibits an increase between 6–16% in the frequency of occurrence mainly located in the eastern Colombian plains and the border between Colombia, Ecuador and Peru. Although the diurnal cycle of the OLLJ windspeed remains unaltered during the ENSO phases the maximum decrease (increase) up to -2m/s (up to 1 m/s) during El Niño (La Niña). Regarding moisture transport there is a gradual reduction during the season in both ENSO phases reaching up to 18 gm-1 kgm-1 during El Niño, and the precipitation also shows a reduction of around 5 mm/day. In conclusion, during DJF at the ENSO canonical phases the OLLJ shows changes in its occurrence along the jet corridor, and the region experiences changes in both moisture transport and precipitation.

How to cite: Builes, A., Yepes, J., and Salas, H. D.: El Niño Southern Oscillation influence over the Orinoco low-level jet variability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-408, https://doi.org/10.5194/egusphere-egu23-408, 2023.

EGU23-410 | Orals | CL2.2

Phase-Locking between precipitation and El Niño-Southern Oscillation over northern South America 

Hernán D. Salas, Germán Poveda, Óscar J. Mesa, Alejandro Builes-Jaramillo, Niklas Boers, and Jürgen Kurths

We study phase-locking between the El Niño - Southern Oscillation (ENSO) and precipitation at inter-annual time scales over northern South America. To this end, we characterize the seasonality of the regional patterns of sea surface temperature, surface pressure levels, and precipitation anomalies associated with the states of the canonical ENSO. We find that the positive (negative) precipitation anomalies experienced in northern South America differ from those previously reported in the literature in some continental regions. In particular, the Orinoco Low-level Jet corridor separates two regions with negative (positive) rainfall anomalies during El Niño (La Niña), which are located in the Guianas (northeastern Amazon) and the Caribbean. Moreover, we show that the ENSO signal is phase-locked with the inter-annual rainfall variability in most of the study regions although some areas exhibit phase-locking without a significant change in the anomalies of precipitation. This suggests that ENSO could induce changes only in terms of phases and not so in terms of magnitude. This work provides new insights into the non-linear interactions between ENSO and hydro-climatic processes over the tropical Americas.

How to cite: Salas, H. D., Poveda, G., Mesa, Ó. J., Builes-Jaramillo, A., Boers, N., and Kurths, J.: Phase-Locking between precipitation and El Niño-Southern Oscillation over northern South America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-410, https://doi.org/10.5194/egusphere-egu23-410, 2023.

EGU23-1522 | ECS | Orals | CL2.2

Future climate response to observed extreme El Niño analogues 

Paloma Trascasa-Castro, Yohan Ruprich-Robert, and Amanda Maycock

Model simulations show a robust increase in ENSO-related precipitation variability in a warmer climate, but there remains uncertainty in whether the characteristics of ENSO events themselves may change in the future. Our study aims to disentangle these effects by addressing how the global impacts of observed large El Niño events would change in different background climate states covering the preindustrial, present and future periods.

Pacemaker simulations with the EC-Earth3-CC model were performed replaying the 3 strongest observed El Niño events from the historical record (1982/83, 1997/98, 2015/16). Model tropical Pacific sea surface temperature (SST) anomalies were restored towards observations, while imposing different background states, mimicking past, present and future climate conditions (following the SSP2-4.5). All variables outside the restoring region evolve freely in a coupled-atmosphere ocean transient simulation. For each start date, 30 ensemble members with different initial conditions were run for 2 years. Using this approach we ask ‘what impacts would arise if the observed El Niño occurred in the past or future’?

In response to the same imposed El Niño SST anomalies, precipitation anomalies are shifted towards the Eastern equatorial Pacific in the future compared to the present day, leading to changes in the extratropical response to El Niño. Some examples are an amplification of the surface temperature response over north-eastern North America, northern South America and Australia in boreal winter. We link the changes of El Niño related tropical Pacific precipitation to a decrease in the climatological zonal SST gradient in the equatorial Pacific, as we move from past to future climatologies, which potentially leads to a higher convection sensitivity to SST anomalies over the Central and Eastern equatorial Pacific in the future. Interestingly, the simulations indicate there has already been an intensification of El Niño impacts between present day and preindustrial, which is comparable to the differences found between future and present. This nonlinear behaviour highlights the need to understand potential changes to convection thresholds in the tropical Pacific to be able to explain El Niño teleconnections under climate change scenarios. Ongoing work is exploring the changes in atmospheric circulation that lead to the overall intensification of El Niño impacts that we show in our study.

How to cite: Trascasa-Castro, P., Ruprich-Robert, Y., and Maycock, A.: Future climate response to observed extreme El Niño analogues, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1522, https://doi.org/10.5194/egusphere-egu23-1522, 2023.

EGU23-1960 | Posters on site | CL2.2

Two regimes of inter-basin interactions between the Atlantic and Pacific Oceans on interannual timescales 

Jae-Heung Park, Sang-Wook Yeh, Jong-Seong Kug, Young-Mean Yang, Hyun-Su Jo, Hyo-Jeong Kim, and Soon-Il An

Understanding the inter-basin interactions between the Atlantic and Pacific Oceans is of great concern due to their substantial global climatic implications. By analyzing observational reanalysis datasets (1948-2020), we found that there are two regimes in Atlantic–Pacific inter-basin interactions: (i) the Pacific-driven regime, and (ii) the Atlantic-driven regime. In the Pacific-driven regime before the mid-1980s, the El Niño-Southern Oscillation (ENSO) in winter effectively drives the primary mode of sea surface temperature anomaly (SSTA) in the tropical Atlantic (i.e., NTA mode) in boreal spring. The NTA mode has a meridional contrast of SSTA along the Atlantic Intertropical convergence zone due to the ENSO effect, similar to the Atlantic Meridional Mode. Whereas, in the Atlantic-driven regime after the mid-1980s, the ENSO effect on the NTA becomes remarkably weaker, so that the NTA mode is featured with a SSTA monopole. Notably, the NTA mode without the meridional contrast of SSTA is capable of modulating an ENSO event. Our analyses of the latest climate models participating in the Coupled Model Intercomparison Project (CMIP) phases 6 support the hypothesis that the two regimes engendered by the Atlantic-Pacific inter-basin interactions are likely due to natural variability.

How to cite: Park, J.-H., Yeh, S.-W., Kug, J.-S., Yang, Y.-M., Jo, H.-S., Kim, H.-J., and An, S.-I.: Two regimes of inter-basin interactions between the Atlantic and Pacific Oceans on interannual timescales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1960, https://doi.org/10.5194/egusphere-egu23-1960, 2023.

EGU23-2136 | ECS | Posters on site | CL2.2

A multi-modal representation of El-Niño Southern Oscillation Diversity 

Jakob Schlör, Antonietta Capotondi, and Bedartha Goswami

Sea surface temperature anomalies (SSTA) associated with the El-Niño Southern Oscillation (ENSO) show strong event-to-event variability, known as ENSO diversity. El Niño and La Niña events are typically divided into Eastern Pacific (EP) and Central Pacific (CP) types based on the zonal location of peak SSTA. The separation of these types is usually based on temperature differences between pairs of predefined indices, such as averages over boxes in the Eastern and Central Pacific or the two leading Principal Components of tropical SSTA. 
Using results from unsupervised learning of SSTA data, we argue that ENSO diversity is not well described by distinctly separate classes but rather forms a continuum with events grouping into "soft'' clusters. We apply a Gaussian mixture model (GMM) to a low-dimensional projection of tropical SSTA to describe the multi-modal distribution of ENSO events. We find that El-Niño events are best described by three overlapping clusters while La-Niña events only show two "soft'' clusters. The three El-Niño clusters are described by i) maximum SSTA in the CP, ii) maximum SSTA in the EP, and iii) strong basin-wide warming of SSTA which we refer to as the "super El-Niño'' cluster. The "soft'' clusters of La-Niña correspond to i) anomalous cool SST in the CP and ii) anomalously cool SST in the EP. We estimate the probability that a given ENSO event belongs to a chosen cluster and use these probabilities as weights for estimating averages of atmospheric variables corresponding to each cluster. These weighted composites show qualitatively similar patterns to the typically used averages over EP and CP events. However, the weighted composites show a higher signal-to-noise ratio in the mid-latitudes for the "super El-Niño'' events. 
We further apply our approach to CESM2 model data and discuss the potential of GMM clustering for evaluating how well ENSO diversity is captured in Global Circulation models.

How to cite: Schlör, J., Capotondi, A., and Goswami, B.: A multi-modal representation of El-Niño Southern Oscillation Diversity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2136, https://doi.org/10.5194/egusphere-egu23-2136, 2023.

An information theory based framework is developed to assess the predictability of the ENSO complexity, which includes different types of the ENSO events with diverse characteristics in spatial patterns, peak intensities and temporal evolutions. The information theory advances a unique way to quantify the forecast uncertainty and allows to distinguish the predictability limit of each type of event. With the assistance of a recently developed multiscale stochastic conceptual model that succeeds in capturing both the large-scale dynamics and many crucial statistical properties of the observed ENSO complexity, it is shown that different ENSO events possess very distinct predictability limits. Beyond the ensemble mean value, the spread of the ensemble members also has remarkable contributions to the predictability. Specifically, while the result indicates that predicting the onset of the eastern Pacific (EP) El Ninos is challenging, it reveals a universal tendency to convert strong predictability to skillful forecast for predicting many central Pacific (CP) El Ninos about two years in advance. In addition, strong predictability is found for the La Nina events, corresponding to the effectiveness of the El Nino to La Nina transitions. In the climate change scenario with the strengthening of the background Walker circulation, the predictability of sea surface temperature in the CP region has a significant response with a notable increase in summer and fall. Finally, the Gaussian approximation exhibits to be accurate in computing the information gain, which facilitates the use of more sophisticated models to study the ENSO predictability.

How to cite: Fang, X. and Chen, N.: Quantifying the Predictability of ENSO Complexity Using a Statistically Accurate Multiscale Stochastic Model and Information Theory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2209, https://doi.org/10.5194/egusphere-egu23-2209, 2023.

EGU23-2470 | ECS | Orals | CL2.2

The Dynamics of the El-Niño Southern Oscillation (ENSO) Diversity 

Priyamvada Priya, Dietmar Dommenget, and Shayne McGregor

This study investigates the observed El-Niño Southern Oscillation (ENSO) dynamics for the eastern Pacific (EP) and central Pacific (CP) events. Here we use the recharge oscillator (ReOsc) model concept to describe the ENSO phase space, based on the interaction of sea surface temperatures in the eastern equatorial Pacific (T) and thermocline depth (h), for the different types of ENSO events. We further look at some important statistical characteristics, such as power spectrum and cross-correlation, as essential parameters for understanding the dynamics of ENSO. The results show that the CP and EP events are very different in the ENSO phase space and less well described by the ReOsc model than a T index-based model. The EP events are closer to the idealised ReOsc model, with clear propagation through all phases of the ENSO cycle and strongly skewed towards the El-Niño and subsurface ocean heat discharge states. The CP events, in turn, do not have a clear propagation through all phases and are strongly skewed towards the La-Nina state. Also, the CP events have a slower cycle (67 months) than the EP events (50 months). Further, the CP events collapse after the La-Nina phase, whereas the EP events appear to collapse after the discharging phase. The characteristics out-of-phase cross-correlation between T and h is nearly absent for the CP events, suggesting that the interaction between T and h is not as important as for the EP or the canonical ENSO events. Furthermore, the coupling factor of T and h is smaller for the CP events than the EP events, implying that the CP events are not influenced much by T and h interactions. This study will provide new insight to understand these events by developing a dynamical approach to explain the observed ENSO dynamics for the EP and CP events in the ReOsc model framework.

How to cite: Priya, P., Dommenget, D., and McGregor, S.: The Dynamics of the El-Niño Southern Oscillation (ENSO) Diversity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2470, https://doi.org/10.5194/egusphere-egu23-2470, 2023.

EGU23-2477 | Posters on site | CL2.2

ENSO phase space dynamics with an improved estimate of the thermocline depth 

Dietmar Dommenget and Priyamvada Priya

The recharge oscillator model of the El Niño Southern Oscillation (ENSO) describes the ENSO dynamics as an interaction and oscillation between the eastern tropical Pacific sea surface temperatures (T) and subsurface heat content (thermocline depth; h), describing a cycle of ENSO phases. h is often approximated on the basis of the depth of the 20oC isotherm (Z20). In this study we will address how the estimation of h affects the representation of ENSO dynamics. We will compare the ENSO phase space with h estimated based on Z20 and based on the maximum gradient in the temperature profile (Zmxg). The results illustrate that the ENSO phase space is much closer to the idealised recharge oscillator model if based on Zmxg than if based on Z20. Using linear and non-linear recharge oscillator models fitted to the observed data illustrates that the Z20 estimate leads to artificial phase dependent structures in the ENSO phase space, which result from an in-phase correlation between h and T. Based on the Zmxg estimate the ENSO phase space diagram show very clear non-linear aspects in growth rates and phase speeds. Based on this estimate we can describe the ENSO cycle dynamics as a non-linear cycle that grows during the recharge and El Nino state, and decays during the La Nina states. The most extreme ENSO states are during the El Nino and discharge states, while the La Nina and recharge states do not have extreme states. It further has faster phase speeds after the El Nino state and slower phase speeds during and after the La Nina states. The analysis suggests that the ENSO phase speed is significantly positive in all phases, suggesting that ENSO is indeed a cycle. However, the phase speeds are closest to zero during and after the La Nina state, indicating that the ENSO cycle is most likely to stall in these states.

How to cite: Dommenget, D. and Priya, P.: ENSO phase space dynamics with an improved estimate of the thermocline depth, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2477, https://doi.org/10.5194/egusphere-egu23-2477, 2023.

EGU23-3263 | ECS | Orals | CL2.2

Model Resolution Effects on ENSO and its Teleconnections 

Ned Williams, Adam Scaife, and James Screen

The El Niño-Southern Oscillation (ENSO) influences climate on a global scale and is a source of long-range predictability. Accurate modelling of the impact of ENSO requires accurate representation of teleconnections as well as of ENSO itself. We consider a set of CMIP6 models and assess the effect of increasing model resolution on ENSO and its boreal winter teleconnections. The spatial structure, strength and asymmetry of both ENSO and its teleconnection to the extratropical North Pacific are considered. We find evidence of an improved El Niño teleconnection in high resolution models, but this effect is weaker for La Niña. We aim to establish whether ocean or atmospheric resolution is the primary driver of resolution-based trends, and we evaluate the relevance of mean state biases on these trends. 

How to cite: Williams, N., Scaife, A., and Screen, J.: Model Resolution Effects on ENSO and its Teleconnections, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3263, https://doi.org/10.5194/egusphere-egu23-3263, 2023.

EGU23-3278 | ECS | Posters on site | CL2.2

Oceanic and Atmospheric Feedbacks Associated with the Spreading of Pacific Coastal Niño Events 

Daniel Rudloff and Joke Lübbecke

In early 2017 a very strong coastal warming occurred off the coast of Peru. This event, which caused heavy rainfalls and flooding over land, marked the strongest so called ‘Pacific Coastal Niño Event’ observed. Most intriguing about this event was the fact that the central Pacific was not showing any significant anomalies during that time. Since then several studies have investigated Pacific Coastal Niños but the exact mechanisms of how such events behave are still not clear. While most studies focus on their onset mechanisms, we here analyze their evolution and decay and in particular their connection to the central equatorial Pacific.

To address those questions, we are using the coupled climate model FOCI (Flexible Ocean Climate Infrastructure). Starting from a long control simulation with pre-industrial conditions we perform sets of 2-year long sensitivity experiments in which a coastal warming is generated by a local wind stress anomaly utilizing a partial coupling approach. Once the warming is initiated by reduced upwelling the wind forcing is switched off and the model can evolve freely, which enables us to investigate the evolution and decay of the warming. The approach allows to vary the forcing in strength, location and timing. By starting from different conditions in terms of equatorial heat content and applying the forcing during different months, the influences of both the background state of the equatorial Pacific during the Coastal Niño and the seasonality of the coastal warming are investigated. To understand which factors influence the spreading of the warm anomaly we analyze both local coastal feedbacks, which lead to an alongshore extension of the anomaly, and equatorial feedbacks that are crucial for a spreading along the equator.

How to cite: Rudloff, D. and Lübbecke, J.: Oceanic and Atmospheric Feedbacks Associated with the Spreading of Pacific Coastal Niño Events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3278, https://doi.org/10.5194/egusphere-egu23-3278, 2023.

EGU23-3440 | ECS | Posters on site | CL2.2

New insight into multi-year La Niña dynamics from the perspective of a near-annual ocean process 

Fangyu Liu, Wenjun Zhang, Fei-Fei Jin, Feng Jiang, Julien Boucharel, and Suqiong Hu

The El Niño-Southern Oscillation (ENSO) exhibits highly asymmetric temporal evolutions between its warm and cold phases. While El Niño events usually terminate rapidly after their mature phase and show an already established transition into the cold phase by the following summer, many La Niña events tend to persist throughout the second year and even re-intensify in the ensuing winter. While many mechanisms were proposed, no consensus has been reached yet and the essential physical processes responsible for the multi-year behavior of La Niña remain to be illustrated. Observations show that a unique ocean physical process operates during multi-year La Niña events. It is characterized by rapid double reversals of zonal ocean current anomalies in the equatorial Pacific which exhibits a fairly regular near-annual periodicity. Analyses of mixed-layer heat budget reveal comparable contributions of the thermocline and zonal advective feedbacks to the SST anomaly growth for the first year of multi-year La Niña events; however, the zonal advective feedback plays a dominant role in the re-intensification of La Niña events. Furthermore, the unique ocean process is identified to be closely associated with the preconditioning heat content state in the central to eastern equatorial Pacific before the first year of La Niña, which sets the stage for the future re-intensification of La Niña. The above-mentioned oceanic process can be largely reproduced by state-of-the-art climate models despite systematic underestimation, providing a potential predictability source for the multi-year La Niña events.

How to cite: Liu, F., Zhang, W., Jin, F.-F., Jiang, F., Boucharel, J., and Hu, S.: New insight into multi-year La Niña dynamics from the perspective of a near-annual ocean process, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3440, https://doi.org/10.5194/egusphere-egu23-3440, 2023.

EGU23-3598 | Orals | CL2.2 | Highlight

Prediction Challenges from Errors in Tropical Pacific Sea Surface Temperature Trends 

Michelle L'Heureux, Michael Tippett, and Wanqiu Wang

Initialized, monthly mean predictions of the tropical Pacific Ocean are made against the backdrop of a warming climate, and it is unclear to what extent these predictions are impacted by trends.  Here, we analyze the forecast models that comprise the North American Multi-Model Ensemble (NMME) and uncover significant linear trend errors that have consequences for the tropical Pacific basin and ENSO variability.  All models show positive trend errors over the eastern equatorial Pacific over the 1982-2020 hindcast and real-time period.  These positive trend errors interact with the mean bias of each respective model, reducing, over time, the bias of models that are too cold and increasing the bias of models that are too warm.  These trend errors lead to a tropical Pacific that is too warm and too wet over the basin, and is significantly correlated with an increase in El Niño false alarms.  Finally, we explore the consequences of these tropical Pacific Ocean trend errors on predictions of global precipitation anomalies. 

How to cite: L'Heureux, M., Tippett, M., and Wang, W.: Prediction Challenges from Errors in Tropical Pacific Sea Surface Temperature Trends, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3598, https://doi.org/10.5194/egusphere-egu23-3598, 2023.

EGU23-3631 | Posters on site | CL2.2

Multiyear ENSO dynamics as revealed in observations, CMIP6 models, and linear theory 

Tomoki Iwakiri and Masahiro Watanabe

El Niño–Southern Oscillation (ENSO) events occasionally recur one after the other in the same polarity, called multiyear ENSO. However, the dynamical processes are not well understood. This study aims to elucidate the unified mechanisms of multiyear ENSO using observations, CMIP6 models, and the theoretical linear recharge oscillator (RO) model. We found that multiyear El Niño and La Niña events are roughly symmetric except in some cases. The composite multiyear ENSO reveals that anomalous ocean heat content (OHC) in the equatorial Pacific persists beyond the first peak, stimulating another event. This prolonged OHC anomaly is caused by meridional Ekman heat transport counteracting geostrophic transport induced recharge–discharge process that otherwise acts to change the OHC anomaly. A meridionally wide pattern of sea surface temperature observed during multiyear event is responsible for the Ekman heat transport. CMIP6 multi-model ensemble shows a significant correlation between the ENSO meridional width and the occurrence ratio of multiyear ENSO. A multiyear ENSO-like oscillation was simulated using the linear RO model that incorporates a seasonally varying Bjerknes growth rate and a weak recharge efficiency representing the effect of Ekman transport. When the recharge efficiency parameter was estimated using reanalysis data based on geostrophic transport alone, a multiyear ENSO rarely occurred, confirming the importance of Ekman transport in retarding the recharge–discharge process.

How to cite: Iwakiri, T. and Watanabe, M.: Multiyear ENSO dynamics as revealed in observations, CMIP6 models, and linear theory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3631, https://doi.org/10.5194/egusphere-egu23-3631, 2023.

EGU23-3637 | ECS | Posters on site | CL2.2

Is a Preceding Strong El Niño Required to Generate Multi-year La Niña? 

Ji-Won Kim, Jin-Yi Yu, and Baijun Tian

By analyzing observational data covering the period from 1900 to 2021, we show that the known mechanism linking multi-year La Niña with a preceding strong El Niño has been overemphasized. A majority of multi-year La Niña (64%; 7 out of 11 events) do not require a preceding strong El Niño to generate their 2nd-year La Niña. We find that the negative phase of the Pacific Meridional Mode (PMM) during 1st-year La Niña’s decaying spring, rather than the preceding strong El Niño, offers the key mechanism to produce 2nd-year La Niña, resulting in a multi-year La Niña. It is further found that the westward extension of the 1st-year La Niña cold sea surface temperature anomalies, which interacts with the eastern edge of the western Pacific warm pool, is a key factor inducing the negative PMM. The negative PMM mechanism to generate multi-year La Niña is also applied to the 3rd-year La Niña of multi-year La Niña, giving rise to a triple-dip event. The possible reason(s) how and why a multi-year La Niña can become either a double-dip or a triple-dip event will be discussed.

How to cite: Kim, J.-W., Yu, J.-Y., and Tian, B.: Is a Preceding Strong El Niño Required to Generate Multi-year La Niña?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3637, https://doi.org/10.5194/egusphere-egu23-3637, 2023.

EGU23-4180 | Orals | CL2.2

Why is El Nino warm? 

Stephan Fueglistaler, Laure Resplandy, and Allison Hogikyan

El Nino years stand out in the global average temperature time series as record-warm years. The coupled atmosphere-ocean dynamics leading to warming in the climatologically cold equatorial Eastern Pacific are well understood, but cannot be the cause for the very strong signal in global average temperarture. The latter must be caused by an increase in subcloud Moist Static Energy (MSE) in the domain of highest subcloud MSE where atmospheric deep convection couples the surface, boundary layer and free troposphere. Transformation of the data from geographical space to sea-surface temperature (SST) percentiles eliminates the large spatial see-saws in all variables arising from the geographic reorganization of the general circulation, and brings to light the mechanism: While in the Eastern Pacific region oceanic heat uptake is reduced (corresponding to a heat flux out of the ocean), the deep convective domain sees a heat flux from the atmosphere into the ocean. We show that this heat flux into the ocean at the high end of SSTs - the opposite of the canonical perspective of a warming due to a heat flux from the ocean to the atmosphere - is mechanically forced: surface wind speeds are lower in regions of active deep convection than in ENSO neutral (and La Nina) years. The resulting reduced evaporation leads to the increase in subcloud MSE that causes the global temperature signal.

How to cite: Fueglistaler, S., Resplandy, L., and Hogikyan, A.: Why is El Nino warm?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4180, https://doi.org/10.5194/egusphere-egu23-4180, 2023.

The equatorial Atlantic zonal sea surface temperature (SST) gradient, which has significant climatic and biogeochemical effects, is closely associated with the equatorial Pacific zonal SST gradient through Walker circulation on seasonal and interannual time scales. However, discrepancies in current SST datasets mean that its long-term trend is not well understood. Here, using multiple datasets, we find a robust weakening long-term trend (i.e., greater warming in the east than west) in the equatorial Atlantic zonal SST gradient over the period 1900–2010 in all datasets. We also find that this weakening trend is closely linked to the tropical Pacific cold tongue mode (CTM), which corresponds to a strong increasing long-term trend of zonal SST gradient along the equatorial Pacific (i.e., warming in the west and cooling in the east). Specifically, the long-term cooling SST anomalies associated with the CTM modify the Walker circulation, and leads to weaker trade winds over the western equatorial Atlantic. These in turn deepen the thermocline in the eastern equatorial Atlantic, and cause the weakening long-term trend of SST gradient along the equatorial Atlantic. The long-term trend of the CTM is induced by ocean dynamical feedback in response to global warming, suggesting that global warming could affect the equatorial Atlantic zonal SST gradient via the CTM. Our results provide a novel explanation of the linkages between the long-term trend of equatorial Atlantic zonal SST gradient and the CTM under global warming, which carries important implications for the relationship between global warming and the equatorial Atlantic zonal SST gradient.

How to cite: Li, Y.: Long-term trend of equatorial Atlantic zonal SST gradient linked to the tropical Pacific cold tongue mode under global warming, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4360, https://doi.org/10.5194/egusphere-egu23-4360, 2023.

EGU23-4971 | ECS | Orals | CL2.2

Indo-Pacific teleconnection changes during the Holocene: model-proxy comparison 

Isma Abdelkader Di Carlo, Pascale Braconnot, Mary Elliot, and Olivier Marti

The teleconnections between the Indian and Pacific Oceans are very complex, involving multiple modes of variability and phenomena such as the El Niño-Southern Oscillation, Indian Ocean Dipole, Indian Ocean Basin mode, and the Asian monsoon. Their interactions are complex because changes in one of these phenomena affect the others. Insufficient agreement exists on the predicted evolution of mean states of both basins and the impacts of climate variability in this region in response to increasing CO2 emissions. To better constrain Indo-Pacific interactions, we have studied the Holocene period. We consider four transient simulations from three General Circulation Models (GCM) and a collection of paleo-archives from the Holocene in the Indo-Pacific region. Our study allows us to put into perspective the links between long-term changes in variability and in the mean state. The main driver is insolation and trace gases (CO2) that have increased the mean sea surface temperature of the tropical ocean over the last 6,000 years. Our first results show that modeled trends in the regional long-term variability are in agreement, but differences are observed when we analyze the data at shorter interannual timescales. We also explain why the simulations differ or agree with the paleoclimate reconstructions. One way is to look at the relative role of temperature and salinity in determining the changes in δ18O recorded by the various climate archives. 

How to cite: Abdelkader Di Carlo, I., Braconnot, P., Elliot, M., and Marti, O.: Indo-Pacific teleconnection changes during the Holocene: model-proxy comparison, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4971, https://doi.org/10.5194/egusphere-egu23-4971, 2023.

EGU23-5205 | ECS | Posters on site | CL2.2

Impact of tropical SSTs on the monthly signal over the North Atlantic-European region 

Sara Ivasić, Ivana Herceg Bulić, and Margareta Popović

Targeted numerical simulations were designed to test the potential impact of tropical sea surface temperatures (SSTs) on the geopotential heights at 200 hPa (GH200) signal over the North Atlantic-European region. Five experiments with SST anomalies prescribed in different areas, acting as lower boundary forcing, were created with an intermediately complex atmospheric general circulation model (ICTP AGCM). In the AGCM experiments, the SST forcing was prescribed globally, in the tropical zone of all oceans, only in the tropical Atlantic, tropical Indian Ocean and limited to the tropical Pacific. All of the simulations covered a 156-year-long period.

The monthly GH200 signal was calculated based on the difference between the ensemble mean of each experiment and the climatological mean for the considered period. In addition, to inspect the impact of the El Niño-Southern Oscillation (ENSO), the signal was calculated for ENSO and non-ENSO years, respectively. Here, the ENSO years were classified according to the value of the late-winter Niño3.4 index.

Additionally, each experiment’s monthly signal was averaged over the signal maximum over the North Atlantic-European region. The characteristics of the spatially averaged signal were compared to the signal averaged over a similar signal maximum observed over the Pacific North American region.

Results have shown that the GH200 signal is the strongest in the late-winter months in all experiments. The AGCM experiment with SST boundary forcing prescribed only in the tropical Atlantic consistently had the smallest signal amplitude. The strongest signal linked to ENSO events was found in the experiment with the SST forcing prescribed only in the tropical Pacific. The signal averaged over the NAE maximum generally yields smaller values than the PNA maximum average. Also, the differences between the (non) ENSO signal and the signal for all years are less pronounced in the case of the NAE maximum average.

How to cite: Ivasić, S., Herceg Bulić, I., and Popović, M.: Impact of tropical SSTs on the monthly signal over the North Atlantic-European region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5205, https://doi.org/10.5194/egusphere-egu23-5205, 2023.

EGU23-5310 | ECS | Orals | CL2.2

Distinct and reproductible northem hemisphere winter teleconnection pattern during strong El Niño events : relative roles of Sea Surface Temperature forcing and atmospheric nonlinearities 

Margot Beniche, Jérôme Vialard, Matthieu Lengaigne, Aurore Voldoire, Srinivas Gangiredla, and Nicholas Hall

The strengthening and north-eastward shift of El Niño Northern hemisphere winter teleconnections relative to those of La Niña is a well-known asymmetry of ENSO (El Niño Southern Oscillation). It is generally attributed to atmospheric nonlinearities associated with the Sea Surface Temperature (SST) threshold for tropical deep convection. Here, we re-examine these teleconnection asymmetries in the context of ENSO SST pattern diversity. We find that the asymmetries are mainly attributable to strong El Niño events (eg. 1982-83, 1997-98, 2015-16), both in observations and in ensemble simulations with the atmospheric component of the CNRM-CM6 model. This strong El Niño teleconnection pattern also results in specific impacts, characterized by enhanced rainfall along the United States (US) west coast and warm anomalies over Canada and the Northern US. Our ensemble simulations further indicate that moderate “Eastern Pacific” El Niño events exhibit teleconnection patterns that are similar to those of “Central Pacific” El Niño, or to the opposite of La Niña events. We also find that the teleconnection spread between ensemble members or events is reduced for strong El Niño relative to moderate El Niño or La Niña events, with important implications for predictability. Sensitivity experiments in which the atmospheric model is forced by the opposite of observed SST anomalies are used to assess the mechanisms inducing the strong El Niño teleconnection pattern. In addition to the well-known influence of atmospheric nonlinearities, these experiments reveal an important contribution from the Eastward-shifted SST pattern during strong El Niño events.

 

How to cite: Beniche, M., Vialard, J., Lengaigne, M., Voldoire, A., Gangiredla, S., and Hall, N.: Distinct and reproductible northem hemisphere winter teleconnection pattern during strong El Niño events : relative roles of Sea Surface Temperature forcing and atmospheric nonlinearities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5310, https://doi.org/10.5194/egusphere-egu23-5310, 2023.

The amplitude of El Niño/Southern Oscillation (ENSO) varied considerably over the last 140 years, for which we have relatively reliable Sea Surface Temperature (SST) observations over the tropical Pacific. The difference between periods of high and low ENSO amplitude results mainly from the number of strong Eastern Pacific (EP) El Niños, while the amplitude of Central Pacific (CP) El Niños is comparable in both periods. Further, the asymmetry of ENSO, i.e. that the SST anomalies during El Niño are on average stronger and located further to the east than during La Niña, covaries with ENSO amplitude in observations, indicating that the number of strong EP El Niño events dominates both ENSO amplitude and asymmetry variations.

We find similar relations in the 40 historical runs of the Large Ensemble with the CESM1-CAM5-BGC model that can simulate the ENSO asymmetry quite realistically.  Further, there is a strong relation between the ENSO amplitude and the tropical Pacific mean state, indicating that a warmer eastern equatorial Pacific favors more EP El Niños due to a lower convective threshold in that area. We also analyze the spatial asymmetry and amplitude asymmetry of the atmospheric and oceanic feedbacks and show that the spatial asymmetry is more pronounced in the atmospheric feedbacks, while the amplitude asymmetry is more pronounced in the oceanic feedbacks, and that both together form the observed asymmetry of ENSO.  A comparison with 360 years-long CESM1 experiments with a -4.0 K colder and +3.7 K warmer mean state indicates that the present-day ENSO may be in a transition zone between a CP El Niño dominated ENSO state and an EP El Niño dominated ENSO state and that ENSO may lock-in into the EP El Niño dominated state under global warming.

Finally, our analysis of ENSO-amplitude variability in preindustrial control simulations of the CMIP6 database supports a strong relation of ENSO amplitude and asymmetry with the number of strong EP El Niño events.

How to cite: Bayr, T., Lübbecke, J. F., and Latif, M.: The role of strong Eastern Pacific El Nino events in ENSO-amplitude variability in Observations and Climate Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6109, https://doi.org/10.5194/egusphere-egu23-6109, 2023.

Using observational analysis and numerical experiments, we identify that the dipole mode of 
spring surface wind speed (SWS) over the Tibetan Plateau (TP) could act as a trigger for subsequent winter El 
Niño–Southern Oscillation events. During the positive phase of spring SWS dipole mode (south-positive and 
north-negative), a self-sustaining “negative sensible heating–baroclinic structure” prevails over the western TP, 
which is characterized by negative surface sensible heating anomalies, anomalous low-level anticyclones, and 
mid–high-level cyclones. The “negative sensible heating–baroclinic structure” stimulates the surface westerly 
wind anomalies over the tropical western Pacific in May through two pathways, favoring the occurrence of 
subsequent El Niño events. One is through weakening the zonal monsoon circulation over the tropical Indian 
Ocean and the Walker circulation over the tropical western Pacific. The other is modulating the air–sea 
interaction over the North Pacific through triggering Rossby waves. The negative SWS dipole mode tends to 
induce La Niña events.

How to cite: Yu, W.: Potential Impact of Spring Thermal Forcing Over the Tibetan Plateau on the Following Winter El Niño–Southern Oscillation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6401, https://doi.org/10.5194/egusphere-egu23-6401, 2023.

EGU23-7693 | Orals | CL2.2

Atmospheric nonlinearities strong contribution to the skewed ENSO amplitude and phase transition 

Jérôme Vialard, Srinivas Gangiredla, Matthieu Lengaigne, Aurore Voldoire, Takeshi Izumo, and Eric Huilyardi

ENSO features prominent asymmetries, in terms of amplitude, spatial pattern and phase-transition between warm and cold events. Here we examine the contribution of atmospheric nonlinearities to ENSO asymmetries through a set of forced experiments with the CNRM-CM6 AGCM and the NEMO OGCM. Control experiments can reproduce the major atmospheric and oceanic asymmetries of ENSO, with stronger signals east of the dateline for strong El Niño events, and west of it for strong La Niñas. Ensemble atmospheric experiments forced by observed ENSO SST anomalies and their opposites allow diagnosing asymmetries in air-sea heat and momentum fluxes directly attributable to atmospheric nonlinearities. They indicate that atmospheric nonlinearities are largely attributable to nonlinearities in the rainfall-SST relation and act to enhance El Niño atmospheric signals east of the dateline and those of La Niña west of it. An ocean simulation where the non-linear signature of air-sea fluxes is removed from the forcing reveals that asymmetries in the ENSO SST pattern are primarily due to atmospheric nonlinearities, and result in a doubling of eastern Pacific warming during the peak of strong El Niño events and a 33% reduction during that of strong La Niña events. Atmospheric nonlinearities also explain most of the observed prolonged eastern Pacific warming into boreal summer after the peak of strong El Niño events. Overall, these results imply that properly simulating the nonlinear relationship between SST and rainfall in CGCMs is essential to accurately simulate asymmetries in ENSO amplitude, spatial pattern and phase transition. Finally, we discuss the inherent limitations to our two-tier forced approach.

How to cite: Vialard, J., Gangiredla, S., Lengaigne, M., Voldoire, A., Izumo, T., and Huilyardi, E.: Atmospheric nonlinearities strong contribution to the skewed ENSO amplitude and phase transition, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7693, https://doi.org/10.5194/egusphere-egu23-7693, 2023.

EGU23-7791 | Posters on site | CL2.2

The multiverse future of ENSO diversity in large ensembles of climate models 

Bastien Dieppois, Nicola Maher, Antonietta Capotondi, and John O'Brien

El Niño Southern Oscillation (ENSO) shows large differences from one event to another in terms of its intensity, spatial pattern, and temporal evolution, which are typically referred to as “ENSO diversity”. While such differences in ENSO patterns are associated with different regional climate impacts throughout the world, influencing the skill of impact prediction systems, large uncertainties remain concerning its potential future evolution and trends. The location and intensity of ENSO events are indeed strongly influenced by internal/natural climate variations, limiting the detection of forced changes.

Here, we exploit the power of single model initial-condition large ensembles (SMILEs) from 13 fully coupled climate models from both CMIP5 and CMIP6 (totalling 580 realizations in historical and SSP-RCP scenarios) to first examine the ability of climate models to simulate realistic diversity of ENSO events compared to multiple observational datasets, and then use those models to characterize future trajectories in the location and intensity of El Niño and La Niña events. We define the location of ENSO events as the longitude of the absolute maximum (the intensity) of sea-surface temperature anomalies (SSTa) during boreal Winter (December-February) in the equatorial Pacific. Future projections of ENSO diversity are assessed in terms of joint probability distributions of ENSO events’ location and intensity.

While some models show a degree of diversity in the location and intensity of events that are comparable with observed statistics, other models tend to favour the occurrence of eastern or central Pacific events. Such contrasting performances during the historical period are found to be associated with different future trajectories of ENSO diversity: i) models favouring the occurrence of eastern Pacific events (e.g., ACCESS-ESM1-5, CanESM2, and 5) show a westward shift in event location over the 21st century; ii) models simulating ENSO events anomalously westward tend to show an eastward shift in event locations and an increased intensity in the 21st century (e.g., CESM1 and 2, CSIRO-MK3-6-0, GFDL-CM3, GFDL-ESM2M, MIROC-ES2L, MIROC6). Nevertheless, we note that models showing the closest match to observed statistics during the historical period also present a westward shift in ENSO locations and a slight increase in intensity in the 21st century (e.g., GFDL-SPEAR and IPSL-CM6-LR).

Although the physical cause of model discrepancies remains unclear, this study provides a broader perspective on expected ENSO changes over the 21st century in different models and highlights the spread of projections among models.

How to cite: Dieppois, B., Maher, N., Capotondi, A., and O'Brien, J.: The multiverse future of ENSO diversity in large ensembles of climate models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7791, https://doi.org/10.5194/egusphere-egu23-7791, 2023.

EGU23-8299 | ECS | Orals | CL2.2

Effect of Indian Ocean Dipole on ocean meridional heat transport depends on ENSO 

Kay McMonigal and Sarah Larson

Meridional heat transport within the Indian Ocean can drive climate and ecosystem impacts, by changing ocean temperature. Previous studies have linked variability in meridional heat transport to Indian Ocean Dipole (IOD) and El Niño-Southern Oscillation (ENSO). Recent studies have shown that some IOD events are caused by ENSO (termed “ENSO forced IOD”), while other events occur without ENSO (termed “internal IOD”). It is unclear whether these different kinds of IOD have different effects on the ocean. By comparing a climate model that includes ENSO to the same climate model but with ENSO dynamically removed, we show that internal IOD does not lead to variability in Indian Ocean meridional heat transport. However, ENSO forced IOD does lead to meridional heat transport variability. This is due to differing wind patterns associated with each kind of IOD event. These results suggest that the ecosystem and climate effects of IOD likely depend upon whether the IOD occurs with or without ENSO. 

How to cite: McMonigal, K. and Larson, S.: Effect of Indian Ocean Dipole on ocean meridional heat transport depends on ENSO, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8299, https://doi.org/10.5194/egusphere-egu23-8299, 2023.

EGU23-8733 | ECS | Orals | CL2.2

Stochastic perturbations of El Nino Southern Oscillations (ENSO) : a Wiener chaos approach 

Yusuf Aydogdu, Peter Baxendale, and N. Sri Namachchivaya

The phenomena of El Nino Southern Oscillations (ENSO) is modeled by coupled atmosphere-ocean mechanism together with sea surface temperature (SST) budget at the equatorial Pacific and has a significant impact on the global climate.  We consider a modeling framework that was originally developed by Majda and co-workers in (Chen et al. 2018; Thual et al. 2016), which is physically consistent and amenable to detailed analysis. The coupled model is mainly governed by the equatorial atmospheric and oceanic Kelvin and Rossby waves and it is shown that stochastic forcing gives rise to the model anomalies and unpredictable behavior. The purpose of our work is to investigate the influence of randomness on the model dynamics,  construct the appropriate model components with stochastic noise and calculate the statistical properties. We also provide analytical and numerical solutions of the model to prove the convergence of the numerical scheme developed in our work. 

We use Wiener-Chaos Expansion (WCE) to study stochastic ENSO models. The WCE method is based on reducing stochastic partial differential equations (SPDEs) into an infinite hierarchy of deterministic PDEs called propagators-Fourier modes (Lototsky and Rozovsky, 2006) and represents the stochastic solution as a spectral decomposition of deterministic components with respect to a set of random Hermite bases. We solve the WCE propagators, which are forced by a set of complete orthonormal bases,  by applying numerical integration and finite-difference methods. We compare WCE-based results with Monte Carlo simulations of SPDEs.

Our results depict that the mean and variance of the solutions obtained from the WCE method provide remarkably accurate results with a reasonable convergence rate and error range.  We first test the WCE-based method on the ocean  model with white noise and show that 10-Fourier modes are able to approach the theoretical variance values. We also show that the OU process with a specific noise strength and dissipation over a one-time period can be recovered with less than 50-Fourier modes for the ENSO model.  To illustrate the particular weight of variance, we also generate the ensembles of solutions by using different stochastic bases. We also derive the analytical formulation of propagators for the coupled model with nonlinear SST by using the properties of Wick polynomials that construct the foundation of numerical schemes. 

How to cite: Aydogdu, Y., Baxendale, P., and Namachchivaya, N. S.: Stochastic perturbations of El Nino Southern Oscillations (ENSO) : a Wiener chaos approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8733, https://doi.org/10.5194/egusphere-egu23-8733, 2023.

EGU23-8904 | Orals | CL2.2

Forecasting the El Niño type well before the spring predictability barrier 

Josef Ludescher, Armin Bunde, and Hans Joachim Schellnhuber

The El Niño Southern Oscillation (ENSO) is the most important driver of interannual global climate variability and can trigger extreme weather events and disasters in various parts of the globe. Depending on the region of maximal warming, El Niño events can be partitioned into 2 types, Eastern Pacific (EP) and Central Pacific (CP) events. The type of an El Niño has a major influence on its impact and can even lead to either dry or wet conditions in the same areas on the globe. Here we show that the zonal difference ΔTWP-CP between the sea surface temperature anomalies (SSTA) in the equatorial western Pacific and central Pacific is predictive of the type of an upcoming El Niño. When at the end of a calendar year, ΔTWP-CP is positive, an El Niño event developing in the following year will probably be an EP event, otherwise a CP event. Between 1950 and present, the index correctly indicates the type of 18 out of 21 El Niño events (p = 9.1⋅10-4).
For early actionable forecasts, the index has to be combined with a forecast for the actual onset of an El Niño event. The previously introduced climate network-based forecasting approach provides such forecasts for the onset of El Niño events also by the end of the calendar year before onset. Thus a combined approach can provide reliable forecasts for both the onset and the type of an event: at a lead time of about one year, 2/3 of the EP El Niño forecasts and all CP El Niño forecasts in the regarded period are correct. The combined model has considerably more predictive power than the current operational type forecasts with a mean lead time of about 1 month and should allow early mitigation measures.

How to cite: Ludescher, J., Bunde, A., and Schellnhuber, H. J.: Forecasting the El Niño type well before the spring predictability barrier, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8904, https://doi.org/10.5194/egusphere-egu23-8904, 2023.

Since the early 1990s the Pacific Walker circulation has strengthened, while SSTs in the eastern equatorial Pacific became colder, which is opposite to future model projections. Whether these trends, evident in many climate indices especially before the 2015 El Niño, reflect the coupled ocean-atmosphere response to global warming or the negative phase of the Pacific Decadal Oscillation (PDO) remains debated. Here we show that sea surface temperature (SST) trends during 1980-2020 are dominated by three signals: a spatially uniform warming trend, a negative PDO pattern, and a Northern Hemisphere/Indo-West Pacific warming pattern. The latter pattern, which closely resembles the transient ocean thermostat-like response to global warming emerging in a subset of CMIP6 models, shows cooling in the central-eastern equatorial Pacific but warming in the western Pacific and tropical Indian ocean. Together with the PDO, this pattern drives the Walker circulation strengthening. CMIP6 historical simulations appear to underestimate this pattern, contributing to the models’ inability to replicate the Walker cell strengthening. We further discuss how such changes in the Walker circulation can effect ENSO.

Reference:  Heede, U. and A.V. Fedorov, 2023: Colder eastern equatorial Pacific and stronger Walker circulation in the early 21st century: separating the forced response to global warming from natural variability. In press, GRL

How to cite: Fedorov, A. and Heede, U.: Colder eastern equatorial Pacific and stronger Walker circulation in the early 21st century: an Indo-Pacific ocean thermostat  versus natural variability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10347, https://doi.org/10.5194/egusphere-egu23-10347, 2023.

EGU23-10801 | Orals | CL2.2 | Highlight

Causes and Consequences of the Prolonged 2020-2023 La Niña 

Michael J. McPhaden, Nahid Hasan, and Yoshimitsu Chikamoto

The tropical Pacific has witnessed three successive years of unusually cold sea surface temperatures, with peak anomalies in late 2020, 2021 and 2022.  These conditions represent the first "triple dip" La Niña of the 21st century with major climatic impacts felt around the world.  Three year La Niña events are rare but not unprecedented; similar events occurred in 1998-2001 and in 1973-76.  A leading hypothesis for multi-year La Niñas is that they occur on the rebound from preceding extreme El Niños which, through recharge oscillator dynamics, drain the equatorial band of upper ocean heat content leaving a large heat deficit that takes multiple years to recover. The current multi-year La Niña does not conform to this scenario--antecedent conditions in the tropical Pacific in 2019 were characterized by a borderline El Niño that did not lead to a large upper ocean heat content discharge. What caused the this La Niña is thus a topic of considerable interest.  In this presentation we hypothesize that tropical inter-basin interactions were instrumental in initiating and prolonging the event. In particular, we suggest that the event was triggered from the Indian Ocean by a record Indian Ocean Dipole in late 2019, then boosted in 2021 by unusually warm conditions in the tropical Atlantic involving the strongest Atlantic Niño since the 1970s. Whether climate change may have played a role in these developments will be discussed.

How to cite: McPhaden, M. J., Hasan, N., and Chikamoto, Y.: Causes and Consequences of the Prolonged 2020-2023 La Niña, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10801, https://doi.org/10.5194/egusphere-egu23-10801, 2023.

EGU23-11500 | Orals | CL2.2

Representation of tropical SST trends in ECMWF seasonal hindcasts and implications for recent ENSO forecasts 

Michael Mayer, Magdalena Alonso Balmaseda, and Steffen Tietsche

Operational seasonal forecasts are routinely issued with their bias removed, which is estimated from hindcasts covering a sufficiently long period. An increased number of false alarms for the occurrence of El Nino by various dynamical forecasting systems in recent years challenges the view that forecast biases are stationary. Here we assess the ability of ECMWF’s operational seasonal prediction system SEAS5 to represent observed trends in tropical SSTs since 1993, with a focus on the Pacific.

SEAS5 hindcasts overestimate SST warming in the equatorial Pacific when compared to observations. Although present for all start dates, the trend error is most pronounced for May starts. As a result, SEAS5 forecasts in recent years tended to predict too warm ENSO states despite bias correction. The hindcasts also fail to reproduce the observed meridional dipole in SST trends in the eastern Pacific, with warming in the northern and cooling in the southern subtropics. We assess several numerical experiments to investigate the role of the evolving ocean observing system, the ocean data assimilation system, and the atmospheric model. Results show that the increase in Argo observations amplifies the spurious trends in the hindcasts, which points to biases in the ocean initial conditions when observational constraints are lacking prior to Argo. Furthermore, observed-SST experiments show that the atmospheric model is unable to reproduce the magnitude of increasingly northward winds that are observed in the eastern equatorial Pacific, which are associated with the meridional structure of observed SST trends and have been speculated to reduce ENSO variability. This suggests that shortcomings of the atmospheric model physics further contribute to the system’s inability to predict the recent triple La Nina period. The results call for more sophisticated calibration methods of seasonal forecasts and ultimately improved models and initialization to provide more reliable ENSO forecasts under varying background conditions.

How to cite: Mayer, M., Alonso Balmaseda, M., and Tietsche, S.: Representation of tropical SST trends in ECMWF seasonal hindcasts and implications for recent ENSO forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11500, https://doi.org/10.5194/egusphere-egu23-11500, 2023.

There are large interannual variations in the area integral of the Pacific-wide annual-mean net surface heat fluxes within 5o of the equator. They are shown to be very well correlated (r2 = 0.75) with the zonal-mean, annual-mean, zonal component of the surface wind stress on the equator, both in UK-HadGEM3 coupled climate simulations and in the ERA5 wind-stress and DEEPC net surface heat flux re-analyses. For the model data the corresponding correlations are small for monthly means (r2 = 0.25) but are large (r2 > 0.6) for time-mean periods between 6 months and 10 years (the latter being calculated from 700 year pre-industrial control simulations). The amplitude of these annual mean fluctuations in the DEEPC net surface heat fluxes is almost twice as large as that in the UK-HadGEM3 simulations. Comparison of the area-mean fields in the Nino3 and Nino4 regions from 4 member ensembles of N216O025 historical simulations with the ERA5 winds, DEEPC heat fluxes and EN4 ocean re-analyses shows that the model’s mean values and seasonal cycle of the zonal wind stress and net surface heat flux agree well with the re-analyses. In the Nino3 region however the model’s surface temperature is 1.5oC colder than the re-analyses and the depth of the 20oC isotherm (t20d) is between 10 and 15 m shallower than that in EN4.  Comparison of the amplitudes of El Nino and La Nina composite anomalies in the Nino3 and Nino4 regions shows that the surface temperature anomalies are well simulated but that the amplitudes of the wind stress anomalies in Nino4 and the t20d anomalies and surface heat flux anomalies in Nino3 are about half those in ERA5, EN4 and DEEPC respectively. These findings are somewhat similar to those from the (lower resolution)  Kiel Climate Model. The characteristic spatial patterns of the surface fields might be used to attribute the differences between the model and re-analysis net surface fluxes to particular component fluxes (e.g. the surface latent heat flux and the surface solar flux). It is also a plausible hypothesis that the under-estimation of these variations in the net surface heat fluxes is a significant contributor to the signal-to-noise paradox.       

 

How to cite: Bell, M.: HadGEM3  underestimates interannual variations in heat fluxes, zonal winds and thermocline displacements  in the tropical Pacific, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12824, https://doi.org/10.5194/egusphere-egu23-12824, 2023.

EGU23-13335 | ECS | Posters on site | CL2.2

Using Causal Discovery to Clarify Observed and Simulated Relationships Between ENSO and Other Ocean Basins 

Rebecca Herman and Jakob Runge

Observed sea-surface temperatures in various ocean basins are confounded by anthropogenic and natural radiative forcing and by teleconnections to modes of internal variability, especially the El Nino Southern Oscillation (ENSO). While confounding due to anthropogenic and natural forcing can be removed in coupled simulations, confounding due to ENSO is unavoidable. When not appropriately characterized and quantified, this confounding can obscure causal relationships between various ocean basins and atmospheric phenomena of huge humanitarian import, such as monsoon rainfall, with implications for attribution of past disasters and prediction of the future. These relationships have been difficult to characterize in part because observational data is limited and simulated data may not represent the observed climate system. This study uses causal discovery to examine the coupled relationships between ENSO and other ocean basins in simulations and observations. We begin by evaluating the (L)PCMCI(+) causal discovery algorithms under various conditions and assumptions on data generated by two continuous idealized models of ENSO: the classic Zebiak-Cane model and a simple stochastic dynamical model proposed by Thual, Majda, Chen, and Stechmann. We then apply the causal discovery algorithms to seasonally and spatially-averaged sea surface temperature (SST) indices for ENSO and other ocean basins in preindustrial control simulations from the Coupled Model Intercomparison Project Phase 6. We discuss the robustness of the results, and the differences between the causal relationships in different General Circulation Models. Finally, we apply the causal learning algorithm to observed SST, and discuss to what extent simulated relationships can be used to learn about the observed climate system. We additionally demonstrate the implications of this study for other scientific questions, specifically for understanding variability in Sahel Monsoon rainfall.

How to cite: Herman, R. and Runge, J.: Using Causal Discovery to Clarify Observed and Simulated Relationships Between ENSO and Other Ocean Basins, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13335, https://doi.org/10.5194/egusphere-egu23-13335, 2023.

EGU23-13812 | ECS | Orals | CL2.2

ENSO–IOD Inter-Basin Connection Is Controlled by the Atlantic Multidecadal Oscillation 

Jiaqing Xue, Jing-Jia Luo, Wenjun Zhang, and Toshio Yamagata

The interactions between El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are known to have great implications for global climate variability and seasonal climate predictions. Observational analysis suggests that the ENSO–IOD inter-basin connection is time-varying and related to the Atlantic Multidecadal Oscillation (AMO) with weakened ENSO–IOD relationship corresponding to AMO warm phases. A suite of Atlantic pacemaker simulations successfully reproduces the decadal fluctuations in ENSO–IOD relationship and its link to the AMO. The warm sea surface temperature (SST) anomalies associated with the AMO drive a series of Indo-Pacific mean climate changes through tropical-wide teleconnections, including the La Niña-like mean SST cooling over the central Pacific and the deepening of mean thermocline depth in the eastern Indian Ocean. By modulating ocean–atmosphere feedback strength, those mean state changes decrease both ENSO amplitude and the Indian Ocean sensitivity to ENSO forcing, therefore decoupling the IOD from ENSO.

How to cite: Xue, J., Luo, J.-J., Zhang, W., and Yamagata, T.: ENSO–IOD Inter-Basin Connection Is Controlled by the Atlantic Multidecadal Oscillation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13812, https://doi.org/10.5194/egusphere-egu23-13812, 2023.

EGU23-15824 | ECS | Orals | CL2.2

Future Changes in the early winter ENSO teleconnections to the North Atlantic European region 

Muhammad Adnan Abid and Fred Kucharski

North Atlantic European (NAE) winter climate variability is strongly modulated through the stratospheric and tropospheric pathways, where El Niño-Southern Oscillation (ENSO) teleconnections play an important role. Recent studies showed intra-seasonal changes of the ENSO response in the NAE circulation anomalies from early to late winter.  One mechanism for this behavior is that the Indian Ocean (IO) dominate over the direct ENSO teleconnections in early winter favoring an in-phase North Atlantic Oscillation (NAO) response over NAE region. On the other hand, the direct ENSO response dominates in latter half of winter, where it projects onto the opposite phase of the NAO. In present study, we analyze the early to late winter ENSO-NAE teleconnections in future climate projections by adopting the sixth assessment report Coupled Model Intercomparison Project (CMIP6) model datasets. During early winter, we noted an increase in the ENSO-induced precipitation variability in the Pacific as well as over western and central Indian Ocean, while decrease is noted over the eastern IO. Moreover, a strengthening of the ENSO and Indian connections are noted in almost all models except few, where these connections are not well represented in the present climate. Interestingly, the changes in ENSO forced wave train are noted, which may lead to the negative NAO like circulation anomalies over the NAE region in future compared to the present climate. 

How to cite: Abid, M. A. and Kucharski, F.: Future Changes in the early winter ENSO teleconnections to the North Atlantic European region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15824, https://doi.org/10.5194/egusphere-egu23-15824, 2023.

EGU23-16921 | Orals | CL2.2

The role of spatial shifting in El Niño/Southern Oscillation complexity 

Sulian Thual and Boris Dewitte

The El Niño-Southern Oscillation (ENSO) represents the most consequential fluctuation of the global climate system, with dramatic societal and environmental impacts. Here we show that the spatial shifting movements of the Walker circulation control the ENSO space-time complexity in a major way. First, we encapsulate the process in a conventional recharge-discharge oscillator for the ENSO by replacing the regionally fixed sea surface temperatures (SST) index against a warm pool edge index. By doing so, we can model essential ingredients of ENSO diversity and nonlinear behavior without increasing the complexity of the dynamical model. Second, we propose a data-driven method for estimating equatorial Pacific SST variability resulting from spatial shifting. It consists in time-averaging conditions respective to the evolving warm pool edge position, then generating back SST data with reduced dimensionality (one degree of freedom) from the movements of the resulting "shifted-mean" profile. It is shown that the shifted-mean SST generated in this fashion reasonably reconstructs observed interannual SSTs both in terms of amplitude and pattern diversity. We discuss implications of the present paradigm of spatial shifting for understanding ENSO complexity, including tropical basins interactions.

How to cite: Thual, S. and Dewitte, B.: The role of spatial shifting in El Niño/Southern Oscillation complexity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16921, https://doi.org/10.5194/egusphere-egu23-16921, 2023.

EGU23-103 | ECS | Orals | CL4.7

Energetic Constraints on Baroclinic Eddy Heat Transport in a Rotating Annulus 

Cheng Qian, Peter Read, and David Marshall

We measure baroclinic eddy heat transport in a differentially heated rotating annulus laboratory experiment to test mesoscale ocean eddy parameterization frameworks. The differentially heated rotating annulus comprises a fluid placed between two upright coaxial cylinders which are maintained at different temperatures, usually with a cooled inner cylinder and a heated outer.  The annular tank is placed on a rotating table which provides conditions for baroclinic eddies to develop and equilibrate in different flow regimes, depending upon the imposed conditions. As the rotation speed is increased, the equilibrated flow changes from a steady or periodically varying low wavenumber pattern to a more complex, time-varying flow dominated by higher wavenumbers. With a topographic beta effect produced by conically sloping upper boundary, more complex flow regimes are observed combining zonal jets and eddies forming one or more parallel storm tracks. With this possibility to explore varied flow regimes, our experimental approach combines laboratory calorimetry and visualization measurements along with numerical simulations to derive the eddy heat transport properties. In the following, we focus on the visualisation measurement to test related assumptions and parametric dependencies for eddy transport. We first test the assumptions of a down-gradient temperature flux-gradient relationship, determining coefficients of the eddy transport tensor, and exploring scaling relations for the eddy coefficients. A clear statistical scaling is found between eddy heat fluxes and physical variables such as eddy energy, the beta effect, and the temperature contrast.

How to cite: Qian, C., Read, P., and Marshall, D.: Energetic Constraints on Baroclinic Eddy Heat Transport in a Rotating Annulus, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-103, https://doi.org/10.5194/egusphere-egu23-103, 2023.

EGU23-273 | ECS | Posters virtual | CL4.7

Understanding the variability and trend of the regional Hadley Cell over Asia-Pacific 

Pratiksha Priyam Baruah and Neena Joseph Mani

The zonal mean Hadley Cell (HC) has been reported to be expanding poleward in the last few decades. However, there has been no consensus on whether the zonal mean HC is strengthening or weakening. The features of longitudinally averaged HC are collectively modulated by various regional HCs, controlled by the regional differences in land-ocean distribution and topography. However, there have not been many studies exploring the variability and trend of regional HCs in a detailed manner. In this study, we examine the variability and long-term trend of the regional HC over the Asia-Pacific and explore the different factors contributing to the regional HC variability. Moist convection can regulate regional HCs on synoptic time scales through equatorial wave dynamics. The ocean–atmosphere coupled variability associated with the El Niño-Southern Oscillation (ENSO), and the modulation of tropical convection and equatorial waves are considered to exert a dominant control on the regional HC variability in the interannual timescale. In addition to the tropical forcing, the regional HC variability is also affected by fluxes transported by the midlatitude eddies from the subtropics to the tropics. In this study, we will be quantifying the relative role of these tropical and extratropical forcings in modulating the variability of regional HC over Asia-Pacific.

 

How to cite: Baruah, P. P. and Joseph Mani, N.: Understanding the variability and trend of the regional Hadley Cell over Asia-Pacific, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-273, https://doi.org/10.5194/egusphere-egu23-273, 2023.

EGU23-972 | ECS | Posters on site | CL4.7

Vanishing the El Niño-induced delay effect on the ice mass loss of West Antarctica under global warming 

Hyunju Lee, Emilia Kyung Jin, Byeong-Hoon Kim, and Won Sang Lee

West Antarctica has been losing their ice mass due to global warming, and the El Niño has delayed the ice mass loss by inducing weakening of the Amundsen Sea Low (ASL), encouraging of poleward moisture flux and consequent extreme precipitation. However, it is not yet revealed whether the delay effect will continue in the future. We analyzed future scenarios from the CMIP6 Earth system models (ESMs) to identify future change and identified that the El Niño-driven mass increase by precipitation will vanish in the high-emission future scenarios. Precipitation anomaly in response to El Niño starts to be negative from the 2050s in the SSP5-8.5 and from the 2060s in the SSP3-7.0, which means that the El Niño-driven delay effect disappears. It is because the moisture transport into West Antarctica is prevented due to east-equatorward migration of El Niño-induced ASL anomaly as global warming intensifies. The strengthened polar jet associated with positive Southern Annular Mode (SAM) trend moves the ASL anomaly east- and equatorward under global warming.

How to cite: Lee, H., Jin, E. K., Kim, B.-H., and Lee, W. S.: Vanishing the El Niño-induced delay effect on the ice mass loss of West Antarctica under global warming, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-972, https://doi.org/10.5194/egusphere-egu23-972, 2023.

 We show that the most prominent of the work theorems, the Jarzynski equality and the Crooks relation, can be applied to the momentum transfer at the air-sea interface using a hierarchy of local models. In the more idealized models, with and without a Coriolis force, the variability is provided from a Gaussian white-noise which modifies the shear between the atmosphere and the ocean. The dynamics is Gaussian and the Jarzynski equality and Crooks relation can be obtained analytically solving stochastic differential equations. The more involved model consists of interacting atmospheric and oceanic boundary-layers, where only the dependence on the vertical direction is resolved, the turbulence is modeled through standard turbulent models and the stochasticity comes from a randomized drag coefficient. It is integrated  numerically and can give rise to a non-Gaussian dynamics. Also in this case the Jarzynski equality allows for calculating a dynamic-beta ßD of the turbulent fluctuations (the equivalent of the thermodynamic-beta  ß=(kB T)-1 in thermal fluctuations). The Crooks relation gives the ßD as a function of the magnitude of the work fluctuations. It is well defined (constant) in the Gaussian models and can show a slight variation in the  more involved models. This demonstrates that recent concepts of stochastic thermodynamics used to study micro-systems subject to thermal fluctuations can further the understanding of geophysical fluid dynamics with turbulent fluctuations.

How to cite: Wirth, A.: Jarzynski equality and Crooks relation for local models of air-sea interaction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1066, https://doi.org/10.5194/egusphere-egu23-1066, 2023.

The Arctic is warming at a rate faster than any other oceans, a phenomenon known as Arctic amplification that has widespread impact on the global climate. In contrast, the Southern Ocean (SO) and Antarctica have been cooling over the past decades. The projection of these regions under global warming has a non-negligible model spread. Here we show that under a strong warming scenario from 1950 to 2100, comparing a cutting-edge high-resolution climate model to a low-resolution model version, the increase of Arctic amplification is 3 °C more and the SO and Antarctica warming is 2°C less. Previously ice-covered Arctic Ocean will exhibit greater SST variability under future global warming. This is due to an increased SST increase in summer due to sea ice retreat. Extreme warming events in the Arctic and SO, known as marine heat waves (MHW) that influence the ecology, are largely unknown. We find that the MHWs in the Arctic and SO are twice as strong in the high-resolution model version, where the increasing intensity of MHWs in the Arctic corresponds to strong decline (<-6% per decade) of sea ice. In both the high-resolution and low-resolution models, the duration of MHWs in the Arctic and SO shows a declining trend under global warming. The much stronger MHWs in the high-resolution model could be caused by two orders of magnitude more ocean turbulent energy. For example, the spatial patterns of SO MHW intensity correspond to the pattern of SO EKE. We conclude that the Arctic amplification and MHWs at high latitudes might be underestimated by the current generation of climate models with low resolution, and the SO and Antarctica warming might be overestimated. Our eddy- and storm-resolving model is expected to open new frontiers on how the system responds to human activities in a high CO2 world by evaluating the impact on past and future climate and environmental extremes.

How to cite: Gou, R., Lohmann, G., and Wu, L.: Increase in Arctic amplification and high-latitude marine extremes in the 21st century as obtained from high-resolution modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1649, https://doi.org/10.5194/egusphere-egu23-1649, 2023.

EGU23-1773 | ECS | Posters on site | CL4.7

Tropical Instability Waves in a High-Resolution Oceanic and Coupled GCM 

Li Tianyan and Yu Yongqiang

Tropical instability waves (TIWs) are the dominant mesoscale variability in the eastern equatorial Pacific Ocean. TIWs have direct impacts on the local hydrology, biochemistry and atmospheric boundary layer, and feedback on ocean circulations and climate variability. In this study, the basic characteristics of Pacific Ocean TIWs simulated by an eddy-resolving ocean model and a coupled general circulation model are evaluated. The simulated TIW biases mainly result from the mean climatology state, as TIWs extract eddy energy from the mean potential and kinetic energy. Both the oceanic and coupled models reproduce the observed westward propagating large-scale Rossby waves between approximately 2-8N, but the simulated TIWs have shorter wavelengths than the observed waves due to the shallower thermocline. Meanwhile, the weak meridional shears of background zonal currents and the less-tilted pycnocline in these two models compared to the observations causes weak barotropic and baroclinic instability, which decreases the intensity of the simulated TIWs. We then contrast the TIWs from these two models and identify the roles of atmospheric feedback in modulating TIWs. The latent heat flux feedback is similar to observation in the coupled model but absent in the ocean model, contributing to the stronger standard deviation (STD) of the TIW SST in the ocean model. The ocean model is not able to capture realistic air-sea interaction processes when forced with prescribed atmospheric forcing. However, the misrepresented atmospheric feedback in the ocean model tends to decrease the sea surface height (SSH) variability, and the current feedback damping effect is stronger in the ocean model than in the coupled model. Combined with weaker barotropic conversion rate and baroclinic conversion rate in the ocean model than in the coupled model, the STD of the TIW SSH in the ocean model is weaker.

How to cite: Tianyan, L. and Yongqiang, Y.: Tropical Instability Waves in a High-Resolution Oceanic and Coupled GCM, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1773, https://doi.org/10.5194/egusphere-egu23-1773, 2023.

EGU23-1985 | ECS | Posters on site | CL4.7

Sensitivity of ocean heat content to atmospheric forcing in the period of global warming hiatus 

Chavely Albert Fernández, Armin Köhl, and Detlef Stammer

Between 1998 and 2012, there was a smaller rate of global warming, known as the "global warming hiatus". One of the suggested causes is that during this period additional heat sequestration occurs into the deep ocean layers such that deep layers warm at a greater rate than the upper layers. This research is focused on the origins of changes in ocean heat content during the hiatus period, defined in this case as the last 10 years of adjoint model run, where the cost function is defined. Adjoint sensitivities are used to determine the influence of atmospheric forcing (heat and freshwater fluxes and wind stress) on the ocean heat content.

The MIT General Circulation Model with a resolution of 2° x 2° is used over the period 1978-2008 to determine adjoint sensitivities of the globally and temporally (over the last 10 years, defined as hiatus period) integrated vertical heat fluxes across various depth levels. The contributions of different forcing components to the vertical heat flux anomalies are obtained from the scalar product between sensitivities and the anomalies of the atmospheric forcing.  For this, the atmospheric forcing anomalies are computed with respect to the climatology calculated over the period 1948-1968 when there was almost no change in the ocean heat content.

A more pronounced increase in ocean heat uptake during the hiatus period has been evidenced by the forward run of the model. Wind anomalies represent more than half of the contribution to the increase in heat flux across 300m, suggesting that the excess of heat stored by the ocean is transferred adiabatically to the deeper layers and that the zonal wind is one of the major drivers of ocean heat uptake. In the Southern Ocean, the sensitivities to the wind stress change from positive to negative when the hiatus starts. This indicates that, during the hiatus, the rate of change of ocean heat content is opposite to the one of the wind stress. The Southern Ocean presents smaller values of the computed amplitude weighted mean time, meaning that this region has the fastest response to changes in surface atmospheric forcing.

How to cite: Albert Fernández, C., Köhl, A., and Stammer, D.: Sensitivity of ocean heat content to atmospheric forcing in the period of global warming hiatus, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1985, https://doi.org/10.5194/egusphere-egu23-1985, 2023.

 Widespread observed and projected increases in warm extremes, along with decreases in cold extremes, have been confirmed as being consistent with global and regional warming. However, based on observational datasets and state-of-the-art CMIP6 model simulations, we disclose that the decadal variation in the frequency of the surface air temperature (SAT) extremes over mid- to high latitudes over Eurasia (MHEA) in winter is primarily dominated by the thermodynamical effect of the surface heat fluxes release over the midlatitude North Atlantic induced by Atlantic multidecadal oscillation (AMO), which even masks the dynamical large-scale Rossby wave propagation. Besides, the stronger Atlantic meridional overturning circulation (AMOC) gives rise to both warm and cold extremes through increasing the variance of winter SAT over MHEA due to thermodynamical heat release and enhanced dynamical Rossby wave propagation.

How to cite: Wang, H.: Frequency of winter temperature extremes over Eurasia dominated by variabilities over the Atlantic Ocean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3106, https://doi.org/10.5194/egusphere-egu23-3106, 2023.

EGU23-3282 | ECS | Posters on site | CL4.7

Physics of the Eddy Memory Kernel of a Baroclinic Midlatitude Atmosphere 

Elian Vanderborght, Jonathan Demeayer, Henk Dijkstra, Georgy Manucharyan, and Woosok Moon

In recent theory trying to explain the origin of low-frequency atmospheric variability, the concept of eddy-memory has been suggested. In this view, the effect of synoptic scale heat fluxes on the mean flow depends on the history of the mean meridional temperature gradient. Mathematically, this involves a convolution of an integral kernel with the mean meridional temperature gradient over past times. In atmospheric studies, it has been proposed that the shape of this integral kernel is linked to the baroclinic wave life cycle. However, this hypothesis has yet to be supported by numerical and observational evidence. In this study we use a low-order two layer quasi-geostrophic atmospheric model (Demaeyer et al., 2020). By perturbing the model with a known forcing, linear response theory can be used to estimate the shape of the integral kernel. Using this methodology, we find an integral kernel that resembles the shape of an exponentially decaying oscillation, different from the simple exponentially decaying integral kernel assumed in most previous studies. By computing the energies and performing a sensitivity analysis, we link the shape of the integral kernel to atmospheric dynamical processes.

References:

J. Demaeyer, L. De Cruz, and S. Vannitsem. qgs: A flexible python framework of reduced-order multiscale climate
models. Journal of Open Source Software, 5(56):2597, 2020.

How to cite: Vanderborght, E., Demeayer, J., Dijkstra, H., Manucharyan, G., and Moon, W.: Physics of the Eddy Memory Kernel of a Baroclinic Midlatitude Atmosphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3282, https://doi.org/10.5194/egusphere-egu23-3282, 2023.

EGU23-3492 | Orals | CL4.7

Variability of summer-time Arctic sea ice: the drivers and the contribution to the sea ice trend and extremes 

Mehdi Pasha Karami, Torben Koenigk, and Bruno Tremblay

Understanding the variability of summer-time Arctic sea ice at interannual to multidecadal time scales in the midst of anthropogenically forced sea ice decline is crucial for better predictions of sea ice conditions in the future climate and rapid changes in sea ice. Here, we apply time-frequency analysis to study the modes of variability, extreme events and the trend in the September Arctic sea ice in 100–150 year datasets. We extract the non-linear trend for the sea ice area and provide an estimate for the anthropogenic-driven sea ice loss. For the used dataset, the anthropogenic-related sea ice loss is found to have a rate of ~-0.25 million km2 per decade in the 1980’s and accelerating to ~-0.47 million km2 per decade in 2010’s. By assuming the same rate of sea ice loss in the future, and without the contribution of the internal variability and feedbacks, we can approximate the occurrence of summer sea-ice free Arctic to be around 2060. Regarding the dominant modes of variability for the September sea ice, we find that they have periods of around 3, 6, 17, 28 and 55 years, and show what drives these modes and how they contribute to sea ice extreme events. The main atmospheric and oceanic drivers of sea ice modes include the Arctic oscillation and Arctic dipole anomaly for the 3-year mode, variability of sea surface temperature (SST) in Gulf Stream region for the 6-year mode, decadal SST variability in the northern North Atlantic Ocean for the 17-year mode, Pacific decadal oscillation (PDO) for the 28-year mode, and Atlantic multidecadal Oscillation (AMO) for the 55-year mode. Results show that changes in the sea ice due to internal variability can be as large as forced changes thus can slow down or accelerate the background anthropogenic-driven sea ice loss. By applying the same method, we also present modes of variability and trend of sea ice in the large ensemble global model simulations of EC-Earth model (SMHI-LENS) for the future climate projections and different climate scenarios.

How to cite: Karami, M. P., Koenigk, T., and Tremblay, B.: Variability of summer-time Arctic sea ice: the drivers and the contribution to the sea ice trend and extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3492, https://doi.org/10.5194/egusphere-egu23-3492, 2023.

Global circulation patterns are analysed using the mean meridional circulation (MMC) from ERA-Interim for the period of 1979 – 2017. The global isentropic MMC consists of a single overturning cell in each hemisphere with net heat transport from the equator to the pole. Six clusters are identified from daily data that are associated with one of four seasons. Two solstitial MMC clusters represent either stronger or weaker circulation in the winter hemisphere. We show that long-term trends do not reflect a gradual change in the atmospheric circulation, but rather a change in the frequency of preferred short-term circulation regimes. Before the late 1990s the clusters showing a stronger (weaker) winter circulation are becoming less (more) frequent; from around year 2000 the trends have paused. These trends are in close agreement with the change in the low-stratospheric Antarctic ozone trends reported by earlier studies. Our findings also reveal a strong coupling between Southern and Northern Hemispheres during boreal winter. Following Hartmann et al. (2022), we hypothesize that anomalous polar vortex over Antarctica leads to anomalies in the sea surface temperatures (SST) in the tropical Pacific that impact the circulation in both hemispheres. Furthermore, we show that consecutive solstice season demonstrates coherent anomalies in the frequency of circulation regimes. We discuss possible reasons for such relationship.

References:
Hartmann, D. L., Kang, S., Polvani, L. & Xie, S.-P. The Antarctic ozone hole and the pattern effect on climate sensitivity. (2022) doi:10.1073/pnas.

How to cite: Rudeva, I., Boschat, G., and Lucas, C.: How can atmospheric trends be explained by changes in frequency of short-term circulation regimes and what is the role of the Antarctic ozone?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4557, https://doi.org/10.5194/egusphere-egu23-4557, 2023.

EGU23-5305 | ECS | Orals | CL4.7

Linking ITCZ migrations to the AMOC and North Atlantic/Pacific SST decadal variability 

Eduardo Moreno-Chamarro, John Marshall, and Tom L. Delworth

This contribution discusses the link between migrations in the intertropical convergence zone (ITCZ) and changes in the Atlantic meridional overturning circulation (AMOC), Atlantic multidecadal variability (AMV), and Pacific decadal oscillation (PDO). We use a coupled climate model that allows us to integrate over climate noise and assess underlying mechanisms. We use an ensemble of ten 300-yr-long simulations forced by a 50-yr oscillatory North Atlantic Oscillation (NAO)-derived surface heat flux anomaly in the North Atlantic, and a 4000-yr-long preindustrial control simulation performed with GFDL CM2.1. In both setups, an AMV phase change induced by a change in the AMOC’s cross-equatorial heat transport forces an atmospheric interhemispheric energy imbalance that is compensated by a change in the cross-equatorial atmospheric heat transport due to a meridional ITCZ shift. Such linkages occur on decadal time scales in the ensemble driven by the imposed forcing, and internally on multicentennial time scales in the control. Regional precipitation anomalies differ between the ensemble and the control for a zonally averaged ITCZ shift of similar magnitude, which suggests a dependence on timescale. Our study supports observational evidence of an AMV–ITCZ link in the twentieth century and further links it to the AMOC, whose long-time-scale variability can influence the phasing of ITCZ migrations. In contrast to the AMV, our calculations suggest that the PDO does not drive ITCZ migrations, because the PDO does not modulate the interhemispheric energy balance.

How to cite: Moreno-Chamarro, E., Marshall, J., and Delworth, T. L.: Linking ITCZ migrations to the AMOC and North Atlantic/Pacific SST decadal variability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5305, https://doi.org/10.5194/egusphere-egu23-5305, 2023.

EGU23-5962 | Orals | CL4.7

Tropical precipitation biases in nextGEMS storm-resolving Earth System Models 

Simona Bordoni, Roberta D'Agostino, and Adrian M. Tompkins

Global Earth System Models at storm-resolving resolutions (SR-ESM, with horizontal resolutions of ~4km) are being developed as part of the nextGEMS collaborative European EU’s Horizon 2020 programme. Through breakthroughs in simulation realism, these models will eventually allow us to understand and reliably quantify how the climate will change on a global and regional scale, and how the weather, including its extreme events, will look like in the future.

As part of the Storms & Ocean theme, we are exploring how resolving convective storms, ocean mesoscale eddies, and air-sea interaction on these scales influences the development of tropical SST anomalies and their influence on the mean climate (ITCZ and circulation biases) and its variability. Existing biases in the SR-ESM simulations in the first two development cycles are interpreted using the vertically integrated atmospheric energy budget to disentangle local and remote influences on tropical precipitation. More specifically, these biases are decomposed in hemispherically symmetric and antisymmetric components, which are linked, respectively, to biases in the atmospheric net energy input near the equator (tropical SST biases, low level clouds, etc) and to the cross-equatorial atmospheric energy flux (driven by inter-hemispheric contrast in net energy input, for instance biases in clouds in the southern ocean). We also explore the role that transient eddies, both of extratropical and tropical origin that are usually neglected in this framework, play in the global energetics and tropical precipitation patterns.

How to cite: Bordoni, S., D'Agostino, R., and Tompkins, A. M.: Tropical precipitation biases in nextGEMS storm-resolving Earth System Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5962, https://doi.org/10.5194/egusphere-egu23-5962, 2023.

    Poleward atmospheric energy transport is determined by the overall equator-to-pole radiative imbalance. As this imbalance is projected to remain fairly constant in end-of-century greenhouse gas forcing scenarios, an increase in poleward latent heat transport must be accompanied by a reduction in dry static energy flux. Since midlatitude energy transport is dominated by the eddy component, changes in the energy budget go hand in hand with changes in cyclone characteristics. From a dynamical perspective, the enhanced condensation due to climate change promotes intensification, prolongs lifetime of cyclones, and can increase stationarity of anticyclones. However, it also tends to increase static stability and thereby reduce baroclinicity, which is another important driver of cyclone development. Additionally, baroclinicity is projected to increase at upper levels due to tropical amplification, to decrease at low levels as a result of Arctic amplification, and to be affected by land-sea temperature contrast changes. As these processes are at play simultaneously, isolating the role of moisture is rather complicated. Therefore, in addition to coupled climate model simulations we use idealized aquaplanet simulations to single out the effects of individual physical mechanisms and address the question: if the overall poleward energy transport remains largely unaffected by global warming, how do cyclone characteristics change in the presence of increased moisture in the atmosphere?

    For bridging the gap between the global energy flux and synoptic-scale features, we analyse the role of increasing moisture for shaping midlatitude storm tracks in present and future climates from both an Eulerian and a Lagrangian perspective. We apply the moist static energy (MSE) framework that allows partitioning atmospheric energy fluxes into eddy and mean, dry and moist components. Here, eddies are related to cyclones and anticyclones, while the mean energy flux is associated with planetary waves and the mean meridional overturning circulation. The goal is to relate the eddy MSE fluxes to feature-based results including extratropical cyclone number, lifetime, intensity, location, and tilt. By combining results from both global-scale eddy energy fluxes and synoptic-scale feature quantities, we aim to improve the understanding of the role of latent heating in shaping the mean properties of extratropical storm tracks. Therefore, a central question of this project is whether and how changes in cyclone quantities can be linked to changes in latent heat transport and release. Building on what we learn from bringing the two perspectives together, we will proceed to investigating the impact of increased latent heating on midlatitude storm tracks. 

How to cite: Zibell, J., Schemm, S., and Hermoso Verger, A.: Combining global-scale atmospheric heat transport and synoptic-scale extratropical cyclone characteristics to understand the role of latent heating for midlatitude storm tracks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7216, https://doi.org/10.5194/egusphere-egu23-7216, 2023.

EGU23-7348 | ECS | Orals | CL4.7

An Energy Transport View of ENSO Responses to Volcanic Forcing 

Shih-Wei Fang and Claudia Timmreck

El Niño-Southern Oscillation (ENSO) is one of the major climate phenomena impacting the globe. When a volcanic eruption happens, how ENSO will respond has still no consensus in proxy data though climate models tend to have an El Niño tendency. In this study, using 100 members of diverse (in locations and magnitude) idealized volcanic forcing ensembles, we found that the ENSO responses to north and south extra-tropical eruptions are related to the energy transport to the cooling hemisphere and involve direct and indirect responses through atmospheric and oceanic transport. The north extratropical forcing leads to more El Niño up to three years after eruptions, which is related to the direct atmospheric responses of the southward movement of ITCZ for transporting more energy to the north. The indirect oceanic transport then takes over afterward, leading to more La Niña due to more upwelling in the equatorial eastern Pacific. The south extra-tropical eruptions have less El Niño tendency due to the northward replacement of ITCZ. As the indirect oceanic transport also results in equatorial mean state changes, which may lead to distinct ENSO responses. The long-term ENSO responses from extra-tropical cooling will also be investigated through the simulations from the Extratropical-Tropical Interaction Model Intercomparison Project (ETIN-MIP) experiment.

How to cite: Fang, S.-W. and Timmreck, C.: An Energy Transport View of ENSO Responses to Volcanic Forcing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7348, https://doi.org/10.5194/egusphere-egu23-7348, 2023.

EGU23-9914 | Posters on site | CL4.7

Changes in river temperature, discharge and heat flux based on new observational data for Yenisei basin and modeling 

Alexander Shiklomanov, Richard Lammers, Alexander Prusevich, Irina Panyushkina, and David Meko

River temperature plays an important role in numerous biological and ecological processes within the Yenisei River basin and it is very sensitive to changes in climatic characteristics and anthropogenic disturbances. Water temperature and river discharge characterize heat or energy flux, which is important in northern latitudes for river freeze-up and ice break-up processes and thermal riverbank erosion. The changes in heat flux in river estuary can also significantly impact various biophysical processes in coastal ocean waters.

We use new water temperature data and river discharge records for 12 observational gauges in the Yenisei River basin to analyze changes in water temperature and heat flux from upstream to downstream over 1950-2018. Preliminary results show significant increases for most gauges in maximum annual water temperature as well as in 10-days mean water temperature during May-June and September-October. There were no significant changes in river temperature during July-August unless the gauges were impacted by reservoir regulations. The river heat flux has significantly increased in central and northern parts of the Yenisei basin and decreased in the south, mainly due to discharge variability.

The gridded hydrological Water Balance Model (WBM) developed at the University of New Hampshire, that takes into account various anthropogenic activities, was used to simulate river discharge and water temperature for entire Yenisei basin with a 5 minute spatial resolution river network using several climate reanalysis products (MERRA2, ERA5 and NCEP-NCAR).  The modeled results were verified with observational data and simulations using the MERRA2 climate drivers demonstrated the best match with observations (Nash-Sutcliffe model efficiencies coefficients were greater than 0.5 for both river temperature and discharge). Maps of modeled changes in runoff, river temperature and heat flux show the opposite changes in the southern and northern parts of Yenisei basin. The model simulations correspond well with observational data even for heavily disturbed river reaches. For example, they show unfrozen water with positive temperatures during the winter below large dams and reservoirs.       

The WBM was also applied to project changes in water temperature, discharge and heat flux up to 2100 for several SSPs and GCMs from CMIP6. In spite of heterogenous projected changes in these parameters across Yenisei basin, significant increases in discharge and heat flux to the Arctic Ocean are expected.

How to cite: Shiklomanov, A., Lammers, R., Prusevich, A., Panyushkina, I., and Meko, D.: Changes in river temperature, discharge and heat flux based on new observational data for Yenisei basin and modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9914, https://doi.org/10.5194/egusphere-egu23-9914, 2023.

EGU23-11159 | ECS | Orals | CL4.7

Causal model evaluation of Arctic-midlatitude process during the boreal cold season in CMIP6 

Evgenia Galytska, Katja Weigel, Dörthe Handorf, Ralf Jaiser, Raphael Köhler, Jakob Runge, and Veronika Eyring

Linked to increased sea ice loss, the Arctic region has warmed at least four times faster than the global average over the past 40 years. Mutual links between amplified Arctic warming with changes and variability in midlatitude weather have been discussed in several studies. Nevertheless, the lack of consistent conclusions between observations and model simulations obfuscates the interpretation behind the mechanisms of Arctic-midlatitude teleconnections. To contribute to the understanding of Arctic-midlatitude connections that occur in conditions of amplified Arctic warming, we applied causal discovery to analyse causal and contemporaneous links. Initially, we calculated causal dependencies for monthly mean ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and Hadley Centre Sea Ice and Sea Surface Temperature among local and remote processes. Then, by comparing causal graphs detected in reanalyses data with a number of climate model historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we assessed the performance of climate models and evaluated the robustness of the observed Arctic-midlatitude connections in the current climate. By comparing causal graphs from the CMIP6 historical and Scenario Model Intercomparison Project (ScenarioMIP) we estimated future changes in Arctic-midlatitude teleconnections towards the end of the century. In this study, we focus on the differences in the mechanism of Arctic-midlatitude teleconnections that occur during the boreal cold season, i.e. early winter (October-November-December), winter (December-January-February), and late winter (January-February-March). In this study, we will present the major findings of Galytska et al., 2022 discussing how causal model evaluation helps to summarize major differences between causal interdependencies detected in observations and simulated by a number of climate models. Understanding these differences can be the basis for further improvement of the representation of Arctic-midlatitude teleconnections in climate models.

References. 

Evgenia Galytska, Katja Weigel, Dörthe Handorf, Ralf Jaiser, Raphael Köhler, Jakob Runge, and Veronika Eyring. Causal model evaluation of Arctic-midlatitude teleconnections in CMIP6. Authorea. October 06, 2022. DOI: 10.1002/essoar.10512569.1

 

How to cite: Galytska, E., Weigel, K., Handorf, D., Jaiser, R., Köhler, R., Runge, J., and Eyring, V.: Causal model evaluation of Arctic-midlatitude process during the boreal cold season in CMIP6, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11159, https://doi.org/10.5194/egusphere-egu23-11159, 2023.

EGU23-12377 | ECS | Posters on site | CL4.7

Thermodynamic assessment of simulations of the last deglaciation with an Earth system model of intermediate complexity 

Muriel Racky, Irene Trombini, Klaus Pfeilsticker, Nils Weitzel, and Kira Rehfeld

As we observe and expect severe changes in the Earth’ climate, the analyses of past climate state transitions is of major value for improving our Earth system understanding. Under this objective, the last deglaciation (~ 21 ka to 9 ka before present), the transition from the Last Glacial Maximum (LGM) to the Holocene, is an ideal case study. During this transition, the orbital configuration gradually changed and greenhouse gases have risen, which caused a sharp decline in northern hemisphere ice sheets and an increase in the global mean surface temperature.

We create an ensemble of deglaciation simulations with a modified version of the Planet Simulator, an Earth system model of intermediate complexity (EMIC). We produce single and combined forcing simulations for further investigation from a thermodynamic perspective. The response to the transiently changing radiative forcing is investigated in terms of energy and entropy budgets of the atmosphere. Here, we focus on the deglacial evolution of the material entropy production (MEP). Its contributions represent the strength of major climate features such as the kinetic energy generation rate, vertical and horizontal heat transport and the hydrological cycle. Preliminary results show an increase of the global mean MEP from the LGM to the Holocene because of a strengthening of the hydrological contribution. In contrast, the relative importance of kinetic energy dissipation and turbulent heat diffusion in the boundary layer decrease. Our work can provide the basis for investigating the MEP as a diagnostic quantity with other models and for other climate state transitions.

How to cite: Racky, M., Trombini, I., Pfeilsticker, K., Weitzel, N., and Rehfeld, K.: Thermodynamic assessment of simulations of the last deglaciation with an Earth system model of intermediate complexity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12377, https://doi.org/10.5194/egusphere-egu23-12377, 2023.

EGU23-12939 | Orals | CL4.7

The contribution of Arctic marine heatwaves to the minimum sea ice extent as compound events 

Armineh Barkhordarian, David Nielsen, and Johanna Baehr

On the global scale, the frequency of marine heatwaves (MHWs) is projected to increase further in the twenty-first Century. In our earlier study we demonstrate that the high-impact major marine heatwaves over the northeast Pacific are co-located with a systematically-forced long-term warming pool, which we attribute to forcing by elevated greenhouse gases levels (GHG), and the recent industrial aerosol-load decrease (Barkhordarian et al., 2022).  In current study we show that the magnitude of Arctic MHWs has significantly increased since 2006, and has exceeded the pre-industrial climate bounds since then. We here perform statistical attribution methodologies, and provide a quantitative assessment of whether GHG forcing was necessary for the Arctic MHWs to occur, and whether it is a sufficient cause for such events to continue to repeatedly occur in the future.

The probability of necessary causation of Arctic MHWs intensity, increases with increasing the severity of MHWs, and saturate to 1.0 by the time MHWs intensity exceeds the 2°C, indicating that any MHWs over the Arctic with an intensity higher than 2°C is entirely attributable to the inclusion of GHG forcing. These amplified extreme MHWs in the Arctic have each been accompanied by a record decline in Arctic Sea ice, in particular in the years 2007, 2012, 2016 and 2020. Over the last decade, MHWs occur in the Arctic where sea ice melt in June is 4 %/year faster, the ice-free season is ~3 months longer, the ocean heat-uptake is 50 W/m2 higher, and the sea surface temperature is ~2°C warmer, in comparison with the previous decade. In autumn surface evaporation rate is increasing, the increased low clouds favor more sea ice melt via emitting stronger longwave radiation. In summary, prolonged Arctic marine heatwaves, triggered by faster early summer sea ice melt, will accelerate Arctic warming, and cause Arctic Sea ice extent to shrink even faster in the near future.

Barkhordarian, A., Nielsen, D.M. & Baehr, J. Recent marine heatwaves in the North Pacific warming pool can be attributed to rising atmospheric levels of greenhouse gases. Nature Communications Earth & Environment, 3, 131 (2022). https://doi.org/10.1038/s43247-022-00461-2

 

How to cite: Barkhordarian, A., Nielsen, D., and Baehr, J.: The contribution of Arctic marine heatwaves to the minimum sea ice extent as compound events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12939, https://doi.org/10.5194/egusphere-egu23-12939, 2023.

EGU23-13091 | Posters on site | CL4.7

Emergent constraints on the future Arctic lapse-rate feedback 

Olivia Linke, Nicole Feldl, and Johannes Quaas

Arctic amplification (AA) is largely attributed to the effect of sea ice decline leading to greater surface solar absorption and further ice melt, and the vertical structure of the warming. The latter aspect evokes the positive lapse-rate feedback (LRF), which is commonly understood as an effect of stable stratification: The warming in the Arctic is particularly strong close to the surface, but muted aloft. This limits the outgoing long-wave radiative flux at the top-of-the-atmosphere (TOA) relative to vertically uniform warming.

We estimate the future Arctic LRF in 43 global climate models (GCMs) from the highest emission pathway SSP5 of CMIP6. The GCMs simulate a large spread of future AA (2-8 K above global warming) and Arctic LRF (1-4 K warming contribution) at the end of the century 2070-2099. Our work aims to identify emerging relationships between this spread and observable aspects of the current climate to ultimately narrow down the range of future Arctic climate predictions.

Previous studies have identified an emerging relationship for the surface-albedo feedback based on the observed seasonal cycle of Arctic sea ice. We similarly derive a positive relationship (r=0.70) between future and seasonal LRF, but due to its nature, no direct observation of the LRF exists. However, we find relationships between the future LRF and observable sea ice metrics, namely sea ice concentration, seasonality, extent and area. From these relationships, the sea ice concentration provides the strongest correlation (r=-0.76) for the area-averaged Arctic sea ice cover. This relationship implies a contribution of the LRF to future Arctic warming of approx. 2 K, which further relates to an AA of 4 K above global average at the end of the century.

We further emphasise the physical meaning behind our constraint: The negative emerging relationship implies that models with a lower Pan-Arctic sea ice concentration produce a larger LRF in the future. However, when dividing the entire sea ice area into regions of sea ice retreat (SIR) and persisting sea ice (PSI) in the future prediction, the relationship becomes positive over these two area-averaged regions. Thereby, the negative overall relationship is merely a result of the area-size distribution of SIR vs. PSI across the spread of model simulations. We conclude that while the Pan-Arctic perspective enables the emergent constraint, the physical meaning is hidden: A higher initial sea ice concentration produces a stronger positive Arctic LRF by setting the stage for greater sea ice retreat.

How to cite: Linke, O., Feldl, N., and Quaas, J.: Emergent constraints on the future Arctic lapse-rate feedback, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13091, https://doi.org/10.5194/egusphere-egu23-13091, 2023.

EGU23-14141 | Orals | CL4.7

Underestimation of Arctic warming trends in sub-seasonal forecasts 

Steffen Tietsche, Frederic Vitart, Michael Mayer, Antje Weisheimer, and Magdalena Balmaseda

The Arctic has warmed substantially over the last decades and will continue to do so owing to global warming in conjunction with polar amplification. The changing mean state poses many challenges to the construction, evaluation and calibration of subseasonal-to-seasonal forecasting systems, because it puts into question the representativeness of the system's retrospective forecasts (reforecasts). Furthermore, any inconsistencies with observed trends degrade the forecast skill and point to deficiencies in either the physical modelling or the initialization methods. Here, we assess the consistency of boreal winter trends of surface air temperature (SAT) in the Eurasian Arctic between the ERA5 reanalysis and ECMWF sub-seasonal reforecasts initialised from ERA5, for the 35-year period 1986-2021. We present methods to quantify robustness and importance of the observed trends, and to quantify the consistency of reforecast trends with these observed trends. We find that, in large parts of the marine Arctic, the reforecasts clearly underestimate the reanalsyis warming trend of about 1 K per decade at lead times beyond two weeks. For longer lead times, the reforecast trend is less than half of the reanalysis trend, with very high statistical significance. We present a series of numerical experiments to investigate potential reasons for the trend underestimation. These concern the sea-ice thermodynamic coupling to the atmosphere, impact of sea surface temperatures, and possible remote atmospheric influences from the North Atlantic and the Tropics. The outcome of these experiments provides guidance for future improvements in the physical forecast model and data assimilation methods needed to faithfully represent and predict Arctic climate variability and change.

How to cite: Tietsche, S., Vitart, F., Mayer, M., Weisheimer, A., and Balmaseda, M.: Underestimation of Arctic warming trends in sub-seasonal forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14141, https://doi.org/10.5194/egusphere-egu23-14141, 2023.

EGU23-14470 | Posters on site | CL4.7

A first look at the new PolarRES ensemble of polar regional climate model storylines to 2100 

Ruth Mottram, Priscilla Mooney, and Jose Abraham Torres and the PolarRES Consortium

The Horizon 2020 project PolarRES is coordinating a large international consortium of regional climate modelling groups in building a new ensemble of regional climate projections out to 2100. The ensemble is built at very high resolution (~12km) and using common domains, and set-ups to give directly comparable model outputs. At the same time, all regional climate models have been upgraded to a next-generation set-up, producing an ensemble of unprecedented sophistication.

We use a storyline approach, focused on Arctic amplification and cyclones in the northern hemisphere and Southern Annular Mode variability in Antarctica, to select global climate models for forcing on the boundaries. Each regional climate modelling group will downscale ERA5 and multiple global climate models. The data produced from these simulations will be used to improve process understanding under present and future conditions as well as to identify impacts of climate change in the polar regions.

Here, we present the experimental protocol developed in PolarRES and give details of the different regional climate models used, their setup, processes and domains as well as an overview of the outputs and planned applications. We show preliminary analysis of hindcast outputs to assess the performance of the ensemble. We invite other regional climate modelling groups outside the PolarRES consortium to consider using the same CORDEX -compatible model set-up and we are happy to receive suggestions of further spin-off studies or requests for collaboration.

 

How to cite: Mottram, R., Mooney, P., and Torres, J. A. and the PolarRES Consortium: A first look at the new PolarRES ensemble of polar regional climate model storylines to 2100, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14470, https://doi.org/10.5194/egusphere-egu23-14470, 2023.

EGU23-15746 | Orals | CL4.7

Simulating oceanic mesoscale eddy dynamics: A comparison of novel parameterizations and energy diagnostics and their impact on the global ocean circulation 

Stephan Juricke, Sergey Danilov, Marcel Oliver, Anton Kutsenko, and Kai Bellinghausen

In this study, we present a variety of parameterizations for simulating ocean eddy dynamics including novel viscous and kinetic energy backscatter closures. Their effect is analyzed using new diagnostics that allow for application on unstructured meshes.

Ocean mesoscale eddy dynamics play a crucial role for large-scale ocean currents as well as for the variability in the ocean and climate. The interactions between eddies and the mean flow affect strength, position and variations of ocean currents. Mesoscale eddies have a substantial impact on oceanic heat transport and the coupling between the atmosphere and ocean. However, at so-called eddy-permitting model resolutions around ¼°, eddy kinetic energy and variability is often substantially underestimated due to excessive dissipation of energy. Despite ever-increasing model resolutions, eddy-permitting simulations will still be used in uncoupled and coupled climate and Earth system simulations for years to come.

To improve the presentation of eddy dynamics in such resolution regimes, we present and systematically compare a set of viscous and kinetic energy backscatter parameterization with different complexity. These schemes are implemented in the unstructured grid, finite volume ocean model FESOM2 and tested in both idealized channel and global ocean simulations. We show that kinetic energy backscatter and adjusted viscosity parameterizations can alleviate some of the substantial eddy related biases, for example biases in sea surface height variability, mean currents and in water mass properties. We then further analyze the effect of these schemes on energy and dissipation spectra using new diagnostics that can be extended to the unstructured grid used by FESOM2. The rigorous intercomparison allows to make informed decisions on which schemes are the most suitable for a given application, considering the complexity of the schemes, their computational costs, their adaptability to various model resolutions and any simulation improvements related to a specific scheme. We will show that novel viscous and kinetic energy backscatter schemes outperform previously used, classical viscous closures. Furthermore, when compared to higher resolution simulations, they are computationally less expensive but achieve similar results.

How to cite: Juricke, S., Danilov, S., Oliver, M., Kutsenko, A., and Bellinghausen, K.: Simulating oceanic mesoscale eddy dynamics: A comparison of novel parameterizations and energy diagnostics and their impact on the global ocean circulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15746, https://doi.org/10.5194/egusphere-egu23-15746, 2023.

EGU23-5 | ECS | PICO | HS1.3.2

Probabilistic analysis of river levees under consideration of time-dependent loads 

Marco Albert Öttl, Jens Bender, and Jürgen Stamm

In the analysis regarding the stability of river dikes, the interactions between the load magnitude of the flood level and the resulting percolation are found to be a highly relevant process. After all, the seepage line separates the cross-sectional area into the water-saturated and the unsaturated crosssectional parts. For homogeneous levees, the position of the seepage line in the stationary case is imprinted in the system by the outer cubature and is well on the safe side for real flood events. In the non-stationary case, the position of the seepage line depends primarily on the changing water level of a flood hydrograph, the resulting water content and suction stresses in the dike, as well as the saturated permeability of the dike construction materials. In the current dimensioning practice according to DIN 19712 and the German DWA-M-507, the characteristic of the hydrograph is not directly applied. So far, for example, the resulting damming duration of a flood hydrograph is only considered indirectly.
This paper presents a methodology, which quantifies natural dependency structures for a selected dike section by synthetically generated dimensioning hydrographs in a probabilistic design. These results are then integrated directly into the geohydraulic process of water penetration. Based on selected water level and discharge time series at a dike section, flood waves can be described in five parameters using the extended flood characteristic simulation according to MUNLV1. After successfully adapting suitable distribution functions, dependencies in the load structure are quantified in the next step using Copula function. Subsequently, any number of synthetic flood hydrographs can be generated by combining these parameters. In keeping with the principle of the Monte Carlo simulation, a sufficiently high number of synthetic events results in extreme conditions with a low probability of occurrence being reliably represented.
Using a developed routine, the process of moisture penetration for the individual flood hydrographs can be simulated and visualized in a transient, geohydraulically numerical model at different points in times. Finally, statements regarding the behavior patterns of the resulting seepage lines, based on the loading situation can be derived and predicted. Based on these results, a reliability analysis then shows the stability of the dike section under the given extreme conditions.

1Ministerium für Umwelt, Landwirtschaft, Natur und Verbraucherschutz des Landes Nordrhein-Westfalen

How to cite: Öttl, M. A., Bender, J., and Stamm, J.: Probabilistic analysis of river levees under consideration of time-dependent loads, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5, https://doi.org/10.5194/egusphere-egu23-5, 2023.

EGU23-487 | ECS | PICO | HS1.3.2

A physics-informed machine learning approach to estimate surface soil moisture 

Abhilash Singh and Kumar Gaurav

We propose Physics Informed Machine Learning (PIML) algorithms to estimate surface soil moisture from Sentinel-1/2 satellite images based on Artificial Neural Networks (ANN). We have used Improved Integral Equation Model (I2EM) to simulate the radar images backscatter in VV polarisation. In addition, we selected a set of different polarisations, i.e.; (VH, VH/VV, VH-VV), incidence angle, Normalised Difference Vegetation Index (NDVI), and topography as input features to map surface soil moisture. We have used two different approaches to predict soil moisture using PIML. In the first approach, we developed an observation bias in which we selected the difference of backscatter value at each pixel in VV polarisation from satellite and derived from theoretical model derived as one of the input features. Our second approach is based on learning bias, in which we modified the loss function with the help of the I2EM model. Our result shows the learning bias PIML outperforms the observation bias PIML with R = 0.94, RMSE = 0.019 m3/m3, and bias = -0.03. We have also compared the performance with the standalone benchmark algorithms. We observed the learning bias PIML emerged as the most accurate model to estimate the surface soil moisture. The proposed approach is a step forward in estimating accurate surface soil moisture at high spatial resolution from remote sensing images.

How to cite: Singh, A. and Gaurav, K.: A physics-informed machine learning approach to estimate surface soil moisture, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-487, https://doi.org/10.5194/egusphere-egu23-487, 2023.

EGU23-4039 | ECS | PICO | HS1.3.2

UNITE: A toolbox for unified diagnostic evaluation of physics-based, data-driven and hybrid models based on information theory 

Manuel Álvarez Chaves, Anneli Guthke, Uwe Ehret, and Hoshin Gupta

The use of “hybrid” models that combine elements from physics-based and data-driven modelling approaches has grown in popularity and acceptance in recent years, but these models also present a number of challenges that must be addressed in order to ensure their effectiveness and reliability. In this project, we propose a toolbox of methods based on information theory as a step towards a unified framework for the diagnostic evaluation of “hybrid" models. Information theory provides a set of mathematical tools that can be used to study input data, model architecture and predictions, which can be helpful in understanding the performance and limitations of “hybrid” models.

Through a comprehensive case study of rainfall-runoff hydrological modelling, we show how a very simple physics-based model can be coupled in different ways with neural networks to develop “hybrid” models. The proposed toolbox is then applied to these “hybrid” models to extract insights which guide towards model improvement and refinement. Diagnostic scores based on the entropy (H) of individual predictions and the Kullback-Leibler divergence (KLD) between predictions and observations are introduced. Mutual information (I) is also used as a more all-encompassing metric which informs on the aleatory and epistemic uncertainties of a particular model. In order to address the challenge of calculating quantities from information theory on continuous variables (such as streamflow), the toolbox takes advantage of different estimators of differential entropy, namely: binning, kernel density estimation (KDE) and k-nearest neighbors (k-NN).

How to cite: Álvarez Chaves, M., Guthke, A., Ehret, U., and Gupta, H.: UNITE: A toolbox for unified diagnostic evaluation of physics-based, data-driven and hybrid models based on information theory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4039, https://doi.org/10.5194/egusphere-egu23-4039, 2023.

We introduce and illustrate our recently developed Augmented Information Physical Systems Intelligence (AIPSI), leveraging and enhancing our proprietary Information Physical Artificial Intelligence (IPAI) and Earth System Dynamical Intelligence (ESDI) to further the mathematically robust, physically consistent and computationally efficient holistic articulation and integration across the latest advances in fundamental physics, geophysical sciences and information technologies.

In theoretical terms, AIPSI brings out a more general principled lingua franca and formal construct to complex system dynamics and analytics beyond traditional hybridisation among stochastic-dynamic, information-theoretic, artificial intelligence and mechanistic techniques.

In practical terms, it empowers improved high-resolution spatiotemporal early detection, robust attribution, high-performance forecasting and decision support across multissectorial theatres of operation pertaining multiple interacting hazards, natural, social and hybrid.

With operational applications in mind, AIPSI methodologically improves the sharp trade-off between speed and accuracy of multi-hazard phenomena sensing, analysis and simulation techniques, along with the quantification and management of the associated uncertainties and predictability with sharper spatio-temporal resolution, robustness and lead.

This is further supported by the advanced Meteoceanics QITES constellation providing coordinated volumetric dynamic sensing and processing of gravitational and electrodynamic fluctuations, thereby providing an instrumentation ecosystem for anticipatory early detection of extreme events such as flash floods, explosive cyclogenesis and imminent disruptive structural critical transitions across built and natural environments.

With the methodological developments at hand, a diverse set of applications to critical theatres of operation are presented, ranging from early detection, advance modelling and decision support to environmental and security agencies entrusted with the protection and nurturing of our society and the environment. Contributing to empowering a more robust early detection, preparedness, response, mitigation and recovery across complex socio-environmental hazards such as those involving massive wildfires, floods and their nonlinear compound interplay, their underlying mechanisms and consequences.

The presentation concludes with an overview of a new large-scale international initiative on multi-hazard risk intelligence networks, where an eclectic diversity of actors ranging from academia and industry to institutions and the civil society come together to co-create emerging pathways for taking this challenging quest even further, in a fundamental coevolution between cutting-edge science, groundbreaking technology and socio-environmental insights to further enrich the ever-learning system dynamic framework at the core of our multi-hazard research and service.

Acknowledgement: This contribution is funded by the Εuropean Union under the Horizon Europe grant 101074004 (C2IMPRESS).

 

How to cite: Perdigão, R. A. P. and Hall, J.: Augmented Information Physical Systems Intelligence (AIPSI) for enhanced spatiotemporal early detection, attribution, prediction and decision support on multi-hazards, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6617, https://doi.org/10.5194/egusphere-egu23-6617, 2023.

EGU23-8388 | ECS | PICO | HS1.3.2 | Highlight

An ML-based Probabilistic Approach for Irrigation Scheduling 

Shivendra Srivastava, Nishant Kumar, Arindam Malakar, Sruti Das Choudhury, Chittaranjan Ray, and Tirthankar Roy

Globally, agriculture irrigation accounts for 70% of water use and is facing extensive and increasing water constraints. Well-designed irrigation scheduling can help determine the appropriate timing and water requirement for crop development and consequently improve water use efficiency. This research aims to assess the probability of irrigation needed for agricultural operations, considering soil moisture, evaporation, and leaf area index as indicators of crop water requirement. The decision on irrigation scheduling is taken based on a three-step methodology. First, relevant variables for each indicator are identified using a Random Forest regressor, followed by the development of a Long Short-Term Memory (LSTM) model to predict the three indicators. Second, errors in the simulation of each indicator are calculated by comparing the predicted values against the actual values, which are then used to calculate the error weights (normalized) of the three indicators for each month (to capture the seasonal variations). Third, the empirical distribution of each indicator is obtained for each month using the estimated error values, which are then adjusted based on the error weights calculated in the previous step. The probabilities of three threshold values (for each indicator) are considered, which correspond to three levels of irrigation requirement, i.e., low, medium, and high. The proposed approach provides a probabilistic framework for irrigation scheduling, which can significantly benefit farmers and policymakers in more informed decision-making related to irrigation scheduling.

How to cite: Srivastava, S., Kumar, N., Malakar, A., Choudhury, S. D., Ray, C., and Roy, T.: An ML-based Probabilistic Approach for Irrigation Scheduling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8388, https://doi.org/10.5194/egusphere-egu23-8388, 2023.

Inference of causality and understanding of extreme events are two intensively developing multidisciplinary areas highly relevant for the Earth sciences. Surprisingly, there is only a limited interaction of the two research areas.

Quantification of causality in terms of improved predictability was proposed by the father of cybernetics N. Wiener [1] and formulated for time series by C.W.J. Granger [2]. The Granger causality evaluates predictability in bivariate autoregressive models. This concept has been generalized for nonlinear systems using methods rooted in information theory [3]. The information theory of Shannon, however, usually ignores two important properties of Earth system dynamics: the evolution on multiple time scales and heavy-tailed probability distributions. While the multiscale character of complex dynamics, such as the air temperature variability, can be studied within the Shannonian framework in combination with the wavelet transform [4], the entropy concepts of Rényi and Tsallis have been proposed to cope with variables with heavy-tailed probability distributions. We will discuss how such non-Shannonian entropy concepts can be applied in inference of causality in systems with heavy-tailed probability distributions and extreme events. Using examples from the climate system, we will focus on causal effects of the North Atlantic Oscillation, blocking events and the Siberian high on winter and spring cold waves in Europe, including the April 2021 frosts endangering French vineyards. Using the non-Shannonian information-theoretic concepts we bridge the inference of causality and understanding of the occurrence of extreme events.

Supported by the Czech Academy of Sciences, Praemium Academiae awarded to M. Paluš.

[1] N. Wiener, in: E. F. Beckenbach (Editor), Modern Mathematics for Engineers (McGraw-Hill, New York, 1956)

[2] C.W.J. Granger, Econometrica 37 (1969) 424

[3] K. Hlaváčková-Schindler  et al., Phys. Rep. 441 (2007)  1; M. Paluš, M. Vejmelka, Phys. Rev. E 75 (2007) 056211; J. Runge et al., Nature Communications 6 (2015) 8502

[4] M. Paluš, Phys. Rev. Lett. 112 (2014) 078702; N. Jajcay, J. Hlinka, S. Kravtsov, A. A. Tsonis, M. Paluš, Geophys. Res. Lett. 43(2) (2016) 902–909

How to cite: Paluš, M.: Non-Shannonian information theory connects inference of causality and understanding of extreme events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10707, https://doi.org/10.5194/egusphere-egu23-10707, 2023.

This research investigated the applicability of a probabilistic physics-informed Deep Learning (DL) algorithm, i.e.,deep autoregressive network (DeepAR), for rainfall-runoff modeling across the continental United States (CONUS). Various catchment physical parameters were incorporated into the probabilistic DeepAR algorithm with various spatiotemporal variabilities to simulate rainfall-runoff processes across Hydrologic Unit Code 8 (HUC8). We benchmarked our proposed model against several physics-based hydrologic approaches such as Sacramento Soil Moisture Accounting Model (SAC-SMA), Variable Infiltration Capacity (VIC), Framework for Understanding Structural Errors (FUSE), Hydrologiska Byråns Vattenbalansavdelning (HBV), and the mesoscale hydrologic model (mHM). These approaches were implemented using Catchment Attributes and Meteorology for Large-sample Studies (CAMELS), Maurer datasets. Analysis suggested that catchment physical attributes such as drainage area have significant impacts on rainfall-runoff generation mechanisms while catchment fraction of carbonate sedimentary rocks parameter’s contribution were insignificant. The results of the proposed physics-informed DeepAR simulation were comparable and somewhat superior to the well-known conceptual hydrologic models across CONUS.  

How to cite: Sadeghi Tabas, S. and Samadi, V.: A Probabilistic Physics-informed Deep Learning Model for Rainfall-runoff Prediction across Continental United States, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11222, https://doi.org/10.5194/egusphere-egu23-11222, 2023.

EGU23-13068 | ECS | PICO | HS1.3.2

Identifying the drivers of lake level dynamics using a data-driven modeling approach 

Márk Somogyvári, Ute Fehrenbach, Dieter Scherer, and Tobias Krueger

The standard approach of modeling lake level dynamics today is via process-based modeling. The development of such models requires an extensive knowledge about the investigated system, especially the different hydrological flow processes. When some of this information is missing, these models could provide distorted results and could miss important system characteristics.

In this study, we show how data-driven modeling can help the identification of the key drivers of lake level changes. We are using the example of the Groß Glienicker Lake, a glacial, groundwater fed lake near Berlin. This lake has been experiencing a drastic loss of water in recent decades, whose trend became even faster in the last few years. There is a local controversy whether these changes are mainly weather driven, or caused by water use; and what mitigation measures could be used to counteract them. Due to the strong anthropogenic influence from multiple water-related facilities near the lake, and the lack of geological information from the catchment, there are many unknows about the properties of the hydrological processes, hence the development of a process-based model in the area is challenging. To understand the system better we combine data-driven models with water balance approaches and use this methodology as an alternative to classic hydrological modeling.

The climatic model input (catchment-average precipitation and actual evapotranspiration) is generated by the Central European Refinement dataset (CER), which is a meteorological dataset generated by dynamically downscaling the Weather Research and Forecasting model (Jänicke et al., 2017). First, a data-driven model is constructed to predict the changes in lake levels one day ahead by using precipitation and evapotranspiration values from the last two months, a time interval that was selected after an extensive parameter analysis. This model is then further extended by additional inputs, such as water abstraction rates, river and groundwater levels. The fits of the different simulated lake levels are evaluated to identify the effects of the relevant drivers of the lake level dynamics. For a more mechanistic interpretation, a monthly water balance model was created using the same dataset. By calculating the different fluxes within the system, we were able to estimate the magnitudes of unobserved hydrological components.

With the help of our modeling approach, we could rule out the influence of one of the nearby waterworks and a river. We have also found that the lake level dynamics over the last two decades was mainly weather-driven, and the lake level fluctuations could be explained with changes in precipitation and evapotranspiration. With the water balance modeling, we have shown that the long-term net outflux from the lake catchment has increased in the last few years. These findings are used to support the development of a local high-resolution hydrogeological model, which could be used to further analyze these processes.

References

Jänicke, B., Meier, F., Fenner, D., Fehrenbach, U., Holtmann, A., Scherer, D. (2017): Urban-rural differences in near-surface air temperature as resolved by the Central Europe Refined analysis (CER): sensitivity to planetary boundary layer schemes and urban canopy models. Int. J. Climatol. 37 (4), 2063-2079. DOI: 10.1002/joc.4835

How to cite: Somogyvári, M., Fehrenbach, U., Scherer, D., and Krueger, T.: Identifying the drivers of lake level dynamics using a data-driven modeling approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13068, https://doi.org/10.5194/egusphere-egu23-13068, 2023.

Streamflow monitoring is a key input to water resource management, as it is an important source of information for understanding hydrological processes and prediction catchment behaviour and resulting flows. Both the monitored and the predicted flows support important decisions in areas such as infrastructure design, flood forecasting and resource allocation. It is therefore essential that the predictive information we have about our water resources serves these various needs.

Since observations are from the past and our decisions affect the future, models are needed to extrapolate measurements in time. Similarly, streamflow is not always measured at places where the information is needed, so interpolation or extrapolation is needed in space or across catchment properties and climates. Recent advances in publicly available large datasets of streamflow records and corresponding catchment characteristics have enabled succesful applications of machine learning to this prediction problem, leading to increased predictability in ungauged basins.

Since information content is related to surprise, we could see the objective of monitoring networks as manufacturing surprising data. This is formalized in approaches for monitoring network design based on information theory, where often the information content of the sources, i.e. the existing monitoring stations, has been investigated, including the effects of redundancy due to shared information between stations.

In this research, we argue that information content is related to unpredictability, but is inevitably filtered through several layers, which should be considered for monitoring network design. Examples of such filters are the models used for extrapolation to ungauged sites of interest, the target statistics of interest to be predicted, and the decision making purpose of those predictions. This means that the optimal monitoring strategy (where to measure, with how much precision and resolution, and for how long) depend on evolving modeling capabilities and representation of societal needs. Also, biases in the current neworks may exist as a function of how they are funded.

In this presentation, these theoretical aspects are investigated with examples from an ongoing project to investigate the streamflow monitoring network in British Columbia, Canada, which recently experienced record-breaking floods. 

How to cite: Weijs, S., Werenka, A., and Kovacek, D.: Manufacturing surprise: How information content, modeling capabilities and decision making purpose influence optimal streamflow monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14704, https://doi.org/10.5194/egusphere-egu23-14704, 2023.

EGU23-15968 | PICO | HS1.3.2

Differentiable modeling to unify machine learning and physical models and advance Geosciences 

Chaopeng Shen, Alison Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin Gupta, Alexandre Tartakovsky, Marco Baity-Jesi, Fabrizio Fenicia, Daniel Kifer, Xiaofeng Liu, Li Li, Dapeng Feng, Wei Ren, Yi Zheng, Ciaran Harman, Martyn Clark, Matthew Farthing, and Praveen Kumar

Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift. For decades, PBM offered benefits in interpretability and physical consistency but struggled to efficiently leverage large datasets. ML methods, especially deep networks, presented strong predictive skills yet lacked the ability to answer specific scientific questions. While various methods have been proposed for ML-physics integration, an important underlying theme  — differentiable modeling — is not sufficiently recognized. Here we outline the concepts, applicability, and significance of differentiable geoscientific modeling (DG). “Differentiable” refers to accurately and efficiently calculating gradients with respect to model variables, critically enabling the learning of high-dimensional unknown relationships. DG refers to a range of methods connecting varying amounts of prior knowledge to neural networks and training them together, capturing a different scope than physics-guided machine learning and emphasizing first principles. In this talk we provide examples of DG in global hydrology, ecosystem modeling, water quality simulations, etc. Preliminary evidence suggests DG offers better interpretability and causality than ML, improved generalizability and extrapolation capability, and strong potential for knowledge discovery, while approaching the performance of purely data-driven ML. DG models require less training data while scaling favorably in performance and efficiency with increasing amounts of data. With DG, geoscientists may be better able to frame and investigate questions, test hypotheses, and discover unrecognized linkages. 

How to cite: Shen, C., Appling, A., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., Baity-Jesi, M., Fenicia, F., Kifer, D., Liu, X., Li, L., Feng, D., Ren, W., Zheng, Y., Harman, C., Clark, M., Farthing, M., and Kumar, P.: Differentiable modeling to unify machine learning and physical models and advance Geosciences, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15968, https://doi.org/10.5194/egusphere-egu23-15968, 2023.

EGU23-16510 | PICO | HS1.3.2 | Highlight

Evolution of Causal Structure of Interactions in Turbulence at the Biosphere-Atmosphere interface 

Praveen Kumar and Leila Hernandez Rodriguez

Turbulence at the biosphere-atmosphere interface refers to the presence of chaotic and chaotic-like fluctuations or patterns in the exchange of energy, matter, or information between the biosphere and atmosphere. These fluctuations can occur at various scales. Turbulence at the biosphere-atmosphere interface can affect the transfer of heat, moisture, and gases. In this study, we use causal discovery to explore how high-frequency data (i.e., 10 Hz) of different variables at a flux tower, such as wind speed, air temperature, and water vapor, exhibit interdependencies. We use Directed Acyclic Graphs (DAGs) to identify how these variables influence each other at a high frequency. We tested the hypothesis that there are different types of DAGs present during the daytime at the land-atmosphere interface, and we developed an approach to identify patterns of DAGs that have similar behavior. To do this, we use distance-based classification to characterize the differences between DAGs and a k-means clustering approach to identify the number of clusters. We look at sequences of DAGs from 3-minute periods of high-frequency data to study how the causal relationships between the variables change over time. We compare our results from a clear sky day to a solar eclipse to see how changes in the environment affect the relationships between the variables. We found that during periods of high primary productivity, the causal relationship between water vapor and carbon dioxide shows a strong coupling between photosynthesis and transpiration. At high frequencies, we found that thermodynamics influences the dynamics of water vapor and carbon dioxide. Our framework makes possible the study of how dependence in turbulence is manifested at high frequencies at the land-atmosphere interface.

How to cite: Kumar, P. and Rodriguez, L. H.: Evolution of Causal Structure of Interactions in Turbulence at the Biosphere-Atmosphere interface, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16510, https://doi.org/10.5194/egusphere-egu23-16510, 2023.

Recent years have seen an uptick in the frequency of flood records occurring in the United States, with South Carolina (SC) being particularly hard hit. This study developed various deep recurrent neural networks (DRNNs) such as Vanilla RNN, long short-term memory (LSTM), and Gated Recurrent Unit (GRU) for flood event simulation. Precipitation and the USGS gaging data were preprocessed and fed into the DRNNs to predict flood events across several catchments in SC. The DRNNs are trained and evaluated using hourly datasets, and the outcomes were then compared with the observed data and the National Water Model (NWM) simulations. Analysis suggested that LSTM and GRU networks skillfully predicted the shape of flood hydrographs, including rising/falling limb, peak rates, flood volume, and time to peak, while the NWM vastly overestimated flood hydrographs. Among different climatic variables that were forced into the DRNNs, rainfall amount and spatial distribution were the most dominant input variables for flood prediction in SC.

How to cite: Heidari, E. and Samadi, V.: Application of Deep Recurrent Neural Networks for Flood Prediction and Assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16678, https://doi.org/10.5194/egusphere-egu23-16678, 2023.

NP3 – Scales, Scaling and Nonlinear Variability

The Eocene-Oligocene transition (EOT) is the turning point of Earth’s Cenozoic climate, during which it stepped into the current “icehouse” state. The absence of a high-resolution, global, evolutionary timeline has limited understanding of the linkages between marine biodiversity and this environmental change. Here, we present a new 28-Myr-long foraminiferal species-richness history with an average temporal resolution of ~26 kyr based on a global dataset and quantitative stratigraphic method, CONOP. A significant richness decline accompanied the EOT, eliminating a great number of foraminifera species. The extinction events in planktonic foraminiferal (PF) and larger benthic foraminiferal (LBF) near the EOT appear to be associated with the combination of a rapid decrease in deep ocean temperature, a eustatic sea-level fall and a positive carbon isotopic excursion. In contrast, the much longer richness decline of small benthic foraminifera (SBF) across the EOT occurred in two phases: the first coincided with turnover of marine primary producers, and the second appears to have been temporally coincident with Afar-Arabian LIP activity, which led to expansion of oceanic anoxia and euxinia. Thus, mega-climatic changes are reflected in the species richness of foraminifera during the Eocene-Oligocene “warmhouse-icehouse” transition.

How to cite: Lu, Z. and Fan, J.: Coupled patterns of foraminiferal species richness and mega-climatic change across the Eocene-Oligocene transition, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1832, https://doi.org/10.5194/egusphere-egu23-1832, 2023.

EGU23-1833 | Posters on site | NP3.1 | Highlight

Coral record from Bicol in the Philippines in 1770-1850 reveals volcanic cooling 

Hodaka kawahata, Mayuri Inoue, Mutsumi Chihara, Fernando P. Siringan, and Atsuhi Suzuki

Historical variations of surface temperature in relation to anthropogenic warming has been extensively studied to understand and explain changes in the contemporary climate and to estimate future impacts of climate.Inoue and others (in press) reported 228-year records of SST and salinity based on Sr/Ca and d18O analyses with monthly time resolution in Porites coral collected from Bicol, the south of Luzon, Philippines. From the record, we investigated the relationship between the reconstructed temperature and the volcanic eruptions in late 18th and early 19th centuries. There were three great famines during the Edo period (1603-1868), almost corresponding to the Little Ice Age in Japan. Of these, the two were Tenmei-famine in 1782-88 and Tempo-famine in 1833-1837(1839). Both famines killed more than one million people out of a population of 30 million at the time. Our reconstructed SST anomaly fluctuated between -1.5 degree and 1.0 degree. The age model may have the age error of 1 to 3 years before around 1885. Large minima occurred in 1785-1789, 1815-1819, 1822-25, 1827-1830, 1834-1835, and 1843-45. Although Laki eruption, Iceland in 1783 has not been described as large eruption in previous studies, their impact on climatic conditions around the Northern Hemisphere and the globe was widely reported. Local eruption of Asama, Japan in 1783 released volcanic ash over eastern part of Japanese islands, In addition, El Nino event, which often cooled down Japanese islands, occurred around those days. These factors could have been responsible for the coldest anomaly in 1785-1789 recorded in our coral samples. After Tambora eruption in 1815, sharp cooling of around 2.0˚C was observed in our coral sample and almost all over the world. However, this world-scale cooling event have no or little influence on the climate in Japanese islands based upon the historical documents and agriculture records. This indicates that there are areas that do not become exceptionally cold, even by major volcanic eruptions. Large eruption of Galunggung in 1822 brought appreciable degree of cooling anomaly in our coral record. Just after Agung exploded largely in 1843, reconstructed SST significantly dropped. This might be also influenced by another large eruption of Cosiguina in Nicaragua, central America. Cold climate was reported in Japan, New York in USA, Copenhagen, UK in 1840s. It was most likely global in scale in the northern hemisphere.

How to cite: kawahata, H., Inoue, M., Chihara, M., Siringan, F. P., and Suzuki, A.: Coral record from Bicol in the Philippines in 1770-1850 reveals volcanic cooling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1833, https://doi.org/10.5194/egusphere-egu23-1833, 2023.

EGU23-2679 | ECS | Orals | NP3.1

Mechanisms behind ocean variability in transient simulations of the early deglaciation 

Marie-Luise Kapsch, Marlene Klockmann, and Uwe Mikolajewicz

The last deglaciation was accompanied by a gradual warming with superimposed abrupt climate changes. In transient simulations of the last deglaciation with the comprehensive Max Planck Institute Earth System Model (MPI-ESM) we show that the timing and occurrence of abrupt climate changes are highly dependent on the utilized ice-sheet boundary condition. Simulations with different ice-sheet reconstructions show that the variability of North Atlantic surface temperatures are dominated by the timing and amplitude of meltwater fluxes from ice sheets, as derived from reconstructions. While some abrupt climate events (e.g. the Younger Dryas) only occur under certain boundary conditions in the transient simulations, other climate events such as the Bølling Allerød warming (about 14.7-14.2 ka BP) cannot be simulated with any of the applied and widely used reconstructions. However, in a sensitivity experiment with changing ice sheets but no addition of meltwater into the ocean, the North Atlantic experiences a warming during the time of the Bølling-Allerød. This warming is associated with a reorganization of the ocean circulation and deep-water formation sites. Prior to this reorganization, during the glacial and early part of the deglaciation, a rather zonal jet stream maintains a strong subpolar gyre in the North Atlantic. In addition, salty and dense water masses form in the Arctic. Until about 16.5 ka BP the Arctic freshens significantly and the surface elevation over the Laurentide ice sheet reduces. The latter leads to a shift in the atmospheric circulation at around 14.2 ka BP. The resulting changes in wind stress strongly reduce the eastward extent of the North Atlantic subpolar gyre. Here, we examine the physical mechanisms behind the reorganization and explore additional simulations with fixed deglacial key parameters (e.g. CO2, insolation, ice sheets) to identify the key drivers of the climate changes during the early deglaciation and Bølling Allerød.

How to cite: Kapsch, M.-L., Klockmann, M., and Mikolajewicz, U.: Mechanisms behind ocean variability in transient simulations of the early deglaciation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2679, https://doi.org/10.5194/egusphere-egu23-2679, 2023.

EGU23-3231 | Orals | NP3.1

Synchronization theory for Pleistocene glacial-interglacial cycles 

Takahito Mitsui, Matteo Willeit, and Niklas Boers

The dominant periodicity of glacial cycles changed from 41 kyr to roughly 100 kyr across the Mid-Pleistocene Transition (MPT) around 1 Myr ago. The mechanisms leading to these dominant periodicities and their changes during the MPT remain debated. We propose a synchronization theory explaining these features of glacial cycles and confirm it using an Earth system model that reproduces the MPT under gradual changes in volcanic CO2 outgassing rate and regolith cover. We show that the model exhibits self-sustained oscillations without astronomical forcing. Before the MPT, glacial cycles synchronize to the 41-kyr obliquity cycles because the self-sustained oscillations have periodicity relatively close to 41 kyr. After the MPT the time scale of internal oscillations becomes too long to follow every 41-kyr obliquity cycle, and the Earth's climate system synchronizes to the 100-kyr eccentricity cycles that modulate the amplitude of climatic precession. The latter synchronization is only possible with the help of the 41-kyr obliquity forcing through a mechanism that we term vibration-enhanced synchronization.

How to cite: Mitsui, T., Willeit, M., and Boers, N.: Synchronization theory for Pleistocene glacial-interglacial cycles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3231, https://doi.org/10.5194/egusphere-egu23-3231, 2023.

EGU23-4446 | Posters on site | NP3.1

The Fractional Macro Evolution Model: A simple quantitative scaling macroevolution model 

Shaun Lovejoy and Andrej Spiridinov

Scaling fluctuation analyses of the marine animal diversity, extinction and origination rates based on the Paleobiology Database occurrence data have opened new perspectives on macroevolution, supporting the hypothesis that the environment (climate proxies) and life (extinction and origination rates) are scaling over the “megaclimate” biogeological regime (from ≈ 1 Myr to at least 400Myrs).   In the emerging picture, biodiversity is a scaling “cross-over” phenomenon being dominated by the environment at short time scales and by life at long times scales with a cross-over at ≈40Myrs.  These findings provide the empirical basis for constructing the Fractional MacroEvolution Model (FMEM), a simple stochastic model combining destabilizing and stabilizing tendencies in macroevolutionary dynamics.  The FMEM is driven by two scaling processes: temperature and turnover rates. 

Macroevolution models are typically deterministic (albeit sometimes perturbed by random noises), and based on integer ordered differential equations.  In contrast, the FMEM is stochastic and based on fractional ordered equations.   Stochastic models are natural for systems with large numbers of degrees of freedom and fractional equations naturally give rise to scaling processes. 

The basic FMEM drivers are fractional Brownian motions (temperature, T) and fractional Gaussian noises (turnover rates E+) and the responses (solutions), are fractionally integrated fractional Relaxation processes (diversity (D), extinction (E), origination (O) and E- = O - E).  We discuss the impulse response (itself a model for impulse perturbations such as bolide impacts) and derive the full statistical properties including cross covariances.  By numerically solving the model, we verified the mathematical analysis and compared both uniformly and irregularly sampled model outputs to paleobiology series. 

The six series (T, E+, D, E-, O, E) had fluctuation statistics that varied realistically with time scales Δt (lags) over the observed range (≈3 Myrs to ≈ 400 Myrs).  In addition, the 15 pairwise fluctuation correlations (of the six variables) as functions of Δt were also very close to observations even though only two correlations were specified in the model (TE+and TD).  The ability to simulate the effects of irregular temporal sampling was important since model – data agreement was much better with realistic (irregular) sampling than with uniform sampling.  Although the model could easily be made more complex, this may not be warranted until much higher resolution series become available.

How to cite: Lovejoy, S. and Spiridinov, A.: The Fractional Macro Evolution Model: A simple quantitative scaling macroevolution model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4446, https://doi.org/10.5194/egusphere-egu23-4446, 2023.

EGU23-6069 | Posters on site | NP3.1

An ocean surface paradox: gas equilibrium with atmosphere 

Brian Durham and Christian Pfrang

Marine science tells us that the surface of Earth’s oceans is in gaseous equilibrium with its atmosphere (Yingxu Wu et al 2022). In the case of the key atmospheric trace gas CO2, the partition across the phase boundary is given by Henry constants as established by Li and Tsui 1971 and by Weiss 1974, while outside the laboratory there are extensive datasets for the atmospheric mol fraction (ppm CO2) embodied in the familiar `Keeling curves’, as measured at oceanic, polar and continental locations (Yuan et al 2019).

Sea surface temperatures are widely available for Earth’s oceans (Kent and Kennedy 2021). We have therefore interpreted the Henry Constants from Li and Tsui (1971) and from Weiss (1974) as headspace mol fractions (ppm CO2) against temperature, and added representative field data from Mauna Loa (https://gml.noaa.gov/ccgg/trends/).

A disparity is evident, which we address as follows: In case the well-known differential between the two 1970s laboratory curves is somehow attributable to pre-treatment including acid in both cases and a biocide in one, we speculate that the outcomes of both might be different if the seawater samples had been treated as biological fluids.

Expanding therefore our studies of atmospheric gas partitioning at a growing ice surface reported to recent EGU conferences, and building on valued conversations with colleagues at EGU 2022, we will present provisional results from gas equilibration in the headspace above freshly-collected (≈`live’) seawater from UK’s Atlantic coast.  

How to cite: Durham, B. and Pfrang, C.: An ocean surface paradox: gas equilibrium with atmosphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6069, https://doi.org/10.5194/egusphere-egu23-6069, 2023.

EGU23-6870 | ECS | Posters on site | NP3.1

A natural history of networks: Modelling higher-order interactions in geohistorical data 

Alexis Rojas, Anton Holmgren, Magnus Neuman, Daniel Edler, Christopher Blöcker, and Martin Rosvall

Geohistorical records, either stratigraphic sections, boreholes, ice cores, or archaeological sites, are inherently complex. Despite their limitations, the high-dimensional and spatiotemporally resolved data retrieved from individual geohistorical records allow for evaluation of past biotic responses to natural and human-induced environmental changes. Network analysis is becoming an increasingly popular alternative for modelling the dynamics of geohistorical data. However, the complexity of geohistorical data raises questions about the limitations of standard network models widely used in paleobiology research. They risk obscuring large-scale patterns by washing out higher-order node interactions when assuming independent pairwise links. Recently introduced higher-order representations and models better suited for the complex relational structure of geohistorical data provide an opportunity to move paleobiology research beyond these challenges. Higher-order networks can represent the spatiotemporal constraints on the information paths underlying geohistorical data, capturing the high-dimensional patterns more accurately. Here we describe how to design higher-order network models of geohistorical data, address some practical decisions involved in modeling complex dependencies, and discuss critical methodological and conceptual issues that make it difficult to compare results across studies in the growing body of network paleobiology. We illustrate multilayer networks, hypergraphs, and varying Markov time models through case studies on the fossil record from continental shelf ecosystems and delineate future research directions for current challenges in the emerging field of network paleobiology.

How to cite: Rojas, A., Holmgren, A., Neuman, M., Edler, D., Blöcker, C., and Rosvall, M.: A natural history of networks: Modelling higher-order interactions in geohistorical data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6870, https://doi.org/10.5194/egusphere-egu23-6870, 2023.

EGU23-7748 | ECS | Orals | NP3.1

Response of atmospheric variability in the Northern Hemisphere winter to past climate conditions and elevated CO2 levels 

Arthur Oldeman, Michiel Baatsen, Anna von der Heydt, Aarnout van Delden, and Henk Dijkstra

A specific feature where future climate projections fail to see a consistent response to increasing CO2 levels is Northern Hemisphere winter atmospheric dynamics and variability. This holds specifically for the Northern Annular Mode (NAM) and its regional expression, the North Atlantic Oscillation (NAO). The lack of consensus in future projections is caused in part due to the large internal variations of these modes of atmospheric variability compared to the response to elevated CO2.

The response of interannual and decadal climate variability to warm conditions can be isolated in climate simulations equilibrated at elevated CO2 concentrations. However, we cannot perform a future model-data comparison. Fortunately, we can turn to the past. The last time the Earth saw similar CO2 concentration as the present day was approximately 3 million years ago, in the mid-Pliocene epoch. The mid-Pliocene is often considered the ‘best analog’ to an equilibrated climate at present or near-future CO2 levels. However, can the mid-Pliocene be used to assess the response of Northern Hemisphere winter atmospheric variability, such as the NAO and NAM, to a warm climate?

To answer this question, we have performed a set of sensitivity experiments using a global coupled climate model (CESM1.0.5). We have performed sensitivity studies using a pre-industrial and a mid-Pliocene geography, as well as two levels of radiative forcing (280 ppm and 560 ppm), as a part of intercomparison project PlioMIP2. Our mid-Pliocene simulations generally compare well to proxy reconstructions of sea-surface temperature.

We consider the sea-level pressure (SLP) and zonal wind at 200 hPa using 200 years of January-mean data, and perform principal component analysis. In response to the mid-Pliocene boundary conditions (other than CO2), we find a large increase in the mean SLP along with a decreased variance over the North Pacific Ocean. This is accompanied with a weakened jet stream over the western North Pacific, as well as increased occurrence of a split jet condition over the eastern North Pacific. These findings are connected to a regime shift in the modes of atmospheric variability in the Northern Hemisphere, where the so-called North Pacific Oscillation (NPO) becomes the most dominant mode of variability. We do not see tendencies towards similar behavior in the CO2 doubling experiment indicating that the Pliocene boundary conditions are the main driver of the observed shifts in variability. This suggests that the mid-Pliocene is not a good analog for a warm future climate when considering Northern hemisphere winter atmospheric variability.

How to cite: Oldeman, A., Baatsen, M., von der Heydt, A., van Delden, A., and Dijkstra, H.: Response of atmospheric variability in the Northern Hemisphere winter to past climate conditions and elevated CO2 levels, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7748, https://doi.org/10.5194/egusphere-egu23-7748, 2023.

EGU23-7943 | Posters on site | NP3.1 | Highlight

Framing origins and dynamics of biodiversity in the paradigm of space and time scaling 

Andrej Spiridonov, Shaun Lovejoy, and Lauras Balakauskas

The biodiversity is the fundamental aspect and transitive measure of biota and the evolutionary process itself. The biodiversity is usually understood as the diversity of morphological or structural types, and also as the number of taxa (species, genera, families etc.) or branches of different ranks in evolutionary trees or networks. The biodiversity is hierarchical and universal feature of biological systems. Despite its conceptual simplicity, the origins and patterns of variability of diversity, except the fact that they are based on the evolutionary process, are rather hardly comprehensively understood. Therefore, the determination of origins, and the dynamics of biodiversity through the space and time, is one of the most fundamental open questions of biology.

                             The theory of evolution reveals a number of possibilities on how biodiversity can change, and also predicts patterns which underlie the mechanism: if the biodiversity is autonomous and self-regulating process, or if opposite is true – the biodiversity is a driven variable dependant on many varying Earth system, and possibly astrophysical components. One of the most promising approaches in characterizing the dynamics of biodiversity, and discriminating between the underlying causes of the dynamics, is the analysis of scaling.

                             The spatial scaling of biodiversity is a well developed field of science. The dependence of species richness on the geographical area is described by power laws. The values of parameters could be interpreted with respect to possible controlling genealogical and ecological mechanisms of evolution. The scaling of biodiversity in space, also suggests the scaling of biodiversity as a function of time scale. The scaling is time scale symmetry which connects the large and small scales, and it reveals the uniformity of a mechanism in a scaling range. Presented approach allows the summarization of macroevolution in very simple terms.

                             Here we present a case of global marine animal biodiversity, and based on the revealed crossover-like time scaling pattern, we suggest that two competing time symmetric scaling mechanisms, with opposite effects on biodiversity (stabilizing versus destabilizing), are responsible for the evolution of the biota at the eon scale. The presented results can serve as a null model in understanding global evolution, and also can serve in sharpening and strengthening of our intuitions in exploring and explaining macroevolutionary patterns.

                             The research was supported by the project S-MIP-21-9 “The role of spatial structuring in major transitions in macroevolution”.

How to cite: Spiridonov, A., Lovejoy, S., and Balakauskas, L.: Framing origins and dynamics of biodiversity in the paradigm of space and time scaling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7943, https://doi.org/10.5194/egusphere-egu23-7943, 2023.

EGU23-8513 | ECS | Posters on site | NP3.1 | Highlight

Exploring evolution of feather function in early birds and dinosaurs 

Pierre Cockx and Michael Benton

Feathers are key innovations that underpin the evolutionary success of birds, and biologists have achieved a solid understanding of modern feather types and their functions. Nonetheless, the unexpected recent discoveries of several specialized feather morphologies in extinct birds and related dinosaurs, challenges our views of the overall evolution of feathers. Such discoveries raise large evolutionary questions in a wider group than simply birds (i.e., dinosaurs as well as pterosaurs). These are related, for instance, to the initial function of feathers, subsequent feather diversification and functions, and potential links between such evolution and external factors. We have differentiated and inventoried fossil feather types based on their general morphological structure, and coded these as traits that relate to feather functions. We analyse the dataset through computational phylogenetic comparative methods, including ancestral state reconstructions, to identify the points of origin for each feature and estimate patterns and rates of evolution. Monofilamentous integumentary structures appear synapomorphic to Avemetatarsalia. A loss of monofilamentous integumentary structures occurred within Pennaraptora. While the presence of pennaceous feathers is synapomorphic for Pennaraptora, the presence of pennaceous feathers on the hindlimbs is a synapomorphy of Paraves. There is greater complexity, however, in feather evolution, with uncertainty over convergence and uniqueness of some feather types not seen in modern birds. The analysis allows some connection from feather morphology evolution to the sequence of regulatory gene switches in modern feather ontogenetic development, but the fossils suggest a richness of evolution not directly seen in studies of feather evo-devo.

How to cite: Cockx, P. and Benton, M.: Exploring evolution of feather function in early birds and dinosaurs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8513, https://doi.org/10.5194/egusphere-egu23-8513, 2023.

EGU23-9455 | Orals | NP3.1

Was the 4.2 ka event unusual in context of global Holocene climate variability? 

Nicholas McKay, Darrell Kaufman, Stéphanie Arcusa, and Hannah Kolus

Abrupt climate changes are commonly observed between 4500 and 4000 years ago, and particular attention has been paid to the “4.2 ka event”, which now serves as a stratigraphic marker to subdivide the mid and late Holocene globally. However, proxy climate records are commonly marked by large, and often abrupt, changes in temperature and moisture throughout the Holocene, and it remains unclear how abrupt change in the mid-Holocene compares to changes throughout the epoch. Here, we assess how regional and global temperature and moisture changes between 4.5 and 4.0 ka compare with other major climate events across the Holocene, in particular the 8.2 ka event. To conduct this analysis objectively, we assess more than a 1000 previously published paleoclimate datasets that span all continents and oceans and include a wide variety of archive and proxy types. All of the data are open access, and the analyses were conducted using the open-source “Abrupt Change Toolkit in R (actR)” software package to determine the timing and significance of multiple types of abrupt change. These include excursion events (significant short-term deviations from the mean state), regime change events (significant rapid shifts in millennial-scale means) and trend change events (significant changes in the long-term trend). We detect multiple significant abrupt change events throughout the Holocene, and therefore evaluate the spatiotemporal significance of events against a null hypothesis of observed background variability. Events at 8.2 ka stand out as large spatiotemporally coherent excursions of temperature and moisture centered in the North Atlantic and globally significant. In contrast, although we detect multiple types of abrupt change in moisture and temperature during the between 4.5 and 4.0 ka, the event does not significantly exceed the expectation of occurrence from our robust null model nor stand out as a regionally coherent anomaly. These results suggest that local abrupt changes are common throughout the Holocene; many of these are regionally coherent, but few are hemispheric or global in extent.

How to cite: McKay, N., Kaufman, D., Arcusa, S., and Kolus, H.: Was the 4.2 ka event unusual in context of global Holocene climate variability?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9455, https://doi.org/10.5194/egusphere-egu23-9455, 2023.

EGU23-11039 | ECS | Posters on site | NP3.1

New Land- vs. Ocean based Global Mean Temperature Reconstructions reveal high consistency except for early 20th Century Ocean Cold Anomaly 

Sebastian Sippel, Nicolai Meinshausen, Erich Fischer, Vincent Humphrey, Robert A. Rohde, Iris de Vries, and Reto Knutti

Global mean surface air temperature (GSAT) is a key diagnostic for understanding and constraining historical climate variability and change, and for climate policy. Yet, global temperature estimates (1) are usually based on blending sea surface temperatures (SST) with near-surface air temperature over land (LSAT), and (2) contain many missing values due to incomplete coverage in the historical record. While these issues are usually accounted for in model-observation comparisons, elucidating the consistency of LSAT and SST recordsand their contribution to GSAT variability and change, remains difficult.

Here, we present a set of new GSAT reconstructions based separately on either the historical LSAT or SST record. The method is based on regularized linear regression models trained on climate model simulations to optimally predict GSAT from the climate model’s LSAT or SST patterns, respectively. We then predict GSAT from the HadSST4 and CRUTEM5 observational data, respectively, for each month from January 1850 up to December 2020.

We demonstrate that the land- or ocean based GSAT estimates show very similar variability and long-term changes, both in the early (1850-1900) as well as in the late record (post-1950). For example, GSAT of the past decade (2011-2020) increased by 1.15°C (LSAT-based) and 1.17°C (SST-based) relative to an early reference period (1850-1900), which is both well within IPCC AR6 estimates.However, the GSAT estimates show pronounced disagreement in the early 20th century (1900 up to around 1930), when the SST-based GSAT estimates appear on average around 0.3°C colder than the LSAT-based estimates. Decadal changes in the LSAT-based estimates are well explained by the multi-model mean of CMIP6 simulations driven with historical forcings, thus implying only a small role of unforced decadal global variability. In contrast, the SST-based estimate highlights pronounced variability during the early 20th century cold anomaly, which may be related to concerns about instrumental cold biases in SST measurements, but overall reasons for the disagreement remain unclear. Further analysis based on physical reasoning, climate models, and proxy reconstructions, indicates that the ocean data may indeed be implausibly cold.

In conclusion, our methodology and results may help to constrain the magnitude of early 20th century warming, and thus to better understand and attribute decadal climate variability.

How to cite: Sippel, S., Meinshausen, N., Fischer, E., Humphrey, V., Rohde, R. A., de Vries, I., and Knutti, R.: New Land- vs. Ocean based Global Mean Temperature Reconstructions reveal high consistency except for early 20th Century Ocean Cold Anomaly, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11039, https://doi.org/10.5194/egusphere-egu23-11039, 2023.

EGU23-11519 | ECS | Posters on site | NP3.1

“HespDiv” method allows to quantify the dark matter of biosystems 

Liudas Daumantas and Andrej Spiridonov

Bretskyan hierarchy of eco-evolutionary entities is a useful theoretical concept that defines hierarchies of communities of species which reside in the same geographical space and are tied together by ecological interactions and evolutionary history. Bretskyan hierarchy can be employed to define and track the evolving hierarchy of bioregions, allowing all sorts of (paleo)biogeographical investigations to be carried out: from finding the causes why bioregions split or fuse and how this happens at many spatial scales, ie. what drives the internal structure of bioregions in Bretskyan hierarchy. Unfortunately, methodical applications of this concept are challenging due to the hybrid nature of Bretskyan hierarchy entities and fuzziness of their boundaries.

In order to help solve the presented problem of explicit subdivision of contiguous spatial regions, which are in our understanding the units of the Bretskyan hierarchy, we propose a new method, within the newly developed R package “HespDiv” which presents a range of functionalities for the determination of spatial structures/bioregions. The method uses fossil taxa distribution data to subdivide a provided territory into hierarchically related (each bioregion is a strict sub-set of larger bioregion) and topologically contiguous bioregional units. It produces split-lines which are used to subdivide bioregions. This subdivision can be done by employing linear or nonlinear divisor lines inside predetermined area polygons. In a latter case, the inferred bioregions can obtain more realistic shapes. The application of “HespDiv” method to Miocene fauna from the contiguous United States was performed in order to demonstrate the potential of the method. Morisita-Horn similarity index was used to measure differences between fossil taxa communities. The results revealed 25 distinct, topologically contiguous and hierarchically related bioregions had the structure dominated by longitudinal and diagonal boundaries, and the three most distinct bioregions were: West Coast, Central Plains and south-east US.

 The numerical analyses of real world paleobiogeographical data with newly developed “HespDiv” method indeed show a high potential of the approach in objectively defining the hierarchical units of the Bretskyan hierarchy of (paleo)bioregions in sufficiently densely sampled regions and time bins.

                      Presented research is funded by project S-MIP-21-9 “The role of spatial structuring in major transitions in macroevolution”.

How to cite: Daumantas, L. and Spiridonov, A.: “HespDiv” method allows to quantify the dark matter of biosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11519, https://doi.org/10.5194/egusphere-egu23-11519, 2023.

EGU23-12346 | ECS | Posters on site | NP3.1

Do tree-rings match the low-frequency patterns represented in climate models? 

Mara McPartland, Raphaël Hébert, and Thomas Laepple

Whether tree-rings faithfully archive the low-frequency variability (LFV) in climate remains debated. In theory, trees are fundamentally limited by being relatively short-lived and therefore unable to capture variations in the climate that are longer than their own lifespans. In addition, near universal practices of “detrending” tree-ring records to remove individualistic age-growth trends place further constraints on the amount of LFV that is maintained in final chronologies. Detrending methods designed to boost LFV may increase low-frequency signals, but how well those patterns reflect true variations in climate as opposed to long growth trends is still unclear. In this study, we first compared the spectral properties of the PAGES North America 2k dataset of temperature-sensitive tree-ring records against long temperature records to determine how much variability is retained in tree-rings after detrending, and how detrending method influences agreement in tree-ring power spectra across space. Then, we compare the spectral properties of tree-rings to the CMIP6 last millennium simulation to validate climate models against long proxy records. This research works to resolve discrepancies between temperature proxies and climate models on long timescales in order to improve our understanding of centennial-scale variability in the Earth’s climate system.

How to cite: McPartland, M., Hébert, R., and Laepple, T.: Do tree-rings match the low-frequency patterns represented in climate models?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12346, https://doi.org/10.5194/egusphere-egu23-12346, 2023.

EGU23-13323 | Posters on site | NP3.1

Centennial to millennial climate variability across climate states; proxy reconstructions vs. transient model simulations. 

Andrew M. Dolman, Marie Kapsch, Uwe Mikolajewicz, Lukas Jonkers, and Thomas Laepple

In previous model-data comparisons, the centennial to millennial scale variance of local climate (e.g., SST) reconstructed from proxies was significantly higher than that simulated by climate models. One possible explanation is the lack of long-term feedback mechanisms, e.g. from the representation of changes in ice-sheets in models. Additionally, proxy records are short, and sparse, and the climate signal is significantly modified during the processes of encoding, archiving, and recovery.

Here we introduce new methods to infer the climate variability of the past from proxy data and compare them to new transient model simulations of the last deglaciation. This will allow us to estimate the amplitude of climate variability and to evaluate whether climate models are capable of capturing changes in climate variability between different climate states (e.g. glacial vs. interglacial periods), which is also relevant for the accuracy of future projections. We compare the variability of marine d18O reconstructed from proxies with that simulated by a state-of-the-art Earth System Model.

From the proxy side, our analysis is based on a new dataset of marine oxygen isotope data from planktonic foraminifera compiled for the PALMOD project. We use new methods to first calculate power-spectra for the LGM, transition and Holocene and then to correct these spectra by fitting a Bayesian model describing the effects of bioturbation and measurement error on the reconstructed climate signal. From the model side we use marine d18O variability calculated using temperature and salinity from transient model simulations of the last deglaciation, performed within the PALMOD project, that include changes in the ice sheets.

This combination of new data and methods will allow us to investigate the effect of different ice-sheet configurations and physical parametrizations in the model on their ability to characterise long-timescale climate variability and its dependence on climate state.

How to cite: Dolman, A. M., Kapsch, M., Mikolajewicz, U., Jonkers, L., and Laepple, T.: Centennial to millennial climate variability across climate states; proxy reconstructions vs. transient model simulations., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13323, https://doi.org/10.5194/egusphere-egu23-13323, 2023.

EGU23-14335 | Orals | NP3.1

Multi-annual variability of a new proxy-constrained modeled AMOC from 1450-1780 CE 

Eric Samakinwa, Christoph Riable, Ralf Hand, Andrew Friedman, and Stefan Brönnimann

The ongoing discussion about the AMOC slowdown over the 21st century requires a detailed understanding of preindustrial AMOC variability. Here, we present a surface nudging technique to reconstruct the AMOC variability during the Little Ice Age from 1450–1780 CE. The AMOC reconstruction is based on a 10-member ensemble ocean model simulation nudged to proxy-reconstructed sea surface temperature. This approach validates and improves existing knowledge of the AMOC variability, showing that the AMOC slowdown under stable atmospheric CO2 conditions is mainly driven by a 4 to 7 year lagged effect of surface heat flux associated with the North Atlantic Oscillation.

How to cite: Samakinwa, E., Riable, C., Hand, R., Friedman, A., and Brönnimann, S.: Multi-annual variability of a new proxy-constrained modeled AMOC from 1450-1780 CE, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14335, https://doi.org/10.5194/egusphere-egu23-14335, 2023.

EGU23-15090 | Orals | NP3.1 | Highlight

Heat extremes in scenario projections: the role of variability 

Claudia Simolo and Susanna Corti

Heat extremes have grown disproportionately since the advent of industrialization and are expected to intensify further under unabated greenhouse warming, spreading unevenly across the globe. However, amplification mechanisms are highly uncertain because of the complex interplay between the regional physical responses to human forcing and the statistical properties of atmospheric temperatures. Here, focusing on the latter, we explain how and to what extent the leading moments of daily thermal distributions sway the future trajectories of heat extremes. We show that historical and future temperature variability are the key to understanding the global patterns of change in the frequency and severity of the extremes and their exacerbation over many areas. Variability is crucial to unravel the highly differential regional sensitivities and may well outweigh the background warming. These findings provide fundamental insights for assessing the reliability of climate models and improving their scenario projections.

How to cite: Simolo, C. and Corti, S.: Heat extremes in scenario projections: the role of variability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15090, https://doi.org/10.5194/egusphere-egu23-15090, 2023.

One of the most telling effects of the weather and climate is the occurrence of rare extreme events. As extremes are typically sudden and climate variability is a slower process, it is important to assess how severe changes have become and to aim to understand why. As the climate dynamics of the mean state are altering, can we also establish accurately if there are systematic changes to the extreme temperature process? One main challenge for assessing such climate dynamic alterations across these time scales is how to analyse records across the pre-industrial paleo and instrumental eras of the past 500 years. This analysis focusses on Northern European temperatures and their mean state and extremes changes. The analysis is done using a form of Dominant Frequency State Analysis where the extreme process (modelled as a Generalised Extreme Value process) can be distinguished from the variation of the mean state. The methods used in this approach are generic and can be applied in any study of extremes provided there is data (instrumental, simulated or paleo-proxies) that is of sufficient quality. This work reports how the extreme temperature process properties for Northern Europe appear to have altered across 500 years and I’ll discuss the climate dynamics interpretation of these results.

How to cite: Bruun, J.: Climatic warming changes to Northern European extreme temperature processes over the past 500 years, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15117, https://doi.org/10.5194/egusphere-egu23-15117, 2023.

EGU23-15592 | ECS | Orals | NP3.1

Recurrence analysis of large-scale dynamical properties of terrestrial mammal evolution 

Robertas Stankevič, Simona Bekeraitė, Andrej Spiridonov, and Ivona Juchnevičiūtė

Contrary to ecology, biology and climate science, analysis of nonlinear dynamics in paleontological time series is still relatively uncommon. Palaeontology tend to focus on events such as mass extinctions or radiations over dynamical processes and relationships. However, all parts of the Earth system, including the biota, are interrelated at multiple scales, showing feedback relations and nonlinearity. Nonlinear analysis of global palaeodiversity dynamics and its coupling with abiotic variables could offer a fresh view into a long-running question of the relative importance of biotic and abiotic factors in macroevolution by identifying interactions and responses not amenable to classical methods of time series analysis.
As a part of our inquiry into causal explanation of the drivers of mammal evolution, we present our analysis of the dynamics of Cenozoic land mammal evolution, based on high resolution time series data and methods of recurrence plots and causal inference.
Using PyRate, a Bayesian palaeodiversity analysis framework, we estimate diversification parameters and individual taxon lifetimes of several extinct Paleogene mammal orders and several extant large bodied orders Carnivora, Proboscidea, Artiodactyla and Perissodactyla. We then use recurrence analysis tools developed by the author to investigate dynamics of the evolution of the aforementioned taxons, identifying regime transitions and regions of deterministic and chaotic regimes over multi-million year timescales.
Abrupt changes in species composition are indentified particularly in Perissodactyla recurrence plots. First and the most abrupt change occured at ca. 32 Ma, corresponding to Eocene-Oligocene extinction event. Another prominent change indentified at ca. 17 Ma, corresponding to Middle Miocene disruption. Both concide with changes in δ13C and δ18O isotopic record (Westerhold et al. 2020).
In search of signatures of general synchronisation, we performed joint-recurrence plot analysis between matrices of diversity composition, δ13C isotopic record and δ18O-derived global temperature time series.
Our preliminary results shed light on diversification dynamics of the main terrestrial mammal orders and similarity over time and coupling with the climatic and carbon cycle dynamics of the Earth. We compare them with findings of causal analysis of climate and diversification time series, using the same datasets and transfer-entropy based causal inference tools. The relative degrees of herbivore and carnivore diversity couplings with climate is also discussed.

How to cite: Stankevič, R., Bekeraitė, S., Spiridonov, A., and Juchnevičiūtė, I.: Recurrence analysis of large-scale dynamical properties of terrestrial mammal evolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15592, https://doi.org/10.5194/egusphere-egu23-15592, 2023.

EGU23-17369 | Posters on site | NP3.1

Spatial and temporal variations of the precipitation regime and trend of the last century in mainland Spain 

Jose Carlos Gonzalez-Hidalgo, Victor Trullenque-Blanco, Dhais Peña-Angulo, and Santiago Beguería

The evolution of the seasonal rainfall regime and trend in mainland Spain (western Mediterranean basin) in the period 1916-2015 has been analyzed with the new high spatial resolution grid of the MOPREDAS_century (10 km2) database, included in CLICES project.

Seasonal rainfall regime changes have been analysed in mainland Spain. Comparison of the seasonal precipitation values in four different periods of 25 years in length were done. The spatial distribution of seasonal rainfall highlights a winter regime to the north and west, autumn to the eastern Mediterranean coastland, and spring predominates in between the aforementioned areas, but the analysis shows that the seasonal distribution of precipitation has undergone remarkable changes between 1916 and 2015. During the first 25 years’ period (1916-1940) winter predominates in 44% of grid, increasing to 55% in 1941-1965 and to 60% in 1966-1990 to decrease in the most recent period (1991-2015) to 38%; in the meantime, spring remain around 30% until 1991-2015 when decrease to 16%, and autumn, initially occupying 33%, decrease in the 2nd and 3rth period to increase in the most recent ones to 50% of grid. These changes have been produce by the different behaviour of rainfall trends, particularly in spring (mostly related to march) and autumn (particularly October). Global spatial changes show the substitution of areas of winter regime by spring, and on the other hand spring substitution by autumn regime, but the analyses of detailed period discover a more complex pattern.
In general, the seasonal rainfall trend present changes throughout the analyzed period, with clear decreases in spring and increases in autumn in the final decades. However, the analysis of temporal scale allows us to observe that since the mid-1970s the seasonal precipitation trends are not statistically significant in the study area. The analysis in four periods of 25 years of the seasonal average values shows that the dominant seasonal regime of precipitations has undergone changes. Between 1916-2015, a replacement of the winter and spring regime for the autumn regime has been detected in extensive areas of the central western peninsular. The results are in line with previous analyzes carried out in the whole of the Mediterranean basin and especially in its western sector and suggest that they may be partly related to the variations in time of the NAO and WeMO atmospheric variability modes. Possible effects on natural systems and human activities are discussed, as a step prior to the adoption of mitigating measures.
The objectives and results obtained in the CLICES project are available on the website www.CLICES.unizar.es.

How to cite: Gonzalez-Hidalgo, J. C., Trullenque-Blanco, V., Peña-Angulo, D., and Beguería, S.: Spatial and temporal variations of the precipitation regime and trend of the last century in mainland Spain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17369, https://doi.org/10.5194/egusphere-egu23-17369, 2023.

EGU23-17584 | Orals | NP3.1 | Highlight

Climate change detection and attribution using proxy system models 

Jörg Franke, Mike Evans, Andrew Schurer, and Gabriele Hegerl
Until now, pre-instrumental climate change detection and attribution studies were based on the regression of statistical reconstructions on simulations. This approach is limited by stationarity assumptions and the univariate linear response of the underlying paleoclimatic observations. Here, we present a new procedure, in which we model paleoclimate data observations as a function of paleoclimatic data simulations using a proxy system model. Specifically, we detect and attribute tree-ring width (TRW) observations as a linear function of TRW simulations. These are nonlinear and multivariate TRW simulation driven by climate simulations with single or multiple external forcing. 
 
Temperature- and moisture-sensitive TRW simulations detect distinct patterns in time and space. Northern Hemisphere averages of temperature-sensitive TRW observations and simulations are significantly correlated. We can attribute their variation to volcanic forcing. In decadally smoothed temporal fingerprints, we find the observed responses to be significantly larger and/or more persistent than the simulated responses. The pattern of simulated TRW of moisture-limited trees is consistent with the observed anomalies in the two years following major volcanic eruptions. We can for the first time attribute this spatiotemporal fingerprint in moisture-limited tree-ring records to volcanic forcing. These results suggest that the use of nonlinear and multivariate proxy system models in paleoclimatic detection and attribution studies may permit more realistic, spatially resolved and multivariate fingerprint detection studies and evaluation of the climate sensitivity to external radiative forcing than has previously been possible.

How to cite: Franke, J., Evans, M., Schurer, A., and Hegerl, G.: Climate change detection and attribution using proxy system models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17584, https://doi.org/10.5194/egusphere-egu23-17584, 2023.

Geomorphological mapping is one of the primary research methods used to collect data on glacial landforms and reconstruct glaciological processes. The most common approach is a combination of field-based and remote mapping using data obtained from various sensors. However, one of the crucial methodical problems is collecting remote sensing data in the appropriate spatial resolution for the analyzed landform, which directly affects the data collection time and costs. This study aims to find the optimal resolution of digital elevation models (DEMs) to map subtle glacial landforms: kame terraces, eskers, flutes, and push moraine. Such landforms contain valuable information about the glacial process–form relationships, however, are often too subtle to be recognized on satellite data, and therefore more detailed data (e.g., UAV-based) are required. By “optimal”, we mean the resolution high enough to enable recognition of the landforms mentioned above, and at the same time, as low as possible to minimize the time spent on data collection during the fieldwork.

To find out the optimal resolution, we used detailed (0.02 – 0.04 m ground sampling distance [GSD]) DEMs of the glacier forelands in Iceland (Kvíárjökull, Fjallsjökull and Svinafellsjökull), created based high-resolution images from an unmanned aerial vehicle (UAV). The DEMs were resampled to 0.05, 0.10, 0.15, 0.20, 0.30, 0.40, 0.50, 1.00 and 2.00 m GSD and selected glacial landforms were mapped independently by two operators and cross-checked. The results indicate that 2.0 m resolution is insufficient to properly recognize landforms such as pushed moraines or flutes; however, it can be sufficient to detect kame terraces and major glacifluvial channels. For general mapping of locations of forms such as annual pushed moraines or fluting, the 0.5 m resolution is required. However, to obtain geomorphometric characteristics of the landforms (e.g., height, width, volume) resolution between 0.1 and 0.2 m is necessary. Finer resolution (better than 0.05 m GSD) does not increase the ability to detect landforms or better characterize their geometric properties; however, in some cases might be useful to obtain information about clast characteristics. The experiment proved that decimeter-scale spatial resolution is sufficient for mapping of some geomorphological forms (annual pushed moraines, flutes), which allows for planning UAV missions at a higher elevation above the ground and, therefore, minmizing the duration of field surveys. Moreover, some of the more prominent landforms (e.g., kame terraces, larger moraines) can be successfully detected from aerial or satellite-based DEMs (e.g. freely available ArcticDEM) with a resolution of 2.00 m, the use of which reduces the costs of field research to a minimum.

This research was funded by the National Science Centre, Poland, Grant Number 2019/35/B/ST10/03928.

How to cite: Śledź, S. and Ewertowski, M.: Optimal resolution of UAV-based digital elevation models (DEMs) for mapping of selected subtle glacial landforms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-151, https://doi.org/10.5194/egusphere-egu23-151, 2023.

EGU23-3292 | Posters on site | GI6.1

CO2 concentration and stable isotope surveys in the ambient air of populated areas of La Palma (Canary Islands) by means of mobile Delta Ray measurements using an electrical car 

Nemesio M. Pérez, María Asensio-Ramos, José Barrancos, Eleazar Padrón, Gladys V. Melián, Fátima Rodríguez, Germán D. Padilla, Violeta T. Albertos, Pedro A. Hernández, Antonio J. Álvarez Díaz, Héctor de los Ríos Díaz, David Afonso Falcón, and Juan Cutillas

Anomalous CO2 degassing of volcanic origin was observed by the end of November 2021 in the neighborhoods of La Bombilla and Puerto Naos, located in the western flank of La Palma, about 5 km distance southwestern of the 2021 Tajogaite eruption vents (Hernández et al., 2021). In this study zone, continuous monitoring of CO2 concentration in the outdoors ambient air at 200 cm from the surface has reached a daily average of maximum and mean values about 28,000 and 10,000 ppm, respectively. We started recently to perform CO2 concentration and stable isotope surveys in the outdoors ambient air of Puerto Naos at 140 cm from the surface by means of a Delta Ray analyzer installed in an electrical car which was driving through the streets of Puerto Naos. This instrument is a high performance, mid-infrared laser-based, isotope ratio infrared spectrometer (IRIS) which offers the possibility of performing simultaneous determination of δ13C and δ18O in CO2 at ambient concentrations with a precision as low as 0.05‰. One major advantage of IRIS techniques with respect to more traditional ones (e.g., isotopic ratio mass spectrometry -IRMS-) is the possibility to perform (semi)continuous measurements at high temporal resolution. Since October 2022, seven surveys have been performed at Puerto Naos making up a total of about 600 measurements. The observed CO2 concentrations and the δ13C-CO2 values in the outdoors ambient air ranged from 420 to 3,500 ppm and from -9.0 to -3.2 ‰ vs. VPDB, respectively. Survey data analysis showed a good spatial correlation between relatively high CO2 concentrations with δ13C-CO2 values less 13C-depleted (i.e., volcanic CO2). These observations highlight that stable isotope surveys allow to evaluate the impact of volcanic degassing on the air CO2 concentration and provide valuable results to identify the volcanic CO2 gas hazard zones.

Hernández, P. A., Padrón, E., Melián, G. V., Pérez, N. M., Padilla, G., Asensio-Ramos, M., Di Nardo, D., Barrancos, J., Pacheco, J. M., and Smit, M.: Gas hazard assessment at Puerto Naos and La Bombilla inhabited areas, Cumbre Vieja volcano, La Palma, Canary Islands, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7705, https://doi.org/10.5194/egusphere-egu22-7705, 2022.

How to cite: Pérez, N. M., Asensio-Ramos, M., Barrancos, J., Padrón, E., Melián, G. V., Rodríguez, F., Padilla, G. D., Albertos, V. T., Hernández, P. A., Álvarez Díaz, A. J., de los Ríos Díaz, H., Afonso Falcón, D., and Cutillas, J.: CO2 concentration and stable isotope surveys in the ambient air of populated areas of La Palma (Canary Islands) by means of mobile Delta Ray measurements using an electrical car, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3292, https://doi.org/10.5194/egusphere-egu23-3292, 2023.

EGU23-3620 | ECS | Posters on site | GI6.1

SO2 emissions during the post-eruptive phase of the Tajogaite eruption (La Palma, Canary Islands) by means of ground-based miniDOAS measurements in transverse mode using a car and UAV 

Oscar Rodríguez, José Barrancos, Juan Cutillas, Victor Ortega, Pedro A. Hernández, Iván Cabrera, and Nemesio M. Pérez

Throughout the 85 days that lasted the Tajogaite eruption at Cumbre Vieja volcano (La Palma, Canary Islands, Spain), observations of SO2 emissions were made using ground-based instruments, in transverse mode, static scanners and on-board drones, as well as by numerous satellite instruments. The initial estimates of the total SO2 emission from the eruption were 2.4 Mt from TROPOMI and 1.2 Mt from the traverse data. These measurements formed part of the official monitoring effort, providing insights into the eruption’s evolution and informing the civil defence response throughout the eruption (Hayer C. et al., 2022; Albertos V. T. et al., 2022). Once the Tajogaite eruption was over, we continued performing a SO2 monitoring release to the atmosphere by the Tajogaite volcanic vent since the low ambient concentrations of SO2 make it an ideal volcanic gas monitoring candidate even during the post-eruptive phase. SO2 measurements had been carried out a using a car-mounted and UAV-mounted ground-based miniDOAS measurements throughout this post-eruptive phase. About 80 measurements of SO2 emission rates were performed from December 15, 2021 to December 17, 2022. The standard deviation of the estimated values obtained daily was ~ 20%. The range of estimated SO2 emission values has been from 670 to 17 tons per day, observing a clear decreasing trend of SO2 emissions during the post-eruptive phase. During the first month of the post-eruptive phase, it was observed that the average value of the estimated SO2 emission was about 219 tons/day, while it dropped to 107 tons/day during the second and third month after the end of the Tajogaite eruption. This average value continued decreasing during the fourth month of the post-eruptive phase, about 67 tons/day, and recently measurements provide an average SO2 emission value of 13 tons/day. These relatively low observed SO2 emissions during the post eruptive of the Tajogaite eruption phase seems to be clearly related to shallow magma cooling processes within the Tajogaite volcanic edificie.

Hayer, C., Barrancos, J., Burton, M., Rodríguez, F., Esse, B., Hernández, P., Melián, G., Padrón, E., Asensio-Ramos, M., and Pérez, N.: From up above to down below: Comparison of satellite- and ground-based observations of SO2 emissions from the 2021 eruption of Cumbre Vieja, La Palma, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12201, https://doi.org/10.5194/egusphere-egu22-12201, 2022.

Albertos, V. T., Recio, G., Alonso, M., Amonte, C., Rodríguez, F., Rodríguez, C., Pitti, L., Leal, V., Cervigón, G., González, J., Przeor, M., Santana-León, J. M., Barrancos, J., Hernández, P. A., Padilla, G. D., Melián, G. V., Padrón, E., Asensio-Ramos, M., and Pérez, N. M.: Sulphur dioxide (SO2) emissions by means of miniDOAS measurements during the 2021 eruption of Cumbre Vieja volcano, La Palma, Canary Islands, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5603, https://doi.org/10.5194/egusphere-egu22-5603, 2022.

How to cite: Rodríguez, O., Barrancos, J., Cutillas, J., Ortega, V., Hernández, P. A., Cabrera, I., and Pérez, N. M.: SO2 emissions during the post-eruptive phase of the Tajogaite eruption (La Palma, Canary Islands) by means of ground-based miniDOAS measurements in transverse mode using a car and UAV, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3620, https://doi.org/10.5194/egusphere-egu23-3620, 2023.

EGU23-3819 | Posters virtual | GI6.1

Using tunable diode laser (TDL) system in urban environments to measure anomalous CO2 concentrations: the case of Puerto Naos, La Palma, Canary Islands 

José Barrancos, Germán D. Padilla, Gladys V. Melián, Fátima Rodríguez, María Asensio-Ramos, Eleazar Padrón, Pedro A. Hernández, Jon Vilches Sarasate, and Nemesio M. Pérez

Carbon dioxide (CO2) is a colourless and odourless gas. It is non-flammable, chemically non-reactive and 1.5 times as heavy as air; therefore, may accumulate at low elevations. CO2 is a toxic gas at high concentration, as well as an asphyxiant gas (due to reduction in oxygen). Irritation of the eyes, nose and throat occurs only at high concentrations. Since the Tajogaite eruption ended on December 13, 2021, high concentrations of CO2 up to 20% (200.000 ppmv) have been observed inside of buildings of the neighborhoods of La Bombilla and Puerto Naos (La Palma, Canary Islands), which are located about 5 km distance from the Tajogaite eruption vent. Anomalous concentrations of CO2 are manily detected in the ground-floor and basement of the buildings in Puerto Naos, and the distribution of relatively high CO2 concentrations  is not homogeneous or uniform throughout the Puerto Naos area (Hernández P.A. et al, 2022).

The purpose of this study was to use the Tunable Laser Diode (TDL) absorption spectroscopy method to monitor the indoor CO2 concentration of the ground-floor of one of the buildings of Puerto Naos. A CO2-TDL was installed on 9 January 2022 and continues measuring the CO2 concentration along an optical path of about 6 meters. During the period January-March 2022, daily averages of CO2 concentrations from fifteen-minute data ranged from 5000 to 25000 ppmv reaching values up to 40000 ppmv (4%). Over time, a clear decreasing trend of the indoor CO2 concentration has been observed at this observation site and the daily CO2 averages from fifteen-minute data during the last 3 months (October-December 2022) ranged from 1000 to 2500 ppmv. This clear decreasing trend over time has not been observed at other observation sites where the concentration of CO2 inside buildings is being monitored. This observation indicates the complexity of the problem and the need to install a dense network of sensors to monitor CO2 for civil protection purposes.

 

Hernández, P. A., Padrón, E., Melián, G. V., Pérez, N. M., Padilla, G., Asensio-Ramos, M., Di Nardo, D., Barrancos, J., Pacheco, J. M., and Smit, M.: Gas hazard assessment at Puerto Naos and La Bombilla inhabited areas, Cumbre Vieja volcano, La Palma, Canary Islands, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7705, https://doi.org/10.5194/egusphere-egu22-7705, 2022.

How to cite: Barrancos, J., Padilla, G. D., Melián, G. V., Rodríguez, F., Asensio-Ramos, M., Padrón, E., Hernández, P. A., Vilches Sarasate, J., and Pérez, N. M.: Using tunable diode laser (TDL) system in urban environments to measure anomalous CO2 concentrations: the case of Puerto Naos, La Palma, Canary Islands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3819, https://doi.org/10.5194/egusphere-egu23-3819, 2023.

EGU23-3834 | Posters on site | GI6.1

Modeling outdoor dispersion of CO2 at Puerto Naos (La Palma, Canary Islands) 

Luca D Auria, Alba Santos, Pedro A. Hernández, Gladys V. Melián, Antonio J. Álvarez Díaz, María Asensio-Ramos, Alexis M. González Pérez, and Nemesio M. Pérez

The 2021 Tajogaite eruption in Cumbre Vieja volcano (La Palma, Canary Islands), which started on Sep. 19, 2021, and lasted 85 days, caused extensive damages because of the lava flows and ash fall. However, since the middle of Nov. 2021, some areas located about 5 km SW of the eruptive center started to be affected by intense diffuse CO2 emission. Among them are the urban centers of La Bombilla and Puerto Naos (Hernández et al., 2022). These emissions prevented the population of these two centers from returning to their houses because of high  concentrations of CO2 in indoor and outdoor environments.

In this work, we model the CO2 dispersion process in Puerto Naos to obtain hazard maps with the maximum CO2 concentrations which can be reached in the town in the outdoor environment. To achieve these results, we combined field observations with numerical modelling. Field surveys were realized in low wind conditions, measuring the CO2 concentration with portable sensors  at 15 and 150 cm from the ground at measurement points spaced approximately 10 m from each other along the streets of Puerto Naos.

We realized numerical modelling using the software TWODEE-2, a code for modeling the dispersion of heavy gases based on the solution of shallow water equations (Folch et al., 2009). For this purpose, we used a detailed digital topographic model, including the edifices of Puerto Naos. Using a trial-and-error approach, we determined the gas emission rates from a set of discrete source points in no-wind conditions. Subsequently, we repeated the numerical modelling, keeping the same sources and simulating all the realistic wind conditions in terms of direction and intensity. For each simulation, we determined the maximum CO2 concentration at different elevations from the ground. This allowed obtaining a hazard map with the maximum CO2 outdoor concentrations for each part of the town

The main results highlight that the outdoor environment is affected by a dense layer of CO2, whose flow is strongly conditioned by the urban infrastructures. Furthermore, we evidenced how even light winds can change the gas concentration pattern radically in a few minutes, evidencing the possibility of sudden changes in the CO2 concentration outdoors with no warning.

Folch A., Costa A., Hankin R.K.S., 2009. TWODEE-2: A shallow layer model for dense gas dispersion on complex topography, Comput. Geosci., doi:10.1016/j.cageo.2007.12.017

Hernández, P. A., Padrón, E., Melián, G. V., Pérez, N. M., Padilla, G., Asensio-Ramos, M., Di Nardo, D., Barrancos, J., Pacheco, J. M., and Smit, M.: Gas hazard assessment at Puerto Naos and La Bombilla inhabited areas, Cumbre Vieja volcano, La Palma, Canary Islands, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7705, https://doi.org/10.5194/egusphere-egu22-7705, 2022.

How to cite: D Auria, L., Santos, A., Hernández, P. A., Melián, G. V., Álvarez Díaz, A. J., Asensio-Ramos, M., González Pérez, A. M., and Pérez, N. M.: Modeling outdoor dispersion of CO2 at Puerto Naos (La Palma, Canary Islands), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3834, https://doi.org/10.5194/egusphere-egu23-3834, 2023.

EGU23-5223 | Orals | GI6.1

Event-oriented observation across scales and environmental systems: MOSES started operation. 

Ute Weber and Claudia Schuetze and the MOSES-Team

The novel observing system „Modular Observation Solutions for Earth Systems (MOSES)“, is an initiative of the Helmholtz Association of German Research Centers that aims at investigating the interactions of short-term events and long-term trends across environmental systems. MOSES is a mobile and modular infrastructure and its component measuring systems are managed by the participating research centers. By quantifying energy, water, nutrient and greenhouse gas states and fluxes during events such as heat waves, droughts, heavy precipitation, floods, rapid thaw of permafrost or of ocean eddies, and subsequently along the related event chains, the system delivers data to examine potential long-term impacts of these events and to gain a better understanding of extreme events that are expected to increase in frequency and intensity in a changing climate. In order to obtain comprehensive data sets, a cross-system approach is followed, covering the atmosphere, land surface and hydrosphere. These event-related data sets complement long-term and/or large scale data sets of established national and international monitoring programs and satellite data such as TERENO, ICOS, eLTER, SENTINEL, etc. After a 5-year setup period, MOSES was successfully put into operation in 2022 (Weber et al., 2022, https://doi.org/10.1175/BAMS-D-20-0158.1).

While long-term trends are typically assessed with stationary observation networks and platforms specifically designed for long-term monitoring, proven event-oriented observation systems and strategies are still missing. Event-oriented observation campaigns require a combination of a) measuring systems that can be rapidly deployed at “hot spots” and in “hot moments”, b) mobile equipment to monitor spatial dynamics in high-resolution, c) in situ measuring systems to record temporal dynamics in high-resolution, and d) interoperable measuring systems to monitor the interactions between atmosphere, land surface and hydrosphere. We will present the observation system and the observing strategy on examples from two past test campaigns: 1) The “Swabian MOSES campaign” of 2021 that captured the formation and evolution of supercells, hail and heavy precipitation and the resulting local flash floods (Kunz et al., 2022, https://doi.org/10.3389/feart.2022.999593). 2) The MOSES campaign of 2019 that captured the historical low flow situation along the Elbe River and into the German Bight (e.g., Kamjunke et al., 2021, https://doi.org/10.1002/lno.11778). As an outlook, upcoming national and international campaigns and potential future deployments will be presented.

How to cite: Weber, U. and Schuetze, C. and the MOSES-Team: Event-oriented observation across scales and environmental systems: MOSES started operation., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5223, https://doi.org/10.5194/egusphere-egu23-5223, 2023.

EGU23-5684 | ECS | Posters on site | GI6.1

Random Forest Classification of Proterozoic and Paleozoic rock types of Tsagaan-uul area, Mongolia 

Munkhsuren Badrakh, Narantsetseg Tserendash, Erdenejargal Choindonjamts, and Gáspár Albert

The Tsagaan-uul area of the Khatanbulag ancient massif in the Central Asian Orogenic Belt is located in the southern part of Mongolia, which belongs to the Gobi Desert. It has a low vegetation cover, and because of this, remotely sensed data can be used without difficulty for geological investigations. Factors such as sparse population and underdeveloped infrastructure in the region further create a need for combining traditional geological mapping with remote sensing technologies. In existing geology maps of the area, the formations are lithologically very diverse and their boundaries were mapped variously, so a need for a more precise lithology-based map arouse.

This study investigated combinations of fieldwork, multispectral data, and petrography for the rock type classification. A random forest classification method using multispectral Sentinel-2A data was employed in order to distinguish different rocks within Proterozoic Khulstai (NP1hl) metamorphic complex, which is dominated by gneiss, andesite, sandstone, limestone, amphibolite, as well as the Silurian terrigenous-carbonate Khukh morit (S1hm) formation, Tsagaan-uul area. Based on the ground samples collected from field surveys, ten kinds of rock units plus Quaternary sediments were chosen as training areas. In addition, morphometric parameters derived from SRTM data and band ratios used for iron-bearing minerals from Sentinel 2 bands are selected as variables in the accuracy of classification. The result showed that gneisses were recognized with the highest accuracy in the Khulstai complex, and limestones and Quaternary sediments were also well predicted. Moreover, the tectonic pattern was also well recognized from the results and compared to the existing maps provided a more detailed geological image of the area. This study emphasized the need for samples as baseline data to improve the machine learning methods, and the method provides an appropriate basis for fieldwork.

 

How to cite: Badrakh, M., Tserendash, N., Choindonjamts, E., and Albert, G.: Random Forest Classification of Proterozoic and Paleozoic rock types of Tsagaan-uul area, Mongolia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5684, https://doi.org/10.5194/egusphere-egu23-5684, 2023.

EGU23-5689 | Posters on site | GI6.1

Post-earthquake geoenvironmental changes in landslide-affected watersheds in Atsuma, Hokkaido (Japan) 

Yuichi S. Hayakawa, Tennyson Lo, Azim Zulhilmi, Xinyue Yu, and Xiaoxiao Wang

Following drastic changes in geoenvironmental components by coseismic landslides in mountainous watersheds, more gradual changes can be observed in the elements, including bare-land surface conditions, sediment connectivity, and vegetation recovery on sloping terrains. Such geoenvironmental changes may continue for years to decades, with complex interrelationships among various geomorphological and ecological factors. Their assessments are also crucial for local to regional environmental management. After the occurrence of numerous coseismic landslides triggered by the 2018 Hokkaido Eastern Iburi Earthquake in northern Japan, geomorphological and geoecological changes were explored using optical and laser sensors on uncrewed aerial systems. Morphological characteristics of the landslide-affected slopes in the watersheds were assessed with structure-from-motion multi-view stereo photogrammetry and light detection and ranging topographic datasets, while vegetation recovery on the slopes was examined with visible-light and near-infrared images. Although spatial relationships among morphological developments, sediment mobility, and vegetation recovery were not clearly observed, their general temporal trends may be correspondent. Dominant processes affecting the morphological developments are supposed to be frost heave in the cold climate and non-frequent high-intensity rainfalls, and these can be conditioning vegetation growth. Such local changes will be further examined on a wider, regional scale. 

How to cite: Hayakawa, Y. S., Lo, T., Zulhilmi, A., Yu, X., and Wang, X.: Post-earthquake geoenvironmental changes in landslide-affected watersheds in Atsuma, Hokkaido (Japan), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5689, https://doi.org/10.5194/egusphere-egu23-5689, 2023.

EGU23-5750 | Posters on site | GI6.1

Aseismic creep and coseismic dislocation at an active fault in volcanic area: the case of Ischia Island 

Stefano Carlino, Nicola Alessandro Pino, Lisa Beccaro, and Prospero De Martino

Understanding the fault dynamics in volcanic areas is not a simple task, mainly due to both the heterogeneity of volcanic structures and the local stress distribution. The presence of high temperature-high pressure geothermal fluids and relative high strain rates, and the occurrence of viscous processes in the deeper part of the volcano further contribute to generate complex patterns of strain load and release, possibly with aseismic creep and differential movements along the faults.

We present the case of an active fault located Casamicciola Terme town – in northern area of the volcanic caldera of Ischia Island (Southern Italy) – where repeated destructive earthquakes occurred at least since 1769, even causing thousands of victims in a single event, with the last one striking in 2017. To assess a possible mechanism leading to the activation of the Ischia main seismogenic fault, its cyclic nature and the related hazard, we performed a joined analysis of the ground vertical movements, obtained from cGPS (2001-present), DInSAR (2015-2018) time-series, and levelling data of the island (1987-2010). The geodetic data indicate that Casamicciola seismogenic fault is characterized by a complex dynamic, with some pre- and post-seismic aseismic dislocation, along sectors that move differentially, in response to the long-term subsidence of the island. Based on the ground deformation rate and on the distribution of degassing areas, we speculate that fluid pressure variations may have a major role in modulating the apparent non-stationarity of the Ischia stronger earthquakes. Furthermore, we suggest that a punctual monitoring of the distribution in space and time of the aseismic creep could provide clues on the state of strain of the seismogenic fault.

How to cite: Carlino, S., Pino, N. A., Beccaro, L., and De Martino, P.: Aseismic creep and coseismic dislocation at an active fault in volcanic area: the case of Ischia Island, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5750, https://doi.org/10.5194/egusphere-egu23-5750, 2023.

EGU23-6832 | ECS | Orals | GI6.1

Quantifying karstic geomorphologies using Minkowski tensors and graph theory: Applications to SLAM Lidar data from carbonate caves in Northern Bavaria (Germany) 

Rahul Prabhakaran, Ruaridh Smith, Daniel Koehn, Pierre-Olivier Bruna, and Giovanni Bertotti

Karstification is a ubiquitous feature in carbonate rocks. The origins can be hypogenic or epigenic based on the source of the reacting fluids. The presence of karstified lithologies and their spatial heterogeneity poses a major risk in subsurface energy utilization goals (hydrocarbons, geothermal etc). Such dissolution features tend to organize as spatial networks, with their evolution controlled by a complex interplay of several factors, including natural mineralogical variations in host rocks, effects of pre-existing structures, directional history of palaeo-flow paths, and competition between convective transport and dissolution. Accurate quantification of the spatial distribution of karst is difficult owing to resolution issues in 3D data such as seismic and ground penetrating radar. Recent advances in Simultaneous Location and Mapping (SLAM) Lidar technology have made possible to acquire karst cave passage geometries at very high-resolution with relative ease compared to conventional terrestrial lidar. In this contribution, we present a unique dataset of more than 80 caves, scanned using SLAM lidar, in Jurassic carbonates from northern Bavaria, Germany. We introduce a methodology for robustly deriving morphometrics of karstic caves using Minkowski tensors and spatial graph theory. The method is based on a combination representation of cave passage skeletons as spatial graphs and 2D passage cross-sections using Minkowski functionals. The enriched topological representation enables detailed analysis of internal spatial variation within a single cave and also comparison with cave geometries from other caves. We derive a typology of cave systems based on the degree of structural control on karstification using the database.

How to cite: Prabhakaran, R., Smith, R., Koehn, D., Bruna, P.-O., and Bertotti, G.: Quantifying karstic geomorphologies using Minkowski tensors and graph theory: Applications to SLAM Lidar data from carbonate caves in Northern Bavaria (Germany), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6832, https://doi.org/10.5194/egusphere-egu23-6832, 2023.

EGU23-7265 | Posters on site | GI6.1

Low Power, Rugged Edge Computing provides a low cost, powerful solution for on the ground remote sensing in extreme environments 

Nicholas Frearson, Terry Plank, Einat Lev, LingLing Dong, and Conor Bacon

Ground based remote sensing devices increasingly incorporate low cost single board computers such as a Raspberry Pi to capture and analyze images and data from the environment. Useful and cheap as these devices are, they are not designed for use in extreme conditions and as a consequence often suffer from early failure. Here we describe a system that incorporates a commercially available rugged Edge Computer running embedded Linux that is designed to operate in remote and extreme environments. The AVERT system  (Anticipating Volcanic Eruptions in  Real Time) developed at Columbia University in New York and funded by the Moore Foundation uses solar and wind powered Sensor nodes configured in a spoke and hub architecture currently operating on two volcanoes overseen by the Alaska Volcano Observatory in the Aleutian Islands, Alaska. Multiple Nodes distributed around the volcanoes are each controlled by an Edge Computer which manages and monitors local sensors, processes and parses their data via radio link to a central Hub and schedules system components to wake and sleep to conserve power. The Hub Edge Computer collects and assembles data from multiple Nodes and passes it via satellite, cellular modem or radio links to servers located elsewhere in the world or cloud for near real-time analysis. The local computer enables us to minimize local power demand to just a few watts in part due to the extremely low power sleep modes that are incorporated into these devices. For instance, a Node incorporating a webcam, IRCam, weather station, Edge Computer, network switch, communications radio and power management relays draws only 4.5W on average. In addition, this level of local computing power and a mature Linux operating environment enables us to run AI algorithms at source that process image and other data to flag precursory indicators of an impending eruption. This also helps to reduce data volume passed across the network at times of low network connectivity. We can also remotely interrogate any part of the system and implement new data schemes to best monitor and react to ongoing events. Future work on the AI algorithm development will incorporate local multisensor data analytics to enhance our anticipatory capability.

How to cite: Frearson, N., Plank, T., Lev, E., Dong, L., and Bacon, C.: Low Power, Rugged Edge Computing provides a low cost, powerful solution for on the ground remote sensing in extreme environments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7265, https://doi.org/10.5194/egusphere-egu23-7265, 2023.

EGU23-8673 | Orals | GI6.1

Are they radon or random signals? Analysis of time series of 222Rn activity concentrations in populated areas of La Palma (Canary Islands, Spain) 

Antonio Eff-Darwich, Germán D. Padilla, José Barrancos, José A. Rodríguez-Losada, Pedro A. Hernández, Nemesio M. Pérez, Antonio J. Álvarez Díaz, Alexis M. González Pérez, Jesús García, José M. Santana, and Eleazar Padrón

Radon, 222Rn, is a radioactive constituent of the surface layer of the atmosphere. The analysis of the temporal and spatial variations in the flux of radon across the soil–air interface is a promising tool to study geo-dynamical processes. However, many of these variations are induced by external variables, such as temperature, barometric pressure, rainfall, or the location of the instrumentation, among others.

Anomalous CO2 degassing has been observed since the end of November 2021 in the neighborhoods of La Bombilla and Puerto Naos, located in the western flank of La Palma, about 5 km distance southwestern of the 2021 Tajogaite eruption vents (Hernández et al. 2022). In order to complement these observations with other independent parameters, a set of radon monitoring stations have been deployed in that area. In an attempt to filter out non-endogenous variations in the radon signal, we have implemented time-series numerical filtering techniques based on multi-variate and frequency domain analysis. A background level for radon emissions at various locations could therefore be defined, by which correlations between radon concentration, gaseous emissions and dynamical processes could be carried out. Some preliminary results corresponding to the first 3 months of data (october-december 2022) are presented.

Hernández, P. A., Padrón, E., Melián, G. V., Pérez, N. M., Padilla, G., Asensio-Ramos, M., Di Nardo, D., Barrancos, J., Pacheco, J. M., and Smit, M.: Gas hazard assessment at Puerto Naos and La Bombilla inhabited areas, Cumbre Vieja volcano, La Palma, Canary Islands, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7705, https://doi.org/10.5194/egusphere-egu22-7705, 2022.

How to cite: Eff-Darwich, A., Padilla, G. D., Barrancos, J., Rodríguez-Losada, J. A., Hernández, P. A., Pérez, N. M., Álvarez Díaz, A. J., González Pérez, A. M., García, J., Santana, J. M., and Padrón, E.: Are they radon or random signals? Analysis of time series of 222Rn activity concentrations in populated areas of La Palma (Canary Islands, Spain), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8673, https://doi.org/10.5194/egusphere-egu23-8673, 2023.

EGU23-8795 | ECS | Orals | GI6.1

Integration of Seismic and Quasi-Static Signals for Improved Volcanic Monitoring 

Joe Carthy, Alejandra Vásquez Castillo, Manuel Titos, Luciano Zuccarello, Flavio Cannavò, and M. Carmen Benitez

The time scale of ground displacement at volcanoes varies between short, sub second seismic events, to days, months or even years. This study is focused on data from seismic and GNSS stations located around Mount Etna. The GNSS and seismic stations operate at different time scales. Data from these different time scales is extracted and combined in order to better understand the subsurface dynamics. The overall aim of this research is to improve volcanic forecasting and monitoring. It does this in a novel way by applying signal processing and machine learning techniques to the rich dataset.

Mount Etna offers an interesting case study as it is a widely monitored volcano with a variety of sensors and with a rich pool of data to analyse. Additionally the volcanic dynamics at Mount Etna are complex. This is a volcano where there is a variety of different sub-surface dynamics due to the movement of both deep and shallow magma. This allows for rich insights to be drawn through the combination of different signal types.

This study looks at combining the information obtained from the seismic array at Mount Etna, with the information obtained from various GNSS stations on the volcano. The seismic array has been able to capture ground velocity data in the frequency range 0.025 Hz to 50 Hz from a range of stations at different locations across the volcano. The GNSS stations measure ground displacement with a sampling frequency of 1 Hz, and they allow for longer term ground dynamic analysis.

We analyse different seismic events, and relate the type and number of the seismic events to the long term ground deformation that we see in the recorded GNSS data. Where links between the two signal types have been identified, research is ongoing to establish a direct connection with known volcanic activity on Mount Etna. This will help establish what the relationship that we are seeing signifies. This integration of data from different types of sensors is a significant step into bridging the gap between seismic and quasi-static ground displacement at active volcanoes and should open the path toward more in depth volcanic monitoring and forecasting.

How to cite: Carthy, J., Vásquez Castillo, A., Titos, M., Zuccarello, L., Cannavò, F., and Benitez, M. C.: Integration of Seismic and Quasi-Static Signals for Improved Volcanic Monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8795, https://doi.org/10.5194/egusphere-egu23-8795, 2023.

EGU23-10069 | ECS | Orals | GI6.1

Vredefort impact site modelling through inhomogeneous depth weighted inversion. 

Andrea Vitale and Maurizio Fedi

We are showing an application of the 3D self-constrained depth weighted inversion of the inhomogeneous gravity field (Vitale and Fedi, 2020) of the Vredefort impact site.

This method is based on two steps, the first being the search in the 3D domain of the homogenous degree of the field, and the second being the inversion of the data using a power-law weighting function with a 3D variable exponent. It does not involve directly data at different altitudes, but it is heavily conditioned by a multiscale search of the homogeneity degree.

The main difference between this inversion approach and the one proposed by Li and Oldenburg algorithm (1996) and Cella and Fedi (2012) is therefore about the depth weighting function, whose exponent is a constant through the whole space in the original Li and Oldenburg and Cella and Fedi approaches, while it is a 3D function in the method which we will discuss here.

The model volume of the area reaches 20 km in depth, while along x and y its extension is respectively 41 by 63 km. The trend at low and middle altitudes of the estimated β related to the main structures is fitting the expectations because the results relate to two main structures, which are geometrically different: the core is like a spheroid body (β ≈ 3) and the distal rings are like horizontal pipes or dykes (1 < β < 2).

With a homogeneous depth weighting function, we recover a smooth solution and both the main sources, the main core and the rings of the impact, are still visible at the bottom of the model (20 km). This is not in agreement with the result by Henkel and Reimold (1996, 1998), which, based on gravity and magnetic inversion supported by seismic data, proposed a model where the bottom of the rings is around 10 km and the density contrast effect due to the core structure loses its effectiveness around 15 km.

Instead, using an inhomogeneous depth weighting function (figure 28) we can retrieve information regarding the position at depth of both core and distal ring structures that better fits the above model. In fact, the bottom of the distal ring structure, that should be around 10 km according to Henkel and Reimold (1996, 1998), is recovered very well using an inhomogeneous depth weighting function, while in the homogeneous case we saw that the interpreted structure was still visible at large depths.

In addition, also the core structure is shallower compared to the homogeneous approach and seems more reliable if we compare it with the model of Henkel and Reimold (1996, 1998).

Instead, the inhomogeneous approach presented in this paper leads naturally us to a better solution because it takes into account during the same inversion process of the inhomogeneous nature of the structural index within the entire domain.

How to cite: Vitale, A. and Fedi, M.: Vredefort impact site modelling through inhomogeneous depth weighted inversion., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10069, https://doi.org/10.5194/egusphere-egu23-10069, 2023.

EGU23-11065 | ECS | Posters on site | GI6.1

The Dynamics of Climate Change Science and Policy in Panama: A Review 

Gustavo Cárdenas-Castillero, Steve Paton, Rodrigo Noriega, and Adriana Calderón

The local studies and reports indicate that the temperature of Panama has increased by approximately 1°C since the 1970s. More evidence shows a constantly rising sea level in the Guna Yala archipelago, coral bleaching on both coasts, and increasingly more frequent and extreme precipitation events throughout Panama. This study includes an analysis of over 400 scientific publications made by researchers from multiple centres and more than 20 Panamanian official reports due to Panama's mandate and duties under the international climate accords. To summarise the results, the studies were gathered according to the climate change effects by Panamanian locations and analysed posteriorly using Rstudio and ArcMAP. The results indicate a significant increase in climate change research beginning in 2007.

This study identified and examined the essential findings per hydroclimatic region, showing the trends, limitations, collaborations, and international contributions. Climate change research in Panama includes some of the longest-term meteorological, hydrological, oceanographic, and biological studies in the neotropics. The most significant number of identified climate change-related studies were conducted, at least in part, in the Barro Colorado Natural Monument located in central Panama. Other frequently used sites include Metropolitan Natural Park, Soberania Park, the Panama Canal Watershed and the Caribbean coast of Colón and Bocas del Toro, primarily due to research conducted by Smithsonian Tropical Research-affiliated investigators. The tropical forests of Panama are some of the bests studied in the world; however, research has been concentrated in a relatively small number of locations and should be expanded to include additional areas to achieve a more complete and comprehensive understanding of climate change will impact Panama in the future.

How to cite: Cárdenas-Castillero, G., Paton, S., Noriega, R., and Calderón, A.: The Dynamics of Climate Change Science and Policy in Panama: A Review, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11065, https://doi.org/10.5194/egusphere-egu23-11065, 2023.

EGU23-12050 | Orals | GI6.1

Stress field analysis from induced earthquakes caused by deep fluid injection: the 2013 St. Gallen (Switzerland) seismic sequence. 

Bruno Massa, Guido Maria Adinolfi, Vincenzo Convertito, and Raffaella De Matteis

The city of St. Gallen is located in the Molasse Basin of northeast Switzerland. Mesozoic units of the substratum are affected by a fault system hosting a hydrothermal reservoir. In 2013 a deep geothermal drilling project started in an area close to the city. During a phase of reservoir stimulation, a sequence of more than 340 earthquakes was induced with a maximum magnitude ML 3.5. Stress inversion of seismological datasets became an essential tool to retrieve the stress field of active tectonics areas. With this aim, a dataset of the best constrained Fault Plane Solutions (FPSs) was processed in order to qualitatively retrieve stress-fields active in the investigated volume. FPSs were obtained by jointly inverting the long-period spectral-level P/S ratios and the P-wave polarities following a Bayesian approach (BISTROP). Data were preliminarily processed by the Multiple Inverse Method to evaluate the possible dataset heterogeneity and separate homogeneous FPS populations. The resulting dataset was then processed using the Bayesian Right Trihedra Method (BRTM). Considering that hypocentral depths range between 4.1 and 4.6 km b.s.l., in order to emphasize depth-related stresses, we performed a first step of raw stress inversion procedure splitting the data into five subsets, grouping events located inside 100-m depth ranges. Once the presence of stress variations with depth has been excluded, the second step of fine stress inversion procedure was performed on the entire dataset. The stress-inversion procedure highlights an active stress field dominated by a well-constrained NE low-plunging σ3 and a corresponding NW low-plunging σ1. The corresponding Bishop ratio confirms the stability of the retrieved attitudes. Results are in good accordance with the regional stress field derived from regional natural seismicity. Additionally, the retrieved, dominant, stress field is coherent with the regional tectonic setting.

This research has been supported by PRIN-2017 MATISSE project (No. 20177EPPN2).

How to cite: Massa, B., Adinolfi, G. M., Convertito, V., and De Matteis, R.: Stress field analysis from induced earthquakes caused by deep fluid injection: the 2013 St. Gallen (Switzerland) seismic sequence., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12050, https://doi.org/10.5194/egusphere-egu23-12050, 2023.

EGU23-13693 | ECS | Orals | GI6.1

Assessing the transfer factors (TFs) of contaminants from soil to plants: the case study of Campania region (Southern Italy) 

Lucia Rita Pacifico, Annalise Guarino, Gianfranco Brambilla, Antonio Pizzolante, and Stefano Albanese

The presence of potentially toxic elements (PTEs) derived from anthropogenic sources in soil represents a serious issue for animal and human health. These elements can easily move from the geological compartment to the biological compartment through to the food chain. (Jarup, 2003).

The geochemical knowledge of a territory allows to assess the degree of contamination of the environment, to locate the sources of environmental hazard and, possibly, to manage the anomalous concentrations of the PTEs in environmental matrices with the purpose of eliminating or minimizing their negative impact on the health of living beings. (Reimann et al. 2005).

Several studies have been already carried out to determine the distribution patterns of PTEs in the soil of Campania region (Southern Italy) (De Vivo et al., 2022) but little is known about the transfer processes of contaminants from soils to agricultural products.

In light of above, we present the results of a new study whose purpose was to determine the Transfer Factors (TFs) of PTEs from soil to a series of agricultural products commonly grown in Campania.

Considering the complex geological and geomorphological settings of the region and the diffuse presence of an historical anthropization related to the industry, agriculture, and urbanization, TFs were calculated for a relevant number of fruit and vegetable samples (3731 specimens). They were collected across the whole regional territory to detect differences between analysed species and to highlight the spatial changes in TFs occurring for individual species.

The TFs were calculated starting from the quasi-total (based on Aqua Regia leaching) and bioavailable (based on Ammonium Nitrate leaching) concentrations of PTEs in 7000 and 1500 soil samples, respectively.

Preliminary results show that TFs determined for the various agricultural species vary in space and in amount independently from the original elemental concentrations in soils. High values of TFs are found in areas where PTE concentrations in soil are low and vice versa, thus suggesting that multiple regression and multivariate analyses could be performed to investigate if some additional chemical and physical characteristics of soil (pH, grainsize, OM, etc.) could have a relevant weight on the transfer processes of contaminant from the soil to the plant life.

 

References

Järup L. 2003. Hazards of heavy metal contamination. Br. Med. Bull. 68, 167–182.

Reimann C., de Caritat P. 2005. Distinguishing between natural and anthropogenic sources for elements in the environment: regional geochemical surveys versus enrichment factors. Science of The Total Environment, Volume 337, Issues 1–3, pages 91-107.

De Vivo B. et al. 2022. Monitoraggio geochimico-ambientale dei suoli e dell'aria della Regione Campania. Piano Campania trasparente. Volume 4. Aracne Editore, Genzano di Roma.

How to cite: Pacifico, L. R., Guarino, A., Brambilla, G., Pizzolante, A., and Albanese, S.: Assessing the transfer factors (TFs) of contaminants from soil to plants: the case study of Campania region (Southern Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13693, https://doi.org/10.5194/egusphere-egu23-13693, 2023.

EGU23-13853 | Posters on site | GI6.1

Analysis and Modelling of 2009-2013 vs. 2019-2022 Unrest Episodes at Campi Flegrei Caldera 

Raffaele Castaldo, Andrea Barone, De Novellis Vincenzo, Pepe Antonio, Pepe Susi, Solaro Giuseppe, Tizzani Pietro, and Tramelli Anna

Geodetic modelling is a significant procedure for detecting and characterizing unrest and eruption episodes and it represents a valuable tool to infer volume and geometry of volcanic source system.

In this study, we analyse the 2009–2013 and the ongoing 2019-2022 uplift phenomena at Campi Flegrei (CF) caldera in terms of spatial and temporal variations of the stress/strain field. In particular, we investigate the characteristics of the inflating sources responsible of these main deformation unrests occurred in the last twenty years. We separately perform for the two considered periods a 3D stationary Finite Element (FE) modelling of geodetic datasets to retrieve the geometry and location of the deformation sources. The geometry of FE domain takes into account both the topography and the bathymetry of the whole caldera. For what concern the definition of domain elastic parameters, we take into account the Vp/Vs distribution from seismic tomography. In order to optimize the nine model parameters (center coordinates, sferoid axes, dip, strike and over-pressure), we use the statistical random sampling Monte Carlo method by exploiting both geodetic datasets: the DInSAR measurements obtained from the processing of COSMO-SkyMed and Sentinel-1 satellite images. The modelling results for the two analysed period are compared revealing that the best-fit source is a three-axis oblate spheroid ~3.5 km deep, similar to a sill-like body. Furthermore, in order to verify the reliability of the geometry model results, we calculate the Total Horizontal Derivative (THD) of the vertical velocity component and compare it with those performed directly on the two DInSAR dataset.

Finally, we compare the modelled shear stress with the natural seismicity recorded during the 2000-2022 period, highlighting high values of modelled shear stress at depths of about 3.5 km, where high-magnitude earthquakes nucleate.

How to cite: Castaldo, R., Barone, A., Vincenzo, D. N., Antonio, P., Susi, P., Giuseppe, S., Pietro, T., and Anna, T.: Analysis and Modelling of 2009-2013 vs. 2019-2022 Unrest Episodes at Campi Flegrei Caldera, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13853, https://doi.org/10.5194/egusphere-egu23-13853, 2023.

EGU23-15127 | ECS | Orals | GI6.1

Multiscale magnetic modelling in the ancient abbey of San Pietro in Crapolla 

Luigi Bianco, Maurizio Fedi, and Mauro La Manna

We present a multiscale analysis of magnetic data in the archaeological site of San Pietro in Crapolla (Massa Lubrense, near Naples, Italy). The site consists of the ruins of an ancient abbey. We computed the Wavelet Transform of the Gradiometric measurements and decomposed the data at different scales and positions by a multiresolution analysis, allowing an effective extraction of local anomalies. Modelling of the filtered anomalies was performed by multiscale methods known as “Multiridge analysis” and “DEpth from eXtreme Points (DEXP)”.  The first method analyses a multiscale dataset at the zeroes of the first horizontal and vertical derivatives besides the potential field data themselves (ridges).  The Wavelet Transform Modulus Maxima  lines converged to buried remains. The field, scaled by a power law of the altitude (DEXP transformation) allowed estimates of source depths at its extreme points. The depth estimations for the buried structures obtained from the two methods are very close each other and fairly agree with those from the modelling of GPR anomalies. On the basis of these results, an archaeological excavation followed our indications and brought to light ancient walls.

How to cite: Bianco, L., Fedi, M., and La Manna, M.: Multiscale magnetic modelling in the ancient abbey of San Pietro in Crapolla, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15127, https://doi.org/10.5194/egusphere-egu23-15127, 2023.

EGU23-15190 | Orals | GI6.1

Synthetic aperture radar burst overlapped interferometry for the analysis of large ground instabilities: Experiments in volcanic regions. 

Antonio Pepe, Andrea Barone, Pietro Mastro, Pietro Tizzani, and Raffaele Castaldo

This work presents an overview of some applications of synthetic aperture radar (SAR) interferometry technology for the detection and analysis of large ground displacements occurring in volcanic areas, with the aim to retrieve the three-dimensional (3-D) ground displacement field (up-down, east-west, north-south). Specifically, the work summarizes and investigates the potential of Bursted Overlapped Interferometry (BOI) that properly combined can allow the retrieval, at different scales of resolution and accuracies, of the north-south components of the ground deformations, which are usually not available considering conventional SAR interferometry techniques. In this context, the almost global coverage and the weekly revisit times of the European Copernicus Sentinel-1 SAR sensors permit nowadays to perform extensive analyses with the aim to assess the accuracy of the BOI techniques. More recently, Spectral Diversity (SD) methods have been exploited for the fine co-registration of SAR data acquired with the Terrain Observation with Progressive Scans (TOPS) mode. In this case, considering that TOPS acquires images in a burst mode, there is an overlap region between consecutive bursts where the Doppler frequency variations is large enough to allow estimating and compensating for, with great accuracy, potential bursts co-registration errors. Additionally, and more importantly, in the case of non-stationary scenarios, it allows detecting the ground displacements occurring along the azimuthal directions (almost aligned along north-south) with centimeter accuracy. This is done by computing the difference between the right and left interferograms, i.e., the burst overlapped interferogram, and relating it to the ongoing deformation signals.

This work aims to apply the BOI technique in selected volcanic and seismic areas to evaluate the impact of this novel technology for the analysis of quantifying, over small, covered regions, the accumulated ground displacements in volcanic areas. In such regions, the interest is on quantifying the accuracy of integrated BOI systems for the retrieval of 3-D displacements. To this aim, we selected as a test site the Galapagos Island and we analyze with BOI the north-south ground displacements. At the next EGU symposium, the results of the BOI analyses will be presented, thus also providing comparative analyses with the results obtained from the use of potential field method applied on the ground displacements in volcanic areas. More specifically, by adopting this technique, we are able to estimate independently the north-south components of the ground displacement by exploiting the harmonic properties of the elasticity field.

How to cite: Pepe, A., Barone, A., Mastro, P., Tizzani, P., and Castaldo, R.: Synthetic aperture radar burst overlapped interferometry for the analysis of large ground instabilities: Experiments in volcanic regions., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15190, https://doi.org/10.5194/egusphere-egu23-15190, 2023.

EGU23-16132 | ECS | Orals | GI6.1

Multiscale imaging of low-enthalpy geothermal reservoir of the Phlegraean Fields caldera from gravity and resistivity data. 

Maurizio Milano, Giuseppe Cavuoto, Alfonso Corniello, Vincenzo Di Fiore, Maurizio Fedi, Nicola Massarotti, Nicola Pelosi, Michele Punzo, Daniela Tarallo, Gian Paolo Donnarumma, and Marina Iorio

The central‐eastern sector of the Phlegraean Fields caldera, southern Italy, is one of the most intensely studied and monitored volcanic active area of the word. This area reveals typical characters of a high‐ enthalpy geothermal systems. However, recently the presence of two different geothermal reservoirs has been outlined: one located in the central sector dominated by highly active vapours generated by episodic arrival of CO2‐rich magmatic fluids and the other one located in the eastern sector (Agnano zone) characterized by a shallow (400-500 m b.s.l.) still hot reservoir, heated by the upward circulation of deep no magmatic hot vapor.

In this study we present preliminary results deriving from the integration of different geophysical surveys carried out in the Agnano plain area, in the frame of the GEOGRID research project. We acquired high-resolution gravity data along two parallel profiles and we investigated the depth, shape and density contrast of the subsurface structures by the CompactDEXP (CDEXP) method, a multiscale iterative imaging technique based on the DEXP method. The resulting density models, together with DC resistivity and stratigraphic data, outlines the presence of a complex morphology of the Agnano subsoil characterized by a horst-graben structure. The importance of the structural lines identified by geophysical data, is also confirmed by the alignment of correlate outcropping thermal waters.

How to cite: Milano, M., Cavuoto, G., Corniello, A., Di Fiore, V., Fedi, M., Massarotti, N., Pelosi, N., Punzo, M., Tarallo, D., Donnarumma, G. P., and Iorio, M.: Multiscale imaging of low-enthalpy geothermal reservoir of the Phlegraean Fields caldera from gravity and resistivity data., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16132, https://doi.org/10.5194/egusphere-egu23-16132, 2023.

EGU23-706 | ECS | PICO | HS7.1

Multi-scale comparison of rainfall measurement with the help of a disdrometer and a mini vertically pointing Doppler radar 

Mateus Seppe Silva, Rodrigo Vieira Casanova Monteiro, Jerry Jose, Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer

Local rainfall measurements can be done in a significant range of methods which rely on very different underlying measurement concepts and assumptions. As an illustration, mechanical rain gauges collect small rainfall amounts; optical disdrometers assess size and velocity of each drop passing through a sampling area, while  Doppler sensors derive a rain rate from estimated average fall velocity. Hence, the quality of the measurements can vary a lot, depending on factors such as rain drop size, wind velocity, rain rate etc. Understanding the differences between various technologies enables us to determine the most reliable device depending on each raining condition. This research aims to compare the performance of two of those devices: the optical disdrometer Parsivel2 (manufactured by OTT) and a mini Doppler radar part of a mini Meteorological Station (manufactured by Thies). The comparison was done with two research focuses: by evaluating the scaling features of the fields measured by both instruments utilizing the framework of Universal Multifractals (UM) to have a performance assessment valid across scales and not only separated scales, and by analyzing the influence of physical parameters namely drop size, wind velocity and rainfall rate in the performance of the devices.

The data used was collected on a meteorological mast located in the Pays d’Othe wind farm, 110km southeast of Paris. This measurement campaign is part of the RW-Turb project (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency (ANR-19-CE05-0022). The mast is operated with two sets of devices, one around 75m in height and the other around 45m. The observation time step of the Parsivel2 is of 30 seconds, and it measures full binned drop size and velocity distribution, while the mini station provides data (rainfall, 2D wind, temperature, pressure, humidity) with 1 second time step. In general, the mini-doppler radar is found to measure a smaller amount of rain with regards to the  Parsivel2. More precisely, we found that the mini doppler radar returned very low rain measurements when subjected to rain conditions with a bigger mean drop size (Dm), and that heavy wind was related to a non-detection of the field in situations with light rain. Scaling analysis enabled us to show that mini Doppler radar exhibited white noise from observation scale smaller than 4s. Hence, it was used only with large time steps. UM analysis also revealed different scaling behaviour for mini Doppler radar rain data at finer temporal resolution than that of Parsivel (30 s).

 

 

How to cite: Seppe Silva, M., Vieira Casanova Monteiro, R., Jose, J., Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Multi-scale comparison of rainfall measurement with the help of a disdrometer and a mini vertically pointing Doppler radar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-706, https://doi.org/10.5194/egusphere-egu23-706, 2023.

EGU23-2689 | PICO | HS7.1

Precipitation measurement based on satellite data and machine learning 

Lu Yi, Zhangyang Gao, Zhehui Shen, Haitao Lin, Zicheng Liu, Siqi Ma, Stan Z. Li, and Ling Li

Satellite infrared (IR) data, with high temporal resolution and wide coverages, have been commonly used in precipitation measurement. However, existing IR-based precipitation retrieval algorithms suffer from various problems such as overestimation in dry regions, poor performance in extreme rainfall events, and reliance on an empirical cloud-top brightness-rain rate relationship. To solve these problems, a deep learning model using a spherical convolutional neural network was constructed to properly represent the Earth's spherical surface. With data inputted directly from IR band 3, 4, and 6 of the operational Geostationary Operational Environmental Satellite (GOES), the new model of Precipitation Estimation based on IR data with Spherical Convolutional Neural Network (PEISCNN) was first trained, tested and validated. Compared to the commonly used IR-based precipitation product PERSIANN CCS (the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Network, Cloud Classification System), PEISCNN showed significant improvement in the metrics of POD, CSI, RMSE and CC, especially in the dry region and for extreme rainfall events. The PEISCNN model may provide a promising way to produce an improved IR-based precipitation product to benefit a wide range of hydrological applications.

How to cite: Yi, L., Gao, Z., Shen, Z., Lin, H., Liu, Z., Ma, S., Li, S. Z., and Li, L.: Precipitation measurement based on satellite data and machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2689, https://doi.org/10.5194/egusphere-egu23-2689, 2023.

EGU23-4992 | PICO | HS7.1

The Fresnel Platform for Greater Paris: enhancing the urban resilience with the fully distributed and physically based model, Multi-Hydro 

Guillaume Drouen, Daniel Schertzer, Auguste Gires, and Ioulia Tchiguirinskaia

The aim of the Fresnel platform of École des Ponts ParisTech is to develop research and innovation on multiscale urban resilience. To achieve this goal, it is therefore conceived as a SaaS (Software as a Service) platform, providing data over a wide range of space-time scales and appropriate softwares to analyse and simulate them over this range.

To study the different technical solutions of the water cycle in an urban environment at different scales, RadX now provides a user-friendly graphical user interface to run simulation using a fully distributed and physically based model: Multi-Hydro.

This model that has been developed at École des Ponts ParisTech, from four open-source software applications already used separately by the scientific community. Its modular structure includes a surface flow module, sewer flow module, a ground flow module and a precipitation module. It is able to simulate the quantity of runoff and the quantity of rainwater infiltrated into unsaturated soil layers from any temporally-spatially varied rainfall event at any point of the peri-urban watersheds. The spatial and temporal variation of meteorological, hydrological, geological and hydrogeological data across the model area is described in gridded form of the input as well as the output from the model.

The use of RadX as a graphical user interface gives users the ability to easily customize the input data for their simulation. They can, for instance, modify the land use to study the effect of urban climate mitigation strategies like green roofs. They can select real hydrological events measured by the ENPC X-Band radar as rainfall input, but also generate virtual rainfall events. To ease the interpretation of the simulation, RadX can render interactive 2D and 3D graphics directly in the users' web browser by the use of open source libraries that focus on performance using low level graphic API. For example, it gives the user an intuitive and efficient way to spot singular points of the infiltration output display. Users can also download the file outputs to use in their GIS software.

Other components can be integrated to RadX to satisfy the particular needs with the help of visual tools and forecasting systems, eventually from third parties. Developments are still in progress, with a constant loop of requests and feedback from the scientific and professional world.

How to cite: Drouen, G., Schertzer, D., Gires, A., and Tchiguirinskaia, I.: The Fresnel Platform for Greater Paris: enhancing the urban resilience with the fully distributed and physically based model, Multi-Hydro, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4992, https://doi.org/10.5194/egusphere-egu23-4992, 2023.

The microphysical processes were found to be vital in facilitating the system evolution for a merger-formation bow echo (MFBE) in southeast China, where the reinforced precipitation enhanced the cold pool strength via evaporation cooling. However, current numerical model failed to accurately perform such processes, suggesting the large uncertainties for microphysical schemes in simulating MFBE events in southeast China. In this study, three microphysics schemes including Thompson (THOM), Morrison (MORR), and Weather Research and Forecasting Double-Moment 6-Class (WDM6) schemes were evaluated by comparing against polarimetric observations and Variational Doppler Radar Analysis System (VDRAS) analyses. The three schemes captured the basic kinematic structures for this MFBE event after assimilating radar radial velocities, but all underpredicted the cold pool strength by ∼25%. Particularly, THOM produced the best raindrop size distributions (DSDs) and precipitation pattern, and the larger raindrop size bias and the weak cold pool strength were owing to the relatively low rain breakup efficiency and inefficient rain evaporation, respectively. By decreasing the cutoff diameter of rain breakup parameterization from the default 1.6–1.2 mm (i.e., increasing breakup efficiency) and increasing evaporation efficiency by threefold in THOM, the simulated DSDs and precipitation were greatly improved, and the cold pool strength was significantly increased from 77% to 99% compared to that in VDRAS analyses. This study illustrated a plausible approach of combining polarimetric radar retrievals and VDRAS analyses as bases to adjust THOM default settings in simulating a MFBE event in southeast China with physical characteristics more consistent with observations. Since microphysical processes vary from convective organizations and climate regions, it is recognized more cases studies are needed in the future to examine the validity and approach in this study to improve simulations and predictions of MFBEs in southeast China.

How to cite: Zhao, K., Zhou, A., Lee, W.-C., and Huang, H.: Evaluation and Modification of Microphysics Schemes on the Cold Pool Evolution for a Simulated Bow Echo in Southeast China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6808, https://doi.org/10.5194/egusphere-egu23-6808, 2023.

EGU23-7987 | ECS | PICO | HS7.1

Spatiotemporal pattern of precipitation in the Pearl River basin, China from 1951 to 2015 

shirong Cai, Kunlong Niu, Xiaolin Mu, and Xiankun Yang

Precipitation is one of the most important factors in hydrological cycle and climate change. Due to global climate change, the global and regional hydrological cycle has been changed significantly, and the precipitation pattern has changed, which made natural disasters happened more frequent. In this study, we taken the Pearl River Basin as a case study area and used APHRODITE dataset to investigate the spatiotemporal trend of precipitation during the period of 1951-2015 based on six extreme rainfall indices recommended by the WMO. Then, the MK test was used to verify their trend and analyze the temporal and spatial variability. The results indicated that: (1) The annual PRCPTOT in the Pearl River Basin displayed an increasing trend with an increasing rate of 0.019mm/yr. Although the number of annual rainy days was decreasing, the annual SDII exhibited an increasing trend. The annual R95P and RX1day exhibited an increasing trend, but the R95D and CWD showed a decreasing trend. The seasonal PRCPTOT increased in summer and winter, but decreased in spring and autumn. R95P and SDII displayed an increasing trend in four seasons. (2) The annual variation of PRCPTOT increased from west to east, the trend of SDII, R95P and RX1day were similar with PRCPTOT, but the high value of R95D happened in the middle and lower reaches of Xijiang River, and CWD increased from north to south. Except autumn, the seasonal spatial distribution of PRCPTOT, SDII and R95P were similar. In spring and winter, the spatial distribution of PRCPTOT, SDII and R95P increased from west to east, and from north to south in summer, indicating that the Beijiang River basin and Dongjiang River basin had a higher flood risk. (3) MK test of indices shown that the Yunnan-Guizhou Plateau was becoming drier, and the risk of extreme rainfall was increasing in the Beijiang River basin and Dongjiang River basin. The study results are valuable for future water resources management and ecological environment protection in the Pearl River Basin.

How to cite: Cai, S., Niu, K., Mu, X., and Yang, X.: Spatiotemporal pattern of precipitation in the Pearl River basin, China from 1951 to 2015, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7987, https://doi.org/10.5194/egusphere-egu23-7987, 2023.

EGU23-9256 | PICO | HS7.1

Improving shape-dependent snow fall speed relationships using different particle size parameters 

Thomas Kuhn, Salomon Eliasson, and Sandra Vázquez-Martín

Meteorological forecast models, notably snowfall predictions, require accurate knowledge of the properties of snow particles, such as their size, cross-sectional area, mass, shape, and fall speed. Therefore, measurements of individual snow particles’ fall speed and their cross-sectional area, from which a size parameter and area ratio can be derived, provide very useful datasets. We have compiled such a dataset from measurements with the Dual Ice Crystal Imager (D-ICI) in Kiruna during several winter seasons from 2014 to 2019. Using that data, we have previously studied shape-dependent relationships between fall speed and particle size, cross-sectional area, and particle mass. While we had used maximum dimension as the size parameter, we have found that it seems unsuitable for certain shapes like columnar particles. Here, we investigate which particle size parameter should be used depending on the shape or if one size parameter is suitable for all shapes. With a more suitable particle size parameter, we aim to improve the relationships between fall speed and particle size and mass.

How to cite: Kuhn, T., Eliasson, S., and Vázquez-Martín, S.: Improving shape-dependent snow fall speed relationships using different particle size parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9256, https://doi.org/10.5194/egusphere-egu23-9256, 2023.

EGU23-10190 | PICO | HS7.1

A novel methodology for remote sensing retrieval of rainfall rates 

Massimiliano Ignaccolo and Carlo De Michele
We propose a new methodology for rainfall rate retrieval from remote sensing observations using 166 datasets from 76 different locations on Earth's surface. The method rests upon the data science parametrization of the drop size distribution [Ignaccolo and De Michele (2022) : https://doi.org/10.1175/JHM-D-21-0211.1]. It retrieves the possible triplets (drop count, mean diameter of the drop size distribution, skewness of the drop size distribution) associated with given values of the horizontal and vertical reflectivities. We demonstate how this novel approach is superior to a standard one based upon the mass weighted diameter, normalized intercept and gamma functional form for the drop size distribution. 
 

How to cite: Ignaccolo, M. and De Michele, C.: A novel methodology for remote sensing retrieval of rainfall rates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10190, https://doi.org/10.5194/egusphere-egu23-10190, 2023.

In previous work [Aguilar Flores et al., Stoch. Environ. Res. Risk Assess. (2021) 35: 1681-1687], distributional convergence of breakdown coefficients (BDCs) to symmetric probability distribution functions of weights in discrete-scale multiplicative cascades has been shown. Asymmetric weights distributions, however, cannot become the limiting functions of symmetric BDC distributions. A procedure has been devised and is presented herein for the computation of the limiting distributions in the aforementioned cases, involving a convolution that is identified with the first-level BDCs probability distribution, and thereby can be used for the purpose of model validation in otherwise non-ergodic single realizations of multiplicative cascade models.

How to cite: Aguilar Flores, C. and Carsteanu, A. A.: Breakdown coefficients of multiplicative cascades having asymmetrically distributed generators with bounded essential range, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10237, https://doi.org/10.5194/egusphere-egu23-10237, 2023.

EGU23-12061 | ECS | PICO | HS7.1

Classification of snow and rainfall using commercial microwave links 

Erlend Øydvin, Rasmus Falkeid Hagland, Vegard Nilsen, Mareile Astrid Wolff, and Nils-Otto Kitterød

The use of Commercial microwave links (CMLs) to estimate rainfall has been under investigation for the past 15 years. CMLs still seem like a promising supplement to standard measurement methods. So far, CMLs have almost exclusively been applied for rainfall only situations. It is expected that different precipitation types affect the CML signal strength and error sources differently. For CML applications in high latitude countries with frequent and extended periods with snowfall and mixed precipitation, an extension of the classification methods for these precipitation types is needed. 

In this presentation we study how the CML signal attenuation is affected by different precipitation types and how those can be used to classify the different events. We use nearby disdrometers as a ground truth reference and CML data from different climatological conditions in Norway.

How to cite: Øydvin, E., Hagland, R. F., Nilsen, V., Wolff, M. A., and Kitterød, N.-O.: Classification of snow and rainfall using commercial microwave links, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12061, https://doi.org/10.5194/egusphere-egu23-12061, 2023.

EGU23-12265 | PICO | HS7.1

Using Opportunistic Rainfall Sensing to improve Areal Precipitation Estimates and Run-off Modelling – The Case Study of the Ahr Flood in July 2021 

Jochen Seidel, András Bárdossy, Micha Eisele, Abbas El Hachem, Christian Chwala, Maximilian Graf, Harald Kunstmann, Norbert Demuth, and Nicole Gerlach

On 14 and 15 July 2021, heavy and prolonged precipitation caused flooding in large areas in western Germany and adjacent regions. The Ahr River valley in the Federal State of Rhineland-Palatinate was particularly affected, with numerous fatalities and large-scale damage. Due to the spatio-temporal variability of precipitation and failure of several gauging stations, the estimation of the flood triggering areal precipitation as well as determination of peak discharges is associated with high uncertainties.

In this study, we present results where data from opportunistic sensors (commercial microwave links (CML) and personal weather stations (PWS)) were used to interpolate hourly precipitation sums for the Ahr catchment. The data from the opportunistic sensors was quality controlled, filtered and interpolated using the methods from Graf et al. (2021). This precipitation data was compared to a gauge adjusted weather radar product from the German Weather Service DWD as well as interpolated rain gauge data. In order to determine the maximum discharges at the gauges in the Ahr, flood was simulated with the water balance model LARSIM (Large Area Runoff Simulation Model) using the aforementioned precipitation products as input data.

The results show that the areal precipitation obtained from opportunistic sensors yielded higher sums than the gauge adjusted radar products and the interpolated gauge data, especially in the northern part of the Ahr catchment where the station density of the conventional rain gauges was not sufficient to capture the spatial variability of this extreme event. Furthermore, the modelled run-offs using the precipitation input from opportunistic sensors yielded higher and more plausible peak discharges than the ones with the gauge adjusted weather radar product. This suggests that the radar underestimated precipitation due to attenuation. The difference in the resulting peak discharges point to the fact that due to the saturated soils any additional precipitation during the flood event in July 2021 lead to a direct run-off effect.

 

References:

Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H., & Bárdossy, A. (2021). Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales. Journal of Hydrology: Regional Studies, 37, 100883.

How to cite: Seidel, J., Bárdossy, A., Eisele, M., El Hachem, A., Chwala, C., Graf, M., Kunstmann, H., Demuth, N., and Gerlach, N.: Using Opportunistic Rainfall Sensing to improve Areal Precipitation Estimates and Run-off Modelling – The Case Study of the Ahr Flood in July 2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12265, https://doi.org/10.5194/egusphere-egu23-12265, 2023.

EGU23-12880 | PICO | HS7.1

Modelling Typhoon Rainfall with Universal Multifractal 

Ching-Chun Chou, Auguste Gires, and Li-Pen Wang

Universal Multifractal (UM) has been a useful tool to model rainfall processes across a wide range of spatialtemporal scales. Double Trace Moment (DTM) is a technique that helps estimate parameters for the UM model. Based upon the estimated UM parameters, a discrete random cascade process can be used to generate samples with realistic rainfall properties. UM parameters are of physical meanings, representing the levels of mean intermittence (C1) and the changing rate of the mean intermittency deviating from the average field (α, know as the multifractality index), respectively. Therefore, these parameters are also widely used to characterise rainfall features across scales. UM has been tested in many countries under various weather conditions. However, its applications to extreme storm events, such as typhoons, are limited. In light of this, this study intends to analyse UM’s capacity of capturing and modelling extreme storm events recorded by a rainfall monitoring network in the South of Taipei City. On the roof of the Civil Engineering Research Building at National Taiwan University, an innovative extreme rainfall monitoring campaign has been set up and collecting high-quality rainfall measurements at fine timescales over the past two years. Rainfall data from several extreme rainfall events, including four typhoons and 10+ thunderstorms, has been collected. In this work, high-resolution rainfall time series from the laser disdrometer for typhoon Nalgae is used for analysis. Rainfall measurements are first aggregated from the native 10-second resolution to 80-second and coarser resolution and then downscaled back to 10-second to verify the downscaling results. The UM analysis is conducted in three different ways. The first way is to apply UM analysis to the entire time series. The resulting parameters are α = 1.32 and C1 = 0.108. Then, the time series is equally divided into 16 sections such that the temporal variations in rainfall features can be observed. Similarly to the first way, the second way applies the ’standard’ UM analysis but to each section. This leads to α ranging from 1.1 to 1.9 and C1 from 0.05 to 0.18. Finally, the third way applies ’ensemble’ UM analysis that concatenates divided sections into a single matrix. This results in α = 1.55 and C1 = 0.125. The derived parameters are then used to sample 10-second rainfall estimates with a discrete cascade process. The performance is quantified based upon the capacity of preserving observed extreme features. We first analyse the ranges of α and C1 resulting from the samples downscaled from the first and the third ways. We can see that the resulting α ranging from 1.2 to 1.8 and C1 from 0.06 to 0.16, which fails resembling the aforementioned variability of the UM parameters (i.e. 1.1−1.9 and 0.05−0.18). In fact, only the second way leads to satisfactory result. This preliminary study suggests that typhoon rainfall experiences drastic behaviour changes within a short period, which requires a more ’dynamic’ way to model these changes well. Similar analyses will be conducted over other collected typhoons and thunderstorm events to see if the findings can be generalised.

How to cite: Chou, C.-C., Gires, A., and Wang, L.-P.: Modelling Typhoon Rainfall with Universal Multifractal, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12880, https://doi.org/10.5194/egusphere-egu23-12880, 2023.

EGU23-13080 | ECS | PICO | HS7.1

Challenges in the usage of commercial microwave links for the generation of transboundary German-Czech rainfall maps 

Nico Blettner, Martin Fencl, Vojtěch Bareš, Christian Chwala, and Harald Kunstmann

Attenuation data from commercial microwave links (CMLs) has proven useful for estimating rainfall. Their major benefits are a high abundance in most regions on earth, a high resolution in time, close to ground measurement, and the absence of installation costs and efforts. The spatial and temporal coverage of CMLs would theoretically enable the generation of continental rainfall maps for various aggregation times.

However, there exist limitations that have so far inhibited rainfall estimation on larger scales. The data is generally obtained on a national basis from different network providers and networks can vary significantly in characteristics such as frequency and length distributions. CML data requires careful processing that depends on these characteristics and which has so far been adjusted to independent data sets only.

In this study we investigate what kind of processing is required to use independent and heterogeneous CML data sets for the generation of transboundary rainfall maps. We use 3900 CMLs from Germany and 2500 CMLs from the Czech Republic. The German data set is rather evenly distributed with respect to spatial coverage, frequencies and lengths. The Czech data set, on the other hand, varies significantly more in all these regards: it is characterized by dense networks of short CMLs in the cities, a large share of CMLs with E-Band frequency, and hence a large range of sensitivities.

We find that quality control is important especially when dealing with independent data sets. We propose several algorithms and the consideration of network characteristics when combining two CML data sets, and show how adapted but straightforward processing allows the generation of transboundary rainfall maps.

How to cite: Blettner, N., Fencl, M., Bareš, V., Chwala, C., and Kunstmann, H.: Challenges in the usage of commercial microwave links for the generation of transboundary German-Czech rainfall maps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13080, https://doi.org/10.5194/egusphere-egu23-13080, 2023.

In an attempt to get the best parameter estimations of the theoretically consistent IDF (Intensity Duration Frequency) models of rainfall intensity for the entire state of Baden Wuerttemberg, three well-defined optimization algorithms such as Differential Evolution (DE), Nelder Mead (NM), and TNC Truncated Newton (TNC) are taken into account for comparison.

Seven-parametric IDF model contains mean intensity µ, intensity scale parameters λ1, λ2 , time scale parameter α, fractal/smoothness parameter Μ, Hurst parameter Η, exponent of the expression of probability dry θ,  and tail index ξ, which are obtained by minimizing the error between empirical k-moments and model quantiles. Error metric focusing on distribution quantiles x(k,T) is thus minimized for all available scales k and a series of return periods T . Non-linear solver is chosen to perform this step as these errors are non-linear functions of the parameters.

All results are demonstrated visually, and a final decision is made on the basis of precisely fitted parameter values to the model. This crucial step will also assist us in finding the optimum design values for stormwater and floods.

How to cite: Amin, B. and Bárdossy, A.: Comparative Analysis of Parameter Optimization of Theoretically Consistent IDF Models of Rainfall Intensity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14215, https://doi.org/10.5194/egusphere-egu23-14215, 2023.

EGU23-14295 | PICO | HS7.1

OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden 

Remco (C.Z.) van de Beek, Jafet Andersson, Jonas Olsson, and Jonas Hansryd

In a changing climate accurate measurements and near-real time rainfall monitoring are essential for sustainable societies. Commercial microwave links (CMLs) offer a great alternative, or addition, to traditional sensors, like rain gauges and radar. While CMLs are a great source of opportunistic sensors the data from CMLs are usually limited by their accessibility for both research and actual implementation. To help in gaining better access and research into CML-derived rainfall we present a dataset at 10 second resolution with true coordinates for 364 bi-directional CMLs gathered during a pilot study in Gothenburg, Sweden over a three-month period (June-August 2015). These data are complemented by additional data from 11 high-resolution rain gauges (ten 1 min and one 15 min) and radar data (5 min and 2 km resolution) from the Swedish operational weather radar composite over the Gothenburg area.

Analysis of the data show that data collection is very complete, with 99.99% of the CMLs, 100% rain gauges and 99.6% of the radar data available. The gauge data shows that around 260mm rainfall was measured during this period with 6% precipitation during 15-minute intervals. At the Torslanda gauge on 28 July 2015 one the of the most intense events was observed during the three-month period with a peak intensity of 1.1 mm min−1. The CML data reflect this event well and show a drop of around 27 dB during the peak intensity. Radar data also showed a good distribution of the reflectivity of the precipitation with some measurements above 40 dBZ, which is commonly taken as an indication of convective precipitation. Some low intensity clutter was also found, mostly around -15 dBZ.

The data are accessible at https://doi.org/10.5281/zenodo.7107689 (Andersson et al., 2022). The sharing of these Open high-resolution data of Microwave links, radar and gauges (OpenMRG) should enable further research in microwave-link based environmental monitoring. In the longer term we hope that this dataset will also contribute to easier access of CML data and help in the development of the merging of multi-sensor products.

How to cite: van de Beek, R. (C. Z. )., Andersson, J., Olsson, J., and Hansryd, J.: OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14295, https://doi.org/10.5194/egusphere-egu23-14295, 2023.

The safety of autonomous vehicles will depend critically on the performance of sensors (such as 77GHz radar), which will degrade in the presence of propagation losses during severe weather events. Variations in the drop size distribution lead to significant uncertainty in attenuation estimates. As part of the UK government's commitment to the safe introduction of autonomous vehicles, and in collaboration with the National Physical Laboratory, we have set up a series of observing platforms at Met Office Cardington to measure a multitude of weather-related variables such as temperature, pressure, illumination, precipitation particles, fog, etc. In this contribution, I will cover our work on characterising the rain drop size distribution, using a network of 5 disdrometers located 125m apart, and returning a drop size distribution every minute. From the spectra, we derived an estimate of the attenuation, including an estimate of the uncertainty.

How to cite: Husnoo, N. and Jones, D.: The impact of drop size distribution variability and rainfall attenuation on autonomous vehicle sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14407, https://doi.org/10.5194/egusphere-egu23-14407, 2023.

Weather radar provides rainfall estimates at high resolution in both space and time, which is useful for many hydrological applications. Despite this, the radar rainfall estimation process introduces many sources of error, impacting the reliability of results obtained from the radar rainfall estimates. Key error sources include signal attenuation, radar calibration issues, ground clutter contamination, variability in the drop-size distribution and variation in the vertical profile of reflectivity. To gain an improved understanding of potential limitations, and the corresponding uncertainty of rainfall rates, the impact of these errors has been systematically investigated, developing a radar error model by inverting the rainfall estimation process.

To this end, an ensemble of realistic rainfall events is simulated, and working backwards in a stochastic manner gives an ensemble of weather radar images, corresponding to each rainfall event, at each time step. The radar error model includes random noise effects, drop-size distribution errors, sampling estimation variance and importantly, attenuation effects. To allow for direct comparisons, standard radar processing methods are applied to each radar image, to obtain corrected ‘best guess’ rainfall estimates which would be obtained from each weather radar ensemble member in real world applications. The difference between the simulated and corrected rainfall for each ensemble member is then treated as the uncertainty corresponding to the radar rainfall estimation process.

A simple measure is introduced, to help understand how often errors result in a rainfall signal completely irretrievable, referred to as ‘rainfall shadow’. Areas of rainfall that are ‘shadowed’ are defined as pixels where the simulated ‘true’ rainfall rate is significant, but the ensemble member has less than 10% of the original signal. This is equivalent to considering where a significant rainfall rate has been completely lost, and would therefore be irretrievable using standard correction methods, to quantify the frequency of occurrence in real-world radar rainfall applications. The impact of location of rainfall within images is considered, by introducing the second moment of area for radar images, in order to quantify the proximity of intense rainfall to the radar transmitter.

Results show relationships between rainfall shadows and high bias and uncertainty in rainfall estimates, related to the amount of rainfall (i.e. proportion and rates) in images. More central rainfall also results in higher errors and higher variability. The minimum likelihood of occurrence of rainfall shadows showed that 50% of event images have at least 3% of significant rainfall shadowed. In addition, 25% of images had a shadowed area of over 45km2, with the minimum largest shadow in one area for 5% of images exceeding an area of 50km2. This gap would result in an underestimation of the impact of potential floods, showing that weather radar has potential for important information to be lost. A model framework for representing this uncertainty in the radar rainfall estimation process provides methodology for assessing the impacts of radar rainfall errors on hydrological applications.

How to cite: Green, A., Kilsby, C., and Bardossy, A.: Quantifying the uncertainty corresponding to the radar rainfall estimation process:  an inverse model for radar attenuation error, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14569, https://doi.org/10.5194/egusphere-egu23-14569, 2023.

EGU23-14766 | PICO | HS7.1

Multiscale Characteristics of West African Summer Monsoon Precipitation Derived from UCadMet Network Observations 

Belen Rodríguez de Fonseca, Luis Durán Montejano, Alvaro González Cervera, Auguste Gires, Cheikh Modou Noreyni Fall, Abdou Lahat Dieng, Amadou Thierno Gaye, and Elsa Mohino

Since 2012 a joint Université Cheikh Anta Diop de Dakar and Universidad Complutense de Madrid meteorological observation network (UCadMet) has been in place in the city of Dakar (Senegal). During the last years, the observation and data storage systems have been considerably improved. Last summer of 2022, a laser disdrometer was installed providing  detailed information on the size and speed of precipitation with a time resolution of one minute. Observations from several tipping bucket rain gauges are available also at the same site. Summer 2022 has been anomalously rainy in West Africa, with large precipitation events during the African monsoon season, which seems to be enhanced by a La Niña situation in the Pacific. These events have proven to be particularly suitable for evaluating the performance of the installed observing systems and for drawing some conclusions about the characteristics of monsoon precipitation in this region not only at different time scales, but also across scales (from 1 min to season). Commonly used rain rate together with drop size distribution are used to access information on rainfall microphysics. This analysis allows the design of future lines of action considering climate change, for which large precipitation events are expected to become more frequent.

How to cite: Rodríguez de Fonseca, B., Durán Montejano, L., González Cervera, A., Gires, A., Fall, C. M. N., Dieng, A. L., Gaye, A. T., and Mohino, E.: Multiscale Characteristics of West African Summer Monsoon Precipitation Derived from UCadMet Network Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14766, https://doi.org/10.5194/egusphere-egu23-14766, 2023.

EGU23-15902 | ECS | PICO | HS7.1

Correction of hourly radar precipitation data based on rain-gauges values: what is the most efficient method for hydrologic modeling purposes? 

Andrea Citrini, Georgia Lazoglou, Adriana Bruggeman, George Zittis, Giovanni Pietro Beretta, and Corrado Camera

The effectiveness of a hydrologic model is largely driven by the availability and nature of the input data. Among these, many studies proved precipitation to be the most important because it regulates the amount of water entering the system. Spatially continuous precipitation data can be obtained from radar technology. However, radar precipitation values are an indirect measure, and it is widely believed that their use in hydrologic modelling is complicated due to the presence of bias. The use of radar data is increasingly problematic in mountain regions where elevation plays a key role on precipitation, creating significant variations in few kilometers. Also, mountains can lead to a shadow effect of the radar beam.

The research objective is to integrate precipitation data derived from the radar into a partially distributed hydrologic model, running in an area with complex morphology. The study area is a portion of Upper Valtellina valley (about 2300 km2), located within the Alpine belt on the border between Italy and Switzerland, and characterized by an elevation range between 350 and 3400 m a.s.l. The hourly series of 22 rain-gauges (18 Italian and 4 Swiss stations) and hourly precipitation from a radar dataset (1km x 1km resolution, from MeteoSWISS) from 2010 to 2020 are used. The mean bias between the series extracted in the radar cells at the station locations and the series measured by rain-gauge is around -28%, indicating a general underestimation of the radar data. The targets of the correction techniques are the precipitation series at the centroids of the sub-basins defined by the hydrologic model.

For the correction, two approaches are tested: (i) the radar precipitation is corrected in every centroid of the hydrologic model subbasins (point-based correction); (ii) the radar precipitation is adjusted by spatializing the radar-station error (interpolation-based correction). The first approach is based on finding the statistical relations between the radar-station series of the three closet stations to the target centroid and applying the statistical correction (Copula or Cumulative Distribution Function (CDF) matching bias correction) to the precipitation series in the centroid cell. The result of the correction is a combination of the statistical relationships weighted according to a Triangular Irregular Network. The second technique focusses instead on the interpolation of the error (residuals) calculated as the difference between radar and rain-gauge values, which is subsequently added to the original radar raster. Two different interpolation techniques are used: Thin Plate Splines and Inverse Distance Weighting. All methods are evaluated through performance indices (KGE and RMSE) at the station locations by Leave One Out cross validation.

Point-based applications are cost-effective and require less computational effort than spatial interpolations. Preliminary results show that the point-based corrections through Copula and CDF have similar performances. In detail, the KGE increases from 0.18 to 0.52 and 0.55 for Copula and CDF, respectively. RMSE decreases from 0.78 mm to 0.53 mm (Copula) and 0.62 mm (CDF). Interpolation-based corrections are still ongoing, therefore there are no definite results regarding the comparative effectiveness of one type of correction over the other.

How to cite: Citrini, A., Lazoglou, G., Bruggeman, A., Zittis, G., Beretta, G. P., and Camera, C.: Correction of hourly radar precipitation data based on rain-gauges values: what is the most efficient method for hydrologic modeling purposes?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15902, https://doi.org/10.5194/egusphere-egu23-15902, 2023.

EGU23-15924 | ECS | PICO | HS7.1

Information-based approach for quantifying uncertainty in precipitation estimates from commercial microwave links 

Anna Špačková, Martin Fencl, and Vojtěch Bareš

Opportunistic sensors have great potential for rainfall monitoring, as the density of their networks can outperform standard rainfall monitoring networks. The commercial microwave link (CML) network enables indirect monitoring of path-averaged rainfall intensity. It is retrieved from signal attenuation caused by raindrops, which can be related to rainfall intensity by a simple power law. Quantitative precipitation estimates from CMLs are, however, affected by uncertainty, which is still challenging to estimate.

This study proposes, for the first time, to use information theory methods to quantify uncertainty in CML QPEs. This method enables measuring the firmness of relationships between different variables using discrete probability distributions and also estimates the uncertainty. The advantage resides also in the fact that it allows any type of data to be used. This approach was recently applied by Neuper and Ehret (2019) to evaluate quantitative precipitation estimates with weather radar.

Data from non-winter periods of 2014 – 2016 are used at a temporal resolution of 15 min. The target (reference) data are the rain gauge adjusted radar observation. The CML data (signal attenuation and its processing) from the Prague network and its hardware characteristics are used as predictors. Additionally, other predictors, e.g., temperature and synoptic types, are used as further predictors. First, the information content of individual predictors of the target rain gauge adjusted radar data is measured. Specifically, we tested how different combinations of predictors reduce uncertainty. Second, the effect of the sample size on uncertainty is investigated. Different sizes of random samples are selected from the dataset and their information content for the target is quantified.

Depending on the choice of the predictor(s), their abilities to estimate the target variable can be compared. Their predictive uncertainties are different, which results in a ranking of suitability of available predictors and their combinations.

 

References
Neuper, M. and Ehret, U. (2019) Quantitative precipitation estimation with weather radar using a data- and information-based approach, Hydrol. Earth Syst. Sci., 23, 3711–3733, https://doi.org/10.5194/hess-23-3711-2019.

 

This study is supported by the Student Grant Competition grant of Czech Technical University in Prague no. SGS22/045/OHK1/1T/11.

How to cite: Špačková, A., Fencl, M., and Bareš, V.: Information-based approach for quantifying uncertainty in precipitation estimates from commercial microwave links, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15924, https://doi.org/10.5194/egusphere-egu23-15924, 2023.

The future evolution of the West Antarctic Ice Sheet (WAIS) will strongly influence the global sea-level rise in the coming decades. Ice shelf melting in that sector is partly controlled by the low-pressure system located off the West Antarctic coast, namely the Amundsen Sea Low (ASL). When the ASL is deep, an overall increase in ice shelf melting is noticed. Because of the sparse observational network and the strong internal variability, our understanding of the long-term climate changes in the atmospheric circulation is limited, and therefore its impact on ice melting as well. Among all the processes involved in the West Antarctic climate variability, an increasing number of studies have pointed out the strong impact of the climate in the tropical Pacific. However, most of those studies focus on the past decades, which prevents the analysis of the role of the multi-decadal tropical variability on the West Antarctic climate. Here, we combine annually-resolved paleoclimate records, in particular ice core and coral records, and the physics of climate models through paleoclimate data assimilation to provide a complete spatial multi-field reconstruction of climate variability in the tropics and Antarctic. This allows for studying both the year-to-year and multi-decadal variability of the tropical-Antarctic teleconnections. As data assimilation provides a climate reconstruction that is dynamically constrained, the contribution of the tropical variability on the West Antarctic climate changes can be directly assessed. Our results indicate that climate variability in the tropical Pacific is the main driver of ASL variability at the multi-decadal time scale, with a strong link to the Interdecadal Pacific Oscillation (IPO). However, the deepening of the Amundsen Sea Low over the 20th century cannot be explained by tropical climate variability. By using large ensembles of climate model simulations, our analysis suggests anthropogenic forcing as the primary driver of this 20th century ASL deepening. In summary, the 20th century ASL deepening is explained by the forcing, but the multi-decadal variability related to the  IPO is superimposed on this long-term trend.

How to cite: Dalaiden, Q., Abram, N., and Goosse, H.: Tropical Pacific variability and anthropogenic forcing are the key drivers of the West Antarctic atmospheric circulation variability over the 20th century, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-683, https://doi.org/10.5194/egusphere-egu23-683, 2023.

EGU23-991 | Orals | CR3.2

Future irreversible loss of Thwaites Glacier relative to global warming 

Emilia Kyung Jin, In-Woo Park, Hyun Joo Lee, and Won Sang Lee

The speed of West Antarctic melting is a very important factor in determining the degree of future global sea level rise. Loss of the Thwaites glacier due to global warming will have various regime changes in line with changes in the Earth system. The basal melting as a result of ocean warming can cause loss at an inhomogeneous rate across the underlying topography and overlying ice volume, while the change in precipitation from snow to rain as atmospheric warming can accelerate surface melting and trigger the irreversible loss.  

In this study, the ISSM model was driven with the ocean and atmospheric forcings obtained from the CMIP6 earth system model results, and future prediction experiments were performed until 2300. As a result, the accelerated period of melting of the Thwaites glacier related with forcings and the period of irreversible loss according to the structural characteristics and degree of warming are investigated. The mechanisms and timing that cause rapid ice loss are analyzed and the tipping point at which irreversible losses are triggered has been proposed as a function of warming.

How to cite: Jin, E. K., Park, I.-W., Lee, H. J., and Lee, W. S.: Future irreversible loss of Thwaites Glacier relative to global warming, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-991, https://doi.org/10.5194/egusphere-egu23-991, 2023.

EGU23-1329 | ECS | Orals | CR3.2

Characterizing the influence of idealized atmospheric forcings on firn using the SNOWPACK firn model 

Megan Thompson-Munson, Jennifer Kay, and Bradley Markle

The porous layer of snow and firn that blankets ice sheets can store meltwater and buffer an ice sheet’s contribution to sea level rise. A warming climate threatens this buffering capacity and will likely lead to depletion of the air-filled pore space, known as the firn air content. The timing and nature of the firn’s response to climate change is uncertain. Thus, understanding how the firn may evolve in different climate scenarios remains important. Here we use a one-dimensional, physics-based firn model (SNOWPACK) to simulate firn properties over time. To force the model, we generate idealized, synthetic atmospheric datasets that represent distinct climatologies on the Antarctic and Greenland Ice Sheets. The forcing datasets include temperature, precipitation, humidity, wind speed and direction, shortwave radiation, and longwave radiation, which SNOWPACK uses as input to simulate a firn column through time. We perturb the input variables to determine how firn properties respond to the perturbation, and how long it takes for those properties to reach a new equilibrium. We explore how different combinations of perturbations impact the firn to assess the effects of, for example, a warmer and wetter climate versus a warmer and drier climate. The firn properties of greatest interest are the firn air content, liquid water content, firn temperature, density, and ice slab content since these quantities help define the meltwater storage capacity of the firn layer. In our preliminary analysis, we find that with a relatively warm and wet base climatology representative of a location in southern Greenland, increasing the air temperature by 1 K yields a 48% decrease in firn air content and a 3% increase in the deep firn temperature 100 years after the perturbation. SNOWPACK also simulates near-surface, low-permeability ice slabs that inhibit potential meltwater storage in deeper firn. Conversely, decreasing the air temperature by 1 K yields a 7% increase in firn air content and a <1% decrease in the deep firn temperature in the same amount of time. In this scenario, the effects of warming are more extreme and have more adverse impacts on the firn’s meltwater storage capacity when compared to cooling. This work highlights the sensitivity of the firn to changing atmospheric variables and provides a framework for estimating the timescales and magnitude of firn responses to a changing climate.

How to cite: Thompson-Munson, M., Kay, J., and Markle, B.: Characterizing the influence of idealized atmospheric forcings on firn using the SNOWPACK firn model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1329, https://doi.org/10.5194/egusphere-egu23-1329, 2023.

EGU23-3405 | ECS | Orals | CR3.2

Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model 

Violaine Coulon, Ann Kristin Klose, Christoph Kittel, Ricarda Winkelmann, and Frank Pattyn

Recent observations show that the Antarctic ice sheet is currently losing mass at an accelerating rate in areas subject to high sub-shelf melt rates. The resulting thinning of the floating ice shelves reduces their ability to restrain the ice flowing from the grounded ice sheet towards the ocean, hence raising sea level by increased ice discharge. Despite a relatively good understanding of the drivers of current Antarctic mass changes, projections of the Antarctic ice sheet are associated with large uncertainties, especially under high‐emission scenarios. This uncertainty may notably be explained by unknowns in the long-term impacts of basal melting and changes in surface mass balance. Here, we use an observationally-calibrated ice-sheet model to investigate the future trajectory of the Antarctic ice sheet until the end of the millennium related to uncertainties in the future balance between sub-shelf melting and ice discharge on the one hand, and the changing surface mass balance on the other. Our large ensemble of simulations, forced by a panel of CMIP6 climate models, suggests that the ocean will be the main driver of short-term Antarctic mass loss, triggering ice loss in the West Antarctic ice sheet (WAIS) already during this century. Under high-emission pathways, ice-ocean interactions will result in a complete WAIS collapse, likely completed before the year 2500 CE, as well as significant grounding-line retreat in the East Antarctic ice sheet (EAIS). Under a more sustainable socio-economic scenario, both the EAIS and WAIS may be preserved, though the retreat of Thwaites glacier appears to be already committed under present-day conditions. We show that with a regional near-surface warming higher than +7.5°C, which may occur by the end of this century under unabated emission scenarios, major ice loss is expected as the increase in surface runoff outweighs the increase in snow accumulation, leading to a decrease in the mitigating role of the ice sheet surface mass balance.

How to cite: Coulon, V., Klose, A. K., Kittel, C., Winkelmann, R., and Pattyn, F.: Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3405, https://doi.org/10.5194/egusphere-egu23-3405, 2023.

EGU23-4042 | Posters on site | CR3.2

Experimental design for the the 2nd marine ice sheet and ocean model intercomparison project (MISOMIP2) 

Nicolas Jourdain, Jan De Rydt, Yoshihiro Nakayama, Ralph Timmermann, and Mathias Van Caspel

The 2nd Marine Ice Sheet and Ocean Model Intercomparison Project (MISOMIP2) is a natural progression of previous and ongoing model intercomparison exercises that have focused on the simulation of ice-sheet--ocean processes in Antarctica. The previous exercises motivate the move towards more realistic configurations and more diverse model parameters and resolutions. The first objective of MISOMIP2 is to investigate the robustness of ocean and ocean--ice-sheet models in a range of Antarctic environments, through comparisons to interannual observational data. We will assess the status of ocean--ice-sheet modelling as a community and identify common characteristics of models that are best able to capture observed features. As models are highly tuned based on present-day data, we will also compare their sensitivity to abrupt atmospheric perturbations leading to either very warm or slightly warmer ocean conditions than present-day. The approach of MISOMIP2 is to welcome contributions of models as they are, but we request standardised variables and common grids for the outputs. There will be two target regions, the Amundsen Sea and the Weddell Sea, chosen because they describe two extremely different ocean environments and have been relatively well observed compared to other parts of Antarctica. An observational "MIPkit" is provided to evaluate ocean and ice sheet models in these two regions.

How to cite: Jourdain, N., De Rydt, J., Nakayama, Y., Timmermann, R., and Van Caspel, M.: Experimental design for the the 2nd marine ice sheet and ocean model intercomparison project (MISOMIP2), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4042, https://doi.org/10.5194/egusphere-egu23-4042, 2023.

EGU23-6642 | ECS | Orals | CR3.2

Snow evolution through the Last Interglacial with a multi-layer snow model 

Thi Khanh Dieu Hoang, Aurélien Quiquet, Christophe Dumas, and Didier M. Roche

The Last Interglacial period (LIG), which occurred approximately between 130 and 116 kyr BP, is characterized by similar/warmer temperatures and higher sea levels compared to the present-day conditions due to the orbital variation of the Earth. Hence, the period provides insights into the behavior of the Earth's system components under stable and prolonged warm climates and their subsequent evolution into a glacial state. 

To better understand the ice sheet's surface mass balance that ultimately drives the advance and retreat of ice-sheets, we study the snow cover changes in the Northern Hemisphere during the LIG. In order to do so, we used BESSI (BErgen Snow Simulator), a physical energy balance model with 15 vertical snow layers and high computational efficiency, to simulate the snowpack evolution. First, BESSI was validated using the regional climate model MAR (Modèle Atmosphérique Régional) as forcing and benchmark for snow cover over the Greenland and Antarctica Ice Sheets under present-day climate. Using two distinct ice sheet climates helps constrain the different processes in place (e.g., albedo and surface melt for Greenland and sublimation for Antarctica). 

For the LIG simulations, the latest version of an Earth system model of intermediate complexity iLOVECLIM was used to force BESSI in different time slices to fully capture the snow evolution in the Northern Hemisphere throughout this period. Impacts of the downscaling component of iLOVECLIM, which provides higher resolution data and accounts for the influences of the topography, on BESSI performance are also discussed.  

The results show that BESSI performs well compared to MAR for the present-day climate, even with a less complex model set-up. Through the LIG, with the ability to model the snow compaction, the change of snow density and snow depth, BESSI simulates the snow cover evolution in the studied area better than the simple snow model (bucket model) included in iLOVECLIM. 

The findings suggest that BESSI can provide a more physical surface mass balance scheme to ice sheet models such as GRISLI of iLOVECLIM to improve simulations of the ice sheet - climate interactions.  

How to cite: Hoang, T. K. D., Quiquet, A., Dumas, C., and Roche, D. M.: Snow evolution through the Last Interglacial with a multi-layer snow model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6642, https://doi.org/10.5194/egusphere-egu23-6642, 2023.

EGU23-7020 | ECS | Orals | CR3.2

Uncertainties in Greenland ice sheet evolution and related sea-level projections until 2100 

Charlotte Rahlves, Heiko Goelzer, Petra Langebroek, and Andreas Born

The Greenland ice sheet is currently one of the main contributors to sea-level rise and mass loss from the ice sheet is expected to continue under increasing Arctic warming. Since sea-level rise is threatening coastal communities worldwide, reducing uncertainties in projections of future sea-level contribution from the Greenland ice sheet is of high importance. In this study we address the response of the ice sheet to future climate change. We determine rates of sea-level contribution that can be expected from the ice sheet until 2100 by performing an ensemble of standalone ice sheet simulations with the Community Ice Sheet Model (CISM). The ice sheet is initialized to resemble the presently observed geometry by inverting for basal friction. We examine a range of uncertainties, associated to stand alone ice sheet modeling by prescribing forcing from various global circulations models (GCMs) for different future forcing scenarios (shared socioeconomic pathways, SSPs). Atmospheric forcing is downscaled with the regional climate model MAR. The response of marine terminating outlet glaciers to ocean forcing is represented by a retreat parameterization and sampled by considering different sensitivities. Furthermore, we investigate how the initialization of the ice sheet with forcing from different global circulation models affects the projected rates of sea-level contribution. In addition, sensitivity of the results to the grid spacing of the ice sheet model is assessed. The observed historical mass loss is generally well reproduced by the ensemble. The projections yield a sea-level contribution in the range of 70 to 230 mm under the SSP5-8.5 scenario until 2100. Climate forcing constitutes the largest source of uncertainty for projected sea-level contribution, while differences due to the initial state of the ice sheet and grid resolution are minor.

 

 

How to cite: Rahlves, C., Goelzer, H., Langebroek, P., and Born, A.: Uncertainties in Greenland ice sheet evolution and related sea-level projections until 2100, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7020, https://doi.org/10.5194/egusphere-egu23-7020, 2023.

The precession of the equinoxes has a strong influence on the intensity of summer insolation according to most metrics and we would therefore expect the 23-Kyr and 19-Kyr precession cycles to be strongly reflected in our records of global ice volume, if summer insolation is indeed important for pacing glacial-interglacial cycles as proposed by Milutin Milankovitch. Instead, the precession signal is reduced in amplitude compared with the obliquity cycle in the Late Pleistocene, and in the Early Pleistocene (EP) precession appears completely absent in the δ18O stack. For this reason, the ‘40-Kyr world’ of the EP has been referred to as Milankovitch's other unsolved mystery. Indeed, numerous models of the Northern Hemisphere (NH) ice sheets simulated across the Plio-Pleistocene predict both a strong precessional and obliquity variability during the EP, at odds with the δ18O record. This points to the possibility of a dynamic Antarctic Ice Sheet in the EP that varied out-of-phase with the NH ice sheets at the precession period. In the original theory proposed by Raymo et al., (2006), from 3 to 1 Ma the East Antarctic Ice Sheet may have been land-terminating between 70S to 65S and sensitive to local summer insolation forcing. As precession is out-of-phase between the hemispheres, these variations could be cancelled out in globally integrated proxies of sea-level, concealing the true precession variability of both hemispheres in the marine sediment record. While studies have demonstrated  that precession-driven variations of the Antarctic Ice Sheet could cancel out NH variations in the deep-ocean record, no studies have investigated the actual feasibility of strong precession variability of the Antarctic Ice Sheet in the EP driven by local summer insolation, and whether it would have the magnitudes necessary to offset larger variations of the NH ice sheets. The question remains under what CO2 concentrations and orbital configuration can the East Antarctic Ice Sheet realistically be sensitive to local summer insolation forcing and possibly deglaciated from 70S to 65S, as postulated by Raymo et al. (2006). Can this produce the 10-30 m of sea-level necessary to offset NH variations in ice volume? To investigate the feasibility for anti-phased precession variability between the NH ice sheets and Antarctica in the EP, we use a zonally-averaged energy balance model coupled to a 1-D ice sheet model of a northern and southern hemisphere ice sheet, forced by atmospheric CO2 concentrations and daily insolation fields. The model will simulate glacial cycles across the Quaternary for different CO2 scenarios and determine whether anti-phased precessional cycles in ice volume between the hemispheres is a viable mechanism to explain the 40-Kyr world found in the δ18O record.

How to cite: Gunning, D.: Investigating precession cancellation across the MPT using a zonally averaged energy balance model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7385, https://doi.org/10.5194/egusphere-egu23-7385, 2023.

EGU23-7422 | ECS | Orals | CR3.2 | Highlight

(Ir)reversibility of future Antarctic mass loss on multi-millennial timescales 

Ann Kristin Klose, Violaine Coulon, Frank Pattyn, and Ricarda Winkelmann

Given the potentially high magnitudes and rates of future warming, the long-term evolution of the Antarctic Ice Sheet is highly uncertain. While recent projections under Representative Concentration Pathway 8.5 estimate the Antarctic sea-level contribution by the end of this century between -7.8 cm and 30.0 cm sea-level equivalent (Seroussi et al., 2020), sea-level might continue to rise for millennia to come due to ice sheet inertia, resulting in a substantially higher long-term committed sea-level change. In addition, potentially irreversible ice loss due to several self-amplifying feedback mechanisms may be triggered within the coming centuries, but evolves thereafter over longer timescales depending on the warming trajectory. It is therefore necessary to account for the timescale difference between forcing and ice sheet response in long-term sea-level projections by (i) determining the resulting gap between transient and committed sea-level contribution with respect to changing boundary conditions, (ii) testing the reversibility of large-scale ice sheet changes, as well as (iii) exploring the potential for safe overshoots of critical thresholds when reversing climate conditions from enhanced warming to present-day.

Here, we assess the sea-level contribution from mass balance changes of the Antarctic Ice Sheet on multi-millennial timescales, as well as ice loss reversibility. The Antarctic sea-level commitment is quantified using the Parallel Ice Sheet Model (PISM) and the fast Elementary Thermomechanical Ice Sheet (f.ETISh) model by fixing forcing conditions of warming trajectories from state-of-the-art climate models available from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) at regular intervals in time. The ice sheet then evolves for several millennia under constant climate conditions. Finally, the climate forcing is reversed to present-day starting from different stages of ice sheet decline to test for the reversibility of ice loss.

Our results suggest that the Antarctic Ice Sheet may be committed to a strong grounding-line retreat or even a collapse of the West Antarctic Ice Sheet when keeping climate conditions constant at warming levels reached during this century. Fixing climate conditions later in time may additionally trigger a substantial decline of the East Antarctic Ice Sheet. We show that the reversibility of Antarctic ice loss as well as the potential for safe overshoots strongly depend on the timing of the reversal of the forcing.

How to cite: Klose, A. K., Coulon, V., Pattyn, F., and Winkelmann, R.: (Ir)reversibility of future Antarctic mass loss on multi-millennial timescales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7422, https://doi.org/10.5194/egusphere-egu23-7422, 2023.

EGU23-7507 | ECS | Posters on site | CR3.2

The influence of temperature variability on the Greenland ice sheet 

Mikkel Lauritzen, Guðfinna Aðalgeirsdóttir, Nicholas Rathmann, Aslak Grinsted, Brice Noël, and Christine Hvidberg

The projected contribution of the Greenland ice sheet to sea-level rise in response to future warming relies upon the state of the present-day ice sheet, and one of the main contributors to uncertainties in projections is due to uncertainties in the initial state of the simulated ice sheet. A previous study showed that including the inter-annual climate variability in an idealized ice sheet model leads to an increased mass loss rate, but the effect on the Greenland ice sheet is not known. Here we present a study using the PISM model to quantify the influence of inter-annual variability in climate forcing on the Greenland ice sheet. 
We construct an ensemble of climate-forcing fields that account for inter-annual variability in temperature using reanalysis data products from RACMO and NOAA-CIRES, and we investigate the steady state and the sensitivity of the simulated Greenland ice sheet under these different scenarios.
We find that the steady state volume decreases by 0.24-0.38% when forced with a variable temperature forcing compared to a constant temperature forcing, corresponding to 21.7±5.0 mm of sea level rise, and the response to abrupt warming is 0.03-0.21 mm SLE a-1 higher depending on climate scenario. The northern basins are particularly sensitive with a change in volume of 1.2-0.9%. Our results emphasize the importance of including climate variability in projections of future mass loss.

How to cite: Lauritzen, M., Aðalgeirsdóttir, G., Rathmann, N., Grinsted, A., Noël, B., and Hvidberg, C.: The influence of temperature variability on the Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7507, https://doi.org/10.5194/egusphere-egu23-7507, 2023.

EGU23-7553 | ECS | Orals | CR3.2

Examining Possible Retreat Scenarios for the Greenland Ice Sheet during the MIS-11c Interglacial 

Brian Crow, Lev Tarasov, Matthias Prange, and Michael Schulz

The interglacial period spanning ca. 423 to 398 ka and known as Marine Isotope Stage (MIS) 11c has been the subject of much study, due largely to the unique evolution of global temperatures, greenhouse gas levels, and sea levels relative to other interglacials of the late Pleistocene. Particularly concerning is some geological evidence and prior modeling studies which have suggested that a large majority of the Greenland ice sheet (GrIS) disappeared during this period, despite global mean air temperatures only modestly higher than those of the preindustrial period. However, uncertainty is high as to the extent and spatiotemporal evolution of this melt due to a dearth of direct geological constraints. Our study therefore endeavors to better constrain these large uncertainties by using spatiotemporally interpolated climate forcing from CESM v1.2 time slice simulations and an ensemble of ice sheet model parameter vectors derived from a GrIS history matching over the most recent glacial cycle from the Glacial Systems Model (GSM). The use of different ice sheet initialization states from simulations of the previous glacial-interglacial transition helps to capture the large initial condition uncertainty. Two different regional present-day climate modeling datasets are utilized for anomaly correction of CESM precipitation and temperature fields. 

Preliminary analysis indicates that the most robust retreat across most ensemble members happens in the northern, western, and central portions of the ice sheet, while the higher terrain of the south and east retain substantial amounts of ice. This is broadly consistent with indications that ice may have survived the MIS-11c interglacial at the Summit ice core location, but not at DYE-3. Simulations indicate a maximum MIS-11c sea level contribution from the GrIS centered between 408 and 403 ka, with minimum GrIS volumes reaching between 25% and 70% of modern-day values. In part due to the prior constraint of ice-sheet model ensemble parameters from history matching, ensemble parameters controlling downscaling and climate forcing bias correction are the largest parametric sources of output variance in our simulations.  Though CESM uncertainties are unassessed in this study, it is likely they dominate given that the choice of present-day reference temperature climatology for anomaly correction of the climate model output has the largest effect on the GrIS melt response in our simulations.

How to cite: Crow, B., Tarasov, L., Prange, M., and Schulz, M.: Examining Possible Retreat Scenarios for the Greenland Ice Sheet during the MIS-11c Interglacial, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7553, https://doi.org/10.5194/egusphere-egu23-7553, 2023.

EGU23-7920 | ECS | Orals | CR3.2

The Divergent Futures of Greenland Surface Mass Balance Estimates from Different Regional Climate Models 

Quentin Glaude, Brice Noel, Martin Olesen, Fredrik Boberg, Michiel van den Broeke, Ruth Mottram, and Xavier Fettweis

Arctic amplification is causing global warming to have a more intense impact on arctic regions, with consequences on the surface mass balance and glacier coverage of Greenland. The glaciers of Greenland are also shrinking, contributing to sea level rise as well. Projecting the future evolution of these changes is crucial for understanding the likely impacts of climate change on sea level rise.

In this study, we compared three state-of-the-art Regional Climate Models (RCMs) (MAR, RACMO, and HIRHAM) using a common grid and forcing data from Earth System Models to assess their ability to project future changes in Greenland's surface mass balance up to 2100. We also considered the impact of different Earth System Models and Shared Socioeconomic Pathways.

The results of this comparison showed significant differences in the projections produced by these different models, with a factor-2 difference in mass loss between MAR and RACMO on cumulative Surface Mass Balance anomalies. These differences are important as RCMs are often used as inputs for ice sheet models, which are used to make predictions about sea level rise. Furthermore, we aim to investigate the causes of these differences, as understanding them will be key to improving the accuracy of sea level rise projections.

The uncertainty of the RCMs projections are translated into uncertainties in Sea-Level-Rise projections. The results presented here open the door for deeper investigations in the climate modeling community and the physical reasons linked to these divergences. Our study highlighted the importance of continued research and development of RCMs to better understand the physics implemented in these models and ultimately improve the accuracy of future sea level rise projections.

How to cite: Glaude, Q., Noel, B., Olesen, M., Boberg, F., van den Broeke, M., Mottram, R., and Fettweis, X.: The Divergent Futures of Greenland Surface Mass Balance Estimates from Different Regional Climate Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7920, https://doi.org/10.5194/egusphere-egu23-7920, 2023.

EGU23-8341 | Orals | CR3.2 | Highlight

Antarctic Ice Sheet tipping points in the last 800,000 years 

David Chandler, Petra Langebroek, Ronja Reese, Torsten Albrecht, and Ricarda Winkelmann

Stability of the Antarctic Ice Sheet in the present-day climate, and in future warming scenarios, is of growing concern as increasing evidence points towards the prospect of irreversible ice loss from the West Antarctica Ice Sheet (WAIS) with little or no warming above present. Here, in transient ice sheet simulations for the last 800,000 years (9 glacial-interglacial cycles), we find evidence for strong hysteresis between ice volume and ocean temperature forcing through each glacial cycle, driven by rapid WAIS collapse and slow recovery. Additional equilibrium simulations at several climate states show this hysteresis does not arise solely from the long ice sheet response time, instead pointing to consistent tipping-point behaviour in the WAIS. Importantly, WAIS collapse is triggered when continental shelf bottom water is maintained above a threshold of 0 to 0.25°C above present, and there are no stable states for the WAIS in conditions warmer than present. Short excursions to warmer temperatures (marine isotope stage 7) may not initiate collapse (‘borrowed time’), while the more sustained interglacials (stages 11, 9, 5e) demonstrate an eventual WAIS collapse. Cooling of ca. 2°C below present-day is then required to initiate recovery. Despite the differing climatic characteristics of each glacial cycle, consistency between both the transient and equilibrium behaviour of the ice sheet through several cycles shows there is some intrinsic predictability at millennial time scales, supporting the use of Pleistocene ice sheet simulations and geological evidence as constraints on likely future behaviour.

How to cite: Chandler, D., Langebroek, P., Reese, R., Albrecht, T., and Winkelmann, R.: Antarctic Ice Sheet tipping points in the last 800,000 years, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8341, https://doi.org/10.5194/egusphere-egu23-8341, 2023.

EGU23-8690 | ECS | Orals | CR3.2

Antarctic sensitivity to oceanic melting parameterizations 

Antonio Juárez-Martínez, Javier Blasco, Marisa Montoya, Jorge Alvarez-Solas, and Alexander Robinson

Ice in Antarctica has been experiencing dramatic changes in the last decades. These variations have consequences in terms of sea level, which could have an impact on human societies and life on the planet in the future. The Antarctic Ice Sheet (AIS) could become the main contributor to sea-level rise in the coming centuries, but there is a great uncertainty associated with its contribution, which is due in part to the complexity of the coupled ice-ocean processes. In this study we investigate the contribution of the AIS to sea-level rise in the coming centuries in the context of the Ice Sheet Model Intercomparison Project (ISMIP6), but covering a range beyond 2100, using the higher-order ice-sheet model Yelmo. We test the sensitivity of the model  to basal melting parameters using several forcings and scenarios for the atmosphere and ocean, obtained from different GCM models. The results show a strong  dependency on variations of the parameter values of the basal melting laws and also on the forcing that is chosen. Higher values of the heat exchange velocity between ice and ocean lead to higher sea-level rise, varying the contribution depending on the forcing. Ice-ocean interactions therefore can be expected to contribute significantly to the uncertainty associated with the future evolution of the AIS.

 

How to cite: Juárez-Martínez, A., Blasco, J., Montoya, M., Alvarez-Solas, J., and Robinson, A.: Antarctic sensitivity to oceanic melting parameterizations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8690, https://doi.org/10.5194/egusphere-egu23-8690, 2023.

EGU23-8853 | ECS | Orals | CR3.2

Sensitivity of Heinrich events to boundary forcing perturbations in a coupled ice sheet-solid Earth model 

Clemens Schannwell, Uwe Mikolajewicz, Marie Kapsch, and Florian Ziemen

Heinrich events are one of the prominent signals of glacial climate variability. They are characterised as abrupt, quasi-periodic episodes of ice-sheet instabilities during which large numbers of icebergs are released from the Laurentide ice sheet. These events affect the evolution of the global climate by modifying the ocean circulation through the addition of freshwater and the atmospheric circulation through changes in ice-sheet height. However, the mechanisms controlling the timing and occurrence of Heinrich events remain enigmatic to this day. Here, we present simulations with a coupled ice-sheet solid Earth model that aim to quantify the importance of different boundary forcings for the timing of Heinrich events. We focus the analysis on two prominent ice streams of the Laurentide ice sheet with the land-terminating Mackenzie ice stream and the marine-terminating Hudson ice stream. Our simulations identify different surge characteristics for the Mackenzie ice stream and the Hudson ice stream. Despite their different glaciological and climatic settings, both ice streams exhibit responses of similar magnitude to perturbations to the surface mass balance and the geothermal heat flux. However, Mackenzie ice stream is more sensitive to changes in the surface temperature. Changes to the ocean temperature and the global sea level have a negligible effect on the timing of Heinrich events in our simulations for both ice streams. We also show that Heinrich events for both ice streams only occur in a certain parameter space. Transitioning from an oscillatory Heinrich event state to a persistent streaming state can lead to an ice volume loss of up to 30%. Mackenzie ice stream is situated in a climate that is particularly close to this transition point, underlining the potential of the ice stream to have contributed to prominent abrupt climate events during glacial-interglacial transitions.

How to cite: Schannwell, C., Mikolajewicz, U., Kapsch, M., and Ziemen, F.: Sensitivity of Heinrich events to boundary forcing perturbations in a coupled ice sheet-solid Earth model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8853, https://doi.org/10.5194/egusphere-egu23-8853, 2023.

EGU23-8973 | ECS | Posters on site | CR3.2

How does the Greenland ice sheet respond on a medium-term time scale to various levels of warming? 

Alison Delhasse, Johanna Beckmann, and Christoph Kittel

The Greenland ice sheet is considered as one of the main causes of sea level rise (SLR) at the end of the 21st century. But what if it is already too late to reverse the loss of ice from the Greenland ice sheet? The mass balance (MB) resulting from the coupling between the Regional Atmospheric Model (MAR, ULiège) and the Parallel Ice Sheet Model (PISM, PIK) over Greenland following the CESM2 ssp585 climate indicates that even if we stop the CESM2 warming in 2100 and continue with a +7°C climate until 2200 with respect to the reference period (1961-1990), the GrIS continues to lose mass up to a contribution equivalent to 60 cm of SLR in 2200. From this coupling experiment, we ran several coupled simulations by stabilizing the warming at different thresholds (+ 1, 2, 3, ... °C) with respect to our reference period in order to highlight a kind of tipping point of the ice sheet with respect to atmospheric warming. Other experiments have been launched by reversing the climate imposed by CESM2 from 2100 to 2000, for example, with the aim of identifying whether the GrIS could gain ice mass again with a climate as warm as the present one.

How to cite: Delhasse, A., Beckmann, J., and Kittel, C.: How does the Greenland ice sheet respond on a medium-term time scale to various levels of warming?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8973, https://doi.org/10.5194/egusphere-egu23-8973, 2023.

EGU23-9449 | Posters on site | CR3.2

Interactive coupling of the Antarctic Ice Sheet and the global ocean 

Moritz Kreuzer, Willem Huiskamp, Torsten Albrecht, Stefan Petri, Ronja Reese, Georg Feulner, and Ricarda Winkelmann

Increased sub-shelf melting and ice discharge from the Antarctic Ice sheet has both regional and global impacts on the ocean and the overall climate system. Additional meltwater, for example, can reduce the formation of Antarctic Bottom Water, potentially affecting the global thermohaline circulation. Similarly, increased input of fresh and cold water around the Antarctic margin can lead to a stronger stratification of coastal waters, and a potential increase in sea-ice formation, trapping warmer water masses below the surface, which in turn can lead to increased basal melting of the ice shelves.

So far these processes have mainly been analysed in simple unidirectional cause-and-effect experiments, possibly neglecting important interactions and feedbacks. To study the long-term and global effects of these interactions, we have developed a bidirectional offline coupled ice-ocean model framework. It consists of the global ocean and sea-ice model MOM5/SIS and an Antarctic instance of the Parallel Ice Sheet Model PISM, with the ice-shelf cavity module PICO representing the ice-ocean boundary layer physics. With this setup we are analysing the aforementioned interactions and feedbacks between the Antarctic Ice Sheet and the global ocean system on multi-millenial time scales.

How to cite: Kreuzer, M., Huiskamp, W., Albrecht, T., Petri, S., Reese, R., Feulner, G., and Winkelmann, R.: Interactive coupling of the Antarctic Ice Sheet and the global ocean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9449, https://doi.org/10.5194/egusphere-egu23-9449, 2023.

EGU23-9747 | Orals | CR3.2

Climate variability as a major forcing of recent Antarctic ice-mass change 

Matt King, Kewei Lyu, and Xuebin Zhang

Antarctica has been losing ice mass for decades, but its link to large-scale modes of climate forcing is not clear. Shorter-period variability has been partly associated with El Niño Southern Oscillation (ENSO), but a clear connection with the dominant climate mode, the Southern Annular Mode (SAM), is yet to be found. We show that space gravimetric estimates of ice-mass variability over 2002-2021 may be substantially explained by a simple linear relation with detrended, time-integrated SAM and ENSO indices, from the whole ice sheet down to individual drainage basins. Approximately 40% of the ice-mass trend over the GRACE period can be ascribed to increasingly persistent positive SAM forcing which, since the 1940s, is likely due to anthropogenic activity. Similar attribution over 2002-2021 could connect recent ice-sheet change to human activity.

How to cite: King, M., Lyu, K., and Zhang, X.: Climate variability as a major forcing of recent Antarctic ice-mass change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9747, https://doi.org/10.5194/egusphere-egu23-9747, 2023.

EGU23-9842 | ECS | Orals | CR3.2

The choice of present-day climate forcing can significantly affect modelled future and past Antarctic Ice Sheet evolution 

Christian Wirths, Johannes Sutter, and Thomas Stocker

Model simulations of past and future Antarctic ice sheet (AIS) evolution depend on the applied climatic forcing. To model the present and future Antarctic ice sheet, several different forcings from regional climate models are available. It is therefore critical to understand the influence and the resulting model differences and uncertainties associated with the choice of present-day reference forcing.  

We apply present-day climatic forcings from regional models (RACMO2.3p2, MAR3.10, HIRHAM5 and COSMO-CLM2) combined with climate anomalies from a global climate model (HadGEM2-ES). With this setup, we investigate the future evolution of the AIS under the RCP2.6, RCP4.5 and RCP8.5 scenarios using the Parallel Ice Sheet Model (PISM). We find substantial differences in the future evolution of the AIS depending on the choice of the present-day reference field even under an extreme scenario such as RCP8.5. We discuss the influence of those forcing choices on the projected future AIS dynamics and sea-level contribution, considering a variety of ice sheet model parameterizations. 

With this analysis, we aim to gain a better understanding of the role of climate forcing choices and parameterization-induced uncertainties of sea-level rise projections. 

 

How to cite: Wirths, C., Sutter, J., and Stocker, T.: The choice of present-day climate forcing can significantly affect modelled future and past Antarctic Ice Sheet evolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9842, https://doi.org/10.5194/egusphere-egu23-9842, 2023.

EGU23-9904 | Orals | CR3.2

Response of the Greenland Ice Sheet to temperature overshoot scenarios  

Michele Petrini, Heiko Goelzer, Petra Langebroek, Charlotte Rahlves, and Jörg Schwinger

As there is no evidence for the implementation of sufficiently ambitious global CO2 emission reductions, it is very unlikely that we will be able to keep the global mean warming at the end of the century below the 1.5 C limit set in the Paris Agreement. However, the development of CO2 removal techniques could potentially allow us to reach the 1.5 C target after a period of temperature overshoot, by offsetting past and current high levels of emissions with net-negative emissions in the future. To assess the effectiveness and the risks associated to such mitigation options, we need to better understand the impact of temperature overshoot scenarios on various components of the Earth System.  

Here, we focus on the Greenland Ice Sheet. We force an ice-sheet model (CISM2) with Surface Mass Balance (SMB) from an ensemble of 400 years-long idealized overshoot simulations, carried out with the Norwegian Earth System Model NorESM2. The SMB, which is calculated in NorESM2 using an energy balance scheme at multiple elevation classes, is downscaled during runtime to the ice-sheet model grid, thus allowing to account explicitly for the SMB-height feedback. In this presentation, we will assess the sea-level contribution of the Greenland Ice Sheet for overshoot pathways, compared to reference pathways without overshoot. Moreover, we will assess the impact of individual processes, such as the SMB-height feedback and the ocean-driven mass loss at marine-terminating margins, on the sea-level contribution of the Greenland Ice Sheet.  

How to cite: Petrini, M., Goelzer, H., Langebroek, P., Rahlves, C., and Schwinger, J.: Response of the Greenland Ice Sheet to temperature overshoot scenarios , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9904, https://doi.org/10.5194/egusphere-egu23-9904, 2023.

EGU23-10165 | Orals | CR3.2

Competing climate feedbacks of ice sheet freshwater discharge in a warming world 

Dawei Li, Robert DeConto, and David Pollard

Earth's polar ice sheets are projected to undergo significant retreat in the coming centuries if anthropogenic warming were to continue unabated, injecting freshwater stored on land over millennia into oceans and raise the global mean sea level. Ice sheet freshwater flux alters the status of ocean stratification and ocean-atmosphere heat exchange, inducing oceanic surface cooling and subsurface warming, hence an impact on the global climate. How the climate effects of ice sheet freshwater would feedback to influence the retreat of ice sheets, however, remains unsettled. Here we develop a two-way coupled climate-ice sheet modeling tool to assess the interactions between retreating polar ice sheets and the climate, considering a variety of greenhouse gas emission scenarios and modeled climate sensitivities. Results from coupled ice sheet-climate modeling show that ice sheet-ocean interactions give rise to multi-centennial oscillations in ocean temperatures around Antarctica, which would make it challenging to isolate anthropogenic signals from observational data. Future projections unveil both positive and negative feedbacks associated with freshwater discharge from the Antarctic Ice Sheet, while the net effect is scenario-dependent. The West Antarctic Ice Sheet collapses in high-emission scenarios, but the process is slowed significantly by cooling induced by ice sheet freshwater flux.

How to cite: Li, D., DeConto, R., and Pollard, D.: Competing climate feedbacks of ice sheet freshwater discharge in a warming world, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10165, https://doi.org/10.5194/egusphere-egu23-10165, 2023.

EGU23-10204 | ECS | Orals | CR3.2

Parameter ensemble simulations of the North American and Greenland ice sheets and climate of the Last Glacial Maximum with Famous-BISICLES 

Sam Sherriff-Tadano, Niall Gandy, Ruza Ivanovic, Lauren Gregoire, Jonathan Owen, Charlotte Lang, Jonathan Gregory, Robin Smith, and Tamsin Edwards
Testing the ability of climate-ice sheet coupled models to simulate past ice sheets and climates can provide a way to evaluate the models. One example is the Last Glacial Maximum (LGM), when huge ice sheets covered the Northern Hemisphere, especially over the North America. Here, we perform 200 ensemble member simulations of the North American and Greenland ice sheets and climate of the LGM with an ice sheet-atmosphere-slab ocean coupled model Famous-BISICLES. 16 parameters associated with climate and ice dynamics are varied. The simulated results are evaluated against the LGM global temperature, the total ice volume and the ice extent at the southern margin of the North American ice sheet. In the ensemble simulations, the global temperature is controlled by the combination of precipitation efficiency in the large-scale condensation and entrainment rate in the cumulus convection. Under reasonable LGM global temperature, we find that the surface albedo and Weertman coefficient in the basal sliding law control the North American ice volume. In contrast, the ice volume of Greenland is found to be controlled by the Weertman coefficient. Based on the constraints, the model produces 6 good simulations with reasonable global temperature and North American ice sheet. We also find that warm summer surface temperature biases at the ice sheet interior as well as downscaling of surface mass balance based on altitude can cause strong local ice melting. This implies the need of better representing the atmospheric conditions and surface mass balance in the ice sheet interior.

How to cite: Sherriff-Tadano, S., Gandy, N., Ivanovic, R., Gregoire, L., Owen, J., Lang, C., Gregory, J., Smith, R., and Edwards, T.: Parameter ensemble simulations of the North American and Greenland ice sheets and climate of the Last Glacial Maximum with Famous-BISICLES, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10204, https://doi.org/10.5194/egusphere-egu23-10204, 2023.

EGU23-10231 | ECS | Orals | CR3.2

The effect of an evolving Greenland ice sheet in NorESM2 projections 

Konstanze Haubner, Heiko Goelzer, Petra Langebroek, and Andreas Born

The Greenland ice sheet's mass loss is increasing and so is its impact to the climate system. Yet, Earth System models mostly keep ice sheets at a constant extent or treat interactions with the ice sheets fairly simple.

Here, we present the first simulations of NorESM2 coupled to the ice sheet model CISM over Greenland. We compare NorESM2 simulations from 1850 to 2300 with and without an evolving ice sheet over Greenland based on the ssp585 scenario and its extension to 2300. Ocean and atmosphere horizontal resolution are on 1deg, while the coupled ice sheet module CISM is running on 4km. The coupling setup is based on CESM2. Ice extent and elevation are provided to the atmosphere every 5years and the land model every year. Whereas the ice sheet receives updated surface mass balance every year.
We show the evolution of the Greenland ice sheet and changes in atmosphere, ocean and sea ice.

Overall global mean surface air temperatures (SAT) change from 14°C to 24°C by 2300 with the steepest increase between 2070-2200.
Over the Southern ocean and Antarctica, SAT are increasing by 10°C, while over the Northern hemisphere we see a change of 15-28°C by 2300. 
At the end of the simulations (year 2300), SAT over Greenland are 6°C warmer when including an evolving ice sheet. In contrast, the ocean surrounding Greenland shows SAT that are 2°C colder in the coupled system, compared to the simulation with a fixed Greenland ice sheet. Sea surface temperatures show the same ~2°C difference around Greenland in coupled and uncoupled simulation. The overall change in sea surface temperatures is 12°C.
Minimum and maximum sea ice extent differs only slightly with and without the coupling, indicating that the overall warming seems to dictate speed of the sea ice retreat.

How to cite: Haubner, K., Goelzer, H., Langebroek, P., and Born, A.: The effect of an evolving Greenland ice sheet in NorESM2 projections, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10231, https://doi.org/10.5194/egusphere-egu23-10231, 2023.

The mid-Pleistocene Transition (MPT) from 41 kyr to 100 kyr glacial cycles was one of the largest changes in the Earth system over the past 2 million years. The transition happened in the absence of a relevant change in orbital forcing. As such, it presents a challenge for the Milankovitch theory of glacial cycles. A change from a low to high friction bed under the North American Ice Complex through the removal of pre-glacial regolith has been hypothesized to play a critical role in the transition. For testing, this hypothesis requires constraint on pre-glacial regolith cover and topography as well as mechanistic constraint on whether the appropriate amount of regolith can be removed from the required regions to enable MPT occurrence at the right time. To date, however, Pleistocene regolith removal has not been simulated for a realistic, 3D North American ice sheet fully resolving relevant basal processes. A further challenge is very limited constraints on pre-glacial bed elevation and sediment thickness.

Herein, we address these challenges with an appropriate computational model and ensemble-based analysis addressing parametric and initial mean sediment cover uncertainties. We use the 3D Glacial Systems Model that incorporates relevant glacial processes. Specifically, it includes: 3D thermomechanically coupled hybrid SIA/SSA ice physics, fully coupled sediment production and transport, subglacial linked-cavity and tunnel hydrology, isostatic adjustment from dynamic loading and erosion, and climate from a 2D non-linear energy balance model and glacial index. The sediment model includes quarrying and abrasion for sediment production with both englacial and subglacial transport. The coupled system is driven only by atmospheric CO2 and insolation.

We show that the ice, climate, and sediment processes encapsulated in this fully coupled glacial systems model enables capture of the evolution of the Pleistocene North American glacial system. Specifically and within observational uncertainty, our model captures: the shift from 41 to 100 kyr glacial cycles, early Pleistocene extent, LGM ice volume, deglacial ice extent, and the broad present-day sediment distribution. We also find that pre-glacial sediment thickness and topography have a strong influence on the strength and duration of early Pleistocene glaciations.

How to cite: Drew, M. and Tarasov, L.: The pre-Pleistocene North American bed from coupled ice-climate-sediment physics and its strong influence on glacial cycle evolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10318, https://doi.org/10.5194/egusphere-egu23-10318, 2023.

EGU23-10677 | Orals | CR3.2 | Highlight

Impacts of regional sea-level changes due to GRD effects on multi-centennial projections of Antarctic Ice Sheet under the ISMIP6-2300 experimental protocol  

Holly Han, Matt Hoffman, Xylar Asay-Davis, Trevor Hillebrand, and Mauro Perego

Evolution of ice sheets contribute to sea-level change globally by exchanging mass with the ocean, and regionally by causing the solid Earth deformation and perturbation of the Earth’s rotation and gravitational field, so-called “gravitational, rotational and deformational (GRD) effects”. In the last decade, much work has been done to establish the importance of coupling GRD effects particularly in modeling of marine-based ice sheets (e.g., West Antarctic Ice Sheet; WAIS) to capture the interactions between ice sheets, sea level and the solid Earth at the grounding lines. However, coupling of GRD effects has not yet been done widely within the ice-sheet modeling community; for example, GRD effects were not included in any of the ice sheet models that contributed to the most recent recent ice-sheet model intercomparison through 2100 (Ice Sheet Model Intercomparison Project for CMIP6: ISMIP6-2100; Serrousi et al., 2020) cited by the latest IPCC AR6 report.

In this work, we couple the US Department of Energy’s MPAS-Albany Land Ice model (which was one of the models that participated in the ISMIP6-2100 project) to a 1D sea-level model and perform coupled simulations of Antarctica under the new ISMIP6-2300 protocol in which climate forcing is extended beyond 2100 to 2300. Comparing to the standalone ice-sheet simulations with fix bed topography without GRD effects, the results from our coupled simulations show multi-decadal to centennial-scale delays in the retreat of the Thwaites glacier in the West Antarctica. Our results further suggest that the strength of the negative feedback of sea-level changes on the WAIS retreat becomes weaker as the strength of the applied forcing increases, implying the pertinence of our commitment to limiting greenhouse gas emissions. In addition, within our coupled ice sheet-sea level modeling frame, we introduce a new workflow work in which the ISMIP6 protocol-provided ocean thermal forcing is re-extrapolated based on the updated ocean bathymetry. Our preliminary results indicate that bedrock uplift due to ice mass loss can block the bottom warm ocean, providing additional negative feedback, but also can block cold water when/if the vertical ocean temperature profile gets inverted due to climate change (e.g., as represented in the UKESM model - SSP585 scenario results).

How to cite: Han, H., Hoffman, M., Asay-Davis, X., Hillebrand, T., and Perego, M.: Impacts of regional sea-level changes due to GRD effects on multi-centennial projections of Antarctic Ice Sheet under the ISMIP6-2300 experimental protocol , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10677, https://doi.org/10.5194/egusphere-egu23-10677, 2023.

EGU23-11678 | ECS | Posters on site | CR3.2

Antarctic ice sheet response to AMOC shutdowns during the penultimate deglaciation 

Maxence Menthon, Pepijn Bakker, Aurélien Quiquet, and Didier M. Roche

According to geological records, the sea level during the Last Interglacial (∼ 129–116 ka) peaked 6 to 9 m higher than during the pre-industrial with a major contribution from the Antarctic ice sheet (Dutton et al. 2015). According to Clark et al. 2020, a longer period of reduced Atlantic Meridional Overturning Circulation (AMOC) during the penultimate deglaciation compared to the last deglaciation could have led to greater subsurface warming and subsequent larger Antarctic Ice Sheet retreat.

Here we study the response of the Antarctic ice sheet to climate forcing with a forced AMOC shutdown at different timing and duration during the penultimate deglaciation (∼ 138–128 ka). The simulations are done with the Earth System Model of Intermediate Complexity iLOVECLIM (Roche et al. 2014) and the ice sheet model GRISLI (Quiquet et al. 2018), using the recently implemented sub-shelf melt module PICO (Reese et al. 2018). In the present simulations the GRISLI is forced with the iLOVECLIM simulations and is a step towards a fully coupled climate - ice sheet set up to take into account the climate - ice sheet interactions in a physical way.

We hypothesize that both the duration and timing of reduced AMOC can significantly affect the sensitivity of the Antarctic Ice Sheet. A longer period of AMOC reduction will lead to a larger subsurface warming in the Southern Ocean and subsequently a larger ice sheet retreat. On the other hand, an AMOC reduction earlier (later) in the deglaciation implies that the ice sheet that is affected by this subsurface warming is still fairly large (already small). We will discuss both the individual as well as combined effect of duration and timing on the ice sheet evolution.

How to cite: Menthon, M., Bakker, P., Quiquet, A., and Roche, D. M.: Antarctic ice sheet response to AMOC shutdowns during the penultimate deglaciation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11678, https://doi.org/10.5194/egusphere-egu23-11678, 2023.

EGU23-11845 | ECS | Orals | CR3.2

An Adimensional Ice-Sheet-Climate Model for glacial cycles 

Sergio Pérez-Montero, Jorge Alvarez-Solas, Alexander Robinson, and Marisa Montoya

Although the ultimate trigger of glacial cycles is Milankovitch insolation cycles, there are still uncertainties concerning their timing and transitions. These unknowns are believed to be due to intrinsic nonlinearities in the climate system, and there is a deep interest in their solution. However, the longer timescales involved make it infeasible to use comprehensive climate models because of the large computational cost involved. In this context, conceptual models are built to mimic complex processes in a simpler, computationally efficient way. Here we present an adimensional ice-sheet–climate model (AMOD), which aims to study these outstanding paleoclimatic topics. AMOD represents ice sheet dynamics by using common assumptions as in state-of-the-art ice-sheet models, adapted to its dimensionless nature, and it solves surface mass balance processes and the aging of snow and ice. In this way, AMOD is able to run several glacial cycles in seconds and produces results comparable to those of paleoclimatic proxies. Preliminary results indicate nonlinearities related to both ice dynamics and snow aging that determine the timing and shape of deglaciations.

How to cite: Pérez-Montero, S., Alvarez-Solas, J., Robinson, A., and Montoya, M.: An Adimensional Ice-Sheet-Climate Model for glacial cycles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11845, https://doi.org/10.5194/egusphere-egu23-11845, 2023.

EGU23-12206 | ECS | Orals | CR3.2

The Glacier-climate Interaction over the High-Mountain Asia during the Last Glacial Maximum 

Qiang Wei, Yonggang Liu, Yongyun Hu, and Qing Yan

Glacier advances affect the local climate, and in turn, can either promote or prohibit its own growth. Such feedback has not been considered in modeling the High-Mountain Asia (HMA) glaciers during the Last Glacial Maximum (LGM; ~28-23 ka), which may contribute to the large spread in some of the published modeling work, with some notable discrepancy with existing reconstruction data. By coupling an ice sheet model (ISSM) with a climate model (CESM1.2.2), we find that the total glacial area is reduced by 10% due to the glacier-climate interaction; glacier growth is promoted along the western rim of HMA, and yet reduced in the interior. Such changes in spatial pattern improve model-data comparison. Moreover, the expansion of glaciers causes an increase in the winter surface temperature of the eastern Tibetan Plateau by more than 2 K, and a decrease of precipitation almost everywhere, especially the Tarim basin, by up to 60%. These changes are primarily due to the increase in surface elevation, which blocks the water vapor brought by westerlies and southwesterlies, reducing precipitation and increasing surface temperatures to the east and northeast of the newly grown glaciers.

How to cite: Wei, Q., Liu, Y., Hu, Y., and Yan, Q.: The Glacier-climate Interaction over the High-Mountain Asia during the Last Glacial Maximum, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12206, https://doi.org/10.5194/egusphere-egu23-12206, 2023.

The Greenland ice sheet comprises a volume of 7.4 m sea level equivalent and is losing mass rapidly as a result of global warming. It is widely thought that the ice sheet will exhibit tipping behaviour in a warmer climate. In other words, due to ice sheet – climate feedbacks (some of) its contribution to sea level rise may become irreversible once critical thresholds are crossed. This would severely affect the increasing number of people living in low-lying coastal areas worldwide. However, the current understanding of such thresholds and tipping behaviour is very limited, because most modelling studies up to date do not include (local) interactions or feedbacks between the ice sheet (topography and ice extent) and other climate system components (surface mass balance and atmosphere).

To investigate the irreversibility of Greenland’s ice mass loss and the associated processes, we coupled our high-resolution Greenland Ice Sheet Model (GISM) with a renowned high-resolution regional climate model, the Modèle Atmosphérique Régional (MAR). The two-way coupling between both models provides a (more) realistic representation of (local) ice sheet – climate interactions for future ice sheet simulations.

Like all regional climate models, MAR needs 6 hourly atmospheric forcing from a global climate model (GCM). Several coupled model runs with forcing from different GCMs are envisioned over the coming months and years. As they are computationally intensive, simulations up to the end of the century and beyond take several weeks to a few months to complete.

The poster will present the preliminary results from our first coupled model run in an envisioned series of experiments: a two-way coupled MAR-GISM run forced by the IPSL-CM6 6 hourly output, which is available up to 2300. For this timescale, our coupled models can still be run in fully interactive mode, which means the information (surface mass balance and ice sheet extent/topography) between both models can be exchanged on a yearly basis. In addition to its long duration, the IPSL forcing is of particular interest as it is on the high end of the CMIP6 model ensemble projections regarding warming over Greenland. We thus expect the experiment to provide valuable insights regarding Greenland’s potential contribution to future sea-level rise and the associated ice sheet – climate interactions or feedbacks.  

How to cite: Paice, C. M., Fettweis, X., and Huybrechts, P.: Quantifying the response of the Greenland ice sheet in a high-end scenario until 2300 from a coupled high-resolution regional climate and ice sheet model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12281, https://doi.org/10.5194/egusphere-egu23-12281, 2023.

EGU23-13350 | Orals | CR3.2

Large effects of ocean circulation change on Greenland ice sheet mass loss 

Miren Vizcaino, Julia Rudlang, Laura Muntjewerf, Sotiria Georgiou, Raymond Sellevold, and Michele Petrini

The Greenland ice sheet (GrIS) is currently losing mass at an accelerated rate, due to atmospheric and ocean warming causing respectively enhanced melt and ice discharge to the ocean. A large part of the uncertainty on future GrIS contribution to sea level rise relates to unknown atmospheric and ocean circulation change. For the later, AR6 models project a weakening of the North Atlantic Meridional Overturning Circulation (NAMOC) during the 21st century. The magnitude of this weakening depends on the greenhouse gas scenario and model, but none of the models project a complete collapse.

Projections of future GrIS evolution in the last IPCC report AR6 are mostly based on simulations with ice sheet models forced with the output of climate models (e.g., Goelzer et al. (2020)). This method permits large ensembles of simulations, however the coupling between climate and GrIS is not represented. Here, we use a coupled Earth System and Ice Sheet Model (ESM-ISM), the CESM2-CISM2 (Muntjewerf et al. 2021) to examine the multi-millennial evolution of the GrIS surface mass balance for a middle-of-the-road CO2 scenario. The model couples realistic simulation of global climate (Danabasoglu et al. 2020), surface processes (van Kampenhout et al. 2020) and ice dynamics (Lipscomb et al. 2019). We use an idealized scenario of 1% CO2 increase until stabilization at two times pre-industrial values.  compare our results with pre-industrial and 1% to 4xCO2 simulations (Muntjewerf et al. 2020).

We find small increases and even reduction of annual temperatures in the GrIS area in connection with strong NAMOC weakening in the first two centuries of simulation. Summer temperatures and surface melt increase moderately with respect to pre-industrial. From simulation year 500, the NAMOC recovers, resulting in strong increases in GrIS melt rates and contribution to sea level rise. We compare the deglaciation pattern over a period of 3,000 years with deglaciation simulations with the same model for the last interglacial (Sommers et al. 2021).

 

How to cite: Vizcaino, M., Rudlang, J., Muntjewerf, L., Georgiou, S., Sellevold, R., and Petrini, M.: Large effects of ocean circulation change on Greenland ice sheet mass loss, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13350, https://doi.org/10.5194/egusphere-egu23-13350, 2023.

EGU23-13907 | ECS | Orals | CR3.2

First results of RACMO2.4: A new model version with updated surface and atmospheric processes 

Christiaan van Dalum and Willem Jan van de Berg

In recent years, considerable progress in surface and atmospheric physics parameterizations has been made by the scientific community that could benefit regional climate modelling of polar regions. Therefore, we developed a major update to the Regional Atmospheric Climate Model, referred to as RACMO2.4, that includes several new and updated parameterizations. Most importantly, the surface and atmospheric processes from the European Center for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), which are embedded in RACMO, are updated to cycle 47r1. This includes, among other changes, updates in the cloud, aerosol and radiation scheme, a new lake model, and a new multilayer snow module for non-glaciated regions. Furthermore, a new spectral albedo and radiative transfer scheme in snow scheme, which has been introduced and evaluated in a previous, yet inoperative version, is now operational. Here, we shortly introduce the aforementioned changes and present the first results of RACMO2.4 for several domains, particularly of the Greenland ice sheet.

How to cite: van Dalum, C. and van de Berg, W. J.: First results of RACMO2.4: A new model version with updated surface and atmospheric processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13907, https://doi.org/10.5194/egusphere-egu23-13907, 2023.

EGU23-14088 | Posters on site | CR3.2

Reconstructing the Greenland ice sheet in past warm climates 

Christine S. Hvidberg, Mikkel Lauritzen, Nicholas M. Rathmann, Anne M. Solgaard, and Dorthe Dahl-Jensen

The stability of the Greenland ice sheet through past glacial-interglacial cycles provides knowledge that can contribute to understanding the future mass loss and contribution to sea level from the Greenland ice sheet in a warmer climate. Paleo-climatic records from ice cores provide constraints on the past climate and ice sheet thickness in Greenland through the current interglacial, the Holocene, 11.7 kyr to present, but is limited to a few ice cores from the central areas. In the previous interglacial period, the Eemian, 130 kyr to 110 kyr before present, the ice core constraints are sparse, and beyond the Eemian, the climate evolution is known from Antarctic ice cores and marine sediments. The limited constraints on the past climate in Greenland presents a challenge for reconstructions based on ice flow modelling. Here we present initial results from an ice flow modelling study using the PISM ice flow model to simulate the evolution of the Greenland ice sheet in the Eemian and the Holocene periods. We discuss how paleo-climatic data from ice cores and marine sediments can be combined with ice flow modelling. We find that the Greenland ice sheet retreated to a minimum volume of up to ∼1.2 m sea-level equivalent smaller than present in the early or mid-Holocene, and that the ice sheet has continued to recover from this minimum up to present day. In all our runs, the ice sheet is approaching a steady state at the end of the 20th century. Our studies show that the Greenland ice sheet evolves in response to climate variations on shorter and longer timescales, and that assessment of future mass loss must take into account the history and current state.

How to cite: Hvidberg, C. S., Lauritzen, M., Rathmann, N. M., Solgaard, A. M., and Dahl-Jensen, D.: Reconstructing the Greenland ice sheet in past warm climates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14088, https://doi.org/10.5194/egusphere-egu23-14088, 2023.

EGU23-14236 | ECS | Orals | CR3.2

Sensitivity of future projections of ice sheet retreat to initial conditions 

Tijn Berends, Jorjo Bernales, Caroline van Calcar, and Roderik van de Wal

Both the Greenland and Antarctic ice sheets are expected to experience substantial mass loss in the case of unmitigated anthropogenic climate change. The exact rate of future mass loss under high warming scenarios remains uncertain, depending strongly on physical quantities that are difficult to constrain from observations, such as basal sliding and sub-shelf melt. We apply a novel model initialisation protocol, that combines elements from existing approaches such as the equilibrium spin-up, basal inversion, and palaeo spin-up, to models of both the Greenland and Antarctic ice sheets. We show the results in term of sea-level projections including the uncertainties, under different warming scenarios, following the ISMIP6 protocol.

This abstract is a companion to “On the initialisation of ice sheet models: equilibrium assumptions, thermal memory, and present-day states” by Bernales et al. We hope that, if both abstracts are lucky enough to be accepted, the conveners can program the two talks in sequence.

How to cite: Berends, T., Bernales, J., van Calcar, C., and van de Wal, R.: Sensitivity of future projections of ice sheet retreat to initial conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14236, https://doi.org/10.5194/egusphere-egu23-14236, 2023.

EGU23-14412 | ECS | Orals | CR3.2

Self-adaptive Laurentide Ice Sheet evolution towards the Last Glacial Maximum 

Lu Niu, Gregor Knorr, Uta Krebs-Kanzow, Paul Gierz, and Gerrit Lohmann

Northern Hemisphere summer insolation is regarded as a main control factor of glacial-interglacial cycles. However, internal feedbacks between ice sheets and other climate components are non-negligible. Here we apply a state-of-the-art Earth system model (AWI-ESM) asynchronously coupled to the ice sheet model PISM, focusing on the period when ice sheet grows from an intermediate state (Marine isotope stage 3, around 38 k) to a maximum ice sheet state (the Last Glacial Maximum). Our results show that initial North American ice sheet differences at 38 k are erased by feedbacks between atmospheric circulation and ice sheet geometry that modulate the ice sheet development during this period. Counter-intuitively, moisture transported from the North Atlantic warm pool during summer is the main controlling factor for the ice sheet advance. A self-adaptative mechanism is proposed in the development of a fully-grown NA ice sheet which indicates how the Earth system stabilizes itself via interactions between different Earth System components.

How to cite: Niu, L., Knorr, G., Krebs-Kanzow, U., Gierz, P., and Lohmann, G.: Self-adaptive Laurentide Ice Sheet evolution towards the Last Glacial Maximum, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14412, https://doi.org/10.5194/egusphere-egu23-14412, 2023.

EGU23-14469 | ECS | Orals | CR3.2 | Highlight

Has the (West) Antarctic Ice Sheet already tipped? 

Ronja Reese, Julius Garbe, Emily A. Hill, Benoît Urruty, Kaitlin A. Naughten, Olivier Gagliardini, Gael Durand, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, David Chandler, Petra M. Langebroek, and Ricarda Winkelmann

Observations of ocean-driven grounding line retreat in the Amundsen Sea Embayment in Antarctica raise the question of an imminent collapse of the West Antarctic Ice Sheet. Here we analyse the committed evolution of Antarctic grounding lines under the present-day climate. To this aim, we run an ensemble of historical simulations with a state-of-the-art ice sheet model to create model instances of possible present-day ice sheet configurations. Then, we extend the simulations to investigate their evolution under constant present-day climate forcing and bathymetry. We test for reversibility of grounding line movement at different stages of the simulations to analyse when and where irreversible grounding line retreat, or tipping, is initiated.

How to cite: Reese, R., Garbe, J., Hill, E. A., Urruty, B., Naughten, K. A., Gagliardini, O., Durand, G., Gillet-Chaulet, F., Gudmundsson, G. H., Chandler, D., Langebroek, P. M., and Winkelmann, R.: Has the (West) Antarctic Ice Sheet already tipped?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14469, https://doi.org/10.5194/egusphere-egu23-14469, 2023.

EGU23-14648 | ECS | Orals | CR3.2

On the initialisation of ice sheet models: equilibrium assumptions, thermal memory, and present-day states 

Jorjo Bernales, Tijn Berends, Caroline van Calcar, and Roderik van de Wal

A significant portion of the spread in future projections of ice sheet volume changes is attributed to uncertainties in their present-day state, and the way this state is represented in ice-sheet models. The scientific literature already contains a variety of classic initialisation approaches used by modelling groups around the globe, each with its own advantages and limitations. We propose a generalised protocol that allows for the quantification of the impact of individual initialisation choices, such as steady-state assumptions, the inclusion of internal paleoclimatic thermal signals, sea level and glacial isostatic effects, and calibration methods. We then apply this protocol to an ensemble of multi-millennia model spin-ups of the present-day Greenland and Antarctic ice sheets and show the importance of the choices made during initialisation.

[This abstract is a companion to “Sensitivity of future projections of ice sheet retreat to initial conditions” by Berends et al. We hope that, if both abstracts are lucky enough to be accepted, the conveners can program the two talks in sequence.]

How to cite: Bernales, J., Berends, T., van Calcar, C., and van de Wal, R.: On the initialisation of ice sheet models: equilibrium assumptions, thermal memory, and present-day states, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14648, https://doi.org/10.5194/egusphere-egu23-14648, 2023.

EGU23-14666 | Orals | CR3.2

Sensitivity of of coupled climate and ice sheet of modern Greenland to atmospheric, snow and ice sheet parameters 

Charlotte Lang, Tamsin Edwards, Jonathan Owen, Sam Sherriff-Tadano, Jonathan Gregory, Ruza Ivanovic, Lauren Gregoire, and Robin S. Smith

As part of a project working to improve coupled climate-ice sheet modelling by studying the response of ice sheets to changes in climate across different periods since the Last Glacial Maximum, we present an analysis of an ensemble of coupled climate and ice sheet simulations of the modern Greenland using the FAMOUS-BISICLES model and statistical emulation.

FAMOUS-BISICLES, a variant of FAMOUS-ice (Smith et al., 2021a), is a low resolution (7.5°X5°) global climate model that is two-way coupled to a higher resolution (minimum grid spacing of 1.2 km) adaptive mesh ice sheet model, BISICLES. It uses a system of elevation classes to downscale the lower resolution atmospheric variables onto the ice sheet grid and calculates surface mass balance using a multilayer snow model. FAMOUS-ice is computationally affordable enough to simulate the millennial evolution of the coupled climate-ice sheet system as well as to run large ensembles of simulations. It has also been shown to simulate Greenland well in previous work using the Glimmer shallow ice model (Gregory et al., 2020).

The ice sheet volume and area are sensitive to a number of parametrisations related to atmospheric and snow surface processes and ice sheet dynamics. Based on that, we designed a perturbed parameters ensemble using a Latin Hypercube sampling technique and ran simulations with climate forcings appropriate for the late 20th century.

Gaussian process emulation allows us explore parameter space in a more systematic and faster way than with more complex earth system models and make predictions at input parameter values that are not evaluated in the simulations. We find that the mass balance is most correlated to three parameters:

  • n, the exponent in Glen’s flow law, and beta, the coefficient of the basal drag law, both influencing the amount of ice lost through discharge
  • rho_threshold, a parameter setting the minimum value the dense firn albedo can possibly reach

Finally, using a history matching approach, we built an implausibility metric (based on surface mass balance, ice volume loss, near-surface and sea-surface temperature) to identify the regions of the parameter space that produce plausible runs.

How to cite: Lang, C., Edwards, T., Owen, J., Sherriff-Tadano, S., Gregory, J., Ivanovic, R., Gregoire, L., and Smith, R. S.: Sensitivity of of coupled climate and ice sheet of modern Greenland to atmospheric, snow and ice sheet parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14666, https://doi.org/10.5194/egusphere-egu23-14666, 2023.

EGU23-15230 | Posters on site | CR3.2

Antarctic RINGS to characterize the Antarctic Ice Sheet coastal zone and Antarctic contribution to the global sea-level rise 

Kenichi Matsuoka, Xiangbin Cui, Fausto Ferraccioli, Rene Forsberg, Tom Jordan, Felicity McCormack, Geir Moholdt, and Kirsty Tinto and the Antarctic RINGS

Regions where the Antarctic Ice Sheet reaches the coast are fundamental to our understanding of the linkages between Antarctica and the global climate system. These coastal regions contain multiple potential tipping points for the Antarctic Ice Sheet in the ongoing 2oC warming world, which must be better understood to predict future sea-level rise. The Antarctic Ice Sheet constitutes the largest uncertainty source in future sea-level projections, and this uncertainty is mainly rooted in poorly known bed topography under the ice sheet. Bed topography matters the most in the coastal regions as it controls the stability of the ice sheet. Together with an overview of the current multidisciplinary understandings of the Antarctic coastal regions, we present ensemble analysis of published datasets to present data and knowledge gaps, and their regional distribution is discussed in the context of ice-sheet evolution and instability. Finally, we identify outstanding science priorities and discuss protocols of airborne surveys to develop a comprehensive dataset uniformly all-around Antarctica.

How to cite: Matsuoka, K., Cui, X., Ferraccioli, F., Forsberg, R., Jordan, T., McCormack, F., Moholdt, G., and Tinto, K. and the Antarctic RINGS: Antarctic RINGS to characterize the Antarctic Ice Sheet coastal zone and Antarctic contribution to the global sea-level rise, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15230, https://doi.org/10.5194/egusphere-egu23-15230, 2023.

EGU23-15361 | ECS | Posters virtual | CR3.2

Sea ice extent and subsurface temperatures in the Labrador Sea across Heinrich events during MIS 3 

Henrieka Detlef, Mads Mørk Jensen, Rasmus Andreasen, Marianne Glasius, Marit-Solveig Seidenkrantz, and Christof Pearce

Heinrich events associated with millennial-scale climate oscillations during the last glacial period are prominent events of ice-sheet collapse, characterized by the dispersal of ice(berg) rafted debris and freshwater across the North Atlantic. Hudson Strait has been suggested as one of the predominant iceberg source regions. One potential mechanism triggering iceberg release invokes cryosphere-ocean interactions, where subsurface warming destabilizes the Laurentide ice sheet. Subsurface warming is facilitated by the expansion of sea ice in the Labrador Sea in combination with a slow down of the Atlantic Meridional Overturning Circulation, which prevents the release and downward mixing of heat in the water column.

Here we present high-resolution reconstructions of sea ice dynamics in the outer Labrador Sea at IODP Site U1302/03 between 30 ka and 60 ka. Sea ice reconstructions are based on a suite of sympagic and pelagic biomarkers, including highly branched isoprenoids and sterols. The results suggest a transition from reduced/seasonal to extended/perennial sea ice conditions preceding the onset of iceberg rafting associated with Heinrich event 3, 4, 5, and 5a by ~0.9 ± 0.5 ka. Ongoing work on the same core and sample material will have to confirm the timing and extent of subsurface warming compared to sea ice advances. 

How to cite: Detlef, H., Mørk Jensen, M., Andreasen, R., Glasius, M., Seidenkrantz, M.-S., and Pearce, C.: Sea ice extent and subsurface temperatures in the Labrador Sea across Heinrich events during MIS 3, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15361, https://doi.org/10.5194/egusphere-egu23-15361, 2023.

EGU23-16930 | ECS | Orals | CR3.2 | Highlight

Multistability and transient response of the Greenland ice sheet to anthropogenic CO2 emissions 

Dennis Höning, Matteo Willeit, and Andrey Ganopolski

Ongoing CO2 emissions into the atmosphere and associated temperature rise have dramatic consequences for the ice sheets on our planet. In this presentation, we focus on the Greenland ice sheet, which holds so much ice that a complete melting would cause the global sea level to rise by seven meters. However, a prediction of future mass loss of the Greenland ice sheet is challenging because it is a strongly non-linear function of temperature and occurs over very long timescales. With the fully coupled Earth system model of intermediate complexity CLIMBER-X, we study the stability of the Greenland ice sheet and its transient response to CO2 emissions over the next 20 kyr. We find two bifurcation points within a global mean surface air temperature anomaly of 1.5°C. Each of these bifurcation points corresponds to a critical ice volume. If the Greenland ice sheet volume decreases below these critical values, returning to a previous atmospheric CO2 concentration would not cause the ice sheet to grow back to its previous state. We also find increased mass loss rates and increased sensitivity of mass loss to cumulative CO2 emission in the vicinity of these critical ice volumes. Altogether, our results suggest that global warming near the lower 1.5°C limit of the Paris agreement would already cause the Greenland ice sheet to irreversibly melt, although a complete melting would take thousands of years.

How to cite: Höning, D., Willeit, M., and Ganopolski, A.: Multistability and transient response of the Greenland ice sheet to anthropogenic CO2 emissions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16930, https://doi.org/10.5194/egusphere-egu23-16930, 2023.

EGU23-966 | ECS | Orals | GM2.9

What controls deltas failure in the Swiss perialpine lakes? 

Daniela Vendettuoli, Michael Strupler, Flavio S. Anselmetti, Stefano C. Fabbri, Anastasiia Shynkarenko, and Katrina Kremer

Large lacustrine mass movements and delta collapses are increasingly being considered as potential tsunamigenic sources. They are therefore hazardous for the population and infrastructure along lakeshores. In most studies of slope stability and triggered tsunamis, however, subaqueous deltas have largely been excluded as we lack information on their morphodynamic evolution. Thus, a holistic assessment of tsunami hazards in the lacustrine environment is required for a better understanding of how delta lakes evolve through time and space.
Within a study funded by the Federal Office of the Environment, we aim to understand what types of deltas are susceptible to slope failure within the perilapine Swiss lakes. To achieve our goal, we primarily focus on those deltas that present an increased potential for subaqueous erosion and analyse their morphological, morphometric and sedimentological characteristics taking advantage of the existing and publically available datasets. In this contribution, we present the designed approach and preliminary results, using Lake Lucerne as a case study. This approach will then be applied to all lakes with a surface area > 1 km2, for which high-resolution bathymetric data are available. The outcomes of such a study will be summarized in a geodatabase of the different delta-types for the perilpine Swiss lakes and it represents an important milestone for the assessment of tsunami hazard with regard to the lakes of Switzerland.

How to cite: Vendettuoli, D., Strupler, M., Anselmetti, F. S., Fabbri, S. C., Shynkarenko, A., and Kremer, K.: What controls deltas failure in the Swiss perialpine lakes?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-966, https://doi.org/10.5194/egusphere-egu23-966, 2023.

EGU23-1787 | Orals | GM2.9 | Highlight

The seedy underbelly of yield: how measuring verrrrrry slow grain motion changes our view of landscapes 

Douglas Jerolmack and Nakul Deshpande

It is now well established that many lansdcapes are organized to be close to the threshold of sediment motion: rivers, wind-blown dunes and hillslopes. 

Whether explicitly or implicitly, this threshold is almost universally treated as a Mohr-Coulomb failure criterion, which is an opaque barrier that prevents us from viewing and understanding motion beneath the yield point. Below-threshold motion is creep, and the dynamics are creepy indeed: typical continuum descriptions break down, and observed behaviors can be counterintuitive. 

In this talk I present two experiments, using two different optical techniques, that study very slow particle motions below the threshold of motion. Experiments in a scaled-down river use refractive-index matched scanning to image the interior of a sediment bed sheared by a fluid, and track particles over many orders of magnitude in velocity to show that creep is activated deep into the sediment bed. This creep hardens the bed and drives segregation. Tracking creeping grains becomes impractical, however, as it takes several months to measure the slowest particle motions. 

To overcome these simplifications and expand our study of creep, we examine an apparently static sandpile that is isolated from external disturbance. Instead of particle tracking, we use an optical interferometry technique called Diffusive Wave Spectroscopy (DWS) that allows us to measure creep rates as low as nanometers/second. Viewed through the lens of DWS, the model hillslope is alive with motion as internal avalanches of grain rearrangements flicker throughout the pile. We observe similar dynamics to those observed in the river experiment -- albeit over much shorter timescales -- even though the only significant stress is gravity. What causes these grains to creep below their angle of repose? Observations suggest that minute mechanical noise may play a role, but reducing the noise floor beyond our fairly quiescent conditions is very challenging. Instead, we raise the driving stresses through heating, tapping and flow. 

The observations lead to new view of sediment creep as relaxation and rejuvenation of a glassy material, where mechanical noise plays a role akin to thermal fluctuations in traditional glass materials. Sub-yield deformation is a new world to explore, for those patient enough to look for it. 

How to cite: Jerolmack, D. and Deshpande, N.: The seedy underbelly of yield: how measuring verrrrrry slow grain motion changes our view of landscapes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1787, https://doi.org/10.5194/egusphere-egu23-1787, 2023.

EGU23-2362 | Posters on site | GM2.9

Adaptation of an experimental alluvial fan to climate change 

Francois Metivier, Olivier Devauchelle, and Pauline Delorme

We study the effect of an increase in flow discharge on the shape and growth of an experimental alluvial fan. The fan is built by a single-thread channel in which the flow occurs near the threshold of sediment motion. We first define a criterion that predicts the conditions under which a change in discharge leaves an inprint on the morphology of a fan. We then report on experimental runs which allow us to establish the relevance of this criterion. Experiments are carried out during which climatic changes are applied to the feeding channel of a fan. By playing on the initiation time of climate change, on the duration of the rise in flow, or on the total variation in discharge, we scan a range of configurations that allow us to qualitatively and quantitatively test our incision criterion. Qualitatively, we note that the dynamics of the fan seems altered only for values of the criterion which exceed the critical value of 1.5. In these situations, the channel stops moving and entranches. Quantitatively, we extract a characteristic time by autocorrelating spatio-temporal channel migration diagrams and show that this time correlates with the value of the incision criterion.

How to cite: Metivier, F., Devauchelle, O., and Delorme, P.: Adaptation of an experimental alluvial fan to climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2362, https://doi.org/10.5194/egusphere-egu23-2362, 2023.

EGU23-2733 | ECS | Posters on site | GM2.9

Slumping regime in lock-release turbidity currents 

Cyril Gadal, Matthieu Mercier, and Laurent Lacaze

Most gravitational currents occur on sloping topographies, often in the presence of particles that can settle during the current propagation. Yet, an exhaustive exploration of associated parameters in experimental devices is still lacking. Here, we present an extensive experimental investigation on the slumping regime of turbidity (particle-laden) currents in two lock-release (dam-break) systems with inclined bottoms. We identify 3 regimes controlled by the ratio between settling and current inertia. (i) For negligible settling, the turbidity current morphodynamics correspond to those of saline homogeneous gravity currents, in terms of velocity, slumping (constant-velocity) regime duration and current morphology. (ii) For intermediate settling, the slumping regime duration decreases to become fully controlled by a particle settling characteristic time. (iii) When settling overcomes the current initial inertia, the slumping (constant-velocity) regime is not detected anymore. In the first two regimes, the current velocity increases with the bottom slope, of about 35% between and 15°. Finally, our experiments show that the current propagates during the slumping regime with the same shape in the frame of the moving front. Strikingly, the current head (first 10 centimeters behind the nose) is found to be independent of all experimental parameters covered in the present study. We also quantify water entrainment coefficients E, and compare them with previous literature, hence finding them proportional to the current Reynolds numbers.

How to cite: Gadal, C., Mercier, M., and Lacaze, L.: Slumping regime in lock-release turbidity currents, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2733, https://doi.org/10.5194/egusphere-egu23-2733, 2023.

EGU23-2913 | Posters on site | GM2.9

Fractal characteristics of suspended sediment transport in rivers: natural experiment site 

Samuel Pelacani, Francesco Barbadori, Federico Raspini, Francois G. Schmitt, and Sandro Moretti

River flows and associated suspended sediment (SS) transport are intermittent processes possessing fluctuations over a large range of time scales and space, making it challenging to develop predictive models that are applicable across timescales and rivers. A concept of “effective timescales of connectivity” has been used to define the timeframe over which sediment (dis)connectivity occurs, whereby parts of the catchment are “switched on and off” as a response of events with varying frequency-magnitude relationships and antecedent soil moisture. These concepts provide excellent frameworks to understand temporal variability and identify relevant timescales for sediment transport, but do not help in the knowledge of mechanisms for temporal variability in SS transport. The complexity and scale dependency of processes driving SS transport stress the need to detect how sediment generation, storage, and transport are linked across different timescales. Furthermore, the mechanisms that produce travel time distributions over many orders of magnitude are not known precisely. To this end, in this study we have considered SS transport as a fractal system. By approaching SS transport dynamics as a fractal system, it is assumed that patterns of variation in SS transport exist over different timescales, while linkages across those temporal scales are expressed as fractal power-laws.

This work aims to defines the link between (i) sediment transport and deposition and (ii) fractal geometry and fractal storage time distributions in streams.

Here, we present case study where fractals are used to describe and predict patterns over different spatial or temporal scales of dynamics in SSCs. We have considered in these studies the statistics and the dynamics of streamflow, SSCs and associated grain size distribution at event based by considering respectively their probability distribution function and Fourier power spectra.

We set up a natural experiment site of a first-order mixed bedrock and alluvial stream channel by using LISST instrument coupling with LIDAR remote sensing measurement. Here we obtain high-resolution observations of streambed topography and continuously long-term measurements of suspended sediment in natural experimental site located in an agricultural watershed of a Chianti area (Florence, Italy).

The LISST is a submersible laser diffraction particle size analyzer for measuring suspended particle size (range from 2.50 µm to 500 µm), its volume concentration at different time step and depth. We set up at time interval equal to 5 minutes of sample rate.

Preliminary results obtained indicate large fluctuations with heavy tails, and long-range properties, characterized by extreme events much more frequent than what is found for a Gaussian process.

Hence, insights into the degree of fractal power of a SS transport system may provide a useful basis to evaluate and develop the most appropriate predictive models and management strategies.

How to cite: Pelacani, S., Barbadori, F., Raspini, F., Schmitt, F. G., and Moretti, S.: Fractal characteristics of suspended sediment transport in rivers: natural experiment site, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2913, https://doi.org/10.5194/egusphere-egu23-2913, 2023.

EGU23-3735 | ECS | Posters on site | GM2.9

Kinematics of scarp retreat in idealized tilted channel experiments 

Yi-Fan Hung, Hervé Capart, and Colin P. Stark

In some landslides, collapse is accompanied by the upslope retreat of a well-defined scarp whose speed controls the rate of mobilization of debris. Here we examine the evolution of such scarps in an idealized laboratory setting. We conduct tilted channel experiments involving retrogressive dry granular landslides over an erodible substrate. After first tilting up a deep sand layer to close to the angle of repose, then imposing an abrupt base-level drop, granular flow is induced at the downstream outlet. This flow generates an upstream-traveling wave with a well-defined scarp at the upstream tip. Downstream of the moving scarp, sand flows as an avalanching layer of finite depth over the erodible but stationary substrate, and outflows over the lowered outlet sill. A series of such experiments were conducted to determine the influence of channel width and base-level drop height on the speed of scarp retreat and other flow properties. Measurements included the time-evolving profile of the free surface, surface velocities acquired using particle tracking velocimetry, and the time-evolving mass outflow rate at the downstream outlet. Dimensional analysis clarifies the physical mechanisms governing the rate of scarp retreat. These results will help guide and validate numerical models of granular landsliding over erodible substrates.

How to cite: Hung, Y.-F., Capart, H., and Stark, C. P.: Kinematics of scarp retreat in idealized tilted channel experiments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3735, https://doi.org/10.5194/egusphere-egu23-3735, 2023.

EGU23-6809 | ECS | Posters on site | GM2.9 | Highlight

Tales of compacting sand to anticipate strain budget of rupture processes 

Anne Voigtländer, Vadim Sikolenko, Jens M. Turowski, Luc Illien, Jonathan Bedford, and Gunnar Pruß

Prior to earthquake ruptures and slope failures, accelerated surface deformations can sometimes be observed. To anticipate rupture processes, these deformations are interpreted in terms of a strain budget and its stressors. If the budget exceeds an assumed critical value, rupture happens. But not all components of the budget can readily be inferred from the bulk deformation. For example, elastic strain build-up and other ‘silent’ contributions challenge the predictability of these potential natural hazards. We present preliminary experimental results, focussing on deformation by compaction. We report an analogous experiment of loading and unloading to constrain compaction behaviour, elastic strain-build up, and release to understand their ‘silent’ contributions to the strain budget. As analogue material, we use sand to assess emergent bulk behaviour. Using natural quartz crystals allows to apply in-situ neutron diffraction to measure elastic strain during loading and unloading stages. We find that while compaction and remnant compaction scale linearly with load magnitudes, elastic strain build-up seems to be independent of stresses ≥60 MPa. In addition to the in-situ neutron diffraction experiments, we conducted mechanical compaction tests at ramped load stages and analysed the post-compaction changes of the grain size distribution. With increased loading, the mean grain size decreased, leading to increased bulk density in the compacted portion. Based on these observations, we reason that the linear elastic bulk compaction of our samples is due to non-linear local brittle deformation. There is only limited elastic strain built up during the compaction, which is likely released due to local crushing. Localized failure produces a denser material in which strain can build up more homogeneously, causing rupture at its bulk elastic limit. Our experiments show that deducing or simply converting loading and displacement to stress-strain relationships to establish a strain budget may be inadequate. Silent components that are likely due to non-linear and emergent processes can in the short term lead to local elastic strain energy release or bulk dynamic ruptures. Conceptually, to especially anticipate the timing of slope failures and the magnitude of earthquake ruptures, the hidden costs, e.g. due to localized failure, and internal changes, concerning density or elastic properties, are crucial components that need to be constrained while compiling a strain or energy budget of these processes.

How to cite: Voigtländer, A., Sikolenko, V., Turowski, J. M., Illien, L., Bedford, J., and Pruß, G.: Tales of compacting sand to anticipate strain budget of rupture processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6809, https://doi.org/10.5194/egusphere-egu23-6809, 2023.

Geomaterial are complex porous material presenting a wide diversity of structures, which set how a fluid will flow through it. The understanding of the mechanisms controlling the flow kinematics at the pore scale is however decisive to predict and control transport processes (dispersion and mixing) and to relate them to the macroscale behaviour of porous materials. Because of the opaque nature of porous media, the flow visualization and characterization of the velocity fields within a porous media is particularly challenging in three-dimensional (3-D) porous media. However, recent development of experimental techniques including index matching, allow to develop transparent porous media to perform direct visualization of the flow in these artificial material.

I will here discuss about how such approach have already been successfully implemented to study porous media composed of randomly packed solid monodisperse spheres, allowing to directly visualize the flow within the bulk of the 3-D media, and to investigate how a blob of dye stretches and get mixed when injected within such 3-D porous media. Using Particle Image Velocimetry techniques (PIV), these promising techniques also allow to perform successive scans of the velocity field, providing highly resolved experimental reconstruction of the 3-D Eulerian fluid velocity field in the bulk of the porous media. This approach is therefore promising to further investigate flow kinematic in more complex porous media, or to directly visualize other crucial mechanisms in such media, like for instance erosion, clogging, or the effect of strong heterogeneities on the overall flow behavior.

How to cite: Souzy, M.: Direct flow visualization and transport processes in transparent 3D porous media, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7051, https://doi.org/10.5194/egusphere-egu23-7051, 2023.

EGU23-7709 | ECS | Orals | GM2.9 | Highlight

Testing the potential of a submarine fibre optic cable to detect sediment gravity flows using laser interferometry 

Irena Schulten, Cecilia Clivati, Aaron Micallef, Simone Donadello, Davide Calonico, André Xuereb, Alberto Mura, and Filippo Levi

Sediment gravity flows are common processes in the submarine environment. They are important for the global sediment transport, but can destroy offshore infrastructure and may even contribute to tsunami generation. These flows, however, remain poorly understood. There is a lack of direct observations due to difficulties with deploying appropriate instruments and predicting the occurrence and route of these flows, especially on open continental slopes. Deployed instruments are further often destroyed as a result of the gravity flows. Submarine fibre cables are present along almost all continental margins worldwide. They are economically important for telecommunication and internet data transfer. Historic records, however, have shown that submarine gravity flows affect and even severe these cables. 


Recent studies successfully tested the usage of fibre optic cables to detect earthquakes and other processes such changes in the wave height associated with storm events. The aim of this study will be to test whether fibre optic cables can also detect submarine gravity flows using laser interferometry. The study is based on a cooperation between the University of Malta and the Istituto Nazionale di Ricerca Metrologica (INRiM) in Italy and is part of the European funded project “Modern and recent sediment gravity flows offshore eastern Sicily, western Ionian Basin (MARGRAF, ID 101038070)”. The University of Malta has been granted permission to collect data from a 260-km long optical fibre cable that connects Malta and Sicily through the western Ionian Basin. INRiM provided the measurement system and technical support needed to carry out the experiment. The western Ionian Basin is an ideal study site, as it is characterised by many earthquakes, tsunamis and submarine sediment gravity flows. The cable crosses known pathways of these gravity flows and thus provides a high possibility to detect modern sediment flows. The laser interferometry data will be analysed to detect disturbances (e.g., twists, expansions, contractions) on the cable. Any detected disturbances will be compared with oceanographic and seismometer data, both from onshore stations and Ocean Bottom Seismometers (OBS). This comparison will allow us to infer the source of the cable disturbance. In addition, we plan to collect gravity cores in vicinity of the event to assess whether the event was based on a gravity flow or not. Initial results showed earthquakes and various storm events recorded by the cable. 


The findings are expected to improve our current understanding of gravity flows in the region in terms of potential trigger mechanisms and reoccurrence rate. Eastern Sicily is densely populated and hosts touristic and industrial infrastructure, which makes it important to constrain the geohazard implication of these flows. A successful test will further allow to use this application on cables in other regions worldwide. 

How to cite: Schulten, I., Clivati, C., Micallef, A., Donadello, S., Calonico, D., Xuereb, A., Mura, A., and Levi, F.: Testing the potential of a submarine fibre optic cable to detect sediment gravity flows using laser interferometry, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7709, https://doi.org/10.5194/egusphere-egu23-7709, 2023.

EGU23-7952 | ECS | Orals | GM2.9

Influence of suspended sediment concentration on hyperpycnal delta progradation 

Yi-Yun Liang, Chiun-Chau Su, and Hervé Capart

When rivers with high suspended sediment load plunge into lakes and reservoirs, the resulting density currents often cause the formation and progradation of hyperpycnal deltas. Suspended load can contribute to delta progradation through two different mechanisms: (1) indirectly, by increasing the excess density of the underflows, thus enhancing the basal shear stresses that drive along-bed transport; (2) directly, by settling out of suspension onto the evolving bed. In this work, we conducted laboratory experiments designed to investigate the relative importance of these two mechanisms, aided by a conceptual model that includes both processes. The experiments are conducted in a narrow tank of constant slope, supplied with prescribed water, sediment, and/or saline influxes. Both suspended sediment load and salinity can therefore contribute independently to the excess density of the inflow. Simultaneous measurements of delta profile evolution and suspended sediment concentration are then acquired using imaging methods. To interpret the results, we construct a simplified one-dimensional model of delta progradation in which along-bed transport is modelled as a diffusion process, and suspended sediment settling as an advection-deposition process. We then examine the influence of process coefficients on the morphology and rate of evolution of the delta fronts and compare simulations with the experiments. It is found that the evolution of the bed profile alone is not sufficient to distinguish between the two mechanisms, hence the importance of simultaneously measuring suspended sediment concentration. Although obtained in a simplified setting and at reduced scale, the results should provide useful guidance for the modeling and monitoring of reservoir sedimentation at field scales.

How to cite: Liang, Y.-Y., Su, C.-C., and Capart, H.: Influence of suspended sediment concentration on hyperpycnal delta progradation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7952, https://doi.org/10.5194/egusphere-egu23-7952, 2023.

EGU23-8385 | ECS | Posters on site | GM2.9

Experimental investigation of the segregation of a large intruder in bedload sediment transport 

Benjamin Dedieu, Philippe Frey, and Julien Chauchat

Vertical size segregation or sorting of particles in bedload transport, strongly impacts the sediment rate and the river bed morphology. To better account for this process in sediment transport models, it is essential to understand the mechanisms acting at the grain scale (Frey, 2009). Following the work of Rousseau (2021), focus is made on the behaviour of a single large particle segregating upwards in a monodisperse mixture of smaller beads during bedload transport. Experiments are carried out in a narrow flume and the bead dynamics is recovered through image analysis. A great number of repetition is performed for different size ratios (large to small bead diameter) in order to conduct statistical analysis. This work confirms the measurements from Rousseau (2021) and suggests that the time for the large particle to reach the bed surface is minimum for a size ratio of 2. This result supports previous research which, using simpler granular configurations, evidenced a similar tendency in terms of segregation force (Guillard, 2016, Jing, 2020) or segregation velocity (Golick, 2009). Other observations are made on the spatial trajectory of the intruder, which have been previously reported to be linear with a repeatable slope independent of the size ratio. These observations offer interesting insights to understand the mechanisms governing size segregation and could provide new closures to upscale the phenomenon at the continum scale.

Illustration: A 5 mm intruder in a 2 mm bed (size ratio = 2.5), flow from right to left, dimensionless bed shear stress (Shields number) = 0.25.

Frey, P. and Church, M. (2009). “How River Beds Move”. Science, 325(5947), pp. 1509–1510.
Golick, L. A. and Daniels, K. E. (2009). “Mixing and Segregation Rates in Sheared Granular Materials”. Physical Review E, 80(4), p. 042301.
Guillard, F., Forterre, Y., and Pouliquen, O. (2016). “Scaling Laws for Segregation Forces in Dense Sheared Granular Flows”. Journal of Fluid Mechanics, 807.
Jing, L., Ottino, J. M., Lueptow, R. M., and Umbanhowar, P. B. (2020). “Rising and Sinking Intruders in Dense Granular Flows”. Physical Review Research, 2(2), p. 022069.
Rousseau, H. (2021). “From Particle Scale to Continuum Modeling of Size Segregation in Bedload Transport : A Theoretical and Experimental Study.” PhD thesis. Université Grenoble Alpes.
Rousseau, H., Frey, P., and Chauchat, J. (2022). “Experiments on a single large particle segregating in bedload transport”. Physical Review Fluids, 7(6), p. 064305.

How to cite: Dedieu, B., Frey, P., and Chauchat, J.: Experimental investigation of the segregation of a large intruder in bedload sediment transport, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8385, https://doi.org/10.5194/egusphere-egu23-8385, 2023.

EGU23-8707 | ECS | Orals | GM2.9

Grain-scale geometry and force networks in general granular materials 

Jack Moss and Romeo Glovnea

Granular material is nearly ubiquitous in nature.  Some examples include sand, soil, snow, rocks; even the interactions of ice burgs and floes can reasonably be considered as large-scale particle interactions.  It is well accepted that continuum-scale behaviour of a granular body is determined by the grain-scale interactions of its constituent particles, but there is still much to learn regarding those grain-scale interactions and their relationship with continuum-scale inputs.  Vibrating granular beds are a good case study for examining this, since differing flow regions generally form within the bed – depending on both the nature of the vibrations and granular material – and the test conditions can be repeated accurately in a laboratory. 

In this experimental study, various beds of spherical glass beads were subjected to sinusoidal horizontal vibrations of various amplitude and frequency combinations.  The granular beds were framed as quasi two-dimensional: the particles were three-dimensional, contained within a thin transparent tank such that phenomena could only occur in two dimensions.  The tests were designed to provide insight into the grain-scale interactions within granular materials.  That is: how do various load inputs and granular compositions affect general grain-scale response, and in turn, how does this grain-scale response affect the continuum-scale behaviour of the material?

Grain-scale interactions were compared between differing granular beds undergoing equivalent vibrations.  The results are used to discuss how behavioural response of granular material to macro-scale inputs is ultimately tied to the geometric complexity of the internal packing structure and the corresponding network of contact forces that packing structure lends itself to.  The concept of ‘geometric compatibility’ between particles within any granular medium is discussed as an explanation for large behavioural differences between grain-scale, and by extension continuum-scale, responses to vibration – or indeed any mechanical work a granular material is subjected to.

How to cite: Moss, J. and Glovnea, R.: Grain-scale geometry and force networks in general granular materials, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8707, https://doi.org/10.5194/egusphere-egu23-8707, 2023.

Sediment transport in rivers and estuaries is typically monitored infrequently and discontinuously, which is a key reason why sediment budget estimations are often poor. On the contrary, water discharge is often monitored with high accuracy, and continuously, which requires periodic ship measurements for recalibration of rating curves. Even in tidal rivers, continuous flow measurements can be obtained by upscaling transect flow measurements to cover the entire cross-section, which rarely occurs for sediment transport (Kästner et al., 2018). This contribution discusses how existing discharge measurement schemes can be extended to yield continuous measurements of sediment transport, separating between suspended load and bedload sediment fluxes. A new approach is outlined, which relies on repeated cross-river transect measurements, using multiple acoustical and optical instruments. Innovative suspended load measurements make use of acoustic profilers with multiple sound frequencies and a spectrometer, which can measure suspended sediment mean particle size and carbon content from light absorbance (Sehgal et al., 2022). Inference of bedload transport from bedform tracking improves when taking secondary bedforms into account, which can migrate fast and persist in the lee of primary dunes, contributing significantly to the total bedload transport (Zomer et al., 2021). For sand-bed rivers in particular, a generic approach to upgrade existing discharge monitoring programmes to include continuous sediment transport may be feasible with limited additional ship survey time.

Kästner, K., Hoitink, A. J. F., Torfs, P. J. J. F., Vermeulen, B., Ningsih, N. S., & Pramulya, M. (2018). Prerequisites for accurate monitoring of river discharge based on fixed‐location velocity measurements. Water resources research54(2), 1058-1076.

Sehgal, D., Martínez‐Carreras, N., Hissler, C., Bense, V. F., & Hoitink, A. J. F. (2022). Inferring suspended sediment carbon content and particle size at high frequency from the optical response of a submerged spectrometer. Water Resources Research58(5), e2021WR030624.

Zomer, J. Y., Naqshband, S., Vermeulen, B., & Hoitink, A. J. F. (2021). Rapidly migrating secondary bedforms can persist on the lee of slowly migrating primary river dunes. Journal of Geophysical Research: Earth Surface126(3), e2020JF005918.

How to cite: Hoitink, T. (A. J. F. ).: Quantifying sediment transport from periodic transect measurements in rivers and estuaries, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9236, https://doi.org/10.5194/egusphere-egu23-9236, 2023.

EGU23-9662 | ECS | Posters on site | GM2.9 | Highlight

A moored profiling platform to study turbulent mixing in density currents in a large lake 

François Mettra, Rafael Sebastian Reiss, Ulrich Lemmin, Valentin Kindschi, Benjamin Graf, and David Andrew Barry

During calm cooling periods, differential cooling can induce winter cascading which is an important process for littoral-pelagic exchange and deep water renewal in large, deep lakes (Fer et al., 2001; Peeters et al., 2003). Generated in the shallow near-shore regions, such cold-water density currents travel down the sloping lakebed until they reach their depth of neutral buoyancy. The latter is strongly dependent on the entrainment of warmer ambient water, often expressed by the entrainment coefficient (i.e., the ratio of the entrainment velocity to the bulk velocity of the density current, e.g., Legg, 2012). Fer et al. (2001, 2002) studied density currents in Lake Geneva and showed that they occur in the form of cold-water pulses that last 1-2 hours, with a typical thickness of 10 m, a mean velocity of ~5 cm s-1 and an entrainment coefficient of ~0.03.

With recent advances in instrument capabilities, our recent investigations in Lake Geneva reveal also the presence of shorter, but still strong, temperature fluctuations of O(10) min in those density currents. To investigate further the mechanisms of entrainment in cascading flows, we designed a turbulence platform that was deployed on the sloping bed of Lake Geneva at 25-m depth. The platform is equipped with (high frequency) temperature and current velocity sensors which collect data over 3 meters vertically. A connection to the shore via a cable laid on the lakebed enables to control the platform’s vertical position and ensures continuous long-term measurements at high frequency. The background variables, such as velocity and temperature profiles, characterizing the nearshore zone in the surrounding of the platform are measured continuously using lower resolution sensors.

Here, we briefly expose the design of the platform, present a case of cascading flow and give small-scale hydrodynamic details of eddies that are observed intermittently within the density currents. Indeed, instantaneous unstable profiles (warm water intruding below cold water) within the dense cold flow show the presence of large eddies with spatial scales similar to the thickness of the mean current. The preliminary results shed light on the mechanism of warm ambient water entrainment into the cold-water density current. The high intermittency of occurrence of large eddies, i.e., those that contribute the most to entrainment, contrasts with the classic concept of a bulk entrainment coefficient.

Fer, I., Lemmin, U., & Thorpe, S. A. (2001). Cascading of water down the sloping sides of a deep lake in winter. Geophysical Research Letters, 28(10), 2093–2096. https://doi.org/10.1029/2000GL012599

Fer, I., Lemmin, U., & Thorpe, S. A. (2002). Winter cascading of cold water in Lake Geneva. Journal of Geophysical Research: Oceans, 107(C6), 13-1-13–16. https://doi.org/10.1029/2001JC000828

Legg, S. (2012). Overflows and convectively driven flows. In E. Chassignet, C. Cenedese, & J. Verron (Eds.), Buoyancy-Driven Flows (pp. 203-239). Cambridge: Cambridge University Press. doi:10.1017/CBO9780511920196.006

Peeters, F., Finger, D., Hofer, M., Brennwald, M., Livingstone, D. M., & Kipfer, R. (2003). Deep-water renewal in Lake Issyk-Kul driven by differential cooling. Limnology and Oceanography, 48(4), 1419–1431. https://doi.org/10.4319/lo.2003.48.4.1419

How to cite: Mettra, F., Reiss, R. S., Lemmin, U., Kindschi, V., Graf, B., and Barry, D. A.: A moored profiling platform to study turbulent mixing in density currents in a large lake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9662, https://doi.org/10.5194/egusphere-egu23-9662, 2023.

EGU23-12076 | ECS | Orals | GM2.9 | Highlight

Grain shape effects in bed load sediment transport 

Eric Deal, Jeremy Venditti, Santiago Benavides, Ryan Bradley, Qiong Zhang, Ken Kamrin, and Taylor Perron

Predictions of bed load sediment flux are notoriously imprecise despite widespread occurrence and importance in contexts ranging from river restoration to planetary exploration. Natural variations in grain size, shape and density are possible sources of inaccuracy in sediment transport, as well as mixtures of different grain sizesand time-dependent bed structure. While many of these effects have been studied in depth, the effects of grain shape have rarely been quantified, even though shape has long been hypothesized to influence sediment transport.

During bed load transport, the granular bed is sheared by the flow passing over it. Aspherical grains and rough surfaces generally increase the resistance to such shearing, enhancing frictional resistance, and reducing the efficiency of bed load transport. However, aspherical grains also experience higher fluid drag force than spherical grains of the same volume, enhancing transport efficiency under the same flow conditions. These two competing effects generally get stronger as grain shape deviates from spherical, making it challenging to predict the net effect of grain shape on sediment transport. We disentangle these competing effects by formulating a theory that accounts for the influence of grain shape on both fluid-grain and grain-grain interactions. It predicts that the onset and efficiency of transport depend on the average coefficients of drag and bulk friction of the transported grains. Because we use the average statistics of drag and friction to characterize the effect of grain shape, our approach is also applicable to materials like natural gravel that have many different shapes in the same sample.

Using a series of flume experiments with different granular materials of distinct shapes, we show that grain shape can modify bed load transport rates by an amount comparable to the scatter in many sediment transport data sets. Our data also demonstrates that, although bed load transport of aspherical grains is generally inhibited by higher bulk friction and enhanced by higher fluid drag, these two effects do not simply cancel each other. This means that the effect of grain shape on sediment transport can be difficult to intuit from the appearance of grains, with the possibility for grain shape changes to lead to either a reduction or an enhancement of sediment transport efficiency.

How to cite: Deal, E., Venditti, J., Benavides, S., Bradley, R., Zhang, Q., Kamrin, K., and Perron, T.: Grain shape effects in bed load sediment transport, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12076, https://doi.org/10.5194/egusphere-egu23-12076, 2023.

EGU23-12918 | Posters on site | GM2.9

Monitoring sediment processes in different delta systems in Swiss peri-alpine lakes through 4D bathymetric mapping 

Katrina Kremer, Stefano C. Fabbri, Daniela Vendettuoli, Carlo Affentranger, Stéphanie Girardclos, and Flavio S. Anselmetti

Deltas represent transfer zones where sediment is moved from terrestrial to the subaquatic domains. They are depositional areas and a source for sediments simultaneously. One of the aspects in this highly dynamic environment that has experienced so far little attention are slope failures in deltas. Such failures are, however, mentioned as potential cause for large (up to m-scale), graded deposits in the sedimentary record, often referred to as megaturbidites or homogenites. In some cases, they may have generated tsunamis in the near-shore area. These delta failures can be triggered, amongst other causes, by spontaneous slope collapses (e.g. Muota delta 1687 in Lake Lucerne, Switzerland). To better understand the controlling factors of slope stability in deltas, we need to comprehend the interplay between deltaic deposition and erosion through time and monitor their evolution.

Repeated bathymetric mapping has been used as powerful tool to better understand the short-term processes occurring in deltas. In this contribution, repeated bathymetric mapping is used to better characterize, which short-term processes may shape subaqueous delta fronts. Using the dataset acquired in recent years in Swiss lakes, we seek to answer (1) what processes can be visualized based on repeated bathymetric mapping?; (2) which areas are prone to depositional/erosion processes?; and (3) what type of delta is more prone to slope failures? We present the first datasets of differential maps from four deltas in Switzerland that show different processes of erosion and deposition on short and long time scales. In addition, we will present the design of a planned multi-method monitoring campaign for delta processes in a sublacustrine delta in a peri-alpine lake in Switzerland. 

How to cite: Kremer, K., Fabbri, S. C., Vendettuoli, D., Affentranger, C., Girardclos, S., and Anselmetti, F. S.: Monitoring sediment processes in different delta systems in Swiss peri-alpine lakes through 4D bathymetric mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12918, https://doi.org/10.5194/egusphere-egu23-12918, 2023.

EGU23-14185 | ECS | Posters on site | GM2.9

Experimental evidences of the influence of flood magnitude and duration on the morphological evolution of a river: Initial results from the EVOFLOOD project 

Pauline Delorme, Stuart McLelland, Brendan Murphy, and Daniel Parsons and the EvoFlood Team

There is now a clear consensus that climate change will lead to an increase in the frequency and intensity of extreme rainfall events in many parts of the world, which, in turn, will lead to increased flood flows and thus flooding of large areas. Numerical simulation is one way to improve our understanding of flooding processes, especially through Global Flood Modelling (GFM). Current GFMs represent the morphology of river channels and floodplains in a very simplified way. In particular, GFM assumes that the channel morphology remains unchanged over time. However, rivers are dynamic, their morphology evolves by erosion and deposition of sediments carried by the flow. These morphological changes can radically alter the conveyance capacity of the channel and therefore the flood risk. Integrate these morphological changes in the new GFM framework is one of the main objectives of the NERC-funded EVOFLOOD project. 

Here we present the results of the experimental part of the project. We designed a controlled laboratory experiment to identify the factors controlling the morphodynamic response within river channel. In this experiment, we generate a succession of flood events characterised by different magnitudes and durations, and we quantify the evolution of the flooded area and channel width as a function of the duration, intensity and flood history. 
We find that the main parameters controlling morphological changes are flood intensity and flood history. The duration of the flood does not have a significant impact on the morphological changes because the main changes occur during the first period of the flood event. Finally, we show the importance of the upstream sediment discharge on the modification of the conveyance capacity.

 

How to cite: Delorme, P., McLelland, S., Murphy, B., and Parsons, D. and the EvoFlood Team: Experimental evidences of the influence of flood magnitude and duration on the morphological evolution of a river: Initial results from the EVOFLOOD project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14185, https://doi.org/10.5194/egusphere-egu23-14185, 2023.

EGU23-14356 | ECS | Posters on site | GM2.9

Measurements of sediment flux in rivers with a multi-frequency echosounder 

Jakob Höllrigl, Koen Blanckaert, David Hurther, Guillaume Fromant, and Florian R. Storck

At present, SSC fluxes in rivers are typically estimated by multiplying the river discharge with the
average suspended sediment concentration (SSC). The latter is typically obtained from optical turbidity
measurements in one single point of the river cross‐section. The optical turbidity is converted in
average SSC based on a relation that is derived from the laboratory analysis of regular SSC samples.
This method has the disadvantages that it is based on a one‐point measurement and that it is
expensive.

The SSC distribution in an entire profile – vertical or horizontal – can also be derived from the
backscatter of single‐frequency echosounders. The disadvantage of this method is that the particle size
of the suspended sediment needs to be known in order to convert the profile of backscatter into a
profile of SSC.

Here we present a hydro‐acoustic method based on multi‐frequency echosounding. Operating on
multiple acoustic frequencies allows estimating the mean particle size directly from the backscatter at
the different frequencies. The method based on multi‐frequency echosounding is illustrated with
measurements on the Rhône River just upstream of Lake Geneva in Switzerland. The results are
compared to measurements based on optical turbidity measurements and to measurements based on
single‐frequency echosounding.

How to cite: Höllrigl, J., Blanckaert, K., Hurther, D., Fromant, G., and Storck, F. R.: Measurements of sediment flux in rivers with a multi-frequency echosounder, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14356, https://doi.org/10.5194/egusphere-egu23-14356, 2023.

EGU23-15044 | ECS | Orals | GM2.9

Centrifuge model test platform for rainfall simulation triggering shallow landslides 

Joon-Young Park, Enok Cheon, Seung-Rae Lee, Jinhyun Choo, Hwan-hui Lim, and Ye-eun Nam

A centrifuge model test platform was designed and developed to verify the critical continuous rainfalls triggering shallow landslides in natural slopes. Based on literature reviews, in-situ dimensions of shallow landslides on natural slopes were determined to 40 m (Length) × 16 m (Width) × 2 m (Depth) on average. In consequence, considering the model mounting space of the centrifuge test facility, a gravity level was decided (N = 40g) so that the length of a model slope equals 1 m according to scaling law. The width and depth of the model slope were hence determined to 0.4 m and 0.05 m, respectively. On the other hand, a rainfall simulator comprised of a series of air-atomizing spray nozzles was designed and developed considering scaling laws of rainfall infiltration and subsurface water flows. As a simulation result in a 40g condition, rainfall dispersions reduced and its trajectory bending induced by Coriolis’ force was almost vanished. After the development of centrifuge model test platform, several 1g performance tests of the rainfall simulator were conducted to test the spatial uniformity of rainfall distributions and fit the conditions of applying water and air pressures to rainfall intensities. The study also presents preliminary test results of shallow landslides in a 1g condition conducted to find and solve errors and unexpected problems before mounting the platform to the centrifuge test facility.

How to cite: Park, J.-Y., Cheon, E., Lee, S.-R., Choo, J., Lim, H., and Nam, Y.: Centrifuge model test platform for rainfall simulation triggering shallow landslides, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15044, https://doi.org/10.5194/egusphere-egu23-15044, 2023.

EGU23-17200 | ECS | Orals | GM2.9

Laboratory modelling of landslide-generated impulse wave 

Abigaël Darvenne, Sylvain Viroulet, and Laurent Lacaze

Impulse waves are waves generated by subaerial landlsides impacting the free surface of a lake or a sea. These waves differs from earthquake tsunami, even if often associated, as the generation mechanism and the scale of influence are not the same. Although they can travel over much shorter distance than other tsunamis, waves generated by landslides can be locally more dangerous [1]. Consequently, predicting the wave amplitude, and particularly its maximum during the generation remain crucial. Even if several studies have been devoted to the prediction of the wave amplitude at the laboratory scale, the mechanisms involved during the generation and particularly the role of the granular material to mimic landslide are still poorly understood [2, 3]. In this context, the presented study aims to better understand the interaction between the landslide and the generated waves, by understanding the physical mechanisms at the origin of the deformation of the free surface and the dry-wet transition of the granular flow. A laboratory model is used consisting of a 2m long chute of varying slope angle ending in a 4m long water tank. More specifically, the landslide is modelled by a monodisperse granular flow of 1mm spherical glass beads.
A picture of the experiment is represented in Figure 1a. The dynamic of the slide when crossing the air/water interface as well as the spatio-temporal structure of the wave are caracterised as a function of the properties of the impacting granular flow. Figure 1b shows the spatial and temporal evolution of the water free surface elevation during the wave generation process. This figure also highlights that the wave crest is stronlgy correlated to the granular front at early stages, while freely propagates in the far field. Based on physical mecanisms during generation, this study allows to discuss existing models relating the maximum wave amplitude to a so-called impulse parameter [4].

  

                                                                                                                                                                                            
Figure 1: (a): Picture of the granular flow penetrating water, (b): Space-time representation of the free surface elevation, compared with granular flow front position.

References:
[1] Fritz H. M., Mohammed F. & Yoo J. Lituya Bay landslide impact generated mega-tsunami 50th anniversary., Pure and Applied Geophysic 166, 153–175 (2009).
[2] Viroulet S., Sauret A. & Kimmoun O. Tsunami generated by granular collapse down a rough inclined plane., Europhysics Letters. 105, 34004 (2014).
[3] Robbe-Saule M., Morize C., Henaff R., Bertho Y., Sauret A. & Gondret P. Experimental investigation of tsunami waves generated by granular collapse into water., J. Fluid Mech. 907, A11 (2021).
[4] Heller V. & Hager W. H. Impulse Product Parameter in Landslide Generated Impulse Waves., Journal of Waterway, Port, Coastal, and Ocean Engineering. 136, 145–155 (2010).

How to cite: Darvenne, A., Viroulet, S., and Lacaze, L.: Laboratory modelling of landslide-generated impulse wave, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17200, https://doi.org/10.5194/egusphere-egu23-17200, 2023.

NP4 – Time Series and Big Data Methods

Recently, Ehret and Dey (2022) suggested the c-u-curve method to analyze, classify and compare dynamical systems of arbitrary dimension, deterministic or probabilistic, by the two key features uncertainty and complexity. It consists of subdividing the system’s time-trajectory into a number of time slices. For all values in a time slice, the Shannon information entropy is calculated, measuring within-slice variability. System uncertainty is expressed by the mean entropy of all time slices. System complexity is then defined as “uncertainty about uncertainty”, expressed by the entropy of the entropies of all time slices. Calculating and plotting uncertainty u and complexity c for many different numbers of time slices yields the c-u-curve. Systems can be analyzed, compared and classified by the c-u-curve in terms of i) its overall shape, ii) mean and maximum uncertainty, iii) mean and maximum complexity, and iv) its characteristic time scale expressed by the width of the time slice for which maximum complexity occurs.

In our contribution, we will briefly revisit the basic concepts of the c-u-curve method, and then present results from applying it to hydro-meteorological time series of 512 catchments from the CAMELS-US data set (Newman et al., 2015). We will show how c-u-curve properties i) relate to hydro-climatological features, ii) how they can be used for catchment classification, and iii) how the classes compare to existing classifications by Knoben et al. (2018) and Jehn et al. (2020).

References

Ehret, U., and Dey, P.: Technical note: c-u-curve: A method to analyse, classify and compare dynamical systems by uncertainty and complexity, Hydrol. Earth Syst. Sci. Discuss., 2022, 1-12, 10.5194/hess-2022-16, 2022.

Jehn, F. U., Bestian, K., Breuer, L., Kraft, P., and Houska, T.: Using hydrological and climatic catchment clusters to explore drivers of catchment behavior, Hydrol. Earth Syst. Sci., 24, 1081-1100, 10.5194/hess-24-1081-2020, 2020.

Knoben, W. J. M., Woods, R. A., and Freer, J. E.: A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data, Water Resources Research, 54, 5088-5109, https://doi.org/10.1029/2018WR022913, 2018.

Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209-223, 10.5194/hess-19-209-2015, 2015.

How to cite: Ehret, U., Baste, S., and Dey, P.: Analyzing and classifying dynamical hydrological systems by uncertainty and complexity with the c-u-curve method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1464, https://doi.org/10.5194/egusphere-egu23-1464, 2023.

EGU23-2400 | ECS | Orals | NP4.1

Identifying soil signatures from soil moisture time series via a changepoint-based approach 

Mengyi Gong, Rebecca Killick, Christopher Nemeth, John Quinton, and Jessica Davis

Healthy soil plays a critical role in sustaining biodiversity, maintaining food production, and mitigating climate change through carbon capture. Soil moisture is an important measure of soil health that scientists model via soil drydown curves. The typical modelling process requires manually separating the soil moisture time series into segments representing the drying process and fitting exponential decay models to these segments to obtain an estimation of the key parameters. With the advancement of sensor technology, scientists can now obtain higher frequency measurements over longer periods in a larger number of locations. To enable automatic data processing and to obtain a dynamic view of the soil moisture drydown, a changepoint-based approach is developed to automatically identify structural changes in soil moisture time series.

Specifically, timings of the sudden rises in soil moisture over a long time series are captured and the parameters characterising the drying processes following the sudden rises are estimated simultaneously. An algorithm based on the penalised exact linear time (PELT) method was developed to identify the changepoints and estimate the model parameters. This method can be considered as a complement to the conventional soil moisture modelling. It requires little data pre-processing and can be applied to a soil moisture time series directly. Since each drying segment has its unique parameters, the method also has the potential of capturing any temporal variations in the drying process, thus providing a more comprehensive summary of the data.

The method was applied to the hourly soil moisture time series of nine field sites from the NEON data portal (https://data.neonscience.org/). Distributions and summary statistics of key model parameters, such as the exponential decay rate and the asymptotic soil moisture level, are produced for each field site. Investigating and comparing these quantities from different field sites enables the identification of soil signatures which can reflect the hydrological properties of the soil. Visualising the model parameters as a time series reveals the subtle temporal pattern of the drying process in some field sites. 

How to cite: Gong, M., Killick, R., Nemeth, C., Quinton, J., and Davis, J.: Identifying soil signatures from soil moisture time series via a changepoint-based approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2400, https://doi.org/10.5194/egusphere-egu23-2400, 2023.

EGU23-3968 | ECS | Posters on site | NP4.1

Influence of Environmental Parameters on Highly Sensitive Instruments at Moxa Geodynamic Observatory 

Valentin Kasburg, Alexander Breuer, Martin Bücker, and Nina Kukowski

Strainmeters measure the change in length between two known fixed points and are used primarily to identify and estimate tectonic strain. In addition to tectonic strain, these instruments also record changes in strain caused by other phenomena such as Earth tides and fluctuations of meteorological signals like changes in groundwater levels caused by precipitation, which may be much larger than tectonic strain and thus mask its signals. To avoid meteorological influences as far as possible, horizontal strainmeters often are maintained in galleries such that tectonically induced strain signals are not affected by other sources of noise.

At Moxa Geodynamic Observatory, located in central Germany, two laser strainmeters with a base length of 26 m each are maintained in galleries oriented north-south and east-west. Their resolution is in the nano-scale and sampling rate is 0.1 Hz. Mountain overburden in the gallery is comparatively low at approx. 30 m and in addition, the hydrogeological situation of the subsurface surrounding the observatory, is very heterogeneous. Therefore, amplitudes of meteorological phenomena are still quite high. In order to correct meteorological influences in the recorded strain time-series, they first need to be better understood. For doing so, long time-series - a decade at least – are needed. As the laser strainmeters continuously record since summer 2011, these are now available.

We present the results of weighting various meteorological parameters on the strainmeter recordings by training Long Short Term Memory Networks and perturbing input parameters for the test data. In this way, the contribution of each parameter to the meteorologically induced strain signals can be estimated. This knowledge is subsequently used to eliminate meteorological influences from the time-series recordings of strain.

How to cite: Kasburg, V., Breuer, A., Bücker, M., and Kukowski, N.: Influence of Environmental Parameters on Highly Sensitive Instruments at Moxa Geodynamic Observatory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3968, https://doi.org/10.5194/egusphere-egu23-3968, 2023.

EGU23-4698 | ECS | Orals | NP4.1

The power low in information geometry: Attempt from the viscoelastic relaxation of rock 

Mitsuhiro Hirano and Hiroyuki Nagahama

It is known that power law exists in the background of various natural phenomena. One example is the viscoelastic behavior of rocks. In the flow laws of high temperature of rocks, strain rate is in proportion to the power of stress. It can be replaced by the one that relaxation modulus (the ratio of stress to strain) is in proportion to the power of time with fractal dimension as power exponent. From Laplace transform for the relaxation modulus, the distribution of relaxation time (relaxation spectrum) with the power of relaxation time is derived. It indicates the existence of fractal distribution of different relaxation times in material elements in rocks. On the other hand, these strain-relaxation modulus-stress relations can be recaptured as the input-response-output relation in an ideal complex system with the power law of component. When input and output are stochastic with probability functions, the response corresponds to the change in differential geometric structure on a statistical manifold with a point as a probability function. Although previous studies suggested the correspondence between the power exponent (fractal dimension) and the constant (alpha) characterizing invariant geometric structure (alpha-connection), its details have not been discussed yet. In this presentation, we would reveal the correspondence between the power exponent (fractal dimension) and the constant (alpha) based on q-exponential family in information geometry, which is a more general exponential family.

How to cite: Hirano, M. and Nagahama, H.: The power low in information geometry: Attempt from the viscoelastic relaxation of rock, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4698, https://doi.org/10.5194/egusphere-egu23-4698, 2023.

EGU23-4919 | Posters on site | NP4.1

Long-term sensor drift of pressure gauges characterized by a pressure balance 

Hiroyuki Matsumoto, Hiroaki Kajikawa, and Eiichiro Araki

It is postulated that a pressure gauge has potential for detection of vertical crustal deformation associated with plate convergence since the measurement resolution is higher than the expected deformation. However, it has been long known that the sensor drift being a few of hPa (cm) per year or larger rate is identified in the long-term pressure observation at the seafloor. We have investigated the sensor drift of pressure gauges pressurized by a pressure balance in the laboratory. Two types of pressure gauges were examined; one is quartz resonant pressure gauges which are traditionally used for in-situ pressure observations and the other is silicon resonant pressure gauges which can be used for oceanographic observations in the future. Full scale of all pressure gauges examined in the present experiment is 70 MPa. Pressure calibration curves were obtained by applying the standard pressure from zero to full scale to characterize hysteresis and repeatability of pressure gauges. Comparing pressure calibration curves under the different temperature condition, only zero offset is changed for the tested quartz pressure gauges, whereas both zero offset and span are changed for some silicon pressure gauges. Then, static pressure of 20 MPa equivalent to 2000 m water depth is applied to the pressure gauges simultaneously for a period of approximately 100 days under the low temperature condition. Some silicon pressure gauges were pressurized under the normal temperature condition. Pressure calibrations were conducted about 50 times repeatedly by providing the standard pressure of 20 MPa using the pressure balance during the experiment. Differences between the standard pressure and the sensor’s output over time were calculated to evaluate the sensor drift. The results suggest that the lower ambient temperature can contribute to the shorter relaxation time (i.e., the elapsed time to disappear initial abrupt change) and the smaller sensor drift (i.e., the linear trend) in the both types of pressure gauges. The sensor drift between the quartz and the silicon pressure gauges were comparable except for the specific silicon pressure gauges. It is noted that the quartz pressure gauges are more sensitive to temperature than the silicon pressure gauges in the present experiment.

How to cite: Matsumoto, H., Kajikawa, H., and Araki, E.: Long-term sensor drift of pressure gauges characterized by a pressure balance, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4919, https://doi.org/10.5194/egusphere-egu23-4919, 2023.

EGU23-5840 | Orals | NP4.1 | Highlight

Advances in Bayesian time series analysis of palaeoclimate data 

Michel Crucifix, Linda Hinnov, Anne-Christine Da Silva, David De Vleeschouwer, Stephen Meyers, Andrew Parnell, Matthias Sinnesael, Thomas Westerhold, and Sébastien Wouters

Time series analysis of palaeoclimate data is used to identify quasi-periodic changes attributable to astronomical forcing of insolation by Earth’s axial obliquity and precession, and orbital eccentricity, i.e., Milankovitch cycles. Hays et al. (1976) applied time series analysis – including spectral analysis, filtering, tuning and hypothesis testing – on palaeoclimatic data from the most recent 500 Ka of Earth history to demonstrate forcing from these astronomical parameters. The CENOGRID “splice” (Westerhold et al., 2000) has since extended this evidence to 66 Ma. Investigators have also recognised the imprint of Milankovitch cycles in palaeoclimatic records reaching back into the Precambrian. 

Palaeoclimate time series present unique challenges: sample spacing is generally not constant; measured data represent combinations of palaeoenvironmental factors; most problematic of all, palaeoclimate time scales are almost never known with adequate certainty. Important time constraints are provided by geochronology from volcanic ash layers, geomagnetic reversals and selected chemostratigraphic events, but only at isolated, widely spaced points along geologic time, and only extremely rarely do they provide a precision sufficient to determine the time-periodicity of palaeoclimate variations at Milankovitch scales. Investigators must also grapple with uncertainties in celestial mechanics, and in the theory of climate change, sedimentation and alteration. From this collective information, one may choose to investigate mechanisms of climate or environmental change (climate modelling); estimate the chronology and duration of stratigraphic series of palaeoclimate data (cyclostratigraphy); and constrain the celestial mechanics of Earth’s distant past. 

In principle, all of these objectives can be obtained through application of a hierarchical Bayesian model: astronomical forcing -> climate -> environment -> sedimentation -> alteration -> observation. Bayesian theory allows us to reverse all of the arrows and to update information about sedimentation, the environment, climate, and astronomical forcing. However, in Bayesian statistics, expressing a likelihood function is a fundamental step and requires parameterising stochastic quantities. One needs to be clear and explicit about errors. We present an example that considers an explicit-likelihood route for Quaternary data (Carson et al., 2019). In the more distant geologic past, uncertainties about climate and sedimentation are increasingly challenging. Strategies tend to be based on pattern identification by the investigator, with or without numerical techniques. Examples include recognising orbital eccentricity bundling in paleoclimatic data sequences that exhibit precession cycling, and studying the relationships between frequency and amplitudes (Meyers and Malinverno, 2018). We review examples illustrating the relationship between frequency and amplitude together with the supporting theory. 

References: Carson et al., Proc. R. Soc. A (2019), 475, 20180854; Hays et al., Sci. (1976), 194(4270), 1121-1132; Meyers, S.R., Malinverno, A., Proc. Natl. Acad. Sci. U.S.A. (2018), 115(25), 6363-6368; Westerhold et al., Sci. (2020), 369, 1383-1387.

How to cite: Crucifix, M., Hinnov, L., Da Silva, A.-C., De Vleeschouwer, D., Meyers, S., Parnell, A., Sinnesael, M., Westerhold, T., and Wouters, S.: Advances in Bayesian time series analysis of palaeoclimate data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5840, https://doi.org/10.5194/egusphere-egu23-5840, 2023.

EGU23-6327 | Orals | NP4.1

Understanding and modeling meteorological drivers of the number of hospital admissions for malaria in South Africa 

Suzana Blesic, Milica Tosic, Neda Aleksandrov, Thandi Kapwata, Rajendra Maharaj, and Caradee Wright

We preformed statistical analysis of two sets of malaria incidence time series: of daily admissions from two large public hospitals in Limpopo Province in South Africa (records taken in the period 2002-2017), and of weekly epidemiological reports from five districts in the same province (for the period 2000-2020). We analysed these time series in relation to time series of temperature and rainfall ground or satellite data from the same geographical area.

Firstly, we used wavelet transform (WT) cross-correlation analysis to monitor and characterize coincidences in daily changes of meteorological variables and variations in hospital admissions. All our daily admission records had global wavelet power spectra (WTS) of the power-law type, indicating that they are outputs of complex sets of causes acting on different time scales. We found that malaria in South Africa is a seasonal multivariate event, initiated by co-occurrence of heat and rainfall. We then proceeded to utilize obtained results for the analysis of the weekly cases data, using the WTS superposition of signals rule to discern WTS peaks that are time lags between the onset of combined meteorological drivers and hospital admissions for malaria. We presumed that all these peaks are characteristic times connected to the characteristic periods of development, distribution and survival of either mosquitos, as disease vectors, the pathogens they transmit, or the times needed for human incubation of the disease. Thus, we were able to propose a regression model for the number of admissions (for malaria) cases, and to provide critical values of temperature and rainfall for the initiation of the disease spread.

Finally, using the developed model we investigated how future changes of meteorological variables and their combination can affect malaria dynamics, and thus provide information that can be of use for public health preparedness.

How to cite: Blesic, S., Tosic, M., Aleksandrov, N., Kapwata, T., Maharaj, R., and Wright, C.: Understanding and modeling meteorological drivers of the number of hospital admissions for malaria in South Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6327, https://doi.org/10.5194/egusphere-egu23-6327, 2023.

The impact of climate change on weather pattern dynamics over the North Atlantic is explored through the lens of the information theory of forced dissipative dynamical systems.

The predictability problem is first tackled by investigating the evolution of block entropies on observational time series of weather patterns produced by the Met Office, which reveals that predictability is increasing as a function of time in the observations during the 19th century and beginning of the 20th century, while the trend is reversed at the end of the 20th century and beginning of the 21st century. This feature is also investigated in the 15-member ensemble of the UK Met Office CMIP5 model for the 20th and 21st centuries under two climate change scenarios, revealing a wide range of possible evolutions depending on the realization considered, with an overall decrease in predictability in the 21st century for both scenarios.

Lower bounds of the information entropy production are also extracted, providing information on the degree of time asymmetry and irreversibility of the dynamics. The analysis of the UK Met Office model runs suggests that the information entropy production will increase by the end of the 21st century, by a factor of 10% in the Representative Carbon Pathway RCP2.6 scenario and a factor of 30 %–40% in the RCP8.5 one, as compared to the beginning of the 20th century. This allows one to make the conjecture that the degree of irreversibility is increasing, and hence heat production and dissipation will also increase under climate change, corroborating earlier findings based on the analysis of the thermodynamic entropy production.

How to cite: Vannitsem, S.: Weather pattern dynamics over western Europe under climate change: predictability, information entropy and production, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6728, https://doi.org/10.5194/egusphere-egu23-6728, 2023.

EGU23-7450 | ECS | Posters on site | NP4.1

Geodynamic studies in the Pieniny Klippen  Belt in  2004-2020 

Dominika Staniszewska

The Pieniny Klippen Belt is located in the middle part of the zone between the Inner Carpathians and Outer Carpathians. Researches at the Pieniny Geodynamic Test Field dates back to the 1960s.

Previous geodynamic studies in the area of the Pieniny Klippen Belt  have indicated neotectonic activity. Currently, starting in 2004, a GNSS survey campaign is held annually in early September.

The subject of the study was to check whether the Pieniny Klippen Belt (PKB) shows neotectonic activity in the relation to the surrounding structures - the Podhale Flysh (FP) and the Magura Nape (MN).

This study was based on the  survey of the movement of stations located in the area of the aforementioned three structures, which create the Pieniny Geodynamic Test Field.

The Pieniny Geodynamic Test Field consists of 15 GNSS stations, including 6 stations inside the PKB, 5 stations within the MN and 4 stations within the FP. The whole geodynamic test field is supplemented by 4 GNSS stations located in the Tatra Mountains.

To determine the horizontal movements of the geodynamic units, the results of satellite measurements made between 2004 and 2020 were processed. The coordinates and velocities of the stations were determined in two reference systems - IGb08 and IGb14.

To define the IGb08 and IGb14 systems, 24 EUREF stations (Euref Parmanent GNSS Network) were used. The stations were selected based on the following criteria: location, length of available data and the fewest number of discontinuities. The stations were basign to be located at the shortest distance from the Pieniny Geodynamic Test Field, as well as to be distributed evenly. Data from the CODE Analysis Center was used to process the GNSS data. GNSS datasets were processed using Bernese 5.2 GNSS Software. The adjustment was prefared in two variants due to inconsistencies between the orbits of the satellites and the IGb14 system. The differences between the ITRF2008 and ITRF2014 are quite small and are due to new or updated antenna calibrations.

Then, the obtained velocities were converted to ETRF2014. Station velocities were determined in two ways-analytically, using transformation parameters between the ITRF and ETRF2014 systems for the 2010.0 epoch, and using the EPN CB Coordinate Transformation Tool shared by the EUREF Permanent Network (EPN).

Horizontal coordinates were determined in both short-period solutions - daily and long-period solutions - covering sixteen measurement epochs.

To check the validity of the adjustment, a comparison of the velocities calculated for the reference stations with the EUREF model was performed.

The velocities of stations located in the Pieniny Geodynamic Test Field were also compared with those obtained in a study done in 2016.

The realized comparison of calculations allowed us to conclude that the performed alignment does not deviate from the solutions presented in the model and in the previous study.

The obtained results shows the tectonic activity of the Pieniny Klippen Belt and surrounding units. Horizontal point movements are small, i.e. 0.2 - 0.7mm/year, although changes in the position of points show a linear character. The trend in the direction of these changes and their magnitude is also preserved.

How to cite: Staniszewska, D.: Geodynamic studies in the Pieniny Klippen  Belt in  2004-2020, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7450, https://doi.org/10.5194/egusphere-egu23-7450, 2023.

EGU23-7730 | Orals | NP4.1

Power spectrum estimation for extreme events data 

Norbert Marwan and Tobias Braun

The estimation of power spectral density (PSD) of time series is an important task in many quantitative scientific disciplines. However, the estimation of PSD from discrete data, such as extreme event series is challenging. We present a novel approach for the estimation of a PSD of discrete data. Combining the edit distance metric with the Wiener-Khinchin theorem provides a simple yet powerful PSD analysis for discrete time series (e.g., extreme events). This method works directly with the event time series without interpolation. We demonstrate the method's potential on some prototypical examples and on event sequences of atmospheric rivers (AR), narrow filaments of extensive water vapor transport in the lower troposphere. Considering the spatial-temporal event series of ARs over Europe, we investigate the presence of a seasonal cycle as well as periodicities in the multi-annual range for specific regions, likely related to the North-Atlantic Oscillation (NAO).

How to cite: Marwan, N. and Braun, T.: Power spectrum estimation for extreme events data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7730, https://doi.org/10.5194/egusphere-egu23-7730, 2023.

A Measurement Node for the continuous gravity and tilt observations in an active

geodynamic area of southern Italy: the Calabrian Arc system

Anna Albano1, Vincenzo Carbone1, Francesco Lamonaca2

1Dipartimento di Fisica, Università della Calabria, Arcavacata di Rende (CS), Italy

2Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica,
Università della Calabria, Arcavacata di Rende (CS), Italy

 

Calabria (southern Italy) is a site of considerable seismic activity related to the ongoing evolution of the Calabrian Arc system, where a complex lithospheric structure is present. For over a century the Calabrian region has been going through a period of relative seismic quietness, yet its seismic hazard is at the highest levels in the Mediterranean basin due to several catastrophic earthquakes present in the historical records. In order to strengthen the geophysical monitoring of this region, a gravity and tilt recording station was set up in the premises of the University of Calabria. The measurement node is composed by the gravimeter G-1089 from LaCoste & Romberg; the tilt-meter Model 714 from Applied Geomechanics; a 6 and ½ digits multimeter Agilent 34970A data acquisition and switching unit used to converts the analog signals of the gravimeter and tilt-meter in the corresponding digital ones; a computer used to store, elaborate and present the signals. The delay among the input channels of the multimeter is evaluated and the optimal configuration is achieved in order to make such a delay negligible for the time correlation of the input signals. The measurement node is positioned on the ground of the cube 41C. Finally, the information about the temperature and the atmospheric pressure is obtained by the nearby environmental station positioned on the roog of cube 41B. The recorded signals should allow to estimate a tidal anomaly, possibly correlated with the difference between some local feature of the lithosphere or geodynamic activity and the corresponding characteristics of the model used to calculate the reference gravity tide. A reliable model of the gravity tide is necessary for accurate processing of discrete absolute and relative gravimetric measurements and to detect in the gravity signals possible components correlated to major seismic activity. The Ocean Tide Load (OTL) effect was accounted for in the determination of the tidal field spectral parameters. The most widespread DDW99/ NH Earth’s model, adopted here as reference, fits the obtained results well enough.

How to cite: Albano, A., Carbone, V., and Lamonaca, F.: A Measurement Node for the continuous gravity and tilt observations in an activegeodynamic area of southern Italy: the Calabrian Arc system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8485, https://doi.org/10.5194/egusphere-egu23-8485, 2023.

EGU23-9762 | Posters on site | NP4.1

Estimating intermittency significance by means of surrogate data: implications for stationarity 

Eliza Teodorescu, Marius Echim, Jay Johnson, and Costel Munteanu

Intermittency is a property of turbulent astrophysical plasmas, such as the solar wind, that implies non-uniformity in the transfer rate of energy carried by non-linear structures from large to small scales. We evaluate the intermittency level of the turbulent magnetic field measured by the Parker Solar Probe in the slow solar wind in the proximity of the Sun, at about 0.17 AU, during the probe’s first encounter. A quantitative measure of the intermittency of a time-series can be deduced based on the normalized forth order moment of the probability distribution functions, the flatness parameter. We observe that when dividing the data into contiguous samples of various lengths, from three to twenty-four hours, flatness differs significantly from sample to sample, suggestive of alternating intermittency-free time intervals with highly intermittent samples. In order to describe this variability, we apply an elaborate statistical test tailored to identify nonlinear dynamics in a time series which involves the construction of surrogate data that eliminate all nonlinear correlations contained in the dynamics of the signal but are otherwise consistent with an “underlying” linear process, i.e. the null hypothesis that we want to falsify. If a discriminating statistic for the original signal, such as the flatness parameter, is found to be significantly different than that of the ensemble of surrogates, then the null hypothesis is not valid, and we can conclude that the computed flatness reliably reflects the intermittency level of the underlying non-linear processes. We determine that non-stationarity of the time-series strongly influences the flatness of both the data and surrogates and the null hypothesis cannot be falsified. The intermittency level detected in such cases reflects the effects of isolated and, maybe, statistically not meaningful events, consequently, we stress upon the importance of careful data selection and evaluating the significance of the evaluated discriminating statistic.

How to cite: Teodorescu, E., Echim, M., Johnson, J., and Munteanu, C.: Estimating intermittency significance by means of surrogate data: implications for stationarity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9762, https://doi.org/10.5194/egusphere-egu23-9762, 2023.

EGU23-10009 | Posters virtual | NP4.1

26 August 2018 Geomagnetic Storm: Fractal Analysis of Earth Magnetic Field  

Anna Wawrzaszek, Renata Modzelewska, Agata Krasińska, Agnieszka Gil, and Vasile Glavan

We perform a systematic and comparative analysis of the fractal dimension estimators as a proxy for data complexity. In particular, we focus on the analysis of the horizontal geomagnetic field components registered by four stations (Belsk, Hel, Sodankylä and Hornsund) at various latitudes during the period of 22 August–1 September, when the 26 August 2018 geomagnetic storm appeared. To identify the fractal scaling and to compute the fractal dimension, we apply and compare three selected methods: structure function scaling, Higuchi, and detrended fluctuation analysis. The obtained results show the temporal variation of the fractal dimension of horizontal geomagnetic field components, revealing differences between their irregularity (complexity). Moreover, the values of fractal dimension seem to be sensitive to the change of physical conditions related to interplanetary shock, the coronal mass ejection, the corotating interaction region, and the high-speed stream passage during the storm development. Especially, a significant decrease in the fractal dimension for all stations is observed immediately following the interplanetary shock, which was not straightforwardly visible in the geomagnetic field components data.

How to cite: Wawrzaszek, A., Modzelewska, R., Krasińska, A., Gil, A., and Glavan, V.: 26 August 2018 Geomagnetic Storm: Fractal Analysis of Earth Magnetic Field , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10009, https://doi.org/10.5194/egusphere-egu23-10009, 2023.

The dynamics of the Earth's magnetosphere are a tremendously complex system that exhibit nonlinear dynamics in response to variations in the solar wind and interplanetary magnetic field. It has been amply demonstrated in the past that scale-invariant processes define magnetospheric dynamics as measured by geomagnetic indices. Forced and/or Self Organized Criticality, a term coined by T.S. Chang in the 90s, describes the plasma dynamics in the magnetospheric tail region. The multifractal structure of the variations of geomagnetic indices is another distinctive feature of the Earth's magnetospheric response.  Here, we use the joint multifractal measures approach first proposed by Meneveau et al. (1990) and low and high latitude geomagnetic indices  (AE, AL, Sym-H, Asy-H ,etc) to examine the link between the intermittency degrees of high and low latitude dynamics. The findings are examined in regard to the coupling of storms and substorms. 

How to cite: Consolini, G.: Joint-Multifractal Analysis of High and Low Latitude Magnetospheric Dynamics., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11060, https://doi.org/10.5194/egusphere-egu23-11060, 2023.

EGU23-11304 | Posters virtual | NP4.1

Long term investigations at the Mont Terri rock laboratory of tilt and their near and far field influences 

Dorothee Rebscher, Finnegan G. Reichertz, and Senecio Schefer

Underground research laboratories provide advantageous conditions to observe a broad range of various rock parameters to characterise rock matrix and geological features, and to enhance knowledge of their dynamic behaviour, all under relatively undisturbed conditions. One of their favorable features is that the overburden protects against large environmental changes, although those influences cannot be mitigated in full. Especially for long term investigations, a holistic observation of ambient environmental parameters is necessary on the local scale and beyond.

The Swiss Mont Terri rock laboratory is situated in the Jura Mountains about 250 m below the surface. Starting in 1996, the international Mont Terri Consortium has conducted about 150 experiments in the native Opalinus Clay. Embedded in several of the ongoing in situ experiments, platform tiltmeters assist in the often interdisciplinary investigations. Two different types of biaxial instruments with resolutions of 0.1 urad and better than nrad are distributed throughout the laboratory, together forming a small, growing array, with the first tiltmeters installed in April 2019.

Tiltmeters observe the direct local deformation, they are exposed to near field but also far field impacts. Known local influences are mainly temperature, air pressure, and humidity. In Mont Terri, all of these parameters are registered directly at the location of the tilt sensors with the same relatively high sampling of once every few seconds. In addition, Mont Terri's comprehensive database imparts valuable complementing information. However, the detected deformation pattern is also influenced on a much larger spatial scale, e.g. far field, extensive changes in weather patterns, earth tides, and teleseismic events.

Therefore, to allow detection, identification, and realistic interpretation of complex signal responses on different spatial scales, it is mandatory to distinguish transient and long term signals, natural and anthropogenic disturbances. Their understanding is essential for the evaluation of stability and the safety of a rock laboratory for the benefit of its personnel and visitors. Obviously, long term, continuous data series require long term commitments. But the efforts pays off, not the least, as decade-long deformation studies contribute to multifaceted technical and scientific aspects of long term behavior of barrier rocks, and these are relevant for the exploitation of the deep geological subsurface such as nuclear waste disposal, geological storage of carbon dioxide, use of geothermal energy, or inter-seasonal thermal energy storage.

How to cite: Rebscher, D., Reichertz, F. G., and Schefer, S.: Long term investigations at the Mont Terri rock laboratory of tilt and their near and far field influences, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11304, https://doi.org/10.5194/egusphere-egu23-11304, 2023.

EGU23-11340 | ECS | Posters on site | NP4.1

Observing climate zones boundaries change: Kazakhstan's case study 

Kalamkas Yessimkhanova and Mátyás Gede

Presently, climate change is an urging topic and mapping the effects of climate change is a crucial part. According to the evaluation by United Nations Development Programme, more than half of the territory of Kazakhstan exposed ecological crisis as drought, extreme weather events, fires and others. In this regard, this study is important for conducting research on both observation and visualization of the boundaries change of climatic zones on the land area of Kazakhstan. The Köppen climate classification was applied as a reference. In particular, such variables as temperature and precipitation were used for climatic zones classification. Extensive database of Google Earth Engine spatial analysis platform allows to leverage climate reanalysis datasets for many decades. Although, World Metereological Organization recommends considering 30-year period to witness the climate change, there is limited data access for the region of interest. Thus, only 21-year time frame was analyzed, specifically, time range between 2000 and 2021. Results are presented as time-series maps of classified climate zones and may benefit other researchers on their projects related to climate change.

How to cite: Yessimkhanova, K. and Gede, M.: Observing climate zones boundaries change: Kazakhstan's case study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11340, https://doi.org/10.5194/egusphere-egu23-11340, 2023.

Statistical associations between variables of interest are commonly assessed by applying similarity measures like Pearson correlation to corresponding observational time series. Most traditional measures focus on continuous variables and their associated complete variability, while there is a vast amount of practical examples where only times with specific conditions (e.g. extreme events) are of interest. For the latter cases, concepts like event synchronization strength or event coincidence rates have been introduced as proper similarity measures, and have proven their broad applicability across many areas of research. However, recent work has shown that such event based similarity measures may have conceptual as well as practical limitations when studying co-occurrence statistics between temporally clustered or extended events that do not meet the common assumption of serially uncorrelated point processes.

In this work, we introduce and discuss a straightforward extension of event coincidence analysis (ECA) to studying statistical associations between sequences of persistent events, which we tentatively call interval coincidence analysis (InCA). Here, each event of interest corresponds to a well-defined time interval, and the discrete counts of event co-occurrences in ECA are replaced by the fractions of time during which event intervals in two sequences mutually overlap. A statistical significance test for the obtained interval coincidence rates is realized by block bootstrapping event and non-event intervals, retaining the event duration and waiting time distributions of the persistent events in both sequences.

We demonstrate the practical potentials of InCA, as well as its similarities and differences with ECA, for a specific case study on atmospheric dynamics. Specifically, we apply both methods to studying the likelihood of co-occurrences between boreal summer (June to August) heatwaves in different parts of the Northern hemisphere and hemispheric anomalies of the atmospheric circulation, such as a jet stream pattern exhibiting two distinct wind bands known as double-jet. Our analysis reveals large-scale regions of markedly elevated likelihood of co-occurrences over Northern Europe, Central to Eastern Siberia, Northeastern Canada as well as the Middle East, Eastern China, the Southwestern and Northeastern United States and Northwest Africa, indicating a particular vulnerability of those regions to the presence of double-jet patterns.

How to cite: Donner, R., Diedrich, D., Praast, S., and Di Capua, G.: Quantifying statistical associations among persistent events: Interval coincidence analysis between Northern hemisphere heatwaves and different types of circulation anomalies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12070, https://doi.org/10.5194/egusphere-egu23-12070, 2023.

EGU23-12560 | ECS | Posters on site | NP4.1

Understanding monsoonal rainfall patterns with a complex network approach  

Guruprem Bishnoi, Reik Donner, Chandrika Thulaseedharan Dhanya, and Rakesh Khosa

The Indian summer monsoon (ISM), which accounts for the majority of India’s yearly rainfall, has a significant influence on the nation’s economy. Understanding monsoonal dynamics is a challenge because of the related small-scale processes and their spatiotemporal complexity. Nevertheless, in the past decades, complex networks have become a key mathematical tool in the analysis of complex systems like the monsoon. However, multi-scale interactions and the coupling between rainfall and atmospheric circulation have remained underrepresented in the corresponding functional network studies. In this study, we exploit coupled rainfall networks to investigate simultaneous interactions of rainfall with other atmospheric variables. Firstly, rainfall networks are investigated by considering various network measures. Secondly, a coupled network is developed based on several atmospheric variables and their point-wise correlation with rainfall fields. Furthermore, the contrasts between the rainfall network and its coupled equivalent are emphasized. By comparison, the resulting coupled network includes both horizontal and vertical interconnections of the spatially enclosed time sequences, representing both the inherent structure of a single meteorological variable and the interaction structure with rainfall fields. It is expected to help with understanding the dynamics of monsoonal rainfall. This study, therefore, demonstrates the application of a complex network approach to studying highly dynamic phenomena such as the ISM. Our results are anticipated to provide the scientific community with new insights into how the interplay of the atmospheric systems leads to the heavy rainfall episodes that take place during the ISM.

 

 

How to cite: Bishnoi, G., Donner, R., Dhanya, C. T., and Khosa, R.: Understanding monsoonal rainfall patterns with a complex network approach , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12560, https://doi.org/10.5194/egusphere-egu23-12560, 2023.

EGU23-13601 | ECS | Posters virtual | NP4.1

Empirical Distortions in Climate Networks 

Moritz Haas, Bedartha Goswami, and Ulrike von Luxburg

Climate networks have become a popular tool for detecting complex structures in spatio-temporal data. However, they require to estimate correlation values on many edges based on limited and noisy time series. Consequently any constructed network likely contains false and missing edges. To measure how severely and in which ways estimated networks are distorted by statistical errors, we simulate time-dependent isotropic random fields on the sphere. We comprehensively present several patterns of distortion in local as well as global network characteristics and demonstrate which network construction methods enhance statistical robustness. When the data has a locally coherent correlation structure, spurious link bundle teleconnections and spurious high-degree clusters have to be expected. Anisotropic estimation variance can also induce severe biases into empirical networks. We validate all our findings with ERA5 reanalysis data. Finally, we explain why commonly applied resampling procedures  are insufficient for evaluating statistical significance of network structures, and introduce a new ensemble construction framework that aims to alleviate most of the discussed shortcomings.

How to cite: Haas, M., Goswami, B., and von Luxburg, U.: Empirical Distortions in Climate Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13601, https://doi.org/10.5194/egusphere-egu23-13601, 2023.

EGU23-14863 | Orals | NP4.1

Windowed recurrence plot approach in semiarid grasslands 

Ana M. Tarquis, Andres F. Almeida-Ñauñay, Ernesto Sanz, Juan C. Losada, and Rosa M. Benito

Being one of the essential ecosystems, grasslands represent an important ecological area for water and biodiversity conservation. In this line, remote sensing instruments are a helpful tool for assessing vegetation status. The Modified Soil-Adjusted Vegetation Index (MSAVI) time-series are used to monitor drought events and to consider the soil influence in vegetation monitoring. In this sense, Recurrence plots (RPs) techniques have been demonstrated to be one of the most capable tools to unravel the complex dynamics of the time-series analysis. This work highlights the recurrence techniques' benefits in visualising and quantifying vegetation dynamics.

We chose a study area in the centre of Spain, where the Mediterranean climate dominates. We selected the MODQ1.V006 product from the MODIS imagery collection, with a spatial resolution of 250x250m. Then, an average MSAVI time series from pixels that met predefined criteria were analysed. RPs and Cross recurrence plots (CRPs) were computed to reveal the dynamics of the time series. Furthermore, diagonal-wise profiles (DWP)  and windowed-cross recurrence plots (WCRPs) were included in the analysis at different time scales. In the end, RPs, CRPs and WCRPs are quantified through the recurrence quantification analysis (RQA).

RPs displayed different patterns depending on the studied time series. Precipitation showed a stochastic dynamic, emphasising the unstable behaviour of Mediterranean rainfalls. On the opposite, temperature revealed a diagonal-like pattern in the RP. This fact pointed out the temperature's seasonal behaviour over time. Concerning MSAVI, RP presented a mixture of both patterns.

CRPs between precipitation and MSAVI showed a delayed consequence of MSAVI to precipitation events. Contrary to precipitation, CRPs between temperature and MSAVI did not show a delayed response in the studied period. WCRPs indicated characteristic phases in the time series, revealing interactions between vegetation and climate and being different between wet and dry seasons.

RPs techniques have been demonstrated to be a valuable instrument for uncovering the complex dynamics between vegetation and climate. Therefore, they should be considered a viable alternative in the vegetation time series analysis.

 

Acknowledgements: The authors acknowledge the support of Clasificación de Pastizales Mediante Métodos Supervisados - SANTO from Universidad Politécnica de Madrid (project number: RP220220C024).

References

Almeida-Ñauñay, A.F., Benito, R.M., Quemada, M., Losada, J.C., Tarquis, A.M., 2022. Recurrence plots for quantifying the vegetation indices dynamics in a semiarid grassland. Geoderma 406, 115488. https://doi.org/10.1016/j.geoderma.2021.115488

Almeida-Ñauñay, A.F., Benito, R.M., Quemada, M., Losada, J.C., Tarquis, A.M., 2021. The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy 23, 559. https://doi.org/10.3390/e23050559

Martín-Sotoca, J.J., Saa-Requejo, A., Moratiel, R., Dalezios, N., Faraslis, I., Tarquis, A.M., 2019. Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds. Nat. Hazards Earth Syst. Sci. 19, 1685–1702. https://doi.org/10.5194/nhess-19-1685-2019

Sanz, E., Saa-Requejo, A., Díaz-Ambrona, C.H., Ruiz-Ramos, M., Rodríguez, A., Iglesias, E., Esteve, P., Soriano, B., Tarquis, A.M., 2021. Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens. 13, 840. https://doi.org/10.3390/rs13050840

How to cite: Tarquis, A. M., Almeida-Ñauñay, A. F., Sanz, E., Losada, J. C., and Benito, R. M.: Windowed recurrence plot approach in semiarid grasslands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14863, https://doi.org/10.5194/egusphere-egu23-14863, 2023.

EGU23-16971 | Posters on site | NP4.1

Ocean Networks Canada: Long-term ocean observing on a Northeast Pacific cabled ocean observatory 

Richard Dewey, Martin Scherwath, Steve Milahy, Martin Heesemann, Fabio De Leo, Lanfranco Muzi, Kohen Bauer, and Kim Juniper

Long time series observations in the ocean are rare. In the Northeast Pacific, Ocean Networks Canada (ONC) of the University of Victoria operates a number of permanent cabled ocean observatories. The first was installed in 2006, and they have successfully produced many interdisciplinary high-resolution time series over the years, the longest being over 16 years in duration. The cabled observatories operated by ONC include the VENUS coastal observatory and the NEPTUNE off-shore deep-sea observatory. Each observatory has several sites where an observatory node provides continuous power and high bandwidth communications to a wide range of ocean and geophysical sensors. Various long high-resolution time series will be presented and the assessment of climate, decadal, inter-seasonal, annual, and even daily cycles, variations, and signals will be discussed. Such long time series, including environmental baselines, are key for evaluating physicochemical and biological change in the oceans in response to natural variations and climate change. In this way, recent efforts to leverage our time series data in robust monitoring, measurement, reporting, and verification (M2RV) frameworks in the context of different marine carbon dioxide removal (mCDR) approaches, will also be presented.

How to cite: Dewey, R., Scherwath, M., Milahy, S., Heesemann, M., De Leo, F., Muzi, L., Bauer, K., and Juniper, K.: Ocean Networks Canada: Long-term ocean observing on a Northeast Pacific cabled ocean observatory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16971, https://doi.org/10.5194/egusphere-egu23-16971, 2023.

Large-scale climate variability is analysed, modelled, and predicted mainly based on general circulation models and low-dimensional association analysis. The models’ equational basis makes it difficult to produce mathematical analysis results and clear interpretations, whereas the association analysis cannot establish causation sufficiently to make invariant predictions. However, the macroscale causal structures of the climate system may accomplish the tasks of analysis, modelling, and prediction according to the concepts of causal emergence and causal prediction’s invariance.

Under the assumptions of no unobserved confounders and linear Gaussian models, we examine whether the macroscale causal structures of the climate system can be inferred not only to model but also to predict the large-scale climate variability. Specifically, first, we obtain the causal structures of the macroscale air-sea interactions of El Niño–Southern Oscillation (ENSO), which are interpretable in terms of physics. The structural causal models constructed accordingly can model the ENSO diversity realistically and predict the ENSO variability. Second, this study identifies the joint effect of ENSO and three other winter climate phenomena on the interannual variability in the East Asian summer monsoon. Using regression, these causal precursors can predict the monsoon one season ahead, outperforming association-based empirical models and several climate models. Third, we introduce a framework that infers ENSO’s air-sea interactions from high-dimensional data sets. The framework is based on aggregating the causal discovery results of bootstrap samples to improve high-dimensional variable selection. It is also based on spatial-dimension reduction to allow of clear interpretations at the macroscale.

While further integration with nonlinear non-Gaussian models will be necessary to establish the full benefits of inferring causal structures as a standard practice in research and operational predictions, our study may offer a route to providing concise explanations of the climate system and reaching accurate invariant predictions.

How to cite: He, S., Yang, S., and Chen, D.: Inferring Causal Structures to Model and Predict ENSO and Its Effect on Asian Summer Monsoon, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-102, https://doi.org/10.5194/egusphere-egu23-102, 2023.

EGU23-239 | ECS | Orals | ITS1.14/CL5.8

Toward a hybrid tropical cyclone global model 

Roberto Ingrosso and Mathieu Boudreault

The future evolution of tropical cyclones (TCs) in a warming world is an important issue, considering their potential socio-economic impacts on the areas hit by these phenomena. Previous studies provide robust responses about the future increase in intensity and in the global proportion of major TCs (Category 4–5). On the other hand, high uncertainty is associated to a projected future decrease in global TCs frequency and to potential changes in TC tracks and translation speed.

Risk management and regulatory actions require more robust quantification in how the climate change affects TCs dynamics.  A probabilistic hybrid TC model based upon statistical and climate models, physically coherent with TCs dynamics, is being built to investigate the potential impacts of climate change. Here, we provide preliminary results, in terms of present climate reconstruction (1980-2021) and future projections (2022-2060) of cyclogenesis locations and TC tracks, based on different statistical models, such as logistic and multiple linear regressions and random forest.  Physical predictors associated with the TC formation and motion and produced by reanalysis (ERA5) and the Community Earth System Model (CESM) ensemble are considered in this study.

 

How to cite: Ingrosso, R. and Boudreault, M.: Toward a hybrid tropical cyclone global model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-239, https://doi.org/10.5194/egusphere-egu23-239, 2023.

EGU23-492 | ECS | Posters on site | ITS1.14/CL5.8

Separation of climate models and observations based on daily output using two machine learning classifiers 

Lukas Brunner, Sebastian Sippel, and Aiko Voigt

Climate models are primary tools to investigate processes in the climate system, to project future changes, and to inform decision makers. The latest generation of models provides increasingly complex and realistic representations of the real climate system while there is also growing awareness that not all models produce equally plausible or independent simulations. Therefore, many recent studies have investigated how models differ from observed climate and how model dependence affects model output similarity, typically drawing on climatological averages over several decades.

Here, we show that temperature maps from individual days from climate models from the CMIP6 archive can be robustly identified as “observation” or “model” even after removing the global mean. An important exception is a prototype high-resolution simulation from the ICON model family that can not be so  unambiguously classified into one category. These results highlight that persistent differences between observed and simulated climate emerge at very short time scales already, but very high resolution modelling efforts may be able to overcome some of these shortcomings.

We use two different machine learning classifiers: (1) logistic regression, which allows easy insights into the learned coefficients but has the limitation of being a linear method and (2) a convolutional neural network (CNN) which represents rather the other end of the complexity spectrum, allowing to learn nonlinear spatial relations between features but lacking the easy interpretability logistic regression allows. For CMIP6 both methods perform comparably, while the CNN is also able to recognize about 75% of samples from ICON as coming from a model, while linear regression does not have any skill for this case.

Overall, we demonstrate that the use of machine learning classifiers, once trained, can overcome the need for multiple decades of data to investigate a given model. This opens up novel avenues to test model performance on much shorter times scales.

How to cite: Brunner, L., Sippel, S., and Voigt, A.: Separation of climate models and observations based on daily output using two machine learning classifiers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-492, https://doi.org/10.5194/egusphere-egu23-492, 2023.

EGU23-753 | ECS | Orals | ITS1.14/CL5.8 | Highlight

Finding regions of similar sea level variability with the help of a Gaussian Mixture Model 

Lea Poropat, Céline Heuzé, and Heather Reese

In climate research we often want to focus on a specific region and the most prominent processes affecting it, but how exactly do we select the borders of that region? We also often need to use long-term in situ observations to represent a larger area, but which area exactly are they representative for? In ocean sciences we usually consider basins as separate regions or even simpler, just select a rectangle of the ocean, but that does not always correspond to the real, physically relevant borders. As alternative, we use an unsupervised classification model, Gaussian Mixture Model (GMM), to separate the northwestern European seas into regions based on the sea level variability observed by altimetry satellites.

After performing a principal component (PC) analysis on the 24 years of monthly sea level data, we use the stacked PC maps as input for the GMM. We used the Bayesian Information Criterion to determine into how many regions our area should be split because GMM requires the number of classes to be selected a priori. Depending on the number of PCs used, the optimal number of classes was between 12 and 18, more PCs typically allowing the separation into more regions. Due to the complexity of the data and the dependence of the results on the starting randomly chosen weights, the classification can differ to a degree with every new run of the model, even if we use the exact same data and parameters. To tackle that, instead of using one model, we use an ensemble of models and then determine which class does each grid point belong to by soft voting, i.e., each of the models provides a probability that the point belongs to a particular class and the class with the maximal sum of probabilities wins. As a result, we obtain both the classification and the likelihood of the model belonging to that class.

Despite not using the coordinates of the data points in the model at all, the obtained classes are clearly location dependent, with grid points belonging to the same class always being close to each other. While many classes are defined by bathymetry changes, e.g., the continental shelf break and slope, sometimes other factors come into play, such as for the split of the Norwegian coast into two classes or for the division in the Barents Sea, which is probably based on the circulation. The North Sea is also split into three distinct regions, possibly based on sea level changes caused by dominant wind patterns.

This method can be applied to almost any atmospheric or oceanic variable and used for larger or smaller areas. It is quick and practical, allowing us to delimit the area based on the information we cannot always clearly see from the data, which can facilitate better selection of the regions that need further research.

How to cite: Poropat, L., Heuzé, C., and Reese, H.: Finding regions of similar sea level variability with the help of a Gaussian Mixture Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-753, https://doi.org/10.5194/egusphere-egu23-753, 2023.

EGU23-849 | ECS | Orals | ITS1.14/CL5.8

Drivers of sea level variability using neural networks 

Linn Carlstedt, Lea Poropat, and Céline Heuzé

Understanding the forcing of regional sea level variability is crucial as many people all over the world live along the coasts and are endangered by the sea level rise. The adding of fresh water into the oceans due to melting of the Earth’s land ice together with thermosteric changes has led to a rise of the global mean sea level (GMSL) with an accelerating rate during the twentieth century, and has now reached a mean rate of 3.7 mm per year according to IPCCs latest report. However, this change varies spatially and the dynamics behind what forces sea level variability on a regional to local scale is still less known, thus making it hard for decision makers to mitigate and adapt with appropriate strategies.

Here we present a novel approach using machine learning (ML) to identify the dynamics and determine the most prominent drivers forcing coastal sea level variability. We use a recurrent neural network called Long Short-Term Memory (LSTM) with the advantage of learning data in sequences and thus capable of storing some memory from previous timesteps, which is beneficial when dealing with time series. To train the model we use hourly ERA5 10-m wind, mean sea level pressure (MSLP), sea surface temperature (SST), evaporation and  precipitation data between 2009-2017 in the North Sea region. To reduce the dimensionality of the data but still preserve maximal information we conduct principal component analysis (PCA) after removing the climatology which are calculated by hourly means over the years. Depending on the explained variance of the PCs for each driver, 2-4 PCs are chosen and cross-correlated to eliminate collinearity, which could affect the model results. Before being used in the ML model the final preprocessed data are normalized by min-max scaling to optimize the learning. The target data in the model are hourly in-situ sea level observations from West-Terschelling in the Netherlands. Using in-situ observations compared to altimeter data enhances the ability of making good predictions in coastal zones as altimeter data has a tendency to degrade along the coasts. The sea level time series is preprocessed by tidal removal and de-seasoned by subtracting the hourly means. To determine which drivers are most prominent for the sea surface variability in our location, we mute one driver at a time in the training of the network and evaluate the eventual improvement or deterioration of the predictions.

Our results show that the zonal wind is the most prominent forcing of sea level variability in our location, followed by meridional wind and MSLP. While the SST greatly affects the GMSL, SST seems to have little to no effect on local sea level variability compared to other drivers. This approach shows great potential and can easily be applied to any coastal zone and is thus very useful for a broad body of decision makers all over the world. Identifying the cause of local sea level variability will also enable the ability of producing better models for future predictions, which is of great importance and interest.

How to cite: Carlstedt, L., Poropat, L., and Heuzé, C.: Drivers of sea level variability using neural networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-849, https://doi.org/10.5194/egusphere-egu23-849, 2023.

EGU23-984 | ECS | Orals | ITS1.14/CL5.8

Data-driven Attributing of Climate Events with Climate Index Collection based on Model Data (CICMoD) 

Marco Landt-Hayen, Willi Rath, Sebastian Wahl, Nils Niebaum, Martin Claus, and Peer Kröger

Machine learning (ML) and in particular artificial neural networks (ANNs) push state-of-the-art solutions for many hard problems e.g., image classification, speech recognition or time series forecasting. In the domain of climate science, ANNs have good prospects to identify causally linked modes of climate variability as key to understand the climate system and to improve the predictive skills of forecast systems. To attribute climate events in a data-driven way with ANNs, we need sufficient training data, which is often limited for real world measurements. The data science community provides standard data sets for many applications. As a new data set, we introduce a collection of climate indices typically used to describe Earth System dynamics. This collection is consistent and comprehensive as we use control simulations from Earth System Models (ESMs) over 1,000 years to derive climate indices. The data set is provided as an open-source framework that can be extended and customized to individual needs. It allows to develop new ML methodologies and to compare results to existing methods and models as benchmark. Exemplary, we use the data set to predict rainfall in the African Sahel region and El Niño Southern Oscillation with various ML models. We argue that this new data set allows to thoroughly explore techniques from the domain of explainable artificial intelligence to have trustworthy models, that are accepted by domain scientists. Our aim is to build a bridge between the data science community and researchers and practitioners from the domain of climate science to jointly improve our understanding of the climate system.

How to cite: Landt-Hayen, M., Rath, W., Wahl, S., Niebaum, N., Claus, M., and Kröger, P.: Data-driven Attributing of Climate Events with Climate Index Collection based on Model Data (CICMoD), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-984, https://doi.org/10.5194/egusphere-egu23-984, 2023.

EGU23-1135 | ECS | Posters on site | ITS1.14/CL5.8

Curation of High-level Molecular Atmospheric Data for Machine Learning Purposes 

Vitus Besel, Milica Todorović, Theo Kurtén, Patrick Rinke, and Hanna Vehkamäki

As cloud and aerosol interactions remain large uncertainties in current climate models (IPCC) they are of special interest for atmospheric science. It is estimated that more than 70% of all cloud condensation nuclei origin from so-called New Particle Formation, which is the process of gaseous precursors clustering together in the atmosphere and subsequent growth into particles and aerosols. After initial clustering this growth is driven strongly by condensation of low volatile organic compounds (LVOC), that is molecules with saturation vapor pressures (pSat) below 10-6 mbar [1]. These origin from organic molecules emitted by vegetation that are subsequently rapidly oxidized in the air, so-called Biogenic LVOC (BLVOC).

We have created a big data set of BLVOC using high-throughput computing and Density Functional Theory (DFT), and use it to train Machine Learning models to predict pSat of previously unseen BLVOC. Figure 1 illustrates some sample molecules form the data.

Figure 1: Sample molecules, for small, medium large sizes.     Figure 2: Histogram of the calculated saturation vapor pressures.

Initially the chemical mechanism GECKO-A provides possible BLVOC molecules in the form of SMILES strings. In a first step the COSMOconf program finds and optimizes structures of possible conformers and provides their energies for the liquid phase on a DFT level of theory. After an additional calculation of the gas phase energies with Turbomole, COSMOtherm calculates thermodynamical properties, such as the pSat, using the COSMO-RS [1] model. We compressed all these computations together in a highly parallelised high-throughput workflow to calculate 32k BLVOC, that include over 7 Mio. molecular conformers. See a histogram of the calculated pSat in Figure 2.

We use the calculated pSat to train a Gaussian Process Regression (GPR) machine learning model with the Topological Fingerprint as descriptor for molecular structures. The GPR incorporates noise and outputs uncertainties for predictions on the pSat. These uncertainties and data cluster techniques allow for the active choosing of molecules to include in the training data, so-called Active Learning. Further, we explore using SLISEMAP [2] explainable AI methods to correlate Machine Learning predictions, the high-dimensional descriptors and human-readable properties, such as functional groups.

[1] Metzger, A. et al. Evidence for the role of organics in aerosol particle formation under atmospheric conditions. Proc. Natl. Acad. Sci. 107, 6646–6651, 10.1073/pnas.0911330107 (2010)
[2] Klamt, A. & Schüürmann, G. Cosmo: a new approach to dielectric screening in solvents with explicit expressions for the
screening energy and its gradient. J. Chem. Soc., Perkin Trans. 2 799–805, 10.1039/P29930000799 (1993).
[3] Björklund, A., Mäkelä, J. & Puolamäki, K. SLISEMAP: supervised dimensionality reduction through local explanations. Mach Learn (2022). https://doi.org/10.1007/s10994-022-06261-1

How to cite: Besel, V., Todorović, M., Kurtén, T., Rinke, P., and Vehkamäki, H.: Curation of High-level Molecular Atmospheric Data for Machine Learning Purposes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1135, https://doi.org/10.5194/egusphere-egu23-1135, 2023.

EGU23-1244 | Posters on site | ITS1.14/CL5.8

Machine learning for non-orographic gravity waves in a climate model 

Steven Hardiman, Adam Scaife, Annelize van Niekerk, Rachel Prudden, Aled Owen, Samantha Adams, Tom Dunstan, Nick Dunstone, and Melissa Seabrook

There is growing use of machine learning algorithms to replicate sub-grid parametrisation schemes in global climate models.  Parametrisations rely on approximations, thus there is potential for machine learning to aid improvements.  In this study, a neural network is used to mimic the behaviour of the non-orographic gravity wave scheme used in the Met Office climate model, important for stratospheric climate and variability.  The neural network is found to require only two of the six inputs used by the parametrisation scheme, suggesting the potential for greater efficiency in this scheme.  Use of a one-dimensional mechanistic model is advocated, allowing neural network hyperparameters to be trained based on emergent features of the coupled system with minimal computational cost, and providing a test bed prior to coupling to a climate model.  A climate model simulation, using the neural network in place of the existing parametrisation scheme, is found to accurately generate a quasi-biennial oscillation of the tropical stratospheric winds, and correctly simulate the non-orographic gravity wave variability associated with the El Nino Southern Oscillation and stratospheric polar vortex variability.  These internal sources of variability are essential for providing seasonal forecast skill, and the gravity wave forcing associated with them is reproduced without explicit training for these patterns.

How to cite: Hardiman, S., Scaife, A., van Niekerk, A., Prudden, R., Owen, A., Adams, S., Dunstan, T., Dunstone, N., and Seabrook, M.: Machine learning for non-orographic gravity waves in a climate model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1244, https://doi.org/10.5194/egusphere-egu23-1244, 2023.

EGU23-1502 | ECS | Orals | ITS1.14/CL5.8

Adapting Transfer Learning for Multiple Channels in Satellite Data Applications 

Naomi Simumba and Michiaki Tatsubori

Transfer learning is a technique wherein information learned by previously trained models is applied to new learning tasks. Typically, weights learned by a network pretrained on other datasets are copied or transferred to new networks. These new networks, or downstream models, are then are then used for assorted tasks. Foundation models extend this concept by training models on large datasets. Such models gain a contextual understanding which can then be used to improve performance of downstream tasks in different domains. Common examples include GPT-3 in the field on natural language processing and ImageNet trained models in the field of computer vision.

Beyond its high rate of data collection, satellite data also has a wide range of meaningful applications including climate impact modelling and sustainable energy. This makes foundation models trained on satellite data very beneficial as they would reduce the time, data, and computational resources required to obtain useful downstream models for these applications.

However, satellite data models differ from typical computer vision models in a crucial way. Because several types of satellite data exist, each with its own benefits, a typical use case for satellite data involves combining multiple data inputs in configurations that are not readily apparent during pretraining of the foundation model. Essentially, this means that the downstream application may have a different number of input channels from the pretrained model, which raises the question of how to successfully transfer information learned by the pretrained model to the downstream application.

This research proposes and examines several architectures for the downstream model that allow for pretrained weights to be incorporated when a different number of input channels is required. For evaluation, models pretrained with self-supervised learning on precipitation data are applied to a downstream model which conducts temporal interpolation of precipitation data and requires two inputs. The effect of including perceptual loss to enhance model performance is also evaluated. These findings can be used to guide adaptation for applications ranging from flood modeling, land use detection, and more.

How to cite: Simumba, N. and Tatsubori, M.: Adapting Transfer Learning for Multiple Channels in Satellite Data Applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1502, https://doi.org/10.5194/egusphere-egu23-1502, 2023.

Unprecedented flash floods (FF) in urban regions are increasing due to heavy rainfall intensity and magnitude as a result of human-induced climate and land-use changes. The changes in weather patterns and various anthropogenic activities increase the complexity of modelling the FF at different spatiotemporal scales: which indicates the importance of multi-resolution forcing information. Towards this, developing new methods for processing coarser resolution spatio-temporal datasets are essential for the efficient modelling of FF. While a wide range of methods is available for spatial and temporal downscaling of the climate data, the multi-temporal downscaling strategy has not been investigated for ungauged stations of streamflow. The current study proposed a multi-temporal downscaling (MTD) methodology for gauged and ungauged stations using Adaptive Emulator Modelling concepts for daily to sub-daily streamflows. The proposed MTD framework for ungauged stations comprise a hybrid framework with conceptual and machine learning-based approaches to analyze the catchment behavior and downscale the model outputs from daily to sub-daily scales. The study area, Peachtree Creek watershed (USA), frequently experiences flash floods; hence, selected to validate the proposed framework. Further, the study addresses the critical issues of model development, seasonality, and diurnal variation of MTD data. The study obtained MTD data with minimal uncertainty on capturing the hydrological signatures and nearly 95% of accuracy in predicting the flow attributes over ungauged stations. The proposed framework can be highly useful for short- and long-range planning, management, and mitigation measurements, where the absence of fine resolution data prohibits flash flood modeling.

How to cite: Budamala, V., Wadhwa, A., and Bhowmik, R. D.: Multi-Temporal Downscaling of Streamflow for Ungauged Stations/ Sub-Basins from Daily to Sub-Daily Interval Using Hybrid Framework – A Case Study on Flash Flood Watershed, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1855, https://doi.org/10.5194/egusphere-egu23-1855, 2023.

EGU23-2289 | ECS | Posters on site | ITS1.14/CL5.8

Towards understanding the effect of parametric aerosol uncertainty on climate using a chemical transport model perturbed parameter ensemble. 

Meryem Bouchahmoud, Tommi Bergman, and Christina Williamson

Aerosols in the climate system have a direct link to the Earth’s energy balance. Aerosols interact directly with the solar radiation through scattering and absorption; and indirectly by changing cloud properties. The effect aerosols have on climate is one of the major causes of radiative forcing (RF) uncertainty in global climate model simulations. Thus, reducing aerosol RF uncertainty is key to improving climate prediction. The objective of this work is to understand the magnitude and causes of aerosol uncertainty in the chemical transport model TM5.

Perturbed Parameter Ensembles (PPEs) are a set of model runs created by perturbing an ensemble of parameters. Parameters are model inputs, for this study we focus on parameters describing aerosol emissions, properties and processes, such as dry deposition, aging rate, emissions to aerosols microphysics. PPEs vary theses parameters over their uncertainty range all at once to study their combine effect on TM5.

Varying these parameters along with others through their value range, will reflect on TM5 outputs. The TM5 outputs parameters we are using in our sensitivity study are the cloud droplet number concentration and the ambient aerosol absorption optical thickness at 550nm.

Here we discuss the design of the PPE, and one-at-a-time sensitivity studies used in this process. The PPE samples the parameter space to enable us to use emulation. Emulating is a machine learning technique that uses a statistical surrogate model to replace the chemical transport model. The aim is to provide output data with more dense sampling throughout the parameter space. We will be using a Gaussian process emulator, which has been shown to be an efficient technique for quantifying parameter sensitivity in complex global atmospheric models.

We also describe plans to extend this work to emulate an aerosol PPE for EC-Earth. The PPE for EC-Earth will also contain cloud parameters that will vary over their uncertainty range together with the aerosol parameters to examine the influence of aerosol parametric uncertainty on RF.

 

How to cite: Bouchahmoud, M., Bergman, T., and Williamson, C.: Towards understanding the effect of parametric aerosol uncertainty on climate using a chemical transport model perturbed parameter ensemble., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2289, https://doi.org/10.5194/egusphere-egu23-2289, 2023.

EGU23-2541 | ECS | Posters on site | ITS1.14/CL5.8

Machine learning based automated parameter tuning of ICON-A using satellite data 

Pauline Bonnet, Fernando Iglesias-Suarez, Pierre Gentine, Marco Giorgetta, and Veronika Eyring

Global climate models use parameterizations to represent the effect of subgrid scale processes on the resolved state. Parameterizations in the atmosphere component usually include radiation, convection, cloud microphysics, cloud cover, gravity wave drag, vertical turbulence in the boundary layer and other processes. Parameterizations are semi-empirical functions that include a number of tunable parameters. Because these parameters are loosely constraint with experimental data, a range of values are typically explored by evaluating model runs against observations and/or high resolution runs. Fine tuning a climate model is a complex inverse problem due to the number of tunable parameters and observed climate properties to fit. Moreover, parameterizations are sources of uncertainties for climate projections, thus fine tuning is a crucial step in model development.

Traditionally, tuning is a time-consuming task done manually, by iteratively updating the values of the parameters in order to investigate the parameter space with user-experience driven choices. To overcome such limitation and search efficiently through the parameter space one can implement automatic techniques. Typical steps in automatic tuning are: (i) constraining the scope of the study (model, simulation setup, parameters, metrics to fit and corresponding reference values); (ii) conducting a sensitivity analysis to reduce the parameter space and/or building an emulator for the climate model; and (iii) conducting a sophisticated grid search to define the optimum parameter set or its distribution (e.g., rejection sampling and history matching). The ICOsahedral Non-hydrostatic (ICON) model is a modelling framework for numerical weather prediction and climate projections. We implement a ML-based automatic tuning technic to tune a recent version of ICON-A with a spatial resolution typically used for climate projections. We evaluate the tuned ICON-A model against satellite observations using the Earth System Model Evaluation Tool (ESMValTool). Although automatic tuning technics allow to reach the optimum parameter values in less steps than with the manual tuning, they still require some experience-driven choices throughout the tuning process. Moreover, the performances of the tuned model is limited by the structural errors of the model, inherent to the mathematical description of the parameterizations included in the model.

How to cite: Bonnet, P., Iglesias-Suarez, F., Gentine, P., Giorgetta, M., and Eyring, V.: Machine learning based automated parameter tuning of ICON-A using satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2541, https://doi.org/10.5194/egusphere-egu23-2541, 2023.

EGU23-3404 | ECS | Posters on site | ITS1.14/CL5.8 | Highlight

Deep learning-based generation of 3D cloud structures from geostationary satellite data 

Sarah Brüning, Stefan Niebler, and Holger Tost

Clouds and their interdependent feedback mechanisms remain a source of insecurity in climate science. This said, overcoming relating obstacles especially in the context of a changing climate emphasizes the need for a reliable database today more than ever. While passive remote sensing sensors provide continuous observations of the cloud top, they lack vital information on subjacent levels. Here, active instruments can deliver valuable insights to fill this gap in knowledge.

This study sets on to combine the benefits of both instrument types. It aims (1) to reconstruct the vertical distribution of volumetric radar data along the cloud column and (2) to interpolate the resultant 3D cloud structure to the satellite’s full disk by applying a contemporary Deep-Learning approach. Input data was derived by an automated spatio-temporally matching between high-resoluted satellite channels and the overflight of the radar. These samples display the physical predictors that were fed into the network to reconstruct the cloud vertical distribution on each of the radar’s height levels along the whole domain. Data from the entire year 2017 was used to integrate seasonal variations into the modeling routine.

The results demonstrate not only the network’s ability to reconstruct the cloud column along the radar track but also to interpolate coherent structures into a large-scale perspective. While the model performs equally well over land and water bodies, its applicable time frame is limited to daytime predictions only. Finally, the generated data can be leveraged to build a comprehensive database of 3D cloud structures that is to be exploited in proceeding applications.

How to cite: Brüning, S., Niebler, S., and Tost, H.: Deep learning-based generation of 3D cloud structures from geostationary satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3404, https://doi.org/10.5194/egusphere-egu23-3404, 2023.

EGU23-3418 | ECS | Posters on site | ITS1.14/CL5.8

Building a physics-constrained, fast and stable machine learning-based radiation emulator 

Guillaume Bertoli, Sebastian Schemm, Firat Ozdemir, Fernando Perez Cruz, and Eniko Szekely

Modelling the transfer of radiation through the atmosphere is a key component of weather and climate models. The operational radiation scheme in the Icosahedral Nonhydrostatic Weather and Climate Model (ICON) is ecRad. The radiation scheme ecRad is accurate but computationally expensive. It is operationally run in ICON on a grid coarser than the dynamical grid and the time step interval between two calls is significantly larger. This is known to reduce the quality of the climate prediction. A possible approach to accelerate the computation of the radiation fluxes is to use machine learning methods. Machine learning methods can significantly speed up computation of radiation, but they may cause climate drifts if they do not respect essential physical laws. In this work, we study random forest and neural network emulations of ecRad. We study different strategies to compare the stability of the emulations. Concerning the neural network, we compare loss functions with an additional energy penalty term and we observe that modifying the loss function is essential to predict accurately the heating rates. The random forest emulator, which is significantly faster to train than the neural network is used as a reference model that the neural network must outperform. The random forest emulator can become extremely accurate but the memory requirement quickly become prohibitive. Various numerical experiments are performed to illustrate the properties of the machine learning emulators.

How to cite: Bertoli, G., Schemm, S., Ozdemir, F., Perez Cruz, F., and Szekely, E.: Building a physics-constrained, fast and stable machine learning-based radiation emulator, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3418, https://doi.org/10.5194/egusphere-egu23-3418, 2023.

EGU23-3457 | Orals | ITS1.14/CL5.8

Evaluating Vegetation Modelling in Earth System Models with Machine Learning Approaches 

Ranjini Swaminathan, Tristan Quaife, and Richard Allan

The presence and amount of vegetation in any given region controls Gross Primary Production (GPP) or  the flux of carbon into the land driven by the process of photosynthesis. Earth System Models (ESMs) give us the ability to simulate GPP through modelling the various interactions between the atmosphere and biosphere including under different climate change scenarios in the future. GPP is the largest flux of the global carbon cycle and plays an important role including in carbon budget calculations.  However, GPP estimates from ESMs not only vary widely but also have much uncertainty in the underpinning attributors for this variability.  

We use data from pre-industrial Control (pi-Control) simulations to avail of the longer time period to sample data from as well as to exclude the influence of anthropogenic forcing in GPP estimation thereby leaving GPP to be largely attributable to two factor - (a) input atmospheric forcings and (b) the processes using those input climate variables to diagnose GPP. 

We explore the processes determining GPP with a physically-guided Machine Learning framework applied to a set of Earth System Models (ESMs) from the Sixth Coupled Model Intercomparison Project (CMIP6). We use this framework to examine whether differences in GPP across models are caused by differences in atmospheric state or process representations. 

Results from our analysis show that models with similar regional atmospheric forcing do not always have similar GPP distributions. While there are regions where climate models largely agree on what atmospheric variables are most relevant for GPP, there are regions such as the tropics where there is more uncertainty.  Our analysis highlights the potential of ML to identify differences in atmospheric forcing and carbon cycle process modelling across current state-of-the-art ESMs. It also allows us to extend the analysis with observational estimates of forcings as well as GPP for model improvement. 

How to cite: Swaminathan, R., Quaife, T., and Allan, R.: Evaluating Vegetation Modelling in Earth System Models with Machine Learning Approaches, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3457, https://doi.org/10.5194/egusphere-egu23-3457, 2023.

EGU23-3619 | ECS | Posters on site | ITS1.14/CL5.8

TCDetect: A new method of Detecting the Presence of Tropical Cyclones using Deep Learning 

Daniel Galea, Julian Kunkel, and Bryan Lawrence

Tropical cyclones are high-impact weather events which have large human and economic effects, so it is important to be able to understand how their location, frequency and structure might change in a future climate.

Here, a lightweight deep learning model is presented which is intended for detecting the presence of tropical cyclones during the execution of numerical simulations for use in an online data reduction method. This will help to avoid saving vast amounts of data for analysis after the simulation is complete. With run-time detection, it might be possible to reduce the need for some of the high-frequency high-resolution output which would otherwise be required.

The model was trained on ERA-Interim reanalysis data from 1979 to 2017 and the training concentrated on delivering the highest possible recall rate (successful detection of cyclones) while rejecting enough data to make a difference in outputs.

When tested using data from the two subsequent years, the recall or probability of detection rate was 92%. The precision rate or success ratio obtained was that of 36%. For the desired data reduction application, if the desired target included all tropical cyclone events, even those which did not obtain hurricane-strength status, the effective precision was 85%.

The recall rate and the Area Under Curve for the Precision/Recall (AUC-PR) compare favourably with other methods of cyclone identification while using the smallest number of parameters for both training and inference. 

Work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-843612

How to cite: Galea, D., Kunkel, J., and Lawrence, B.: TCDetect: A new method of Detecting the Presence of Tropical Cyclones using Deep Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3619, https://doi.org/10.5194/egusphere-egu23-3619, 2023.

EGU23-3875 | ECS | Posters on site | ITS1.14/CL5.8

Explainable AI for oceanic carbon cycle analysis of CMIP6 

Paul Heubel, Lydia Keppler, and Tatiana Iliyna

The Southern Ocean acts as one of Earth's major carbon sinks, taking up anthropogenic carbon from the atmosphere. Earth System Models (ESMs) are used to project its future evolution. However, the ESMs in the Coupled Model Intercomparison Project version 6 (CMIP6) disagree on the biogeochemical representation of the Southern Ocean carbon cycle, both with respect to the phasing and the magnitude of the seasonal cycle of dissolved inorganic carbon (DIC), and they compare poorly with observations.

We develop a framework to investigate model biases in 10 CMIP6 ESMs historical runs incorporating explainable artificial intelligence (xAI) methodologies. Using both a linear Random Forest feature relevance approach to a nonlinear self organizing map - feed forward neural network (SOM-FFN) framework, we relate 5 drivers of the seasonal cycle of DIC in the Southern Ocean in the different CMIP6 models. We investigate temperature, salinity, silicate, nitrate and dissolved oxygen as potential drivers. This analysis allows us to determine dominant statistical drivers of the seasonal cycle of DIC in the different models, and how they compare to the observations. Our findings inform future model development to better constrain the seasonal cycle of DIC.

How to cite: Heubel, P., Keppler, L., and Iliyna, T.: Explainable AI for oceanic carbon cycle analysis of CMIP6, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3875, https://doi.org/10.5194/egusphere-egu23-3875, 2023.

EGU23-4044 | ECS | Orals | ITS1.14/CL5.8

DailyMelt: Diffusion-based Models for Spatiotemporal Downscaling of (Ant-)arctic Surface Meltwater Maps 

Björn Lütjens, Patrick Alexander, Raf Antwerpen, Guido Cervone, Matthew Kearney, Bingkun Luo, Dava Newman, and Marco Tedesco

Motivation. Ice melting in Greenland and Antarctica has increasingly contributed to rising sea levels. Yet, the exact speed of melting, existence of abrupt tipping points, and in-detail links to climate change remain uncertain. Ice shelves essentially prevent the ice sheet from slipping into the ocean and better prediction of collapses is needed. Meltwater at the surface of ice shelves indicates ice shelf collapse through destabilizing ice shelves via fracturing and flexural processes (Banwell et al., 2013) and is likely impacted by a warming climate ( Kingslake et al., 2017). Maps of meltwater have been created from in-situ and remote observations, but their low and irregular spatiotemporal resolution severely limits studies (Kingslake et al., 2019).

Research Gap. In particular, there does not exist daily high-resolution (< 500m) maps of surface meltwater. We propose the first daily high-resolution surface meltwater maps by developing a deep learning-based downscaling method, called DailyMelt, that fuses observations and simulations of varying spatiotemporal resolution, as illustrated in Fig.1. The created maps will improve understanding of the origin, transport, and controlling physical processes of surface meltwater. Moreover, they will act as unified source to improve sea level rise and meltwater predictions in climate models. 

Data. To synthesize surface meltwater maps, we leverage observations from satellites (MODIS, Sen-1 SAR) which are high-resolution (500m, 10m), but have substantial temporal gaps due to repeat time and cloud coverage. We fuse them with simulations (MAR) and passive microwave observations (MEaSURE) that are daily, but low-resolution (6km, 3.125km). In a significant remote sensing effort, we have downloaded, reprojected, and regridded all products into daily observations for our study area over Greenland’s Helheim glacier. 

Approach and expected results. Within deep generative vision models, diffusion-based models promise sharp and probabilistic predictions. We have implemented SRDiff (Li H. et al., 2022) and tested it on spatially downscaling external data. As a baseline model, we have implemented a statistical downscaling model that is a local hybrid physics-linear regression model (Noel et al., 2016). In our planned benchmark, we expect a baseline UNet architecture that minimizes RMSE to create blurry maps and a generative adversarial network that minimizes adversarial loss to create sharp but deterministic maps. We have started with spatial downscaling and will include temporal downscaling. 

In summary, we will create the first daily high-resolution (500m) surface meltwater maps, have introduced the first diffusion-based model for downscaling Earth sciences data, and have created the first benchmark dataset for downscaling surface meltwater maps.

 

References.

Banwell, A. F., et al. (2013), Breakup of the Larsen B Ice Shelf triggered by chain reaction drainage of supraglacial lakes, Geophys. Res. Lett., 40 

Kingslake J, et al. (2017), Widespread movement of meltwater onto and across Antarctic ice shelves, Nature, 544(7650)

Kingslake J., et al. (2019), Antarctic Surface Hydrology and Ice Shelf Stability Workshop report, US Antarctic Program Data Center

Li H., et al. (2022), SRDiff: Single image super-resolution with diffusion probabilistic models, Neurocomputing, 479

Noël, B., et al. (2016), A daily, 1 km resolution data set of downscaled Greenland ice sheet surface mass balance (1958–2015), The Cryosphere, 10

How to cite: Lütjens, B., Alexander, P., Antwerpen, R., Cervone, G., Kearney, M., Luo, B., Newman, D., and Tedesco, M.: DailyMelt: Diffusion-based Models for Spatiotemporal Downscaling of (Ant-)arctic Surface Meltwater Maps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4044, https://doi.org/10.5194/egusphere-egu23-4044, 2023.

EGU23-4350 | ECS | Orals | ITS1.14/CL5.8

Physics-Constrained Deep Learning for Downscaling 

Paula Harder, Venkatesh Ramesh, Alex Hernandez-Garcia, Qidong Yang, Prasanna Sattigeri, Daniela Szwarcman, Campbell Watson, and David Rolnick

The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and, therefore, often generate coarse-resolution predictions. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often using image super-resolution methods from computer vision. However, despite achieving visually compelling results in some cases, such models frequently violate conservation laws when predicting physical variables. In order to conserve physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep learning downscaling model while also improving their performance according to traditional metrics. We compare different constraining approaches and demonstrate their applicability across different neural architectures as well as a variety of climate and weather data sets, including ERA5 and WRF data sets.

How to cite: Harder, P., Ramesh, V., Hernandez-Garcia, A., Yang, Q., Sattigeri, P., Szwarcman, D., Watson, C., and Rolnick, D.: Physics-Constrained Deep Learning for Downscaling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4350, https://doi.org/10.5194/egusphere-egu23-4350, 2023.

EGU23-5431 | ECS | Orals | ITS1.14/CL5.8

Towards Robust Parameterizations in Ecosystem-level Photosynthesis Models 

Shanning Bao, Nuno Carvalhais, Lazaro Alonso, Siyuan Wang, Johannes Gensheimer, Ranit De, and Jiancheng Shi

Photosynthesis model parameters represent vegetation properties or sensitivities of photosynthesis processes. As one of the model uncertainty sources, parameters affect the accuracy and generalizability of the model. Ideally, parameters of ecosystem-level photosynthesis models, i.e., gross primary productivity (GPP) models, can be measured or inversed from observations at the local scale. To extrapolate parameters to a larger spatial scale, current photosynthesis models typically adopted fixed values or plant-functional-type(PFT)-specific values. However, the fixed and PFT-based parameterization approaches cannot capture sufficiently the spatial variability of parameters and lead to significant estimation errors. Here, we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome these issues. 

SPIE refers to predicting model parameters using an artificial neural network (NN) constrained by both model loss and ecosystem features including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition and soil properties. Taking a light use efficiency (LUE) model as an example, we evaluated SPIE at 196 FLUXNET eddy covariance flux sites. The LUE model accounts for the effects of air temperature, vapor pressure deficit, soil water availability (SW), light saturation, diffuse radiation fraction and CO2 on GPP using five independent sensitivity functions. The SW was represented using the water availability index and can be optimized based on evapotranspiration. Thus, we optimized the NN by minimizing the model loss which consists of GPP errors, evapotranspiration errors, and constraints on sensitivity functions. Furthermore, we compared SPIE with 11 typical parameter extrapolating approaches, including PFT- and climate-specific parameterizations, global and PFT-based parameter optimization, site-similarity, and regression methods using Nash-Sutcliffe model efficiency (NSE), determination coefficient (R2) and normalized root mean squared error (NRMSE).

The results in ten-fold cross-validation showed that SPIE had the best performance across various temporal and spatial scales and across assessing metrics. None of the parameter extrapolating approaches reached the same performance as the on-site calibrated parameters (NSE=0.95), but SPIE was the only approach showing positive NSE (=0.68) in cross-validation across sites. Moreover, the site-level NSE, R2, and NRMSE of SPIE all significantly outperformed per biome and per climate type. Ranges of parameters were more constrained by SPIE than site calibrations.

Overall, SPIE is a robust parameter extrapolation approach that overcomes strong limitations observed in many of the standard model parameterization approaches. Our approach suggests that model parameterizations can be determined from observations of vegetation, climate and soil properties, and expands from customary clustering methods (e.g., PFT-specific parameterization). We argue that expanding SPIE to other models overcomes current limits in parameterization and serves as an entry point to investigate the robustness and generalization of different models.

How to cite: Bao, S., Carvalhais, N., Alonso, L., Wang, S., Gensheimer, J., De, R., and Shi, J.: Towards Robust Parameterizations in Ecosystem-level Photosynthesis Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5431, https://doi.org/10.5194/egusphere-egu23-5431, 2023.

EGU23-5487 * | ECS | Posters on site | ITS1.14/CL5.8 | Highlight

Harvesting historical spy imagery by evaluating deep learning models for state-wide mapping of land cover changes between 1965-1978 

Lucas Kugler, Christopher Marrs, Eric Kosczor, and Matthias Forkel

Remote sensing has played a fundamental role for land cover mapping and change detection at least since the launch of the Landsat satellite program in 1972. In 1995, the Central Intelligence Agency of the United States of America released previously classified spy imagery taken from 1960 onwards with near-global coverage from the Keyhole programme, which includes the CORONA satellite mission. CORONA imagery is a treasure because it contains information about land cover 10 years before the beginning of the civilian Earth observation and has a high spatial resolution < 2m. However, this imagery is only pan-chromatic and usually not georeferenced, which has so far prevented a large-scale application for land cover mapping or other geophysical and environmental applications.

Here, we aim to harvest the valuable information about past land cover from CORONA imagery for a state-wide mapping of past land cover changes between 1965 and 1978 by training, testing and validating various deep learning models.

To the best of our knowledge, this is the first work to analyse land cover from CORONA data on a large scale, dividing land cover into six classes based on the CORINE classification scheme. The particular focus of the work is to test the transferability of the deep learning approaches to unknown CORONA data.

To investigate the transferability, we selected 27 spatially and temporally distributed study areas (each 23 km²) in the Free State of Saxony (Germany) and created semantic masks to train and test 10 different U-shaped neuronal network architectures to extract land cover from CORONA data. As input, we use either the original panchromatic pixel values and different texture measures. From these input data, ten different training datasets and test datasets were derived for cross-validation.

The training results show that a semantic segmentation of land cover from CORONA data with the used architectures is possible. Strong differences in model performance (based on cross validation and the intersection over union metric, IOU) were detected among the classes. Classes with many sample data achieve significantly better IOU values than underrepresented classes. In general, a U-shaped architecture with a Transformer as Encoder (Transformer U-Net) achieved the best results. The best segmentation performance (IOU 83.29%), was obtained for forests, followed by agriculture (74.21%). For artificial surfaces, a mean IOU of 68.83% was achieved. Water surfaces achieved a mean IOU of 66.49%. For the shrub vegetation and open areas classes only IOU values mostly below 25% were achieved. The deep learning models were successfully transferable in space (between test areas) and time (between CORONA imagery from different years) especially for classes with many sample data. The transferability of deep learning models was difficult for the mapping of water bodies. Despite the general good model performance and successful transferability for most classes, the transferability was limited especially for imagery of very poor quality. Our approach enabled the state-wide mapping of land cover in Saxony between 1965 and 1978 with a spatial resolution of 2 m. We identify an increase in urban cover and a decrease in cropland cover

How to cite: Kugler, L., Marrs, C., Kosczor, E., and Forkel, M.: Harvesting historical spy imagery by evaluating deep learning models for state-wide mapping of land cover changes between 1965-1978, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5487, https://doi.org/10.5194/egusphere-egu23-5487, 2023.

EGU23-5583 | ECS | Posters on site | ITS1.14/CL5.8

Identifying and Locating Volcanic Eruptions using Convolutional Neural Networks and Interpretability Techniques 

Johannes Meuer, Claudia Timmreck, Shih-Wei Fang, and Christopher Kadow

Accurately interpreting past climate variability can be a challenging task, particularly when it comes to distinguishing between forced and unforced changes. In the  case of large volcanic eruptions, ice core records are a very valuable tool but still often not sufficient to link reconstructed anomaly patterns to a volcanic eruption at all or to its geographical location. In this study, we developed a convolutional neural network (CNN) that is able to classify whether a volcanic eruption occurred and its location (northern hemisphere extratropical, southern hemisphere extratropical, or tropics) with an accuracy of 92%.

To train the CNN, we used 100 member ensembles of the MPI-ESM-LR global climate model, generated using the easy volcanic aerosol (EVA) model, which provides the radiative forcing of idealized volcanic eruptions of different strengths and locations. The model considered global sea surface temperature and precipitation patterns 12 months after the eruption over a time period of 3 months.

In addition to demonstrating the high accuracy of the CNN, we also applied layer-wise relevance propagation (LRP) to the model to understand its decision-making process and identify the input data that influenced its predictions. Our study demonstrates the potential of using CNNs and interpretability techniques for identifying and locating past volcanic eruptions as well as improving the accuracy and understanding of volcanic climate signals.

How to cite: Meuer, J., Timmreck, C., Fang, S.-W., and Kadow, C.: Identifying and Locating Volcanic Eruptions using Convolutional Neural Networks and Interpretability Techniques, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5583, https://doi.org/10.5194/egusphere-egu23-5583, 2023.

EGU23-5967 | ECS | Posters on site | ITS1.14/CL5.8

Potentials and challenges of using Explainable AI for understanding atmospheric circulation 

Sebastian Scher, Andreas Trügler, and Jakob Abermann

Machine Learning (ML) and AI techniques, especially methods based on Deep Learning, have long been considered as black boxes that might be good at predicting, but not explaining predictions. This has changed recently, with more techniques becoming available that explain predictions by ML models – known as Explainable AI (XAI). These have seen adaptation also in climate science, because they could have the potential to help us in understanding the physics behind phenomena in geoscience. It is, however, unclear, how large that potential really is, and how these methods can be incorporated into the scientific process. In our study, we use the exemplary research question of which aspects of the large-scale atmospheric circulation affect specific local conditions. We compare the different answers to this question obtained with a range of different methods, from the traditional approach of targeted data analysis based on physical knowledge (such as using dimensionality reduction based on physical reasoning) to purely data-driven and physics-unaware methods using Deep Learning with XAI techniques. Based on these insights, we discuss the usefulness and potential pitfalls of XAI for understanding and explaining phenomena in geosciences. 

How to cite: Scher, S., Trügler, A., and Abermann, J.: Potentials and challenges of using Explainable AI for understanding atmospheric circulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5967, https://doi.org/10.5194/egusphere-egu23-5967, 2023.

EGU23-6061 | ECS | Orals | ITS1.14/CL5.8 | Highlight

Using reduced representations of atmospheric fields to quantify the causal drivers of air pollution 

Sebastian Hickman, Paul Griffiths, Peer Nowack, and Alex Archibald

Air pollution contributes to millions of deaths worldwide every year. The concentration of a particular air pollutant, such as ozone, is controlled by physical and chemical processes which act on varying temporal and spatial scales. Quantifying the strength of causal drivers (e.g. temperature) on air pollution from observational data, particularly at extrema, is challenging due to the difficulty of disentangling correlation and causation, as many drivers are correlated. Furthermore, because air pollution is controlled in part by large scale atmospheric phenomena, using local (e.g. individual grid cell level) covariates for analysis is insufficient to fully capture the effect of these phenomena on air pollution. 

 

Access to large spatiotemporal datasets of air pollutant concentrations and atmospheric variables, coupled with recent advances in self-supervised learning, allow us to learn reduced representations of spatiotemporal atmospheric fields, and therefore account for non-local and non-instantaneous processes in downstream tasks.

 

We show that these learned reduced representations can be useful for tasks such as air pollution forecasting, and crucially to quantify the causal effect of varying atmospheric fields on air pollution. We make use of recent advances in bounding causal effects in the presence of unobserved confounding to estimate, with uncertainty, the causal effect of changing atmospheric fields on air pollution. Finally, we compare our quantification of the causal drivers of air pollution to results from other approaches, and explore implications for our methods and for the wider goal of improving the process-level treatment of air pollutants in chemistry-climate models.

How to cite: Hickman, S., Griffiths, P., Nowack, P., and Archibald, A.: Using reduced representations of atmospheric fields to quantify the causal drivers of air pollution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6061, https://doi.org/10.5194/egusphere-egu23-6061, 2023.

EGU23-6306 | ECS | Orals | ITS1.14/CL5.8 | Highlight

Data-Driven Cloud Cover Parameterizations 

Arthur Grundner, Tom Beucler, Pierre Gentine, Marco A. Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring

A promising approach to improve cloud parameterizations within climate models, and thus climate projections, is to train machine learning algorithms on storm-resolving model (SRM) output. The ICOsahedral Non-hydrostatic (ICON) modeling framework permits simulations ranging from numerical weather prediction to climate projections, making it an ideal target to develop data-driven parameterizations for sub-grid scale processes. Here, we systematically derive and evaluate the first data-driven cloud cover parameterizations with coarse-grained data based on ICON SRM simulations. These parameterizations range from simple analytic models and symbolic regression fits to neural networks (NNs), populating a performance x complexity plane. In most models, we enforce sparsity and discourage correlated features by sequentially selecting features based on the models' performance gains. Guided by a set of physical constraints, we use symbolic regression to find a novel equation to parameterize cloud cover. The equation represents a good compromise between performance and complexity, achieving the highest performance (R^2>0.9) for its complexity (13 trainable parameters). To model sub-grid scale cloud cover in its full complexity, we also develop three different types of NNs that differ in the degree of vertical locality they assume for diagnosing cloud cover from coarse-grained atmospheric state variables. Using the game-theory based interpretability library SHapley Additive exPlanations, we analyze our most non-local NN and identify an overemphasis on specific humidity and cloud ice as the reason why it cannot perfectly generalize from the global to the regional coarse-grained SRM data. The interpretability tool also helps visualize similarities and differences in feature importance between regionally and globally trained NNs, and reveals a local relationship between their cloud cover predictions and the thermodynamic environment. Our results show the potential of deep learning and symbolic regression to derive accurate yet interpretable cloud cover parameterizations from SRMs.

How to cite: Grundner, A., Beucler, T., Gentine, P., Giorgetta, M. A., Iglesias-Suarez, F., and Eyring, V.: Data-Driven Cloud Cover Parameterizations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6306, https://doi.org/10.5194/egusphere-egu23-6306, 2023.

EGU23-6450 | ECS | Orals | ITS1.14/CL5.8

The key role of causal discovery to improve data-driven parameterizations in climate models 

Fernando Iglesias-Suarez, Veronika Eyring, Pierre Gentine, Tom Beucler, Michael Pritchard, Jakob Runge, and Breixo Solino-Fernandez

Earth system models are fundamental to understanding and projecting climate change, although there are considerable biases and uncertainties in their projections. A large contribution to this uncertainty stems from differences in the representation of clouds and convection occurring at scales smaller than the resolved model grid. These long-standing deficiencies in cloud parameterizations have motivated developments of computationally costly global high-resolution cloud resolving models, that can explicitly resolve clouds and convection. Deep learning can learn such explicitly resolved processes from cloud resolving models. While unconstrained neural networks often learn non-physical relationships that can lead to instabilities in climate simulations, causally-informed deep learning can mitigate this problem by identifying direct physical drivers of subgrid-scale processes. Both unconstrained and causally-informed neural networks are developed using a superparameterized climate model in which deep convection is explicitly resolved, and are coupled to the climate model. Prognostic climate simulations with causally-informed neural network parameterization are stable, accurately represent mean climate and variability of the original climate model, and clearly outperform its non-causal counterpart. Combining causal discovery and deep learning is a promising approach to improve data-driven parameterizations (informed by causally-consistent physical fields) for both their design and trustworthiness.

How to cite: Iglesias-Suarez, F., Eyring, V., Gentine, P., Beucler, T., Pritchard, M., Runge, J., and Solino-Fernandez, B.: The key role of causal discovery to improve data-driven parameterizations in climate models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6450, https://doi.org/10.5194/egusphere-egu23-6450, 2023.

EGU23-7457 | ECS | Posters on site | ITS1.14/CL5.8

Towards the effective autoencoder architecture to detect weather anomalies 

Dusan Fister, Jorge Pérez-Aracil, César Peláez-Rodríguez, Marie Drouard, Pablo G. Zaninelli, David Barriopedro Cepero, Ricardo García-Herrera, and Sancho Salcedo-Sanz

To organise weather data as images, pixels represent coordinates and magnitude of pixels represents the state of the observed variable in a given time. Observed variables, such as air temperature, mean sea level pressure, wind components and others, may be collected into higher dimensional images or even into a motion structure. Codification of formers as a spatial and the latter as a spatio-temporal allows them to be processed using the deep learning methods, for instance autoencoders and autoencoder-like architectures. The objective of the original autoencoder is to reproduce the input image as much as possible, thus effectively equalising the input and output during the training. Then, an advantage of autoencoder can be utilised to calculate the deviations between (1) true states (effectively the inputs), which are derived by nature, and the (2) expected states, which are derived by means of statistical learning. Calculated deviations can then be interpreted to identify the extreme events, such as heatwaves, hot days or any other rare events (so-called anomalies). Additionally, by modelling deviations by statistical distributions, geographical areas with higher probabilities of anomalies can be deduced at the tails of the distribution. The capability of reproduction of the (original input) images is hence crucial in order to avoid addressing arbitrary noise as anomaly. We would like to run experiments to realise the effective architecture that give reasonable solutions, verify the benefits of implementing the variational autoencoder, realise the effect of selecting various statistical loss functions, and find out the effective architecture of the decoder part of the autoencoder.

How to cite: Fister, D., Pérez-Aracil, J., Peláez-Rodríguez, C., Drouard, M., G. Zaninelli, P., Barriopedro Cepero, D., García-Herrera, R., and Salcedo-Sanz, S.: Towards the effective autoencoder architecture to detect weather anomalies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7457, https://doi.org/10.5194/egusphere-egu23-7457, 2023.

EGU23-7465 | ECS | Posters on site | ITS1.14/CL5.8

Invertible neural networks for satellite retrievals of aerosol optical depth 

Paolo Pelucchi, Jorge Vicent, J. Emmanuel Johnson, Philip Stier, and Gustau Camps-Valls

The retrieval of atmospheric aerosol properties from satellite remote sensing is a complex and under-determined inverse problem. Traditional retrieval algorithms, based on radiative transfer models, must make approximations and assumptions to reach a unique solution or repeatedly use the expensive forward models to be able to quantify uncertainty. The recently introduced Invertible Neural Networks (INNs), a machine learning method based on Normalizing Flows, appear particularly suited for tackling inverse problems. They simultaneously model both the forward and the inverse branches of the problem, and their generative aspect allows them to efficiently provide non-parametric posterior distributions for the retrieved parameters, which can be used to quantify the retrieval uncertainty. So far INNs have successfully been applied to low-dimensional idealised inverse problems and even to some simpler scientific retrieval problems. Still, satellite aerosol retrievals present particular challenges, such as the high variability of the surface reflectance signal and the often comparatively small aerosol signal in the top-of-the-atmosphere (TOA) measurements.

In this study, we investigate the use of INNs for retrieving aerosol optical depth (AOD) and its uncertainty estimates at the pixel level from MODIS TOA reflectance measurements. The models are trained with custom synthetic datasets of TOA reflectance-AOD pairs made by combining the MODIS Dark Target algorithm’s atmospheric look-up tables and a MODIS surface reflectance product. The INNs are found to perform emulation and inversion of the look-up tables successfully. We initially train models adapted to different surface types by focusing our application on limited regional and seasonal contexts. The models are applied to real measurements from the MODIS sensor, and the generated AOD retrievals and posterior distributions are compared to the corresponding Dark Target and AERONET retrievals for evaluation and discussion.

How to cite: Pelucchi, P., Vicent, J., Johnson, J. E., Stier, P., and Camps-Valls, G.: Invertible neural networks for satellite retrievals of aerosol optical depth, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7465, https://doi.org/10.5194/egusphere-egu23-7465, 2023.

The rapid development of deep learning approaches has conquered many fields, and precipitation prediction is one of them. Precipitation modeling remains a challenge for numerical weather prediction or climate models, and parameterization is required for low spatial resolution models, such as those used in climate change impact studies. Machine learning models have been shown to be capable of learning the relationships between other meteorological variables and precipitation. Such models are much less computationally intensive than explicit modeling of precipitation processes and are becoming more accurate than parametrization schemes.

Most existing applications focus either on precipitation extremes aggregated over a domain of interest or on average precipitation fields. Here, we are interested in spatial extremes and focus on the prediction of heavy precipitation events (>95th percentile) and extreme events (>99th percentile) over the European domain. Meteorological variables from ERA5 are used as input, and E-OBS data as target. Different architectures from the literature are compared in terms of predictive skill for average precipitation fields as well as for the occurrence of heavy or extreme precipitation events (threshold exceedance). U-Net architectures show higher skills than other variants of convolutional neural networks (CNN). We also show that a shallower U-Net architecture performs as well as the original network for this application, thus reducing the model complexity and, consequently, the computational resources. In addition, we analyze the number of inputs based on the importance of the predictors provided by a layer-wise relevance propagation procedure.

How to cite: Horton, P. and Otero, N.: Predicting spatial precipitation extremes with deep learning models. A comparison of existing model architectures., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7862, https://doi.org/10.5194/egusphere-egu23-7862, 2023.

EGU23-8085 | ECS | Posters on site | ITS1.14/CL5.8

Improving the spatial accuracy of extreme tropical cyclone rainfall in ERA5 using deep learning 

Guido Ascenso, Andrea Ficchì, Leone Cavicchia, Enrico Scoccimarro, Matteo Giuliani, and Andrea Castelletti

Tropical cyclones (TCs) are one of the costliest and deadliest natural disasters due to the combination of their strong winds and induced storm surges and heavy precipitation, which can cause devastating floods. Unfortunately, due to its high spatio-temporal variability, complex underlying physical process, and lack of high-quality observations, precipitation is still one of the most challenging aspects of a TC to model. However, as precipitation is a key forcing variable for hydrological processes acting across multiple space-time scales, accurate precipitation input is crucial for reliable hydrological simulations and forecasts.

A popular source of precipitation data is the ERA5 reanalysis dataset, frequently used as input to hydrological models when studying floods. However, ERA5 systematically underestimates TC-induced precipitation compared to MSWEP, a multi-source observational dataset fusing gauge, satellite, and reanalysis-based data, currently one of the most accurate precipitation datasets. Moreover, the spatial distribution of TC-rainfall in ERA5 has large room for improvement.

Here, we present a precipitation correction scheme based on U-Net, a popular deep-learning architecture. Rather than only adjusting the per-pixel precipitation values at each timestep of a given TC, we explicitly design our model to also adjust the spatial distribution of the precipitation; to the best of our knowledge, we are the first to do so. The key novelty of our model is a custom-made loss function, based on the combination of the fractions skill score (FSS) and mean absolute error (MAE) metrics. We train and validate the model on 100k time steps (with an 80:20 train:test split) from global TC precipitation events. We show how a U-Net trained with our loss function can reduce the per-pixel MAE of ERA5 precipitation by nearly as much as other state-of-the-art methods, while surpassing them significantly in terms of improved spatial patterns of precipitation. Finally, we discuss how the outputs of our model can be used for future research.

How to cite: Ascenso, G., Ficchì, A., Cavicchia, L., Scoccimarro, E., Giuliani, M., and Castelletti, A.: Improving the spatial accuracy of extreme tropical cyclone rainfall in ERA5 using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8085, https://doi.org/10.5194/egusphere-egu23-8085, 2023.

EGU23-8496 | ECS | Posters on site | ITS1.14/CL5.8

Utilizing AI emulators to Model Stratospheric Aerosol Injections and their Effect on Climate 

Eshaan Agrawal and Christian Schroder de Witt

With no end to anthropogenic greenhouse gas emissions in sight, policymakers are increasingly debating artificial mechanisms to cool the earth's climate. One such solution is stratospheric atmospheric injections (SAI), a method of solar geoengineering where particles are injected into the stratosphere in order to reflect the sun’s rays and lower global temperatures. Past volcanic events suggest that SAI can lead to fast substantial surface temperature reductions, and it is projected to be economically feasible. Research in simulation, however, suggests that SAI can lead to catastrophic side effects. It is also controversial among politicians and environmentalists because of the numerous challenges it poses geopolitically, environmentally, and for human health. Nevertheless, SAI is increasingly receiving attention from policymakers. In this research project, we use deep reinforcement learning to study if, and by how much, carefully engineered temporally and spatially varying injection strategies can mitigate catastrophic side effects of SAI. To do this, we are using the HadCM3 global circulation model to collect climate system data in response to artificial longitudinal aerosol injections. We then train a neural network emulator on this data, and use it to learn optimal injection strategies under a variety of objectives by alternating model updates with reinforcement learning. We release our dataset and code as a benchmark dataset to improve emulator creation for solar aerosol engineering modeling. 

How to cite: Agrawal, E. and Schroder de Witt, C.: Utilizing AI emulators to Model Stratospheric Aerosol Injections and their Effect on Climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8496, https://doi.org/10.5194/egusphere-egu23-8496, 2023.

Multiple studies have now demonstrated that machine learning (ML) can give improved skill for simulating fairly typical weather events in climate simulations, for tasks such as downscaling to higher resolution and emulating and speeding up expensive model parameterisations. Many of these used ML methods with very high numbers of parameters, such as neural networks, which are the focus of the discussion here. Not much attention has been given to the performance of these methods for extreme event severities of relevance for many critical weather and climate prediction applications, with return periods of more than a few years. This leaves a lot of uncertainty about the usefulness of these methods, particularly for general purpose models that must perform reliably in extreme situations. ML models may be expected to struggle to predict extremes due to there usually being few samples of such events. 
 
This presentation will review the small number of studies that have examined the skill of machine learning methods in extreme weather situations. It will be shown using recent results that machine learning methods that perform reasonably for typical weather events can have very large errors in extreme situations, highlighting the necessity of testing the performance for these cases. Extrapolation to extremes is found to work well in some studies, however. 
 
It will be argued that more attention needs to be given to performance for extremes in work applying ML in climate science. Research gaps that seem particularly important are identified. These include investigating the behaviour of ML systems in events that are multiple standard deviations beyond observed records, which have occurred in the past, and evaluating performance of complex generative models in extreme events. Approaches to address these problems will be discussed.

How to cite: Watson, P.: Machine learning applications for weather and climate need greater focus on extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8615, https://doi.org/10.5194/egusphere-egu23-8615, 2023.

EGU23-8661 | Posters on site | ITS1.14/CL5.8

An urban climate neural network screening tool 

Robert von Tils and Sven Wiemers

Microscale RANS (Reynolds Averaged Navier Stokes) models are able to simulate the urban climate for entire large cities with a high spatial resolution of up to 5 m horizontally. They do this using data from geographic information systems (GIS) that must be specially processed to provide the models with information about the terrain, buildings, land use, and resolved vegetation. If high-performance computers, for example from research institutions, are not available for the simulations or are beyond the financial scope, the calculation on commercially available servers can take several weeks. The calculation of a reference initial state for a city is often followed by questions regarding adaptation measures due to climate change or the influence of smaller and larger future building developments on the urban climate. These changes lead locally to a change of the urban climate but are also influenced by the urban climate itself.

In order to save computational time and to comfortably give a quantitative fast initial assessment, we trained a neural network that predicts the simulation results of a RANS model (for example: air temperature at night and during the day, wind speed, cold air flow) and implemented this network in a GIS. The tool allows to calculate the impact of development projects on the urban climate in a fraction of the time required by a RANS simulation and comes close to the RANS model in terms of accuracy. It can also be used by people without in-depth knowledge of urban climate modeling and is therefore particularly suitable for use, for example, in specialized offices of administrative departments or by project developers.

How to cite: von Tils, R. and Wiemers, S.: An urban climate neural network screening tool, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8661, https://doi.org/10.5194/egusphere-egu23-8661, 2023.

EGU23-8666 | ECS | Posters on site | ITS1.14/CL5.8

Drivers of Natural Gas Use in United States Buildings 

Rohith Teja Mittakola, Philippe Ciais, Jochen Schubert, David Makowski, Chuanlong Zhou, Hassan Bazzi, Taochun Sun, Zhu Liu, and Steven Davis

Natural gas is the primary fuel used in U.S. residences, especially during winter, when cold temperatures drive the heating demand. In this study, we use daily county-level gas consumption data to assess the spatial patterns of the relationships and sensitivities of gas consumption by U.S. households considering outdoor temperatures. Linear-plus-plateau functions are found to be the best fit for gas consumption and are applied to derive two key coefficients for each county: the heating temperature threshold (Tcrit) below which residential heating starts and the rate of increase in gas consumption when the outdoor temperature drops by one degree (Slope). We then use interpretable machine learning models to evaluate the key building properties and socioeconomic factors related to the spatial patterns of Tcrit and Slope based on a large database of individual household properties and population census data. We find that building age, employment rates, and household size are the main predictors of Tcrit, whereas the share of gas as a heating fuel and household income are the main predictors of Slope. The latter result suggests inequalities across the U.S. with respect to gas consumption, with wealthy people living in well-insulated houses associated with low Tcrit and Slope values. Finally, we estimate potential reductions in gas use in U.S. residences due to improvements in household insulation or a hypothetical behavioral change toward reduced consumption by adopting a 1°C lower Tcrit than the current value and a reduced slope. These two scenarios would result in 25% lower gas consumption at the national scale, avoiding 1.24 million MtCO2 of emissions per year. Most of these reductions occur in the Midwest and East Coast regions. The results from this study provide new quantitative information for targeting efforts to reduce household gas use and related CO2 emissions in the U.S.

How to cite: Mittakola, R. T., Ciais, P., Schubert, J., Makowski, D., Zhou, C., Bazzi, H., Sun, T., Liu, Z., and Davis, S.: Drivers of Natural Gas Use in United States Buildings, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8666, https://doi.org/10.5194/egusphere-egu23-8666, 2023.

EGU23-8921 | ECS | Posters on site | ITS1.14/CL5.8

Identification of sensitive regions to climate change and anticipation of climate events in Brazil 

Angelica Caseri and Francisco A. Rodrigues

In Brazil, the water system is essential for the electrical system and agribusiness. Understanding climate changes and predicting long-term hydrometeorological phenomena is vital for developing and maintaining these sectors in the country. This work aims to use data from the SIN system (National Interconnected System) in Brazil, from the main hydrological basins, as well as historical rainfall data, in complex networks and deep learning algorithms, to identify possible climate changes in Brazil and predict future hydrometeorological phenomena. Through the methodology developed in this work, the predictions generated showed satisfactory results, which allows identifying regions more sensitive to climate change and anticipating climate events. This work is expected to help the energy generation system in Brazil and the agronomy sector, the main sectors that drive the country's economy.

How to cite: Caseri, A. and A. Rodrigues, F.: Identification of sensitive regions to climate change and anticipation of climate events in Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8921, https://doi.org/10.5194/egusphere-egu23-8921, 2023.

EGU23-9337 | ECS | Posters on site | ITS1.14/CL5.8

Modeling landscape-scale vegetation response to climate: Synthesis of the EarthNet challenge 

Vitus Benson, Christian Requena-Mesa, Claire Robin, Lazaro Alonso, Nuno Carvalhais, and Markus Reichstein

The biosphere displays high heterogeneity at landscape-scale. Vegetation modelers struggle to represent this variability in process-based models because global observations of micrometeorology and plant traits are not available at such fine granularity. However, remote sensing data is available: the Sentinel 2 satellites with a 10m resolution capture aspects of localized vegetation dynamics. The EarthNet challenge (EarthNet2021, [1]) aims at predicting satellite imagery conditioned on coarse-scale weather data. Multiple research groups approached this challenge with deep learning [2,3,4]. Here, we evaluate how well these satellite image models simulate the vegetation response to climate, where the vegetation status is approximated by the NDVI vegetation index.

Achieving the new vegetation-centric evaluation requires three steps. First, we update the original EarthNet2021 dataset to be suitable for vegetation modeling: EarthNet2021x includes improved georeferencing, a land cover map, and a more effective cloud mask. Second, we introduce the interpretable evaluation metric VegetationScore: the Nash Sutcliffe model efficiency (NSE) of NDVI predictions over clear-sky observations per vegetated pixel aggregated through normalization to dataset level. The ground truth NDVI time series achieves a VegetationScore of 1, the target period mean NDVI a VegetationScore of 0. Third, we assess the skill of two deep neural networks with the VegetationScore: ConvLSTM [2,3], which combines convolutions and recurrency, and EarthFormer [4], a Transformer adaptation for Earth science problems. 

Both models significantly outperform the persistence baseline. They do not display systematic biases and generally catch spatial patterns. Yet, both neural networks achieve a negative VegetationScore. Only in about 20% of vegetated pixels, the deep learning models do beat a hypothetical model predicting the true target period mean NDVI. This is partly because models largely underestimate the temporal variability. However, the target variability may partially be inflated by the noisy nature of the observed NDVI. Additionally, increasing uncertainty for longer lead times decreases scores: the mean RMSE in the first 25 days is 50% lower than between 75 and 100 days lead time. In general, consistent with the EarthNet2021 leaderboard, the EarthFormer outperforms the ConvLSTM. With EarthNet2021x, a more narrow perspective to the EarthNet challenge is introduced. Modeling localized vegetation response is a task that requires careful adjustments of off-the-shelf computer vision architectures for them to excel. The resulting specialized approaches can then be used to advance our understanding of the complex interactions between vegetation and climate.



 [1] Requena-Mesa, Benson, Reichstein, Runge and Denzler. EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task. CVPR Workshops, 2021.

 [2] Diaconu, Saha, Günnemann and Zhu. Understanding the Role of Weather Data for Earth Surface Forecasting Using a ConvLSTM-Based Model. CVPR Workshops, 2022.

 [3] Kladny, Milanta, Mraz, Hufkens and Stocker. Deep learning for satellite image forecasting of vegetation greenness. bioRxiv, 2022.

 [4] Gao, Shi, Wang, Zhu, Wang, Li and Yeung. Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. NeurIPS, 2022.

How to cite: Benson, V., Requena-Mesa, C., Robin, C., Alonso, L., Carvalhais, N., and Reichstein, M.: Modeling landscape-scale vegetation response to climate: Synthesis of the EarthNet challenge, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9337, https://doi.org/10.5194/egusphere-egu23-9337, 2023.

EGU23-9434 | ECS | Posters on site | ITS1.14/CL5.8

Enhancing environmental sensor data quality control with graph neural networks 

Elżbieta Lasota, Julius Polz, Christian Chwala, Lennart Schmidt, Peter Lünenschloß, David Schäfer, and Jan Bumberger

The rapidly growing number of low-cost environmental sensors and data from opportunistic sensors constantly advances the quality as well as the spatial and temporal resolution of weather and climate models. However, it also leads to the need for effective tools to ensure the quality of collected data.

Time series quality control (QC) from multiple spatial, irregularly distributed sensors is a challenging task, as it requires the simultaneous integration and analysis of observations from sparse neighboring sensors and consecutive time steps. Manual QC is very often time- and labour- expensive and requires expert knowledge, which introduces subjectivity and limits reproducibility. Therefore, automatic, accurate, and robust QC solutions are in high demand, where among them one can distinguish machine learning techniques. 

In this study, we present a novel approach for the quality control of time series data from multiple spatial, irregularly distributed sensors using graph neural networks (GNNs). Although we applied our method to commercial microwave link attenuation data collected from a network in Germany between April and October 2021, our solution aims to be generic with respect to the number and type of sensors, The proposed approach involves the use of an autoencoder architecture, where the GNN is used to model the spatial relationships between the sensors, allowing for the incorporation of contextual information in the quality control process. 

While our model shows promising results in initial tests, further research is needed to fully evaluate its effectiveness and to demonstrate its potential in a wider range of environmental applications. Eventually, our solution will allow us to further foster the observational basis of our understanding of the natural environment.

How to cite: Lasota, E., Polz, J., Chwala, C., Schmidt, L., Lünenschloß, P., Schäfer, D., and Bumberger, J.: Enhancing environmental sensor data quality control with graph neural networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9434, https://doi.org/10.5194/egusphere-egu23-9434, 2023.

EGU23-9810 | ECS | Orals | ITS1.14/CL5.8

Integration of a deep-learning-based fire model into a global land surface model 

Rackhun Son, Nuno Carvalhais, Lazaro Silva, Christian Requena-Mesa, Ulrich Weber, Veronika Gayler, Tobias Stacke, Reiner Schnur, Julia Nabel, Alexander Winkler, and Sönke Zaehle

Fire is an ubiquitous process within the Earth system that has significant impacts in terrestrial ecosystems. Process-based fire models quantify fire disturbance effects in stand-alone dynamic global vegetation models (DGVMs) and within coupled Earth system models (ESMs), and their advances have incorporated both descriptions of natural processes and anthropogenic drivers. However, we still observe a limited skill in modeling and predicting fire at global scale, mostly due to the stochastic nature of fire, but also due to the limits in empirical parameterizations in these process-based models. As an alternative, statistical approaches have shown the advantages of machine learning in providing robust diagnostics of fire damages, though with limited value for process-based modeling frameworks. Here, we develop a deep-learning-based fire model (DL-fire) to estimate gridded burned area fraction at global scale and couple it within JSBACH4, the land surface model used in the ICON ESM. We compare the resulting hybrid model integrating DL-fire into JSBACH4 (JDL-fire) against the standard fire model within JSBACH4 and the stand-alone DL-fire results. The stand-alone DL-fire model forced with observations shows high performance in simulating global burnt fraction, showing a monthly correlation (Rm) with the Global Fire Emissions Database (GFED4) of 0.78 and of 0.8 at global scale during the training (2004-10) and validation periods (2011-15), respectively. The performance remains nearly the same when evaluating the hybrid modeling approach JDL-fire (Rm=0.76 and 0.86 in training and evaluation periods, respectively). This outperforms the currently used standard fire model in JSBACH4 (Rm=-0.16 and 0.22 in training and evaluation periods, respectively) by far. We further evaluate the modeling results across specific fire regions and apply layer-wise relevance propagation (LRP) to quantify importance of each predictor. Overall, land properties, such as fuel amount and water contents in soil layers, stand out as the major factors determining burnt fraction in DL-fire, paralleled by meteorological conditions, over tropical and high latitude regions. Our study demonstrates the potential of hybrid modeling in advancing the predictability of Earth system processes by integrating statistical learning approaches in physics-based dynamical systems.

How to cite: Son, R., Carvalhais, N., Silva, L., Requena-Mesa, C., Weber, U., Gayler, V., Stacke, T., Schnur, R., Nabel, J., Winkler, A., and Zaehle, S.: Integration of a deep-learning-based fire model into a global land surface model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9810, https://doi.org/10.5194/egusphere-egu23-9810, 2023.

EGU23-10219 | ECS | Posters on site | ITS1.14/CL5.8

Identifying compound weather prototypes of forest mortality with β-VAE 

Mohit Anand, Friedrich Bohn, Lily-belle Sweet, Gustau Camps-Valls, and Jakob Zscheischler

Forest health is affected by many interacting and correlated weather variables over multiple temporal scales. Climate change affects weather conditions and their dependencies. To better understand future forest health and status, an improved scientific  understanding of the complex relationships between weather conditions and forest mortality is required. Explainable AI (XAI) methods are increasingly used to understand and simulate physical processes in complex environments given enough data. In this work, an hourly weather generator (AWE-GEN) is used  to simulate 200,000 years of daily weather conditions representative of central Germany. It is capable of simulating low and high-frequency characteristics of weather variables and also captures the inter-annual variability of precipitation. These data are then used to drive an individual-based forest model (FORMIND) to simulate the dynamics of a beech, pine, and spruce forest. A variational autoencoder β-VAE is used to learn representations of the generated weather conditions, which include radiation, precipitation and temperature. We learn shared and specific variable latent representations using a decoder network which remains the same for all the weather variables. The representation learning is completely unsupervised. Using the output of the forest model, we identify single and compounding weather prototypes that are associated with extreme forest mortality. We find that the prototypes associated with extreme mortality are similar for pine and spruce forests and slightly different for beech forests. Furthermore, although the compounding weather prototypes represent a larger sample size (2.4%-3.5%) than the single prototypes (1.7%-2.2%), they are associated with higher levels of mortality on average. Overall, our research illustrates how deep learning frameworks can be used to identify weather patterns that are associated with extreme impacts.

 

How to cite: Anand, M., Bohn, F., Sweet, L., Camps-Valls, G., and Zscheischler, J.: Identifying compound weather prototypes of forest mortality with β-VAE, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10219, https://doi.org/10.5194/egusphere-egu23-10219, 2023.

Hydrological models and machine learning models are widely used in streamflow simulation and data reconstruction. However, a global assessment of these models is still lacking and no synthesized catchment-scale streamflow product derived from multiple models is available over the globe. In this study, we comprehensively evaluated four conceptual hydrological models (GR2M, XAJ, SAC, Alpine) and four machine learning models (RF, GBDT, DNN, CNN) based on the selected 16,218 gauging stations worldwide, and then applied multi-model weighting ensemble (MWE) method to merge streamflow simulated from these models. Generally, the average performance of the machine learning model for all stations is better than that of the hydrological model, and with more stations having a quantified simulation accuracy (KGE>0.2); However, the hydrological model achieves a higher percentage of stations with a good simulation accuracy (KGE>0.6). Specifically, for the average accuracy during the validation period, there are 67% (27%) and 74% (21%) of stations showed a “quantified” (“good”) level for the hydrological models and machine learning models, respectively. The XAJ is the best-performing model of the four hydrological models, particularly in tropical and temperate zones. Among the machine learning models, the GBDT model shows better performance on the global scale. The MWE can effectively improve the simulation accuracy and perform much better than the traditional multi-model arithmetic ensemble (MAE), especially for the constrained least squares prediction combination method (CLS) with 82% (28%) of the stations having a “qualified” (“good”) accuracy. Furthermore, by exploring the influencing factors of the streamflow simulation, we found that both machine-learning models and hydrological models perform better in wetter areas.

How to cite: Zhang, J. and Liu, J.: Simulation and reconstruction of global monthly runoff based on hydrological models and machine learning models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10391, https://doi.org/10.5194/egusphere-egu23-10391, 2023.

Physical process-based numerical prediction models (NWPs) and radar-based probabilistic methods have been mainly used for short-term precipitation prediction. Recently, radar-based precipitation nowcasting models using advanced machine learning (ML) have been actively developed. Although the ML-based model shows outstanding performance in short-term rainfall prediction, it significantly decreases performance due to increased lead time. It has the limitation of being a black box model that does not consider the physical process of the atmosphere. To address these limitations, we aimed to develop a hybrid precipitation nowcasting model, which combines NWP and an advanced ML-based model via an ML-based ensemble method. The Weather Research and Forecasting (WRF) model was used as NWP to generate a physics-based rainfall forecast. In this study, we developed the ML-based precipitation nowcasting model with conditional Generative Adversarial Network (cGAN), which shows high performance in the image generation tasks. The radar reflectivity data, WRF hindcast meteorological outputs (e.g., temperature and wind speed), and static information of the target basin (e.g., DEM, Land cover) were used as input data of cGAN-based model to generate physics-informed rainfall prediction at the lead time up to 6 hours. The cGAN-based model was trained with the data for the summer season of 2014-2017. In addition, we proposed an ML-based blending method, i.e., XGBoost, that combines cGAN-based model results and WRF forecast results. To evaluate the hybrid model performance, we analyzed the performance of precipitation predictions on three heavy rain events in South Korea. The results confirmed that using the blending method to develop a hybrid model could provide an improved precipitation nowcasting approach.

 

Acknowledgements

 This work was supported by a grant from the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (2020R1A2C2007670).

How to cite: Choi, S. and Kim, Y.: Developing hybrid precipitation nowcasting model with WRF and conditional GAN-based model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10431, https://doi.org/10.5194/egusphere-egu23-10431, 2023.

EGU23-10568 | ECS | Orals | ITS1.14/CL5.8

Extended-range predictability of stratospheric extreme events using explainable neural networks 

Zheng Wu, Tom Beucler, and Daniela Domeisen

Extreme stratospheric events such as extremely weak vortex events and strong vortex events can influence weather in the troposphere from weeks to months and thus are important sources of predictability of tropospheric weather on subseasonal to seasonal (S2S) timescales. However, the predictability of weak vortex events is limited to 1-2 weeks in state-of-the-art forecasting systems, while strong vortex events are more predictable than weak vortex events. Longer predictability timescales of the stratospheric extreme events would benefit long-range surface weather prediction. Recent studies showed promising results in the use of machine learning for improving weather prediction. The goal of this study is to explore the potential of a machine learning approach in extending the predictability of stratospheric extreme events in S2S timescales. We use neural networks (NNs) to predict the monthly stratospheric polar vortex strength with lead times up to five months using the first five principal components (PCs) of the sea surface temperature (SST), mean sea level pressure (MSLP), Barents–Kara sea-ice concentration (BK-SIC), poleward heat flux at 100 hPa, and zonal wind at 50, 30, and 2 hPa as precursors. These physical variables are chosen as they are indicated as potential precursors for the stratospheric extremes in previous studies. The results show that the accuracy and Brier Skill Score decrease with longer lead times and the performance is similar between weak and strong vortex events. We then employ two different NN attribution methods to uncover feature importance (heat map) in the inputs for the NNs, which indicates the relevance of each input for NNs to make the prediction. The heat maps suggest that precursors from the lower stratosphere are important for the prediction of the stratospheric polar vortex strength with a lead time of one month while the precursors at the surface and the upper stratosphere become more important with lead times longer than one month. This result is overall consistent with the previous studies that subseasonal precursors to the stratospheric extreme events may come from the lower troposphere. Our study sheds light on the potential of explainable NNs in searching for opportunities for skillful prediction of stratospheric extreme events and, by extension, surface weather on S2S timescales.

How to cite: Wu, Z., Beucler, T., and Domeisen, D.: Extended-range predictability of stratospheric extreme events using explainable neural networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10568, https://doi.org/10.5194/egusphere-egu23-10568, 2023.

One of the main challenges for forecasting fire activity is the tradeoff between accuracy at finer spatial scales relevant to local decision making and predictability over seasonal (next 2-4 months) and subseasonal-to-seasonal (next 2 weeks to 2 months) timescales. To achieve predictability at long lead times and high spatial resolution, several analyses in the literature have constructed statistical models of fire activity using only antecedent climate predictors. However, in this talk, I will present preliminary seasonal forecasts of wildfire frequency and burned area for the western United States using SMLFire1.0, a stochastic machine learning (SML) fire model, that relies on both observed antecedent climate and vegetation predictors and seasonal forecasts of fire month climate. In particular, I will discuss results obtained by forcing the SMLFire1.0 model with seasonal forecasts from: a) downscaled and bias-corrected North American Multi-Model Ensemble (NMME) outputs, and b) skill-weighted climate analogs constructed using an autoregressive ML model. I will also comment upon the relative contribution of uncertainties, from climate forecasts and fire model simulations respectively, in projections of wildfire frequency and burned area across several spatial scales and lead times. 

How to cite: Buch, J., Williams, A. P., and Gentine, P.: Seasonal forecasts of wildfire frequency and burned area in the western United States using a stochastic machine learning fire model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11238, https://doi.org/10.5194/egusphere-egu23-11238, 2023.

EGU23-11355 | Posters on site | ITS1.14/CL5.8

Estimation of Fine Dust Concentration from BGR Images in Surveillance Cameras 

Hoyoung Cha, Jongyun Byun, Jongjin Baik, and Changhyun Jun

  This study proposes a novel approach on estimation of fine dust concentration from raw video data recorded by surveillance cameras. At first, several regions of interest are defined from specific images extracted from videos in surveillance cameras installed at Chung-Ang University. Among them, sky fields are mainly considered to figure out changes in characteristics of each color. After converting RGB images into BGR images, a number of discrete pixels with brightness intensities in a blue channel is mainly analyzed by investigating any relationships with fine dust concentration measured from automatic monitoring stations near the campus. Here, different values of thresholds from 125 to 200 are considered to find optimal conditions from changes in values of each pixel in the blue channel. This study uses the Pearson correlation coefficient to calculate the correlation between the number of pixels with values over the selected threshold and observed data for fine dust concentration. As an example on one specific date, the coefficients reflect their positive correlations with a range from 0.57 to 0.89 for each threshold. It should be noted that this study is a novel attempt to suggest a new, simple, and efficient method for estimating fine dust concentration from surveillance cameras common in many areas around the world.

 

Keywords: Fine Dust Concentration, BGR Image, Surveillance Camera, Threshold, Correlation Analysis

 

Acknowledgment

  This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838) and this work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-01910 and this work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2020R1G1A1013624).

How to cite: Cha, H., Byun, J., Baik, J., and Jun, C.: Estimation of Fine Dust Concentration from BGR Images in Surveillance Cameras, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11355, https://doi.org/10.5194/egusphere-egu23-11355, 2023.

EGU23-12137 | ECS | Posters on site | ITS1.14/CL5.8

Identifying mechanisms of low-level jets near coast of Kurzeme using Principal Component Analysis 

Maksims Pogumirskis, Tija Sīle, and Uldis Bethers

Low-level jets are maximums in the vertical profile of the wind speed profile in the lowest levels of atmosphere. Low-level jets, when present, can make a significant impact on the wind energy. Wind conditions in low-level jets depart from traditional assumptions about wind profile and low-level jets can also influence the stability and turbulence that are important for wind energy applications.

In literature commonly an algorithm of identifying low-level jets is used to estimate frequency of low-level jets. The algorithm searches for maximum in the lowest levels of the atmosphere with a temperature inversion above the jet maximum. The algorithm is useful in identifying the presence of the low-level jets and estimating their frequency. However, low-level jets can be caused by a number of different mechanisms which leads to differences in low-level jet characteristics. Therefore, additional analysis is necessary to distinguish between different types of jets and characterize their properties. We aim to automate this process using Principal Component Analysis (PCA) to identify main patterns of wind speed and temperature. By analyzing diurnal and seasonal cycles of these patterns a better understanding about climatology of low-level jets in the region can be gained.

This study focuses on the central part of the Baltic Sea. Several recent studies have identified the presence of low-level jets near the coast of Kurzeme. Typically, maximums of low-level jets are located several hundred meters above the surface, while near the coast of Kurzeme maximums of low-level jets are usually within the lowest 100 meters of the atmosphere.

Data from UERRA reanalysis with 11 km horizontal resolution on 12 height levels in the lowest 500 meters of atmosphere was used. The algorithm that identifies low-level jets was applied to the data, to estimate frequency of low-level jets in each grid cell of the model. Jet events were grouped by the wind direction to identify main trajectories of low-level jets in the region. Several atmosphere cross-sections that low-level jets frequently flow through were chosen for further analysis.

Model data was interpolated to the chosen cross-sections and PCA was applied to the cross-section data of wind speed, geostrophic wind speed and temperature. Main patterns of these meteorological parameters, such as wind speed maximum, temperature inversion above the surface of the sea and temperature difference between sea and land were identified by the PCA. Differences of principal components between cross-sections and diurnal and seasonal patterns of principal components helped to gain better understanding of climatology, extent and mechanisms of low-level jets in the region.

How to cite: Pogumirskis, M., Sīle, T., and Bethers, U.: Identifying mechanisms of low-level jets near coast of Kurzeme using Principal Component Analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12137, https://doi.org/10.5194/egusphere-egu23-12137, 2023.

EGU23-12528 | ECS | Orals | ITS1.14/CL5.8

Evaluation of explainable AI solutions in climate science 

Philine Bommer, Marlene Kretschmer, Anna Hedstroem, Dilyara Bareeva, and Marina M.-C. Hoehne

Explainable artificial intelligence (XAI) methods serve as a support for researchers to shed light onto the reasons behind the predictions made by deep neural networks (DNNs). XAI methods have already been successfully applied to climate science, revealing underlying physical mechanisms inherent in the studied data. However, the evaluation and validation of XAI performance is challenging as explanation methods often lack ground truth. As the number of XAI methods is growing, a comprehensive evaluation is necessary to enable well-founded XAI application in climate science.

In this work we introduce explanation evaluation in the context of climate research. We apply XAI evaluation to compare multiple explanation methods for a multi-layer percepton (MLP) and a convolutional neural network (CNN). Both MLP and CNN assign temperature maps to classes based on their decade. We assess the respective explanation methods using evaluation metrics measuring robustness, faithfulness, randomization, complexity and localization. Based on the results of a random baseline test we establish an explanation evaluation guideline for the climate community. We use this guideline to rank the performance in each property of similar sets of explanation methods for the MLP and CNN. Independent of the network type, we find that Integrated Gradients, Layer-wise relevance propagation and InputGradients exhibit a higher robustness, faithfulness and complexity compared to purely Gradient-based methods, while sacrificing reactivity to network parameters, i.e. low randomisation scores. The contrary holds for Gradient, SmoothGrad, NoiseGrad and FusionGrad. Another key observation is that explanations using input perturbations, such as SmoothGrad and Integrated Gradients, do not improve robustness and faithfulness, in contrast to theoretical claims. Our experiments highlight that XAI evaluation can be applied to different network tasks and offers more detailed information about different properties of explanation method than previous research. We demonstrate that using XAI evaluation helps to tackle the challenge of choosing an explanation method.

How to cite: Bommer, P., Kretschmer, M., Hedstroem, A., Bareeva, D., and Hoehne, M. M.-C.: Evaluation of explainable AI solutions in climate science, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12528, https://doi.org/10.5194/egusphere-egu23-12528, 2023.

EGU23-12657 | Orals | ITS1.14/CL5.8 | Highlight

DeepExtremes: Explainable Earth Surface Forecasting Under Extreme Climate Conditions 

Karin Mora, Gunnar Brandt, Vitus Benson, Carsten Brockmann, Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Tonio Fincke, Norman Fomferra, Fabian Gans, Maria Gonzalez, Chaonan Ji, Guido Kraemer, Eva Sevillano Marco, David Montero, Markus Reichstein, Christian Requena-Mesa, Oscar José Pellicer Valero, Mélanie Weynants, Sebastian Wieneke, and Miguel D. Mahecha

Compound heat waves and drought events draw our particular attention as they become more frequent. Co-occurring extreme events often exacerbate impacts on ecosystems and can induce a cascade of detrimental consequences. However, the research to understand these events is still in its infancy. DeepExtremes is a project funded by the European Space Agency (https://rsc4earth.de/project/deepextremes/) aiming at using deep learning to gain insight into Earth surface under extreme climate conditions. Specifically, the goal is to forecast and explain extreme, multi-hazard, and compound events. To this end, the project leverages the existing Earth observation archive to help us better understand and represent different types of hazards and their effects on society and vegetation. The project implementation involves a multi-stage process consisting of 1) global event detection; 2) intelligent subsampling and creation of mini-data-cubes; 3) forecasting methods development, interpretation, and testing; and 4) cloud deployment and upscaling. The data products will be made available to the community following the reproducibility and FAIR data principles. By effectively combining Earth system science with explainable AI, the project contributes knowledge to advancing the sustainable management of consequences of extreme events. This presentation will show the progress made so far and specifically introduce how to participate in the challenges about spatio-temporal extreme event prediction in DeepExtremes.

How to cite: Mora, K., Brandt, G., Benson, V., Brockmann, C., Camps-Valls, G., Fernández-Torres, M.-Á., Fincke, T., Fomferra, N., Gans, F., Gonzalez, M., Ji, C., Kraemer, G., Marco, E. S., Montero, D., Reichstein, M., Requena-Mesa, C., Valero, O. J. P., Weynants, M., Wieneke, S., and Mahecha, M. D.: DeepExtremes: Explainable Earth Surface Forecasting Under Extreme Climate Conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12657, https://doi.org/10.5194/egusphere-egu23-12657, 2023.

EGU23-12889 | Orals | ITS1.14/CL5.8

New Berkeley Earth High Resolution Temperature Data Set 

Robert A. Rohde and Zeke Hausfather

Berkeley Earth is premiering a new high resolution analysis of historical instrumental temperatures.

This builds on our existing work on climate reconstruction by adding a simple machine learning layer to our analysis.  This new approach extracts weather patterns from model, satellite, and reanalysis data, and then layers these weather patterns on top of instrumental observations and our existing interpolation methods to produce new high resolution historical temperature fields.  This has quadrupled our output resolution from the previous 1° x 1° lat-long to a new global 0.25° x 0.25° lat-long resolution.  However, this is not simply a downscaling effort.  Firstly, the use of weather patterns derived from physical models and observations increases the spatial realism of the reconstructed fields.  Secondly, observations from regions with high density measurement networks have been directly incorporated into the high resolution field, allowing dense observations to be more fully utilized.  

This new data product uses significantly more observational weather station data and produces higher resolution historical temperature fields than any comparable product, allowing for unprecedented insights into historical local and regional climate change.  In particular, the effect of geographic features such as mountains, coastlines, and ecosystem variations are resolved with a level of detail that was not previously possible.  At the same time, previously established techniques for bias corrections, noise reduction, and error analysis continued to be utilized.  The resulting global field initially spans 1850 to present and will be updated on an ongoing basis.  This project does not significantly change the global understanding of climate change, but helps to provide local detail that was often unresolved previously.  The initial data product focuses on monthly temperatures, though a proposal exists to also create a high resolution daily temperature data set using similar methods.

This talk will describe the construction of the new data set and its characteristics.  The techniques used in this project are accessible enough that they are likely to be useful for other types of instrumental analyses wishing to improve resolution or leverage basic information about weather patterns derived from models or other sources.

How to cite: Rohde, R. A. and Hausfather, Z.: New Berkeley Earth High Resolution Temperature Data Set, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12889, https://doi.org/10.5194/egusphere-egu23-12889, 2023.

EGU23-12948 | ECS | Orals | ITS1.14/CL5.8

Identifying drivers of river floods using causal inference 

Peter Miersch, Shijie Jiang, Oldrich Rakovec, and Jakob Zscheischler

River floods are among the most devastating natural hazards, causing thousands of deaths and billions of euros in damages every year. Floods can result from a combination of compounding drivers such as heavy precipitation, snowmelt, and high antecedent soil moisture. These drivers and the processes they govern vary widely both between catchments and between flood events within a catchment, making a causal understanding of the underlying hydrological processes difficult.

Modern causal inference methods, such as the PCMCI framework, are able to identify drivers from complex time series through causal discovery and build causally aware statistical models. However, causal inference tailored to extreme events remains a challenge due to data length limitations. To overcome data limitations, here we bridge the gap between synthetic and real world data using 1,000 years of simulated weather to drive as state-of-the-art hydrological model (the mesoscale Hydrological Model, mHM) over a wide range of European catchments. From the simulated time series, we extract high runoff events, on which we evaluate the causal inference approach. We identify the minimum data necessary for obtaining robust causal models, evaluate metrics for model evaluation and comparison, and compare causal flood drivers across catchments. Ultimately, this work will help establish best practices in causal inference for flood research to identify meteorological and catchment specific flood drivers in a changing climate.

How to cite: Miersch, P., Jiang, S., Rakovec, O., and Zscheischler, J.: Identifying drivers of river floods using causal inference, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12948, https://doi.org/10.5194/egusphere-egu23-12948, 2023.

EGU23-13250 | ECS | Posters on site | ITS1.14/CL5.8

From MODIS cloud properties to cloud types using semi-supervised learning 

Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic

Clouds are classified into types, classes, or regimes. The World Meteorological Organization distinguishes stratus and cumulus clouds and three altitude layers. Cloud types exhibit very different radiative properties and interact in numerous ways with aerosol particles in the atmosphere. However, it has proven difficult to define cloud regimes objectively and from remote sensing data, hindering the understanding we have of the processes and adjustments involved.

Building on the method we previously developed, we combine synoptic observations and passive satellite remote-sensing retrievals to constitute a database of cloud types and cloud properties to eventually train a cloud classification algorithm. The cloud type labels come from the global marine meteorological observations dataset (UK Met Office, 2006) which is comprised of near-global synoptic observations. This data record reports back information about cloud type and other meteorological quantities at the surface. The cloud classification model is built on different cloud-top and cloud optical properties (Level 2 products MOD06/MYD06 from the MODIS sensor) extracted temporally close to the observation time and on a 128km x 128km grid around the synoptic observation location. To make full use of the large quantity of remote sensing data available and to investigate the variety in cloud settings, a convolutional variational auto-encoder (VAE) is applied as a dimensionality reduction tool in a first step. Furthermore, such model architecture allows to account for spatial relationships while describing non-linear patterns in the input data. The cloud classification task is subsequently performed drawing on the constructed latent representation of the VAE. Associating information from underneath and above the cloud enables to build a robust model to classify cloud types. For the training we specify a study domain in the Atlantic ocean around the equator and evaluate the method globally. Further experiments and evaluation are done on simulation data produced by the ICON model.

How to cite: Lenhardt, J., Quaas, J., and Sejdinovic, D.: From MODIS cloud properties to cloud types using semi-supervised learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13250, https://doi.org/10.5194/egusphere-egu23-13250, 2023.

EGU23-13462 | ECS | Orals | ITS1.14/CL5.8

Double machine learning for geosciences 

Kai-Hendrik Cohrs, Gherardo Varando, Markus Reichstein, and Gustau Camps-Valls

Hybrid modeling describes the synergy between parametric models and machine learning [1]. Parts of a parametric equation are substituted by non-parametric machine learning models, which can then represent complex functions. These are inferred together with the parameters of the equation from the data. Hybrid modeling promises to describe complex relationships and to be scientifically interpretable. These promises, however, need to be taken with a grain of salt. With too flexible models, such as deep neural networks, the problem of equifinality arises: There is no identifiable optimal solution. Instead, many outcomes describe the data equally well, and we will obtain one of them by chance. Interpreting the result may lead to erroneous conclusions. Moreover, studies have shown that regularization techniques can introduce a bias on jointly estimated physical parameters [1].

We propose double machine learning (DML) to solve these problems [2]. DML is a theoretically well-founded technique for fitting semi-parametric models, i.e., models consisting of a parametric and a non-parametric component. DML is widely used for debiased treatment effect estimation in economics. We showcase its use for geosciences on two problems related to carbon dioxide fluxes: 

  • Flux partitioning, which aims at separating the net carbon flux (NEE) into its main contributing gross fluxes, namely, RECO and GPP.
  • Estimation of the temperature sensitivity parameter of ecosystem respiration Q10.

First, we show that in the case of synthetic data for Q10 estimation, we can consistently retrieve the true value of Q10 where the naive neural network approach fails. We further apply DML to the carbon flux partitioning problem and find that it is 1) able to retrieve the true fluxes of synthetic data, even in the presence of strong (and more realistic) heteroscedastic noise, 2) retrieves main gross carbon fluxes on real data consistent with established methods, and 3) allows us to causally interpret the retrieved GPP as the direct effect of the photosynthetically active radiation on NEE. This way, the DML approach can be seen as a causally interpretable, semi-parametric version of the established daytime methods. We also investigate the functional relationships inferred with DML and the drivers modulating the obtained light-use efficiency function. In conclusion, DML offers a solid framework to develop hybrid and semiparametric modeling and can be of widespread use in geosciences.

 

[1] Reichstein, Markus, et al. “Combining system modeling and machine learning into hybrid ecosystem modeling.” Knowledge-Guided Machine Learning (2022). https://doi.org/10.1201/9781003143376-14

[2] Chernozhukov, Victor, et al. “Double/debiased machine learning for treatment and structural parameters.” The Econometrics Journal, Volume 21, Issue 1, 1 (2018): C1–C68. https://doi.org/10.1111/ectj.12097

How to cite: Cohrs, K.-H., Varando, G., Reichstein, M., and Camps-Valls, G.: Double machine learning for geosciences, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13462, https://doi.org/10.5194/egusphere-egu23-13462, 2023.

EGU23-13622 | ECS | Posters on site | ITS1.14/CL5.8

Towards explainable marine heatwaves forecasts 

Ayush Prasad and Swarnalee Mazumder

In recent years, both the intensity and extent of marine heatwaves have increased across the world. Anomalies in sea surface temperature have an effect on the health of marine ecosystems, which are crucial to the Earth's climate system. Marine Heatwaves' devastating impacts on aquatic life have been increasing steadily in recent years, harming aquatic ecosystems and causing a tremendous loss of marine life. Early warning systems and operational forecasting that can foresee such events can aid in designing effective and better mitigation techniques. Recent studies have shown that machine learning and deep learning-based approaches can be used for forecasting the occurrence of marine heatwaves up to a year in advance. However, these models are black box in nature and do not provide an understanding of the factors influencing MHWs. In this study, we used machine learning methods to forecast marine heatwaves. The developed models were tested across four historical Marine Heatwave events around the world. Explainable AI methods were then used to understand and analyze the relationships between the drivers of these events.

How to cite: Prasad, A. and Mazumder, S.: Towards explainable marine heatwaves forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13622, https://doi.org/10.5194/egusphere-egu23-13622, 2023.

EGU23-14493 | ECS | Orals | ITS1.14/CL5.8

Interpretable probabilistic forecast of extreme heat waves 

Alessandro Lovo, Corentin Herbert, and Freddy Bouchet
Understanding and predicting extreme events is one of the major challenges for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. Extreme heatwaves are, and likely will be in the future, among the deadliest weather events. They also increase strain on water resources, food security and energy supply. Developing the ability to forecast their probability of occurrence a few days, weeks, or even months in advance would have major consequences to reduce our vulnerability to these events. Beyond the practical benefits of forecasting heat waves, building statistical models for extreme events which are interpretable is also highly beneficial from a fundamental point of view. Indeed, they enable proper studies of the processes underlying extreme events such as heat waves, improve dataset or model validation, and contribute to attribution studies. Machine learning provides tools to reach both these goals.
We will first demonstrate that deep neural networks can predict the probability of occurrence of long-lasting 14-day heatwaves over France, up to 15 days ahead of time for fast dynamical drivers (500 hPa geopotential height field), and at much longer lead times for slow physical drivers (soil moisture). Those results are amazing in terms of forecasting skill. However, these machine learning models tend to be very complex and are often treated as black boxes. This limits our ability to use them for investigating the dynamics of extreme heat waves.
To gain physical understanding, we have then designed a network architecture which is intrinsically interpretable. The main idea of this architecture is that the network first computes an optimal index, which is an optimal projection of the physical fields in a low-dimensional space. In a second step, it uses a fully non-linear representation of the probability of occurrence of the event as a function of the optimal index. This optimal index can be visualized and compared with classical heuristic understanding of the physical process, for instance in terms of geopotential height and soil moisture. This fully interpretable network is slightly less efficient than the off-the-shelf deep neural network. We fully quantify the performance loss incurred when requiring interpretability and make the connection with the mathematical notion of committor functions.
This new machine learning tool opens the way for understanding optimal predictors of weather and climate extremes. This has potential for the study of slow drivers, and the effect of climate change on the drivers of extreme events.

How to cite: Lovo, A., Herbert, C., and Bouchet, F.: Interpretable probabilistic forecast of extreme heat waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14493, https://doi.org/10.5194/egusphere-egu23-14493, 2023.

EGU23-14856 | ECS | Orals | ITS1.14/CL5.8

Classification of Indoor Air Pollution Using Low-cost Sensors by Machine Learning 

Andrii Antonenko, Viacheslav Boretskij, and Oleksandr Zagaria

Air pollution has become an integral part of modern life. The main source of air pollution can be considered combustion processes associated with energy-intensive corporate activities. Energy companies consume about one-third of the fuel produced and are a significant source of air pollution [1]. State and public air quality monitoring networks were created to monitor the situation. Public monitoring networks are cheaper and have more coverage than government ones. Although the state monitoring system shows more accurate data, an inexpensive network is sufficient to inform the public about the presence or absence of pollution (air quality). In order to inform the public, the idea arose to test the possibility of detecting types of pollution using data from cheap air quality monitoring sensors. In general, to use a cheap sensor for measurements, it must first be calibrated (corrected) by comparing its readings with a reference device. Various mathematical methods can be used for this. One of such method is neural network training, which has proven itself well for correcting PM particle readings due to relative humidity impact [2].

The idea of using a neural network to improve data quality is not new, but it is quite promising, as the authors showed in [3]. The main problem to implement this method is connected with a reliable dataset for training the network. For this, it is necessary to register sensor readings for relatively clean air and for artificially generated or known sources of pollution. Training the neural network on the basis of collected data can be used to determine (classify) types of air: with pollution (pollutant) or without. For this, an experiment was set up in the "ReLab" co-working space at the Taras Shevchenko National University of Kyiv. The sensors were placed in a closed box, in which airflow ventilation is provided. The ZPHS01B [4] sensor module was used for inbox measurements, as well as, calibrated sensors PMS7003 [5] and BME280 [6]. Additionally, IPS 7100 [7] and SPS30 [8] were added to enrich the database for ML training. A platform based on HiLink 7688 was used for data collecting, processing, and transmission.

Data was measured every two seconds, independently from each sensor. Before each experiment, the room was ventilated to avoid influence on the next series of experiments.

References

1. Zaporozhets A. Analysis of means for monitoring air pollution in the environment. Science-based technologies. 2017, Vol. 35, no3. 242-252. DOI: 10.18372/2310-5461.35.11844

2. Antonenko A, (2021) Correction of fine particle concentration readings depending on relative humidity, [Master's thesis, Taras Shevchenko National University of Kyiv], 35 pp.

3. Lee, J. Kang, S. Kim, Y. Im, S. Yoo , D. Lee, “Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor”, Sensors 2020, vol. 20, 3617, 24 pp., 2020.`

4. ZPHS01B Datasheet URL: https://pdf1.alldatasheet.com/datasheet-pdf/view/1303697/WINSEN/ZPHS01B.html

5. Plantower PMS7003 Datasheet URL: https://www.espruino.com/datasheets/PMS7003.pdf

6. Bosch 280 Datasheet URL: https://www.mouser.com/datasheet/2/783/BST-BME280-DS002-1509607.pdf

7. https://pierasystems.com/intelligent-particle-sensors/

8. https://sensirion.com/products/catalog/SPS30/

How to cite: Antonenko, A., Boretskij, V., and Zagaria, O.: Classification of Indoor Air Pollution Using Low-cost Sensors by Machine Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14856, https://doi.org/10.5194/egusphere-egu23-14856, 2023.

EGU23-15000 | ECS | Orals | ITS1.14/CL5.8 | Highlight

Causal inference to study food insecurity in Africa 

Jordi Cerdà-Bautista, José María Tárraga, Gherardo Varando, Alberto Arribas, Ted Shepherd, and Gustau Camps-Valls

The current situation regarding food insecurity in the continent of Africa, and the Horn of Africa in particular, is at an unprecedented risk level triggered by continuous drought events, complicated interactions between food prices, crop yield, energy inflation and lack of humanitarian aid, along with disrupting conflicts and migration flows. The study of a food-secure environment is a complex, multivariate, multiscale, and non-linear problem difficult to understand with canonical data science methodologies. We propose an alternative approach to the food insecurity problem from a causal inference standpoint to discover the causal relations and evaluate the likelihood and potential consequences of specific interventions. In particular, we demonstrate the use of causal inference for understanding the impact of humanitarian interventions on food insecurity in Somalia. In the first stage of the problem, we apply different data transformations to the main drivers to achieve the highest degree of correlation with the interested variable. In the second stage, we infer causation from the main drivers and interested variables by applying different causal methods such as PCMCI or Granger causality. We analyze and harmonize different time series, per district of Somalia, of the global acute malnutrition (GAM) index, food market prices, crop production, conflict levels, drought and flood internal displacements, as well as climate indicators such as the NDVI index, precipitation or land surface temperature. Then, assuming a causal graph between the main drivers causing the food insecurity problem, we estimate the effect of increasing humanitarian interventions on the GAM index, considering the effects of a changing climate, migration flows, and conflict events. We show that causal estimation with modern methodologies allows us to quantify the impact of humanitarian aid on food insecurity.

 

References

 

[1] Runge, J., Bathiany, S., Bollt, E. et al. Inferring causation from time series in Earth system sciences. Nat Commun 10, 2553 (2019). https://doi.org/10.1038/s41467-019-10105-3

[2] Sazib Nazmus, Mladenova lliana E., Bolten John D., Assessing the Impact of ENSO on Agriculture Over Africa Using Earth Observation Data, Frontiers in Sustainable Food Systems, 2020, 10.3389/fsufs.2020.509914. https://www.frontiersin.org/article/10.3389/fsufs.2020.509914

[3] Checchi, F., Frison, S., Warsame, A. et al. Can we predict the burden of acute malnutrition in crisis-affected countries? Findings from Somalia and South Sudan. BMC Nutr 8, 92 (2022). https://doi.org/10.1186/s40795-022-00563-2

How to cite: Cerdà-Bautista, J., Tárraga, J. M., Varando, G., Arribas, A., Shepherd, T., and Camps-Valls, G.: Causal inference to study food insecurity in Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15000, https://doi.org/10.5194/egusphere-egu23-15000, 2023.

EGU23-15185 | ECS | Posters on site | ITS1.14/CL5.8

Deep learning to support ocean data quality control 

Mohamed Chouai, Felix Simon Reimers, and Sebastian Mieruch-Schnülle

In this study, which is part of the M-VRE [https://mosaic-vre.org/about] project, we aim to improve a quality control (QC) system on arctic ocean temperature profile data using deep learning. For the training, validation, and evaluation of our algorithms, we are using the UDASH dataset [https://essd.copernicus.org/articles/10/1119/2018/]. In the classical QC setting, the ocean expert or "operator", applies a series of thresholding (classical) algorithms to identify, i.e. flag, erroneous data. In the next step, the operator visually inspects every data profile, where suspicious samples have been identified. The goal of this time-consuming visual QC is to find "false positives", i.e. flagged data that is actually good, because every sample/profile has not only a scientific value but also a monetary one. Finally, the operator turns all "false positive" data back to good. The crucial point here is that although these samples/profiles are above certain thresholds they are considered good by the ocean expert. These human expert decisions are extremely difficult, if not impossible, to map by classical algorithms. However, deep-learning neural networks have the potential to learn complex human behavior. Therefore, we have trained a deep learning system to "learn" exactly the expert behavior of finding "false positives" (identified by the classic thresholds), which can be turned back to good accordingly. The first results are promising. In a fully automated setting, deep learning improves the results and fewer data are flagged. In a subsequent visual QC setting, deep learning relieves the expert with a distinct workload reduction and gives the option to clearly increase the quality of the data.
Our long-term goal is to develop an arctic quality control system as a series of web services and Jupyter notebooks to apply automated and visual QC online, efficient, consistent, reproducible, and interactively.

How to cite: Chouai, M., Simon Reimers, F., and Mieruch-Schnülle, S.: Deep learning to support ocean data quality control, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15185, https://doi.org/10.5194/egusphere-egu23-15185, 2023.

EGU23-15286 | ECS | Orals | ITS1.14/CL5.8

Spatio-temporal downscaling of precipitation data using a conditional generative adversarial network 

Luca Glawion, Julius Polz, Benjamin Fersch, Harald Kunstmann, and Christian Chwala

Natural disasters caused by cyclones, hail, landslides or floods are directly related to precipitation. Global climate models are an important tool to adapt to these hazards in a future climate. However, they operate on spatial and temporal discretizations that limit the ability to adequately reflect these fast evolving, highly localized phenomena which has led to the development of various downscaling approaches .

Conditional generative adversarial networks (cGAN) have recently been applied as a promising downscaling technique to improve the spatial resolution of climate data. The ability of GANs to generate ensembles of solutions from random perturbations can be used to account for the stochasticity of climate data and quantify uncertainties. 

We present a cGAN for not only downscaling the spatial, but simultaneously also the temporal dimension of precipitation data as a so-called video super resolution approach. 3D convolutional layers are exploited for extracting and generating temporally consistent  rain events with realistic fine-scale structure. We downscale coarsened gauge adjusted and climatology corrected precipitation data from Germany from a spatial resolution of 32 km to 2 km and a temporal resolution of 1 hr to 10 min, by applying a novel training routine using partly normalized and logarithmized data, allowing for improved extreme value statistics of the generated fields.

Exploiting the fully convolutional nature of our model we can generate downscaled maps for the whole of Germany in a single downscaling step at low latency. The evaluation of these maps using a spatial and temporal power spectrum analysis shows that the generated temporal and spatial structures are in high agreement with the reference. Visually, the generated temporally evolving and advecting rain events are hardly classifiable as artificial generated. The model also shows high skill regarding pixel-wise error and localization of high precipitation intensities, considering the FSS, CRPS, KS and RMSE. Due to the underdetermined downscaling problem a probabilistic cGAN approach yields additional information to deterministic models which we use for comparison. The method is also capable of preserving the climatology, e.g., expressed as the annual precipitation sum. Investigations of temporal aggregations of the downscaled fields revealed an interesting effect. We observe that structures generated in networks with convolutional layers are not placed completely at random, but can generate recurrent structures, which can also be discovered within other prominent DL downscaling models. Although they can be mitigated by adequate model selection, their occurrence remains an open research question.

We conclude that our proposed approach can extend the application of cGANs for downscaling to the time dimension and therefore is a promising candidate to supplement conventional downscaling methods due to the high performance and computational efficiency.

How to cite: Glawion, L., Polz, J., Fersch, B., Kunstmann, H., and Chwala, C.: Spatio-temporal downscaling of precipitation data using a conditional generative adversarial network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15286, https://doi.org/10.5194/egusphere-egu23-15286, 2023.

EGU23-15540 | ECS | Posters on site | ITS1.14/CL5.8 | Highlight

USCC: A Benchmark Dataset for Crop Yield Prediction under Climate Extremes 

Adrian Höhl, Stella Ofori-Ampofo, Ivica Obadic, Miguel-Ángel Fernández-Torres, Ridvan Salih Kuzu, and Xiaoxiang Zhu

Climate variability and extremes are known to represent major causes for crop yield anomalies. They can lead to the reduction of crop productivity, which results in disruptions in food availability and nutritional quality, as well as in rising food prices. Extreme climates will become even more severe as global warming proceeds, challenging the achievement of food security. These extreme events, especially droughts and heat waves, are already evident in major food-production regions like the United States. Crops cultivated in this country such as corn and soybean are critical for both domestic use and international supply. Considering the sensitivity of crops to climate, here we present a dataset that couples remote sensing surface reflectances with climate variables (e.g. minimum and maximum temperature, precipitation, and vapor pressure) and extreme indicators. The dataset contains the crop yields of various commodities over the USA for nearly two decades. Given the advances and proven success of machine learning in numerous remote sensing tasks, our dataset constitutes a benchmark to advance the development of novel models for crop yield prediction, and to analyze the relationship between climate and crop yields for gaining scientific insights. Other potential use cases include extreme event detection and climate forecasting from satellite imagery. As a starting point, we evaluate the performance of several state-of-the-art machine and deep learning models to form a baseline for our benchmark dataset.

How to cite: Höhl, A., Ofori-Ampofo, S., Obadic, I., Fernández-Torres, M.-Á., Salih Kuzu, R., and Zhu, X.: USCC: A Benchmark Dataset for Crop Yield Prediction under Climate Extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15540, https://doi.org/10.5194/egusphere-egu23-15540, 2023.

EGU23-15817 | ECS | Posters on site | ITS1.14/CL5.8

Evaluating the generalization ability of a deep learning model trained to detect cloud-to-ground lightning on raw ERA5 data 

Gregor Ehrensperger, Tobias Hell, Georg Johann Mayr, and Thorsten Simon

Atmospheric conditions that are typical for lightning are commonly represented by proxies such as cloud top height, cloud ice flux, CAPE times precipitation, or the lightning potential index. While these proxies generally deliver reasonable results, they often need to be adapted for local conditions in order to perform well. This suggests that there is a need for more complex and holistic proxies. Recent research confirms that the use of machine learning (ML) approaches for describing lightning is promising.

In a previous study a deep learning model was trained on single spatiotemporal (30km x 30km x 1h) cells in the summer period of the years 2010--2018 and showed good results for the unseen test year 2019 within Austria. We now improve this model by using multiple neighboring vertical atmospheric columns to also address for horizontal moisture advection. Furthermore data of successive hours is used as input data to enable the model to capture the temporal development of atmospheric conditions such as the build-up and breakdown of convections.

In this work we focus on the summer months June to August and use data from parts of Central Europe. This spatial domain is thought to be representative for Continental Europe since it covers mountainous aswell as coastal regions. We take raw ERA5 parameters beyond the tropopause enriched with a small amount of meta data such as the day of the year and the hour of the day for training. The quality of the resulting paramaterized model is then evaluated on Continental Europe to examine the generalization ability.

Using parts of Central Europe to train the model, we evaluate its ability to generalize on unseen parts of Continental Europe using EUCLID data. Having a model that generalizes well is a building block for a retrospective analysis back into years where the structured recording of accurate lightning observations in a unified way was not established yet.

How to cite: Ehrensperger, G., Hell, T., Mayr, G. J., and Simon, T.: Evaluating the generalization ability of a deep learning model trained to detect cloud-to-ground lightning on raw ERA5 data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15817, https://doi.org/10.5194/egusphere-egu23-15817, 2023.

EGU23-16098 | Posters on site | ITS1.14/CL5.8

Identifying Lightning Processes in ERA5 Soundings with Deep Learning 

Tobias Hell, Gregor Ehrensperger, Georg J. Mayr, and Thorsten Simon

Atmospheric environments favorable for lightning and convection are commonly represented by proxies or parameterizations based on expert knowledge such as CAPE, wind shears, charge separation, or combinations thereof. Recent developments in the field of machine learning, high resolution reanalyses, and accurate lightning observations open possibilities for identifying tailored proxies without prior expert knowledge. To identify vertical profiles favorable for lightning, a deep neural network links ERA5 vertical profiles of cloud physics, mass field variables and wind to lightning location data from the Austrian Lightning Detection & Information System (ALDIS), which has been transformed to a binary target variable labelling the ERA5 cells as lightning and no lightning cells. The ERA5 parameters are taken on model levels beyond the tropopause forming an input layer of approx. 670 features. The data of 2010 - 2018 serve as training/validation. On independent test data, 2019, the deep network outperforms a reference with features based on meteorological expertise. Shapley values highlight the atmospheric processes learned by the network which identifies cloud ice and snow content in the upper and mid-troposphere as relevant features. As these patterns correspond to the separation of charge in thunderstorm cloud, the deep learning model can serve as physically meaningful description of lightning. 

How to cite: Hell, T., Ehrensperger, G., Mayr, G. J., and Simon, T.: Identifying Lightning Processes in ERA5 Soundings with Deep Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16098, https://doi.org/10.5194/egusphere-egu23-16098, 2023.

EGU23-16163 | ECS | Posters on site | ITS1.14/CL5.8

A comparison of methods for determining the number of classes in unsupervised classification of climate models 

Emma Boland, Dani Jones, and Erin Atkinson

Unsupervised classification is becoming an increasingly common method to objectively identify coherent structures within both observed and modelled climate data. However, the user must choose the number of classes to fit in advance. Typically, a combination of statistical methods and expertise is used to choose the appropriate number of classes for a given study, however it may not be possible to identify a single ‘optimal’ number of classes. In this
work we present a heuristic method for determining the number of classes unambiguously for modelled data where more than one ensemble member is available. This method requires robustness in the class definition between simulated ensembles of the system of interest. For demonstration, we apply this to the clustering of Southern Ocean potential temperatures in a CMIP6 climate model, and compare with other common criteria such as Bayesian Information Criterion (BIC) and the Silhouette Score.

How to cite: Boland, E., Jones, D., and Atkinson, E.: A comparison of methods for determining the number of classes in unsupervised classification of climate models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16163, https://doi.org/10.5194/egusphere-egu23-16163, 2023.

EGU23-16186 | ECS | Posters on site | ITS1.14/CL5.8

A review of deep learning for weather prediction 

Jannik Thümmel, Martin Butz, and Bedartha Goswami

Recent years have seen substantial performance-improvements of deep-learning-based
weather prediction models (DLWPs). These models cover a large range of temporal and
spatial resolutions—from nowcasting to seasonal forecasting and on scales ranging from
single to hundreds of kilometers. DLWPs also exhibit a wide variety of neural architec-
tures and training schemes, with no clear consensus on best practices. Focusing on the
short-to-mid-term forecasting ranges, we review several recent, best-performing models
with respect to critical design choices. We emphasize the importance of self-organizing
latent representations and inductive biases in DLWPs: While NWPs are designed to sim-
ulate resolvable physical processes and integrate unresolvable subgrid-scale processes by
approximate parameterizations, DLWPs allow the latent representation of both kinds of
dynamics. The purpose of this review is to facilitate targeted research developments and
understanding of how design choices influence performance of DLWPs. While there is
no single best model, we highlight promising avenues towards accurate spatio-temporal
modeling, probabilistic forecasts and computationally efficient training and infer

How to cite: Thümmel, J., Butz, M., and Goswami, B.: A review of deep learning for weather prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16186, https://doi.org/10.5194/egusphere-egu23-16186, 2023.

EGU23-16443 | ECS | Orals | ITS1.14/CL5.8

Hybrid machine learning model of coupled carbon and water cycles 

Zavud Baghirov, Basil Kraft, Martin Jung, Marco Körner, and Markus Reichstein

There is evidence for a strong coupling between the terrestrial carbon and water cycles and that these cycles should be studied as an interconnected system (Humphrey et al. 2018). One of the key methods to numerically represent the Earth system is process based modelling, which is, however, still subject to large uncertainties, e.g., due to wrong or incomplete process knowledge (Bonan and Doney 2018). Such models are often rigid and only marginally informed by Earth observations. This is where machine learning (ML) approaches can be advantageous, due to their ability to learn from data in a flexible way. These methods have their own shortcomings, such as their “black-box” nature and lack of physical consistency.

Recently, it has been suggested by Reichstein et al. (2019) to combine process knowledge with ML algorithms to model environmental processes. The so-called hybrid modelling approach has already been used to model different components of terrestrial water storage (TWS) in a global hydrological model (Kraft et al. 2022). This study follows-up on this work with the objective to improve the parameterization of some processes (e.g., soil moisture) and to couple the model with the carbon cycle. The coupling could potentially reduce model uncertainties and help to better understand water-carbon interactions.

The proposed hybrid model of the coupled water and carbon cycles is forced with reanalysis data from ERA-5, such as air temperature, net radiation, and CO2 concentration from CAMS. Water-carbon cycle processes are constrained using observational data products of water-carbon cycles. The hybrid model uses a long short-term memory (LSTM) model—a member of the recurrent neural networks family—at its core for processing the time-series Earth observation data. The LSTM simulates a number of coefficients which are used as parameters in the conceptual model of water and carbon cycles. Some of the key processes represented in the conceptual model are evapotranspiration, snow, soil moisture, runoff, groundwater, water use efficiency (WUE), ecosystem respiration, and net ecosystem exchange. The model partitions TWS into different components and it can be used to assess the impact of different TWS components on the CO2 growth rate. Moreover, we can assess the learned system behaviors of water and carbon cycle interactions for different ecosystems.

References:

Bonan, Gordon B, and Scott C Doney. 2018. “Climate, Ecosystems, and Planetary Futures: The Challenge to Predict Life in Earth System Models.” Science 359 (6375): eaam8328.

Humphrey, Vincent, Jakob Zscheischler, Philippe Ciais, Lukas Gudmundsson, Stephen Sitch, and Sonia I Seneviratne. 2018. “Sensitivity of Atmospheric CO2 Growth Rate to Observed Changes in Terrestrial Water Storage.” Nature 560 (7720): 628–31.

Kraft, Basil, Martin Jung, Marco Körner, Sujan Koirala, and Markus Reichstein. 2022. “Towards Hybrid Modeling of the Global Hydrological Cycle.” Hydrology and Earth System Sciences 26 (6): 1579–1614.

Reichstein, Markus, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, Nuno Carvalhais, et al. 2019. “Deep Learning and Process Understanding for Data-Driven Earth System Science.” Nature 566 (7743): 195–204.

How to cite: Baghirov, Z., Kraft, B., Jung, M., Körner, M., and Reichstein, M.: Hybrid machine learning model of coupled carbon and water cycles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16443, https://doi.org/10.5194/egusphere-egu23-16443, 2023.

EGU23-16449 | Orals | ITS1.14/CL5.8

Data-driven seasonal forecasts of European heat waves 

Stefano Materia, Martin Jung, Markus G. Donat, and Carlos Gomez-Gonzalez

Seasonal Forecasts are critical tools for early-warning decision support systems, that can help reduce the related risk associated with hot or cold weather and other events that can strongly affect a multitude of socio-economic sectors. Recent advances in both statistical approaches and numerical modeling have improved the skill of Seasonal Forecasts. However, especially in mid-latitudes, they are still affected by large uncertainties that can limit their usefulness.

The MSCA-H2020 project ARTIST aims at improving our knowledge of climate predictability at the seasonal time-scale, focusing on the role of unexplored drivers, to finally enhance the performance of current prediction systems. This effort is meant to reduce uncertainties and make forecasts efficiently usable by regional meteorological services and private bodies. This study focuses on seasonal prediction of heat extremes in Europe, and here we present a first attempt to predict heat wave accumulated activity across different target seasons. An empirical seasonal forecast is designed based on Machine Learning techniques. A feature selection approach is used to detect the best subset of predictors among a variety of candidates, and then an assessment of the relative importance of each predictor is done, in different European regions for the four main seasons.

Results show that many observed teleconnections are caught by the data-driven approach, while a few features that show to be linked to the heat wave propensity of a season deserve a deeper understanding of the underpinning physical process.

How to cite: Materia, S., Jung, M., Donat, M. G., and Gomez-Gonzalez, C.: Data-driven seasonal forecasts of European heat waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16449, https://doi.org/10.5194/egusphere-egu23-16449, 2023.

EGU23-16846 | ECS | Orals | ITS1.14/CL5.8

Learning causal drivers of PyroCb 

Emiliano Díaz, Gherardo Varando, Fernando Iglesias-Suarez, Gustau Camps-Valls, Kenza Tazi, Kara Lamb, and Duncan Watson-Parris

Discovering causal relationships from purely observational data is often not possible. In this case, combining observational and experimental data can allow for the identifiability of the underlying causal structure. In Earth Systems sciences, carrying out interventional experiments is often impossible for ethical and practical reasons. However, “natural interventions”, are often present in the data, and these represent regime changes caused by changes to exogenous drivers. In [3,4], the Invariant Causal Prediction (ICP) methodology was presented to identify the causes of a target variable of interest from a set of candidate causes. This methodology takes advantage of natural interventions, resulting in different cause variables distributions across different environments.  In [2] this methodology is implemented in a geoscience problem, namely identifying the causes of Pyrocumulunimbus (pyroCb), and storm clouds resulting from extreme wildfires. Although a set of plausible causes is produced, certain heuristic adaptations to the original ICP methodology were implemented to overcome some of the practical. limitations of ICP: a large number of hypothesis tests required and a failure to identify causes when these have a high degree of interdependence. In this work, we try to circumvent these difficulties by taking a different approach. We use a learning paradigm similar to that presented in [3] to learn causal representations invariant across different environments. Since we often don’t know exactly how to define the different environments best, we also propose to learn functions that describe their spatiotemporal extent. We apply the resulting algorithm to the pyroCb database in [1] and other Earth System sciences datasets to verify the plausibility of the causal representations found and the environments that describe the so-called natural interventions.. 

 

[1] Tazi et al. 2022. https://arxiv.org/abs/2211.13052

[2] Díaz et al. 2022 .https://arxiv.org/abs/2211.08883

[3] Arjovsky et al. 2019. https://arxiv.org/abs/1907.02893

[4] Peters et al.2016.  https://www.jstor.org/stable/4482904

[5] Heinze-Deml et al. 2018. https://doi.org/10.1515/jci-2017-0016

How to cite: Díaz, E., Varando, G., Iglesias-Suarez, F., Camps-Valls, G., Tazi, K., Lamb, K., and Watson-Parris, D.: Learning causal drivers of PyroCb, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16846, https://doi.org/10.5194/egusphere-egu23-16846, 2023.

EGU23-17082 | ECS | Posters on site | ITS1.14/CL5.8

A statistical approach on rapid estimations of climate change indices by monthly instead of daily data 

Kristofer Hasel, Marianne Bügelmayer-Blaschek, and Herbert Formayer

Climate change indices (CCI) defined by the expert team on climate change detection and indices (ETCCDI) profoundly contribute to understanding climate and its change. They are used to present climate change in an easy to understand and tangible way, thus facilitating climate communication. Many of the indices are peak over threshold indices needing daily and, if necessary, bias corrected data to be calculated from. We present a method to rapidly estimate specific CCI from monthly data instead of daily while also performing a simple bias correction as well as a localisation (downscaling). Therefore, we used the ERA5 Land data with a spatial resolution of 0.1° supplemented by a CMIP6 ssp5-8.5 climate projection to derive different regression functions which allow a rapid estimation by monthly data. Using a climate projection as a supplement in training the regression functions allows an application not only on historical periods but also on future periods such as those provided by climate projections. Nevertheless, the presented method can be adapted to any data set, allowing an even higher spatial resolution.

How to cite: Hasel, K., Bügelmayer-Blaschek, M., and Formayer, H.: A statistical approach on rapid estimations of climate change indices by monthly instead of daily data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17082, https://doi.org/10.5194/egusphere-egu23-17082, 2023.

EGU23-17197 | Posters on site | ITS1.14/CL5.8

Machine learning workflow for deriving regional geoclimatic clusters from high-dimensional data 

Sebastian Lehner, Katharina Enigl, and Matthias Schlögl

Geoclimatic regions represent climatic forcing zones, which constitute important spatial entities that serve as a basis for a broad range of analyses in earth system sciences. The plethora of geospatial variables that are relevant for obtaining consistent clusters represent a high-dimensionality, especially when working with high-resolution gridded data, which may render the derivation of such regions complex. This is worsened by typical characteristics of geoclimatic data like multicollinearity, nonlinear effects and potentially complex interactions between features. We therefore present a nonparametric machine learning workflow, consisting of dimensionality reduction and clustering for deriving geospatial clusters of similar geoclimatic characteristics. We demonstrate the applicability of the proposed procedure using a comprehensive dataset featuring climatological and geomorphometric data from Austria, aggregated to the recent climatological normal from 1992 to 2021.
 
The modelling workflow consists of three major sequential steps: (1) linear dimensionality reduction using Principal Component Analysis, yielding a reduced, orthogonal sub-space, (2) nonlinear dimensionality reduction applied to the reduced sub-space using Uniform Manifold Approximation and Projection, and (3) clustering the learned manifold projection via Hierarchical Density-Based Spatial Clustering of Applications with Noise. The contribution of the input features to the cluster result is then assessed by means of permutation feature importance of random forest models. These are trained by treating the clustering result as a supervised classification problem. Results show the flexibility of the defined workflow and exhibit good agreement with both quantitatively derived and synoptically informed characterizations of geoclimatic regions from other studies. However, this flexibility does entail certain challenges with respect to hyperparameter settings, which require careful exploration and tuning. The proposed workflow may serve as a blueprint for deriving consistent geospatial clusters exhibiting similar geoclimatic attributes.

How to cite: Lehner, S., Enigl, K., and Schlögl, M.: Machine learning workflow for deriving regional geoclimatic clusters from high-dimensional data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17197, https://doi.org/10.5194/egusphere-egu23-17197, 2023.

EGU23-17333 | ECS | Posters on site | ITS1.14/CL5.8

Emulating the regional temperature responses (RTPs) of short-lived climate forcers 

Maura Dewey, Hans Christen Hansson, and Annica M. L. Ekman

Here we develop a statistical model emulating the surface temperature response to changes in emissions of short-lived climate forcers as simulated by an Earth system model. Short-lived climate forcers (SLCFs) are chemical components in the atmosphere that interact with radiation and have both an immediate effect on local air quality, and regional and global effects on the climate in terms of changes in temperature and precipitation distributions. The short atmospheric residence times of SLCFs lead to high atmospheric concentrations in emission regions and a highly variable radiative forcing pattern. Regional Temperature Potentials (RTPs) are metrics which quantify the impact of emission changes in a given region on the temperature or forcing response of another, accounting for spatial inhomogeneities in both forcing and the temperature response, while being easy to compare across models and to use in integrated assessment studies or policy briefs. We have developed a Gaussian-process emulator using output from the Norwegian Earth System Model (NorESM) to predict the temperature responses to regional emission changes in SLCFs (specifically back carbon, organic carbon, sulfur dioxide, and methane) and use this model to calculate regional RTPs and study the sensitivity of surface temperature in a certain region, e.g. the Arctic, to anthropogenic emission changes in key policy regions. The main challenge in developing the emulator was creating the training data set such that we included maximal SLCF variability in a realistic and policy relevant range compared to future emission scenarios, while also getting a significant temperature response. We also had to account for the confounding influence of greenhouse gases (GHG), which may not follow the same future emission trajectories as SLCFs and can overwhelm the more subtle temperature response that comes from the direct and indirect effects of SLCF emissions. The emulator can potentially provide accurate and customizable predictions for policy makers to proposed emission changes with minimized climate impact.

How to cite: Dewey, M., Hansson, H. C., and Ekman, A. M. L.: Emulating the regional temperature responses (RTPs) of short-lived climate forcers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17333, https://doi.org/10.5194/egusphere-egu23-17333, 2023.

EGU23-582 | ECS | Posters on site | ITS1.13/AS5.2

Modeling the Variability of Terrestrial Carbon Fluxes using Transformers 

Swarnalee Mazumder and Ayush Prasad

The terrestrial carbon cycle is one of the largest sources of uncertainty in climate projections. The terrestrial carbon sink which removes a quarter of anthropogenic CO2 emissions; is highly variable in time and space depending on climate. Previous studies have found that data-driven models such as random forest, artificial neural networks and long short-term memory networks can be used to accurately model Net Ecosystem Exchange (NEE) and Gross Primary Productivity (GPP) accurately, which are two important metrics to quantify the direction and magnitude of CO2 transfer between the land surface and the atmosphere. Recently, a new class of machine learning models called transformers have gained widespread attention in natural language processing tasks due to their ability to learn from large volumes of sequential data. In this work, we use Transformers to model NEE and GPP from 1996-2022 at 39 Flux stations in the ICOS Europe network using ERA5 reanalysis data. We can compare our results with traditional machine learning approaches to evaluate the generalisability and predictive performance of transformers for carbon flux modelling.

How to cite: Mazumder, S. and Prasad, A.: Modeling the Variability of Terrestrial Carbon Fluxes using Transformers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-582, https://doi.org/10.5194/egusphere-egu23-582, 2023.

EGU23-1825 | ECS | Orals | ITS1.13/AS5.2

Spatial representation learning for ensemble weather simulations using invariant variational autoencoders 

Jieyu Chen, Kevin Höhlein, and Sebastian Lerch

Weather forecasts today are typically issued in the form of ensemble simulations based on multiple runs of numerical weather prediction models with different perturbations in the initial states and the model physics. In light of the continuously increasing spatial resolutions of operational weather models, this results in large, high-dimensional datasets that nonetheless contain relevant spatial and temporal structure, as well as information about the predictive uncertainty. We propose invariant variational autoencoder (iVAE) models based on convolutional neural network architectures to learn low-dimensional representations of the spatial forecast fields. We specifically aim to account for the ensemble character of the input data and discuss methodological questions about the optimal design of suitable dimensionality reduction methods in this setting. Thereby, our iVAE models extend previous work where low-dimensional representations of single, deterministic forecast fields were learned and utilized for incorporating spatial information into localized ensemble post-processing methods based on neural networks [1], which were able to improve upon model utilizing location-specific inputs only [2]. By additionally incorporating the ensemble dimension and learning representation for probability distributions of spatial fields, we aim to enable a more flexible modeling of relevant predictive information contained in the full forecast ensemble. Additional potential applications include data compression and the generation of forecast ensembles of arbitrary size.

We illustrate our methodological developments based on a 10-year dataset of gridded ensemble forecasts from the European Centre for Medium-Range Weather Forecasts of several meteorological variables over Europe. Specifically, we investigate alternative model architectures and highlight the importance of tailoring the loss function to the specific problem at hand.

References:

[1] Lerch, S. & Polsterer, K.L. (2022). Convolutional autoencoders for spatially-informed ensemble post-processing. ICLR 2022 AI for Earth and Space Science Workshop, https://arxiv.org/abs/2204.05102.

[2] Rasp, S. & Lerch, S. (2018). Neural networks for post-processing ensemble weather forecasts. Monthly Weather Review, 146, 3885-3900.

How to cite: Chen, J., Höhlein, K., and Lerch, S.: Spatial representation learning for ensemble weather simulations using invariant variational autoencoders, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1825, https://doi.org/10.5194/egusphere-egu23-1825, 2023.

EGU23-3117 | Orals | ITS1.13/AS5.2

AtmoRep: Large Scale Representation Learning for Atmospheric Data 

Christian Lessig, Ilaria Luise, and Martin Schultz

The AtmoRep project asks if one can train one neural network that represents and describes all atmospheric dynamics. AtmoRep’s ambition is hence to demonstrate that the concept of large-scale representation learning, whose principle feasibility and potential was established by large language models such as GPT-3, is also applicable to scientific data and in particular to atmospheric dynamics. The project is enabled by the large amounts of atmospheric observations that have been made in the past as well as advances on neural network architectures and self-supervised learning that allow for effective training on petabytes of data. Eventually, we aim to train on all of the ERA5 reanalysis and, furthermore, fine tune on observational data such as satellite measurements to move beyond the limits of reanalyses.

We will present the theoretical formulation of AtmoRep as an approximate representation for the atmosphere as a stochastic dynamical system. We will also detail our transformer-based network architecture and the training protocol for self-supervised learning so that unlabelled data such as reanalyses, simulation outputs and observations can be employed for training and re-fining the network. Results will be presented for the performance of AtmoRep for downscaling, precipitation forecasting, the prediction of tropical convection initialization, and for model correction. Furthermore, we also demonstrate that AtmoRep has substantial zero-short skill, i.e., it is capable to perform well on tasks it was not trained for. Zero- and few-shot performance (or in context learning) is one of the hallmarks of large-scale representation learning and to our knowledge has never been demonstrated in the geosciences.

How to cite: Lessig, C., Luise, I., and Schultz, M.: AtmoRep: Large Scale Representation Learning for Atmospheric Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3117, https://doi.org/10.5194/egusphere-egu23-3117, 2023.

Numerical Earth system models (ESMs) are our primary tool for projecting future climate scenarios. Their simulation output is used by impact models that assess the effect of anthropogenic global warming, e.g., on flood events, vegetation changes or crop yields. Precipitation, an atmospheric variable with arguably one of the largest socio-economic impacts, involves various processes on a wide range of spatial-temporal scales. However, these cannot be completely resolved in ESMs due to the limited discretization of the numerical model. 
This can lead to biases in the ESM output that need to be corrected in a post-processing step prior to feeding ESM output into impact models, which are calibrated with observations [1]. While established post-processing methods successfully improve the modelled temporal statistics for each grid cell individually, unrealistic spatial features that require a larger spatial context are not addressed.
Here, we apply a cycle-consistent generative adversarial network (CycleGAN) [2] that is physically constrained to the precipitation output from Coupled Model Intercomparison Project phase 6 (CMIP6)  ESMs to correct both temporal distributions and spatial patterns. The CycleGAN can be naturally trained on daily ESM and reanalysis fields that are unpaired due to the deviating trajectories of the ESM and observation-based ground truth. 
We evaluate our method against a state-of-the-art bias adjustment framework (ISIMIP3BASD) [3] and find that it outperforms it in correcting spatial patterns and achieves comparable results on temporal distributions. We further discuss the representation of extreme events and suitable metrics for quantifying the realisticness of unpaired precipitation fields.

 [1] Cannon, A.J., et al. "Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?." Journal of Climate 28.17 (2015): 6938-6959.

[2] Zhu, J.-Y., et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." Proceedings of the IEEE international conference on computer vision. 2017.

[3] Lange, S. "Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0)." Geoscientific Model Development 12.7 (2019): 3055-3070.

How to cite: Hess, P., Lange, S., and Boers, N.: Improving global CMIP6 Earth system model precipitation output with generative adversarial networks for unpaired image-to-image translation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3128, https://doi.org/10.5194/egusphere-egu23-3128, 2023.

EGU23-3256 | Orals | ITS1.13/AS5.2

Emulating radiative transfer in a numerical weather prediction model 

Matthew Chantry, Peter Ukkonen, Robin Hogan, and Peter Dueben

Machine learning, and particularly neural networks, have been touted as a valuable accelerator for physical processes. By training on data generated from an existing algorithm a network may theoretically learn a more efficient representation and accelerate the computations via emulation. For many parameterized physical processes in weather and climate models this being actively pursued. Here, we examine the value of this approach for radiative transfer within the IFS, an operational numerical weather prediction model where both accuracy and speed are vital. By designing custom, physics-informed, neural networks we achieve outstanding offline accuracy for both longwave and shortwave processes. In coupled testing we find minimal changes to forecast scores at near operational resolutions. We carry out coupled inference on GPUs to maximise the speed benefits from the emulator approach.

How to cite: Chantry, M., Ukkonen, P., Hogan, R., and Dueben, P.: Emulating radiative transfer in a numerical weather prediction model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3256, https://doi.org/10.5194/egusphere-egu23-3256, 2023.

EGU23-3321 | ECS | Orals | ITS1.13/AS5.2

Using machine learning to improve dynamical predictions in a coupled model 

Zikang He, Julien Brajard, Yiguo Wang, Xidong Wang, and Zheqi Shen

Dynamical models used in climate prediction often have systematic errors that can bias the predictions. In this study, we utilized machine learning to address this issue. Machine learning was applied to learn the error corrected by data assimilation and thus build a data-driven model to emulate the dynamical model error. A hybrid model was constructed by combining the dynamical and data-driven models. We tested the hybrid model using synthetic observations generated by a simplified high-resolution coupled ocean-atmosphere model (MAOOAM, De Cruz et al., 2016) and compared its performance to that of a low-resolution version of the same model used as a standalone dynamical model.

To evaluate the forecast skill of the hybrid model, we produced ensemble predictions based on initial conditions determined through data assimilation. The results show that the hybrid model significantly improves the forecast skill for both atmospheric and oceanic variables compared to the dynamical model alone. To explore what affects short-term forecast skills and long-term forecast skills, we built two other hybrid models by correcting errors either only atmospheric or only oceanic variables. For short-term atmospheric forecasts, the results show that correcting only oceanic errors has no effect on atmosphere variables forecasts but correcting only atmospheric variables shows similar forecast skill to correcting both atmospheric and oceanic errors. For the long-term forecast of oceanic variables, correcting the oceanic error can improve the forecast skill, but correcting both atmospheric and oceanic errors can obtain the best forecast skill. The results indicate that for the long-term forecast of oceanic variables, bias correction of both oceanic and atmospheric components can have a significant effect.

How to cite: He, Z., Brajard, J., Wang, Y., Wang, X., and Shen, Z.: Using machine learning to improve dynamical predictions in a coupled model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3321, https://doi.org/10.5194/egusphere-egu23-3321, 2023.

EGU23-3340 | ECS | Orals | ITS1.13/AS5.2

An iterative data-driven emulator of an ocean general circulation model 

Rachel Furner, Peter Haynes, Dan(i) Jones, Dave Munday, Brooks Paige, and Emily Shuckburgh

Data-driven models are becoming increasingly competent at tasks fundamental to weather and climate prediction. Relative to machine learning (ML) based atmospheric models, which have shown promise in short-term forecasting, ML-based ocean forecasting remains somewhat unexplored. In this work, we present a data-driven emulator of an ocean GCM and show that performance over a single predictive step is skilful across all variables under consideration. Iterating such data-driven models poses additional challenges, with many models suffering from over-smoothing of fields or instabilities in the predictions. We compare a variety of methods for iterating our data-driven emulator and assess them by looking at how well they agree with the underlying GCM in the very short term and how realistic the fields remain for longer-term forecasts. Due to the chaotic nature of the system being forecast, we would not expect any model to agree with the GCM accurately over long time periods, but instead we expect fields to continue to exhibit physically realistic behaviour at ever increasing lead times. Specifically, we expect well-represented fields to remain stable whilst also maintaining the presence and sharpness of features seen in both reality and in GCM predictions, with reduced emphasis on accurately representing the location and timing of these features. This nuanced and temporally changing definition of what constitutes a ‘good’ forecast at increasing lead times generates questions over both (1) how one defines suitable metrics for assessing data-driven models, and perhaps more importantly, (2) identifying the most promising loss functions to use to optimise these models.

How to cite: Furner, R., Haynes, P., Jones, D., Munday, D., Paige, B., and Shuckburgh, E.: An iterative data-driven emulator of an ocean general circulation model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3340, https://doi.org/10.5194/egusphere-egu23-3340, 2023.

EGU23-4337 | Orals | ITS1.13/AS5.2 | Highlight

Towards a new surrogate model for predicting short-term NOx-O3 effects from aviation using Gaussian processes 

Pratik Rao, Richard Dwight, Deepali Singh, Jin Maruhashi, Irene Dedoussi, Volker Grewe, and Christine Frömming

While efforts have been made to curb CO2 emissions from aviation, the more uncertain non-CO2 effects that contribute about two-thirds to the warming in terms of radiative forcing (RF), still require attention. The most important non-CO2 effects include persistent line-shaped contrails, contrail-induced cirrus clouds and nitrogen oxide (NOx) emissions that alter the ozone (O3) and methane (CH4) concentrations, both of which are greenhouse gases, and the emission of water vapour (H2O). The climate impact of these non-CO2 effects depends on emission location and prevailing weather situation; thus, it can potentially be reduced by advantageous re-routing of flights using Climate Change Functions (CCFs), which are a measure for the climate effect of a locally confined aviation emission. CCFs are calculated using a modelling chain starting from the instantaneous RF (iRF) measured at the tropopause that results from aviation emissions. However, the iRF is a product of computationally intensive chemistry-climate model (EMAC) simulations and is currently restricted to a limited number of days and only to the North Atlantic Flight Corridor. This makes it impossible to run EMAC on an operational basis for global flight planning. A step in this direction lead to a surrogate model called algorithmic Climate Change Functions (aCCFs), derived by regressing CCFs (training data) against 2 or 3 local atmospheric variables at the time of emission (features) with simple regression techniques and are applicable only in parts of the Northern hemisphere. It was found that in the specific case of O3 aCCFs, which provide a reasonable first estimate for the short-term impact of aviation NOx on O3 warming using temperature and geopotential as features, can be vastly improved [1]. There is aleatoric uncertainty in the full-order model (EMAC), stemming from unknown sources (missing features) and randomness in the known features, which can introduce heteroscedasticity in the data. Deterministic surrogates (e.g. aCCFs) only predict point estimates of the conditional average, thereby providing an incomplete picture of the stochastic response. Thus, the goal of this research is to build a new surrogate model for iRF, which is achieved by :

1. Expanding the geographical coverage of iRF (training data) by running EMAC simulations in more regions (North & South America, Eurasia, Africa and Australasia) at multiple cruise flight altitudes,

2. Following an objective approach to selecting atmospheric variables (feature selection) and considering the importance of local as well as non-local effects,

3. Regressing the iRF against selected atmospheric variables using supervised machine learning techniques such as homoscedastic and heteroscedastic Gaussian process regression.

We present a new surrogate model that predicts iRF of aviation NOx-O3 effects on a regular basis with confidence levels, which not only improves our scientific understanding of NOx-O3 effects, but also increases the potential of global climate-optimised flight planning.

References

[1] Rao, P.; et al. Case Study for Testing the Validity of NOx-Ozone Algorithmic Climate Change Functions for Optimising Flight Trajectories. Aerospace 20229, 231. https://doi.org/10.3390/aerospace9050231

How to cite: Rao, P., Dwight, R., Singh, D., Maruhashi, J., Dedoussi, I., Grewe, V., and Frömming, C.: Towards a new surrogate model for predicting short-term NOx-O3 effects from aviation using Gaussian processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4337, https://doi.org/10.5194/egusphere-egu23-4337, 2023.

Time transfer functions describe the change of state variables over time in geoscientific numerical simulation models. The identification of these functions is an essential but challenging step in model building. While traditional methods rely on qualitative understanding or first order principles, the availability of large spatio-temporal data sets from direct measurements or extremely detailed physical-based system modelling has enabled the use of machine learning methods to discover the time transfer function directly from data. In this study we explore the feasibility of this data driven approach for numerical simulation of the co-evolution of soil, hydrology, vegetation, and grazing on landscape scale, at geological timescales. From empirical observation and hyper resolution (1 m, 1 week) modelling (Karssenberg et al, 2017) it has been shown that a hillslope system shows complex behaviour with two stable states, respectively high biomass on deep soils (healthy state) and low biomass on thin soils (degraded or desertic state). A catastrophic shift from healthy to degraded state occurs under changes of external forcing (climate, grazing pressure), with a transient between states that is rapid or slow depending on system characteristics. To identify and use the time transfer functions of this system at hillslope scale we follow four procedural steps. First, an extremely large data set of hillslope average soil and vegetation state is generated by a mechanistic hyper resolution (1 m, 1 week) system model, forcing it with different variations in grazing pressure over time. Secondly, a machine learning model predicting the rate of change in soil and vegetation as function of soil, vegetation, and grazing pressure, is trained on this data set. In the third step, we explore the ability of this trained machine learning model to predict the rate of system change (soil and vegetation) on untrained data. Finally, in the fourth step, we use the trained machine learning model as time transfer function in a forward numerical simulation of a hillslope to determine whether it is capable of representing the known complex behaviour of the system. Our findings are that the approach is in principle feasible. We compared the use of a deep neural network and a random forest. Both can achieve great fitting precision, although the latter performs much faster and requires less training data. Even though the machine learning based time transfer function shows differences in the rates of change in system state from those calculated using expert knowledge in Karssenberg et al. (2017), forward simulation appeared to be possible with system behaviour generally in line with that observed in the data from the hyper resolution model. Our findings indicate that discovery of time transfer functions from data is possible. Next steps need to involve the use observational data (e.g., from remote sensing) to test the approach using data from real-world systems.

 

Karssenberg, D., Bierkens, M.F.P., Rietkerk, M., Catastrophic Shifts in Semiarid Vegetation-Soil Systems May Unfold Rapidly or Slowly. The American Naturalist 2017. Vol. 190, pp. E145–E155.

How to cite: Pomarol Moya, O. and Karssenberg, D.: Machine learning for data driven discovery of time transfer functions in numerical modelling: simulating catastrophic shifts in vegetation-soil systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4454, https://doi.org/10.5194/egusphere-egu23-4454, 2023.

EGU23-4695 | Posters on site | ITS1.13/AS5.2

Development of PBL Parameterization Emulator using Neural Networks 

Jiyeon Jang, Tae-Jin Oh, Sojung An, Wooyeon Park, Inchae Na, and Junghan Kim

Physical parameterization is one of the major components of Numerical Weather Prediction system. In Korean Integrated Model (KIM), physical parameterizations account for about 30 % of the total computation time. There are many studies of developing neural network based emulators to replace and accelerate physics based parameterization. In this study, we develop a planetary boundary layer(PBL) emulator which is based on Shin-Hong (Hong et al., 2006, 2010; Shin and Hong, 2013, 2015) scheme that computes the parameterized effects of vertical turbulent eddy diffusion of momentum, water vapor, and sensible heat fluxes. We compare the emulator performance with Multi-Layer Perceptron (MLP) based architectures: simple MLP, MLP application version, and MLP-mixer(Tolstikhin et al., 2021). MLP application version divides data into several vertical groups for better approximation of each vertical group layers. MLP-mixer is MLP based architecture that performs well in computer vision without using convolution and self-attention. We evaluate the resulting MLP based emulator performance. MLP application version and MLP-mixer showed significant performance improvement over simple MLP.

How to cite: Jang, J., Oh, T.-J., An, S., Park, W., Na, I., and Kim, J.: Development of PBL Parameterization Emulator using Neural Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4695, https://doi.org/10.5194/egusphere-egu23-4695, 2023.

EGU23-4817 | ECS | Posters on site | ITS1.13/AS5.2

Algorithmic optimisation of key parameters of OpenIFS 

Lauri Tuppi, Madeleine Ekblom, Pirkka Ollinaho, and Heikki Järvinen

Numerical weather prediction models contain parameters that are inherently uncertain and cannot be determined exactly. Traditionally, the parameter tuning has been done manually, which can be an extremely labourious task. Tuning the entire model usually requires adjusting a relatively large amount of parameters. In case of manual tuning, the need to balance a number of requirements at the same time can lead the tuning process being a maze of subjective choices. It is, therefore, desirable to have reliable objective approaches for estimation of optimal values and uncertainties of these parameters. In this presentation we present how to optimise 20 key physical parameters having a strong impact on forecast quality. These parameters belong to the Stochastically Perturbed Parameters Scheme in the atmospheric model Open Integrated Forecasting System.

The results show that simultaneous optimisation of O(20) parameters is possible with O(100) algorithm steps using an ensemble of O(20) members, and that the optimised parameters lead to substantial enhancement of predictive skill. The enhanced predictive skill can be attributed to reduced biases in low-level winds and upper-tropospheric humidity in the optimised model. We find that the optimisation process is dependent on the starting values of the parameters that are optimised (starting from better suited values results in a better model). The results also show that the applicability of the tuned parameter values across different model resolutions is somewhat questionable since the model biases seem to be resolution-specific. Moreover, our optimisation algorithm tends to treat the parameter covariances poorly limiting its ability to converge to the global optimum.

How to cite: Tuppi, L., Ekblom, M., Ollinaho, P., and Järvinen, H.: Algorithmic optimisation of key parameters of OpenIFS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4817, https://doi.org/10.5194/egusphere-egu23-4817, 2023.

EGU23-5003 | ECS | Posters on site | ITS1.13/AS5.2

Towards machine-learning calibration of cloud parameters in the kilometre-resolution ICON atmosphere model 

Hannah Marie Eichholz, Jan Kretzschmar, Duncan Watson-Parris, Josefine Umlauft, and Johannes Quaas

In the preparation of the global kilometre-resolution coupled ICON climate model, it is necessary to calibrate cloud microphysical parameters. Here we explore the avenue towards optimally calibrating such parameters using machine learning. The emulator developed by Watson-Parris et al. (2021) is employed in combination with a perturbed-parameter ensemble of limited-area atmosphere-only ICON simulations for the North Atlantic ocean. In a first step, the autoconversion scaling parameter is calibrated, using satellite-retrieved top-of-atmosphere and bottom-of-atmosphere radiation fluxes. For this purpose, limited area simulations of the north atlantic are performed with ICON. In which different cloud microphysical parameters are changed, in order to evaluate possible influences on the output of radiation fluxes.

How to cite: Eichholz, H. M., Kretzschmar, J., Watson-Parris, D., Umlauft, J., and Quaas, J.: Towards machine-learning calibration of cloud parameters in the kilometre-resolution ICON atmosphere model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5003, https://doi.org/10.5194/egusphere-egu23-5003, 2023.

EGU23-5149 | ECS | Posters on site | ITS1.13/AS5.2

Machine Learning Parameterization for Super-droplet Cloud Microphysics Scheme 

Shivani Sharma and David Greenberg

Machine learning approaches have been widely used for improving the representation of subgrid scale parameterizations in Earth System Models. In our study we target the Cloud Microphysics parameterization, in particular the two-moment bulk scheme of the ICON (Icosahedral Non-hydrostatic) Model. 

 

Cloud microphysics parameterization schemes suffer from an accuracy/speed tradeoff. The simplest schemes, often heavy with assumptions (such as the bulk moment schemes) are most common in operational weather prediction models. Conversely, the more complex schemes with fewer assumptions –e.g. Lagrangian schemes such as the super-droplet method (SDM)– are computationally expensive and used only within research and development. SDM allows easy representation of complex scenarios with multiple hydrometeors and can also be used for simulating cloud-aerosol interactions. To bridge this gap and to make the use of more complex microphysical schemes feasible within operational models, we use a data-driven approach. 

 

Here we train a neural network to mimic the behavior of SDM simulations in a warm-rain scenario in a dimensionless control volume. The network behaves like a dynamical system that converts cloud droplets to rain droplets–represented as bulk moments–with only the current system state as the input. We use a multi-step training loss to stabilize the network over long integration periods, especially in cases with extremely low cloud water to start with. We find that the network is stable across various initial conditions and in many cases, emulates the SDM simulations better than the traditional bulk moment schemes. Our network also performs better than any previous ML-based attempts to learn from SDM. This opens the possibility of using the trained network as a proxy for imitating the computationally expensive SDM within operational weather prediction models with minimum computational overhead. 

How to cite: Sharma, S. and Greenberg, D.: Machine Learning Parameterization for Super-droplet Cloud Microphysics Scheme, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5149, https://doi.org/10.5194/egusphere-egu23-5149, 2023.

EGU23-5523 | ECS | Orals | ITS1.13/AS5.2

Using weak constrained neural networks to improve simulations in the gray zone 

Yvonne Ruckstuhl, Raphael Kriegmair, Stephan Rasp, and George Craig

Machine learning represents a potential method to cope with the gray zone problem of representing motions in dynamical systems on scales comparable to the model resolution. Here we explore the possibility of using a neural network to directly learn the error caused by unresolved scales. We use a modified shallow water model which includes highly nonlinear processes mimicking atmospheric convection. To create the training dataset, we run the model in a high- and a low-resolution setup and compare the difference after one low-resolution time step, starting from the same initial conditions, thereby obtaining an exact target. The neural network is able to learn a large portion of the difference when evaluated on single time step predictions on a validation dataset. When coupled to the low-resolution model, we find large forecast improvements up to 1 d on average. After this, the accumulated error due to the mass conservation violation of the neural network starts to dominate and deteriorates the forecast. This deterioration can effectively be delayed by adding a penalty term to the loss function used to train the ANN to conserve mass in a weak sense. This study reinforces the need to include physical constraints in neural network parameterizations.

How to cite: Ruckstuhl, Y., Kriegmair, R., Rasp, S., and Craig, G.: Using weak constrained neural networks to improve simulations in the gray zone, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5523, https://doi.org/10.5194/egusphere-egu23-5523, 2023.

EGU23-5766 | ECS | Orals | ITS1.13/AS5.2

Best Practices for Fortran-Python Bridges to Integrate Neural Networks in Earth System Models 

Caroline Arnold, Shivani Sharma, Tobias Weigel, and David Greenberg

In recent years, machine learning (ML) based parameterizations have become increasingly common in Earth System Models (ESM). Sub-grid scale physical processes that would be computationally too expensive, e.g., atmospheric chemistry and cloud microphysics, can be emulated by ML algorithms such as neural networks.

Neural networks are trained first on simulations of the sub-grid scale process that is to be emulated. They are then used in so-called inference mode to make predictions during the ESM run, replacing the original parameterization. Training usually requires GPUs, while inference may be done on CPU architectures.

At first, neural networks are evaluated offline, i.e., independently of the ESM on appropriate datasets. However, their performance can ultimately only be evaluated in an online setting, where the ML algorithm is coupled to the ESM, including nonlinear interactions.

We want to shorten the time spent in neural network development and offline testing and move quickly to online evaluation of ML components in our ESM of choice, ICON (Icosahedral Nonhydrostatic Weather and Climate Model). Since ICON is written in Fortran, and modern ML algorithms are developed in the Python ecosystem, this requires efficient bridges between the two programming languages. The Fortran-Python bridge must be flexible to allow for iterative development of the neural network. Changes to the ESM codebase should be as few as possible, and the runtime overhead should not limit development.

In our contribution we explore three strategies to call the neural network inference from within Fortran using (i) embedded Python code compiled in a dynamic library, (ii) pipes, and (iii) MPI using the ICON coupler YAC. We provide quantitative benchmarks for the proposed Fortran-Python bridges and assess their overall suitability in a qualitative way to derive best practices. The Fortran-Python bridge enables scientists and developers to evaluate ML components in an online setting, and can be extended to other parameterizations and ESMs.

How to cite: Arnold, C., Sharma, S., Weigel, T., and Greenberg, D.: Best Practices for Fortran-Python Bridges to Integrate Neural Networks in Earth System Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5766, https://doi.org/10.5194/egusphere-egu23-5766, 2023.

EGU23-6287 | Orals | ITS1.13/AS5.2

Approximation and Optimization of Atmospheric Simulations in High Spatio-Temporal Resolution with Neural Networks 

Elnaz Azmi, Jörg Meyer, Marcus Strobl, Michael Weimer, and Achim Streit

Accurate forecasts of the atmosphere demand large-scale simulations with high spatio-temporal resolution. Atmospheric chemistry modeling, for example, usually requires solving a system of hundreds of coupled ordinary partial differential equations. Due to the computational complexity, large high performance computing resources are required, which is a challenge as the spatio-temporal resolution increases. Machine learning methods and specially deep learning can offer an approximation of the simulations with some factor of speed-up while using less compute resources. The goal of this study is to investigate the feasibility, opportunities but also challenges and pitfalls of replacing the compute-intensive chemistry of a state-of-the-art atmospheric chemistry model with a trained neural network model to forecast the concentration of trace gases at each grid cell and to reduce the computational complexity of the simulation. In this work, we introduce a neural network model (ICONET) to forecast trace gas concentrations without executing the traditional compute-intensive atmospheric simulations. ICONET is equipped with a multifeature Long Short Term Memory (LSTM) model to forecast atmospheric chemicals iteratively in time. We generated the training and test dataset, our ground truth for ICONET, by execution of an atmospheric chemistry simulation in ICON-ART. Applying the ICONET trained model to forecast a test dataset results in a good fit of the forecast values compared to our ground truth dataset. We discuss appropriate metrics to evaluate the quality of models and present the quality of the ICONET forecasts with RMSE and KGE metrics. The variety in the nature of trace gases limits the model's learning and forecast skills according to the variable. In addition to the quality of the ICONET forecasts, we described the computational efficiency of ICONET as its run time speed-up in comparison to the run time of the ICON-ART simulation. The ICONET forecast showed a speed-up factor of 3.1 over the run time of the atmospheric chemistry simulation of ICON-ART, which is a significant achievement, especially when considering the importance of ensemble simulations.

How to cite: Azmi, E., Meyer, J., Strobl, M., Weimer, M., and Streit, A.: Approximation and Optimization of Atmospheric Simulations in High Spatio-Temporal Resolution with Neural Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6287, https://doi.org/10.5194/egusphere-egu23-6287, 2023.

EGU23-6836 | ECS | Posters on site | ITS1.13/AS5.2

Parameterising melt at the base of Antarctic ice shelves with a feedforward neural network 

Clara Burgard, Nicolas C. Jourdain, Pierre Mathiot, and Robin Smith

One of the largest sources of uncertainty when projecting the Antarctic contribution to sea-level rise is the ocean-induced melt at the base of Antarctic ice shelves. This is because resolving the ocean circulation and the ice-ocean interactions occurring in the cavity below the ice shelves is computationally expensive.

Instead, for large ensembles and long-term projections of the ice-sheet evolution, ice-sheet models currently rely on parameterisations to link the ocean temperature and salinity in front of ice shelves to the melt at their base. However, current physics-based parameterisations struggle to accurately simulate basal melt patterns.

As an alternative approach, we explore the potential use of a deep feedforward neural network as a basal melt parameterisation. To do so, we train a neural network to emulate basal melt rates simulated by highly-resolved circum-Antarctic ocean simulations. We explore the influence of different input variables and show that the neural network struggles to generalise to ice-shelf geometries unseen during training, while it generalises better on timesteps unseen during training. We also test the parameterisation on separate coupled ocean-ice simulations to assess the neural network’s performance on independent data.  

How to cite: Burgard, C., Jourdain, N. C., Mathiot, P., and Smith, R.: Parameterising melt at the base of Antarctic ice shelves with a feedforward neural network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6836, https://doi.org/10.5194/egusphere-egu23-6836, 2023.

EGU23-7281 | ECS | Posters on site | ITS1.13/AS5.2

Neural network surrogate models for multiple scattering: Application to OMPS LP simulations 

Michael Himes, Natalya Kramarova, Tong Zhu, Jungbin Mok, Matthew Bandel, Zachary Fasnacht, and Robert Loughman

Retrieving ozone from limb measurements necessitates the modeling of scattered light through the atmosphere.  However, accurately modeling multiple scattering (MS) during retrieval requires excessive computational resources; consequently, operational retrieval models employ approximations in lieu of the full MS calculation.  Here we consider an alternative MS approximation method, where we use radiative transfer (RT) simulations to train neural network models to predict the MS radiances.  We present our findings regarding the best-performing network hyperparameters, normalization schemes, and input/output data structures.  Using RT calculations based on measurements by the Ozone Mapping and Profiling Suite's Limb Profiler (OMPS/LP), we compare the accuracy of these neural-network models with both the full MS calculation as well as the current MS approximation methods utilized during OMPS/LP retrievals.

How to cite: Himes, M., Kramarova, N., Zhu, T., Mok, J., Bandel, M., Fasnacht, Z., and Loughman, R.: Neural network surrogate models for multiple scattering: Application to OMPS LP simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7281, https://doi.org/10.5194/egusphere-egu23-7281, 2023.

EGU23-7368 | ECS | Posters on site | ITS1.13/AS5.2

Comparison of Methods for Learning Differential Equations from Data 

Christof Schötz

Some results from the DEEB (Differential Equation Estimation Benchmark) are presented. In DEEB, we compare different machine learning approaches and statistical methods for estimating nonlinear dynamics from data. Such methods constitute an important building block for purely data-driven earth system models as well as hybrid models which combine physical knowledge with past observations.

Specifically, we examine approaches for solving the following problem: Given time-state-observations of a deterministic ordinary differential equation (ODE) with measurement noise in the state, predict the future evolution of the system. Of particular interest are systems with chaotic behavior - like Lorenz 63 - and nonparametric settings, in which the functional form of the ODE is completely unknown (in particular, not restricted to a polynomial of low order). To create a fair comparison of methods, a benchmark database was created which includes datasets of simulated observations from different dynamical systems with different complexity and varying noise levels. The list of methods we compare includes: echo state networks, Gaussian processes, Neural ODEs, SINDy, thin plate splines, and more.

Although some methods consistently perform better than others throughout different datasets, there seems to be no silver bullet.

How to cite: Schötz, C.: Comparison of Methods for Learning Differential Equations from Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7368, https://doi.org/10.5194/egusphere-egu23-7368, 2023.

EGU23-7391 | ECS | Posters on site | ITS1.13/AS5.2

Learning fluid dynamical statistics using stochastic neural networks 

Martin Brolly
Many practical problems in fluid dynamics demand an empirical approach, where statistics estimated from data inform understanding and modelling. In this context data-driven probabilistic modelling offers an elegant alternative to ad hoc estimation procedures. Probabilistic models are useful as emulators, but also offer an attractive means of estimating particular statistics of interest. In this paradigm one can rely on proper scoring rules for model comparison and validation, and invoke Bayesian statistics to obtain rigorous uncertainty quantification. Stochastic neural networks provide a particularly rich class of probabilistic models, which, when paired with modern optimisation algorithms and GPUs, can be remarkably efficient. We demonstrate this approach by learning the single particle transition density of ocean surface drifters from decades of Global Drifter Program observations using a Bayesian mixture density network. From this we derive maps of various displacement statistics and corresponding uncertainty maps. Our model also offers a means of simulating drifter trajectories as a discrete-time Markov process, which could be used to study the transport of plankton or plastic in the upper ocean.

How to cite: Brolly, M.: Learning fluid dynamical statistics using stochastic neural networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7391, https://doi.org/10.5194/egusphere-egu23-7391, 2023.

EGU23-7492 | Posters on site | ITS1.13/AS5.2

Machine Learning and Microseism as a Tool for Sea Wave Monitoring 

Flavio Cannavo', Vittorio Minio, Susanna Saitta, Salvatore Alparone, Alfio Marco Borzì, Andrea Cannata, Giuseppe Ciraolo, Danilo Contrafatto, Sebastiano D’Amico, Giuseppe Di Grazia, and Graziano Larocca

Monitoring the state of the sea is a fundamental task for economic activities in the coastal zone, such as transport, tourism and infrastructure design. In recent years, regular wave height monitoring for marine risk assessment and mitigation has become unavoidable as global warming impacts in more intense and frequent swells.
In particular, the Mediterranean Sea has been considered as one of the most responsive regions to global warming, which may promote the intensification of hazardous natural phenomena as strong winds, heavy precipitation and high sea waves. Because of the high density population along the Mediterranean coastlines, heavy swells could have major socio-economic consequences. To reduce the impacts of such scenarios, the development of more advanced monitoring systems of the sea state becomes necessary.
In the last decade, it has been demonstrated how seismometers can be used to measure sea conditions by exploiting the characteristics of a part of the seismic signal called microseism. Microseism is the continuous seismic signal recorded in the frequency band of 0.05 and 0.4 Hz that is likely generated by interactions of sea waves together and with seafloor or shorelines.
In this work, in the framework of i-WaveNET INTERREG project, we performed a regression analysis to develop a model capable of predicting the sea state in the Sicily Channel (Italy) using microseism, acquired by onshore instruments installed in Sicily and Malta. Considering the complexity of the relationship between spatial sea wave height data and seismic data measured at individual stations, we used supervised machine learning (ML) techniques to develop the prediction model. As input data we used the hourly Root Mean Squared (RMS) amplitude of the seismic signal recorded by 14 broadband stations, along the three components, and in different frequency bands, during 2018 - 2021. These stations, belonging to the permanent seismic networks managed by the National Institute of Geophysics and Volcanology INGV and the Department of Geosciences of the University of Malta, consist of three-component broadband seismometers that record at a sampling frequency of 100 Hz.
As for the target, the significant sea wave height data from Copernicus Marine Environment Monitoring Service (CMEMS) for the same period were used. Such data is the hindcast product of the Mediterranean Sea Waves forecasting system, with hourly temporal resolution and 1/24° spatial resolution. After a feature selection step, we compared three different kinds of ML algorithms for regression: K-Nearest-Neighbors (KNN), Random Forest (RF) and Light Gradient Boosting (LGB). The hyperparameters were tuned by using a grid-search algorithm, and the best models were selected by cross-validation.  Different metrics, such as MAE, R2 and RMSE, were considered to evaluate the generalization capabilities of the models and special attention was paid to evaluate the predictive ability of the models for extreme wave height values.
Results show model predictive capabilities good enough to develop a sea monitoring system to complement the systems currently in use.

How to cite: Cannavo', F., Minio, V., Saitta, S., Alparone, S., Borzì, A. M., Cannata, A., Ciraolo, G., Contrafatto, D., D’Amico, S., Di Grazia, G., and Larocca, G.: Machine Learning and Microseism as a Tool for Sea Wave Monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7492, https://doi.org/10.5194/egusphere-egu23-7492, 2023.

EGU23-7561 | ECS | Posters on site | ITS1.13/AS5.2

Deep Learning guided statistical downscaling of climate projections for use in hydrological impact modeling in Danish peatlands 

Thea Quistgaard, Peter L. Langen, Tanja Denager, Raphael Schneider, and Simon Stisen

A course of action to combat the emission of greenhouse gasses (GHG) in a Danish context is to re-wet previously drained peatlands and thereby return them to their natural hydrological state acting as GHG sinks. GHG emissions from peatlands are known to be closely coupled to the hydrological dynamics through the groundwater table depth (WTD). To understand the effect of a changing and variable climate on the spatio-temporal dynamics of hydrological processes and the associated uncertainties, we aim to produce a high-resolution local-scale climate projection ensemble from the global-scale CMIP6 projections.

With focus on hydrological impacts, uncertainties and possible extreme endmembers, this study aims to span the full ensemble of local-scale climate projections in the Danish geographical area corresponding to the CMIP6-ensemble of Global Climate Models (GCMs). Deep learning founded statistical downscaling methods are applied bridge the gap from GCMs to local-scale climate change and variability, which in turn will be used in field-scale hydrological modeling. The approach is developed to specifically accommodate the resolutions, event types and conditions relevant for assessing the impacts on peatland GHG emissions through their relationship with WTD dynamics by applying stacked conditional generative adversarial networks (CGANs) to best downscale precipitation, temperature, and evaporation. In the future, the approach is anticipated to be extended to directly assess the impacts of climate change and ensemble uncertainty on peatland hydrology variability and extremes.

How to cite: Quistgaard, T., Langen, P. L., Denager, T., Schneider, R., and Stisen, S.: Deep Learning guided statistical downscaling of climate projections for use in hydrological impact modeling in Danish peatlands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7561, https://doi.org/10.5194/egusphere-egu23-7561, 2023.

EGU23-8288 | Orals | ITS1.13/AS5.2

Learning operational altimetry mapping from ocean models 

Quentin Febvre, Ronan Fablet, Julien Le Sommer, Clément Ubelmann, and Simon Benaïchouche

In oceanography, altimetry products are used to measure the height of the ocean surface, and ocean modeling is used to understand and predict the behavior of the ocean. There are two main types of gridded altimetry products: operational sea level products, such as DUACS, which are used for forecasting and reconstruction, and ocean model reanalyses, such as Glorys 12, which are used to forecast seasonal trends and assess physical characteristics. However, advances in ocean modeling do not always directly benefit operational forecast or reconstruction products.

In this study, we investigate the potential for deep learning methods, which have been successfully applied in simulated setups, to leverage ocean modeling efforts for improving operational altimetry products. Specifically, we ask under what conditions the knowledge learned from ocean simulations can be applied to real-world operational altimetry mapping. We consider the impact of simulation grid resolution, observation data reanalysis, and physical processes modeled on the performance of a deep learning model.

Our results show that the deep learning model outperforms current operational methods on a regional domain around the Gulfstream, with a 50km improvement in resolved scale. This improvement has the potential to enhance the accuracy of operational altimetry products, which are used for a range of important applications, such as climate monitoring and understanding mesoscale ocean dynamics.

How to cite: Febvre, Q., Fablet, R., Le Sommer, J., Ubelmann, C., and Benaïchouche, S.: Learning operational altimetry mapping from ocean models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8288, https://doi.org/10.5194/egusphere-egu23-8288, 2023.

EGU23-9285 | ECS | Orals | ITS1.13/AS5.2

Stabilized Neural Differential Equations for Hybrid Modeling with Conservation Laws 

Alistair White and Niklas Boers

Neural Differential Equations (NDEs) provide a powerful framework for hybrid modeling. Unfortunately, the flexibility of the neural network component of the model comes at the expense of potentially violating known physical invariants, such as conservation laws, during inference. This shortcoming is especially critical for applications requiring long simulations, such as climate modeling, where significant deviations from the physical invariants can develop over time. It is hoped that enforcing physical invariants will help address two of the main barriers to adoption for hybrid models in climate modeling: (1) long-term numerical stability, and (2) generalization to out-of-sample conditions unseen during training, such as climate change scenarios. We introduce Stabilized Neural Differential Equations, which augment an NDE model with compensating terms that ensure physical invariants remain approximately satisfied during numerical simulations. We apply Stabilized NDEs to the double pendulum and Hénon–Heiles systems, both of which are conservative, chaotic dynamical systems possessing a time-independent Hamiltonian. We evaluate Stabilized NDEs using both short-term and long-term prediction tasks, analogous to weather and climate prediction, respectively. Stabilized NDEs perform at least as well as unstabilized models at the “weather prediction” task, that is, predicting the exact near-term state of the system given initial conditions. On the other hand, Stabilized NDEs significantly outperform unstabilized models at the “climate prediction” task, that is, predicting long-term statistical properties of the system. In particular, Stabilized NDEs conserve energy during long simulations and consequently reproduce the long-term dynamics of the target system with far higher accuracy than non-energy conserving models. Stabilized NDEs also remain numerically stable for significantly longer than unstabilized models. As well as providing a new and lightweight method for combining physical invariants with NDEs, our results highlight the relevance of enforcing conservation laws for the long-term numerical stability and physical accuracy of hybrid models.

How to cite: White, A. and Boers, N.: Stabilized Neural Differential Equations for Hybrid Modeling with Conservation Laws, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9285, https://doi.org/10.5194/egusphere-egu23-9285, 2023.

EGU23-10135 | ECS | Orals | ITS1.13/AS5.2

Exploring physics-informed machine learning for accelerated simulation of permafrost processes 

Brian Groenke, Moritz Langer, Guillermo Gallego, and Julia Boike

Permafrost, i.e. ground material that remains perennially frozen, plays a key role in Arctic ecosystems. Monitoring the response of permafrost to rapid climate change remains difficult due to the sparse availability of long-term, high quality measurements of the subsurface. Numerical models are therefore an indispensable tool for understanding the evolution of Arctic permafrost. However, large scale simulation of the hydrothermal processes affecting permafrost is challenging due to the highly nonlinear effects of phase change in porous media. The resulting computational cost of such simulations is especially prohibitive for sensitivity analysis and parameter estimation tasks where a large number of simulations may be necessary for robust inference of quantities such as temperature, water fluxes, and soil properties. In this work, we explore the applicability of recently developed physics-informed machine learning (PIML) methods for accelerating numerical models of permafrost hydrothermal dynamics. We present a preliminary assessment of two possible applications of PIML in this context: (1) linearization of the nonlinear PDE system according to Koopman operator theory in order to reduce the computational burden of large scale simulations, and (2) efficient parameterization of the surface energy balance and snow dynamics on the subsurface hydrothermal regime. By combining the predictive power of machine learning with the underlying conservation laws, PIML can potentially enable researchers and practitioners interested in permafrost to explore complex process interactions at larger spatiotemporal scales.

How to cite: Groenke, B., Langer, M., Gallego, G., and Boike, J.: Exploring physics-informed machine learning for accelerated simulation of permafrost processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10135, https://doi.org/10.5194/egusphere-egu23-10135, 2023.

EGU23-10256 | ECS | Posters on site | ITS1.13/AS5.2

Foehn Wind Analysis using Unsupervised Deep Anomaly Detection 

Tobias Milz, Marte Hofsteenge, Marwan Katurji, and Varvara Vetrova

Foehn winds are accelerated, warm and dry winds that can have significant environmental impacts as they descend into the lee of a mountain range. For example, in the McMurdo Dry Valleys in Antarctica, foehn events can cause ice and glacial melt and destabilise ice shelves, which if lost, resulting in a rise in sea level. Consequently, there is a strong interest in a deeper understanding of foehn winds and their meteorological signatures. Most current automatic detection methods rely on rule-based methodologies that require static thresholds of meteorological parameters. However, the patterns of foehn winds are hard to define and differ between alpine valleys around the world. Consequently, data-driven solutions might help create more accurate detection and prediction methodologies. 

State-of-the-art machine learning approaches to this problem have shown promising results but follow a supervised learning paradigm. As such, these approaches require accurate labels, which for the most part, are being created by imprecise static rule-based algorithms. Consequently, the resulting machine-learning models are trained to recognise the same static definitions of the foehn wind signatures. 

In this paper, we introduce and compare the first unsupervised machine-learning approaches for detecting foehn wind events. We focus on data from the Mc Murdo Dry Valleys as an example, however, due to the unsupervised nature of these approaches, our solutions can recognise a more dynamic definition of foehn wind events and are therefore, independent of the location. The first approach is based on multivariate time-series clustering, while the second utilises a deep autoencoder-based anomaly detection method to identify foehn wind events. Our best model achieves an f1-score of 88%, matching or surpassing previous machine-learning methods while providing a more flexible and inclusive definition of foehn events. 

How to cite: Milz, T., Hofsteenge, M., Katurji, M., and Vetrova, V.: Foehn Wind Analysis using Unsupervised Deep Anomaly Detection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10256, https://doi.org/10.5194/egusphere-egu23-10256, 2023.

EGU23-10351 | ECS | Orals | ITS1.13/AS5.2

Deep learning of systematic sea ice model errors from data assimilation increments 

William Gregory, Mitchell Bushuk, Alistair Adcroft, and Yongfei Zhang

Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short-term corrections, or analysis increments, closely mirror the systematic bias patterns of the dynamical model. In this work, we show that Convolutional Neural Networks (CNNs) can be used to learn a mapping from model state variables to analysis increments, thus promoting the feasibility of a data-driven model parameterization which predicts state-dependent model errors. We showcase this problem using an ice-ocean data assimilation system within the fully coupled Seamless system for Prediction and EArth system Research (SPEAR) model at the Geophysical Fluid Dynamics Laboratory (GFDL), which assimilates satellite observations of sea ice concentration. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are sea ice concentration, sea-surface temperature, ice velocities, ice thickness, net shortwave radiation, ice-surface skin temperature, and sea-surface salinity. We show that the CNN is able to make skilful predictions of the increments, particularly between December and February in both the Arctic and Antarctic, with average daily spatial pattern correlations of 0.72 and 0.79, respectively. Initial investigation of implementation of the CNN into the fully coupled SPEAR model shows that the CNN can reduce biases in retrospective seasonal sea ice forecasts by emulating a data assimilation system, further suggesting that systematic sea ice biases could be reduced in a free-running climate simulation.

How to cite: Gregory, W., Bushuk, M., Adcroft, A., and Zhang, Y.: Deep learning of systematic sea ice model errors from data assimilation increments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10351, https://doi.org/10.5194/egusphere-egu23-10351, 2023.

Current numerical weather prediction models contain significant systematic errors, due in part to indeterminate ground forcing (GF). This study considers an optimal virtual GF (GFo) derived by training observed and simulated datasets of 10-m wind speeds (WS10) for summer and winter. The GFo is added to an offline surface multilayer model (SMM) to revise predictions of WS10 in China by the Weather Research and Forecasting model (WRF). This revision is a data-based optimization under physical constraints. It reduces WS10 errors and offers wide applicability. The resulting model outperforms two purely physical forecasts (the original WRF forecast and the SMM with physical GF parameterized using urban, vegetation, and subgrid topography) and two purely data-based revisions (i.e., multilinear regression and multilayer perceptron). Compared with original WRF forecasting, using the GFo scheme reduces the Root Mean Square Error (RMSE) in WS10 across China by 25% in summer and 32% in winter. The frontal area index of GFo indicates that it includes both the effects of indeterminate GF and other possible complex physical processes associated with WS10.

How to cite: Feng, J.: Mitigate forecast error in surface wind speed using an offline single-column model with optimal ground forcing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10394, https://doi.org/10.5194/egusphere-egu23-10394, 2023.

EGU23-10726 | Posters virtual | ITS1.13/AS5.2

A hybrid VMD-WT-InceptionTime model for multi-horizon short-term air temperature forecasting in Alaska 

Jaakko Putkonen, M. Aymane Ahajjam, Timothy Pasch, and Robert Chance

The lack of ground level observation stations outside of settlements makes monitoring and forecasting local weather and permafrost challenging in the Arctic. Such predictive pieces of information are essential to help prepare for potentially hazardous weather conditions, especially during winter. In this study, we aim at enhancing predictive analytics in Alaska of permafrost and temperature by using a hybrid forecasting technique. In particular, we propose VMD-WT-InceptionTime model for short-term air temperature forecasting.

This proposed technique incorporates data preprocessing techniques and deep learning to enhance the accuracy of the next seven days air temperature forecasts. Initially, the Spearman correlation coefficient is utilized to examine the relationship between different inputs and the forecast target temperature. Following this, Variational Mode Decomposition (VMD) is used to decompose the most output-correlated input variables (i.e., temperature and relative humidity) to extract intrinsic and non-stationary time-frequency features from the original sequences. The Wavelet Transform (WT) is then employed to further extract intrinsic multi-resolution patterns from these decomposed input variables. Finally, a deep InceptionTime model is used for multi-step air temperature forecasting using these processed sequences. This forecasting technique was developed using an open dataset holding 20+ years of data from three locations in Alaska: North Slope, Alaska, Arctic National Wildlife Refuge, Alaska, and Diomede Island region, Bering Strait. Model performance has been rigorously evaluated of metrics including RMSE, MAPE and error.

Results highlight the effectiveness of the proposed hybrid model in providing more accurate short-term forecasts than several baselines (GBDT, SVR, ExtraTrees, RF, ARIMA, LSTM, GRU, and Transformer). More specifically, this technique reported RMSE and MAPE average increase rates amounting to 11.21% and 16.13% in North Slope, 30.01% and 34.97% in Arctic National Wildlife Refuge, and 16.39%, 23.46% in Diomede Island region. In addition, the proposed technique produces forecasts over all seven horizons with a maximum error of <1.5K, a minimum error of >-1.2K, and an average error lower than 0.18K for North Slope. For Arctic National Wildlife Refuge, a maximum error of <1K, a minimum error of >-0.9K, and an average of < 0.1K. While a maximum error of <0.9K, a minimum error of >-0.8K, and an average of <0.13K, for Diomede Island region. However, the worst performances achieved were errors of around 6K in the third horizon (i.e., 3rd day) for North Slope and the Arctic National Wildlife Refuge and the last horizon (i.e., 7th day) for the Diomede Islands region. Most of the worst performances of the proposed technique in all three locations can be attributed to having to produce forecasts of higher variations and wider temperature ranges than their averages.

Overall, this research highlights the potential of the decomposition techniques and deep learning to: 1) reveal and effectively learn the underlying cyclicity of air temperatures at varying resolutions that allows for accurate predictions without any knowledge of the governing physics, 2) produce accurate multi-step temperature forecasts in Arctic climates.

How to cite: Putkonen, J., Ahajjam, M. A., Pasch, T., and Chance, R.: A hybrid VMD-WT-InceptionTime model for multi-horizon short-term air temperature forecasting in Alaska, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10726, https://doi.org/10.5194/egusphere-egu23-10726, 2023.

EGU23-10810 | ECS | Orals | ITS1.13/AS5.2

Oceanfourcast: Emulating Ocean Models with Transformers for Adjoint-based Data Assimilation 

Suyash Bire, Björn Lütjens, Dava Newman, and Chris Hill

Adjoints have become a staple of the oceanic and atmospheric numerical modeling community over the past couple of decades as they are useful for tuning of dynamical models, sensitivity analyses, and data assimilation. One such application is generation of reanalysis datasets, which provide an optimal record of our past weather, climate, and ocean. For example, the state-of-the-art ocean-ice renanalysis dataset, ECCO, is created by optimally combining a numerical ocean model with heterogeneous observations through a technique called data assimilation. Data assimilation in ECCO minimizes the distance between model and observations by calculating adjoints, i.e., gradients of the loss w.r.t. simulation forcing fields (wind and surface heat fluxes). The forcing fields are iteratively updated and the model is rerun until the loss is minimized to ensure that the numerical model does not drastically deviate from the observations. Calculating adjoints, however, either requires  disproportionately high computational resources  or rewriting the dynamical model code to be autodifferentiable. 

Therefore, we ask if deep learning-based emulators can provide fast and accurate adjoints. Ocean data is smooth, high-dimensional, and has complex spatiotemporal correlations. Therefore, as an initial foray into ocean emulators, we leverage a combination of neural operators and transformers. Specifically, we have adapted the FourCastNet architecture, which has successfully emulated ERA5 weather data in seconds rather than hours, to emulate an idealized ocean simulation.

We generated a ground-truth dataset by simulating a double-gyre, an idealized representation of the North Atlantic Ocean, using MITgcm, a state-of-the-art dynamical model. The model was forced by zonal wind at the surface and relaxation to a meridional profile of temperature — warm/cold temperatures at low/high latitudes. This simulation produced turbulent western boundary currents embedded in the large-scale gyre circulation. We performed 4 additional simulations by modifying the magnitude of SST relaxation and wind forcing to introduce diversity in the dataset. From these simulations, we used 4 state variables (meridional and zonal surface velocities, pressure, and temperature) as well as the forcing fields (zonal wind velocity and relaxation SST profile) sampled in 10-day steps. The dataset was split into training, validation, and test datasets such that validation and test datasets were unseen during training. These datasets provide an ideal testbed for evaluating and comparing the performance of data-driven ocean emulators.

We used this data to train and evaluate Oceanfourcast. Our initial results in the following figure show that our model, Oceanfourcast, can successfully predict the streamfunction and pressure for a lead time of 1 month. 

We are currently working on generating adjoints from Oceanfourcast.  We expect the adjoint calculation to require significantly less compute time than that from a full-scale dynamical model like MITgcm.  Our work shows a promising path towards deep-learning augmented data assimilation and uncertainty quantification.

How to cite: Bire, S., Lütjens, B., Newman, D., and Hill, C.: Oceanfourcast: Emulating Ocean Models with Transformers for Adjoint-based Data Assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10810, https://doi.org/10.5194/egusphere-egu23-10810, 2023.

EGU23-10904 | ECS | Posters on site | ITS1.13/AS5.2

On the choice of turbulence eddy fluxes to learn from in data-driven methods 

Feier Yan, Julian Mak, and Yan Wang

Recent works have demonstrated the viability of employing data-driven / machine learning 
methods for the purposes of learning more about ocean turbulence, with applications to turbulence parameterisations in ocean general circulation models. Focusing on mesoscale geostrophic turbulence in the ocean context, works thus far have mostly focused on the choice of algorithms and testing of trained up models. Here we focus instead on the choice of eddy flux data to learn from. We argue that, for mesoscale geostrophic turbulence, it might be beneficial from a theoretical as well as practical point of view to learn from eddy fluxes with dynamically inert rotational fluxes removed (ideally in a gauge invariant fashion), instead of the divergence of the eddy fluxes as has been considered thus far. Outlooks for physically constrained and interpretable machine learning will be given in light of the results. 

How to cite: Yan, F., Mak, J., and Wang, Y.: On the choice of turbulence eddy fluxes to learn from in data-driven methods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10904, https://doi.org/10.5194/egusphere-egu23-10904, 2023.

EGU23-10959 | Orals | ITS1.13/AS5.2

Deep learning parameterization of small-scale vertical velocity variability for atmospheric models 

Donifan Barahona, Katherine Breen, and Heike Kalesse-Los

Small-scale fluctuations in vertical wind velocity, unresolved by climate and weather forecast models play a particularly important role in determining vapor and tracer fluxes, turbulence and cloud formation. Fluctuations in vertical wind velocity are challenging to represent since they depend on orography, large scale circulation features, convection and wind shear. Parameterizations developed using data retrieved at specific locations typically lack generalization and may introduce error when applied on a wide range of different conditions. Retrievals of vertical wind velocity are also difficult and subject to large uncertainty. This work develops a new data-driven, neural network representation of subgrid scale variability in vertical wind velocity. Using a novel deep learning technique, the new parameterization merges data from high-resolution global cloud resolving model simulations with high frequency Radar and Lidar retrievals.  Our method aims to reproduce observed statistics rather than fitting individual measurements. Hence it is resilient to experimental uncertainty and robust to generalization. The neural network parameterization can be driven by weather forecast and reanalysis products to make real time estimations. It is shown that the new parameterization generalizes well outside of the training data and reproduces much better the statistics of vertical wind velocity than purely data-driven models.

How to cite: Barahona, D., Breen, K., and Kalesse-Los, H.: Deep learning parameterization of small-scale vertical velocity variability for atmospheric models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10959, https://doi.org/10.5194/egusphere-egu23-10959, 2023.

EGU23-11293 | ECS | Posters on site | ITS1.13/AS5.2

National scale agricultural development dynamics under socio-political drivers in Saudi Arabia since 1990 

Ting Li, Oliver López Valencia, Kasper Johansen, and Matthew McCabe

Driven in large part by policy initiatives designed to increase food security and realized via the construction of thousands of center-pivot irrigation fields since the 1970s, agriculture development in Saudi Arabia has undergone tremendous changes. However, little is known about the accurate number, acreage, and the changing dynamics of the fields. To bridge the knowledge gap between the political drivers and in-field response, we leveraged a hybrid machine learning framework by implementing Density-Based Spatial Clustering of Applications with Noise, Convolutional Neural Networks, and Spectral Clustering in a stepwise manner to delineate the center-pivot fields on a national scale in Saudi Arabia using historical Landsat imagery since 1990. The framework achieved producer's and user's accuracies larger than  83.7% and 90.2%, respectively, when assessed against 28,000 manually delineated fields collected from different regions and periods. We explored multi-decadal dynamics of the agricultural development in Saudi Arabia by quantifying the number, acreage, and size distribution of center-pivot fields, along with the first and last detection year of the fields since 1990. The agricultural development in Saudi Arabia experienced four stages, including an initialization stage before 1990, a contraction stage from 1990 to 2010, an expansion stage from 2010 to 2016, and an ongoing contraction stage since 2016. Most of the fields predated 1990, representing over 8,800 km2 in that year, as a result of the policy initiatives to stimulate wheat production, promoting Saudi Arabia as the sixth largest exporter of wheat in the 1980s. A decreasing trend was observed from 1990 to 2010, with an average of 8,011 km2 of fields detected during those two decades, which was a response to the policy initiative implemented to phase-out wheat after 1990. As a consequence of planting fodder crops to promote the dairy industry, the number and extent of fields increased rapidly from 2010 to 2015 and reached its peak in 2016, with 33,961 fields representing 9,400 km2. Agricultural extent has seen a continuous decline since 2016 to a level lower than 1990 values in 2020. This decline has been related to sustainable policy initiatives implemented for the Saudi Vision 2030. There is some evidence of an uptick in 2021 — also observed in an ongoing analysis for 2022 — which might be in response to global influences, such as the COVID-19 pandemic and the more recent conflict in the Ukraine, which has disrupted the international supply of agricultural products. The results provide a historical account of agricultural activity throughout the Kingdom and provide a basis for informed decision-making on sustainable irrigation and agricultural practices, helping to better protect and manage the nation's threatened groundwater resources, and providing insights into the resilience and elasticity of the Saudi Arabian food system to global perturbations.

How to cite: Li, T., López Valencia, O., Johansen, K., and McCabe, M.: National scale agricultural development dynamics under socio-political drivers in Saudi Arabia since 1990, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11293, https://doi.org/10.5194/egusphere-egu23-11293, 2023.

EGU23-11687 | ECS | Orals | ITS1.13/AS5.2

Objectively Determining the Number of Similar Hydrographic Clusters with Unsupervised Machine Learning 

Carola Trahms, Yannick Wölker, and Arne Biastoch

Determining the number of existing water masses and defining their boundaries is subject to ongoing discussion in physical oceanography. Traditionally, water masses are defined manually by experts setting constraints based on experience and previous knowledge about the hydrographic properties describing them. In recent years, clustering, an unsupervised machine learning approach, has been introduced as a tool to determine clusters, i.e., volumes, with similar hydrographic properties without explicitly defining their hydrographic constraints. However, the exact number of clusters to be looked for is set manually by an expert up until now. 

We propose a method that determines a fitting number of clusters for hydrographic clusters in a data driven way. In a first step, the method averages the data in different-sized slices along the time or depth axis as the structure of the hydrographic space changes strongly either in time or depth. Then the method applies clustering algorithms on the averaged data and calculates off-the-shelf evaluation scores (Davies-Bouldin, Calinski-Harabasz, Silhouette Coefficient) for several predefined numbers of clusters. In the last step, the optimal number of clusters is determined by analyzing the cluster evaluation scores across different numbers of clusters for optima or relevant changes in trend. 

For validation we applied this method to the output for the subpolar North Atlantic between 1993 and 1997 of the high-resolution Atlantic Ocean model VIKING20X, in direct exchange with domain experts to discuss the resulting clusters. Due to the change from strong to weak deep convection in these years, the hydrographic properties vary strongly in the time and depth dimension, providing a specific challenge to our methodology. 

Our findings suggest that it is possible to identify an optimal number of clusters using the off-the-shelf cluster evaluation scores that catch the underlying structure of the hydrographic space. The optimal number of clusters identified by our data-driven method agrees with the optimal number of clusters found by expert interviews. These findings contribute to aiding and objectifying water mass definitions across multiple expert decisions, and demonstrate the benefit of introducing data science methods to analyses in physical oceanography.

How to cite: Trahms, C., Wölker, Y., and Biastoch, A.: Objectively Determining the Number of Similar Hydrographic Clusters with Unsupervised Machine Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11687, https://doi.org/10.5194/egusphere-egu23-11687, 2023.

EGU23-11906 | ECS | Orals | ITS1.13/AS5.2

Untapping the potential of geostationary EO data to understand drought impacts with XAI 

Basil Kraft, Gregory Duveiller, Markus Reichstein, and Martin Jung

Ecosystems are affected by extreme climate conditions such as droughts worldwide but we still lack understanding of the involved dynamics. Which factors render an ecosystem more resilient, and on which temporal scales do weather patterns affect vegetation state and physiology? Traditional approaches to tackle such questions involve assumption-based land surface modeling or inversions. Machine learning (ML) methods can provide a complementary perspective on how ecosystems respond to climate in a more data-driven and assumption-free manner. However, ML depends heavily on data, and commonly used observations of vegetation at best contain one observation per day, but most products are provided at 16-daily to monthly temporal resolution. This masks important processes at sub-monthly time scales. In addition, ML models are inherently difficult to interpret, which still limits their applicability for process understanding.

In the present study, we combine modern deep learning models in the time domain with observations from the geostationary Meteosat Second Generation (MSG) satellite, centered over Africa. We model fractional vegetation cover (representing vegetation state) and land surface temperature (as a proxy for water stress) from MSG as a function of meteorology and static geofactors. MSG collects observations at sub-daily frequency, rendering it into an excellent tool to study short- to mid-term land surface processes. Furthermore, we use methods from explainable ML for post-hoc model interpretation to identify meteorological drivers of vegetation dynamics and their interaction with key geofactors.

From the analysis, we expect to gather novel insights into ecosystem response to droughts with high temporal fidelity. Drought response of vegetation can be highly diverse and complex especially in arid to semi-arid regions prevalent in Africa. Also, we assess the potential of explainable machine learning to discover new linkages and knowledge and discuss potential pitfalls of the approach. Explainable machine learning, combined with potent deep learning approaches and modern Earth observation products offers the opportunity to complement assumption-based modeling to predict and understand ecosystem response to extreme climate.

How to cite: Kraft, B., Duveiller, G., Reichstein, M., and Jung, M.: Untapping the potential of geostationary EO data to understand drought impacts with XAI, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11906, https://doi.org/10.5194/egusphere-egu23-11906, 2023.

EGU23-11958 | ECS | Posters on site | ITS1.13/AS5.2

Modelling Soil Temperature and Soil Moisture in Space, Depth, and Time with Machine Learning Techniques 

Maiken Baumberger, Linda Adorf, Bettina Haas, Nele Meyer, and Hanna Meyer

Soil temperature and soil moisture variations have large effects on ecological processes in the soil. To investigate and understand these processes, high-resolution data of soil temperature and soil moisture are required. Here, we present an approach to generate data of soil temperature and soil moisture continuously in space, depth, and time for a 400 km² study area in the Fichtel Mountains (Germany). As reference data, measurements with 1 m long soil probes were taken. To cover many different locations, the available 15 soil probes were shifted regularly in the course of one year. With this approach, around 250 different locations in forest sites, on meadows and on agricultural fields were captured under a variety of meteorological conditions. These measurements are combined with readily available meteorological data, satellite data and soil maps in a machine learning approach to learn the complex relations between these variables. We aim for a model which can predict the soil temperature and soil moisture continuously for our study area in the Fichtel Mountains, with a spatial resolution of 10 m x 10 m, down to 1 m depth with segments of 10 cm each and in an hourly resolution in time. Here, we present the results of our pilot study where we focus on the temperature and moisture change within the depth down to 1 m at one single location. To take temporal lags into account, we construct a Long Short-Term Memory network based on meteorological data as predictors to make temperature and moisture predictions in time and depth. The results indicate a high ability of the model to reproduce the time series of the single location and highlight the potential of the approach for the space-time-depth mapping of soil temperature and soil moisture.

How to cite: Baumberger, M., Adorf, L., Haas, B., Meyer, N., and Meyer, H.: Modelling Soil Temperature and Soil Moisture in Space, Depth, and Time with Machine Learning Techniques, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11958, https://doi.org/10.5194/egusphere-egu23-11958, 2023.

EGU23-12218 | Posters on site | ITS1.13/AS5.2

Bias correction of aircraft temperature observations in the Korean Integrated Model based on a deep learning approach 

Hui-nae Kwon, Hyeon-ju Jeon, Jeon-ho Kang, In-hyuk Kwon, and Seon Ki Park

The aircraft-based observation is one of the important anchor data used in the numerical weather prediction (NWP) models. Nevertheless, the bias has been noted in the temperature observation through several previous studies. As the performance on the hybrid four-dimensional ensemble variational (hybrid-4DEnVar) data assimilation (DA) system of the Korean Integrated Model (KIM) ⸺ the operational model in the Korea Meteorological Administration (KMA) ⸺ has been advanced, the need for the aircraft temperature bias correction (BC) has been confirmed. Accordingly, as a preliminary study on the BC, the static BC method based on the linear regression was applied to the KIM Package for Observation Processing (KPOP) system. However, the results showed there were limitations of a spatial discontinuity and a dependency on the calculation period of BC coefficients.

In this study, we tried to develop the machine learning-based bias estimation model to overcome these limitations. The MultiLayer Perceptron (MLP) based learning was performed to consider the vertical, spatial and temporal characteristics of each observation by flight IDs and phases, and at the same time to consider the correlation among observation variables. As a result of removing the predicted bias from the bias estimation model, the mean of the background innovation (O-B) decreases from 0.2217 K to 0.0136 K in a given test period. Afterwards, in order to verify the analysis field impact for BC, the bias estimation model will be grafted onto the KPOP system and then several DA cycle experiments will be conducted in the KIM.

How to cite: Kwon, H., Jeon, H., Kang, J., Kwon, I., and Park, S. K.: Bias correction of aircraft temperature observations in the Korean Integrated Model based on a deep learning approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12218, https://doi.org/10.5194/egusphere-egu23-12218, 2023.

EGU23-12355 | ECS | Orals | ITS1.13/AS5.2

Comparison of NWP Models Used in Training Surrogate Wave Models 

Ajit Pillai, Ian Ashton, Jiaxin Chen, and Edward Steele

Machine learning is increasingly being applied to ocean wave modelling. Surrogate modelling has the potential to reduce or bypass the large computational requirements, creating a low computational-cost model that offers a high level of accuracy. One approach integrates in-situ measurements and historical model runs to achieve the spatial coverage of the model and the accuracy of the in-situ measurements. Once operational, such a system requires very little computational power, meaning that it could be deployed to a mobile phone, operational vessel, or autonomous vessel to give continuous data. As such, it makes a significant change to the availability of met-ocean data with potential to revolutionise data provision and use in marine and coastal settings.

This presentation explores the impact that an underlying physics-based model can have in such a machine learning driven framework; comparing training the system on a bespoke regional SWAN wave model developed for wave energy developments in the South West of the UK against training using the larger North-West European Shelf long term hindcast wave model run by the UK Met Office. The presentation discusses the differences in the underlying NWP models, and the impacts that these have on the surrogate wave models’ accuracy in both nowcasting and forecasting wave conditions at areas of interest for renewable energy developments. The results identify the importance in having a high quality, validated, NWP model for training such a system and the way in which the machine learning methods can propagate and exaggerate the underlying model uncertainties.

How to cite: Pillai, A., Ashton, I., Chen, J., and Steele, E.: Comparison of NWP Models Used in Training Surrogate Wave Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12355, https://doi.org/10.5194/egusphere-egu23-12355, 2023.

EGU23-12403 | ECS | Orals | ITS1.13/AS5.2

PseudoSpectralNet: A hybrid neural differential equation for atmosphere models 

Maximilian Gelbrecht and Niklas Boers

When predicting complex systems such as parts of the Earth system, one typically relies on differential equations which often can be incomplete, missing unknown influences or include errors through their discretization. To remedy those effects, we present PseudoSpectralNet (PSN): a hybrid model that incorporates both a knowledge-based part of an atmosphere model and a data-driven part, an artificial neural network (ANN). PSN is a neural differential equation (NDE): it defines the right-hand side of a differential equation, combining a physical model with ANNs and is able to train its parameters inside this NDE. Similar to the approach of many atmosphere models, part of the model is computed in the spherical harmonics domain, and other parts in the grid domain. The model consists of ANN layers in each domain, information about derivatives, and parameters such as the orography. We demonstrate the capabilities of PSN on the well-studied Marshall Molteni Quasigeostrophic Model.

How to cite: Gelbrecht, M. and Boers, N.: PseudoSpectralNet: A hybrid neural differential equation for atmosphere models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12403, https://doi.org/10.5194/egusphere-egu23-12403, 2023.

EGU23-12458 | ECS | Posters on site | ITS1.13/AS5.2

Training Deep Data Assimilation Networks on Sparse and Noisy Observations 

Vadim Zinchenko and David Greenberg

Data Assimilation (DA) is a challenging and expensive computational problem targetting hidden variables in high-dimensional spaces. 4DVar methods are widely used in weather forecasting to fit simulations to sparse observations by optimization over numerical model input. The complexity of this inverse problem and the sequential nature of common 4DVar approaches lead to long computation times with limited opportunity for parallelization. Here we propose using machine learning (ML) algorithms to replace the entire 4DVar optimization problem with a single forward pass through a neural network that maps from noisy and incomplete observations at multiple time points to a complete system state estimate at a single time point. We train the neural network using a loss function derived from the weak-constraint 4DVar objective, including terms incorporating errors in both model and data. In contrast to standard 4DVar approaches, our method amortizes the computational investment of training to avoid solving optimization problems for each assimilation window, and its non-sequential nature allows for easy parallelization along the time axis for both training and inference. In contrast to most previous ML-based data assimilation methods, our approach does not require access to complete, noise-free simulations for supervised learning or gradient-free approximations such as Ensemble Kalman filtering. To demonstrate the potential of our approach, we show a proof-of-concept on the chaotic Lorenz'96 system, using a novel "1.5D Unet" architecture combining 1D and 2D convolutions.

How to cite: Zinchenko, V. and Greenberg, D.: Training Deep Data Assimilation Networks on Sparse and Noisy Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12458, https://doi.org/10.5194/egusphere-egu23-12458, 2023.

EGU23-12566 | Posters on site | ITS1.13/AS5.2

Comparison of PM2.5 concentrations prediction model performance using Artificial Intelligence 

Kyung-Hui Wang, Chae-Yeon Lee, Ju-Yong Lee, Min-Woo Jung, Dong-Geon Kim, Seung-Hee Han, Dae-Ryun Choi, and Hui-young Yun

Since PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) directly threatens public health, in order to take appropriate measures(prevention) in advance, the Korea Ministry of Environment(MOE) has been implementing PM10 forecast nationwide since February 2014. PM2.5 forecasts have been implemented nationwide since January 2015. The currently implemented PM forecast by the MOE subdivides the country into 19 regions, and forecasts the level of PM in 4 stages of “Good”, “Moderate”, “Unhealthy”, and “Very unhealthy”.

Currently PM air quality forecasting system operated by the MOE is based on a numerical forecast model along with a weather and emission model. Numerical forecasting model has fundamental limitations such as the uncertainty of input data such as emissions and meteorological data, and the numerical model itself. Recently, many studies on predicting PM using artificial intelligence such as DNN, RNN, LSTM, and CNN have been conducted to overcome the limitations of numerical models.

In this study, in order to improve the prediction performance of the numerical model, past observational data (air quality and meteorological data) and numerical forecasting model data (chemical transport model) are used as input data. The machine learning model consists of DNN and Seq2Seq, and predicts 3 days (D+0, D+1, D+2) using 6-hour and 1-hour average input data, respectively. The PM2.5 concentrations predicted by the machine learning model and the numerical model were compared with the PM2.5 measurements.

The machine learning models were trained for input data from 2015 to 2020, and their PM forecasting performance was tested for 2021. Compared to the numerical model, the machine learning model tended to increase ACC and be similar or lower to FAR and POD.

Time series trend was showed machine learning PM forecasting trend is more similar to PM measurements compared with numerical model. Especially, machine learning forecasting model can appropriately predict PM low and high concentrations that numerical model is used to overestimate.

Machine learning forecasting model with DNN and Seq2Seq can found improvement of PM forecasting performance compared with numerical forecasting model. However, the machine learning model has limitations that the model can not consider external inflow effects.

In order to overcome the drawback, the models should be updated and added some other machine learning module such as CNN with spatial features of PM concentrations.

 

Acknowledgements

This study was supported in part by the ‘Experts Training Graduate Program for Particulate Matter Management’ from the Ministry of Environment, Korea and by a grant from the National Institute of Environmental Research (NIER), funded by the Ministry of Environment (ME) of the Republic of Korea (NIER-2022-04-02-068).

 

How to cite: Wang, K.-H., Lee, C.-Y., Lee, J.-Y., Jung, M.-W., Kim, D.-G., Han, S.-H., Choi, D.-R., and Yun, H.: Comparison of PM2.5 concentrations prediction model performance using Artificial Intelligence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12566, https://doi.org/10.5194/egusphere-egu23-12566, 2023.

EGU23-13013 | ECS | Posters on site | ITS1.13/AS5.2

Using cGAN for cloud classification from RGB pictures 

Markus Rosenberger, Manfred Dorninger, and Martin Weißmann

Clouds of all kinds play a large role in many atmospheric processes including, e.g. radiation and moisture transport, and their type allows an insight into the dynamics going on in the atmosphere. Hence, the observation of clouds from Earth's surface has always been important to analyse the current weather and its evolution during the day. However, cloud observations by human observers are labour-intensive and hence also costy. In addition to this, cloud classifications done by human observers are always subjective to some extent. Finding an efficient method for automated observations would solve both problems. Although clouds have already been operationally observed using satellites for decades, observations from the surface shed a light on a different set of characteristics. Moreover, the WMO also defined their cloud classification standards according to visual cloud properties when observations are done at the Earth’s surface. Thus, in this work a utilization of machine learning methods to classify clouds from RGB pictures taken at the surface is proposed. Explicitly, a conditional Generative Adversarial Network (cGAN) is trained to discriminate between 30 different categories, 10 for each cloud level - low, medium and high; Besides showing robust results in different image classification problems, an additional advantage of using a GAN instead of a classical convolutional neural network is that its output can also artificially enhance the size of the training data set. This is especially useful if the number of available pictures is unevenly distributed among the different classes. Additional background observations like cloud cover and cloud base height can also be used to further improve the performance of the cGAN. Together with a cloud camera, a properly trained cGAN can observe and classify clouds with a high temporal resolution of the order of seconds, which can be used, e.g. for model verification or to efficiently monitor the current status of the weather as well as its short-time evolution. First results will also be presented.

How to cite: Rosenberger, M., Dorninger, M., and Weißmann, M.: Using cGAN for cloud classification from RGB pictures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13013, https://doi.org/10.5194/egusphere-egu23-13013, 2023.

EGU23-13143 | ECS | Posters on site | ITS1.13/AS5.2

Comparison of LSTM, GraphNN, and IrradPhyDNet based Approaches for High-resolution Solar Irradiance Nowcasting 

Petrina Papazek, Irene Schicker, and Pascal Gfähler

With fast parallel computing hardware, particularly GPUs, becoming more accessible in the geosciences the now efficiently running deep learning techniques are ready to handle larger amounts of recorded observation and satellite derived data and are able to learn complex structures across time-series. Thus, a suitable deep learning setup is able to generate highly-resolved weather forecasts in real-time and on demand. Forecasts of irradiance and radiation can be challenging in machine learning as they embrace a high degree of diurnal and seasonal variation.

Continuously extended PV/solar power production grows into one of our most important fossil-fuel free energy sources. Unlike the just recently emerging PV power observations, solar irradiance offers long time-series from automized weather station networks. Being directly linked to PV outputs, forecasting highly resolved solar irradiance from nowcasting to short-range plays a crucial role in decision support and managing PV.

In this study, we investigate the suitability of several deep learning techniques adopted and developed to a set of heterogeneous data sources on selected locations. We compare the forecast results to traditional – however computationally expensive - numerical weather prediction models (NWP) and rapid update cycle models. Relevant input features include 3D-fields from NWP models (e.g.: AROME), satellite data and products (e.g.: CAMS), radiation time series from remote sensing, and observation time time-series (site observations and close sites). The amount of time-series data can be extended by a synthetic data generator, a part of our deep learning framework. Our main models investigated includes a sequence-to-sequence LSTM (long-short-term-memory) model using a climatological background model or NWP for post-processing, a Graph NN model, and an analogs based deep learning method. Furthermore, a novel neural network model based on two other ideas, the IrradianceNet and the PhyDNet, was developed. IrradPhyDNet combines the skills of IrradianceNet and PhyDNet and showed improved performance in comparison to the original models.

Results obtained by the developed methods yield, in general, high forecast-skills. For selected case studies of extreme events (e.g. Saharan dust) all novel methods could outperform the traditional methods.  Different combinations of inputs and processing-steps are part of the analysis.

How to cite: Papazek, P., Schicker, I., and Gfähler, P.: Comparison of LSTM, GraphNN, and IrradPhyDNet based Approaches for High-resolution Solar Irradiance Nowcasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13143, https://doi.org/10.5194/egusphere-egu23-13143, 2023.

EGU23-13322 | ECS | Posters on site | ITS1.13/AS5.2

Nodal Ambient Noise Tomography and automatic picking of dispersion curves with convolutional neural network: case study at Vulcano-Lipari, Italy 

Douglas Stumpp, Elliot Amir Jiwani-Brown, Célia Barat, Matteo Lupi, Francisco Muñoz, Thomas Planes, and Geneviève Savard

The ambient noise tomography (ANT) method is widely adopted to reconstruct shear-wave velocity anomalies and to generate high-resolution images of the crust and upper-mantle. A critical step in this process is the extraction of surface-wave dispersion curves from cross-correlation functions of continuous ambient noise recordings, which is traditionally performed manually on the dispersion spectrograms through human-machine interfaces. Picking of dispersion curves is sometimes prone to bias due to human interpretation. Furthermore, it is a laborious and time-consuming task that needs to be resolved in an automatized manner, especially when dealing with dense seismic network of nodal geophones where the large amount of generated data severely hinders manual picking approaches. In the last decade, several studies successfully employed machine learning methods in Earth Sciences and across many seismological applications. Early studies have shown versatile and reliable solutions by treating dispersion curve extraction as a visual recognition problem. 

We review and adapt a specific machine learning approach, deep convolutional neural networks, for use on dispersion spectrograms generated with the usual frequency-time analysis (FTAN) processing on ambient noise cross-correlations. To train and calibrate the algorithm we use several available datasets acquired from previous experiments across different geological settings. The main dataset consists of records acquired with a dense local geophone network (150 short period stations sampling at 250 Hz) deployed for one month in October 2021. The dataset has been acquired during the volcanic unrest of the Vulcano-Lipari complex, Italy. The network also accounts for additional 17 permanent broadband stations (sampling at 100 Hz) maintained by the National Institute of Geophysics and Volcanology (INGV) in Italy. We evaluate the performance of the dispersion curves extraction algorithm. The automatically-picked dispersion curves will be used to construct a shear-wave velocity model of the Vulcano-Lipari magmatic plumbing system and the surrounding area of the Aeolian archipelago.

 

How to cite: Stumpp, D., Amir Jiwani-Brown, E., Barat, C., Lupi, M., Muñoz, F., Planes, T., and Savard, G.: Nodal Ambient Noise Tomography and automatic picking of dispersion curves with convolutional neural network: case study at Vulcano-Lipari, Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13322, https://doi.org/10.5194/egusphere-egu23-13322, 2023.

EGU23-13367 | ECS | Posters on site | ITS1.13/AS5.2

Framework for creating daily semantic segmentation maps of classified eddies using SLA along-track altimetry data 

Eike Bolmer, Adili Abulaitijiang, Luciana Fenoglio-Marc, Jürgen Kusche, and Ribana Roscher

Mesoscale eddies are gyrating currents in the ocean and have horizontal scales from 10 km up to 100 km and above. They transport water mass, heat, and nutrients and therefore are of interest among others to marine biologists, oceanographers, and geodesists. Usually, gridded sea level anomaly maps, processed from several radar altimetry missions, are used to detect eddies. However, operational processors create multi-mission (processing level 4) SLA grid maps with an effective spatiotemporal resolution far lower than their grid spacing and temporal resolution. 

This drawback leads to erroneous eddy detection. We, therefore, investigate if the higher-resolution along-track data could be used instead to solve the problem of classifying the SLA observations into cyclonic, anticyclonic, or no eddies in a more accurate way than using processed SLA grid map products. With our framework, we aim to infer a daily two-dimensional segmentation map of classified eddies. Due to repeat cycles between 10 and 35 days and cross-track spacing of a few 10 km to a few 100 km, ocean eddies are clearly visible in altimeter observations but are typically covered only by a few ground tracks where the spatiotemporal context within the input data is highly variable each day. However conventional convolutional neural networks (CNNs) rely on data without varying gaps or jumps in time and space in order to use the intrinsic spatial or temporal context of the observations. Therefore, this is a challenge that needs to be addressed with a deep neural network that on the one hand utilizes the spatiotemporal context information within the modality of along-track data and on the other hand is able to output a two-dimensional segmentation map from data of varying sparsity. Our approach with our architecture Teddy is to use a transformer module to encode and process the spatiotemporal information along with the ground track's sea level anomaly data that produces a sparse feature map. This will then be fed into a sparsity invariant convolutional neural network in order to infer a two-dimensional segmentation map of classified eddies. Reference data that is used to train Teddy is produced by an open-source geometry-based approach (py-eddy-tracker [1]). 

The focus of this presentation is on how we implemented this approach in order to derive two-dimensional segmentation maps of classified eddies with our deep neural network architecture Teddy from along-track altimetry. We show results and limitations for the classification of eddies using only along-track SLA data from the multi-mission level 3 product of the Copernicus Marine Environment Monitoring Service (CMEMS) within the 2017 - 2019 period for the Gulf Stream region. We find that using our methodology, we can create two-dimensional maps of classified eddies from along-track data without using preprocessed SLA grid maps.

[1] Evan Mason, Ananda Pascual, and James C. McWilliams, “A new sea surface height–based code for oceanic mesoscale eddy tracking,” Journal of Atmospheric and Oceanic Technology, vol. 31, no. 5, pp. 1181–1188, 2014.

How to cite: Bolmer, E., Abulaitijiang, A., Fenoglio-Marc, L., Kusche, J., and Roscher, R.: Framework for creating daily semantic segmentation maps of classified eddies using SLA along-track altimetry data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13367, https://doi.org/10.5194/egusphere-egu23-13367, 2023.

EGU23-13771 | Orals | ITS1.13/AS5.2

Machine Learning Emulation of 3D Shortwave Radiative Transfer for Shallow Cumulus Cloud Fields 

Jui-Yuan Christine Chiu, Chen-Kuang Kevin Yang, Jake J. Gristey, Graham Feingold, and William I. Gustafson

Clouds play an important role in determining the Earth’s radiation budget. Despite their complex and three-dimensional (3D) structures, their interactions with radiation in models are often simplified to one-dimensional (1D), considering the time required to compute radiative transfer. Such a simplification ignores cloud Inhomogeneity and horizontal photon transport in radiative processes, which may be an acceptable approximation for low-resolution models, but can lead to significant errors and impact cloud evolution predictions in high-resolution simulations. Since model developments and operations are heading toward a higher resolution that is more susceptible to radiation errors, a fast and accurate 3D radiative transfer scheme becomes important and necessary. To address the need, we develop a machine-learning-based 3D radiative transfer emulator to provide surface radiation, shortwave fluxes at all layers, and heating rate profiles. The emulators are trained for highly heterogeneous shallow cumulus under different solar positions. We will discuss the performance of the emulators in accuracy and efficiency and discuss their potential applications.

How to cite: Chiu, J.-Y. C., Yang, C.-K. K., Gristey, J. J., Feingold, G., and Gustafson, W. I.: Machine Learning Emulation of 3D Shortwave Radiative Transfer for Shallow Cumulus Cloud Fields, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13771, https://doi.org/10.5194/egusphere-egu23-13771, 2023.

EGU23-14051 | ECS | Posters on site | ITS1.13/AS5.2

Multi-modal data assimilation of sea surface currents from AIS data streams and satellite altimetry using 4DVARNet 

Simon Benaïchouche, Clément Le Goff, Brahim Boussidi, François Rousseau, and Ronan Fablet

Over the last decades, space oceanography missions, particularly altimeter missions, have greatly advanced our ability to observe sea surface dynamics. However, they still struggle to resolve spatial scales below ~ 100 km. On a global scale, sea surface current are derived from sea surface height by a geostrophical assumption. While future altimeter missions should improve the observation of sea surface height, the observation of sea surface current using altimetry techniques would remains indirect. In the other hands, recent works have considered the use of AIS (automated identification system) as a new mean to reconstruct sea surface current : AIS data streams provide an indirect observational models of total currents including ageostrophic phenomenas. In this work we consider the use of the supervised learning framework 4DVARNet, a supervised data driven approach that allow us to perform multi-modal experiments : We focus on an Observing System Simulation Experiment (OSSE) in a region of the Gulf-Stream and we show that the joint use of AIS and sea surface height (SSH) measurement could improve the reconstruction of sea surface current with respect to product derived solely from AIS or SSH observations in terms of physical and time scale resolved. 

How to cite: Benaïchouche, S., Le Goff, C., Boussidi, B., Rousseau, F., and Fablet, R.: Multi-modal data assimilation of sea surface currents from AIS data streams and satellite altimetry using 4DVARNet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14051, https://doi.org/10.5194/egusphere-egu23-14051, 2023.

EGU23-15183 | ECS | Orals | ITS1.13/AS5.2

Deep learning approximations of a CFD model for operational wind and turbulence forecasting 

Margrethe Kvale Loe and John Bjørnar Bremnes

The Norwegian Meteorological Institute has for many years applied a CFD model to downscale operational NWP forecasts to 100-200m spatial resolution for wind and turbulence forecasting for about 20 Norwegian airports. Due to high computational costs, however, the CFD model can only be run twice per day, each time producing a 12-hour forecast. An approximate approach requiring far less compute resources using deep learning has therefore been developed. In this, the relation between relevant NWP forecast variables at grids of 2.5 km spatial resolution and wind and turbulence from the CFD model has been approximated using neural networks with basic convolutional and dense layers. The deep learning models have been trained on approximately two year of the data separately for each airport. The results show that the models are to a large extent able to capture the characteristics of their corresponding CDF simulations, and the method is in due time intended to fully replace the current operational solution. 

How to cite: Loe, M. K. and Bremnes, J. B.: Deep learning approximations of a CFD model for operational wind and turbulence forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15183, https://doi.org/10.5194/egusphere-egu23-15183, 2023.

EGU23-15684 | ECS | Posters on site | ITS1.13/AS5.2

Semi-supervised feature-based learning for prediction of Mass Accumulation Rate of sediments 

Naveenkumar Parameswaran, Everardo Gonzalez, Ewa Bur­wicz-Ga­ler­ne, David Greenberg, Klaus Wallmann, and Malte Braack

Mass accumulation rates of sediments[g/cm2/yr] or sedimentation rates[cm/yr] on the seafloor are important to understand various benthic properties, like the rate of carbon sequestration in the seafloor and seafloor geomechanical stability. Several machine learning models, such as random forests, and k-Nearest Neighbours have been proposed for the prediction of geospatial data in marine geosciences, but face significant challenges such as the limited amount of labels for training purposes, skewed data distribution, a large number of features etc. Previous model predictions show deviation in the global sediment budget, a parameter used to determine a model's predicitve validity, revealing the lack of accurate representation of sedimentation rate by the state of the art models. 

Here we present a semi-supervised deep learning methodology to improve the prediction of sedimentation rates, making use of around 9x106  unlabelled data points. The semi-supervised neural network implementation has two parts: an unsupervised pretraining using an encoder-decoder network. The encoder with the optimized weights from the unsupervised training is then taken out and fitted with layers that lead to the target dimension. This network is then fine-tuned with 2782 labelled data points, which are observed sedimentation rates from peer-reviewed sources. The fine-tuned model then predicts the rate and quantity of sediment accumulating on the ocean floor, globally.

The developed semi-supervised neural network provide better predictions than supervised models trained only on labelled data. The predictions from the semi-supervised neural network are compared with that of the supervised neural network with and without dimensionality reduction(using Principle Component Analysis).

How to cite: Parameswaran, N., Gonzalez, E., Bur­wicz-Ga­ler­ne, E., Greenberg, D., Wallmann, K., and Braack, M.: Semi-supervised feature-based learning for prediction of Mass Accumulation Rate of sediments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15684, https://doi.org/10.5194/egusphere-egu23-15684, 2023.

EGU23-15756 | ECS | Posters on site | ITS1.13/AS5.2

Physiography improvements in numerical weather prediction digital twin engines 

Thomas Rieutord, Geoffrey Bessardon, and Emily Gleeson

The next generation of numerical weather prediction model (so-called digital twin engines) will reach hectometric scale, for which the existing physiography databases are insufficient. Our work leverages machine learning and open-access data to produce a more accurate and higher resolution physiography database. One component to improve is the land cover map. The reference data gathers multiple high-resolution thematic maps thanks to an agreement-based decision tree. The input data are taken from the Sentinel-2 satellite. Then, the land cover map generation is made with image segmentation. This work implements and compares several algorithms of different families to study their suitability to the land cover classification problem. The sensitivity to the data quality will also be studied. Compared to existing work, this work is innovative in the reference map construction (both leveraging existing maps and fit for end-user purpose) and the diversity of algorithms to produce our land cover map comparison.

How to cite: Rieutord, T., Bessardon, G., and Gleeson, E.: Physiography improvements in numerical weather prediction digital twin engines, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15756, https://doi.org/10.5194/egusphere-egu23-15756, 2023.

EGU23-15892 | ECS | Posters on site | ITS1.13/AS5.2

Towards emulated Lagrangian particle dispersion model footprints for satellite observations 

Elena Fillola, Raul Santos-Rodriguez, and Matt Rigby

Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships (“footprints”) for use in greenhouse gas (GHG) flux inversions. However, because a backward-running model simulation is required for each data point, LPDMs do not scale well to very large datasets, which makes them unsuitable for use in GHG inversions using high-resolution satellite instruments such as TROPOMI. In this work, we demonstrate how Machine Learning (ML) can be used to accelerate footprint production, by first presenting a proof-of-concept emulator for ground-based site observations, and then discussing work in progress to create an emulator suitable to satellite observations. In Fillola et al (2023), we presented a ML emulator for NAME, the Met Office’s LPDM, which outputs footprints for a small region around an observation point using purely meteorological variables as inputs. The footprint magnitude at each grid cell in the domain is modelled independently using gradient-boosted regression trees. The model is evaluated for seven sites, producing a footprint in 10ms, compared to around 10 minutes for the 3D simulator, and achieving R2 values between 0.6 and 0.8 for CH4 concentrations simulated at the sites when compared to the timeseries generated by NAME. Following on from this work, we demonstrate how this same emulator can be applied to satellite data to reproduce footprints immediately around any measurement point in the domain, evaluating this application with data for Brazil and North Africa and obtaining R2 values of around 0.5 for simulated CH4 concentrations. Furthermore, we propose new emulator architectures for LPDMs applied to satellite observations. These new architectures should tackle some of the weaknesses in the existing approach, for example, by propagating information more flexibly in space and time, potentially improving accuracy of the derived footprints and extending the prediction capabilities to bigger domains.

How to cite: Fillola, E., Santos-Rodriguez, R., and Rigby, M.: Towards emulated Lagrangian particle dispersion model footprints for satellite observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15892, https://doi.org/10.5194/egusphere-egu23-15892, 2023.

EGU23-15994 | ECS | Posters on site | ITS1.13/AS5.2

Uncertainty quantification in variational data assimilation with deep learning 

Nicolas Lafon, Philippe Naveau, and Ronan Fablet

The spatio-temporal reconstruction of a dynamical process from some observationaldata is at the core of a wide range of applications in geosciences. This is particularly true for weather forecasting, operational oceanography and climate studies. However, the re35 construction of a given dynamic and the prediction of future states must take into ac36 count the uncertainties that affect the system. Thus, the available observational measurements are only provided with a limited accuracy. Besides, the encoded physical equa38 tions that model the evolution of the system do not capture the full complexity of the real system. Finally, the numerical approximation generates a non-negligible error. For these reasons, it seems relevant to calculate a probability distribution of the state system rather than the most probable state. Using recent advances in machine learning techniques for inverse problems, we propose an algorithm that jointly learns a parametric distribution of the state, the dynamics governing the evolution of the parameters, and a solver. Experiments conducted on synthetic reference datasets, as well as on datasets describing environmental systems, validate our approach.

How to cite: Lafon, N., Naveau, P., and Fablet, R.: Uncertainty quantification in variational data assimilation with deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15994, https://doi.org/10.5194/egusphere-egu23-15994, 2023.

EGU23-16287 | ECS | Posters on site | ITS1.13/AS5.2

A machine learning emulator for forest carbon stocks and fluxes 

Carolina Natel de Moura, David Martin Belda, Peter Antoni, and Almut Arneth

Forests are a significant carbon sink of the total carbon dioxide (CO2) emitted by humans. Climate change is expected to impact forest systems, and their role in the terrestrial carbon cycle in several ways – for example, the fertilization effect of increased atmospheric CO2, and the lengthening of the growing season in northern temperate and boreal areas may increase forest productivity, while more frequent extreme climate events such as storms and windthrows or drought spells, as well as wildfires might reduce disturbances return period, hence increasing forest land loss and reduction of the carbon stored in the vegetation and soils. In addition, forest management in response to an increased demand for wood products and fuel can affect the carbon storage in ecosystems and wood products. State-of-the-art Dynamic Global Vegetation Models (DGVMs) simulate the forest responses to environmental and human processes, however running these models globally for many climate and management scenarios becomes challenging due to computational restraints. Integration of process-based models and machine learning methods through emulation allows us to speed up computationally expensive simulations. In this work, we explore the use of machine learning to surrogate the LPJ-GUESS DGVM. This emulator is spatially-aware to represent forests across the globe in a flexible spatial resolution, and consider past climate and forest management practices to account for legacy effects. The training data for the emulator is derived from dedicated runs of the DGVM sampled across four dimensions relevant to forest carbon and yield: atmospheric CO2 concentration, air Temperature, Precipitation, and forest Management (CTPM). The emulator can capture relevant forest responses to climate and management in a lightweight form, and will support the development of the coupled socio-economic/ecologic model of the land system, namely LandSyMM (landsymm.earth). Other relevant scientific applications include the analysis of optimal forestry protocols under climate change, and the forest potential in climate change mitigation.

 

How to cite: Natel de Moura, C., Belda, D. M., Antoni, P., and Arneth, A.: A machine learning emulator for forest carbon stocks and fluxes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16287, https://doi.org/10.5194/egusphere-egu23-16287, 2023.

EGU23-16597 | Posters on site | ITS1.13/AS5.2 | Highlight

Global Decadal Sea Surface Height Forecast with Conformal Prediction 

Nils Lehmann, Jonathan Bamber, and Xiaoxiang Zhu

One of the many ways in which anthropogenic climate change impacts our planet is
rising sea levels. The rate of sea level rise (SLR) across the oceans is,
however, not uniform in space or time and is influenced by a complex interplay
of ocean dynamics, heat uptake, and surface forcing. As a consequence,
short-term (years to a decade) regional SLR patterns are difficult to model
using conventional deterministic approaches. For example, the latest climate
model projections (called CMIP6) show some agreement in the globally integrated
rate of SLR but poor agreement when it comes to spatially-resolved
patterns. However, such forecasts are valuable for adaptation planning in
coastal areas and for protecting low lying assets.
Rather than a deterministic modeling approach, here we explore the possibility
of exploiting the high quality satellite altimeter derived record of sea surface
height variations, which cover the global oceans outside of ice-infested waters
over a period of 30 years. Alongside this rich and unique satellite record,
several data-driven models have shown tremendous potential for various
applications in Earth System science. We explore several data-driven deep
learning approaches for sea surface height forecasts over multi-annual to
decadal time frames. A limitation of some machine learning approaches is the
lack of any kind of uncertainty quantification, which is problematic for
applications where actionable evidence is sought. As a consequence, we equip
our models with a rigorous measure of uncertainty, namely conformal prediction which
is a model and dataset agnostic method that provides calibrated predictive
uncertainty with proven coverage guarantees. Based on a 30-year satellite
altimetry record and auxiliary climate forcing data from reanalysis such as
ERA5, we demonstrate that our methodology is a viable and attractive alternative
for decadal sea surface height forecasts.

How to cite: Lehmann, N., Bamber, J., and Zhu, X.: Global Decadal Sea Surface Height Forecast with Conformal Prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16597, https://doi.org/10.5194/egusphere-egu23-16597, 2023.

EGU23-16936 | ECS | Orals | ITS1.13/AS5.2

Analysis of marine heat waves using machine learning 

Said Ouala, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, and Ronan Fablet

Sea surface temperature (SST) is a critical parameter in the global climate system and plays a vital role in many marine processes, including ocean circulation, evaporation, and the exchange of heat and moisture between the ocean and atmosphere. As such, understanding the variability of SST is important for a range of applications, including weather and climate prediction, ocean circulation modeling, and marine resource management.

The dynamics of SST is the compound of multiple degrees of freedom that interact across a continuum of Spatio-temporal scales. A first-order approximation of such a system was initially introduced by Hasselmann. In his pioneering work, Hasselmann (1976) discussed the interest in using a two-scale stochastic model to represent the interactions between slow and fast variables of the global ocean, climate, and atmosphere system. In this paper, we examine the potential of machine learning techniques to derive relevant dynamical models of Sea Surface Temperature Anomaly (SSTA) data in the Mediterranean Sea. We focus on the seasonal modulation of the SSTA and aim to understand the factors that influence the temporal variability of SSTA extremes. Our analysis shows that the variability of the SSTA can indeed well be decomposed into slow and fast components. The dynamics of the slow variables are associated with the seasonal cycle, while the dynamics of the fast variables are linked to the SSTA response to rapid underlying processes such as the local wind variability. Based on these observations, we approximate the probability density function of the SSTA data using a stochastic differential equation parameterized by a neural network. In this model, the drift function represents the seasonal cycle and the diffusion function represents the envelope of the fast SSTA response.

 

How to cite: Ouala, S., Chapron, B., Collard, F., Gaultier, L., and Fablet, R.: Analysis of marine heat waves using machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16936, https://doi.org/10.5194/egusphere-egu23-16936, 2023.

EGU23-2843 | ECS | PICO | ESSI1.1

Geography-Aware Masked Autoencoders for Change Detection in Remote Sensing 

Lukas Kondmann, Caglar Senaras, Yuki M. Asano, Akhil Singh Rana, Annett Wania, and Xiao Xiang Zhu

Increasing coverage of commercial and public satellites allows us to monitor the pulse of the Earth in ever-shorter frequency (Zhu et al., 2017). Together with the rise of deep learning in artificial intelligence (AI) (LeCun et al., 2015), the field of AI for Earth Observation (AI4EO) is growing rapidly. However, many supervised deep learning techniques are data-hungry, which means that annotated data in large quantities are necessary to help these algorithms reach their full potential. In many Earth Observation applications such as change detection, this is often infeasible because high-quality annotations require manual labeling which is time-consuming and costly.  

Self-supervised learning (SSL) can help tackle the issue of limited label availability in AI4EO. In SSL, an algorithm is pretrained with tasks that only require the input data without annotation. Notably, Masked Autoencoders (MAE) have shown promising performances recently where a Vision Transformer learns to reconstruct a full image with only 25% of it as input. We hypothesize that the success of MAEs also extends to satellite imagery and evaluate this with a change detection downstream task. In addition, we provide a multitemporal DINO baseline which is another widely successful SSL method. Further, we test a second version of MAEs, which we call GeoMAE. GeoMAE incorporates the location and date of the satellite image as auxiliary information in self-supervised pretraining. The coordinates and date information are passed as additional tokens to the MAE model similar to the positional encoding. 
The pretraining dataset used is the RapidAI4EO corpus which contains multi-temporal Planet Fusion imagery for a variety of locations across Europe. The dataset for the downstream task also uses Planet Fusion in pairs as input data. These are provided on a 600m * 600m patch level three months apart together with a classification if the respective patch has changed in this period. Self-supervised pretraining is done for up to 150 epochs where we take the model with the best validation performance on the downstream task as a starting point for the test set. 

We find that the regular MAE model scores the best on the test set with an accuracy of 81.54% followed by DINO with 80.63% and GeoMAE with 80.02%. Pretraining MAE with ImageNet data instead of satellite images results in a notable performance loss down to 71.36%. Overall, our current pretraining experiments can not yet confirm our hypothesis that GeoMAE is advantageous compared to regular MAE. However, in similar spirit, Cong et al. (2022) recently introduced SatMAE which outlines that for other remote sensing applications, the combination of auxiliary information and novel masking strategies is a key factor. Therefore, it seems that a combination of location and time inputs together with adapted masking may also hold the most potential for change detection. There is ample potential for future research in geo-specific applications of MAEs and we provide a starting point for this with our experimental results for change detection. 

How to cite: Kondmann, L., Senaras, C., Asano, Y. M., Rana, A. S., Wania, A., and Zhu, X. X.: Geography-Aware Masked Autoencoders for Change Detection in Remote Sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2843, https://doi.org/10.5194/egusphere-egu23-2843, 2023.

EGU23-3267 | ECS | PICO | ESSI1.1

Decomposition learning based on spatial heterogeneity: A case study of COVID-19 infection forecasting in Germany 

Ximeng Cheng, Jost Arndt, Emilia Marquez, and Jackie Ma

New models are emerging from Artificial Intelligence (AI) and its sub-fields, in particular, Machine Learning and Deep Learning that are being applied in different application areas including geography (e.g., land cover identification and traffic volume forecasting based on spatial data). Different from well-known datasets often used to develop AI models (e.g., ImageNet for image classification), spatial data has an intrinsic feature, i.e., spatial heterogeneity, which leads to varying relationships across different regions between the independent (i.e., the model input X) and dependent variables (i.e., the model output Y). This makes it difficult to conduct large-scale studies with a single robust AI model. In this study, we draw on the idea of modular learning, i.e., to decompose large-scale tasks into sub-tasks for specific sub-regions and use multiple AI models to achieve these sub-tasks. The decomposition is based on the spatial characteristics to ensure that the relationship between independent and dependent variables is similar in each sub-region. We explore this approach for forecasting COVID-19 cases in Germany using spatiotemporal data (e.g., weather data and human mobility data) as an example and compare the prediction tasks with a single model to the proposed decomposition learning procedure in terms of accuracy and efficiency. This study is part of the project DAKI-FWS which is funded by the Federal Ministry of Economic Affairs and Climate Action in Germany to develop an early warning system to stabilize the German economy.

How to cite: Cheng, X., Arndt, J., Marquez, E., and Ma, J.: Decomposition learning based on spatial heterogeneity: A case study of COVID-19 infection forecasting in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3267, https://doi.org/10.5194/egusphere-egu23-3267, 2023.

EGU23-4929 | PICO | ESSI1.1

Using AI and ML to support marine science research 

Ilaria Fava, Peter Thijsse, Gergely Sipos, and Dick Schaap

The iMagine project is devoted to developing and delivering imaging data and services for aquatic science. Started in September 2022, the project will provide a portfolio of image data collections, high-performance image analysis tools empowered with Artificial Intelligence, and best practice documents for scientific image analysis. These services and documentation will enable better and more efficient processing and analysis of imaging data in marine and freshwater research, accelerating our scientific insights about processes and measures relevant to healthy oceans, seas, and coastal and inland waters. By building on the European Open Science Cloud compute platform, iMagine delivers a generic framework for AI model development, training, and deployment, which researchers can adopt for refining their AI-based applications for water pollution mitigation, biodiversity and ecosystem studies, climate change analysis and beach monitoring, but also for developing and optimising other AI-based applications in this field. The iMagine AI development and testing framework offers neural networks, parallel post-processing of extensive data, and analysis of massive online data streams in distributed environments. The synergies among the eight aquatic use cases in the project will lead to common solutions in data management, quality control, performance, integration, provenance, and FAIRness and contribute to harmonisation across RIs. The resulting iMagine AI development and testing platform and the iMagine use case applications will provide another component to the European marine data management landscape, valid for the Digital Twin of the Ocean, EMODnet, Copernicus, and international initiatives. 

How to cite: Fava, I., Thijsse, P., Sipos, G., and Schaap, D.: Using AI and ML to support marine science research, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4929, https://doi.org/10.5194/egusphere-egu23-4929, 2023.

EGU23-6818 | ECS | PICO | ESSI1.1

Eddy identification from along-track altimeter data with multi-modal deep learning 

Adili Abulaitijiang, Eike Bolmer, Ribana Roscher, Jürgen Kusche, and Luciana Fenoglio-Marc

Eddies are circular rotating water masses, which are usually generated near the large ocean currents, e.g., Gulf Stream. Monitoring eddies and gaining knowledge on eddy statistics over a large region are important for fishery, marine biology studies, and testing ocean models.

At mesoscale, eddies are observed in radar altimetry, and methods have been developed to identify, track and classify them in gridded maps of sea surface height derived from multi-mission data sets. However, this procedure has drawbacks since much information is lost in the gridded maps. Inevitably, the spatial and temporal resolution of the original altimetry data degrades during the gridding process. On the other hand, the task of identifying eddies has been a post-analysis process on the gridded dataset, which is, by far, not meaningful for near-real time applications or forecasts. In the EDDY project at the University of Bonn, we aim to develop methods for identifying eddies directly from along track altimetry data via a machine (deep) learning approach.

Since eddy signatures (eddy boundary and highs and lows on sea level anomaly, SLA) are not possible to extract directly from along track altimetry data, the gridded altimetry maps from AVISO are used to detect eddies. These will serve as the reference data for Machine Learning. The eddy detection on 2D grid maps is produced by open-source geometry-based approach (e.g., py-eddy-tracker, Mason et al., 2014) with additional constraints like Okubo-Weiss parameter. Later, Sea Surface Temperature (SST) maps of the same region and date (also available from AVISO) are used for manually cleaning the reference data. Noting that altimetry grid maps and SST maps have different temporal and spatial resolution, we also use the high resolution (~6 km) ocean modeling simulation dataset (e.g., FESOM, Finite Element Sea ice Ocean Model). In this case, the FESOM dataset provides a coherent, high-resolution SLA and SST, salinity maps for the study area and is a potential test basis to develop the deep learning network.

The single modal training via a Conventional Neural Network (CNN) for the 2D altimetry grid maps produced excellent dice score of 86%, meaning the network almost detects all eddies in the Gulf Stream, which are consistent with reference data. For the multi-modal training, two different training networks are developed for 1D along-track altimetry data and 2D grid maps from SLA and SST, respectively, and then they are combined to give the final classification output. A transformer model is deemed to be efficient for encoding the spatiotemporal information from 1D along track altimetry data, while CNN is sufficient for 2D grid maps from multi-sensors.

In this presentation, we show the eddy classification results from the multi-modal deep learning approach based on along track and gridded multi-source datasets for the Gulf stream area for the period between 2017 and 2019. Results show that multi-modal deep learning improve the classification by more than 20% compared to transformer model training on along-track data alone.

How to cite: Abulaitijiang, A., Bolmer, E., Roscher, R., Kusche, J., and Fenoglio-Marc, L.: Eddy identification from along-track altimeter data with multi-modal deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6818, https://doi.org/10.5194/egusphere-egu23-6818, 2023.

EGU23-8479 | ECS | PICO | ESSI1.1

Model evaluation strategy impacts the interpretation and performance of machine learning models 

Lily-belle Sweet, Christoph Müller, Mohit Anand, and Jakob Zscheischler

Machine learning models are able to capture highly complex, nonlinear relationships, and have been used in recent years to accurately predict crop yields at regional and national scales. This success suggests that the use of ‘interpretable’ or ‘explainable’ machine learning (XAI) methods may facilitate improved scientific understanding of the compounding interactions between climate, crop physiology and yields. However, studies have identified implausible, contradicting or ambiguous results from the use of these methods. At the same time, researchers in fields such as ecology and remote sensing have called attention to issues with robust model evaluation on spatiotemporal datasets. This suggests that XAI methods may produce misleading results when applied to spatiotemporal datasets, but the impact of model evaluation strategy on the results of such methods has not yet been examined.

In this study, machine learning models are trained to predict simulated crop yield, and the impact of model evaluation strategy on the interpretation and performance of the resulting models is assessed. Using data from a process-based crop model allows us to then comment on the plausibility of the explanations provided by common XAI methods. Our results show that the choice of evaluation strategy has an impact on (i) the interpretations of the model using common XAI methods such as permutation feature importance and (ii) the resulting model skill on unseen years and regions. We find that use of a novel cross-validation strategy based on clustering in feature-space results in the most plausible interpretations. Additionally, we find that the use of this strategy during hyperparameter tuning and feature selection results in improved model performance on unseen years and regions. Our results provide a first step towards the establishment of best practices for model evaluation strategy in similar future studies.

How to cite: Sweet, L., Müller, C., Anand, M., and Zscheischler, J.: Model evaluation strategy impacts the interpretation and performance of machine learning models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8479, https://doi.org/10.5194/egusphere-egu23-8479, 2023.

EGU23-9437 | PICO | ESSI1.1

On Unsupervised Learning from Environmental Data 

Mikhail Kanevski

Predictive learning from data usually is formulated as a problem of finding the best connection between input and output spaces by optimizing well-defined cost or risk functions.

In geo-environmental studies input space is usually constructed from the geographical coordinates and features generated from different sources of available information (feature engineering), by applying expert knowledge, using deep learning technologies and taking into account the objectives of the study. Often, it is not known in advance if the input space is complete or contains redundant features. Therefore, unsupervised learning (UL) is essential in environmental data analysis, modelling, prediction and visualization. UL also helps better understand the data and phenomena they describe as well as in interpreting/communicating modelling strategies and the results in the decision-making process.

The main objective of the present investigation is to review some important topics in unsupervised learning from environmental data: 1) quantitative description of the input space (“monitoring network”) structure using global and local topological and fractal measures, 2) dimensionality reduction, 3) unsupervised feature selection and clustering by applying a variety of machine learning algorithms (kernel-based, ensemble learning, self-organizing maps) and visualization tools.

Major attention is paid to the simulated and real spatial data (pollution, permafrost, geomorphological and wind fields data).  Considered case studies have different input space dimensionality/topology and number of measurements. It is confirmed that UL should be considered an integral part of a generic methodology of environmental data analysis. Comprehensive comparisons and discussions of the results conclude the research.

 

 

How to cite: Kanevski, M.: On Unsupervised Learning from Environmental Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9437, https://doi.org/10.5194/egusphere-egu23-9437, 2023.

EGU23-11601 | PICO | ESSI1.1

Clustering Geodata Cubes (CGC) and Its Application to Phenological Datasets 

Francesco Nattino, Ou Ku, Meiert W. Grootes, Emma Izquierdo-Verdiguier, Serkan Girgin, and Raúl Zurita-Milla

Unsupervised classification techniques are becoming essential to extract information from the wealth of data that Earth observation satellites and other sensors currently provide. These datasets are inherently complex to analyze due to the extent across multiple dimensions - spatial, temporal, and often spectral or band dimension – their size, and the high resolution of current sensors. Traditional one-dimensional cluster analysis approaches, which are designed to find groups of similar elements in datasets such as rasters or time series, may come short of identifying patterns in these higher-dimensional datasets, often referred to as data cubes. In this context, we present our Clustering Geodata Cubes (CGC) software, an open-source Python package that implements a set of co- and tri-clustering algorithms to simultaneously group elements across two and three dimensions, respectively. The package includes different implementations to most efficiently tackle datasets that fit into the memory of a single machine as well as very large datasets that require cluster computing. A refining strategy to facilitate data pattern identification is also provided. We apply CGC to investigate gridded datasets representing the predicted day of the year when spring onset events (first leaf, first bloom) occur according to a well-established phenological model. Specifically, we consider spring indices computed at high spatial resolution (1 km) and continental scale (conterminous United States) for the last 40+ years and extract the main spatiotemporal patterns present in the data via CGC co-clustering functionality.  

How to cite: Nattino, F., Ku, O., Grootes, M. W., Izquierdo-Verdiguier, E., Girgin, S., and Zurita-Milla, R.: Clustering Geodata Cubes (CGC) and Its Application to Phenological Datasets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11601, https://doi.org/10.5194/egusphere-egu23-11601, 2023.

EGU23-12773 | PICO | ESSI1.1

Industrial Atmospheric Pollution Estimation Using Gaussian Process Regression 

Anton Sokolov, Hervé Delbarre, Daniil Boldyriev, Tetiana Bulana, Bohdan Molodets, and Dmytro Grabovets

Industrial pollution remains a major challenge in spite of recent technological developments and purification procedures. To effectively monitor atmosphere contamination, data from air quality networks should be coupled with advanced spatiotemporal statistical methods.

Our previous studies showed that standard interpolation techniques (like inverse distance weighting, linear or spline interpolation, kernel-based Gaussian Process Regression, GPR) are quite limited for the simulation of a smoke-like narrow-directed industrial pollution in the vicinity of the source (a few tenths of kilometers). In this work, we try to apply GPR, based on statistically estimated covariances. These covariances are calculated using СALPUFF atmospheric pollution dispersion model for a one-year simulation in the Kryvyi Rih region. The application of GPR permits taking into account high correlations between pollution values in neighboring points revealed by modeling. The result of the GPR covariance-based technique is compared with other interpolation techniques. It can be used then in the estimation and optimization of air quality networks.

How to cite: Sokolov, A., Delbarre, H., Boldyriev, D., Bulana, T., Molodets, B., and Grabovets, D.: Industrial Atmospheric Pollution Estimation Using Gaussian Process Regression, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12773, https://doi.org/10.5194/egusphere-egu23-12773, 2023.

EGU23-12933 | ECS | PICO | ESSI1.1

Estimating vegetation carbon stock components by linking ground databases with Earth observations 

Daniel Kinalczyk, Christine Wessollek, and Matthias Forkel

Land ecosystems dampen the increase of atmospheric CO2 by storing carbon in soils and vegetation. In order to estimate how long carbon stays in land ecosystems, a detailed knowledge about the distribution of carbon in different vegetation components is needed. Current Earth observation products provide estimates about total above-ground biomass but do not further separate between carbon stored in trees, understory vegetation, shrubs, grass, litter or woody debris. Here we present an approach in which we link several Earth observation products with a ground-based database to estimate biomass in various vegetation components. Therefore, we use information about the statistical distribution of biomass components provided by the North American Wildland Fuels Database (NAWFD), which are however not available as geocoded data. We use ESA CCI AGB version 3 data from 2010 as a proxy in order to link the NAWFD data to the spatial information from Earth observation products. The biomass and corresponding uncertainty from the ESA CCI AGB and a map of vegetation types are used to select the likely distribution of vegetation biomass components from the set of in-situ measurements of tree biomass. We then apply Isolation Forest outlier detection and bootstrapping for a robust comparison of both datasets and for uncertainty estimation. We use Random Forest and Gaussian Process regression to predict the biomass of trees, shrubs, snags, herbaceous vegetation, coarse and fine woody debris, duff and litter from ESA CCI AGB and land cover, GEDI canopy height, Sentinel-3 LAI and bioclimatic data. The regression models reach high predictive power and allow to also extrapolate to other regions. Our derived estimates of vegetation carbon stock components provide a more detailed view on the land carbon storage and contribute to an improved estimate of potential carbon emissions from respiration, disturbances and fires.

How to cite: Kinalczyk, D., Wessollek, C., and Forkel, M.: Estimating vegetation carbon stock components by linking ground databases with Earth observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12933, https://doi.org/10.5194/egusphere-egu23-12933, 2023.

EGU23-13196 | ECS | PICO | ESSI1.1

From Super-Resolution to Downscaling - An Image-Inpainting Deep Neural Network for High Resolution Weather and Climate Models 

Maximilian Witte, Danai Filippou, Étienne Plésiat, Johannes Meuer, Hannes Thiemann, David Hall, Thomas Ludwig, and Christopher Kadow

High resolution in weather and climate was always a common and ongoing goal of the community. In this regards, machine learning techniques accompanied numerical and statistical methods in recent years. Here we demonstrate that artificial intelligence can skilfully downscale low resolution climate model data when combined with numerical climate model data. We show that recently developed image inpainting technique perform accurate super-resolution via transfer learning using the HighResMIP of CMIP6 (Coupled Model Intercomparison Project Phase 6) experiments. Its huge data base offers a unique training opportunity for machine learning approaches. The transfer learning purpose allows also to downscale other CMIP6 experiments and models, as well as observational data like HadCRUT5. Combined with the technology of Kadow et al. 2020 of infilling missing climate data, we gain a neural network which reconstructs and downscales the important observational data set (IPCC AR6) at the same time. We further investigate the application of our method to downscale quantities predicted from a numerical ocean model (ICON-O) to improve computation times. In this process we focus on the ability of the model to predict eddies from low-resolution data.

An extension to:

Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nature Geoscience 13, 408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5

How to cite: Witte, M., Filippou, D., Plésiat, É., Meuer, J., Thiemann, H., Hall, D., Ludwig, T., and Kadow, C.: From Super-Resolution to Downscaling - An Image-Inpainting Deep Neural Network for High Resolution Weather and Climate Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13196, https://doi.org/10.5194/egusphere-egu23-13196, 2023.

EGU23-14716 | ECS | PICO | ESSI1.1

Spatial-temporal transferability assessment of remote sensing data models for mapping agricultural land use 

Jayan Wijesingha, Ilze Dzene, and Michael Wachendorf

To assess the impact of anthropogenic and natural causes on land use and land use cover change, mapping of spatial and temporal changes is increasingly applied. Due to the availability of satellite image archives, remote sensing (RS) data-based machine learning models are in particular suitable for mapping and analysing land use and land cover changes. Most often, models trained with current RS data are employed to estimate past land cover and land use using available RS data with the assumption that the trained model predicts past data values similar to the accuracy of present data. However, machine learning models trained on RS data from particular locations and times may not be well transferred to new locations and time datasets due to various reasons. This study aims to assess the spatial-temporal transferability of the RS data models in the context of agricultural land use mapping. The study was designed to map agricultural land use (5 classes: maize, grasslands, summer crops, winter crops, and mixed crops) in two regions in Germany (North Hesse and Weser Ems) between the years 2010 and 2018 using Landsat archive data (i.e., Landsat 5, 7, and 8). Three model transferability scenarios were evaluated, a) temporal - S1, b) spatial - S2 and c) spatial-temporal - S3. Two machine learning models (random forest - RF and Convolution Neural Network - CNN) were trained. For each transferability scenario, class-level F1 and macro F1 values were compared between the reference and targeted transferability systems. Moreover, to explain the results of transferability scenarios, transferability results were further explored using dissimilarity index and area of applicability (AOA) concepts. The average macro F1 value of the trained model for the reference scenario (no transferability) was 0.75. For assessed transferability scenarios, the average macro F1 values were 0.70, 0.65 and 0.60, for S1, S2, and S3 respectively. It shows that, when predicting data from different spatial-temporal contexts, the model performance is decreasing. In contrast, the average proportion of the data inside the AOA did not show a clear pattern for different scenarios. In the context of RS data-related model building, spatial-temporal transferability is essential because of the limited availability of the labelled data. Thus, the results from this case study provide an understanding of how model performance changes when the model is transferred to new settings with data from different temporal and spatial domains.

How to cite: Wijesingha, J., Dzene, I., and Wachendorf, M.: Spatial-temporal transferability assessment of remote sensing data models for mapping agricultural land use, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14716, https://doi.org/10.5194/egusphere-egu23-14716, 2023.

EGU23-16096 | ECS | PICO | ESSI1.1

Limitations of machine learning in a spatial context 

Jens Heinke, Christoph Müller, and Dieter Gerten

Machine learning algorithms have become popular tools for the analysis of spatial data. However, a number of studies have demonstrated that the application of machine learning algorithms in a spatial context has limitations. New geographic locations may lie outside of the data range for which the model was trained, and estimates of model performance may be too optimistic, when spatial autocorrelation of geographic data is not properly accounted for in cross-validation. We here use artificially created spatial data fields to conduct a series of experiments to further investigate the potential pitfalls of random forest regression applied to spatial data. We provide new insights on previously reported limitations and identify further limitations. We demonstrate that the same mechanism that leads to overoptimistic estimates of model performance (when based on ordinary random k-fold cross-validation) can also lead to a deterioration of model performance. When covariates contain sufficient information to deduce spatial coordinates, the model can reproduce any spatial pattern in the training data even if it is entirely or partly unrelated to the covariates. The presence of spatially correlated residuals in the training data changes how the model utilizes the information of the covariates and impedes the identification of the actual relationship between covariates and response. This reduces model performance when the model is applied to data with a different spatial structure. Under such conditions, machine learning methods that are sufficiently flexible to fit to autocorrelated residuals (such as random forest) may not be an optimal choice. Better models may be obtained using less flexible but more transparent approaches such as generalized linear models or additive models.

How to cite: Heinke, J., Müller, C., and Gerten, D.: Limitations of machine learning in a spatial context, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16096, https://doi.org/10.5194/egusphere-egu23-16096, 2023.

EGU23-16768 | PICO | ESSI1.1

Knowledge Representation of Levee Systems - an Environmental Justice Perspective 

Armita Davarpanah, Anthony.l Nguy Robertson, Monica Lipscomb, Jacob.w. McCord, and Amy Morris

Levee systems are designed to reduce the risk of water-related natural hazards (e.g., flooding) in areas behind levees. Most levees in the U.S. are designed to protect people and facilities against the impacts of the 100-year floods. However, the current climate change is increasing the probability of the occurrence of 500-year flood events that in turn increases the likelihood of economic loss, environmental damage, and fatality that disproportionately impacts communities of color and low-income groups facing socio-economic inequities in leveed areas. The increased frequency and intensity of flooding is putting extra pressure on emergency responders that often require diverse, multi-dimensional data originating from different sources to make sound decisions. Currently, the integration of these heterogeneous data acquired by diverse sensors and emergency agencies about environmental, hydrological, and demographic indicators requires costly and complex programming and analysis that hinder rapid disaster management efforts. Our domain ‘Levee System Ontology (LSO)’ resolves the data integration and software interoperability issues by semantically modeling the static aspects, dynamic processes, and information content of the levee systems by extending the well-structured, top-level Basic Formal Ontology (BFO) and mid-level Common Core Ontologies (CCO). LSO’s class and property names follow the terminology of the National Levee Database (NLD), allowing data scientists using NLD data to constrain their classifications based on the knowledge represented in LSO. In addition to modeling the information related to the characteristics and status of the structural components of the levee system, LSO represents the residual risk in leveed areas, economic and environmental losses, and damage to facilities in case of breaching and/or overtopping of levees. LSO enables reasoning to infer components and places along levees and floodwalls where the system requires inspection, maintenance, and repair based on the status of system components. The ontology also represents the impact of flood management activities on different groups of people from an environmental justice perspective, based on the principles of DEI (diversity, equity, inclusion) as defined by the U.N. Sustainable Development Goals.

How to cite: Davarpanah, A., Nguy Robertson, A. L., Lipscomb, M., McCord, J. w., and Morris, A.: Knowledge Representation of Levee Systems - an Environmental Justice Perspective, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16768, https://doi.org/10.5194/egusphere-egu23-16768, 2023.

An LSTM-based distributed hydrologic model for an urban watershed of Korea was developed. The input of the model is the time series of the 10-minute radar-gauge composite rainfall data and 10-minute temperature data at the 239 model grid cells, and the output of the model is the 10-minute flow discharge at the watershed outlet. The Nash-Sutcliffe Efficiency (NSE) coefficients of the calibration period (2013-2016) and validation period (2017-2019) were 0.99 and 0.67, respectively. Normal events were better predicted than the extreme ones. Further in-depth analyses revealed that: (1) the model composes the watershed outlet flow discharge by linearly superimposing multiple time series created by each of the LSTM units. Unlike conventional hydrologic models, most of these time series greatly fluctuated in both positive and negative domain; (2) the runoff to rainfall ratio of each of the model grid cells does not reflect its counterpart parameters of the conceptual hydrologic models  revealing that the model simulates the watershed responses in a unique manner; (3) the model successfully reproduced the soil-moisture dependent runoff processes, which is an essential prerequisite of continuous hydrologic models; (4) Each of the LSTM units have different temporal sensitivity to a unit rainfall stimulus, and the LSTM units that is sensitive to rainfall input have greater output weight factors nearby the watershed outlet, and vice versa. This means that the model learned a mechanism to separately consider the hydrologic components with distinct response time such as direct runoff and the low frequency baseflow. 

Acknowledgement

This research was supported by the Basic Science Research Program (Grant Number: 2021R1A2C2003471) and the Basic Research Laboratory Program (Grant Number: 2022R1A4A3032838) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.

How to cite: Kim, D. and Lee, Y.: Machines simulate hydrologic processes using a simple structure but in a unique manner – a case study of predicting fine scale watershed response on a distributed framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-245, https://doi.org/10.5194/egusphere-egu23-245, 2023.

This study developed a distributed hydrologic model based on Long Short-Term Memory (LSTM) to predict flow discharge of Joongrang stream located in a highly urbanized area in Seoul, Korea. The model inputs are the time series of 10-minute radar-gauge composite precipitation data at 239 grid cells (1km2) in the watershed and the Normalized Difference Vegetation Index (NDVI) data derived from Landsat 8 images and the model output is the 10-minute flow discharge at the watershed outlet as output. The model was trained for the calibration period of 2013-2016 and was validated for the period of 2017-2019. The NSE value over the validation period corresponding to the optimal model architecture (256 LSTM hidden layers) with and without NDVI input data was 0.68 and 0.52, respectively, which suggests that the machine can learn dynamic processes of soil infiltration and plant interception from the remotely sensed information provided by satellite.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838). 

How to cite: Lee, J. and Kim, D.: Effectiveness of Satellite-based Vegetation Index for Simulating Watershed Response Using an LSTM-based model in a Distributed Framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-339, https://doi.org/10.5194/egusphere-egu23-339, 2023.

EGU23-1218 | Posters on site | HS3.3

Exploring the Value of Natural Language Processing for Urban Water Research 

Ina Vertommen, Xin Tian, Tessa Pronk, Siddharth Seshan, Sotirios Paraskevopoulos, and Bas Wols

Natural Language Processing (NLP), empowered by the most recent developments in Deep Learning, demonstrates its potential effectiveness for handling texts. Urban water research  benefits from both subfields of NLP, namely, Natural Language Understanding (NLU) and Natural Language Generation (NLG). In this work, we present three recent studies that use NLP for: (1) automated processing and responding to registered customer complaint within Dutch water utilities, (2) automated collection of up-to-date water-related information from the Internet, (3) extraction of key information about chemical compounds and pathogen characteristics from scientific publications. These applications, using the latest NLP models and tools (e.g., Rasa, Spacy), take into account studies on both water quality and quantity for the water sector. According to our findings, NLU and rule-based text mining are effective in extracting information from unstructured texts. In addition, NLU and NLG can be integrated to build a human-computer interface, such as a value-based Chabot to understand and address the demands made by customers of water utilities.

How to cite: Vertommen, I., Tian, X., Pronk, T., Seshan, S., Paraskevopoulos, S., and Wols, B.: Exploring the Value of Natural Language Processing for Urban Water Research, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1218, https://doi.org/10.5194/egusphere-egu23-1218, 2023.

EGU23-1278 | ECS | Orals | HS3.3

Evaluating Machine Learning Approach for Regional Flood Frequency Analysis in Data-sparse Regions 

Nikunj K. Mangukiya and Ashutosh Sharma

Accurate flood frequency analysis is essential for developing effective flood management strategies and designing flood protection infrastructure, but it is challenging due to the complex, nonlinear hydrological system. In regional flood frequency analysis (RFFA), the flood quantiles at ungauged sites can be estimated by establishing a relationship between interdependent physio-meteorological variables and observed flood quantiles at gauge sites in the region. However, this regional approach implies a loss of information due to the prior aggregation of hydrological data at gauged locations and can be difficult for data-sparse regions due to limited data. In this study, we evaluated an alternate approach or path for RFFA in two case studies: a data-sparse region in India and a data-dense region in the USA. In this approach, daily streamflow is predicted first using a deep learning-based hydrological model, and then flood quantiles are estimated from the predicted daily streamflow using statistical methods. We compared the results obtained using this alternate approach to those from the traditional RFFA technique, which used the Random Forest (RF) and eXtreme Gradient Boosting (XGB) algorithms to model the nonlinear relationship between flood quantiles and relevant physio-meteorological predictor variables such as meteorological forcings, topography, land use, and soil properties. The results showed that the alternate approach produces more reliable results with the least mean absolute error and higher coefficient of determination in the data-sparse region. In the data-dense region, both traditional and alternate approaches produced comparable results. However, the alternate approach has the advantage of being flexible and providing the complete time series of daily flow at the ungauged location, which can be used to estimate other flow characteristics, develop flow duration curves, or estimate flood quantiles of any return period without creating a separate traditional RFFA model. This study shows that the alternate approach can provide accurate flood frequency estimates in data-sparse regions, offering a promising solution for flood management in these areas.

How to cite: Mangukiya, N. K. and Sharma, A.: Evaluating Machine Learning Approach for Regional Flood Frequency Analysis in Data-sparse Regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1278, https://doi.org/10.5194/egusphere-egu23-1278, 2023.

EGU23-1526 | ECS | Orals | HS3.3

Extrapo… what? Predictions beyond the support of the training data 

Ralf Loritz and Hoshin Gupta

Neural networks belong to the best available methods for numerous hydrological model challenges. However, although they have shown to outperform classical hydrological models in several applications there is still some doubt whether neural networks are, despite their excellent interpolation skills, capable to make predictions beyond the support of the training data. This study addresses this issue and proposes an approach to infer the ability of neural network to predict unusual, extreme system states. We show how we can use the concept of data surprise and model surprise in a complementary manner to assess which unusual events a neural network can predict, which it can predict but only with additionally data and which it cannot predict at all hinting toward the wrong model choice or towards an incomplete description of the data.

How to cite: Loritz, R. and Gupta, H.: Extrapo… what? Predictions beyond the support of the training data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1526, https://doi.org/10.5194/egusphere-egu23-1526, 2023.

Having a continuous and complete karst discharge data record is necessary to understand hydrological behaviors of the karst aquifer and manage karst water resources. However, caused by many problems such as equipment errors and failure of observation, lots of hydrological and research dataset contains missing spring discharge values, which becomes a main barrier for further environmental and hydrological modeling and studies. In this work, a novel approach that integrates deep learning algorithms and ensemble empirical mode decomposition (EEMD) is developed to reconstruct missing karst spring discharge values with the local precipitation. EEMD is firstly employed to decompose the precipitation data, extract useful features, and remove noises. The decomposed precipitation components are then fed as input data to various deep learning models for performance comparison, including convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM models to reconstruct the missing discharge values. Root mean squared error (RMSE) and Nash–Sutcliffe efficiency coefficient (NSE), are calculated to evaluate the reconstruction performance as metrics. The models are validated with the spring discharge and precipitation data collected at Barton Spring in Texas. The reconstruction performance of various deep learning models with and without EEMD are compared and evaluated. The main conclusions can be summarized as: 1) by using EEMD, the integrated deep models significantly improve reconstruction performance and outperform the simple deep models; 2) among three integrated models, the LSTM-EEMD model obtains the best reconstruction results among three deep learning algorithms; 3) For models with monthly data, the reconstruction performance decreases greatly with the increase of missing rate: the best reconstruction results are obtained when the missing rate is low. If the missing rate was 50%, the reconstruction results become notably poorer. For models with daily data, the reconstruction performance is less impacted by the missing rate and the models can obtain satisfactory reconstruction results when missing rates range from 10% to 50%.

How to cite: Zhou, R. and Zhang, Y.: Reconstruct karst spring discharge data with hybrid deep learning models and ensemble empirical mode decomposition method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2382, https://doi.org/10.5194/egusphere-egu23-2382, 2023.

Machine Learning and Deep Learning have been proving their potential for streamflow modelling in various studies. In particular, long short-term memory (LSTM) models showed exceptionally good results. However, machine learning models often are considered “black boxes” with limited interpretability. Explainable artificial intelligence (XAI) comprise methods that analyze the internal processes of the machine learning network and allow to have a glance in the “black box”. Most proposed XAI techniques are designed for the analysis of images, and there is currently only limited work on time series data available.

In our study, we applied various XAI algorithms including gradient-based methods (Saliency, InputXGradient, Integrated Gradient, GradientSHAP) but also perturbation-based methods (Feature Ablation, Feature Permutation) to compare their applicability for reasonable interpretation in the hydrological context. To our knowledge, only Integrated Gradient has been applied to a LSTM in hydrology so far. Gradient-based methods analyze the gradient of the output with respect to the input feature. Whereas perturbation-based methods gain information by altering or masking specific input features. The different methods were applied to a LSTM trained for the low-land Ems catchment in Germany, which has a major baseflow share of total streamflow.

We analyzed the results regarding their “timestep of influence”, which describes the amount of past days having importance for the prediction of streamflow at a particular day. All of the algorithms applied result in a comparable annual pattern, characterized by relatively small timesteps of influence in spring (wet season) and increasing timesteps of influence in summer and autumn (dry season). However, the range of the absolute days of attribution varies between the methods. In conclusion, all methods produces reasonable results and appear to be suitable for interpretation purposes.

Furthermore, we compare the results to ERA-5 reanalysis data and gained evidence that the LSTM recognizes soil water storage as the main driver for streamflow generation in the catchment: we found an inverse seasonality of soil moisture and timestep of influence.

How to cite: Ley, A., Bormann, H., and Casper, M.: Exploring different explainable artificial intelligence algorithms applied to a LSTM for streamflow modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3125, https://doi.org/10.5194/egusphere-egu23-3125, 2023.

EGU23-4137 | ECS | Orals | HS3.3

Sequential optimization of temperature measurements to estimate groundwater-surface water interactions 

Robin Thibaut, Ty Ferré, Eric Laloy, and Thomas Hermans
The groundwater-surface water (GW-SW) exchange fluxes are driven by a complex interplay of subsurface processes and their interactions with surface hydrology, which have a significant impact on the water and contaminant exchanges. Due to the complexity of these systems, the accurate estimation of GW-SW fluxes is important for quantitative hydrological studies and should be based on relevant data and careful experimental design. Therefore, the effective design of monitoring networks that can identify relevant subsurface information are essential for the optimal protection of our water resources. In this study, we present novel deep learning (DL)-driven approaches for sequential and static Bayesian optimal experimental design (BOED) in the subsurface, with the goal of estimating the GW-SW exchange fluxes from a set of temperature measurements. We apply probabilistic Bayesian neural networks (PBNN) to conditional density estimation (CDE) within a BOED framework, and the predictive performance of the PBNN-based CDE model is evaluated by a custom objective function based on the Kullback-Leibler divergence to determine optimal temperature sensor locations utilizing the information gain provided by the measurements. This evaluation is used to determine the optimal sequential sampling strategy for estimating GW-SW exchange fluxes in the 1D case, and the results are compared to the static optimal sampling strategy for a 3D conceptual riverbed-aquifer model based on a real case study. Our results indicate that probabilistic DL is an effective method for estimating GW-SW fluxes from temperature data and designing efficient monitoring networks. Our proposed framework can be applied to other cases involving surface or subsurface monitoring and experimental design.

How to cite: Thibaut, R., Ferré, T., Laloy, E., and Hermans, T.: Sequential optimization of temperature measurements to estimate groundwater-surface water interactions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4137, https://doi.org/10.5194/egusphere-egu23-4137, 2023.

Rainfall-runoff (RR) modeling remains a challenging task in the field of hydrology especially when it comes to regional scale hydrology. Recently, the Long Short-Term Memory (LSTM) - which is known for its ability to learn sequential and temporal relations - has been widely adopted in RR modeling. The Convolutional Neural Networks (CNN) have matured enough in computer vision tasks, and trials were conducted to use them in hydrological applications. Different combinations of CNN and LSTM have proved to work; however, questions remain about suitability of different model architectures, the input variables needed for the model and the interpretability of the learning process of the models for regional scale.

 

In this work we trained a sequential CNN-LSTM deep learning architecture to predict daily streamflow between 1980 and 2014, regionally and simultaneously, over 86 catchments from CAMELS dataset in the US. The model was forced using year-long spatially distributed (gridded) input with precipitation, maximum temperature and minimum temperature for each day, to predict one day streamflow. The model takes advantage of the CNN to encode the spatial patterns in the input tensor, and feed them to the LSTM for learning the temporal relations between them. The trained model was further fine-tuned to predict for 3 local sub-clusters of the 86 stations. This was made in order to test the significance of fine-tuning in the performance and model learning process. Also, to interpret the spatial patterns learning process, a perturbation was introduced in the gridded input data and the sensitivity of the model output to the perturbation was shown in spatial heat maps. Finally, to evaluate the performance of the model, different benchmark models were trained using -as possible- a similar training setup as for the CNN-LSTM model. These models are CNN without the LSTM part (regional model), LSTM without CNN part (regional model), simple single-layer ANN (regional model), and LSTM trained for individual stations (considered as state of the art). All of these benchmark models have been fined-tuned for the 3 clusters as well.

 

CNN-LSTM model, after being fine-tuned, performed well predicting daily streamflow over the test period with a median Nash-Sutcliffe efficiency (NSE) of 0.62 and 65% of the 86 stations with NSE > 0.6 outperforming all benchmark models that were trained regionally using the same training setup. The model also achieved a comparable performance as for the -state of the art- LSTM trained for individual stations. Fine-tuning improved the performance for all of the models during the test period. The CNN-LSTM model, was shown to be more sensitive to input perturbations near the stations in which the prediction is intended. This was even clearer for the fine-tuned model, indicating that the model is learning spatially relevant information from the input gridded data, and fine tuning is helping on guiding the model to focus more on the relevant input.  

 

This work shows the potential of CNN and LSTM for regional Rainfall-runoff modeling by capturing spatiotemporal patterns involved in RR process. The work, also, contributes toward more physically interpretable data-driven modeling paradigm.

How to cite: Mohammed, A. and Corzo, G.: Evaluation of regional Rainfall-Runoff modelling using convolutional long short-term memory:  CAMELS dataset in US as a case study., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4177, https://doi.org/10.5194/egusphere-egu23-4177, 2023.

EGU23-4179 | Orals | HS3.3

Improving Data-Driven Flow Forecasting in Large Basins using Machine Learning to Route Flows 

David Lambl, Mostafa Elkurdy, Phil Butcher, Laura K Read, and Alden Keefe Sampson

Producing accurate hourly streamflow forecasts in large basins is difficult without a distributed model to represent both streamflow routing through the river network and the spatial heterogeneity of land and weather conditions. HydroForecast is a theory-guided deep learning flow forecasting product that consists of short-term (hourly predictions out to 10 days), seasonal (10 day predictions out to a year), and daily reanalysis models. This work focuses primarily on the short-term model which has award winning accuracy across a wide range of basins.

In this work, we discuss the implementation of a novel distributed flow forecasting capability of HydroForecast, which splits basins into smaller sub-basins and routes flows from each subbasin to the downstream forecast points of interest. The entire model is implemented as a deep neural network allowing end-to-end training of both sub-basin runoff prediction and flow routing. The model's routing component predicts a unit hydrograph of flow travel time at each river reach and timestep allowing us to inspect and interpret the learned river routing and to seamlessly incorporate any upstream gauge data. 

We compare the accuracy of this distributed model to our original flow forecasting model at selected sites and discuss future improvements that will be made to this model.

How to cite: Lambl, D., Elkurdy, M., Butcher, P., Read, L. K., and Sampson, A. K.: Improving Data-Driven Flow Forecasting in Large Basins using Machine Learning to Route Flows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4179, https://doi.org/10.5194/egusphere-egu23-4179, 2023.

EGU23-4801 | Posters on site | HS3.3

Improving Streamflow Predictions over Indian Catchments using Long Short Term Memory Networks 

Bhanu Magotra, Manabendra Saharia, and Chandrika Thulaseedharan Dhanya

Streamflow modelling plays a critical role in water resource management activities. The “physically based" models require high computation resources and large amounts of input meteorological data which results in high operating costs and longer running times. On the other hand, with advancements in deep-learning techniques, data-driven models such as long short-term memory (LSTM) networks have been shown to successfully model non-linear rainfall-runoff relationships through historically observed data at a fraction of computation cost. Moreover, using physics-informed machine learning techniques, the physical consistency of data-driven models can be further improved. In this study, one such method is applied where we trained a physics-informed LSTM network model over 278 Indian catchments to simulate streamflow at a daily timestep using historically observed precipitation and streamflow data. The ancillary data included meteorological forcings, static catchment attributes, and Noah-MP simulated land surface states and fluxes such as soil moisture, latent heat, and total evapotranspiration. The LSTM model's performance was evaluated using error metrics such as Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE) and its components, along with skill scores based on 2x2 contingency matrix for hydrological extremes. The trained LSTM model shows improved performance in simulating streamflow over the catchments compared to the physically based model. This will be the first study over India to generate reliable streamflow simulations using a hybrid state-of-the-art approach, which will be beneficial to policy makers for effective water resource management in India. 

How to cite: Magotra, B., Saharia, M., and Dhanya, C. T.: Improving Streamflow Predictions over Indian Catchments using Long Short Term Memory Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4801, https://doi.org/10.5194/egusphere-egu23-4801, 2023.

EGU23-4842 | ECS | Orals | HS3.3

Introducing DL-GLOBWB: a deep-learning surrogate of a process-based global hydrological model 

Bram Droppers, Myrthe Leijnse, Marc F.P. Bierkens, and Niko Wanders

Process-based global hydrological models are an important tool for sustainable development and policy making in today’s water-scarce world. These models are able to inform national to regional scale water management with basin-scale accounting of water availability and demand and project the impacts of climate change and adaptation on water resources. However, the increasing need for better and higher resolution hydrological information is proving difficult for these state-of-the-art process-based models as the associated computational requirements are significant.

Recently, the deep-learning community has shown that neural networks (in particular the LSTM network) can provide hydrological information with an accuracy that rivals, if not exceeds, that of process-based hydrological models. Although the training of these neural networks takes time, prediction is fast compared to process-based simulations. Nevertheless, training is mostly done on historical observations and thus projections under climate change and adaptation are uncertain.

Inspired by the complementary strengths and weaknesses of the process-based and deep-learning approaches, we present DL-GLOBWB: a deep-learning surrogate of the state-of-the-art PCR-GLOBWB global hydrological model. DL-GLOBWB predicts all water-balance components from the process-based model, including human water demand and abstraction, with a nRSME of 0.05 (range between 0.0001 and 0.32). The DL-GLOBWB surrogate is orders of magnitudes faster than its process-based counterpart, especially as surrogates trained at low resolutions (e.g. 30 arc-minute) can effectively be downscaled to higher resolutions (e.g. 5 arc-minute).

In addition to introducing DL-GLOBWB, our presentation will explore future applications of this deep-learning surrogate, such as (1) improving model calibration and performance by comparing DL-GLOBWB outputs with ins-situ data and satellite observations; (2) training DL-GLOBWB on  future model projections to include global change; and (3) the implementation of DL-GLOBWB to dynamically, and at high resolution, visualize the impact of climate change and adaptation to stakeholders.

How to cite: Droppers, B., Leijnse, M., Bierkens, M. F. P., and Wanders, N.: Introducing DL-GLOBWB: a deep-learning surrogate of a process-based global hydrological model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4842, https://doi.org/10.5194/egusphere-egu23-4842, 2023.

IMERG is a global satellite-based precipitation dataset, produced by NASA. It has provided valuable rainfall information to facilitate the design or the operation of the disaster and risk management worldwide. In operation, NASA offers three types of IMERG Level 3 (L3) products, with different levels of trade-offs in terms of time latency and accuracy. These are Early run (4-hour latency), Late run (14-hour latency) and Final run(3.5-month latency). The final-run product integrates multi-sensor retrievals and provides the highest-quality precipitation estimates among three IMERG products. It however suffers from a long processing latency, which hinders its applicability to near real-time applications. In the past 10 years, deep learning techniques have made significant breakthroughs in various scientific fields, including short-term rainfall forecasting. Deep learning models have shown to have the potential to learn the complex variations in weather systems and to outperform the Numerical Weather Prediction (NWP) in terms of short lead-time predictability and the required computational resources for operation.

 

In this research, we would like to explore the potential of deep learning (DL) in generating high-quality satellite-based precipitation product with low latency. More specifically, we investigate if DL models can learn the difference between Final- and Early-run products, and thus predict a Final-run-like product using Early-run product as input. Low-latency yet high-quality IMERG precipitation product can be therefore obtained. Various DL techniques are being tested in this work, including Auto-Encoder(AE), ConvLSTM and Deep Generative model. IMERG data between 2018 and 2020 over a rectangular area centred in the UK is used for model training and testing, and ground rain gauge records will be used to evaluate the performance of the original and predicted products. This pilot includes both ocean and land regions, which enables the comparison of the model performance between two different surface conditions. Preliminary analysis suggests that given patterns do exist in the differences between Early- and Final-run products, and the capacity of the selected DL models to learn the differences will be further investigated. The proposed work is of great potential to improve the applicability of IMERG products in an operational context.

How to cite: Hung, H. T. and Wang, L.-P.: IMERG Run Deep: Can we produce a low-latency IMERG Final run product with a deep learning based prediction model?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4887, https://doi.org/10.5194/egusphere-egu23-4887, 2023.

EGU23-4970 | ECS | Posters on site | HS3.3

Use of Long-Short Term Memory network (LSTM) in the reconstruction of missing water level data in the Seine River. 

Imad Janbain, Julien Deloffre, Abderrahim Jardani, Minh Tan Vu, and Nicolas Massei

Missing data is the first major problem that appears in many database fields for a set of reasons. It has always been necessary to fill them, which becomes unavoidable and more complicated when the missing periods are longer. Several machine-learning-based approaches have been introduced to deal with this problem. 

The purpose of this paper is to discuss the effectiveness of a new methodology added prior to the LSTM deep learning algorithm to fill in the missing data in the hourly surface water level time series of some stations installed along the Seine River in Normandy-France. In our study, due to a lack of data, a challenging situation was faced where only the water level data in the same station, which contain many missing parts, were used as input and output variables to fill the station itself in a self-learning approach. This contrasts with the common work on imputing missing data, where several features are available to take advantage of in a multivariate and spatiotemporally way, e.g.: using the same variable from other stations or exploiting other physical variables and metrological data, etc. The reconstruction accuracy of the proposed method depends on both the size of the available/missing data and the parameters of the networks. Therefore, we performed sensitivity analyses on both the properties of the networks and the structuring of the input and output data to better determine the appropriate strategy. During this analysis process, a data preprocessing method was developed and added prior to the LSTM model. This data processing method was discovered by presenting many scenarios, each of which was an updated version of the last one. Along with these scenarios, limitations were also addressed and overcome. Finally, the last model version was able to impute missing values that may reach one year of hourly data with high accuracy (One-year RMSE = 0.14 m) regardless of neither the location of the missing part in the series nor its size.  

How to cite: Janbain, I., Deloffre, J., Jardani, A., Vu, M. T., and Massei, N.: Use of Long-Short Term Memory network (LSTM) in the reconstruction of missing water level data in the Seine River., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4970, https://doi.org/10.5194/egusphere-egu23-4970, 2023.

The objective function plays an important role in the training process for deep learning models, since it largely determines the trained values of the model parameters and influences the model performance. In this study, we establish two application-orientated objective functions, namely high flow balance error (HFBE) and transformed mean absolute percentage error (MAPE*), for the forecasts of high flows and low flows, respectively, in the LSTM model. We examine the strength and weakness of these streamflow forecast models trained on HFBE, MAPE* and mean square error (MSE) based on multiple performance metrics. Furthermore, we propose the objective function-based ensemble model (OEM) framework that integrates the models trained on different objective functions, so as to take advantages of the trained models focusing on different aspects of streamflow and thus achieve a better overall performance. Our results in 273 catchments over USA show that the models trained on HFBE can alleviate underestimation in high flows existing in the models trained on MSE, and perform remarkably better for high flows. It is also found that the models trained on MAPE* outperform the other two models in low flow forecast, no matter what algorithm is used for the model establishment. By incorporating the three models trained on HFBE, MAPE* and MSE, respectively, our proposed OEM performs well in the forecasts of both high flows and low flows, and realistically capture the mean and the variability of the observational streamflow under different scenarios under a variety of hydrometeorological conditions. This study highlights the necessity of applying application-orientated objective functions for given projects and the great potential of the ensemble learning methods for multi-optimization in hydrological modeling.

How to cite: Wang, D.: The role of ensemble learning in multi-optimization for streamflow prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5044, https://doi.org/10.5194/egusphere-egu23-5044, 2023.

EGU23-5199 | ECS | Posters virtual | HS3.3

How do machine learning models deal with inter-catchment groundwater flows? 

Nicolas Weaver, Taha-Abderrahman El-Ouahabi, Thibault Hallouin, François Bourgin, Charles Perrin, and Vazken Andréassian

Machine learning models have recently gained popularity in hydrological modelling at the catchment scale, fuelled by the increasing availability of large-sample data sets and the increasing accessibility of deep learning frameworks, computing environments, and open-source tools. In particular, several large-sample studies at daily and monthly time scales across the globe showed successful applications of the LSTM architecture as a regional model learning of the hydrological behaviour at the catchment scale. Yet, a deeper understanding of how machine learning models close the water balance and how they deal with inter-catchment groundwater flows is needed to move towards better process understanding. We investigate the performance and behaviour of the LSTM architecture at a monthly time step on a large sample French data set coined CHAMEAU – following the CAMELS initiative. To provide additional information to the learning step of the LSTM, we use the parameter sets and fluxes from the conceptual GR2M model that has a dedicated formulation to deal with inter-catchment groundwater flows. We see this study as a contribution towards the development of hybrid hydrological models.

How to cite: Weaver, N., El-Ouahabi, T.-A., Hallouin, T., Bourgin, F., Perrin, C., and Andréassian, V.: How do machine learning models deal with inter-catchment groundwater flows?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5199, https://doi.org/10.5194/egusphere-egu23-5199, 2023.

EGU23-5445 | ECS | Posters on site | HS3.3

Physics-Informed Neural Networks for Statistical Emulation of Hydrodynamical Numerical Models 

James Donnelly, Alireza Daneshkhah, and Soroush Abolfathi

The application of numerical models for flood and inundation modelling has become widespread in the past decades as a result of significant improvements in computational capabilities. Computational approaches to flood forecasting have significant benefits compared to empirical approaches which estimate statistical patterns of hydrological variables from observed data. However, there is still a significant computational cost associated with numerical flood modelling at high spatio-temporal resolutions. This limitation of numerical modelling has led to the development of statistical emulator models, machine learning (ML) models designed to learn the underlying generating process of the numerical model. The data-driven approach to ML involves relying entirely upon a set of training data to inform decisions about model selection and parameterisations. Deep learning models have leveraged data-driven learning methods with improvements in hardware and an increasing abundance of data to obtain breakthroughs in various fields such as computer vision, natural language processing and autonomous driving. In many scientific and engineering problems however, the cost of obtaining data is high and so there is a need for ML models that are able to generalise in the ‘small-data’ regime common to many complex problems. In this study, to overcome extrapolation and over-fitting issues of data-driven emulators, a Physics-Informed Neural Network model is adopted for the emulation of all two-dimensional hydrodynamic models which model fluid according the shallow water equations. This study introduces a novel approach to encoding the conservation of mass into a deep learning model, with additional terms included in the optimisation criterion, acting to regularise the model, avoid over-fitting and produce more physically consistent predictions by the emulator.

How to cite: Donnelly, J., Daneshkhah, A., and Abolfathi, S.: Physics-Informed Neural Networks for Statistical Emulation of Hydrodynamical Numerical Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5445, https://doi.org/10.5194/egusphere-egu23-5445, 2023.

EGU23-5736 | ECS | Orals | HS3.3

A Novel Workflow for Streamflow Prediction in the Presence of Missing Gauge Observations 

Rendani Mbuvha, Peniel Julien Yise Adounkpe, Mandela Coovi Mahuwetin Houngnibo, and Nathaniel Newlands

Streamflow predictions are a vital tool for detecting flood and drought events. Such predictions are even more critical to Sub-Saraharan African regions that are vulnerable to the increasing frequency and intensity of such events. These regions are sparsely gauged, with few available gauging stations that are often plagued with missing data due to various causes, such as harsh environmental conditions and constrained operational resources. 

This work presents a novel workflow for predicting streamflow in the presence of missing gauge observations. We leverage bias correction of the GEOGloWS ECMWF streamflow service (GESS) forecasts for missing data imputation and predict future streamflow using the state-of-the-art Temporal Fusion transformers at ten river gauging stations in the Benin Republic.

We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in poor imputation performance over the ten Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior performance relative to traditional imputation by established methods such as Random Forest, k-Nearest Neighbour, and GESS lookup. We also show that the Temporal Fusion Transformer yields high predictive skill and further provides explanations for predictions through the weights of its attention mechanism. The findings of this work provide a basis for integrating Global streamflow prediction model data and state-of-the-art machine learning models into operational early-warning decision-making systems (e.g., flood/ drought alerts) in resource-constrained countries vulnerable to drought and flooding due to extreme weather events.

How to cite: Mbuvha, R., Adounkpe, P. J. Y., Houngnibo, M. C. M., and Newlands, N.: A Novel Workflow for Streamflow Prediction in the Presence of Missing Gauge Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5736, https://doi.org/10.5194/egusphere-egu23-5736, 2023.

EGU23-6313 | ECS | Posters on site | HS3.3

Moving away from deterministic solutions: A probabilistic machine learning approach to account for geological model uncertainty in groundwater modelling 

Mathias Busk Dahl, Troels Norvin Vilhelmsen, Rasmus Bødker Madsen, and Thomas Mejer Hansen

Decision-making related to groundwater management often relies on results from a deterministic groundwater model representing one ‘optimal’ solution. However, such a single deterministic model lacks representation of subsurface uncertainties. The simplicity of such a model is appealing, as typically only one is needed, but comes with the risk of overlooking critical scenarios and possible adverse environmental effects. Instead, we argue, that groundwater management should be based on a probabilistic model that incorporates the uncertainty of the subsurface structures to the extent that it is known. If such a probabilistic model exists, it is, in principle, simple to propagate the uncertainties of the model parameter using multiple numerical simulations, to allow a quantitative and probabilistic base for decision-makers. However, in practice, such an approach can become computationally intractable. Thus, there is a need for quantifying and propagating the uncertainty numerical simulations and presenting outcomes without losing the speed of the deterministic approach.

This presentation provides a probabilistic approach to the specific groundwater modelling task of determining well recharge areas that accounts for the geological uncertainty associated with the model using a deep neural network. The results of such a task are often part of an investigation for new abstraction well locations and should, therefore, present all possible outcomes to give informative decision support. We advocate for the use of a probabilistic approach over a deterministic one by comparing results and presenting examples, where probabilistic solutions are essential for proper decision support. To overcome the significant increase in computation time, we argue that this problem can be solved using a probabilistic neural network trained on examples of model outputs. We present a way of training such a network and show how it performs in terms of speed and accuracy. Ultimately, this presentation aims to contribute with a method for incorporating model uncertainty in groundwater modelling without compromising the speed of the deterministic models.

How to cite: Busk Dahl, M., Norvin Vilhelmsen, T., Bødker Madsen, R., and Mejer Hansen, T.: Moving away from deterministic solutions: A probabilistic machine learning approach to account for geological model uncertainty in groundwater modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6313, https://doi.org/10.5194/egusphere-egu23-6313, 2023.

EGU23-6466 | ECS | Orals | HS3.3

Neural ODE Models in Large-Sample Hydrology 

Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia

Neural Ordinary Differential Equation (ODE) models have demonstrated high potential in providing accurate hydrologic predictions and process understanding for single catchments (Höge et al., 2022). Neural ODEs fuse a neural network model core with a mechanistic equation framework. This hybrid structure offers both traceability of model states and processes, like in conceptual hydrologic models, and the high flexibility of machine learning to learn and refine model interrelations. Aside of the functional dependence of internal processes on driving forces, like of evapotranspiration on temperature, Neural ODEs are also able to learn the effect of catchment-specific attributes, e.g. land cover types, on processes when being trained over multiple basins simultaneously.

 

We demonstrate the performance of a generic Neural ODE architecture in a hydrologic large-sample setup with respect to both predictive accuracy and process interpretability. Using several hundred catchments, we show the capability of Neural ODEs to learn the general interplay of catchment-specific attributes and hydrologic drivers in order to predict discharge in out-of-sample basins. Further, we show how functional relations learned (encoded) by the neural network can be translated (decoded) into an interpretable form, and how this can be used to foster understanding of processes and the hydrologic system.

 

Höge, M., Scheidegger, A., Baity-Jesi, M., Albert, C., & Fenicia, F.: Improving hydrologic models for predictions and process understanding using Neural ODEs. Hydrol. Earth Syst. Sci., 26, 5085-5102, https://hess.copernicus.org/articles/26/5085/2022/

How to cite: Höge, M., Scheidegger, A., Baity-Jesi, M., Albert, C., and Fenicia, F.: Neural ODE Models in Large-Sample Hydrology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6466, https://doi.org/10.5194/egusphere-egu23-6466, 2023.

EGU23-7347 | ECS | Orals | HS3.3

Deep learning for mapping water bodies in the Sahel 

Mathilde de FLEURY, Laurent Kergoat, Martin Brandt, Rasmus Fensholt, Ankit Kariryaa, Gyula Mate Kovács, Stéphanie Horion, and Manuela Grippa

Inland surface water, especially lakes and small water bodies, are essential resources and have impacts on biodiversity, greenhouse gases and health. This is particularly true in the semi-arid Sahelian region, where these resources remain largely unassessed, and little is known about their number, size and quality. Remote sensing monitoring methods remain a promising tool to address these issues at the large scale, especially in areas where field data are scarce. Thanks to technological advances, current remote sensing systems provide data for regular monitoring over time and offer a high spatial resolution, up to 10 metres.  

Several water detection methods have been developed, many of them using spectral information to differentiate water surfaces from soil, through thresholding on water indices (MNDWI for example), or classifications by clustering. These methods are sensitive to optical reflectance variability and are not straight forwardly applicable to regions, such as the Sahel, where the lakes and their environment are very diverse. Particularly, the presence of aquatic vegetation is an important challenge and source of error for many of the existing algorithms and available databases.  

Deep learning, a subset of machine learning methods for training deep neural networks, has emerged as the state-of-the-art approach for a large number of remote sensing tasks. In this study, we apply a deep learning model based on the U-Net architecture to detect water bodies in the Sahel using Sentinel-2 MSI data, and 86 manually defined lake polygons as training data. This framework was originally developed for tree mapping (Brandt et al., 2020, https://doi.org/10.1038/s41586-020-2824-5).   

Our preliminary analysis indicate that our models achieve a good accuracy (98 %). The problems of aquatic vegetation do not appear anymore, and each lake is thus well delimited irrespective of water type and characteristics. Using the water delineations obtained, we then classify different optical water types and thereby highlight different type of waterbodies, that appear to be mostly turbid and eutrophic waters, allowing to better understand the eco-hydrological processes in this region.  

This method demonstrates the effectiveness of deep learning in detecting water surfaces in the study region. Deriving water masks that account for all kind of waterbodies offer a great opportunity to further characterize different water types. This method is easily reproducible due to the availability of the satellite data/algorithm and can be further applied to detect dams and other human-made features in relation to lake environments. 

How to cite: de FLEURY, M., Kergoat, L., Brandt, M., Fensholt, R., Kariryaa, A., Kovács, G. M., Horion, S., and Grippa, M.: Deep learning for mapping water bodies in the Sahel, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7347, https://doi.org/10.5194/egusphere-egu23-7347, 2023.

EGU23-7828 | ECS | Posters on site | HS3.3

Sub-seasonal daily precipitation forecasting based on Long Short-Term Memory (LSTM) models 

Claudia Bertini, Gerald Corzo, Schalk Jan van Andel, and Dimitri Solomatine

Water managers need accurate rainfall forecasts for a wide spectrum of applications, ranging from water resources evaluation and allocation, to flood and drought predictions. In the past years, several frameworks based on Artificial Intelligence have been developed to improve the traditional Numerical Weather Prediction (NWP) forecasts, thanks to their ability of learning from past data, unravelling hidden relationships among variables and handle large amounts of inputs. Among these approaches, Long Short-Term Memory (LSTM) models emerged for their ability to predict sequence data, and have been successfully used for rainfall and flow forecasting, mainly with short lead-times. In this study, we explore three different multi-variate LSTM-based models, i.e. vanilla LSTM, stacked LSTM and bidirectional LSTM, to forecast daily precipitation for the upcoming 30 days in the area of Rhine Delta, the Netherlands. We use both local atmospheric and global climate variables from the ERA-5 reanalysis dataset to predict rainfall, and we introduce a fuzzy index for the models to account for seasonality effects. The framework is developed within the H2020 project CLImate INTelligence (CLINT), and its outcomes have the potential to improve forecasting precipitation deficit in the study area.

How to cite: Bertini, C., Corzo, G., van Andel, S. J., and Solomatine, D.: Sub-seasonal daily precipitation forecasting based on Long Short-Term Memory (LSTM) models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7828, https://doi.org/10.5194/egusphere-egu23-7828, 2023.

Terrestrial water storage (TWS) anomalies from Gravity Recovery and Climate Experiment (GRACE) and its follow on GRACE-FO satellite missions provide a unique opportunity to measure the impact of different climate extremes and human intervention on water use at regional and continental scales. However, temporal gaps within GRACE and GRACE-FO mission (GRACE: 20 months, between GRACE and GRACE-FO: 11 months and GRACE-FO: 2 months) pose difficulties in analyzing spatiotemporal variations in TWS. In this study, Convolutional Long Short-Term Memory Neural Networks (CNN-LSTM) model was developed to fill these gaps and reconstruct the TWS for the Indian subcontinent (April 2002-July 2022). Various meteorological and climatic variables, such as precipitation, temperature, run-off, evapotranspiration, and vegetation, have been integrated to predict GRACE TWS. The performance of the models was evaluated with the help of Pearson’s correlation coefficient (PR), Nash-Sutcliffe efficiency (NSE), and Normalised Root Mean Square Error (NRMSE). Results indicate that the CNN-LSTM model yielded a mean PR of 0.94 and 0.89, NSE of 0.87 and 0.8, and NRMSE of 0.075 and 0.101 on training and testing, respectively. Overall, the CNN-LSTM achieved good performance except in the northwestern region of India, which showed a relatively poor performance might be due to high anthropogenic activity and arid climatic conditions. Further reconstructed time series were used to study the Spatiotemporal variations of TWS over the Indian Subcontinent.

Keywords: GRACE; Deep Learning; TWSA; Indian subcontinent

How to cite: Moudgil, P. S. and Rao, G. S.: Filling Temporal Gaps within and between GRACE and GRACE-FO Terrestrial Water Storage Changes over Indian Sub-Continent using Deep Learning., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8218, https://doi.org/10.5194/egusphere-egu23-8218, 2023.

Recent years have seen an increase of deep learning applications for flow forecasting. Large-sample hydrological (LSH) studies typically try to predict the runoff of a catchment using some selection of hydrometeorological features from the respective catchment. One aspect of these models that has received little attention in LSH is the effect that data from upstream catchments has on model performance. The number of available and stations and distance between stations is highly variable between catchments, which creates a unique modelling challenge. Existing LSH studies either use some form of linear aggregation of upstream flows as input features or omit them altogether. The potential of upstream data to improve the performance of real-time flow forecasts has not yet been systematically evaluated on a large scale. The objective of our study is to evaluate methods for integrating upstream features for real-time, data-driven flow forecasting models. Our study uses a subset of Canadian catchments (n>150) from the HYSETS database. For each catchment, long-short term memory networks (LSTMs) are used to generate flow forecasts for lead times of 1 to 3 days. We evaluate methods for identifying, selecting, and integrating relevant upstream input features within a deep-learning modelling framework, which include using neighbouring upstream stations, using all upstream stations, and using all stations with embedded dimensionality reduction. Early results indicate that while the inclusion of upstream data often yields improvements in model performance, including too much upstream information can easily have detrimental effects.

How to cite: Snieder, E. and Khan, U.: A large sample study of the effects of upstream hydrometeorological input features for LSTM-based daily flow forecasting in Canadian catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8746, https://doi.org/10.5194/egusphere-egu23-8746, 2023.

EGU23-9726 | ECS | Posters on site | HS3.3

Flood Forecasting with Deep Learning LSTM Networks: Local vs. Regional Network Training Based on Hourly Data 

Tanja Morgenstern, Jens Grundmann, and Niels Schütze

Floods are among the most frequently occurring natural disasters in Germany. Therefore, predicting their occurrence is a crucial task for efficient disaster management and for the protection of life, property, infrastructure and cultural assets. In recent years Deep Learning methods gained popularity on the research field on flood forecasting methods – Long Short-Term Memory (LSTM) networks being part of them.

Efficient disaster management needs a fine temporal resolution of runoff predictions. Past work at TU Dresden on LSTM networks shows certain challenges when using input data with hourly resolution, such as systematically poor timing in peak flow prediction (Pahner et al. (2019) and Morgenstern et al. (2021)). At times, disaster management even requires flood forecasts for hitherto unobserved catchments, so in total a regionally transferable rainfall-runoff model with a fine temporal resolution is needed. We derived the idea for a potential approach from Kratzert et al. (2019) and Fang et al. (2021): they demonstrate that LSTM networks for rainfall(R)-runoff(R)-modeling benefit from an integration of multiple diverse catchments in the training dataset instead of a strictly local dataset, as this allows the networks to learn universal hydrologic catchment behavior. However, their training dataset consists of daily resolution data.

Following this approach, in this study we train the LSTM networks using single catchments ("local network training") as well as combinations of diverse catchments in Saxony, Germany ("regional network training"). The training data (hourly resolution) consist of area averages of observed precipitation as well as of observed discharge at long-term observation gauges in Saxony. The gauges belong to small, fast-responding Saxon catchments and vary in their hydrological and geographical properties, which in turn are part of the network training as well.

We show the preliminary results and investigate the following questions:

  • With a finer temporal resolution than daily values, characteristics of flood waves become more pronounced. Concerning the detailed simulation of flood waves, do regional LSTM-based R-R-models enable more accurate and robust flow predictions compared to local LSTM-based R-R-models – especially for rare extreme events?
  • Are regional LSTM-based R-R-models – trained at this temporal resolution – able to generalize to unobserved areas or areas with discharge observations unsuitable for network training?

 

References

Fang, K., Kifer, D., Lawson, K., Feng, D., Shen, C. (2022). The Data Synergy Effects of Time-Series Deep Learning Models in Hydrology. In: Water Resources Research (58). DOI: 10.1029/2021WR029583

Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., Nearing, G. (2019). Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrology and Earth System Sciences (23), S. 5089–5110. DOI: 10.5194/hess-23-5089-2019

Morgenstern, T., Pahner, S., Mietrach, R., Schütze, N. (2021): Flood forecasting in small catchments using deep learning LSTM networks. DOI: 10.5194/egusphere-egu21-15072

Pahner, S., Mietrach, R., Schütze, N. (2019): Flood Forecasting in small catchments: a comparative application of long short-term memory networks and artificial neural networks. DOI: 10.13140/RG.2.2.36770.89286.

How to cite: Morgenstern, T., Grundmann, J., and Schütze, N.: Flood Forecasting with Deep Learning LSTM Networks: Local vs. Regional Network Training Based on Hourly Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9726, https://doi.org/10.5194/egusphere-egu23-9726, 2023.

EGU23-10317 | ECS | Posters on site | HS3.3

A convolutional LSTM model with high accuracy to predict extreme precipitation space-time fields 

Hyojeong Choi and Dongkyun Kim

Precipitation forecast models based on meteorological radar data using machine learning architectures accurately predict spatio-temporal progress of precipitation. However, these data-driven forecasting models tend to underestimate magnitude of extreme precipitation events because the training of them is based on the observed precipitation data in which the normal precipitation events are included significantly more than the rare extreme events. This study proposes a ConvLSTM-based precipitation nowcasting model that can accurately predict space-time field of extreme precipitation. First, precipitation events were classified into 5 subsets using the k-means clustering algorithm based their statistical properties such as mean, standard deviation, skewness, duration, and the calendar month at which the precipitation event occurred. Then, a ConvLSTM-based neural network was trained based on the subset containing extreme precipitation events (events with large mean, variance, and duration occurred in summer months). The model was trained and tested based on the 4km-10minute resolution radar-gauge composite precipitation field of central part of South Korea (200km x 200km) for the period of 2009-2015 and 2016-2020, respectively. The NSE of the model that was trained based on the whole precipitation data was 0.55 while the one trained based on the subset of extreme precipitation was 0.78 showing a significant improvement.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A2C2003471). 

How to cite: Choi, H. and Kim, D.: A convolutional LSTM model with high accuracy to predict extreme precipitation space-time fields, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10317, https://doi.org/10.5194/egusphere-egu23-10317, 2023.

EGU23-12315 | Posters on site | HS3.3

Meta-modeling with data-driven methods in hydrology 

Tobias Krueger, Mark Somogyvari, Ute Fehrenbach, and Dieter Scherer

Process-based models are the standard tools today when trying to understand how physical systems work. There are situations however, when system understanding is not a primary focus and it is worth substituting existing process-based models with computationally more efficient meta-models (or emulators), i.e. proxies designed for specific applications. In our research we have explored potential data-driven meta-modeling approaches for applications in hydrology, designed to solve specific research questions.

In order to find a suitable meta-modeling approach, we have experimented with a set of different data-driven methods. We have employed a multi-fidelity modeling approach, where we gradually increased the complexity of our models. In total five different approaches were investigated: linear model with ordinary least squares regression, linear model with two different Bayesian methods (Hamiltonian Monte Carlo and transdimensional Monte Carlo) and two machine learning approaches (dense artificial neural network and long short-term memory (LSTM) neural network).

For method development the project case study of the Groß Glienicker Lake was used. This is a glacial lake near Berlin, with a strong negative trend in water levels in the last decades. Supported by the observation model from the Central European Refined analysis, we had a daily, high resolution meteorological dataset (precipitation and actual evapotranspiration) and lake level observations for 16 years.

All of the used models are designed similarly: they predict lake level changes one day ahead using precipitation and evapotranspiration data from the previous 70 days. This interval was selected after an extensive parameter test with the linear model. By predicting the change in stored water, we linearize the problem, and by using a longer time interval we allow the methods to automatically compensate for any lag or memory effects inside the catchment. The different methods are evaluated by comparing the fits between the observed and the reconstructed lake levels.

As expected, increasing the model and inversion complexity improves the quality of the reconstruction. Especially the use of nonlinear models was advantageous, the artificial neural network outperformed every other method. However, in the used example these improvements were relatively small – meaning that in practice the simplest linear method was advantageous due to its computational efficiency and robustness, and ease of use and interpretation.

In this presentation we discuss the challenges of data preparation and optimal model design (especially the memory of the hydrological system), while finding the hyperparameters of the specific methods themselves was relatively straight forward. Our results suggest that problem linearization should be a preferred first step in any meta-modeling application, as it helps the training of nonlinear models as well. We also discuss data requirements, because we found that the size of our dataset was too small for the most complex LSTM method, which yielded unstable results and learned spurious background trends.

How to cite: Krueger, T., Somogyvari, M., Fehrenbach, U., and Scherer, D.: Meta-modeling with data-driven methods in hydrology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12315, https://doi.org/10.5194/egusphere-egu23-12315, 2023.

EGU23-12952 | ECS | Orals | HS3.3

On the generalization of hydraulic-inspired graph neural networks for spatio-temporal flood simulations 

Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina

The high computational cost of detailed numerical models for flood simulation hinders their use in real-time and limits uncertainty quantification. Deep-learning surrogates have thus emerged as an alternative to speed up simulations. However, most surrogate models currently work only for a single topography, meaning that they need to be retrained for different case studies, ultimately defeating their purpose. In this work, we propose a graph neural network (GNN) inspired by the shallow water equations used in flood modeling, that can generalize the spatio-temporal prediction of floods over unseen topographies. The proposed model works similarly to finite volume methods by propagating the flooding in space and time, given initial and boundary conditions. Following the Courant-Friedrichs-Lewy condition, we link the time step between consecutive predictions to the number of GNN layers employed in the model. We analyze the model's performance on a dataset of numerical simulations of river dike breach floods, with varying topographies and breach locations. The results suggest that the GNN-based surrogate can produce high-fidelity spatio-temporal predictions, for unseen topographies, unseen breach locations, and larger domain areas with respect to the training ones, while reducing computational times.

How to cite: Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R.: On the generalization of hydraulic-inspired graph neural networks for spatio-temporal flood simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12952, https://doi.org/10.5194/egusphere-egu23-12952, 2023.

EGU23-13493 | ECS | Posters on site | HS3.3

Comparison of  a conceptual rainfall-runoff  model with an artificial neural network model for streamflow prediction 

fadil boodoo, carole delenne, Renaud hostache, and julien freychet

Accurate streamflow forecasting can help minimizing the negative impacts of hydrological events such as floods and droughts. To address this challenge, we explore here artificial neural networks models (ANNs) for streamflow forecasting. These models, which have been proven successful in other fields, may offer improved accuracy and efficiency compared to traditional conceptually-based forecasting approaches.

The goal of this study is to compare the performance of a traditional conceptual rainfall-runoff (hydrological) model with an artificial neural network (ANN) model for streamflow forecasting. As a test case, we use the Severn catchment in the United Kingdom. The adopted ANN model has a long short-term memory (LSTM) architecture with two hidden layers, each with 256 neurons. The model is trained on a 25-year dataset from 1988 to 2013 and tested on a 3-year dataset (from 2014 to 2016). It is also validated on a 3-year dataset (from 2017 to 2020, 2019 being a particularly wet year), to assess its performance in extreme hydrological conditions. The study focuses on daily and hourly predictions.

To conduct this study, the conceptual hydrological model called Superflex is used as a benchmark. Both models are first evaluated using the Nash-Sutcliffe Efficiency (NSE) score. To enable a fair and accurate comparison, both models share the same inputs (i.e. meteorological forcings: total precipitation, daily maximum and minimum temperatures, daylight duration, mean surface downward short wave radiation flux, and vapor pressure). The ANN model was implemented using the Neuralhydrology library developed by F. Kratzert.

In our study, we found that LSTM model is able to provide more accurate one-day forecasts than the  hydrological model Superflex. For the daily predictions, the average NSE score using the LSTM model is 0.85 (with an average NSE score of 0.99 for training period, and 0.85 for validation period), which is higher than the NSE score of 0.74 achieved by the Superflex model (with a score of 0.84 for training period).

The hourly prediction using NSE with the superflex model had a score of 0.88, with a score of 0.7 during training. The LSTM model had an average NSE score of 0.87, with an average score of 0.99 during training and an average score of 0.85 during validation.

These results were obtained without adjusting the hyperparameters and by training the model only on data from the Severn watershed.The ANN model has demonstrated promising results compared to a state-of-the-art conceptual hydrological model in our studies. We will further compare both models using different training dataset periods, and different catchements. These additional tests will provide more information on the capabilities of the LSTM model and help to confirm its effectiveness.

How to cite: boodoo, F., delenne, C., hostache, R., and freychet, J.: Comparison of  a conceptual rainfall-runoff  model with an artificial neural network model for streamflow prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13493, https://doi.org/10.5194/egusphere-egu23-13493, 2023.

EGU23-14399 | ECS | Orals | HS3.3

LSTMs for Hydrological Modelling in Swiss Catchments 

Christina Lott, Leonardo Martins, Jonas Weiss, Thomas Brunschwiler, and Peter Molnar

Simulation of the catchment rainfall-runoff transformation with physically based watershed models is a traditional way to predict streamflow and other hydrological variables at catchment scales. However, the calibration of such models requires large data inputs and computational power and contains many parameters which are often impossible to constrain or validate. An alternative approach is to use data-driven machine learning for streamflow prediction.

In the past few years, LSTM (long short-term memory) models and its variants have been explored in rainfall-runoff modelling. Typical applications use daily climate variables as inputs and model the rainfall-runoff transformation processes with different timescales of memory. This is especially useful as delays in runoff production by snow accumulation and melt, soil water storage, evapotranspiration, etc., can be included. In contrast to feed-forward ANNs (artificial neural networks), LSTMs are capable of maintaining the sequential temporal order of inputs, and compared to RNNs (recurrent neural networks), of learning the long-term dependencies. [1]

However, current work on LSTMs mostly focuses on the USA, the UK and Brazil, where CAMELS datasets are available [1, 2, 3]. Catchments at higher altitudes with snow-driven dynamics and sometimes glaciers are present in small number in these datasets (if at all). Systematic applications of LSTMs for streamflow prediction in climates where a significant part of the catchments are snow and ice dominated are missing. In this work, an FS-LSTM (fast slow-LSTM) previously applied in Brazil is adapted for Swiss catchments to fill this gap [3]. The FS-LSTM explored builds on the work of Hoedt et al. (2021) that imposed mass constraints on an LSTM, called MC-LSTM [4]. FS-LSTM adds a fast and slow part for streamflow, containing rainfall and soil moisture respectively. We will discuss benchmark results against an existing semi-distributed conceptual model widely used in Switzerland for streamflow simulation [5].

 

References:

[1]: Kratzert et al., Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks, 2018.

[2]: Lees et al., Hydrological concept formation inside long short-term memory (LSTM) networks, 2022.

[3]: Quinones et al., Fast-Slow Streamflow Model Using Mass-Conserving LSTM, 2021.

[4]: Hoedt et al., MC-LSTM: Mass-Conserving LSTM, 2021.

[5]: Viviroli et al., An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools, 2009.

How to cite: Lott, C., Martins, L., Weiss, J., Brunschwiler, T., and Molnar, P.: LSTMs for Hydrological Modelling in Swiss Catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14399, https://doi.org/10.5194/egusphere-egu23-14399, 2023.

Improving the understanding of processes is vital to hydrological modeling. One key challenge is how to extract interpretable information that can describe the complex hydrological system from the growing number of observation data to advance our understanding of processes and modeling. To address the problem, we propose a data-driven framework to discover coordinate transformation, which transfers original observations to a reduced-dimension system. The framework combines deep learning method with sparse regression to approximate the specific hydrological process: deep learning methods have a rich representation to promote generalization, and sparse regression can sparsely identify parsimonious models to promote interpretability. By doing so, we can identify the essential latent variables under a physically meaning-wise coordinate system where the hydrological processes are linearly and sparsity represented to capture the behavior of the system from observations. To demonstrate the framework, we focus on the evaporation process. The relationships between potential evaporation and climate variables including long/short wave radiation, air temperature, air pressure, relative humidity, and wind speed are quantified. The connection between the climate variables and coordinates components extracted are evaluated to capture the pattern of climate variables in the component space. The robustness and statistical stability of the framework is examined based on distributed observations from FluxNet towers over North America. The resulting modeling framework shows the potential of deep learning methods for improving our knowledge of the hydrological system.

How to cite: Hu, X., Tuo, Y., and Disse, M.: Deep learning based coordinates transformations for improving process understanding in hydrological modeling system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14631, https://doi.org/10.5194/egusphere-egu23-14631, 2023.

EGU23-15575 | Orals | HS3.3

Application of deep convolutional neural networks for precipitation estimation through both top-down and bottom-up approaches 

Hamidreza Mosaffa, Paolo Filippucci, Luca Ciabatta, Christian Massari, and Luca Brocca

Reliable and accurate precipitation estimations are a crucial hydrological parameter for various applications, including managing water resources, drought monitoring and natural hazard prediction. The two main approaches for estimating precipitation from satellite data are the top-down and bottom-up. The top-down approach uses data from Geostationary and Low Earth Orbiting satellites to infer precipitation from atmosphere and cloud information, while the bottom-up approach estimates precipitation using soil moisture observations, e.g.  the SM2RAIN algorithm. The main difference between these approaches is that the top-down approach is a more direct method of measuring precipitation that estimates it instantaneously, which may lead to underestimation, while the bottom-up approach measures accumulated rainfall with more reliable precipitation estimation between two consecutive SM measurements. In this study, we develop the deep convolutional neural networks (CNN) algorithm to combine the top-down and bottom-up approaches for estimating precipitation using the satellite level 1 products including the satellite backscatter information from the Advanced SCATterometer (ASCAT), infrared (IR) and water vapor (WV) channels from geostationary satellites. This algorithm is assessed at 0.1° spatial and daily temporal resolution over Italy for the period of 2019-2021. The results show that the developed model improves the accuracy of precipitation estimation. Additionally, it indicates that there is a significant potential for global precipitation estimation using this model.

How to cite: Mosaffa, H., Filippucci, P., Ciabatta, L., Massari, C., and Brocca, L.: Application of deep convolutional neural networks for precipitation estimation through both top-down and bottom-up approaches, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15575, https://doi.org/10.5194/egusphere-egu23-15575, 2023.

EGU23-15604 | ECS | Posters on site | HS3.3

Forecasting discharges through explainable machine learning approaches at an alpine karst spring 

Anna Pölz, Julia Derx, Andreas Farnleitner, and Alfred Paul Blaschke

Karst springs provide drinking water for approximately 700 million people worldwide. Complex subsurface flow processes lead to challenges for modelling spring discharges. Machine learning (ML) models possess the ability to learn non-linear patterns and show promising results in forecasting dynamic spring discharge. We compare the performance of three ML models of varying complexity in forecasting karst spring discharges: the multivariate adaptive regression spline model (MARS), a feed-forward neural network (ANN) and a long short-term memory model (LSTM). The well-studied alpine karst spring LKAS2 in Austria is used as test case. We provide model explanations including feature attribution through Shapley additive explanations (SHAP), a method based on Shapley values. Our results show that the higher the model complexity, the higher the accuracy, based on the evaluated symmetric mean absolute percentage error of the three investigated models. With SHAP every prediction can be explained through each feature in each input time step. We found seasonal model differences. For example, snow influenced the model mostly in winter and spring. Analyzing the combinations of input time steps and features provided further insights into the model performance. For instance, the SHAP results showed that a high electrical conductivity in recent time steps, which indicates that the karst water is less diluted with precipitation, leads to a reduced discharge forecast. These feature attribution results coincide with physical processes within karst systems. Therefore, the introduced SHAP method can increase the confidence in ML model forecasts and emphasizes the raison d’être of complex and accurate deep learning models in hydrology. This allows the operator to better understand and evaluate the model’s output, which is essential for drinking water management.

How to cite: Pölz, A., Derx, J., Farnleitner, A., and Blaschke, A. P.: Forecasting discharges through explainable machine learning approaches at an alpine karst spring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15604, https://doi.org/10.5194/egusphere-egu23-15604, 2023.

EGU23-15629 | ECS | Posters virtual | HS3.3

Peak Hydrological Event Simulation with Deep Learning Algorithm 

Nicole Tatjana Scherer, Muhammad Nabeel Usmann, Markus Disse, and Jingshui Huang

Most floods are caused by heavy rainfall events, including the disaster in the Simbach catchment in 2016. For the Simbach catchment, a study was already carried out using the conceptual Hydrologiska Byråns Vattenbalansavdelning (HBV) model to simulate the extreme event of 2016. While the calibration model performance is classified as very good, the overall validation is classified as unsatisfactory. Recent studies showed that data-driven models outperform benchmark rainfall-runoff models. A widely used data-driven model is the Long-Short-Term-Memory algorithm (LSTM). The main advantage of this algorithm is the ability to learn short-term as well as long term dependencies.

The objective of this work is to determine if a data-driven model outperforms the conceptual model. For this purpose, in a first step a LSTM model is setup and its results are compared with the results of the HBV model. It is assumed that the LSTM model outperforms the HBV model in training and validation but is not able to simulate the extreme event, because the extrapolation capabilities of Neuronal Networks are poor if they operate outside of their training range. In a second step, it is studied if the model performance can be improved by providing more features to the model. Therefore, different feature combinations are provided to the model. Furthermore, it is assumed that providing more data to the model will improve its performance. Therefore, in a third step more events are used for training and validation.

It was concluded that the LSTM model is able to simulate the rainfall-runoff process. A satisfactory overall model performance can be achieved using only precipitation as input data and a small training dataset of four events. But, as the HBV model, the LSTM model is not able to simulate the extreme event, because no extreme event is present within the training dataset. However, the LSTM model outperforms the HBV model, because the LSTM generalizes better. Furthermore, the model performance of the LSTM model using six events can be improved by providing additionally the soil moisture class as input data. Whereas providing more features to the model results in worse model performance. Providing more events to the model does not significantly improve its performance. However, the model improved especially for the event in June 2015. If the model is trained with more events having higher magnitude than the 2015 event, the event in 2015 is no longer classified as an out-of-sample event, resulting in better model performance. Providing the model more events and more input features does not significantly improve the model performance. 

The results show the potential and limitations using the LSTM model in modeling extreme events.

How to cite: Scherer, N. T., Usmann, M. N., Disse, M., and Huang, J.: Peak Hydrological Event Simulation with Deep Learning Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15629, https://doi.org/10.5194/egusphere-egu23-15629, 2023.

EGU23-16658 | ECS | Orals | HS3.3

Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing 

Tadd Bindas, Wen-Ping Tsai, Jiangtao Liu, Farshid Rahmani, Dapeng Feng, Yuchen Bian, Kathryn Lawson, and Chaopeng Shen

Differentiable modeling has been introduced recently as a method to learn relationships from a combination of data and structural priors. This method uses end-to-end gradient tracking inside a process-based model to tune internal states and parameters along with neural networks, allowing us to learn underlying processes and spatial patterns. Hydrologic routing modules are typically needed to simulate flows in stem rivers downstream of large, heterogeneous basins, but obtaining suitable parameterization for them has previously been difficult. In this work, we apply differentiable modeling in the scope of streamflow prediction by coupling a physically-based routing model (which computes flow velocity and discharge in the river network given upstream inflow conditions) to neural networks which provide parameterizations for Manning’s river roughness parameter (n). This method consists of an embedded Neural Network (NN), which uses (imperfect) DL-simulated runoffs and reach-scale attributes as forcings and inputs, respectively, entered into the Muskingum-Cunge method and trained solely on downstream discharge. Our initial results show that while we cannot identify channel geometries, we can learn a parameterization scheme for roughness that follows observed n trends. Training on a short sample of observed data showed that we could obtain highly accurate routing results for the training and inner, untrained gages. This general framework can be applied to small and large scales to learn channel roughness and predict streamflow with heightened interpretability. 

 

How to cite: Bindas, T., Tsai, W.-P., Liu, J., Rahmani, F., Feng, D., Bian, Y., Lawson, K., and Shen, C.: Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16658, https://doi.org/10.5194/egusphere-egu23-16658, 2023.

Although deep learning (DL) models have shown extraordinary performance in hydrologic modeling, they are still hard to interpret and not able to predict untrained hydrologic variables due to lacking physical meanings and constraints. This study established hybrid differentiable models (namely the delta models) with regionalized parameterization and learnable structures based on a DL-based differentiable parameter learning (dPL) framework. The simulation experiments on both US and global basins demonstrate that the delta models can approach the performance of the state-of-the-art long short-term memory (LSTM) network on discharge prediction. Different from the pure data-driven LSTM model, the delta models can output a full set of hydrologic variables not used as training targets. The evaluation with independent data sources showed that the delta models, only trained on discharge observations, can also give decent predictions for ET and baseflow. The spatial extrapolation experiments showed that the delta models can surpass the performance of the LSTM model for predictions in large ungauged regions in terms of the daily hydrographic metrics and multi-year trend prediction. The spatial patterns of the parameters learned by the delta models remain remarkably stable from the in-sample to spatial out-of-sample predictions, which explains the robustness of the delta models for spatial extrapolation. More importantly, the proposed modeling framework enables directly learning new relations between intermediate variables from large observations. This study shows that the model performance and physical meanings can be balanced with the differentiable modeling approach which is promising to large-scale hydrologic prediction and knowledge discovery.

How to cite: Feng, D. and Shen, C.: A differentiable modeling approach to systematically integrating deep learning and physical models for large-scale hydrologic prediction and knowledge discovery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16947, https://doi.org/10.5194/egusphere-egu23-16947, 2023.

EGU23-16974 | Orals | HS3.3

From Hindcast to Forecast with Deep Learning Streamflow Models 

Grey Nearing, Martin Gauch, Daniel Klotz, Frederik Kratzert, Asher Metzger, Guy Shalev, Shlomo Shenzis, Tadele Tekalign, Dana Weitzner, and Oren Gilon

Deep learning has become the de facto standard for streamflow simulation. While there are examples of deep learning based streamflow forecast models (e.g., 1-5), the majority of the development and research has been done with hindcast models. The primary challenge in using deep learning models for forecasting (e.g., flood forecasting) is that the meteorological input data are drawn from different distributions in hindcast vs. forecast. The (relatively small) amount of research that has been done on deep learning streamflow forecasting has largely used an encoder-decoder approach to account for forecast distribution shifts. This is, for example, what Google’s operational flood forecasting model uses [4]. 

In this work we show that the encoder-decoder approach results in artifacts in forecast trajectories that are not detectable with standard hydrological metrics, but which can cause forecasts to have incorrect trends (e.g., rising when they should be falling and vice-versa).  We solve this problem using regularized embeddings, which remove forecast artifacts without harming overall accuracy. 

Perhaps more importantly, input embeddings allow for training models on spatially and/or temporally incomplete meteorological inputs, meaning that a single model can be trained using input data that does not exist everywhere or does not exist during the entire training or forecast period. This allows models to learn from a significantly larger training data set, which is important for high-accuracy predictions. It also allows large (e.g., global) models to learn from local weather data. We demonstrate how and why this is critical for state-of-the-art global-scale streamflow forecasting. 

 

  • Franken, Tim, et al. An operational framework for data driven low flow forecasts in Flanders. No. EGU22-6191. Copernicus Meetings, 2022.
  • Kao, I-Feng, et al. "Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting." Journal of Hydrology 583 (2020): 124631.
  • Liu, Darong, et al. "Streamflow prediction using deep learning neural network: case study of Yangtze River." IEEE access 8 (2020): 90069-90086.
  • Nevo, Sella, et al. "Flood forecasting with machine learning models in an operational framework." Hydrology and Earth System Sciences 26.15 (2022): 4013-4032.
  • Girihagama, Lakshika, et al. "Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism." Neural Computing and Applications 34.22 (2022): 19995-20015.

 

How to cite: Nearing, G., Gauch, M., Klotz, D., Kratzert, F., Metzger, A., Shalev, G., Shenzis, S., Tekalign, T., Weitzner, D., and Gilon, O.: From Hindcast to Forecast with Deep Learning Streamflow Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16974, https://doi.org/10.5194/egusphere-egu23-16974, 2023.

EGU23-338 | ECS | Posters virtual | ITS1.1/NH0.1

A Stacking Ensemble Deep Learning Approach for Post Disaster Building Assessment using UAV Imagery 

Leon Sim, Fang-Jung Tsai, and Szu-Yun Lin

Traditional post-disaster building damage assessments were performed manually by the response team, which was risky and time-consuming. With advanced remote sensing technology, such as an unmanned aerial vehicle (UAV), it would be possible to acquire high-quality aerial videos and operate at a variety of altitudes and angles.  The collected data would be sent into a neural network for training and validating. In this study, the Object Detection model (YOLO) was utilized, which is capable of predicting both bounding boxes and damage levels. The network was trained using the ISBDA dataset, which was created from aerial videos of the aftermath of Hurricane Harvey in 2017, Hurricane Michael and Hurricane Florence in 2018, and three tornadoes in 2017, 2018, and 2019 in the United States. The Joint Damage Scale was used to classify the buildings in this dataset into four categories: no damage, minor damage, major damage, and destroyed. However, the number of major damage and destroyed classes are significantly lower than the number of no damage and minor damage classes in the dataset. Also, the damage characteristics of minor and major damage classes are similar under such type of disaster. These caused the YOLO model prone to misclassify the intermediate damage levels, i.e., minor and major damage in our earlier experiments. This study aimed to improve the YOLO model using a stacking ensemble deep learning approach with a image classification model called Mobilenet. First, the ISBDA dataset was used and refined to train the YOLO network and the Mobilenet network separately, and the latter provides two classes predictions (0 for no damage or minor damage, 1 for major damage or destroyed) rather than the four classes by the former. In the inference phase, the initial predictions from the trained YOLO network, including bounding box coordinates, confidence scores for four damage classes, and the predicted class, were then extracted and passed to the trained Mobilenet to generate the secondary predictions for each building. Based on the secondary predictions, two hyperparameters were utilized to refine the initial predictions by modifying the confidence scores of each class, and the hyperparameters were trained during this phase. Lastly, the trained hyperparameters were applied to the testing dataset to evaluate the performance of the proposed method. The results show that our stacking ensemble method could obtain more reliable predictions of intermediate classes.

 

How to cite: Sim, L., Tsai, F.-J., and Lin, S.-Y.: A Stacking Ensemble Deep Learning Approach for Post Disaster Building Assessment using UAV Imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-338, https://doi.org/10.5194/egusphere-egu23-338, 2023.

        Since Taiwan is located at the Pacific Ring of Fire, seismic activity of varying magnitudes occurs almost every day. Among them, some of these seismic activities have in turn caused severe disasters, resulting in loss of personal property, casualties and damage to important public facilities. Therefore, investigating the long-term spatiotemporal pattern of seismic activities is a crucial task for understanding the causes of seismic activity and to predict future seismic activity, in order to carry out disaster prevention measures in advance. Previous studies mostly focused on the causes of single seismic events on the small spatiotemporal scale. In this study, the data from 1987 to 2020 are used, including seismic events from the United States Geological Survey (USGS), the ambient environmental factors such as daily air temperature from Taiwan Central Weather Bureau (CWB) and daily sea surface temperature data from National Oceanic and Atmospheric Administration (NOAA). Then the temperature difference between the land air temperature and the sea surface temperature (SST) to the correlation between the occurrence of seismic activities and the abnormal occurrence of temperature difference are compared. The results show that lots of seismic activities often have positive and negative anomalies of temperature difference from 21 days before to 7 days after the seismic event. Moreover, there is a specific trend of temperature difference anomalies under different magnitude intervals. In the magnitude range of 2.5 to 4 and greater than 6, almost all of the seismic events have significant anomalous differences in the temperature difference between land air temperature and SST compared with no seismic events. This study uncovers anomalous frequency signatures of seismic activities and temperature differences between land air temperature and SST. The significant difference in temperature difference between seismic events and non-seismic events was compared by using statistical analysis. Additionally, the deep neural network (DNN) of deep learning model, logistic regression and random forest of machine learning model was used to identify whether there will be a seismic event under different magnitude intervals. It is hoped that it can provide relevant information for the prediction of future seismic activity, to more accurately prevent disasters that may be caused by seismic activity.

How to cite: Chen, Y.-H. and Lin, Y.-C.: Investigating the Correlation between the Characteristics of Seismic Activity and Environmental Variables in Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2564, https://doi.org/10.5194/egusphere-egu23-2564, 2023.

The 2010-2011 Canterbury Earthquake sequence (CES) led to unprecedented building damage in the Canterbury region, New Zealand. Commercial and residential buildings were significantly affected. Due to New Zealand’s unique insurance setting, around 80% of the losses were covered by insurance (Bevere & Balz, 2012; King et al., 2014). The Insurance Council of New Zealand (ICNZ) estimated the total economic losses to be more than NZ$40 billion, with the Earthquake Commission (EQC) and private insurers covering NZ$10 billion and NZ$21 billion of the losses, respectively (ICNZ, 2021). As a result of the CES and the 2016 Kaikoura earthquake, EQC’s Natural Disaster Fund was depleted (EQC, 2022). This highlighted the need for improved tools enabling damage and loss analysis for natural hazards.
This research project used residential building claims collected by EQC following the CES to develop a rapid seismic loss prediction model for residential buildings in Christchurch. Geographic information systems (GIS) tools, data science techniques, and machine learning (ML) were used for the model development. Before the training of the ML model, the claims data was enriched with additional information from external data sources. The seismic demand, building characteristics, soil conditions, and information about the liquefaction occurrence were added to the claims data. Once merged and pre-processed, the aggregated data was used to train ML models based on the main events in the CES. Emphasis was put on the interpretability and explainability of the model. The ML model delivered valuable insights related to the most important features contributing to losses. Those insights are aligned with engineering knowledge and observations from previous studies, confirming the potential of using ML for disaster loss prediction and management. Care was also put into the retrainability of the model to ensure that any new data from future earthquake events can rapidly be added to the model. 

How to cite: Roeslin, S.: Development of a Rapid Seismic Loss Prediction Model for Residential Buildings using Machine Learning - Christchurch, New Zealand, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2996, https://doi.org/10.5194/egusphere-egu23-2996, 2023.

EGU23-3928 | Orals | ITS1.1/NH0.1

Comparison of deep learning approaches to monitor trash screen blockage from CCTV cameras 

Remy Vandaele, Sarah L Dance, and Varun Ojha

We investigate the use of CCTV cameras and deep learning to automatically monitor trash screen blockage. 

Trash screens are installed to prevent debris from entering critical parts of river networks (pipes, tunnels, locks,...). When the debris piles up at the trash screens,  it  may block the waterway and can cause flooding. It is thus crucial to clean blocked trash screens and avoid flooding and consequent damage. Currently, the maintenance crews must manually check a camera or river level data or go on site to check the state of the screen to know if it needs cleaning. This wastes valuable time in emergency situations where blocked screens must be urgently cleaned (e.g., in case of forecast  heavy rainfall). Some initial attempts at trying to predict trash screen blockage exist. However, these have not been widely adopted in practice.  CCTV cameras can be easily installed at any location and can thus be used to monitor the state of trash screens, but the images need to be processed by an automated algorithm to inform whether the screen is blocked.

With the help of UK-based practitioners (Environment Agency and local councils), we have created a dataset of 40000 CCTV trash screen images coming from 36 cameras, each labelled with blockage information. Using this database, we have compared 3 deep learning approaches to automate the detection of trash screen blockage: 

  • A binary image classifier, which takes as input a single image, and outputs a binary label that estimates whether the trash screen is blocked.
  • An approach based on anomaly detection which tries to reconstruct the input image with an auto-encoder trained on clean trash screen images.  In consequence, blocked trash screens are detected as anomalies by the auto-encoder.
  • An image similarity estimation approach based on the use of a siamese network, which takes as input two images and outputs a similarity index related, in our case, to whether both images contain trash. 

Using performance criteria chosen in discussion  with practitioners (overall accuracy, false alarm rate, resilience to luminosity / moving fields of view, computing capabilities), we show that deep learning can be used in practice to automate the identification of blocked trash screens. We also analyse the strengths and weaknesses of each of these approaches and provide guidelines for their application.

How to cite: Vandaele, R., Dance, S. L., and Ojha, V.: Comparison of deep learning approaches to monitor trash screen blockage from CCTV cameras, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3928, https://doi.org/10.5194/egusphere-egu23-3928, 2023.

EGU23-4455 | ECS | Posters virtual | ITS1.1/NH0.1

Traffic Monitoring System Design considering Multi-Hazard Disaster Risks 

Michele Gazzea, Reza Arghandeh, and Amir Miraki

Roadways are critical infrastructure in our society, providing services for people through and between cities. However, they are prone to closures and disruptions, especially after extreme weather events like hurricanes.

At the same time, traffic flow data are a fundamental type of information for any transportation system.

We tackle the problem of traffic sensor placement on roadways to address two tasks at the same time. The first task is traffic data estimation in ordinary situations, which is vital for traffic monitoring and city planning. We design a graph-based method to estimate traffic flow on roads where sensors are not present. The second one is enhanced observability of roadways in case of extreme weather events. We propose a satellite-based multi-domain risk assessment to locate roads at high risk of closures. Vegetation and flood hazards are taken into account. We formalize the problem as a search method over the network to suggest the minimum number and location of traffic sensors to place while maximizing the traffic estimation capabilities and observability of the risky areas of a city.

How to cite: Gazzea, M., Arghandeh, R., and Miraki, A.: Traffic Monitoring System Design considering Multi-Hazard Disaster Risks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4455, https://doi.org/10.5194/egusphere-egu23-4455, 2023.

Earthquake-induced land deformation and structure failure are more severe over soft soils than over firm soils and rocks owing to the seismic site effect and liquefaction. The site-specific seismic site effect related to the amplification of ground motion, liquefaction, and landslide has spatial uncertainty depending on the local subsurface, surface geological, and topographic conditions. When the 2017 Pohang earthquake (M 5.4), South Korea’s second strongest earthquake in decades, occurred, the severe damages influenced by variable site response and vulnerability indicators were observed focusing on the basin or basin-edge region deposited unconsolidated Quaternary sediments. Thus, nationwide site characterization is essential considering empirical correlations with geotechnical site response and hazard parameters and surface proxies. Furthermore, in case of so many variables and tenuously related correlations, machine learning classification models can prove to be very precise than the parametric methods. This study established a multivariate seismic site classification system using the machine learning technique based on the geospatial big data platform.

The supervised machine learning classification techniques and more specifically, random forest, support vector machine (SVM), and artificial neural network (ANN) algorithms have been adopted. Supervised machine learning algorithms analyze a set of labeled training data consisting of a group of input data and desired output values. They produce an inferred function that can be used for predictions from given input data. To optimize the classification criteria by considering the geotechnical uncertainty and local site effects, the training datasets applying principal component analysis (PCA) were verified with k-fold cross-validation. Moreover, the optimized training algorithm, proved by loss estimators (receiver operating characteristic curve (ROC), the area under the ROC curve (AUC)) based on confusion matrix, was selected.

For the southeastern region in South Korea, the boring log information (strata, standard penetration test, etc.), geological map (1:50k scale), digital terrain model (having 5 m × 5 m), soil map (1:250k scale) were collected and constructed as geospatial big data. Preliminarily, to build spatially coincided datasets with geotechnical response parameters and surface proxies, the mesh-type geospatial information was built by advanced geostatistical interpolation and simulation methods.

Site classification systems use seismic hazard parameters related to the geotechnical characteristics of the study area as the classification criteria. The current site classification systems in South Korea and the United States recommend Vs30, which is the average shear wave velocity (Vs) up to 30 m underground. This criterion uses only the dynamic characteristics of the site without considering its geometric distribution characteristics. Thus, the geospatial information included the geo-layer thickness, surface proxies (elevation, slope, geological category, soil category), and Vs30. For the liquefaction and landslide hazard estimation, the liquefaction vulnerability indexes (i.e., liquefaction potential or severity index) and landslide vulnerability indexes (i.e., a factor of safety or displacement) were also trained as input features into the classifier modeling. Finally, the composite status against seismic site effect, liquefaction, and landslide was predicted as hazard class (I.e., safe, slight-, moderate-, extreme-failure) based on the best-fitting classifier.  

How to cite: Kim, H.: Machine Learning-based Site Classification System for Earthquake-Induced Multi-Hazard in South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4757, https://doi.org/10.5194/egusphere-egu23-4757, 2023.

EGU23-4816 | ECS | Posters on site | ITS1.1/NH0.1

XAIDA4Detection: A Toolbox for the Detection and Characterization of Spatio-Temporal Extreme Events 

Jordi Cortés-Andrés, Maria Gonzalez-Calabuig, Mengxue Zhang, Tristan Williams, Miguel-Ángel Fernández-Torres, Oscar J. Pellicer-Valero, and Gustau Camps-Valls

The automatic anticipation and detection of extreme events constitute a major challenge in the current context of climate change, which has changed their likelihood and intensity. One of the main objectives within the EXtreme Events: Artificial Intelligence for Detection and Attribution (XAIDA) project (https://xaida.eu/) is related to developing novel approaches for the detection and localization of extreme events, such as tropical cyclones and severe convective storms, heat waves and droughts, as well as persistent winter extremes, among others. Here we introduce the XAIDA4Detection toolbox that allows for tackling generic problems of detection and characterization. The open-source toolbox integrates a set of advanced ML models, ranging in complexity, assumptions, and sophistication, and yields spatio-temporal explicit detection maps with probabilistic heatmap estimates. We included supervised and unsupervised methods, deterministic and probabilistic, neural networks based on convolutional and recurrent nets, and density-based methods. The toolbox is intended for scientists, engineers, and students with basic knowledge of extreme events, outlier detection techniques, and Deep Learning (DL), as well as Python programming with basic packages (Numpy, Scikit-learn, Matplotlib) and DL packages (PyTorch, PyTorch Lightning). This presentation will summarize the available features and their potential to be adapted to multiple extreme event problems and use cases.

How to cite: Cortés-Andrés, J., Gonzalez-Calabuig, M., Zhang, M., Williams, T., Fernández-Torres, M.-Á., Pellicer-Valero, O. J., and Camps-Valls, G.: XAIDA4Detection: A Toolbox for the Detection and Characterization of Spatio-Temporal Extreme Events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4816, https://doi.org/10.5194/egusphere-egu23-4816, 2023.

EGU23-5581 | Posters on site | ITS1.1/NH0.1

Vision Transformers for building damage assessment after natural disasters 

Adrien Lagrange, Nicolas Dublé, François De Vieilleville, Aurore Dupuis, Stéphane May, and Aymeric Walker-Deemin

Damage assessment is a critical step in crisis management. It must be fast and accurate in order to organize and scale the emergency response in a manner adapted to the real needs on the ground. The speed requirements motivate an automation of the analysis, at least in support of the photo-interpretation. Deep Learning (DL) seems to be the most suitable methodology for this problem: on one hand for the speed in obtaining the answer, and on the other hand by the high performance of the results obtained by these methods in the extraction of information from images. Following previous studies to evaluate the potential contribution of DL methods for building damage assessment after a disaster, several conventional Deep Neural Network (DNN) and Transformers (TF) architectures were compared.

Made available at the end of 2019, the xView2 database appears to be the most interesting database for this study. It gathers images of disasters between 2011 and 2018 with 6 types of disasters: earthquakes, tsunamis, floods, volcanic eruptions, fires and hurricanes. For each of these disasters, pre- and post-disaster images are available with a ground truth containing the building footprint as well as the evaluation of the type of damage divided into 4 classes (no damage, minor damage, major damage, destroyed) similar to those considered in the study.

This study compares a wide range DNN architectures all based on an encoder-decoder structure. Two encoder families were implemented: EfficientNet (B0 to B7 configurations) and Swin TF (Tiny, Small, and Base configurations). Three adaptable decoders were implemented: UNet, DeepLabV3+, FPN. Finally, to benefit from both pre- and post-disaster images, the trained models were designed to proceed images with a Siamese approach: both images are processed independently by the encoder, and the extracted features are then concatenated by the decoder.

Taking benefit of global information (such as the type of disaster for example) present in the image, the Swin TF, associated with FPN decoder, reaches the better performances than all other encoder-decoder architectures. The Shifted WINdows process enables the pipe to process large images in a reasonable time, comparable to the processing time of EfficientNet-based architectures. An interesting additional result is that the models trained during this study do not seem to benefit so much from extra-large configurations, and both small and tiny configurations reach the highest scores.

How to cite: Lagrange, A., Dublé, N., De Vieilleville, F., Dupuis, A., May, S., and Walker-Deemin, A.: Vision Transformers for building damage assessment after natural disasters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5581, https://doi.org/10.5194/egusphere-egu23-5581, 2023.

Natural and man-made disasters pose a threat to human life, flora-fauna, and infrastructure. It is critical to detect the damage quickly and accurately for infrastructures right after the occurrence of any disaster. The detection and assessment of infrastructure damage help manage financial strategy as well. Recently, many researchers and agencies have made efforts to create high-resolution satellite imageries database related to pre and post-disaster events. The advanced remote sensing satellite imageries can reflect the surface of the earth accurately up to 30 cm spatial resolution on a daily basis. These high spatial resolutions (HSR) imageries can help access any natural hazard's damage by comparing the pre- and post-disaster data. These remote sensing imageries have limitations, such as cloud occlusions. Building under a thick cloud cannot be recognised in optical images. The manual assessment of the severity of damage to buildings/infrastructure by comparing bi-temporal HSR imageries or airborne will be a tedious and subjective job. On the other hand, the emerging use of unmanned aired vehicles (UAV) can be used to assess the situation precisely. The high-resolution UAV imageries and the HSR satellite imageries can complement each other for critical infrastructure damage assessment. In this study, a novel approach is used to integrate UAV data into HSR satellite imageries for the building damage assessment using a convolution neural network (CNN) based deep learning model. The research work is divided into two fundamental sub-tasks: first is the building localisation in the pre-event images, and second is the damage classification by assigning a unique damage level label reflecting the degree of damage to each building instance on the post-disaster images. For the study, the HSR satellite imageries of 36 pairs of pre- and post natural hazard events is acquired for the year 2021-22, similarly available UAV based data for these events is also collected from the open data source. The data is then pre-processed, and the building damage is assessed using a deep object-based semantic change detection framework (ChangeOS). The mentioned model was trained on the xview2 building damage assessment datasets comprised of ~20,000 images with ~730,000 building polygons of pre and post disaster events over the globe from 2011-2018. The experimental setup in this study includes training on the global dataset and testing on the regional-scale building damage assessment using HSR satellite imageries and local-scale using UAV imageries. The result obtained from the bi-temporal assessment of HSR images for the Indonesia Earthquake 2022 has shown an F1 score of ~67%, while the Uttarakhand flooding event 2021 has reported an F1 score of ~64%. The HSR imageries from the UAV Haiti earthquake event in 2011 have also shown less but promising F1 scores of ~54%. It is inferred that merging HSR imageries from satellite and UAV for building damage assessment using the ChangeOS framework represents a robust tool to further promote future research in infrastructure maintenance strategy and policy management in disaster response.

How to cite: Gupta, S. and Nair, S.: A novel approach for infrastructural disaster damage assessment using high spatial resolution satellite and UAV imageries using deep learning algorithms., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5778, https://doi.org/10.5194/egusphere-egu23-5778, 2023.

EGU23-5913 | ECS | Orals | ITS1.1/NH0.1

Pluto: A global volcanic activity early warning system powered by large scale self-supervised deep learning on InSAR data 

Nikolaos Ioannis Bountos, Dimitrios Michail, Themistocles Herekakis, Angeliki Thanasou, and Ioannis Papoutsis

Artificial intelligence (AI) methods have emerged as a powerful tool to study and in some cases forecast natural disasters [1,2]. Recent works have successfully combined deep learning modeling with scientific knowledge stemming from the SAR Interferometry domain propelling research on tasks like volcanic activity monitoring [3], associated with ground deformation. A milestone in this interdisciplinary field has been the release of the Hephaestus [4] InSAR dataset, facilitating automatic InSAR interpretation, volcanic activity localization as well as the detection and categorization of atmospheric contributions in wrapped interferograms. Hephaestus contains annotations for approximately 20,000 InSAR frames, covering the 44 most active volcanoes in the world. The annotation was performed  by a team of InSAR experts that manually examined each InSAR frame individually. However, even with such a large dataset, class imbalance remains a challenge, i.e. the InSAR samples containing volcano deformation fringes are orders of magnitude less than those that do not. This is anticipated since natural hazards are in principle rare in nature. To counter that, the authors of Hephaestus provide more than 100,000 unlabeled InSAR frames to be used for global large-scale self-supervised learning, which is more robust to class imbalance when compared to supervised learning [5]. 

Motivated by the Hephaestus dataset and the insights provided by [2], we train global, task-agnostic models in a self-supervised learning fashion that can handle distribution shifts caused by spatio-temporal variability as well as major class imbalances. By finetuning such a model to the labeled part of Hephaestus we obtain the backbone for a global volcanic activity alerting system, namely Pluto. Pluto is a novel end-to-end AI based system that provides early warnings of volcanic unrest on a global scale.

Pluto automatically synchronizes its database with the Comet-LiCS [6] portal to receive newly generated Sentinel-1 InSAR data acquired over volcanic areas. The new samples are fed to our volcanic activity detection model. If volcanic activity is detected, an automatic email is sent to the service users, which contains information about the intensity, the exact location and the type (Mogi, Sill, Dyk) of the event. To ensure a robust and ever-improving service we augment Pluto with an iterative pipeline that collects samples that were misclassified in production, and uses them to further improve the existing model. 

 

[1] Kondylatos et al. "Wildfire danger prediction and understanding with Deep Learning." Geophysical Research Letters 49.17 (2022): e2022GL099368.

[2] Bountos et al. "Self-supervised contrastive learning for volcanic unrest detection." IEEE Geoscience and Remote Sensing Letters 19 (2021): 1-5.

[3] Bountos et al. "Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection." IEEE Transactions on Geoscience and Remote Sensing (2022).

[4] Bountos et al. "Hephaestus: A large scale multitask dataset towards InSAR understanding." Proceedings of the IEEE/CVF CVPR. 2022.

[5] Liu et al. "Self-supervised learning is more robust to dataset imbalance." arXiv preprint arXiv:2110.05025 (2021).

[6] Lazecký et al. "LiCSAR: An automatic InSAR tool for measuring and monitoring tectonic and volcanic activity." Remote Sensing 12.15 (2020): 2430.

How to cite: Bountos, N. I., Michail, D., Herekakis, T., Thanasou, A., and Papoutsis, I.: Pluto: A global volcanic activity early warning system powered by large scale self-supervised deep learning on InSAR data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5913, https://doi.org/10.5194/egusphere-egu23-5913, 2023.

It has become increasingly apparent over the past few decades that environmental degradation is something of a common concern for humanity and it is difficult to deny that the present environmental problems are caused primarily by anthropogenic activities rather than natural causes.

To minimize disaster’s risk, the role of geospatial science and technology may be a terribly helpful and necessary technique for hazard zone mapping throughout emergency conditions. 

This approach can definitively help predict harmful events, but also to mitigate damage to the environment from events that cannot be efficiently predicted.

With detailed information obtained through various dataset, decision making has become simpler. This fact is crucial for a quick and effective response to any disaster. Remote sensing, in particular RADAR/SAR data, help in managing a disaster at various stages. 

Prevention for example refers to the outright avoidance of adverse impacts of hazards and related disasters; preparedness refers to the knowledge and capacities to effectively anticipate, respond to, and recover from, the impacts of likely, imminent or current hazard events or conditions.

Finally relief is the provision of emergency services after a disaster in order to reduce damage to environment and people.

Thanks to the opportunity proposed by ASI (Italian Space Agency) to use COSMO-SkyMed data, in NeMeA Sistemi srl we developed two projects: “Ventimiglia Legalità”, “Edilizia Spontanea” and 3xA.

Their main objective is to detect illegal buildings not present in the land Legal registry.

We developed new and innovative technologies using integrated data for the monitoring and protection of environmental and anthropogenic health, in coastal and nearby areas. 

3xA project addresses the highly challenging problem of automatically detecting changes from a time series of high-resolution synthetic aperture radar (SAR) images. In this context, to fully leverage the potential of such data, an innovative machine learning based approach has been developed. 

The project is characterized by an end-to-end training and inference system which takes as input two raw images and produces a vectorized change map without any human supervision.

More into the details, it takes as input two SAR acquisitions at time t1 and t2, the acquisitions are firstly pre-processed, homogenised and finally undergo a completely self-supervised algorithm which takes advantage of DNNs to classify changed/unchanged areas. This method shows promising results in automatically producing a change map from two input SAR images (Stripmap or Spotlight COSMO-SkyMed data), with 98% accuracy.

Being the process automated, results are produced faster than similar products generated by human operators.

A similar approach has been followed to create an algorithm which performs semantic segmentation from the same kind of data.

This time, only one of the two SAR acquisitions is taken as input for pre-processing steps and then for a supervised neural network. The result is a single image where each pixel is labelled with the class predicted by the algorithm. 

Also in this case, results are promising, reaching around 90% of accuracy. 

How to cite: Pennino, I.: A new approach for hazard and disaster prevention: deep learning algorithms for change detection and classification RADAR/SAR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6522, https://doi.org/10.5194/egusphere-egu23-6522, 2023.

EGU23-6790 | ECS | Posters on site | ITS1.1/NH0.1

Deep learning for automatic flood mapping from high resolution SAR images 

Arnaud Dupeyrat, abdullah Almaksour, Joao Vinholi, and tapio friberg

 With the gradual warming of the global climate, natural catastrophes have caused billions of dollars in damage to ecosystems, economies and properties. Along with the damage, the loss of life is a very serious possibility. With the unprecedented growth of the human population, large-scale development activities and changes to the natural environment, the frequency, and intensity of extreme natural events and consequent impacts are expected to increase in the future. 

 To be able to mitigate and to reduce the potential damage of the natural catastrophe, continuous monitoring is required. The collection of data using earth observation (EO) systems has been valuable for tracking the effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.

 Synthetic aperture radar (SAR) imagery provides highly valuable information about our planet that no other technology is capable of. SAR sensors emit their own energy to illuminate objects or areas on Earth and record what’s reflected back from the surface to the sensor. This allows data acquisition day and night since no sunlight is needed. SAR also uses longer wavelengths than optical systems, which gives it the unsurpassed advantage of being able to penetrate clouds, rain, fog and smoke. All of this makes SAR imagery unprecedentedly valuable in sudden events and crisis situations requiring a rapid response.

 In this talk we will be focusing on flood monitoring using our ICEYE SAR images, taking into account multi-satellites, multi-angles and multi-resolutions that are inherent from our constellation and capabilities. We will present the different steps necessary that have allowed us to improve the consistency of our generated flood maps.

How to cite: Dupeyrat, A., Almaksour, A., Vinholi, J., and friberg, T.: Deep learning for automatic flood mapping from high resolution SAR images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6790, https://doi.org/10.5194/egusphere-egu23-6790, 2023.

Increasing climatic extremes resulted in frequency and severity of urban flood events during the last several decades. Significant economic losses were point out the urgency of flood response. In recent years, the government gradually increased the layout of CCTV water level monitoring facilities for the purpose of decision-making in flood event. However, it is difficult for decision makers to recognize multiple images in the same time. Therefore, the aim of this study attempts to establish an automatic water level recognition method for given closed-circuit television (CCTV) system.

In the last years, many advances have been made in the area of automatic image recognition with methods of artificial intelligence. Little literature has been published on real-time water level recognition of closed-circuit television system for disaster management. The purpose of this study is to examine the possibilities in practice of artificial intelligence for real-time water level recognition with deep convolutional neural network. Proposed methodology will demonstrate with several case studies in Taichung. For the potential issue that AI models may lacks of learning target, the generative adversarial network (GAN) may be adopted for this study. The result of this study could be useful to decision makers responsible for organizing response assignments during flood event.

How to cite: Chen, B. and Li, C.-Y.: A study on the establishment of computer vision for disaster identification based on existing closed-circuit television system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7435, https://doi.org/10.5194/egusphere-egu23-7435, 2023.

EGU23-8419 | ECS | Orals | ITS1.1/NH0.1

Synthetic Generation of Extra-Tropical Cyclones’ fields with Generative Adversarial Networks 

Filippo Dainelli, Riccardo Taormina, Guido Ascenso, Enrico Scoccimarro, Matteo Giuliani, and Andrea Castelletti

Extra-Tropical Cyclones are major systems ruling and influencing the atmospheric structure at mid-latitudes. They are characterised by strong winds and heavy precipitation, and can cause considerable storm surges potentially devastating for coastal regions. The availability of historical observations of the extreme events caused by intense ETCs are rather limited, hampering risk evaluation. Increasing the amount of significant data available would substantially help several fields of analysis influenced by these events, such as coastal management, agricultural production, energy distribution, air and maritime transportation, and risk assessment and management.

Here, we address the possibility of generating synthetic ETC atmospheric fields of mean sea level pressure, wind speed, and precipitation in the North Atlantic by training a Generative Adversarial Network (GAN). The purpose of GANs is to learn the distribution of a training set based on a game theoretic scenario where two networks compete against each other, the generator and the discriminator. The former is trained to generate synthetic examples that are plausible and resemble the real ones. The input of the generator is a vector of random Gaussian values, whose domain is known as the “latent space”. The discriminator learns to distinguish whether an example comes from the dataset distribution. The competition set by the game-theoretic approach improves the network until the counterfeits are indistinguishable from the originals.

To train the GAN, we use atmospheric fields extracted from the ERA5 reanalysis dataset in the geographic domain with boundaries 0°- 90°N, 70°W - 20°E and for the period 1st January 1979 - 1st January 2020. We analyse the generated samples’ histograms, the samples’ average fields, the Wasserstein distance and the Kullback-Leibler divergence between the generated samples and the test set distributions. Results show that the generative model has learned the distribution of the values of the atmospheric fields and the general spatial trends of the atmosphere in the domain. To evaluate better the atmospheric structure learned by the network, we perform linear and spherical interpolations in the latent space. Specifically, we consider four cyclones and compare the frames of their tracks to those of the synthetic tracks generated by interpolation. The interpolated tracks show interesting features consistent with the original tracks. These findings suggest that GANs can learn meaningful representations of the ETCs’ fields, encouraging further investigations to model the tracks’ temporal evolution.

How to cite: Dainelli, F., Taormina, R., Ascenso, G., Scoccimarro, E., Giuliani, M., and Castelletti, A.: Synthetic Generation of Extra-Tropical Cyclones’ fields with Generative Adversarial Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8419, https://doi.org/10.5194/egusphere-egu23-8419, 2023.

EGU23-8944 | ECS | Orals | ITS1.1/NH0.1

Towards probabilistic impact-based drought risk analysis – a case study on the Volta Basin 

Marthe Wens, Raed Hamed, Hans de Moel, Marco Massabo, and Anna Mapelli

Understanding the relationships between different drought drivers and observed drought impact can provide important information for early warning systems and drought management planning. Moreover, this relationship can help inform the definition and delineation of drought events. However, currently, drought hazards are often characterized based on their frequency of occurring, rather than based on the impacts they cause. A more data-driven depiction of “impactful drought events”- whereby droughts are defined by the hydrometeorological conditions that, in the past, have led to observable impacts-, has the potential to be more meaningful for drought risk assessments.

In our research, we apply a data-mining method based on association rules, namely fast and frugal decision trees, to link different drought hazard indices to agricultural impacts. This machine learning technique is able to select the most relevant drought hazard drivers (among both hydrological and meteorological indices) and their thresholds associated with “impactful drought events”. The technique can be used to assess the likelihood of occurrence of several impact severities, hence it supports the creation of a loss exceedance curve and estimates of average annual loss. An additional advantage is that such data-driven relations in essence reflect varying local drought vulnerabilities which are difficult to quantify in data-scarce regions.

This contribution exemplifies the use of fast and frugal decision trees to estimate (agricultural) drought risk in the Volta basin and its riparian countries. We find that some agriculture-dependent regions in Ghana, Togo and Côte d’Ivoire face annual average drought-induced maize production losses up to 3M USD, while per hectare, losses can mount to on average 50 USD/ha per year in Burkina Faso. In general, there is a clear north-south gradient in the drought risk, which we find augmented under projected climate conditions. Climate change is estimated to worsen the drought impacts in the Volta Basin, with 11 regions facing increases in annual average losses of more than 50%.

We show that the proposed multi-variate, impact-based, non-parametric, machine learning approach can improve the evaluation of droughts, as this approach directly leverages observed drought impact information to demarcate impactful drought events. We evidence that the proposed technique can support quantitative drought risk assessments which can be used for geographic comparison of disaster losses at a sub-national scale.

How to cite: Wens, M., Hamed, R., de Moel, H., Massabo, M., and Mapelli, A.: Towards probabilistic impact-based drought risk analysis – a case study on the Volta Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8944, https://doi.org/10.5194/egusphere-egu23-8944, 2023.

EGU23-9091 | Orals | ITS1.1/NH0.1

Improving near real-time flood extraction pipeline from SAR data using deep learning 

Mathieu Turgeon-Pelchat, Heather McGrath, Fatemeh Esfahani, Simon Tolszczuk-Leclerc, Thomas Rainville, Nicolas Svacina, Lingjun Zhou, Zarrin Langari, and Hospice Houngbo

The Canada Centre for Mapping and Earth Observation (CCMEO) uses Radarsat Constellation Mission (RCM) data for near-real time flood mapping. One of the many advantages of using SAR sensors, is that they are less affected by the cloud coverage and atmospheric conditions, compared to optical sensors. RCM has been used operationally since 2020 and employs 3 satellites, enabling lower revisit times and increased imagery coverage. The team responsible for the production of flood maps in the context of emergency response are able to produce maps within four hours from the data acquisition. Although the results from their automated system are good, there are some limitations to it, requiring manual intervention to correct the data before publication. Main limitations are located in urban and vegetated areas. Work started in 2021 to make use of deep learning algorithms, namely convolutional neural networks (CNN), to improve the performances of the automated production of flood inundation maps. The training dataset make use of the former maps created by the emergency response team and is comprised of over 80 SAR images and corresponding digital elevation model (DEM) in multiple locations in Canada. The training and test images were split in smaller tiles of 256 x 256 pixels, for a total of 22,469 training tiles and 6,821 test tiles. Current implementation uses a U-Net architecture from NRCan geo-deep-learning pipeline (https://github.com/NRCan/geo-deep-learning). To measure performance of the model, intersection over union (IoU) metric is used. The model can achieve 83% IoU for extracting water and flood from background areas over the test tiles. Next steps include increasing the number of different geographical contexts in the training set, towards the integration of the model into production.

How to cite: Turgeon-Pelchat, M., McGrath, H., Esfahani, F., Tolszczuk-Leclerc, S., Rainville, T., Svacina, N., Zhou, L., Langari, Z., and Houngbo, H.: Improving near real-time flood extraction pipeline from SAR data using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9091, https://doi.org/10.5194/egusphere-egu23-9091, 2023.

EGU23-9426 | ECS | Orals | ITS1.1/NH0.1

Fire hazard modelling with remote sensing data for South America 

Johanna Strebl, Julia Gottfriedsen, Dominik Laux, Max Helleis, and Volker Tresp

Throughout the past couple years, changes in global climate have been turning wildfires into an increasingly unpredictable phenomenon. Many environmental parameters that have been linked to wildfires, such as the number of consecutive hot days, are becoming increasingly unstable. This leads to a twofold problem: adequate fire risk assessment is at the same time more important and more difficult than ever. 

In the past, physical models were the prevalent approach to most questions in the domain of wildfire science. While they tend to provide accurate and transparent results, they require domain expertise and often tedious manual data collection.

In recent years, increased computation capabilities and the improved availability of remote sensing data associated with the new space movement have made deep learning a beneficial approach. Data-driven approaches often yield state of the art performance without requiring expert knowledge at a fraction of the complexity of physical models. The downside, however, is that they are often intransparent and offer no insights into their inner algorithmic workings. 

We want to shed some light on this interpretability/performance tradeoff and compare different approaches for predicting wildfire hazard. We evaluate their strengths and weaknesses with a special focus on explainability. We built a wildfire hazard model for South America based on a spatiotemporal CNN architecture that infers fire susceptibility from environmental conditions that led to fire in the past. The training data used contains selected ECMWF ERA5 Land variables and ESA world cover information. This means that our model is able to learn from actual fire conditions instead of relying on theoretical frameworks. Unlike many other models, we do not make simplifying assumptions such as a standard fuel type, but calculate hazard ratings based on actual environmental conditions. Compared to classical fire hazard models, this approach allows us to account for regional and atypical fire behavior and makes our model readily adaptable and trainable for other ecosystems, too.

The ground truth labels are derived from fusing active fire remote sensing data from 20 different satellites into one active wildfire cluster data set. The problem itself is highly imbalanced with non-fire pixels making up 99.78% of the training data. Therefore we evaluate the ability of our model to correctly predict wildfire hazard using metrics for imbalanced data such as PR-AUC and F1 score. We also compare the results against selected standard fire hazard models such as the Canadian Fire Weather Index (FWI). 

In addition, we assess the computational complexity and speed of calculating the respective models and consider the accuracy/complexity/speed tradeoff of the different approaches. Furthermore, we aim to provide insights why and how our model makes its predictions by leveraging common explainability methods. This allows for insights into which factors tend to influence wildfire hazard the most and to optimize for relatively lightweight, yet performant and transparent architectures.

How to cite: Strebl, J., Gottfriedsen, J., Laux, D., Helleis, M., and Tresp, V.: Fire hazard modelling with remote sensing data for South America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9426, https://doi.org/10.5194/egusphere-egu23-9426, 2023.

For recent years, Machine Learning (ML) models have been proven to be useful in solving problems of a wide variety of fields such as medical, economic, manufacturing, transportation, energy, education, etc. With increased interest in ML models and advances in sensor technologies, ML models are being widely applied even in civil engineering domain. ML model enables analysis of large amounts of data, automation, improved decision making and provides more accurate prediction. While several state-of-the-art reviews have been conducted in each sub-domain (e.g., geotechnical engineering, structural engineering) of civil engineering or its specific application problems (e.g., structural damage detection, water quality evaluation), little effort has been devoted to comprehensive review on ML models applied in civil engineering and compare them across sub-domains. A systematic, but domain-specific literature review framework should be employed to effectively classify and compare the models. To that end, this study proposes a novel review approach based on the hierarchical classification tree “D-A-M-I-E (Domain-Application problem-ML models-Input data-Example case)”. “D-A-M-I-E” classification tree classifies the ML studies in civil engineering based on the (1) domain of the civil engineering, (2) application problem, (3) applied ML models and (4) data used in the problem. Moreover, data used for the ML models in each application examples are examined based on the specific characteristic of the domain and the application problem. For comprehensive review, five different domains (structural engineering, geotechnical engineering, water engineering, transportation engineering and energy engineering) are considered and the ML application problem is divided into five different problems (prediction, classification, detection, generation, optimization). Based on the “D-A-M-I-E” classification tree, about 300 ML studies in civil engineering are reviewed. For each domain, analysis and comparison on following questions has been conducted: (1) which problems are mainly solved based on ML models, (2) which ML models are mainly applied in each domain and problem, (3) how advanced the ML models are and (4) what kind of data are used and what processing of data is performed for application of ML models. This paper assessed the expansion and applicability of the proposed methodology to other areas (e.g., Earth system modeling, climate science). Furthermore, based on the identification of research gaps of ML models in each domain, this paper provides future direction of ML in civil engineering based on the approaches of dealing data (e.g., collection, handling, storage, and transmission) and hopes to help application of ML models in other fields.

How to cite: Kim, J. and Jung, D.: State-of-the-Art Review of Machine Learning Models in Civil Engineering: Based on DAMIE Classification Tree, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11636, https://doi.org/10.5194/egusphere-egu23-11636, 2023.

EGU23-11756 * | Orals | ITS1.1/NH0.1 | Highlight

Digital twin computing for enhancing resilience of disaster response system 

Shunichi Koshimura and Erick Mas

Digital twin is now recognized as digital copies of physical world's objects stored in digital space and utilized to simulate the sequences and consequences of target phenomena. By incorporating physical world’s data into the digital twin, developers and users have a full view of the target through real-time feedback. Recent advances in high-performance computing and large-scale data fusion of sensing and observations of both natural and social phenomena are enhancing applicability of digital twin paradigm to natural disaster research. Artificial intelligence (AI) and machine learning are also being applied more and more widely across the world and contributing as essential elements of digital twin. Those have significant implications for disaster response and recovery to hold out the promise of dramatically improving our understanding of disaster-affected areas and responses in real-time.

A project is underway to enhance resilience of disaster response systems by constructing "Disaster Digital Twin" to support disaster response team in the anticipated tsunami disaster. “Disaster Digital Twin” platform consists of a fusion of real-time hazard simulation, e.g. tsunami inundation forecast, social sensing to identify dynamic exposed population, and multi-agent simulation of disaster response activities to find optimal allocation or strategy of response efforts, and achieve the enhancement of disaster resilience.

To achieve the goal of innovating digital twin computing for enhancing disaster resilience, four preliminary results are shown;

(1) Developing nation-wide real-time tsunami inundation and damage forecast system. The priority target for forecasting is the Pacific coast of Japan, a region where Nankai trough earthquake is likely to occur.

(2) Establishing a real-time estimation of the number of exposed population in the inundation zone and clarifying the relationship between the exposed population and medical demand.

(3) Developing a reinforcement learning-based multi-agent simulation of medical activities in the affected areas with use of damage information, medical demands, and resources in the medical facilities to fid optimal allocation of medical response.

(4) Developing a digital twin computing platform to support disaster medical response activities and find optimal allocation of disaster medical services through what-if analysis of multi-agent simulation.

How to cite: Koshimura, S. and Mas, E.: Digital twin computing for enhancing resilience of disaster response system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11756, https://doi.org/10.5194/egusphere-egu23-11756, 2023.

EGU23-12240 | ECS | Posters on site | ITS1.1/NH0.1

Classification Seismic Spectrograms from Deep Neural Network: Application to Alarm System of Post-failure Landslides 

Jui-Ming Chang, Wei-An Chao, and Wei-Kai Huang

Daman Landslide had blocked one of the three cross-island roads in Taiwan, and a road section has been under control since last October. During the period, more than thousands of small-scale post-failures occurred whose irregular patterns affected the safety of engineering workers for slope protection construction and road users. Therefore, we installed one time-lapse camera and two geophones at the crown and closed to the toe of the Daman landslide, respectively to train a classification model to offer in-situ alarm. According to time-lapse photos, those post failures can be categorized into two types. One is rock/debris moving and stopping above the upper slope or road, named type I, and the other is the rock/debris going through the road to download slope, named type II. Type I was almost recorded by the crown station, and type II was shown by both stations with different arrival times and the toe station’ high-frequency signals gradually rising (up to 100 Hz). Those distinct features were exhibited by spectrograms. To keep characteristics simultaneously, we merge two stations’ spectrograms as one to indicate different types of post-failures. However, frequent earthquakes affect the performance of the landslide’s discrimination, which should be involved in the classification model. A total of three labels, type I, type II, and earthquake, contained more than 15,000 images of spectrogram, have been used for deep neural network (DNN) to be a two-station-based automatic classifier. Further, user-defined parameters for the specific frequency band within fixed time span windows, including a sum of power spectrogram density, the arrival time of peak amplitude, cross-correlation coefficient, and signal-to-noise ratio, have been utilized for the decision tree algorithm. Both model results benefit the automatic classifier for post-failure alarms and can readily extend to monitor other landslides with frequent post-failures by transfer learning.

How to cite: Chang, J.-M., Chao, W.-A., and Huang, W.-K.: Classification Seismic Spectrograms from Deep Neural Network: Application to Alarm System of Post-failure Landslides, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12240, https://doi.org/10.5194/egusphere-egu23-12240, 2023.

EGU23-12716 | ECS | Posters on site | ITS1.1/NH0.1

Investigating causal effects of anthropogenic factors on global fire modeling 

Nirlipta Pande and Wouter Dorigo

Humans significantly control the natural environment and natural processes. Global fire ignitions are a prime example of how human actions change the frequency of occurrence of otherwise rare events like wildfires. However, human controls on fire ignition are insufficiently characterised by global fire models because impacts are often indirect, complex, and collinear. Hence, modelling fire activity while considering the complex relationships amongst the input variables and their effect on global ignitions is crucial to developing fire models reflecting the real world. 

This presentation leverages causal inference and machine learning frameworks applied to global datasets of fire ignitions from Earth observations and potential drivers to uncover anthropogenic pathways on fire ignition. Potential fire controls include human predictors from Earth observations and statistical data combined with variables traditionally associated with fire activity, like weather, and vegetation abundance and state, derived from earth observations and models.

Our research models causal relationships between fire control variables and global ignitions using Directed Acyclic Graphs(DAGs). Here, every edge between variables symbolises a relation between them; the edge weight indicates the strength of the relationship, and the orientation of the edge between the variables signifies the cause-and-effect relationship between the variables. However, defining a fire ignition distribution using DAGs is challenging owing to the large combinatorial sample space and acyclicity constraint. We use Bayesian structure learning to make these approximations and infer the extent of human intervention when combined with climate variables and vegetation properties. Our research demonstrates the need for causal modelling and the inclusion of anthropogenic factors in global fire modelling.

How to cite: Pande, N. and Dorigo, W.: Investigating causal effects of anthropogenic factors on global fire modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12716, https://doi.org/10.5194/egusphere-egu23-12716, 2023.

EGU23-13083 | Orals | ITS1.1/NH0.1

Machine learning modelling of compound flood events 

Agnieszka Indiana Olbert, Sogol Moradian, and Galal Uddin

Flood early warning systems are vital for preventing flood damages and for reducing disaster risks. Such systems are particularly important for forecasting compound events where multiple, often dependent flood drivers co-occur and interact. In this research an early warning system for prediction of coastal-fluvial floods is developed to provide a robust, cost-effective and time-efficient framework for management of flood risks and impacts. This three-step method combines a cascade of three linked models: (1) statistical model that determines probabilities of multiple-driver flood events, (2) hydrodynamic model forced by outputs from the statistical model, and finally (3) machine learning (ML) model that uses hydrodynamic outputs from various probability flood events to train the ML algorithm in order to predict the spatially and temporarily variable inundation patterns resulting from a combination of coastal and fluvial flood drivers occurring simultaneously.

The method has been utilized for the case of Cork City, located in the south-west of Ireland, which has a long history of fluvial-coastal flooding. The Lee  River channelling through the city centre may generate a substantial flood when the downstream river flow draining to the estuary coincides with the sea water propagating upstream on a flood tide. For this hydrological domain the statistical model employs the univariate extreme values analysis and copula functions to calculate joint probabilities of river discharges and sea water levels (astronomical tides and surge residuals) occurring simultaneously. The return levels for these two components along a return level curve produced by the copula function are used to generate synthetic timeseries, which serve as water level boundary conditions for a hydrodynamic flood model. The multi-scale nested flood model (MSN_Flood) was configured for Cork City at 2m resolution to simulate an unsteady, non-uniform flow in the Lee  River and a flood wave propagation over urban floodplains. The ensemble hydrodynamic model outputs are ultimately used to train and test a range machine learning models for prediction of flood extents and water depths. In total, 23 machine learning algorithms including: Artificial Neural Network, Decision Tree, Gaussian Process Regression, Linear Regression, Radial Basis Function, Support Vector Machine, and Support Vector Regression were employed to confirm that the ML algorithm can be used successfully to predict the flood inundation depths over urban floodplains for a given set of compound flood drivers. Here, the limited flood conditioning factors taken into account to analyse floods are the upstream flood hydrographs and downstream sea water level timeseries. To evaluate model performance, different statistical skill scores were computed. Results indicated that in most pixels, the Gaussian Process Regression model performs better than the other models.

The main contribution of this research is to demonstrate the ML models can be used in early warning systems for flood prediction and to give insight into the most suitable models in terms of robustness, accuracy, effectiveness, and speed. The findings demonstrate that ML models do help in flood water propagation mapping and assessment of flood risk under various compound flood scenarios.

How to cite: Olbert, A. I., Moradian, S., and Uddin, G.: Machine learning modelling of compound flood events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13083, https://doi.org/10.5194/egusphere-egu23-13083, 2023.

EGU23-14126 | ECS | Orals | ITS1.1/NH0.1

ML-based fire spread model and data pipeline optimization 

Tobias Bauer, Julia Miller, Julia Gottfriedsen, Christian Mollière, Juan Durillo Barrionuevo, and Nicolay Hammer

Climate change is one of the most pressing challenges to humankind today. The number and severity of wildfires are increasing in many parts of the world, with record-breaking temperatures, prolonged heat waves, and droughts. We can minimize the risks and consequences of these natural disasters by providing accurate and timely wildfire progression predictions through fire spread modeling. Knowing the direction and rate of spread of wildfires over the next hours can help deploy firefighting resources more efficiently and warn nearby populations hours in advance to allow safe evacuation.
Physics-based spread models have proven their applicability on a regional scale but often require detailed spatial input data. Additionally, rendering them in real-time scenarios can be slow and therefore inhibit fast output generation. Deep learning-based models have shown success in specific fire spread scenarios in recent years. But they are limited by their transferability to other regions, explainability, and longer training time. Accurate active fire data products and a fast data pipeline are additional essential requirements of a wildfire spread early-warning system.
In this study, physical models are compared to a deep learning-based CNN approach in terms of computational speed, area accuracy, and spread direction. We use a dataset of the 30 largest wildfires in the US in the year 2021 to evaluate the performance of the model’s predictions.
This work focuses in particular on the optimization of a cloud-based fire spread modeling data pipeline for near-real-time fire progression over the next  2 to 24 hours. We describe our data pipeline, including the collection and pre-processing of ignition points derived from remote sensing-based active fire detections. Furthermore, we use data from SRTM-1 as topography, ESA Land Cover and Corine Land Cover for fuel composition, and ERA-5 Reanalysis products for weather data inputs. The application of the physics-based models is derived from the open-source library ForeFire, to create and execute physical wildfire spread models from single fire ignition points as well as fire fronts. The predictions of the ForeFire model serve as a benchmark for the evaluation of the performance of our Convolutional Neural Network. The CNN forecasts the fire outline based on a spatiotemporal U-Net architecture. 
The scaling of the algorithms to a global setting is enabled by the Leibniz Supercomputing Centre. It enables large-scale cloud-based machine learning to provide a time-sensitive solution for operational fire spread modeling in emergency management based on real-time remote sensing information. 

How to cite: Bauer, T., Miller, J., Gottfriedsen, J., Mollière, C., Durillo Barrionuevo, J., and Hammer, N.: ML-based fire spread model and data pipeline optimization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14126, https://doi.org/10.5194/egusphere-egu23-14126, 2023.

EGU23-15711 | Orals | ITS1.1/NH0.1

A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery 

Filippo Catani, Sansar Raj Meena, Lorenzo Nava, Kushanav Bhuyan, Silvia Puliero, Lucas Pedrosa Soares, Helen Cristina Dias, and Mario Floris

Multiple landslide events occur often across the world which have the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address the mapping of landslides using Earth Observation (EO) data, several gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the HR-GLDD, a high-resolution (HR) dataset for landslide mapping composed of landslide instances from ten different physiographical regions globally: South and South-East Asia, East Asia, South America, and Central America. The dataset contains five rainfall triggered and five earthquake-triggered multiple landslide events that occurred in varying geomorphological and topographical regions. HR-GLDD is one of the first datasets for landslide detection generated by high-resolution satellite imagery which can be useful for applications in artificial intelligence for landslide segmentation and detection studies. Five state-of-the-art deep learning models were used to test the transferability and robustness of the HR-GLDD. Moreover, two recent landslide events were used for testing the performance and usability of the dataset to comment on the detection of newly occurring significant landslide events. The deep learning models showed similar results for testing the HR-GLDD in individual test sites thereby indicating the robustness of the dataset for such purposes. The HR-GLDD can be accessed open access and it has the potential to calibrate and develop models to produce reliable inventories using high-resolution satellite imagery after the occurrence of new significant landslide events. The HR-GLDD will be updated regularly by integrating data from new landslide events.

How to cite: Catani, F., Meena, S. R., Nava, L., Bhuyan, K., Puliero, S., Pedrosa Soares, L., Dias, H. C., and Floris, M.: A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15711, https://doi.org/10.5194/egusphere-egu23-15711, 2023.

EGU23-16626 | ECS | Posters on site | ITS1.1/NH0.1

Danish national early warning system for flash floods based on a gradient boosting machine learning framework 

Grith Martinsen, Yann Sweeney, Jonas Wied Pedersen, Roxana Alexandru, Sergi Capape, Charlotte Harris, Michael Butts, and Maria Diaz

Fluvial and flash floods can have devastating effects if they occur without warning. In Denmark, management of flood risk and performing preventative emergency service actions has been the sole responsibility of local municipalities. However, motivated by the disastrous 2021 floods in Central Europe, the Danish government has recently appointed the Danish Meteorological Institute (DMI) as the national authority for flood warnings in Denmark, and DMI is in the process of building capacity to fulfill this role.

 

One of the most cost-effective ways to mitigate flood damages is a well-functioning early warning system. Flood warning systems can rely on various methods ranging from human interpretation of meteorological and hydrological data to advanced hydrological modelling. The aim of this study is to generate short-range streamflow predictions in Danish river systems with lead times of 4-12 hours. To do so, we train and test models with hourly data on 172 catchments.

 

Machine learning (ML) models have in many cases been shown to outperform traditional hydrological models and offer efficient ways to learn patterns in historical data. Here, we investigate streamflow predictions with LightGBM, which is a gradient boosting framework that employs tree-based ML algorithms and is developed and maintained by Microsoft (Ke et al., 2017). The main argument for choosing a tree-based algorithm is its inherent ability to represent rapid dynamics often observed during flash floods. The main advantages of LightGBM over other tree-based algorithms are efficiency in training and lower memory consumption. We benchmark LightGBM’s performance against persistence, linear regression and various LSTM setups from the Neural Hydrology library (Kratzert et al., 2022).

 

We evaluate the algorithm trained using different input features. This analysis include model explainability, such as SHAP, and the results indicate that simply using lagged real-time observations of streamflow together with precipitation leads to the best performing and most parsimonious models. The results show that the LightGBM setup outperforms the benchmarks and is able to generate predictions with high Klinge-Gupta Efficiency scores > 0.9 in most catchments. Compared to the persistence benchmark it especially shows strong improvements on peak timing errors.

How to cite: Martinsen, G., Sweeney, Y., Pedersen, J. W., Alexandru, R., Capape, S., Harris, C., Butts, M., and Diaz, M.: Danish national early warning system for flash floods based on a gradient boosting machine learning framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16626, https://doi.org/10.5194/egusphere-egu23-16626, 2023.

EGU23-945 | ECS | Orals | SM8.1

The Influence of Fluids on Earthquakes: Insights from Mechanical Modelling 

Valentin Marguin and Guy Simpson

The strength and sliding behavior of faults in the upper crust are largely controlled by friction and effective stress, which is itself modulated by the fluid pressure. However, while many studies have investigated the role of friction on the earthquake cycle, relatively little effort has gone into understanding the effects linked to dynamic changes in fluid pressure. Here, we explore coupled interactions between slow tectonic loading and fluid pressure generation during the interseismic period with rapid sliding and elastic stress transfer during earthquakes on a plane strain thrust fault in two dimensions. Our models incorporate rate- and state-dependent friction along with dramatic changes in the fault permeability during sliding. In these modes, earthquakes are nucleated where fluid pressures are locally high and then propagated as slip pulses onto stronger parts of the fault. For the model without overpressure, the ruptures are more crack-like. Our model produces a wide range of sliding velocities from rapid to slow earthquakes, which occur due to the presence of high pore pressures prior to rupture. The models also show evidence for aftershocks that are driven by fluid transfer along the fault plane after the mainshock. Overall, we find that the presence of relatively modest fluid overpressures tends to reduce coseismic slip, stress drop, maximum sliding velocity, rupture velocity, and the earthquake recurrence time relative to ruptures in a dry crust. This study shows that fluids can exert an important influence on earthquakes in the crust, which is mostly due to modulation of the effective stress and variations in permeability, and to a lesser extent to poroelastic coupling.

How to cite: Marguin, V. and Simpson, G.: The Influence of Fluids on Earthquakes: Insights from Mechanical Modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-945, https://doi.org/10.5194/egusphere-egu23-945, 2023.

EGU23-1678 | ECS | Orals | SM8.1

A synthetic ambient-noise data set fortime-lapsed monitoring 

Sérgio Nunes, Hamzeh Mohammadigheymasi, Nasrin Tavakolizadeh, and Nuno Garcia
Synthetic simulation of seismic wave propagation is a fundamental way to evaluate the accuracy and performance of signal processing methods developed for application to real seismic datasets. Various research papers have introduced state-of-the-art synthetic active and passive seismic datasets to implement this critical step. However, a versatile seismic data set for ambient noise is still missing in the literature. In this study, we conducted synthetic simulations by leveraging the noise simulation modules of SPECFEM3D Cartesian open-source codes. The simulation is carried out for the geometries of station pairs of the YB Cavola Broadband Dense Array temporary network installed in 2004 through the village of Cavola, northern Apennine, Italy. This is a dense array (8m separation one way and
10m the other way) installed on an active landslide through the village of Cavola, northern Apennines, Italy. By considering a fixed crustal velocity model reported for this region, a noise correlation seismogram is computed for each station pair by implementing three processing steps: 1) simulation for generating wavefields, 2) simulation for ensemble forward wavefields, and 3) simulation for ensemble adjoint wavefields and sensitivity kernels. The generated cross-correlation seismograms are post-processed, detrended, and decimated by a factor of 2 to obtain a dataset with a sampling rate of 0.01sec. Then the traces are rotated to the transverse-radial-vertical coordinate system making 3-component data for each station pair. To make the simulation more realistic, the data is contaminated by Gaussian noise (bandpass-filtered in the range of [0.02, 100] Hz) to give a Signal to Noise Ratio (SNR) of 10. The generated dataset provides one epoch of a synthetic time-lapsed ambient noise dataset as a reference for evaluating time-lapsed processing algorithms. This research contributes to the ALLAB project.
 
The authors would like to thank the support of the Instituto de Telecomunicaçõe. This work is funded by FCT/MCTES through national funds.

How to cite: Nunes, S., Mohammadigheymasi, H., Tavakolizadeh, N., and Garcia, N.: A synthetic ambient-noise data set fortime-lapsed monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1678, https://doi.org/10.5194/egusphere-egu23-1678, 2023.

EGU23-2497 | ECS | Posters virtual | SM8.1

Scalar wave equation modeling with dispersion relation based on finite difference method 

Vanga Mounika and Maheswar Ojha

The finite-difference method(FDM) is widely used in the numerical modeling of wave equations. Conventional FDM stencils for spatial derivatives are usually designed in the space domain, which creates difficulty in satisfying the dispersion relations exactly while solving the wave equations. We use an automated and optimized FDM using a genetic algorithm to optimally compute second-order spatial derivatives. In our method, the explicit finite-difference stencils are calculated using the genetic algorithm to minimize the dispersion (phase velocity) for all wavenumbers without using any specific window function. The amplitudes of the pseudo-spectral window are optimized by making the phase velocity close to the analytical solution at each wavenumber, where the stability is close to that of the conventional FDM. Although finite difference coefficients in this method depend on velocity, grid spacing and time step, less dispersive solutions can be achieved by computing suitable finite-difference coefficients for varying cases. We compare our results with the solutions of an existing pseudo-spectral method (with Kaiser window function), conventional FDM, joint time-space optimization method, and the least square method. The normalized phase velocity and the absolute error of our method show very promising results.

How to cite: Mounika, V. and Ojha, M.: Scalar wave equation modeling with dispersion relation based on finite difference method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2497, https://doi.org/10.5194/egusphere-egu23-2497, 2023.

A central problem in earthquake physics and fault mechanics is understanding the coupling of fluid and solid phases during fault slip. This coupling is mostly treated as a one-way coupled problem where the pore pressure is imposed as a perturbation in effective normal stress. However, more recent work indicates that the two-way coupling of a porous fluid-filled bulk and pressure changes in the shear zone significantly alters rupture properties. Further, a qualitative analysis of this problem in a poroelastic medium reveals that pore pressure inside an mm to micron thick frictional shear zone cannot be constant as slip dynamically evolves. This analysis calls into question the practice of imposing pore pressure as a perturbation to effective normal stress at an infinitesimal interface and raises fundamental questions regarding the interpretation of the effective stress principle. Here we explore two ways to couple shear zone processes on a mm-micron scale to the meter-kilometer scale bulk processes. Efficient coupling across these scales is achieved with a spectral boundary integral representation of a poroelastic bulk. Furthermore, the boundary integral representation reduces the dimension of the computational problem that needs to be discretized by one. In other words, it allows us to simulate 3D physics by only discretizing in 2D. We develop boundary integral solutions in 2D and 3D medium that are appropriate for modeling shear zone that can undergo pressure changes, expansion/contraction, and shear localization. First, we explore an efficient approach where shear zone properties are averaged and dimensionally reduced, thus with finite shear zone effects built into the boundary conditions of the bulk in 2D and 3D. Second, we show how a shear zone can be explicitly modeled, but the coupling to the surrounding bulk is done with a boundary integral representation. Thus, offering relatively efficient modeling of processes such as shear localization, dilatancy, thermal pressurization, and how such processes interact with the bulk. We suggest that such use of boundary integrals may be applied more generally to achieve two-way fluid-solid coupling at lower computation expense.

How to cite: Heimisson, E. R. and Wang, Y.: Linking fluid flow in a shear zone to the surrounding bulk with poroelastic boundary integral solutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4283, https://doi.org/10.5194/egusphere-egu23-4283, 2023.

The instantaneous weakening of rocks during the passage of seismic waves has first been observed in laboratory experiments. The change of elastic rock moduli during and after the dynamic perturbations typically includes three phases – a gradual drop of moduli, a dynamically steady state and the recovery over a time scale that is larger than that of the perturbations. Such changes have been referred to as slow dynamics (Johnson and Sutin, 2005). With the development of the long-term continuous monitoring of the velocity field inside the Earth using methods such as ambient noise interferometry, coseismic rock weakening and post-seismic recovery of rock strength have also been recorded in the field over the past two decades. The question that we want to answer is: how relevant is the non-classical nonlinearity observed in the lab to the coseismic velocity drop in the field? To this end, we aim to adapt an analytical model that explains the lab observations and apply it to field observations using numerical simulations. Our first step is to identify the appropriate nonlinear model(s). Most of the proposed physical models that explain the phenomenon contain many parameters and are hard to constrain. Moreover, most of the existing physical models are restricted to 1D analysis and are difficult to generalize to 2D or 3D modeling.

 

We apply two models within the framework of the continuum damage mechanics: (i) the internal variable model (Berjamin et al., 2017) and (ii) the continuum damage model that accounts for parallel micro-cracks oriented perpendicular to the maximum tension or compression (Lyakhovsky et al., 1997). Both models can generalize to 2D and 3D. We formulate both models as nonlinear hyperbolic partial differential equations (PDEs) and solve them with the arbitrary high-order discontinuous Galerkin method using ExaHyPE (Reinarz et al., 2020) in 2D and 3D. We show that both models successfully reproduce the three phases during and after dynamic perturbations observed in the laboratory. We find that the continuum damage model can explain the amplitude- and frequency-dependent damage with a good match against the lab measurements. We also compare the simulation results using both models quantitatively with the observations in a 2D copropagating acousto-elastic testing (Feng et al., 2018). Our sensitivity analysis of the model parameters using the Markov chain Monte Carlo method quantitatively estimates the uncertainties and correlations among the parameters of both models. We believe our work paves the way towards a model of nonlinear rock deformation with slow dynamics that can be used in large scale 2D and 3D seismic wave propagation simulations for direct analysis of field observations, such as the Tohoku earthquake, 2011 (Brenguier et al., 2014).

How to cite: Niu, Z., Gabriel, A.-A., and Igel, H.: Numerical Simulation and Uncertainty Quantification of Models for Coseismic Damage and Healing of Rocks in 1D, 2D and 3D, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4450, https://doi.org/10.5194/egusphere-egu23-4450, 2023.

EGU23-4960 | ECS | Orals | SM8.1

An efficient poroelastic wave simulation based on discontinuous grid and nonuniform time step 

Heng Zhang, Hengxin Ren, Yao-Chong Sun, Mingbo Li, Tao Wang, and Changjiang Fang

The existence of slow P wave, in addition to fast P wave and S wave, makes it tricky for grid-based numerical simulation methods to conduct poroelastic wave modeling. The grid spacing has to be fine enough to capture the slow P wave since the velocity of slow P wave is much smaller than that of the other two waves. Dense space and time steps significantly increase the computation cost. In this study, we propose a poroelastic finite-difference simulation method that combines discontinuous curvilinear collocated-grid and non-uniform time step Runge-Kutta scheme. Only the space and time steps for the areas near interfaces, where the contribution of slow P wave is non-negligible, are refined in an effort to speed up the computation. The refined space step is determined by the velocity of slow P wave, while the coarse space step is determined by the velocity of shear wave. The coarse and refined time steps are set according to the non-uniform time step Runge-Kutta scheme, which is derived with Taylor expansion and avoids interpolation or extrapolation for communication between different time levels. This scheme helps maintain fourth-order accuracy in the whole domain. The accuracy and efficiency of the proposed method are verified by numerical tests. Compared with the conventional curvilinear collocated-grid finite-difference method that uses a uniform space grid as well as a uniform time step, the computation efficiency is improved significantly and the computation time can be saved by more than 80%.

How to cite: Zhang, H., Ren, H., Sun, Y.-C., Li, M., Wang, T., and Fang, C.: An efficient poroelastic wave simulation based on discontinuous grid and nonuniform time step, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4960, https://doi.org/10.5194/egusphere-egu23-4960, 2023.

EGU23-5365 | ECS | Posters on site | SM8.1

Ground motion simulation of the 2021 Mw 5.2 Central Adriatic earthquake 

Helena Latečki, Irene Molinari, and Josip Stipčević

In the last few decades, several series of earthquakes in the Central Adriatic Sea have been detected and analyzed, indicating the complexity of the tectonics within the Adriatic microplate. The most recent earthquake series suggests higher seismic potential than what was previously assumed and opens questions regarding present-day tectonic stress distribution within the Adria microplate in general. Therefore, studying seismic activity and identifying active faults is crucial when it comes to better understanding of the seismotectonics of this area, and consequently, improvement of the seismic hazard estimation. In this work we focus on the Mw 5.2 March 27, 2021 earthquake which occurred in the Central Adriatic Sea close to the island of Vis (Croatia). To evaluate the expected ground motion parameters of the event, we make use of physics-based waveform modelling. We simulate the earthquake using a newly defined 3D crustal model which honors surface topography, reflects main geological features and is embedded within the existing regional crustal model EPCrust. We compute broadband seismograms by making use of the hybrid approach where low-frequency and high-frequency parts are obtained separately and then combined into a single time series. We compare simulated waveforms against the recorded data and validate our results by assessing the goodness of fit for different ground-motion metrics. We then focus on simulating the waveforms using different descriptions of the source in order to investigate how its parametrization affects final results. This allows us to get a better understanding about the physical properties of the driving forces and mechanisms responsible for the seismicity in this region.

How to cite: Latečki, H., Molinari, I., and Stipčević, J.: Ground motion simulation of the 2021 Mw 5.2 Central Adriatic earthquake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5365, https://doi.org/10.5194/egusphere-egu23-5365, 2023.

EGU23-5411 | Posters on site | SM8.1

How asperity size and neighboring segments can change the frictional response and fault slip behavior: insights from laboratory experiments and numerical models 

Fabio Corbi, Giacomo Mastella, Elisa Tinti, Matthias Rosenau, Laura Sandri, Silvio Pardo, and Francesca Funiciello

Accurate assessment of rate and state friction parameters is essential for producing realistic rupture scenarios and, in turn, for seismic hazard analysis. Those parameters can be directly measured in the laboratory, with experimental apparati that reproduce fault conditions in nature. Alternatively, indirect estimates (i.e., inversion) of rate and state parameters are based on postseismic slip evolution studies and numerical modeling. Both direct and indirect approaches require a series of assumptions that might bias the results.

Here we take advantage of a downscaled analog model reproducing experimentally megathrust earthquakes. The analog model shares many characteristics of real subduction zones, although being intentionally oversimplified with respect to nature. This allows reducing the number of potential sources of bias (e.g., fault geometry and asperity size). 

We perform five analog models with a single, rectangular asperity of different lengths embedded in a nearly velocity neutral matrix. We focus on two different physical conditions, namely the along-strike asperity length and the asperity to neighboring segments length ratio, and study systematically how they tune the model seismic behavior. Then, by coupling quasi-dynamic numerical models with the simulated annealing algorithm, we retrieve rate and state parameters that allow reproducing both the recurrence time, rupture duration and slip amplitude of the analog model, in ensemble. 

We identify a tradeoff between (a-b) of the asperity and (a-b) of neighboring creeping segments, with multiple combinations that allow mimicking the analog model behavior and variability. We also identify a negative correlation between (a-b) of the asperity and asperity size, with Dc remaining relatively constant within the investigated asperity size range. When estimating (a-b), poorly constrained properties of neighboring segments are responsible for uncertainties in the order of per mille. Roughly one order of magnitude larger uncertainties derive from asperity size. Those results provide a first order assessment of the variability that rate and state friction estimates retrieved for nature conditions might have when used as constraint to model fault slip behavior.

How to cite: Corbi, F., Mastella, G., Tinti, E., Rosenau, M., Sandri, L., Pardo, S., and Funiciello, F.: How asperity size and neighboring segments can change the frictional response and fault slip behavior: insights from laboratory experiments and numerical models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5411, https://doi.org/10.5194/egusphere-egu23-5411, 2023.

EGU23-5539 | ECS | Posters virtual | SM8.1

Critical nucleation length for frictional slipping of an elastic layer over an elastic half-space 

Abhishek Painuly and Ranjith Kunnath

The interplay of geological forces and shear resistance of slipping surfaces leads to the expansion of earthquake ruptures, which nucleate in creeping zone patches. Once the dimension of the nucleating creeping zone exceeds a critical length, ruptures accelerate dynamically. The present work provides an analytic model to determine the critical nucleation length of a slip rupture. It is determined by performing a linear stability analysis of steady-state sliding of an elastic layer (having a finite height) over an elastic half-space in the quasi-static regime. Interfacial frictional behaviour is modelled using a rate- and state-dependent friction law with velocity weakening behaviour in the steady state, mimicking the experimental observations of interfacial friction. Results for critical nucleation length at the interface with similar and dissimilar materials across the interface are presented and the effect of layer height on the critical nucleation length is explored numerically.

How to cite: Painuly, A. and Kunnath, R.: Critical nucleation length for frictional slipping of an elastic layer over an elastic half-space, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5539, https://doi.org/10.5194/egusphere-egu23-5539, 2023.

EGU23-6056 | Posters on site | SM8.1

Seismic hazard assessment of the Lebanese Restraining Bend: A neo-deterministic approach 

Tony Nemer, Franco Vaccari, and Mustapha Meghraoui

The Lebanese Restraining Bend is an active bend along the Dead Sea Transform Fault in the eastern Mediterranean region where several destructive earthquakes happened throughout history. In this paper, we assess the gross features of seismic hazard of the Lebanese Restraining Bend by applying a neo-deterministic method that involves the generation of synthetic seismograms distributed on a regular grid over the study area. We use the regional seismicity, seismic source zones, focal mechanism solutions, and velocity structural models. We present maps of ground displacement, velocity, and acceleration. This is the first study that generates neo-deterministic seismic hazard maps for the Lebanese Restraining Bend using representative ground motion modelling. Our results show that displacement values of 15-30 cm and velocity values of 30-60 cm/s can be expected along most of Lebanon. In addition, 0.15-0.30 g acceleration values can dominate most of the Lebanese territory and surrounding areas. It is evident from these results that the study area in general and Lebanon in particular constitute a high seismic hazard area, which necessitates further attention from the authorities regarding the precaution measures needed to mitigate the effects of potential catastrophic seismic events; in addition, more detailed investigations are needed at local scale for specific sites of interest.

How to cite: Nemer, T., Vaccari, F., and Meghraoui, M.: Seismic hazard assessment of the Lebanese Restraining Bend: A neo-deterministic approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6056, https://doi.org/10.5194/egusphere-egu23-6056, 2023.

EGU23-6471 | ECS | Orals | SM8.1

SPHY3D: A hybrid seismic computational framework for box-tomography of spherical Earth 

Foivos Karakostas, Andrea Morelli, Irene Molinari, Brandon VanderBeek, and Manuele Faccenda

Computational seismology encountered a dramatic advance during the past decades with the development of SEM codes that use the simultaneous increase of the available computational power. Meanwhile, the use of teleseismic events for regional seismic tomography is suggested with the application of the box-tomography methodology (Masson and Romanowicz, 2017). In this work we use these advances in order to suggest a package for box-tomography, using AxiSEM for 1-D global wavefield simulations (Nissen-Meyer et al., 2014) and SPECFEM3D for 3-D regional seismic simulations (Komatitsch and Tromp, 1999). These codes have been previously used and validated for such hybrid simulations (Monteiller et al., 2021), however with the limitation on the dimensions of the examined region, where 3-D full waveform topography is applied, due to the Cartesian setting that does not honour the curvature of the Earth. Although recent advances solved this limitation for SPECFEM3D Global, by permitting the use of a small Earth chunk, the Cartesian description of the regional model allows computing the injection of the 1-D computed wavefield from the global model to the regional box. Therefore, we developed and present comparative results of a package that transforms the geometry of the Cartesian simulation in a "spherical Earth" setting and allows the performance of hybrid simulations for box tomography in regions larger than a couple of degrees. The code changes the shape of a Cartesian rectangular mesh into a curved one and through a series of interpolations adjusts the geometry of any given structure model, the topography of the surface and the interfaces, and the position of the receivers. The simulations are tested against real data, as we perform our computations on a dynamically interesting area, with the presence of a subduction slab in the central Mediterranean. We test the methodology on seismological inverse models for the local structure (Rappisi et al., 2021).

References:

Komatitsch, D. and Tromp, J., 1999. Introduction to the spectral element method for three-dimensional seismic wave propagation. Geophysical journal international, 139(3), pp.806-822.

Masson, Y. and Romanowicz, B., 2017. Box tomography: localized imaging of remote targets buried in an unknown medium, a step forward for understanding key structures in the deep Earth. Geophysical Journal International, 211(1), pp.141-163.

Monteiller, V., Beller, S., Plazolles, B. and Chevrot, S., 2021. On the validity of the planar wave approximation to compute synthetic seismograms of teleseismic body waves in a 3-D regional model. Geophysical Journal International, 224(3), pp.2060-2076.

Nissen-Meyer, T., van Driel, M., Stähler, S.C., Hosseini, K., Hempel, S., Auer, L., Colombi, A. and Fournier, A., 2014. AxiSEM: broadband 3-D seismic wavefields in axisymmetric media. Solid Earth, 5(1), pp.425-445.

Rappisi, F., VanderBeek, B.P., Faccenda, M., Morelli, A. and Molinari, I., 2022. Slab geometry and upper mantle flow patterns in the Central Mediterranean from 3D anisotropic P-wave tomography. Journal of Geophysical Research: Solid Earth, p.e2021JB023488.

How to cite: Karakostas, F., Morelli, A., Molinari, I., VanderBeek, B., and Faccenda, M.: SPHY3D: A hybrid seismic computational framework for box-tomography of spherical Earth, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6471, https://doi.org/10.5194/egusphere-egu23-6471, 2023.

EGU23-6708 | ECS | Orals | SM8.1

On the importance of 3D stress state in 2D earthquake rupture simulations with off-fault deformation 

Marion Thomas, Louise Jeandet, and Harsha Bhat

During the last decades, many numerical models have been developed to explore the conditions for seismic and aseismic slip. Those models explore the behavior of frictional faults, embedded in either elastic or inelastic mediums, and submitted to a far field loading (seismic cycle models), or initial stresses (single dynamic rupture models). Those initial conditions impact both fault and off-fault dynamics. Because of the sparsity of direct measurements of fault stresses, modelers have to make assumptions about the initial conditions. To these days, Anderson theory is the only framework that can be used to link fault generation and reactivation to the three-dimensional stress field.  In this study, we focus on the initial stresses in 2D plane strain models developed to compute off-fault deformation. It has been demonstrated that initial conditions, in particular the angle between fault and the greatest compressive stress, is of crucial importance for the localization and intensity of off-fault inelastic deformation. However, because those models are performed on a 2D plane, the importance of the out-of-plane stress have never been investigated. We show that it can lead to set up a stress field that is not in agreement with Anderson theory (i.e., modelling a strike-slip fault in a three-dimensional stress field appropriate for reverse faulting). We investigate the influence of initial stresses by comparing equivalent models with “correct” and “incorrect” initial stress fields, keeping constant rupture-related parameters (stress drop, seismic ratio), angle between fault and greatest principal stress, and depth. We first use purely elastic models to study the influence of initial stresses on the assessment of two plastic criteria (Drucker-Prager and Coulomb stress change). We show that setting up the incorrect initial stress field can lead to underestimating the different yield criteria. The error is of the order of magnitude of the dynamic stress drop. Moreover, setting up the incorrect pre-stresses leads to errors in the estimation of potential off-fault failure modes. Then, we explore the influence of pre-stresses conditions on off-fault inelastic deformation. Using two different modelling strategies (a plastic deformation model and a micromechanics model computing dynamic damage), we show that setting up the incorrect stress field can lead to underestimate the size of the damage zone by a factor of 3 to 6 for the studied cases.  Moreover, because of the interactions between fault slip and off-fault deformation, we show that initial stress field influences the rupture propagation. Setting up the correct stress field can significantly slow the rupture, because of the more important quantity of damage induced.

How to cite: Thomas, M., Jeandet, L., and Bhat, H.: On the importance of 3D stress state in 2D earthquake rupture simulations with off-fault deformation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6708, https://doi.org/10.5194/egusphere-egu23-6708, 2023.

EGU23-7207 | Orals | SM8.1

Deciphering earthquake source observations to motivate questions for physics-based models of earthquake simulation 

Rebecca M. Harrington, Yajing Liu, Hongyu Yu, Alessandro Verdecchia, Kilian B. Kemna, Gian Maria Bocchini, Armin Dielforder, Marco P. Roth, James Kirkpatrick, Elizabeth S. Cochran, Hilary Chang, and Rachel E. Abercrombie

Earthquake stress drop values estimated from ground-motion spectra commonly vary by several orders of magnitude, particularly for small earthquakes (~M < 3). Stress-drop values have been found to vary with faulting style, faulting type (intraplate, interplate), depth, and to exhibit differences between natural and induced earthquakes. Nevertheless, distinguishing uncertainties from real trends across data sets is challenging, in part due to the variation in methodological approaches and observational constraints. However, the proliferation of high-quality, dense seismic data in recent years has shown that at least some of the variability in stress drop values almost certainly reflects diversity in fault strength and geological conditions. Coupling well-constrained observations to a variety of modeling approaches will help uncover what controls earthquake rupture processes, but deconvolving observational constraints from real variation in rupture behavior is key.

We present our stress drop estimates from data sets representing a wide range of fault loading conditions and geological environments, from interplate, intraslab and forearc subduction faults, to volcanic, intraplate, and human induced events. Stress-drop values range primarily between 1 – 100 MPa for events that meet the criteria for spectral-ratio analysis.  We present correlations of low relative stress drop values in areas of high seismic attenuation indicative of lower rock strength, and a slight correlation with depth that corresponds to modeled deviatoric stress values. We also show one notable subset of induced events near active injection wells that exhibit stress drop values of ~0.1 MPa and have distinctive low-frequency content. Their spatial distribution, waveform, and source spectral characteristics suggest either slower rupture, lower stress drop values, or a combination of both, and may represent part of the transition between aseismic and seismic slip. We show using a Large-n array that while stress drop values are roughly constant (within 2 orders of magnitude), estimates can vary by roughly 25% when station coverage is limited to 15 stations or less with a maximum azimuthal gap of 90°.  Our findings highlight the importance of using modeling approaches to explore relative influence of fault strength and methodological approaches in stress drop variation. In particular, models that incorporate both frictional and thermoelastic approaches may provide clues to the variability of conditions that can activate faults, both within stable sliding and seismic rupture conditions.

 
 
 
 

How to cite: Harrington, R. M., Liu, Y., Yu, H., Verdecchia, A., Kemna, K. B., Bocchini, G. M., Dielforder, A., Roth, M. P., Kirkpatrick, J., Cochran, E. S., Chang, H., and Abercrombie, R. E.: Deciphering earthquake source observations to motivate questions for physics-based models of earthquake simulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7207, https://doi.org/10.5194/egusphere-egu23-7207, 2023.

EGU23-7795 | ECS | Posters on site | SM8.1

Testing sedimentary basin models for ground motion simulation: the case of the Fucino intramountain basin in the Apennines (Italy) 

Giulia Sgattoni, Irene Molinari, and Giuseppe Di Giulio

Sedimentary basins are of great interest for ground motion simulations, because of their power to amplify seismic motion and because urban areas are often built on sediment covers. Realistic and detailed 3D basin models have shown to significantly improve the physics-based ground motion modeling in terms of fit between recorded and synthetic seismograms. However, discerning between the uncertainties due to source, path or site effects is not simple.

A good proxy of the seismic response of small- to moderate-scale sedimentary basins is their resonance frequencies, often investigated by experimental measurement of the Horizontal to Vertical spectral ratio (H/V) computed on ambient seismic vibrations or earthquake records. Since these parameters strongly depend on the geometry and mechanical properties of the sediment fill, a wavefield numerical simulation in a realistic 3D media should ideally reproduce them. The comparison of resonance frequencies obtained from real and simulated waveforms can help in discerning inconsistencies in the 3D models, and may help in evaluating the goodness of the model and highlighting areas where it may be improved

We apply this approach in the Fucino intermountain sedimentary basin (Central Apennines, Italy) for which several stratigraphic models, exploration and geophysical data are available in the literature. We critically combine the stratigraphic models of the basin with regional crustal models available in the literature and build an appropriate 3D velocity model. We then perform 3D seismic wave propagation simulations using a spectral-element code; and we compare simulated and experimental seismograms and resonance frequencies for different basin models observing similarities and discrepancies.

How to cite: Sgattoni, G., Molinari, I., and Di Giulio, G.: Testing sedimentary basin models for ground motion simulation: the case of the Fucino intramountain basin in the Apennines (Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7795, https://doi.org/10.5194/egusphere-egu23-7795, 2023.

EGU23-8199 | ECS | Posters on site | SM8.1

Grid-based Ray Theory Amplitude Calculation for Teleseismic Moment Tensor Sources 

Anne Mohr and Wolfgang Friederich

Direct numerical modeling of seismic wave propagation at high frequencies remains a computational challenge despite ever-increasing processing capabilities. Ray theory, which is based on a high-frequency solution of the seismic wave equation, provides an alternative to direct numerical modeling for sufficiently smooth velocity models. Here, we present a hybrid 1D-3D approach to model grids of seismic amplitudes of P-phases based on ray theory and dynamic ray tracing. They may serve to construct P-phase synthetic seismograms to be used in high-frequency teleseismic full waveform inversion or the interpretation of scattered and converted waves as done, for example, in receiver function analysis.

The modeling domain is split into two parts: 1D bulk earth and a box encompassing a regional study area for which a 3D model is used. 1D dynamic ray tracing and amplitude calculation for a moment tensor source is performed using ray paths calculated with Obspy TauP and the resulting transformation matrices and amplitudes are stored at the box boundaries. In the regional box ray paths from the box boundary to each grid point are calculated using the FM3D software by Rawlinson and Sambridge (2005) and de Kool, Rawlinson and Sambridge (2006). Subsequently, 3D dynamic ray tracing along all calculated rays is initialized from the box boundaries yielding amplitudes at each grid point.

The 1D method is tested by comparing amplitude ratios with those calculated using the software Gemini (Friederich and Dalkolmo 1995). The 3D method is tested using a 1D model and comparing amplitudes calculated using the hybrid 1D-3D method with amplitudes calculated using only the 1D method. Additionally, a 3D spherical velocity anomaly is inserted into a 1D background model to test the plausibility of the resulting amplitude grid for this model. The calculated amplitude grid clearly shows the expected focusing effects caused by the anomaly.

How to cite: Mohr, A. and Friederich, W.: Grid-based Ray Theory Amplitude Calculation for Teleseismic Moment Tensor Sources, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8199, https://doi.org/10.5194/egusphere-egu23-8199, 2023.

EGU23-8447 | ECS | Posters on site | SM8.1

The effects of 3D normal fault interactions in seismic cycles 

Constanza Rodriguez Piceda, Zoë Mildon, Martijn van den Ende, and Jean Paul Ampuero

Numerical earthquake simulators are valuable tools for investigating the causal dynamics between seismic events and improving our understanding of seismic sequences. This approach has been widely applied to single strike-slip faults, but physics-based simulations of earthquake cycles for normal fault(s) and networks are still limited. This is partly due to the focus on studying the California fault system and the computational cost of modelling dip-slip faults, which involve additional computations related to normal stress changes during the earthquake cycle. We aim to address this gap by focusing on the effect of normal fault interaction in the generation of complex seismic sequences. Using the open-source boundary-element method QDYN, we model two 3D normal faults incorporating rate-and-state friction and elastic interactions. We examine the impact of variable spatial offsets between the faults on different properties of the earthquake cycle, including slip, slip rate, magnitude distribution, and recurrence intervals within and between faults. By doing so, we aim to provide a physical explanation for the spatial and temporal variability observed in the geological record of natural normal fault networks, such as those found in the Central and Southern Apennines in Italy. Our results will shed light on the behaviour of normal fault networks and contribute to a more comprehensive understanding of earthquake cycles in these systems.  

How to cite: Rodriguez Piceda, C., Mildon, Z., van den Ende, M., and Ampuero, J. P.: The effects of 3D normal fault interactions in seismic cycles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8447, https://doi.org/10.5194/egusphere-egu23-8447, 2023.

EGU23-9857 | ECS | Posters on site | SM8.1

Propagation of SH waves in 2-D random media: from ballistic to diffusive behavior 

Malcon Celorio, Emmanuel Chaljub, Ludovic Margerin, and Laurent Stehly

Random inhomogeneities in the earth can highly influence the characteristics of propagating seismic waves. They exist at all scales and can become an important source of epistemic uncertainty in the ground motion estimation. Despite several works have evaluated these effects, few of them have verified the accuracy of their numerical solutions or controlled the propagation regime they were simulating. In this work we present a comprehensive study of SH wave propagation in 2D random media, which covers from ballistic to diffusive behaviors. In order to understand and identify the interaction of these regimes, we analyzed the coherent and incoherent components of the wavefield. The random media consist in correlated density and velocity fluctuations described by von Kármán autocorrelation function with a Hurst coefficient of 0.25 and a correlation length a=500 m. The Birch correlation coefficient which relates density to velocity fluctuations takes 4 possible values between 0.5 and 1, and the standard deviation of the perturbations is either 5% or 10%. Spectral element simulations of SH wave propagation excited by a plane wave are performed for normalized wavenumbers (ka) up to 5. By measuring the amplitude decay of the coherent wave we obtain the scattering attenuation, which is then compared with theoretical predictions from the mean field theory. Similarly, mean intensities from synthetic waveforms are also compared with those from radiative transfer theory. Both sets of comparisons show excellent agreement between numerical and theoretical predictions. Addionally, we perform statistical analyses on the fluctuations of the ballistic peak which exhibits a transition from log-normal to exponential distribution. These two types of distribution characterize the ballistic and diffusive behaviors, respectively, which means that after certain propagation distances the quasi-ballistic peak is composed mainly by multiply-diffused components. Such critical distance is of the order of the scattering mean free path and offers an alternative method to measure this parameter. Finally, we pay particular attention on the attenuation of the quasi-ballistic peak, which in the forward scattering regime appears to decay exponentially over a length scale known as the transport mean free path.

How to cite: Celorio, M., Chaljub, E., Margerin, L., and Stehly, L.: Propagation of SH waves in 2-D random media: from ballistic to diffusive behavior, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9857, https://doi.org/10.5194/egusphere-egu23-9857, 2023.

EGU23-10800 | ECS | Orals | SM8.1

The 1934 Bihar-Nepal Earthquake – Simulation of Broadband Ground Motions and Estimation of Site Amplification 

Jahnabi Basu, Sreejaya kp, and Raghukanth stg

The 1934 Bihar-Nepal earthquake, one of the most catastrophic events ever to occur in the Himalayas, inflicted extensive devastation with reported MMI of IX-VI in the Kathmandu valley and the Indo-Gangetic (IG) basin. The earthquake triggered significant ground liquefaction and landslides as it occurred in the proximity of densely populated river basins causing a huge economic loss and over 15700 fatalities. However, it is unfortunate that there are no ground motion data available for the event, as it remained unrecorded due to a lack of instrumentation. Therefore, simulating ground motions for the 1934 Bihar-Nepal earthquake would provide new insights into the influence of regional characteristics on Himalayan earthquakes. However, incorporating the Himalayan topography and the IG basin in the ground motion simulation is very challenging. In contrast, proper validation of modeling of ground motions is difficult due to the unavailability of recorded data. To circumvent these challenges, we simulated broadband ground motions for the 2015 Nepal earthquake, another significant catastrophe that occurred in the same seismo-tectonic region in the Himalayas which provides a well-recorded database. For the 2015 Nepal earthquake, a thorough comparison of the recorded and simulated ground motion spectra reveals that the simulated ground motions are consistent with the recorded data in terms of amplitude, strong motion duration, and spectral ordinates. Therefore, we considered the same medium characteristics to simulate broadband seismograms for the 1934 Bihar-Nepal earthquake by combining deterministically generated low-frequency (LF) and stochastically simulated high-frequency (HF) ground motions. The HF accelerograms are generated by considering incident and azimuthal angles obtained from rays of P and S waves traced from the finite fault slip model to the station, passing through the regional layered stratified velocity model, free surface factors and energy partition factors (Otarola and Ruiz, 2016). For deterministic simulation, a 3D computational model (Sreejaya et al., 2022) for the study region of approximately 9°×7° (between 80°–89°E longitude and 23°-30°N latitude), incorporated with basin geometry, material properties, and topography of the region is embedded with the finite fault rupture model of the event to generate LF ground motions. For the finite fault source model, five samples with various spatial variability of the slip on the rupture plane are simulated as a random field (Mai and Beroza, 2000; 2002) using the seismic moment and fault dimensions provided by Pettanati et al. (2017). Ultimately, the broadband (0.01–25 Hz) ground motions are obtained at 6461 hypothetically gridded stations with a 0.1°×0.1° spacing by combining the suitably filtered LF and HF ground motions in the frequency domain with the target frequency of 0.3 Hz with a bandwidth up to 0.05 Hz. A systematic comparison of estimated MMI values (Iyengar and Raghukanth, 2003) and the observed MMI values at 459 sites revealed that the PGA between 0.25-0.6g is significant within 200 km of the epicentral distance. Thus, the results can be used for addressing the ground failure and liquefaction caused due to the earthquake and also find applications in seismic hazard assessment of the cities in the basin.

How to cite: Basu, J., kp, S., and stg, R.: The 1934 Bihar-Nepal Earthquake – Simulation of Broadband Ground Motions and Estimation of Site Amplification, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10800, https://doi.org/10.5194/egusphere-egu23-10800, 2023.

EGU23-10831 | ECS | Orals | SM8.1

Do the statistical properties of aftershocks change in fluid-induced settings? 

Omid Khajehdehi and Joern Davidsen

Fluid-induced earthquakes are an adverse effect of industrial operations like hydraulic fracturing (e.g., 4.7 Mw in Alberta, Canada), and enhanced geothermal systems (e.g., 5.5 Mw in Pohang, South Korea). Identifying all underlying physical processes contributing to fluid-induced seismicity presents an open challenge. Recent work reports signatures of event-event triggering or aftershocks --- common for tectonic settings --- within the context of fluid-induced seismicity. In particular, the statistical properties including the productivity relation and the Omori-Utsu relation appear to hold for fluid-induced seismicity as well. Here, we investigate the underlying potential cause of these field observations from a modelling perspective. By extending a novel conceptual model by integrating (non-)linear viscoelastic effects with a combination of fluid diffusion and invasion percolation associated with a point source, we are able to capture the essential characteristics of crustal rheology and stress interactions in a porous medium. We show that this gives rise to realistic aftershock behaviour with statistical properties indistinguishable from the case of seismicity resulting from tectonic loading. This is even true if the loading due to fluid injections occurs at time scales much faster than the tectonic loading. In our model framework, such tectonic loading can be mimicked by a spatially uniform drive replacing the point source of the fluid injection and its propagation to initiate slips and earthquakes. This indicates that the emergence of the Omori-Utsu relation is independent of how the system is loaded or driven and it is indeed only controlled by the viscoelasticity of the medium. Similarly, the scaling exponent of the productivity relation --- which quantifies how the number of aftershocks increases with the magnitude of the main shock --- is independent of how the system is driven. At the same time, the spatial footprint of fluid-induced events and its dependence on the permeability field are primarily unaltered by the presence of aftershocks. Finally, within our model framework, we systematically investigate the impact of varying fluid injection rates during the viscoelastic stress redistribution on the detection of aftershocks and event-event triggering sequences. When the injection rate is sufficiently high, the aftershock detection and recovery of the Omori-Utsu and productivity relations is only feasible when the internal stress redistribution is directly accessible. 

How to cite: Khajehdehi, O. and Davidsen, J.: Do the statistical properties of aftershocks change in fluid-induced settings?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10831, https://doi.org/10.5194/egusphere-egu23-10831, 2023.

Both numerical simulations and observational pieces of evidence suggest that the earthquake triggering mechanism depends non-linearly on time. The rate and state friction (RSF) demonstrate these dependencies with a changing weight of healing and weakening terms during its state's evolution. A clock advance due to a nearby rupture using the RSF models either agrees well with the Coulomb's static failure during the fault healing stage or becomes highly susceptible to velocity changes when the failure is imminent. Here we aim to formulate an analytical relation for earthquake triggering effects on nearby faults using transient signals. The dynamic mechanical weakening on the fault interface is quantified as a function of a transient oscillatory signal's peak ground velocity (PGV) and peak spectral frequency (PSF), elastic properties of the fault, and different state weakening terms. So far, the tested numerical simulations show a good agreement with our proposed analytical approach. As a case study, nearby seismic waveforms recorded during the M6.4 (04.07.2019) event that preceded the larger  M7.1 (06.07.2019) Ridgecrest earthquake are used to calculate mechanical weakening, which correlates well with the computed PGV values attenuating with distance. The results support that if inadequate instrumentation exists, those dynamic weakening effects can be approximated empirically using the source parameter of the triggering event as a function of distance and directivity. Derivation of this analytical relation with additional verifications from numerical simulations will contribute to simultaneously including dynamic and static effects. This may lead to a more realistic estimation of increased seismic risk on nearby faults after an earthquake.  

How to cite: Sopacı, E. and Özacar, A. A.: Transient signal-based quantification of earthquake triggering effects on nearby faults using rate and state friction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11198, https://doi.org/10.5194/egusphere-egu23-11198, 2023.

An M6.8 earthquake occurred in Luding, Sichuan province, China on September 5, 2022. Since towns and villages in the earthquake-stricken area are densely populated, the earthquake caused severe fatalities and economic losses. Rapid estimation of earthquake intensity and disaster losses is significantly important for post-earthquake emergency rescue, scientific anti-seismic deployment, and the reduction of casualties and financial losses. Therefore, we make a preliminary rapid estimation of the earthquake intensity and disaster losses in the aftermath of the Luding earthquake. The seismic intensity represents the distribution of earthquake disasters and the degree of ground damages and can be directly converted from the peak acceleration velocity (PGV) map. To obtain a reliable PGV distribution map of this earthquake, we combined the finite-fault model constrained by seismic observations, with the complex three-dimensional (3D) geological environment and topographical features to perform strong ground motion simulation. Then, we compared the consistency between the simulated ground motion waveforms and observations, indicating the plausibility and reliability of simulations. In addition, we transformed the PGV simulation results into intensity and obtained a physics-based map of the intensity distribution of the Luding earthquake. The maximum simulated intensity of this earthquake is IX, which is consistent with the maximum intensity determined from the post-earthquake field survey. Based on the simulated seismic intensity map of the Luding earthquake and the earthquake disaster loss estimation model, we rapidly estimated the death and economic losses caused by this earthquake. The estimation results show that the death toll caused by this earthquake is most probably in the range of 50-300, with a mathematic expectation of 89 The government should launch a Level II earthquake emergency response plan. The economic losses are likely to be in the range of 10-100 billion RMB, with a mathematical expectation of 23.205 billion RMB. Such seismic intensity simulations and rapid estimation of disaster losses are expected to provide a preliminary scientific reference for governments to carry out the targeted deployment of emergency rescue and post-disaster reconstruction.

How to cite: Wang, W. and Zhang, Z.: Rapid Estimation of Disaster Losses by Physics-based Simulation for the M6.8 Luding Earthquake on September 5, 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11250, https://doi.org/10.5194/egusphere-egu23-11250, 2023.

EGU23-11710 | Orals | SM8.1

Deterministic and Stochastic Chaos characterise Laboratory Earthquakes 

Adriano Gualandi, Davide Faranda, Chris Marone, Massimo Cocco, and Gianmarco Mengaldo

We analyze frictional motion for a laboratory fault as it passes through the stability transition from stable sliding to unstable motion. We study frictional stick-slip events, which are the lab equivalent of earthquakes, via dynamical system tools in order to retrieve information on the underlying dynamics and to assess whether there are dynamical changes associated with the transition from stable to unstable motion. We find that the lab seismic cycles exhibit characteristics of a low-dimensional system with average dimension similar to that of natural slow earthquakes (<5). We also investigate local properties of the attractor and find maximum instantaneous dimension >10, indicating that some regions of the phase space require a high number of degrees of freedom (dofs). Our analysis does not preclude deterministic chaos, but the lab seismic cycle is best explained by a random attractor based on rate- and state-dependent friction whose dynamics is stochastically perturbed. We find that minimal variations of 0.05% of the shear and normal stresses applied to the experimental fault influence the large-scale dynamics and the recurrence time of labquakes. While complicated motion including period doubling is observed near the stability transition, even in the fully unstable regime we do not observe truly periodic behavior. Friction's nonlinear nature amplifies small scale perturbations, reducing the predictability of the otherwise periodic macroscopic dynamics. As applied to tectonic faults, our results imply that even small stress field fluctuations (less or about 150 kPa) can induce coefficient of variations in earthquake repeat time of a few percent. Moreover, these perturbations can drive an otherwise fast-slipping fault, close to the critical stability condition, into a mixed behavior involving slow and fast ruptures.

How to cite: Gualandi, A., Faranda, D., Marone, C., Cocco, M., and Mengaldo, G.: Deterministic and Stochastic Chaos characterise Laboratory Earthquakes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11710, https://doi.org/10.5194/egusphere-egu23-11710, 2023.

EGU23-11827 | ECS | Posters on site | SM8.1

The role of frictional heterogeneities, stress-state and fluid flow on fault slip behavior during fluid pressure perturbations. 

Silvio Pardo, Elisa Tinti, Martijn van den Ende, Jean-Paul Ampuero, and Cristiano Collettini

In the last 15 years, activities for geo-energy production are associated to subsurface fluid injection in enhanced geothermal systems,  for enhanced oil recovery, for the disposal of wastewater or for carbon dioxide capture and storage. In several regions, M>3 earthquakes occurred following fluid injection, and some of these earthquakes have caused extensive damage, putting geo-energy production projects at risk of being discontinued. Evaluating the conditions under which fluid injection can induce earthquakes is therefore important to safeguard local infrastructures and to ensure continuity of geo-energy projects.  

To shed light on the effect of fluid injection on a fault located in the proximity of a reservoir, we implemented into the Q-DYN seismic cycle simulator the fluid diffusion equation (one-way coupling). We ran models of seismic cycles on a rate-and-state-dependent fault under a quasi-dynamic approximation, and we developed a systematic study to assess how fault frictional heterogeneities, the stress state of the fault upon injection, the timing of injection relative to the phase of the seismic cycle and factors controlling fluid flow, i.e. permeability, porosity, flow-rate, influence fault slip behavior and earthquake magnitude. 

Our results show that localized pore-pressure perturbations allow us to gain deeper physical insight into the propagation and arrest of earthquake ruptures and that changes in the fault physical properties can promote a spectrum of fault slip behavior and recorded magnitudes.

How to cite: Pardo, S., Tinti, E., van den Ende, M., Ampuero, J.-P., and Collettini, C.: The role of frictional heterogeneities, stress-state and fluid flow on fault slip behavior during fluid pressure perturbations., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11827, https://doi.org/10.5194/egusphere-egu23-11827, 2023.

EGU23-11928 | ECS | Orals | SM8.1

Moment vs local magnitude scaling of small-to-moderate earthquakes from seismic moment estimation of 10 years (2009-2018) of Italian seismicity 

Mariano Supino, Lauro Chiaraluce, Raffaele Di Stefano, Barbara Castello, and Maddalena Michele

We computed moment (Mw) and local magnitude (ML) of about 250,000 earthquakes occurred in Italy from 2009 to 2018 and recorded at seismic stations of the Italian National Network managed by INGV.

For moment magnitude computation, we start from raw velocity waveforms and invert the displacement spectra of more than 2,000,000 S-waves manually picked. We use the probabilistic method of Supino et al. [2019] to estimate the a-posteriori joint probability density function of the source parameters: seismic moment M0, corner frequency fc and high-frequency decay γ. Mw is obtained from M0 using the Kanamori [1977] equation.

We start from the same waveforms to compute local magnitude using two designed on purpose codes, PyAmp and PyML [Di Stefano et al., 2023], and an attenuation law specific for the Italian region, Di Bona et al. [2016], obtaining ML values characterized by quality and homogeneity.

Both magnitude catalogs can be reproduced due to the availability in open databases of all the input and output parameters used for processing.

We observe a self-similar scaling between fc and M0 for Mw larger than ~2.0. For smaller magnitudes, S-wave spectra show an almost constant corner frequency (~10 Hz), which does not scale with the earthquake source (seismic moment). We interpret this as the constant cut-off frequency of the anelastic attenuation, which acts as a low-pass filter and produces an apparent corner frequency. The latter is lower than expected, and corresponds to an apparent larger source duration.

Because of the conservation of total displacement integral after a low-pass filtering, signals must exhibit a maximum amplitude lower than expected to “compensate” the apparent larger source duration. ML values are therefore expected to be underestimated while moment magnitudes, by definition, are not affected by this as they are proportional to the displacement integral.

Coherently, the comparison of our Mw and ML estimates shows the systematic underestimation of ML with respect to Mw for small magnitude events. The deviation from a 1:1 scaling relationship between ML and Mw overlaps the magnitude range where the constant apparent corner frequency arises in the M0-fc scaling (ML <~ 2).

Regarding the upcoming of a new generation of earthquake catalogs characterized by very low completeness magnitudes (MC << 2), our results suggest that a robust analysis of the statistical features of these catalogs (e.g., event size distribution) should consider the use of a precise magnitude estimate such as Mw instead of ML.

How to cite: Supino, M., Chiaraluce, L., Di Stefano, R., Castello, B., and Michele, M.: Moment vs local magnitude scaling of small-to-moderate earthquakes from seismic moment estimation of 10 years (2009-2018) of Italian seismicity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11928, https://doi.org/10.5194/egusphere-egu23-11928, 2023.

Ground motion prediction equations (GMPE) are traditionally used in site specific seismic hazard analysis to obtain design response spectra. These equations are obtained by regression analysis on the available strong motion data in a given tectonic and geological region. Assuming ergodicity regional GMPE are routinely used in site-specific probabilistic seismic hazard analysis. Since these empirical equations are region specific, However the obtained seismic hazard curves are not specific to the site. Due to lack of data for all possible combinations of magnitude and distances, development of site-specific GMPE is not possible in the near future. The only way to develop a site-specific GMPE is through numerical models. Given a 3D velocity structure, topography and source information these models can simulate site-specific ground motion. Once calibrated with the recorded strong motion data, numerical models can be used to simulate ensemble of ground motions by including the uncertainty in the slip models. In regions lacking strong motion data, these models have an additional advantage compared to GMPE. In the present study, an broad band simulation model is developed for a typical site in peninsular India. Spectral finite element method (SPECFEM) is used to simulate the low frequency ground motion by incorporating the 3D velocity structure in the medium. The high frequency ground motion is simulated from the stochastic seismological model (Otarola and Ruiz, 2016). Statistical kinematic rupture model is used to represent the earthquake source (Dhanya and Raghukanth 2018). The rupture length, width and correlation lengths of the random field are estimated from magnitude. Assuming the phase as random, a total of 30 rupture models are simulated for each magnitude. An ensemble of ground motions is simulated at the site for various possible combination of faults and magnitudes in a region around 500 km from the site. The simulated low-frequency and high-frequency ground motions are combined in the frequency domain to obtained broad band ground motions (0-100 Hz). The mean and standard deviation of the response spectra are estimated from these simulated motions for all possible combinations of magnitudes and distances at the given site. Further, probabilistic seismic hazard analysis is carried out using the simulated data to obtain hazard curves for spectral accelerations at various natural periods. Uniform hazard response spectra (UHRS) for 475yr and 2475 yr is obtained from the hazard analysis. A comparison with traditional hazard analysis using region specific GMPE is also presented. It is observed GMPE based UHRS show a smooth trend compared with site-specific UHRS obtained from broad band models. The PGA values obtained from physics based model are slightly higher than that obtained from GMPE based PSHA.

How to cite: Stg, R.: Physics based ground motion model in seismic hazard assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12317, https://doi.org/10.5194/egusphere-egu23-12317, 2023.

EGU23-12897 | ECS | Posters on site | SM8.1

Resolving Hydro-Mechanical Earthquake Cycles with a GPU-based Accelerated Pseudo-Transient Solver 

You Wu, Luca Dal Zilio, Albert De Monserrat, and the Bedretto Team

Modeling earthquake source processes is a multi-physics and multi-scale endeavor that tightly links several disciplines, including seismology, numerical computing, continuum mechanics, materials science, and engineering. In particular, incorporating the full range of coupled mechanisms, including complex fault geometries, off-fault inelastic processes, realistic shear-layer response, and fluid effects, brings significant programming and computational challenges. Furthermore, the development of highly efficient, robust and scalable numerical algorithms lags behind the rapid increase in massive parallelism of modern hardware. To address this challenge, we present a physically motivated derivation of coupled solid-fluid interactions on faults using an innovative accelerated pseudo-transient (PT) iterative method. The general approach involves transforming a time-independent problem into an evolution problem, which allows us to utilize the benefits of the Method-of-Lines (MOL) approach with the accelerated PT method. Additionally, we provide an efficient numerical implementation of PT solvers on graphics processing units (GPUs) using the Julia programming language. Julia solves the “two-language problem”, where developers who write scientific software can achieve desired performance, without sacrificing productivity. As a result, this enables us to develop high-performance code for massively parallel hardware with modern GPU-accelerated supercomputers, without requiring architecture-specific code. We aim to unveil preliminary results on the application of PT solvers to fully compressible poro-visco-elasto-plastic media, wave-mediated fully dynamic effects, rate-and-state dependent friction, and an adaptive time stepping to resolve both long- and short-time scales, ranging from years to milliseconds during the dynamic propagation of dynamic rupture. Our work can contribute to a better understanding of the accelerated PT method and its potential for facilitating the implementation of various numerical models in the field of computational earthquake physics. 

How to cite: Wu, Y., Dal Zilio, L., De Monserrat, A., and Team, T. B.: Resolving Hydro-Mechanical Earthquake Cycles with a GPU-based Accelerated Pseudo-Transient Solver, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12897, https://doi.org/10.5194/egusphere-egu23-12897, 2023.

EGU23-13098 | ECS | Orals | SM8.1

Simulations of ground motion in the Tehran basin based on newly developed 3D velocity model 

Saeed Soltani, Cecile Cornou, Bertrand Guillier, and Ebrahim Haghshenas

Tehran urban area serves as the main hub for economic and social activities in Iran. The city is located on a sedimentary basin including faults and folds, and thus it is vulnerable to large site effects. Analysis of earthquakes recorded by a temporary seismological network has approved a large amplification of seismic ground motion (about 4 to 8) over a broad frequency range.

In order to better understand and predict the effects of the geometry and mechanical properties on surface ground motions, we developed a 3D shear-wave velocity model of Tehran by integrating extensive geophysical surveys including almost 600 single station measurements and 33 ambient vibrations arrays, with geotechnical and geological data. This 3D model shows that the bedrock depth varies between 100 and 900 meters with a general increasing depth from N-NE toward the S-SW. Also, there are two main velocity layers in the basin. A surface layer, which drops from 950 m/s to 600 m/s from NE to SW and a deeper layer with Vs up to 1300 m/s.

We then used the open-source spectral-element code, EfiSpec3D (DiMartin et al., 2011), to simulate ground motion by this new sedimentary basin model at the defined 50*50 kilometers tilted square simulation block up to the maximum target frequency of 2 Hz. The source time function is a 2-Hz lowpass filtered Dirac impulse injected from the defined z-plane at 5 km depth.

The results reveal a good correlation between real and simulated earthquake ground motion by the comparison between experimental and synthetic standard spectral ratios (SSR). The results also reproduced the experimental H/V frequency peaks over the basin relatively well and suggest that 3D geometry always should be considered for an accurate estimation of realistic basin response.

How to cite: Soltani, S., Cornou, C., Guillier, B., and Haghshenas, E.: Simulations of ground motion in the Tehran basin based on newly developed 3D velocity model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13098, https://doi.org/10.5194/egusphere-egu23-13098, 2023.

The model discretization by the grid points has a great influence on the accuracy of the finite-difference seismic waveform simulation. Discretizing the discontinuous velocity model by the medium parameters of local points will lead to artefacts diffraction from stair-step representation and the inaccuracy of the calculated waveforms due to the interface error. To accurately represent layered models and reduce the interface error of finite-difference calculation, many equivalent medium parametrization methods have been developed in recent years. Most of these methods are developed for the fourth-order staggered-grid scheme and may not be accurate enough for coarse grids when applying higher-order and optimized schemes.

In this work, we develop a tilted transversely isotropic equivalent medium parametrization method to suppress the interface error and the artefact diffraction caused by the staircase approximation under the application of coarse grids. We also present an efficient algorithm for implementing equivalent medium parameterization methods for complex layered models.

How to cite: Jiang, L. and Zhang, W.: A discrete representation and the implementation for the finite-difference seismic waveform simulation with coarse grid, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13217, https://doi.org/10.5194/egusphere-egu23-13217, 2023.

EGU23-13574 | ECS | Orals | SM8.1

Evidence of frequency-dependent directivity effects from non-ergodic ground motion modelling of Spectral Acceleration in Central Italy 

Leonardo Colavitti, Giovanni Lanzano, Sara Sgobba, Francesca Pacor, and František Gallovič

Rupture directivity and its potential frequency dependence is an open issue in the seismological community, especially for small-to-moderate earthquake. Directivity itself is the focusing of the radiated seismic wave energy due to the rupture propagation along the direction of the fault.

In this research, we calibrate a non-ergodic ground motion model for the ordinates of the 5% acceleration response spectra (computation interval 0.04-2 sec) and we analyse, earthquake by earthquake, the azimuthal dependence of the aleatory component, i.e. the residual terms corrected for systematic source, site and path contributions. The final aim is the calibration of a prediction model including directivity effects that can be used for engineering purposes such as seismic hazard assessment and shaking scenarios generation.

The study area is the Central Italy, which was affected by several seismic sequences in the last 20 years, occurred on normal fault systems. The dataset we used is composed by almost 300,000 seismic recordings of 456 earthquakes in the magnitude range from 3.4 to 6.5 within the time frame 2008-2018. We find that about one-third of the analysed events are directive, characterized by unilateral ruptures along the Apennine faults direction.

Directivity effects occur over a wide frequency band and can be described by spectral curves peaked in different frequency ranges according to the event magnitude: the stronger the earthquake, the lower the frequency at which these effects are visible. Vice versa, we find no correlation between the amplitude of such peaks and the events magnitude. When normalized to the peak, the directivity curves can be grouped into families characterized by similar amplification trends variable with frequency, with the exception of 16 events, which we classify as "super-directive", that differ markedly from the others generating broadband amplifications.

Preliminary results suggest that is possible to obtain similar shapes of directivity curves for defined frequency families and that they can consequently be modeled for non-ergodic ground motion model and predictive shaking scenarios.

How to cite: Colavitti, L., Lanzano, G., Sgobba, S., Pacor, F., and Gallovič, F.: Evidence of frequency-dependent directivity effects from non-ergodic ground motion modelling of Spectral Acceleration in Central Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13574, https://doi.org/10.5194/egusphere-egu23-13574, 2023.

EGU23-13815 | ECS | Posters on site | SM8.1

Numerical modeling of fault and rupture co-evolution using a damage-breakage rheology with granular rate-and-state friction 

Casper Pranger, Dave May, Ludovic Raess, Yehuda Ben-Zion, and Alice-Agnes Gabriel

A recently developed continuum formulation of rate and state friction (Pranger et al., 2022) treats fault friction as an internal flow process in a granular medium, instead of its conventional treatment as a sliding process on a surface between juxtaposed rocks. The spurious mesh dependency that is typically associated with strain softening rheologies is avoided by including a diffusion process with an associated diffusion length scale.

We show that this granular rate and state friction law can be understood as a flow involving the breakage component of the damage-breakage rheology (DBR) of Lyakhovsky and Ben Zion (2014a,b). Modeling the episodic transitions from local damage accumulation in the solid to the fluid-like granular flow phase during larger collective failure events, the DBR is both significantly broader in scope and better grounded in the thermodynamic theory of irreversible processes than the phenomenological rate and state friction law.

A promising next step is to consider the damage and breakage components simultaneously in coupled continuum models of fault and rupture co-evolution. Doing so at sufficient resolution requires highly performant algorithms and a specialized numerical treatment of the coupled non-linear partial differential equations, including a robust time integration scheme with adaptive step size control and a flexible implicit-explicit split. We aim to discuss our numerical methods and computing paradigms supported by proof-of-concept modeling results of interacting damage and breakage pulses in 2D.

References:
Pranger et al. (2022), Rate and state friction as a spatially regularized transient viscous flow law. Journal of Geophysical Research: Solid Earth, 127, e2021JB023511.
Lyakhovsky and Ben-Zion (2014a), Damage–breakage rheology model and solid-granular transition near brittle instability. Journal of the Mechanics and Physics of Solids, 64, 184-197.
Lyakhovsky and Ben-Zion (2014b), A Continuum Damage–Breakage Faulting Model and Solid-Granular Transitions. Pure and Applied Geophysics, 171, 3099–3123

How to cite: Pranger, C., May, D., Raess, L., Ben-Zion, Y., and Gabriel, A.-A.: Numerical modeling of fault and rupture co-evolution using a damage-breakage rheology with granular rate-and-state friction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13815, https://doi.org/10.5194/egusphere-egu23-13815, 2023.

EGU23-13982 | Posters virtual | SM8.1

Effect of 3D Topography on Physics-Based Earthquake Ground Motion characteristics. 

Vishal Vats, Lav Joshi, and Jay Prakash Narayan

This paper presents the effects of 3D conical topography on the pseudo-dynamically simulated ground motion characteristics. The simulation of pseudo-dynamic ground motion has been carried out using a fourth-order accurate staggered-grid time-domain 3D finite-difference method. In the case of numerical simulations, the radiation of seismic energy from the rupture plane as per Brune’s model as well as avoiding the coherency effects is a challenging job for the simulators. The randomization of slip, rise-time, and peak-time of the source time function and the rupture arrival time, as well as the incorporation of fault-roughness and damage zone, play important roles in seismic energy release from the rupture plane as well as in the reduction of currency effects on the high-frequency seismic radiations. Firstly, the ground motions have been simulated for a hypothetical strike-slip Mw 6.0 earthquake. The efficacy of the presented code has been validated with a good match of the computed average pseudo-spectral acceleration (PSA) using the simulated ground motion with that obtained using NGA-West2 GMPEs in the frequency range 0.1–5.0 Hz. The code has been able to correctly incorporate the rupture directivity effect. Further, the effect of 3D conical topography has been estimated with azimuthal coverage of receivers. The effect of the direction of the source on the topographic amplification has also been estimated. It has been observed that topography plays an important role in the amplification of earthquake ground motion. Also, the direction of the source plays an important role in estimating the pattern of topographic amplification.

How to cite: Vats, V., Joshi, L., and Narayan, J. P.: Effect of 3D Topography on Physics-Based Earthquake Ground Motion characteristics., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13982, https://doi.org/10.5194/egusphere-egu23-13982, 2023.

EGU23-14252 | ECS | Posters on site | SM8.1

Hamiltonian Monte Carlo Method and Symplectic Geometry 

Feyza Öztürk, Çağrı Diner, and Tevfik Mustafa Aktar

Hamiltonian Monte Carlo (HMC) method is an application of non-Euclidean geometry to inverse problems. It is a probabilistic sampling method with the basis of Hamiltonian dynamics. One of the main advantages of the HMC algorithm is to draw independent samples from the state space with a higher acceptance rate than other MCMC methods. In order to understand how a higher acceptance rate is achieved, I have studied HMC in the light of symplectic geometry. Hamiltonian dynamics is defined on the phase space (cotangent bundle), which has a natural symplectic structure, i.e. a differential two-form that is non-degenerate and closed.

Symplectic geometry lies at the very foundations of physics: Geometry is the method of abstracting the solutions of physical phenomena. Once the use of phase space in the solutions of mechanical systems (e.g. simple harmonic motion, or ray-tracing) is abstracted via geometry, then it can be used in other branches such as optimization problems (e.g. Hamiltonian Monte Carlo). I present two different applications of symplectic geometry: Ray-tracing and Hamiltonian Monte Carlo.

First, the Hamiltonian function is defined on the phase space, which corresponds to an invariant of the system (e.g. total energy for the HMC method and wavefront normal for ray-tracing problem), and then by using the non-degeneracy property, a vector field can be found in which Hamiltonian function is invariant along the integral curves of the field. The invariance of the Hamiltonian function results in a high acceptance rate, where we apply the accept-reject test to satisfy the detailed-balance property.

After describing the concept of phase space for both mechanical systems and optimization problems, I am going to show different applications of HMC, including 2-dimensional travel-time tomography on a synthetic complex velocity structure. 

How to cite: Öztürk, F., Diner, Ç., and Aktar, T. M.: Hamiltonian Monte Carlo Method and Symplectic Geometry, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14252, https://doi.org/10.5194/egusphere-egu23-14252, 2023.

EGU23-14727 | Orals | SM8.1

Fast Boundary Element Methods for fault mechanics and earthquake control 

Laura Bagur, Stéphanie Chaillat, Jean-François Semblat, Ioannis Stefanou, and Pierre Romanet

Earthquakes due to either natural or anthropogenic sources cause important human and material damage. In both cases, the presence of pore fluid influence the triggering of seismic instabilities. Preliminary results, done in the context of the European Research Council CoQuake’s project (www.coquake.eu), show that the earthquake instability could be avoided by active control of the fluid pressure [Stefanou, (2019)].
In this contribution, we propose to study the ability of Fast Boundary Element Methods (Fast BEMs) [Chaillat and Bonnet (2013)] to provide a multi-physic large-scale robust model required for modeling earthquake processes, pore-fluid-induced seismicity and their control.
The main challenges concern:

  •  the modelling of a realistic on-fault behaviour as well as hydro-mechanical couplings;
  • the extension of Fast Boundary Element methods to fault mechanic problems incorporating the effect of fluid injection of the on-fault behaviour;
  • the simulation of both small and large time scales corresponding to earthquakes and fluid diffusion respectively by using a single advance in time algorithm.

The main methods used for numerical modeling of earthquake ruptures at a planar interface between two elastic half-spaces are spectral BEMs as in [Lapusta and al. (2000)]. As a first step, we consider this method for a simple problem in crustal faulting. A rate-and-state friction law is considered and different adaptive time stepping algorithms inspired from the literature are tested to take into account both small and large time scales with the correct resolution in time. These solving methods are compared on different benchmarks and convergence studies are conducted on each of them.
Then, poroelastodynamic effects are considered. To this aim, a dimensional analysis of generic poroelastodynamic equations [Schanz (2009)] is performed. It allows determining which of the poroelastodynamics effects are predominant depending on the observation time of the fault. The obtained equations corroborate and justify simplified multiphysics models from the literature, for example [Heimisson and al. (2021)]. A first multi-physics test using Fast BEMs to solve a simplified crustal faulting problem with fluid injection is considered. The objective of this project is to provide a viable efficient tool to explore the advantages and limitations of novel strategies of earthquake control using fluid injection to drive the fault from an unstable state of high potential energy to a stable state of lower potential energy.

References:

S. Chaillat, M. Bonnet. Recent advances on the fast multipole accelerated boundary element method for 3D time-harmonic elastodynamics, Wave Motion, 1090-1104, 2013
E. R. Heimisson, J. Rudnicki, N. Lapusta. Dilatancy and Compaction of a Rate-and-State Fault in a Poroelastic Medium: Linearized Stability Analysis., Journal of Geophysical Research: Solid Earth, 126(8), 2021
N. Lapusta, J. Rice and al.. Elastodynamic analysis for slow tectonic loading with spontaneous rupture episodes on faults with rate- and state-dependent friction, Journal of Geophysical Research: Solid Earth, 23765-23789, 2000.
M. Schanz. Poroelastodynamics: Linear Models, Analytical Solutions, and Numerical Methods., Applied Mechanics Reviews, 62(3)., 2009.
I. Stefanou. Controlling Anthropogenic and Natural Seismicity: Insights From Active Stabilization of the Spring‐Slider Model, Journal of Geophysical Research: Solid Earth, 8786-8802, 2019.

How to cite: Bagur, L., Chaillat, S., Semblat, J.-F., Stefanou, I., and Romanet, P.: Fast Boundary Element Methods for fault mechanics and earthquake control, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14727, https://doi.org/10.5194/egusphere-egu23-14727, 2023.

Large-scale and high-resolution earthquake simulations are very significant to earthquake hazard evaluation and exploration seismology. However, high-resolution earthquake simulations require large computing and storage resources, which increase the economic cost of computing. Compared with single-precision floating-point numbers (FP32), half-precision floating-point numbers (FP16) have faster calculation speed and lower storage requirements, which have been applied to computing platforms such as Nvidia GPUs, Sunway series supercomputers, and Ascend processors. However, the stored range of FP16 is very narrow, and numerical overflow or underflow may occur during the calculations. Therefore, in order to solve the wave equations stably, we introduce two scaling factors Cv and Cs, and rescale physical quantities to the range of the stored values of FP16. Thus, we derive new equations, which can be calculated with FP16. Based on half-precision floating-point arithmetic operations, we develop a multi-GPU earthquake simulation solver using the curved grid finite-difference method (CGFDM). Moreover, we perform several simulations and compare the seismograms with the standard CGFDM to verify the solver. Consequently, the calculation efficiency is remarkably improved, and the memory usage is reduced to 1/2.

How to cite: Wan, J., Wang, W., and Zhang, Z.: The optimization with half-precision floating-point numbers for 3-D seismic simulation based on the curved grid finite-difference method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15141, https://doi.org/10.5194/egusphere-egu23-15141, 2023.

EGU23-17013 | ECS | Posters on site | SM8.1

Physics-Based Ground Motion Simulations Using Kinematic and Dynamic Sources: A Case Study of the 2020 Mw 6.8 Elaziğ, Turkey Earthquake 

Zhongqiu He, Wenqiang Wang, Zhenguo Zhang, Zijia Wang, and Yuhao Gu

Physics-based 3D numerical simulations for earthquake rupture dynamics and ground motion simulations capable of incorporating complex non-planar fault systems, rough surface topography and the heterogeneous structure of the media are playing an increasingly role in the study of the earthquake physics and earthquake engineering. Recent advances in high-performance computing allow deterministic 3D regional-scale broadband ground motion simulations to resolve frequencies up to 10 Hz (e.g., Heinecke et al., 2014; Zhang et al., 2019; Rodgers et al., 2020; Pitarka et al., 2021). Such simulations commonly assume kinematic or dynamic rupture sources. However, systematic analysis of the effects of kinematic and dynamic rupture sources on simulations is lacking. In this work, we first resolve the kinematic rupture model of the 2020 Mw 6.8 Elaziğ, Turkey earthquake from near-field seismic and InSAR observations. We then conduct dynamic rupture scenarios that aim to reproduce the slip characteristics of the preferred kinematic model and to assess its mechanical viability. The curved grid finite-difference method (CG-FDM) is adopted to implement dynamic rupture simulations on complex non-planar fault (Zhang Z. et al., 2014; Zhang W. et al., 2020). The heterogeneous initial stresses are generate from the projection of regional tectonic stress field and the modification of static stress drop calculated from the kinematic model. Ground motion using physics-based numerical methods that consider 3D complexities in topography, medium and source is simulated on the CGFDM3D-EQR platform (Wang et al., 2022). Our result indicates that dynamic source with heterogeneity is an important factor for physics-based seismic hazard assessment.

 

References

Heinecke, A., Breuer, A., Rettenberger, S., Bader, M., Gabriel, A. A., Pelties, C., ... & Dubey, P. (2014, November). Petascale high order dynamic rupture earthquake simulations on heterogeneous supercomputers. In SC'14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 3-14). IEEE.

Pitarka, A., Akinci, A., De Gori, P., & Buttinelli, M. (2022). Deterministic 3D Ground‐Motion Simulations (0–5 Hz) and Surface Topography Effects of the 30 October 2016 M w 6.5 Norcia, Italy, Earthquake. Bulletin of the Seismological Society of America, 112(1), 262-286.

Rodgers, A. J., Pitarka, A., Pankajakshan, R., Sjögreen, B., & Petersson, N. A. (2020). Regional‐Scale 3D ground‐motion simulations of Mw 7 earthquakes on the Hayward fault, northern California resolving frequencies 0–10 Hz and including site‐response corrections. Bulletin of the Seismological Society of America, 110(6), 2862-2881.

Wang, W., Zhang, Z., Zhang, W., Yu, H., Liu, Q., Zhang, W., & Chen, X. (2022). CGFDM3D‐EQR: A Platform for Rapid Response to Earthquake Disasters in 3D Complex Media. Seismological Research Letters, 93 (4): 2320-2334.

Zhang, W., Zhang, Z., Fu, H., Li, Z., & Chen, X. (2019). Importance of spatial resolution in ground motion simulations with 3‐D basins: An example using the Tangshan earthquake. Geophysical Research Letters, 46(21), 11915-11924.

Zhang, W., Zhang, Z., Li, M., & Chen, X. (2020). GPU implementation of curved-grid finite-difference modelling for non-planar rupture dynamics. Geophysical Journal International, 222(3), 2121-2135.

Zhang, Z., Zhang, W., & Chen, X. (2014). Three-dimensional curved grid finite-difference modelling for non-planar rupture dynamics. Geophysical Journal International, 199(2), 860-879.

How to cite: He, Z., Wang, W., Zhang, Z., Wang, Z., and Gu, Y.: Physics-Based Ground Motion Simulations Using Kinematic and Dynamic Sources: A Case Study of the 2020 Mw 6.8 Elaziğ, Turkey Earthquake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17013, https://doi.org/10.5194/egusphere-egu23-17013, 2023.

NP5 – Predictability

EGU23-946 | ECS | Orals | NP5.1

Combining Bayesian Neural Networks with explainable AI techniques for trustworthy probabilistic post-processing 

Mariana Clare, Zied Ben Bouallegue, Matthew Chantry, Martin Leutbecher, and Thomas Haiden

The large data volumes available in weather forecasting make post-processing an attractive field for applying machine learning. In turn, novel statistical machine learning methods that can be used to generate uncertainty information from a deterministic forecast are of great interest to forecast users, especially given the computational cost of running high resolution ensembles. In this work, we show how one such method, a Bayesian Neural Network (BNN), can be used to post-process a single global high resolution forecast for 2m temperature. This methodology improves both the accuracy of the forecast and adds uncertainty information, by predicting the distribution of the forecast error relative to its own analysis.

Here we assess both model and data uncertainty using two different BNN approaches. In the first approach, the BNN’s parameters are defined to be distributions rather than deterministic parameters, thereby generating an ensemble of models that can be used to quantify model uncertainty. In the second approach, the BNN remains deterministic but predicts a distribution rather than a deterministic output thereby quantifying data uncertainty. Our BNN results are benchmarked against simpler statistical methods, as well as statistics from the ECMWF operational ensemble.

Finally, in order to add trustworthiness to the BNN predictions, we apply an explainable AI technique (Layerwise Relevance Propagation) so as to understand whether the variables on which the BNN bases its prediction are physically reasonable or whether it is instead learning spurious correlations.

How to cite: Clare, M., Ben Bouallegue, Z., Chantry, M., Leutbecher, M., and Haiden, T.: Combining Bayesian Neural Networks with explainable AI techniques for trustworthy probabilistic post-processing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-946, https://doi.org/10.5194/egusphere-egu23-946, 2023.

EGU23-1365 | Posters virtual | NP5.1

Improving post-processing of East African precipitation forecasts using a generative machine learning model 

Bobby Antonio, Andrew McRae, Dave MacLeod, Fenwick Cooper, John Marsham, Laurence Aitchison, Tim Palmer, and Peter Watson

Existing weather models are known to have poor skill over Africa, where there are regular threats of drought and floods that present significant risks to people's lives and livelihoods. Improved precipitation forecasts could help mitigate the negative effects of these extreme weather events, as well as providing significant financial benefits to the region. Building on work that successfully applied a state-of-the-art machine learning method (a conditional Generative Adversarial Network, cGAN) to postprocess precipitation forecasts in the UK, we present a novel way to improve precipitation forecasts in East Africa. We address the challenge of realistically representing tropical convective rainfall in this region, which is poorly simulated in conventional forecast models. We use a cGAN to postprocess ECMWF high resolution forecasts at 0.1 degree resolution and 6-18h lead times, using the iMERG dataset as ground truth, and investigate how well this model can correct bias, produce reliable probability distributions and create samples of rainfall with realistic spatial structure. We will also present performance in extreme rainfall events. This has the potential to enable cost effective improvements to early warning systems in the affected areas.

How to cite: Antonio, B., McRae, A., MacLeod, D., Cooper, F., Marsham, J., Aitchison, L., Palmer, T., and Watson, P.: Improving post-processing of East African precipitation forecasts using a generative machine learning model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1365, https://doi.org/10.5194/egusphere-egu23-1365, 2023.

EGU23-2592 | ECS | Orals | NP5.1

U-Net based Methods for the Postprocessing of Precipitation Ensemble Forecasting 

Romain Pic, Clément Dombry, Maxime Taillardat, and Philippe Naveau

Most Numerical Weather Prediction (NWP) systems use statistical postprocessing methods to correct for bias and underdispersion errors made by ensemble forecasting. This underdispersion leads to an underestimation of extreme events. Thus, many statistical postprocessing methods have been used to take into consideration the extremal behavior of meteorological phenomena such as precipitation. State-of-the-art techniques are based on Machine Learning combined with knowledge from Extreme Value Theory in order to improve forecasts. However, some of the best techniques do not consider the spatial dependency between locations. For example, Taillardat et al. (2019) trains a different Quantile Regression Forest at each location of interest and Rasp & Lerch (2018) uses neural networks with an embedding for the station's information in order to train a global model.
The dataset used corresponds to 3-h precipitation amounts produced by the radar-based observations of ANTILOPE and the 17-members ensemble forecast system called PEAROME. The dataset spans over the south of France with a grid resolution of 0.025 degrees. Our method uses a U-Net-like neural network in order to take into account the spatial structure of the data and the output of our model is a parameterized law at each grid point. Among the choices available in the literature, we focused on the Extended Generalized Pareto Distribution  and the truncated logistic with a point mass in 0. The model is trained by minimizing the scoring rules such as the Continuous Ranked Probability Score, the Log-Score or weighted versions of the aforementioned scoring rules. The method developed here is then compared to the raw ensemble as well as state-of-the-art techniques through scoring rules, skill scores and ROC curves.

References :

  • L. Pacchiardi, R. Adewoyin, P. Dueben, and R. Dutta. Probabilistic forecasting with generative networks via scoring rule minimization. Dec. 2021. arXiv:2112.08217
  • M. Taillardat, A.-L. Fougères, P. Naveau, and O. Mestre. Forest-based and semiparametric methods for the postprocessing of rainfall ensemble forecasting. Weather and Forecasting, 34(3):617–634, jun 2019. doi: 10.1175/waf-d-18-0149.1.

How to cite: Pic, R., Dombry, C., Taillardat, M., and Naveau, P.: U-Net based Methods for the Postprocessing of Precipitation Ensemble Forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2592, https://doi.org/10.5194/egusphere-egu23-2592, 2023.

EGU23-2628 | ECS | Posters on site | NP5.1

Seasonal Weather Forecast Biases Dependence on Static and Dynamic Environmental Variables in the Alpine Region 

Sameer Balaji Uttarwar, Anna Napoli, Diego Avesani, and Bruno Majone

Global seasonal weather forecasts have inherent biases compared to observational datasets over mountainous regions. This can be attributed to the model's inaccurate representation of local and global environmental processes on the Earth. In this context, the objective of this study is to assess the variation of seasonal weather forecast biases with respect to static and dynamic environmental variables over the Trentino-South Tyrol region (north-eastern Italian Alps), characterized by complex terrain.

The research employs the latest fifth-generation seasonal weather forecast system (SEAS5) dataset produced by the European Center for Medium-Range Weather Forecast (ECMWF), available at a horizontal grid resolution of 0.125° x 0.125° with 25 ensemble members in a re-forecast period from 1981 to 2016. The reference dataset is a high-resolution gridded observation (250 m x 250 m) over the region of interest. The spatiotemporal variation of monthly weather (i.e., precipitation and temperature) forecast biases over the region is inferred using several statistical indicators at observational dataset grid resolution. The static and dynamic environmental variables (i.e., respectively, terrain characteristics and large-scale atmospheric circulation indices) are used univariately to interpret their relationship with monthly weather forecast biases using the linear regression technique. A statistically significant linear relation between monthly weather forecast biases and terrain characteristics, as well as large-scale atmospheric circulation indices, has been found depending on seasonality and ensemble members.

Given significant univariate linear correlation, a simple linear bias reduction model is developed and assessed by implementing a random subsampling technique in which the regression parameters are simulated by splitting the data into calibration (70%) and validation (30%). The results reveal a reduction in the monthly weather forecast bias over the region.

This study demonstrates that the local and global environmental variables should be explicitly considered in the bias correction and downscaling of the seasonal weather forecasts over complex terrain.

How to cite: Uttarwar, S. B., Napoli, A., Avesani, D., and Majone, B.: Seasonal Weather Forecast Biases Dependence on Static and Dynamic Environmental Variables in the Alpine Region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2628, https://doi.org/10.5194/egusphere-egu23-2628, 2023.

This work investigates several statistical tests in the context of probabilistic weather forecasting and ensemble postprocessing. The tests are commonly used for comparing predictive performance of e.g. two statistical postprocessing models.  

In the first part of the analysis a case study is conducted on temperature data consisting of observations and ensemble forecasts. The tests are applied to compare the performance of two probabilistic temperature forecasts at different stations, for different lead times, investigating several standard verification metrics to measure prediction performance. The analysis shows that the tests generally behave consistently in the context of temperature forecasts. However, for certain scenarios some tests might be be preferred over the others. In general, the combination of the original Diebold-Mariano test with the continuous ranked probability score (CRPS) to assess forecast accuracy leads to the most consistent and reliable results.

The second part of the analysis uses simulated data to investigate the general behaviour of the tests in different postprocessing scenarios as well as their size and power properties. Again, the original Diebold-Mariano test appears to perform most reliably and shows no noticeable inconsistent behaviour for different simulation settings.

How to cite: Möller, A. and Grupe, F.: Investigating properties of statistical tests for comparing predictive performance with application to probabilistic weather forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2701, https://doi.org/10.5194/egusphere-egu23-2701, 2023.

EGU23-2902 | ECS | Posters virtual | NP5.1

D-Vine Copula based Postprocessing of Wind Speed Ensemble Forecasts 

David Jobst, Annette Möller, and Jürgen Groß

Statistical postprocessing of ensemble forecasts has become a common practice in research to correct biases and errors in calibration. Meanwhile, machine learning methods such as quantile regression forests or neural networks are often suggested as promising candidates in literature. However, interpretation of these methods is not always straightforward. 
Therefore, we propose the D-vine (drawable-vine) copula based postprocessing, where for the construction of a multivariate conditional copula the graphical D-vine model serves as building plan. The conditional copula is based on this tracetable model using bivariate copulas, which allow to describe linear as well as non-linear relationships between the response variable and its covariates. Additionally, our highly data-driven model selects the covariates based on their predictive strength and thus provides a natural variable selection mechanism, facilitating interpretability of the model. Finally, (non-crossing) quantiles from the obtained conditional distribution can be utilized as postprocessed ensemble forecasts. 
In a case study for the postprocessing of 10 m surface wind speed ensemble forecasts with 24 hour lead time we compare local and global D-vine copula based models to the zero-truncated ensemble model output statistics (tEMOS) for different sets of predictor variables at 60 surface weather stations in Germany. Furthermore, we investigate different types of training periods for both methods. We observe that the D-vine based postprocessing yields a comparable performance with respect to tEMOS models if wind speed ensemble variables are included only and a significant improvement if additional meteorological and station specific weather variables are integrated. The case study indicates that training periods capturing seasonal patterns are performing best for both models. Additionally, we provide a criterion for calculating the variable importance in D-vine copulas and utilize it to outline which predictor variables are the most important for the correction of 10 m surface wind speed ensemble forecasts.

How to cite: Jobst, D., Möller, A., and Groß, J.: D-Vine Copula based Postprocessing of Wind Speed Ensemble Forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2902, https://doi.org/10.5194/egusphere-egu23-2902, 2023.

EGU23-5821 | ECS | Posters on site | NP5.1

A multivariate approach to combine general circulation models using graph cuts 

Lucas Schmutz, Soulivanh Thao, Mathieu Vrac, and Gregoire Mariethoz

General circulation models (GCMs) are of extreme importance to making future climate projections. Those are used extensively by policymakers to manage responses to anthropogenic global warming and climate change.

To extract a robust global signal and evaluate uncertainties, individual models are often assembled in Multi-Model Ensembles (MMEs). Various approaches to combine individual models have been developed, such as the Multi-Model Mean (MMM) or its weighted variants.

Recently, Thao et al. (2022) proposed a model comparison approach based on graph cuts. Graph cut optimization was developed in the field of computer vision to efficiently approximate a solution for low-level computer vision tasks such as image segmentation (Boykov et al., 2001). Applied to MMEs, it allows selecting for each gridpoint the best-performing model and produces a patchwork of models that maximizes performances while avoiding spatial discontinuities. Thus, it considers the local performance of individual models in contrast with approaches such as MMM or similar methods that use global weights.

Here we propose a new multivariate combination approach of MMEs based on graph cuts. Compared to the existing univariate method, our approach ensures that the relationships between variables, that are present in GCMs, are locally preserved while providing coherent spatial fields. Moreover, we measure the local performance of models using the Hellinger distance between multi-decadal distributions. This allows a combination of models that is not only indicative of the average behavior (e.g. mean temperature or mean precipitation) but of the entire multivariate distribution, including more extreme values that have a high societal and environmental impact.

REFERENCES 

Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), 1222–1239. https://doi.org/10.1109/34.969114

Thao, S., Garvik, M., Mariethoz, G., & Vrac, M. (2022). Combining global climate models using graph cuts. Climate Dynamics, February. https://doi.org/10.1007/s00382-022-06213-4

How to cite: Schmutz, L., Thao, S., Vrac, M., and Mariethoz, G.: A multivariate approach to combine general circulation models using graph cuts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5821, https://doi.org/10.5194/egusphere-egu23-5821, 2023.

EGU23-8594 | ECS | Posters on site | NP5.1

Joint Generalized Neural Models and Censored Spatial Copulas for Probabilistic Rainfall Forecasting 

David Huk, Rilwan Adewoyin, and Ritabrata Dutta

This work develops a novel method for generating conditional probabilistic rainfall forecasts with temporal and spatial dependence. A two-step procedure is employed. Firstly, marginal location-specific distributions are modelled independently of one another. Secondly, a spatial dependency structure is learned in order to make these marginal distributions spatially coherent.
To learn marginal distributions over rainfall values, we propose a class of models termed Joint Generalised Neural Models (JGNMs). These models expand the linear part of generalised linear models with a deep neural network allowing them to take into account non-linear trends of the data while learning the parameters for a distribution over the outcome space.
In order to understand the spatial dependency structure of the data, a model based on censored copulas is presented. It is designed for the particularities of rainfall data and incorporates the spatial aspect into our approach. Uniting our two contributions, namely the JGNM and the Censored Spatial Copulas into a single model, we get a probabilistic model capable of generating possible scenarios on short to long-term timescales, able to be evaluated at any given location, seen or unseen. We show an application of it to a precipitation downscaling problem on a large UK rainfall dataset and compare it to existing methods.

How to cite: Huk, D., Adewoyin, R., and Dutta, R.: Joint Generalized Neural Models and Censored Spatial Copulas for Probabilistic Rainfall Forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8594, https://doi.org/10.5194/egusphere-egu23-8594, 2023.

EGU23-8824 | ECS | Posters on site | NP5.1

Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions 

Maxime Taillardat, Anne-Laure Fougères, Philippe Naveau, and Raphaël De Fondeville

Verifying probabilistic forecasts for extreme events is a highly active research area because popular media and public opinions are naturally focused on extreme events, and biased conclusions are readily made. In this context, classical verification methods tailored for extreme events, such as thresholded and weighted scoring rules, have undesirable properties that cannot be mitigated, and the well-known continuous ranked probability score (CRPS) is no exception.

Here, we define a formal framework for assessing the behavior of forecast evaluation procedures with respect to extreme events, which we use to demonstrate that assessment based on the expectation of a proper score is not suitable for extremes. Alternatively, we propose studying the properties of the CRPS as a random variable by using extreme value theory to address extreme event verification. An index is introduced to compare calibrated forecasts, which summarizes the ability of probabilistic forecasts for predicting extremes. The strengths and limitations of this method are discussed using both theoretical arguments and simulations.

How to cite: Taillardat, M., Fougères, A.-L., Naveau, P., and De Fondeville, R.: Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8824, https://doi.org/10.5194/egusphere-egu23-8824, 2023.

The ERA5 global reanalysis has been compared against a high-resolution regional reanalysis (COSMO-REA6) by means of scale-separation diagnostics based on 2d Haar discrete wavelet transforms. The presented method builds upon existing methods and enables the assessment of bias, error and skill for individual spatial scales, separately. A new skill score (evaluated against random chance) and the Symmetric Bounded Efficiency are introduced. These are compared to the Nash-Sutcliffe and the Kling-Gupta Efficiencies, evaluated on different scales, and the benefits of symmetric statistics are illustrated. As expected, the wavelet statistics show that the coarser resolution ERA5 products underestimate small-to-medium scale precipitation compared to COSMO-REA6. The newly introduced skill score shows that the ERA5 control member (EA-HRES), despite its higher variability, exhibits better skill in representing small-to-medium scales with respect to the smoother ensemble members. The Symmetric Bounded Efficiency is suitable for the intercomparison of reanalyses, since it is invariant with respect to the order of comparison.

How to cite: Casati, B., Lussana, C., and Crespi, A.: Scale-separation diagnostics and the Symmetric Bounded Efficiency for the inter-comparison of precipitation reanalyses, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9083, https://doi.org/10.5194/egusphere-egu23-9083, 2023.

EGU23-9328 | Orals | NP5.1

The EUPPBench postprocessing benchmark 

Jonas Bhend, Jonathan Demaeyer, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem

Statistical postprocessing of forecasts from numerical weather prediction systems is an important component of modern weather forecasting systems. A growing variety of postprocessing methods has been proposed, but a comprehensive, community-driven comparison of their relative performance is yet to be established. Important reasons for this lack include the absence of a fair intercomparison protocol, and, the difficulty of constructing a common comprehensive dataset that can be used to perform such intercomparison. Here we introduce the first version of the EUPPBench, a dataset of time-aligned medium-range forecasts and observations over Central Europe, with the aim to facilitate and standardize the intercomparison of postprocessing methods. This dataset is publicly available [1], includes station and gridded data, ensemble forecasts for training (20 years) and validation (2 years) based on the ECMWF system. The initial dataset is the basis of an ongoing activity to establish a benchmarking platform for postprocessing of medium-range weather forecasts. We showcase a first benchmark of several methods for the adjustment of near-surface temperature forecasts and outline the future plans for the benchmark activity. 

 

[1] https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark

How to cite: Bhend, J., Demaeyer, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9328, https://doi.org/10.5194/egusphere-egu23-9328, 2023.

The established benefits of post-processing the results of multi-model ensembles, even by simple averaging, suggest a more radical approach: The models should be combined more frequently in run-time so as to form a single “supermodel”.  Simple nudging of models to one another, as frequently as the models might assimilate data from observations, combines model fusion with a reasonable degree of model autonomy.

Key to the success of the supermodeling approach is the phenomenon of chaos synchronization, known in the field of nonlinear dynamics, wherein two chaotic systems synchronize when connected through only a few of their variables, despite sensitive dependence on initial conditions. Synchronization gives rise to consensus among models. The nudging coefficients can be trained so that that consensus agrees with observations, because the effective dynamics of the trained supermodel, regarded as a single dynamical system, matches the dynamics of nature. Yet the number of independent nudging coefficients that must be trained is far less than the number of trainable parameters in a typical climate model.

It is expected that supermodeling will be especially useful for improving the representation of localized structures, such as blocking patterns, which will wash out if de-synchronized output fields of different models are combined by averaging.  We confirm a hypothesis that such coherent structures will synchronize even when the underlying fields do not, because the internal synchronization within each structure re-enforces synchronization between models: A configuration of CAM4 and CAM5 models, of different resolution, connected by nudging, exhibits correlated blocking activity even when the flows do not otherwise synchronize.  

We further explore the basis for correlated blocking activity in a pair of coupled quasi-geostrophic channel models. The local synchronization error is lower in a region of the channels where blocks form than elsewhere in the channels. Blocking correlations emerge as a vestige of “chimera synchronization”, the phenomenon in which complete synchronization of two spatially extended systems is intermittent in space as well as time. Such partial synchronization of different models in the regions of blocks - and of other structures such as jets, fronts, and large-scale convection - would be particularly useful for projecting climate-change patterns in extreme events associated with those structures.

How to cite: Duane, G., Schevenhoven, F., and Weiss, J.: Synchronization of Blocking Patterns in Diifferent Models, Connected So As to Form a “Supermodel” of Future Climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10153, https://doi.org/10.5194/egusphere-egu23-10153, 2023.

EGU23-11230 | Posters on site | NP5.1

Mathematical Properties of Continuous Ranked Probability Score Forecasting 

Clément Dombry, Romain Pic, Philippe Naveau, and Maxime Taillardat

The theoretical advances on the properties of scoring rules over the past decades have broaden the use of scoring rules in probabilistic forecasting. In meteorological forecasting, statistical postprocessing techniques are essential to improve the forecasts made by deterministic physical models. Numerous state-of-the-art statistical postprocessing techniques are based on distributional regression evaluated with the Continuous Ranked Probability Score (CRPS). However, theoretical properties of such minimization of the CRPS have mostly considered the unconditional framework (i.e. without covariables) and infinite sample sizes. We circumvent these limitations and study the rate of convergence in terms of CRPS of distributional regression methods. We find the optimal minimax rate of convergence for a given class of distributions. Moreover, we show that the nearest neighbor method and the kernel method for distributional regression reach the optimal rate of convergence in dimension larger than 2 and in any dimension, respectively.
Associated article: https://doi.org/10.1016/j.ijforecast.2022.11.001

How to cite: Dombry, C., Pic, R., Naveau, P., and Taillardat, M.: Mathematical Properties of Continuous Ranked Probability Score Forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11230, https://doi.org/10.5194/egusphere-egu23-11230, 2023.

It is often stated that the goal of probabilistic forecasting is to issue predictive distributions that are as sharp as possible, subject to being calibrated. To assess the calibration of ensemble forecasts, it is customary to employ rank histograms. Rank histograms not only assess whether or not an ensemble prediction system is calibrated, but they also reveal what (if any) systematic biases are present in the forecasts. This information can readily be relayed back to forecasters, helping to improve future predictions. Such is the utility of rank histograms, several extensions have been proposed to evaluate the calibration of probabilistic forecasts for multivariate outcomes. These extensions typically introduce a so-called pre-rank function that condenses the multivariate forecasts and observations into univariate objects, from which a standard rank histogram can be constructed. Several different approaches to construct multivariate rank histograms have been proposed, each of which differs in the choice of pre-rank function. Existing pre-rank functions typically aim to preserve as much information as possible when condensing the multivariate forecasts and observations into univariate objects. Although this is sensible when testing for multivariate calibration, the resulting rank histograms are often difficult to interpret, and are therefore rarely used in practice.        
We argue that the principal utility of these histogram-based diagnostic tools is that they provide forecasters with additional information regarding the deficiencies that exist in their forecasts, in turn allowing them to address these shortcomings more readily; interpretation is therefore a key requirement. We demonstrate that there are very few restrictions on the choice of pre-rank function when constructing multivariate rank histograms, meaning forecasters need not restrict themselves to the few proposed already, but can instead choose a pre-rank function on a case-by-case basis, depending on what information they want to extract from their forecasts. We illustrate this by introducing a range of possible pre-rank functions when assessing the calibration of probabilistic spatial field forecasts. The pre-rank functions that we introduce are easy to interpret, easy to implement, and they provide complementary information. Several pre-rank functions can therefore be employed to achieve a more complete understanding of the multivariate forecast performance. Finally, having chosen suitable pre-rank functions, tests for univariate calibration based on rank histograms can readily be applied to the multivariate rank histograms. We illustrate this here using e-values, which provide a theoretically attractive way to sequentially test for the calibration of probabilistic forecasts.

How to cite: Allen, S. and Ziegel, J.: Assessing the calibration of multivariate ensemble forecasts: E-values and the choice of pre-rank function, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11660, https://doi.org/10.5194/egusphere-egu23-11660, 2023.

EGU23-12232 | ECS | Posters on site | NP5.1

Impacts of uni- and multivariate bias adjustment methods on simulations of hydrological signatures in high latitude catchments 

Faranak Tootoonchi, Andrijana Todorović, Thomas Grabs, and Claudia Teutschbein

Climate models are used to generate future hydroclimatic projections for exploring how climate change may affect water resources. Their outputs, however, feature systematic errors due to parametrization and simplification of processes at the spatiotemporal scales required for impact studies. To minimize the adverse effects of such biases, an additional bias adjustment step is typically required.

Over the past decade, adjustment methods with different levels of complexity have been developed that consider one or several variables at a time, consequently adjusting one or multiple features of climate model simulations. Despite attempts in developing such methods and the growing use of some, the selection of methods for accurate simulation of streamflow remains subjective and still highly debated. In this study, we seek to answer whether sophisticated multivariate bias adjustment methods outperform simple univariate methods in the simulation of streamflow signatures.

To this end, we systematically investigated the ability of two simple univariate and two advanced multivariate methods to accurately represent various hydrological signatures relevant for water resources management in high latitudes. We offer practical guidelines for choosing the most suitable bias adjustment methods based on the objective of each study (i.e., hydrologic signatures of interest) and the hydroclimatic regime of the study catchments.

How to cite: Tootoonchi, F., Todorović, A., Grabs, T., and Teutschbein, C.: Impacts of uni- and multivariate bias adjustment methods on simulations of hydrological signatures in high latitude catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12232, https://doi.org/10.5194/egusphere-egu23-12232, 2023.

Spatial sampling remains a conundrum for verification. The observations that are required are rarely on a grid, nor are they homogenously spaced. They are often located where there are people, easy access and do not sample the variable in a representative way. In an aggregate sense, scores derived from such observation locations, will give areas with greater observation density more weight in the aggregate if the variations in network density are not accounted for. Furthermore the performance in some parts of the domain may not be represented at all if there are no observations there. Gridded analyses on the other hand often provide complete coverage, and offer great ease of use, but adjacent grid boxes are not independent. Given this relative wealth of coverage and uniform sampling, we tend to use all available grid points for computing aggregate scores for an area or region, despite knowing that this is likely to produce too-narrow confidence intervals and inflate any statistical significance that may be present. 

In this presentation a variety of approaches, both empirical and statistical, are explored to establish what we ought to include when computing aggregate scores. Three different empirical sampling approaches are compared to selections from statistical coverage or network design algorithms. The empirical options include what is termed “strict” sub-sampling, whereby a sample is taken from the full grid and the reduction in sample size is explored by systematically continually taking a sub-sample from the sub-sample. The second is a systematic reduction in sample size from the original grid whereby each sample is drawn from the original grid, taken every other grid point, then every 3rd grid point, every 4th etc. The third is a mean computed from N random draws of reducing sample size. These empirical options do not respect land or sea locations. They are purely intended at looking at the behaviour and stability of the sample score. The coverage design algorithms provide a methodology for deriving homogeneous samples for irregularly spaced surface networks over land, and regularly spaced sampling of grids over the ocean, to achieve an optimal blend of sampling for regions that cover both land and sea.  These sample sizes and sample scores are compared to a statistically computed effective sample size. 

Some interesting and surprising results emerge. One of which is that as little as 1% of the total number of grid points may be sufficient for measuring the performance of the forecast on a grid, though the proportion of the total will always be dependent on (to varying degrees) the variable, the threshold or event of interest, the metric or score, and the characteristics of the geographical region of interest. 

How to cite: Mittermaier, M. and Gilleland, E.: Exploring empirical and statistical approaches for determining an appropriate sample size for aggregate scores, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12242, https://doi.org/10.5194/egusphere-egu23-12242, 2023.

EGU23-12316 | Posters on site | NP5.1

On the reliability of bivariate forecasts 

Zied Ben Bouallegue

Reliability is a key attribute of an ensemble forecast. Typically, this means that one expects that the ensemble spread reflects the potential error of the corresponding ensemble mean forecast. In the realistic case of an unperfect forecast, reliability deficiencies can be diagnosed with tools such as the reliability diagram and the rank histogram. Along with the computation of scores, the use of these diagnostic tools is common practice in ensemble forecast verification when assessing univariate forecasts. But what does reliability mean in practical terms when assessing multivariate forecasts? Here the concept of reliability is revisited in the simplest of the multivariate cases: the bivariate forecast. As a result, we propose a set of new diagnostic tools with an emphasis on the cross-variable reliability aspect. Real case examples are used for illustration and discussion.

How to cite: Ben Bouallegue, Z.: On the reliability of bivariate forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12316, https://doi.org/10.5194/egusphere-egu23-12316, 2023.

EGU23-13327 | ECS | Posters on site | NP5.1

Towards a machine learning based multimodel for precipitation forecast over the italian peninsula 

Luca Monaco, Roberto Cremonini, and Francesco Laio

Direct model output forecasts by Numerical Weather Prediction models (NWPs) present some limitations caused by errors mostly due to sensitivity to initial conditions, sensitivity to boundary conditions and deficiencies in parametrization schemes (i.e. orography).
These sources of error are unavoidable, and atmospheric chaotic dynamics make prediction errors spread rapidly in time in the course of the forecast, inducing both systematic and random errors.
Nonetheless, in the last 50 years, NWPs had a significant decrease in the impact of these sources of errors, even in the long-term forecast, thanks for instance to an ever-increasing computational capability, but their relevance is still not neglectable.
Moreover, different NWPs present specific different pros and cons which are findable empirically. For instance, in the case of precipitation forecast in north-west Italy, low-resolution models (e.g. ECMWF-IFS) are more reliable in terms of space and time in predicting the average precipitation, while high-resolution models (e.g. COSMO-2I) tend to forecast better the maximum precipitation. Research purposes apart, actual limitations must be seen in an operational context, where weather forecasts’ skillfulness and associated uncertainty are information of the utmost importance to the forecaster and in general to the user of a certain forecasts system.

To tackle these limitations of NWPs and the need for an uncertainty-quantified meteorological forecast, we propose a machine learning-based multimodel post-processing technique for precipitation forecast. We focus on precipitation since it is the most important variable in the issue of spatially localized weather alert notice by the Italian Civil Protection system and at the same time it is one of the most challenging variables to forecast. 
We use a Convolutional Neural Network (CNN) to obtain deterministic and probabilistic forecast grids over 24h up to 48h focusing on North-West Italy, using several high and low-resolution deterministic NWPs as input and using high-resolution rain-gauge corrected radar observations for the training. The effect of the usage of different convolutional parameters (e.g. stride, padding) is taken into account. The deterministic output grid is chosen as the grid with the lowest mean square error obtained during the training, and it is compared with the linear regression of the input NWPs and with every single model. The probabilistic output grid is generated by considering the statistical ensemble of the twenty grids with the lowest mean square error obtained during the training, and it is compared with the logistic regression of the input NWPs and with ECMWF-EPS as a benchmark, both at different precipitation thresholds.

How to cite: Monaco, L., Cremonini, R., and Laio, F.: Towards a machine learning based multimodel for precipitation forecast over the italian peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13327, https://doi.org/10.5194/egusphere-egu23-13327, 2023.

In recent years neural networks have successfully been applied to probabilistic post-processing of numerical weather prediction forecasts. In the Bernstein Quantile Networks (BQN) method predictive quantile distributions are specified by Bernstein polynomials and their coefficients linked to input features through flexible neural networks. However, precipitation presents an additional challenge due to its mixed distributed nature with a considerable proportion of dry events for short accumulation periods. In this presentation, it is demonstrated how the BQN method can be modified to mixed distributed variables like precipitation by introducing a latent variable and treating zero precipitation cases as censored data. The method is tested on both synthetic and real precipitation forecast data and compared to an approach where a model of the probability of precipitation is combined with a model of precipitation amounts using the laws of probability.

 

How to cite: Bremnes, J. B.: Censored Bernstein quantile networks for probabilistic precipitation forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13849, https://doi.org/10.5194/egusphere-egu23-13849, 2023.

EGU23-14425 | ECS | Posters on site | NP5.1

Lead time continuous statistical post-processing of ensemble weather forecasts 

Jakob Wessel, Chris Ferro, and Frank Kwasniok

Numerical weather prediction (NWP) models usually output their forecasts at a multiplicity of different lead times. For example, the Met Office ensemble prediction system for the UK (MOGREPS-UK) predicts atmospheric variables on a 2.2km grid for up 126h on hourly and sub-hourly timesteps. Even though for applications, information is often required on this range of lead times, many post-processing methods in the literature are either applied at fixed lead time or by fitting individual models for each lead time. This is also the case in systems used in practice such as the Met Office IMPROVER system. However, this is 1) computationally expensive because it requires the training of multiple models if users are interested in information at multiple lead times and 2) prohibitive because it restricts the training data used for training post-processing models and the usability of fitted models.

In this work we investigate lead time dependence of ensemble post-processing methods by looking at ensemble forecasts in an idealized Lorenz96 system as well as temperature forecast data from the Met Office MOGREPS-UK system. First, we investigate the lead time dependence of estimated model parameters in non-homogenous Gaussian regression (NGR -- a standard ensemble post-processing technique) and find substantial smoothness. Secondly, we look at the usability of models fitted for one lead time and employed at another to then thirdly fit models that are “lead time continuous”, meaning they work for multiple lead times simultaneously by including lead time as a covariate using spline regression. We show that these models can achieve similar performance to the classical “lead time separated” models, whilst saving substantial computation time. Fourthly and finally we make first steps towards the development of a cheap computational model including seasonality and working continuously over the lead time, needing to be fit only once.

How to cite: Wessel, J., Ferro, C., and Kwasniok, F.: Lead time continuous statistical post-processing of ensemble weather forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14425, https://doi.org/10.5194/egusphere-egu23-14425, 2023.

EGU23-14560 | ECS | Posters on site | NP5.1

Quantile regression forests for post-processing ECWMF ensemble precipitation forecasts: hyperparameter optimization and comparison to EMOS 

Eva van der Kooij, Antonello Squintu, Kirien Whan, and Maurice Schmeits

Ensemble forecasts are important due to their ability to characterize forecast uncertainty, which is fundamental when forecasting extreme weather. Ensemble forecasts are however often biased and underdispersed and thus need to be post-processed.

A common approach for this is the use of ensemble model output statistics (EMOS), where a parametric distribution is fitted with a limited number of predictors. With recent advances in computer science and increased amounts of data available, machine learning techniques, like random forests, are becoming more popular for high dimensional regression problems. In this research, we explore the use of the quantile regression forest (QRF), a random forest adapted for conditional quantile estimation, applied to medium range gridded probabilistic precipitation forecasts. QRFs are non-parametric and allow for a larger number of predictors, which means they can possibly consider more dependencies that might otherwise not be captured with a simple EMOS.

A QRF takes several hyperparameters that influence the way the decision trees in the forest are constructed. We explore the minimum number of samples needed in a leaf to split it (minimum node size) and the number of predictors explored in each split (mtry). A hyperparameter space is constructed by setting ranges for both minimum node size and mtry, and the optimal hyperparameter set is determined by performing a cross validated grid search. Here, each model is assessed based on the continuous ranked probability skill score (CRPSS). For comparison, EMOS is applied with a zero-adjusted gamma (ZAGA) distribution, using a limited number of predictors that are physically correlated to precipitation. Both methods are verified on a separate testing data set and evaluated using several scores, including CRPSS and Brier skills score (BSS).

We consider 4 years (November 2018 – October 2022) of archived operational ECMWF-IFS ensemble forecasts for the Netherlands. The data is split into November 2018 – October 2021 for training and cross-validation, and October 2021 – October 2022 for testing, separating data for season, initialization time and lead-time. Forecasts are post-processed up to +10 days. Ensemble statistics on 60+ forecast variables are used as predictors. Spatially and temporally aggregated, gauge-adjusted radar observations are used as predictand. The raw ensemble is considered as the benchmark.

The results of this research will determine what method will be used to post-process the ensemble precipitation forecasts in the context of the early warning center (EWC) of the Royal Netherlands Meteorological Institute. The most suitable method could differ between shorter and longer lead times.

How to cite: van der Kooij, E., Squintu, A., Whan, K., and Schmeits, M.: Quantile regression forests for post-processing ECWMF ensemble precipitation forecasts: hyperparameter optimization and comparison to EMOS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14560, https://doi.org/10.5194/egusphere-egu23-14560, 2023.

EGU23-14712 | ECS | Orals | NP5.1

NWP model updates and post-processing: a strategy for an EMOS model on ECMWF wind gusts forecasts 

Antonello A. Squintu, Eva van der Kooij, Kirien Whan, and Maurice Schmeits

In the framework of KNMI’s Early Warning Center (EWC), ECMWF ensemble (ENS) predictions are used to issue medium-range forecasts of severe weather. Timely forecasts of wind gusts extremes are important to prevent potential damage. However, ensemble forecasts are affected by biases and under- or over-dispersion. These errors lead to a reduction in the skill of the forecasts, especially for long lead-times and for extreme cases, such as windstorms and deep convective episodes. Hence, statistical post-processing is a fundamental step in the establishment of a skillful weather alert system for extreme wind gust events.     

However, weather models like ECMWF-IFS are subject to frequent updates, which include changes in the calculation of certain diagnostic variables and by consequence in statistical features of their ensemble distribution. This is the case for ECMWF wind gusts forecasts, whose bias has been reduced with the last update in October 2021. Therefore, the use of pre-update wind gusts forecasts in the training of the post-processing model must be considered with care.

In the context of the development of an Ensemble Model Output Statistics (EMOS) model, this limitation has been tackled by reconstructing wind-gusts forecasts with a preliminary EMOS model. This step has been performed by including in the regression those variables that are used by ECMWF for the calculation of wind gusts, which were less affected by the update.

The reconstructed wind gusts forecasts have been added to a set of summary statistics of the ensemble distribution of variables physically related to wind gusts. A process of forward selection has been applied to identify the most relevant contributions to the general EMOS model, highlighting reconstructed wind gusts as the most important predictor for all lead-times.

The post-processed forecasts obtained with this experimental EMOS model have been verified and compared to those calculated with a conventional EMOS model (performed ignoring the above caveats) and with the results of a non-parametric Quantile Regression Forest. These models have been trained on the same period (2018-2021) and tested on the period that has followed the update (2021-2022), including only grid-points and stations that cover the territory of the Netherlands and distinguishing between summer and winter half-years. The method showing the best performance will be employed operationally for the post-processing of ECMWF-ENS wind gust forecasts over the Netherlands and will be used in the EWC weather alert system.

How to cite: Squintu, A. A., van der Kooij, E., Whan, K., and Schmeits, M.: NWP model updates and post-processing: a strategy for an EMOS model on ECMWF wind gusts forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14712, https://doi.org/10.5194/egusphere-egu23-14712, 2023.

EGU23-15152 | ECS | Posters on site | NP5.1

Towards sub-kilometer resolution probabilistic analysis of surface wind in complex terrain 

Francesco Zanetta, Daniele Nerini, Matteo Buzzi, and Mark A. Liniger

Correctly representing surface wind is critical for applications such as renewable energy, snow modelling or warning systems. However, numerical weather prediction models with their limited resolution cannot fully represent the strong variability due to complex topography. Downscaling techniques – functionally equivalent to postprocessing when the ground truth is given by observational data - can achieve remarkable results in reducing systematic biases of raw models and can be calibrated to yield accurate probabilistic information at any point in space. 

These techniques can be further improved at analysis time by including real-time measurements, allowing to produce a probabilistic sub-grid resolution analysis of surface wind. Such a product would enable other interesting applications, such as detailed climatologies or nowcasting, and could serve as a ground truth for training deep learning-based postprocessing models with generative approaches, allowing to model spatially and temporally consistent ensembles.  

The first important challenge is to integrate measurements in a statistically optimized and efficient way. Here, we share our ongoing work and preliminary results in a comparative analysis of different approaches, from naïve interpolations to geostatistical techniques or novel approaches based on neural networks. The analysis is based on a multi-year archive of hourly wind observations and NWP analyses from the operational COSMO-1E model over Switzerland. 

How to cite: Zanetta, F., Nerini, D., Buzzi, M., and Liniger, M. A.: Towards sub-kilometer resolution probabilistic analysis of surface wind in complex terrain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15152, https://doi.org/10.5194/egusphere-egu23-15152, 2023.

EGU23-17348 | Orals | NP5.1

Postprocessing of ensemble precipitation forecasts over India using weather types 

Martin Widmann, Noemi Gonczol, Michael Angus, and Robert Neal

Accurate predictions of heavy precipitation in India are vital for impact-orientated forecasting, and an essential requirement for mitigating the impact of damaging flood events. Operational forecasts from non-convection-permitting models can have large biases in the intensities of heavy precipitation, and while convection-permitting models can perform better, their operational use over large areas is not yet feasible. Statistical postprocessing can reduce these biases for relatively little computational cost, but few studies have focused on postprocessing forecasts of monsoonal rainfall.

We present a postprocessing method for operational precipitation forecasts based on local precipitation distributions for 30 Indian weather types. It is applied to ensemble forecasts for daily precipitation with 12km spatial resolution and lead times of up to 10 days from the Indian National Centre for Medium Range Weather Forecasting (NCMRWF) Ensemble Prediction System (NEPS). The method yields local probabilistic forecasts that are the weighted mean of the observed local precipitation distributions for each weather type, with weights given by the relative frequency of the weather types in the forecast ensemble.

The general forecast skill is determined through the Continuous Ranked Probability Skill Score (CRPSS) and the skill for predicting the exceedance of the local 90th percentile is quantified through the Brier Skill Score (BSS). The CRPSS shows moderate improvement over most of India for forecasts with one day lead time, and substantial improvements almost everywhere for longer lead times. The BSS for one day forecasts indicates a spatially complex pattern of higher and lower performance, while for longer lead times the forecasts for heavy precipitation are improved almost everywhere. The improvements with respect to both measures are particularly high over mountainous or wet regions. We will also present reliability diagrams for the raw and postprocessed forecasts of threshold exceedances.

 

 

How to cite: Widmann, M., Gonczol, N., Angus, M., and Neal, R.: Postprocessing of ensemble precipitation forecasts over India using weather types, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17348, https://doi.org/10.5194/egusphere-egu23-17348, 2023.

EGU23-1095 | Orals | NP5.2

Recent offline land data assimilation results and future steps towards coupled DA at Meteo-France 

Jean-Christophe Calvet, Bertrand Bonan, and Yiwen Xu

Land data assimilation aims to monitor the evolution of soil and vegetation variables. These variables are driven by climatic conditions and by anthropogenic factors such as agricultural practices. Monitoring terrestrial surfaces involves a number of variables of the soil-plant system such as land cover, snow, surface albedo, soil water content and leaf area index. These variables can be monitored by integrating satellite observations into models. This process is called data assimilation. Integrating observations into land surface models is particularly important in changing climate conditions because environmental conditions and trends never experienced before are emerging. Because data assimilation is able to weight the information coming from contrasting sources of information and to account for uncertainties, it can produce an analysis of terrestrial variables that is the best possible estimation. In this work, data assimilation is implemented at a global scale by regularly updating the model state variables of the ISBA land surface model within the SURFEX modelling platform: the LDAS-Monde sequential assimilation approach. Model-state variable analysis is done for initializing weather forecast atmospheric models. Weather forecast relies on observations to a large extent because of the chaotic nature of the atmosphere. Land variables are not chaotic per se but rapid and complex processes impacting the land carbon budget such as forest management (thinning, deforestation, ...), forest fires and agricultural practices are not easily predictable with a good temporal precision. They cannot be monitored without integrating observations as soon as they are available. We focus on the assimilation of leaf area index (LAI), using land surface temperature (LST) for verification. We show that (1) analyzing LAI together with root-zone soil moisture is needed to monitor the impact of irrigation and heat waves on the vegetation, (2) LAI can be forecasted after properly initializing ISBA. This paves the way to more interactive assimilation of land variables into numerical weather forecast and seasonal forecast models, as well as in atmospheric chemistry models.

 

How to cite: Calvet, J.-C., Bonan, B., and Xu, Y.: Recent offline land data assimilation results and future steps towards coupled DA at Meteo-France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1095, https://doi.org/10.5194/egusphere-egu23-1095, 2023.

EGU23-1846 | Posters on site | NP5.2 | Highlight

Hybrid covariance super-resolution data assimilation 

Sébastien Barthélémy, Julien Brajard, Laurent Bertino, and François Counillon

This work extends the concept of "Super-resolution data assimilation" (SRDA, Barthélémy et al. 2022)) to the case of mixed-resolution ensembles pursuing two goals: (1) emulate the Ensemble Kalman Filter while (2) benefit from high-resolution observations. The forecast step is performed by two ensembles at two different resolutions, high and low-resolution. Before the assimilation step the low-resolution ensemble is downscaled to the high-resolution space, then both ensembles are updated with high-resolution observations. After the assimilation step, the low-resolution ensemble is upscaled back to its low-resolution grid for the next forecast. The downscaling step before the data assimilation step is performed either with a neural network, or with a simple cubic spline interpolation operator. The background error covariance matrix used for the update of both ensembles is a hybrid matrix between the high and low resolution background error covariance matrices. This flavor of the SRDA is called "Hybrid covariance super-resolution data assimilation" (Hybrid SRDA). We test the method with a quasi-geostrophic model in the context of twin-experiments with the low-resolution model being twice and four times coarser than the high-resolution one. The Hybrid SRDA with neural network performs equally or better than its counterpart with cubic spline interpolation, and drastically reduces the errors of the low-resolution ensemble. At equivalent computational cost, the Hybrid SRDA outperforms both the SRDA (8.4%) and the standard EnKF (14%). Conversely, for a given value of the error, the Hybrid SRDA requires as little as  50% of the computational resources of  the EnKF. Finally, the Hybrid SRDA can be formulated as a low-resolution scheme, in the sense that the assimilation is performed in the low-resolution space, encouraging the application of the scheme with realistic ocean models.

How to cite: Barthélémy, S., Brajard, J., Bertino, L., and Counillon, F.: Hybrid covariance super-resolution data assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1846, https://doi.org/10.5194/egusphere-egu23-1846, 2023.

All-sky radiance assimilation often has non-Gaussian observation error distributions, which can be exacerbated by high model spatial resolutions due to better resolved nonlinear physical processes. For ensemble Kalman filters, observation ensemble perturbations can be approximated by linearized observation operator (LinHx) that uses the observation operator Jacobian of ensemble mean rather than full observation operator (FullHx). The impact of observation operator on infrared radiance data assimilation is examined here by assimilating synthetic radiance observations from channel 1025 of GIIRS with increased model spatial resolutions from 7.5 km to 300 m. A tropical cyclone is used, while the findings are expected to be generally applied. Compared to FullHx, LinHx provides larger magnitudes of correlations and stronger corrections around observation locations, especially when all-sky radiances are assimilated at fine model resolutions. For assimilating clear-sky radiances with increasing model resolutions, LinHx has smaller errors and improved vortex intensity and structure than FullHx. But when all-sky radiances are assimilated, FullHx has advantages over LinHx. Thus for regimes with more linearity, LinHx provides stronger correlations and imposes more corrections than FullHx; but for regimes with more nonlinearity, LinHx provides detrimental non-Gaussian prior error distributions in observation space, unrealistic correlations and overestimated corrections, compared to FullHx.

How to cite: Lei, L.: Impacts of Observation Forward Operator on Infrared Radiance Data Assimilation with Fine Model Resolutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3011, https://doi.org/10.5194/egusphere-egu23-3011, 2023.

EGU23-3086 | Posters on site | NP5.2

Comparison of optimization methods for the maximum likelihood ensemble filter 

Takeshi Enomoto and Saori Nakashita

The Newton method, which requires the Hessian matrix, is prohibitively expensive in adjoint-based variational data assimilation (VAR). It may be rather attractive for ensemble-based VAR because the ensemble size is usually several orders of magnitude smaller than that of the state size. In the present paper the Newton method is compared against the conjugate-gradient (CG) method, which is one of the most popular choices in adjoint-based VAR. To make comparisons, the maximum likelihood ensemble filter (MLEF) is used as a framework for data assimilation experiments. The Hessian preconditioning is used with CG as formulated in the original MLEF. Alternatively we propose to use the Hessian in the Newton method. In the exact Newton (EN) method, the Newton equation is solved exactly, i.e. the step size is fixed to unity avoiding a line search. In the 1000-member wind-speed assimilation test, CG is stagnated early in iteration and terminated due to a line search error while EN converges quadratically. This behaviour is consistent with the workings of the EN and CG in the minimization of the Rosenbrock function. In the repetitive cycled experiments using the Korteweg-de Vries-Burgers (KdVB) model with a quadratic observation operator, EN performs competitively in accuracy to CG with significantly enhanced stability. These idealized experiments indicate the benefit of adopting EN for the optimization in MLEF.

How to cite: Enomoto, T. and Nakashita, S.: Comparison of optimization methods for the maximum likelihood ensemble filter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3086, https://doi.org/10.5194/egusphere-egu23-3086, 2023.

EGU23-3761 | ECS | Posters on site | NP5.2

Observation space localizations for the maximum likelihood ensemble filter 

Saori Nakashita and Takeshi Enomoto

The maximum likelihood ensemble filter (MLEF) can handle nonlinearity of observation operators more appropriately than conventional ensemble Kalman filters. Here we consider the observation space localization method for MLEF to enable application to large-scale problems in the atmosphere. Optimization of the cost function in MLEF, however, impedes local analysis, suitable for massive parallel computers, in the same manner as the local ensemble transform Kalman filter (LETKF). In this study two approaches to observation space localization for MLEF (LMLEF) are compared. The first method introduces local gradients to minimize the global cost function (Yokota et al. 2016). An alternative approach, proposed here, defines a local cost function for each grid assuming a constant ensemble weight in the local domain to enable embarrassingly parallel analysis. The two approaches are compared to LETKF in cycled data assimilation experiments using the Lorenz-96 and the SPEEDY models. LMLEFs are found to be more accurate and stable than LETKF when nonlinear observations are assimilated into each model. Our proposed method is comparable to Yokota's global optimization method when dense observations are assimilated into the Lorenz-96 model. This result is consistent with the fact that ensemble weights have high spatial correlations with those at neighboring grids. Although our method also yields similar analysis in the SPEEDY experiments with a more realistic observation network, Yokota’s global optimization method shows faster error convergence in the earlier cycles. The error convergence rate seems to be related to the difference between global and local optimization and the validity of the assumption of constant weights, which depends strongly on the observation density.

How to cite: Nakashita, S. and Enomoto, T.: Observation space localizations for the maximum likelihood ensemble filter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3761, https://doi.org/10.5194/egusphere-egu23-3761, 2023.

EGU23-4668 | ECS | Posters virtual | NP5.2 | Highlight

A particle filter based target observation method and its application to two types of El Niño events 

Meiyi Hou and Youmin Tang

The optimal observational array for improving the El Niño-Southern Oscillation (ENSO) prediction is investigated by exploring sensitive areas for target observations of two types of El Niño events in the Pacific. A target observation method based on the particle filter and pre-industrial control runs from six coupled model outputs in Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments are used to quantify the relative importance of the initial accuracy of sea surface temperature (SST) in different Pacific areas. The initial accuracy of the tropical Pacific, subtropical Pacific, and extratropical Pacific can influence both types of El Niño predictions. The relative importance of different areas changes along with different lead times of predictions. Tropical Pacific observations are crucial for decreasing the root mean square error of predictions of all lead times. Subtropical and extratropical observations play an important role in reducing the prediction uncertainty, especially when the prediction is made before and throughout the boreal spring. To consider different El Niño types and different start months for predictions, a quantitative frequency method based on frequency distribution is applied to determine the optimal observations of ENSO predictions. The final optimal observational array contains 31 grid points, including 21 grid points in the equatorial Pacific and 10 grid points in the North Pacific, suggesting the importance of the initial SST conditions for ENSO predictions in the tropical Pacific and also in the area outside the tropics. Furthermore, the predictions made by assimilating SST in sensitive areas have better prediction skills in the verification experiment, which can indicate the validity of the optimal observational array designed in this study. This result provided guidance on how to initialize models in predictions of El Niño types. 

How to cite: Hou, M. and Tang, Y.: A particle filter based target observation method and its application to two types of El Niño events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4668, https://doi.org/10.5194/egusphere-egu23-4668, 2023.

EGU23-5421 | ECS | Posters on site | NP5.2

Estimation of Spatially and Temporally Varying Biogeochemical Parameters in a Global Ocean Model 

Nabir Mamnun, Christoph Völker, Mihalis Vrekoussis, and Lars Nerger

Ocean biogeochemical (BGC) models are, in addition to measurements, the primary tools for investigating ocean biogeochemistry, marine ecosystem functioning, and the global carbon cycle. These models contain a large number of not precisely known parameters and are highly uncertain regarding those parametrizations.  The values of these parameters depend on the physical and biogeochemical context, but in practice values derived from limited field measurements or laboratory experiments are used in the model keeping them constant in space and time. This study aims to estimate spatially and temporally varying parameters in a global ocean BGC model and to assess the effect of those estimated parameters on model fields and dynamics. Utilizing the BGC model Regulated Ecosystem Model 2 (REcoM2), we estimate ten selected BGC parameters with heterogeneity in parameter values both across space and over time using an ensemble data assimilation technique. We assimilate satellite ocean color and BGC-ARGO data using an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) to simultaneously estimate the BGC model states and parameters. We assess the improvement in the model predictions with space and time-dependent parameters in reference to the simulation with globally constant parameters against both assimilative and independent data. We quantify the spatiotemporal uncertainties regarding the parameter estimation and the prediction uncertainties induced by those parameters. We study the effect of estimated parameters on the biogeochemical fields and dynamics to get deeper insights into modeling processes and discuss insights from spatially and temporally varying parameters beyond parameter values.

How to cite: Mamnun, N., Völker, C., Vrekoussis, M., and Nerger, L.: Estimation of Spatially and Temporally Varying Biogeochemical Parameters in a Global Ocean Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5421, https://doi.org/10.5194/egusphere-egu23-5421, 2023.

EGU23-5506 | ECS | Posters on site | NP5.2

Empirical optimal vertical localization derived from large ensembles 

Tobias Necker, Philipp Griewank, Takemasa Miyoshi, and Martin Weissmann

Ensemble-based estimates of error covariances suffer from limited ensemble size due to computational restrictions in data assimilation systems for numerical weather prediction. Localization of error covariances can mitigate sampling errors and is crucial for ensemble-based data assimilation. However, finding optimal localization methods, functions, or scales is challenging. We present a new approach to derive an empirical optimal localization (EOL) from a large ensemble dataset. The EOL allows for a better understanding of localization requirements and can guide toward improved localization.

Our study presents EOL estimates using 40-member subsamples assuming a 1000-member ensemble covariance as truth. The EOL is derived from a 5-day training period. In the presentation, we cover both model and observation space vertical localization and discuss:

  • vertical error correlations and EOL estimates for different variables and settings;

  • the effect of the EOL compared to common localization approaches, such as distance-dependent localization with a Gaspari-Cohn function;

  • and vertical localization of infrared and visible satellite observations in the context of observation space localization.

Proper observation space localization of error covariances between non-local satellite observations and state space is non-trivial and still an open research question. First, we evaluate requirements for optimal localization for different variables and spectral channels. And secondly, we investigate the situation dependence of vertical localization in convection-permitting NWP simulations, which suggests an advantage of using adaptive situation-dependent localization approaches.

How to cite: Necker, T., Griewank, P., Miyoshi, T., and Weissmann, M.: Empirical optimal vertical localization derived from large ensembles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5506, https://doi.org/10.5194/egusphere-egu23-5506, 2023.

EGU23-6050 | ECS | Posters on site | NP5.2 | Highlight

Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation 

Lei Kong, Xiao Tang, Jiang Zhu, Zifa Wang, Yele Sun, Pingqing Fu, Meng Gao, Huangjian Wu, Jie Li, Xiaole Pan, Lin Wu, Hajime Akimoto, and Gregory R. Carmichael

The unprecedented lockdown of human activities during the COVID-19 pandemic have significantly influenced the social life in China. However, understanding of the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15km) resolutions. Subsequently, contributions of emission changes versus meteorology variations during COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations of multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measurement with NOx emissions decreased substantially by ~40%. However, emissions of other air pollutants were found only decreased by ~10%, both because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over North China Plain (NCP) during lockdown period, the emission change only accounted for 8.6% of PM2.5 reductions, and even led to substantial increases of O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over NCP, which contributed 90% of the PM2.5 reductions over most parts of NCP region. Meanwhile, our results also suggest that the local stagnant meteorological conditions together with inefficient reductions in PM2.5 emissions were the main drivers of the unexpected COVID-19 haze in Beijing. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

How to cite: Kong, L., Tang, X., Zhu, J., Wang, Z., Sun, Y., Fu, P., Gao, M., Wu, H., Li, J., Pan, X., Wu, L., Akimoto, H., and Carmichael, G. R.: Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6050, https://doi.org/10.5194/egusphere-egu23-6050, 2023.

EGU23-7480 | ECS | Orals | NP5.2 | Highlight

Supermodelling: synchronising models to further improve predictions 

Francine Schevenhoven, Mao-Lin Shen, Noel Keenlyside, Jeffrey B. Weiss, and Gregory S. Duane

Instead of combining data from an ensemble of different models after the simulations are already performed, as in a standard multi-model ensemble, we let the models interact with each other during their simulation. This ensemble of interacting models is called a supermodel. By exchanging information, models can compensate for each other's errors before the errors grow and spread to other regions or variables. Effectively, we create a new dynamical system. The exchange between the models is frequent enough such that the models synchronize, in order to prevent loss of variance when the models are combined. In previous work, we experimented successfully with combining atmospheric models of intermediate complexity in the context of parametric error. Here we will show results of combining two different AGCMs, NorESM1-ATM and CESM1-ATM. The models have different horizontal and vertical resolutions. To combine states from the different grids, we convert the individual model states to a ‘common state space’ with interpolation techniques. The weighted superposition of different model states is called a ‘pseudo-observation’. The pseudo-observations are assimilated back into the individual models, after which the models continue their run. We apply recently developed methods to train the weights determining the superposition of the model states, in order to obtain a supermodel that will outperform the individual models and any weighted average of their outputs.

How to cite: Schevenhoven, F., Shen, M.-L., Keenlyside, N., Weiss, J. B., and Duane, G. S.: Supermodelling: synchronising models to further improve predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7480, https://doi.org/10.5194/egusphere-egu23-7480, 2023.

EGU23-7719 | ECS | Orals | NP5.2

The role of anchor observations in disentangling observation and model bias corrections in 4DVar 

Devon Francis, Alison Fowler, Amos Lawless, Stefano Migliorini, and John Eyre

Data assimilation theory relies on the assumption that the background, model, and observations are unbiased. However, this is often not the case and, if biases are left uncorrected, this can cause significant systematic errors in the analysis. When bias is only present in the observations, Variational Bias Correction (VarBC) can correct for observation bias, and when bias is only present in the model, Weak-Constraint 4D Variational Assimilation (WC4DVar) can correct for model bias. However, when both observation and model biases are present, it can be very difficult to understand how the different bias correction methods interact, and the role of anchor (unbiased) observations becomes crucial for providing a frame of reference from which the biases may be estimated. This work presents a systematic study of the properties of the network of anchor observations needed to disentangle between model and observation biases when correcting for one or both types of bias in 4DVar.

We extend the theory of VarBC and WC4DVar to include both biased and anchor observations, to find that the precision and timing of the anchor observations are important in reducing the contamination of model/observation bias in the correction of observation/model bias. We show that anchor observations have the biggest impact in reducing the contamination of bias when they are later in the assimilation window than the biased observations, as such, operational systems that rely on anchor observations that are earlier in the window will be more susceptible to the contamination of model and/or observation biases. We also compare the role of anchor observations when VarBC/WC4DVar/both are used in the presence of both observation and model biases. We find that the ability of VarBC to effectively correct for observation bias when model bias is present, is very dependent on precise anchor observations, whereas correcting model bias with WC4DVar or correcting for both biases performs reasonably well regardless of the precision of anchor observations (although more precise anchor observations reduces the bias in the state analysis compared with less precise anchor observations for all three cases). This demonstrates that, when it is not possible to use anchor observations, it may be better to correct for both observation and model biases, rather than relying on only one bias correction technique.

We demonstrate these results in a series of idealised numerical experiments that use the Lorenz 96 model as a simplified model of the atmosphere.

How to cite: Francis, D., Fowler, A., Lawless, A., Migliorini, S., and Eyre, J.: The role of anchor observations in disentangling observation and model bias corrections in 4DVar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7719, https://doi.org/10.5194/egusphere-egu23-7719, 2023.

EGU23-8030 | Posters on site | NP5.2

Assessment of short-range forecast atmosphere-ocean cross-covariances from the Met Office coupled NWP system 

Amos Lawless, Maria Valdivieso, Nancy Nichols, Daniel Lea, and Matthew Martin

As part of the design of future coupled forecasting systems, operational centres such as the Met Office are starting to include interactions between the atmosphere and the ocean within the data assimilation system. This requires an improved understanding and representation of the correlations between short-range forecast errors in different variables. To understand the potential benefit of further coupling in the data assimilation scheme it is important to understand the significance of any cross-correlations between atmosphere and ocean short-range forecast errors as well as their temporal and spatial variability. In this work we examine atmosphere-ocean cross-covariances from an ensemble of the Met Office coupled NWP system for December 2019, with particular focus on short-range forecast errors that evolve at lead times up to 6 hours.

We find that significant correlations exist between atmosphere and ocean forecast errors on these timescales, and that these vary diurnally, from day to day, spatially and synoptically. Negative correlations between errors in sea-surface temperature (SST) and 10m wind correlations strengthen as the solar radiation varies from zero at night (local time) to a maximum insolation around midday (local time). In addition, there are significant variations in correlation intensities and structures in response to synoptic-timescale forcing. Significant positive correlations between SST and 10m wind errors appear in the western North Atlantic in early December and are associated with variations in low surface pressures and their associated high wind speeds, that advect cold, dry continental air eastward over the warmer Atlantic ocean. Negative correlations across the Indo-Pacific Warm Pool are instead associated with light wind conditions on these short timescales.

When we consider the spatial extent of cross-correlations, we find that in the Gulf Stream region positive correlations between wind speed and sub-surface ocean temperatures are generally vertically coherent down to a depth of about 100m, consistent with the mixing depth; however, in the tropical Indian and West Pacific oceans, negative correlations break down just below the surface layer. This is likely due to the presence of surface freshwater layers that form from heavy precipitation on the tropical oceans, manifested by the presence of salinity-stratified barrier layers within deeper isothermal layers that can effectively limit turbulent mixing of heat between the ocean surface and the deeper thermocline.

How to cite: Lawless, A., Valdivieso, M., Nichols, N., Lea, D., and Martin, M.: Assessment of short-range forecast atmosphere-ocean cross-covariances from the Met Office coupled NWP system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8030, https://doi.org/10.5194/egusphere-egu23-8030, 2023.

EGU23-8640 | Orals | NP5.2

Forecast error growth: A stochastic differential equation model 

Michael Ghil, Eviatar Bach, and Dan Crisan

There is a history of simple error growth models designed to capture the key properties of error growth in operational numerical weather prediction models. We propose here such a scalar model that relies on the previous ones, but captures the effect of small scales on the error growth via additive noise in a nonlinear stochastic differential equation (SDE). We nondimensionalize the equation and study its behavior with respect to the error saturation value, the growth rate of small errors, and the magnitude of noise. We show that the addition of noise can change the curvature of the error growth curve. The SDE model seems to improve substantially the fit to operational error growth curves, compared to the deterministic counterparts.

How to cite: Ghil, M., Bach, E., and Crisan, D.: Forecast error growth: A stochastic differential equation model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8640, https://doi.org/10.5194/egusphere-egu23-8640, 2023.

EGU23-9529 | Orals | NP5.2

Nonlinear Data Assimilation for State and Parameter Estimation in Earthquake Simulation 

Femke Vossepoel, Arundhuti Banerjee, Hamed Diab Montero, Meng Li, Celine Marsman, Rob Govers, and Ylona van Dinther

The highly nonlinear dynamics of earthquake sequences and the limited availability of stress observations near subsurface faults make it very difficult, if not impossible, to forecast earthquakes. Ensemble data-assimilation methods provide a means to estimate state variables and parameters of earthquake sequences that may lead to a better understanding of the associated fault-slip process and contribute to the forecastability of earthquakes. We illustrate the challenges of data assimilation in earthquake simulation with an overview of three studies, each with different objectives and experiments.

In the first study, by reconstructing a laboratory experiment with an advanced numerical simulator we perform synthetic twin experiments to test the performance of an ensemble Kalman Filter (EnKF) and its ability to reconstruct fault slip behaviour in 1D and 3D simulations. The data assimilation estimates and forecasts earthquakes, even when having highly uncertain observations of the stress field. In these experiments, we assume the friction parameters to be perfectly known, which is typically not the case in reality.

A bias in a friction parameter can cause a significant change in earthquake dynamics, which will complicate the application of data assimilation in realistic cases. The second study addresses how well state estimation and state-parameter estimation can account for friction-parameter bias. For this, we use a 0D model for earthquake recurrence with a particle filter with sequential importance resampling. This shows that in case of intermediate to large uncertainty in friction parameters, combined state-and-parameter estimation is critical to correctly estimate earthquake sequences. The study also highlights the advantage of a particle filter over an EnKF for this nonlinear system.

The post- and inter-seismic deformations following an earthquake are rather gradual and do not pose the same challenges for data assimilation as the deformation during an earthquake event. To estimate the model parameters of surface displacements during these phases, a third study illustrates the application of the Ensemble Smoother-Multiple Data Assimilation and the particle filter with actual GPS data of the Tohoku 2011 earthquake.

Based on the comparison of the various experiments, we discuss the choice of data-assimilation method and -approach in earthquake simulation and suggest directions for future research.

How to cite: Vossepoel, F., Banerjee, A., Diab Montero, H., Li, M., Marsman, C., Govers, R., and van Dinther, Y.: Nonlinear Data Assimilation for State and Parameter Estimation in Earthquake Simulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9529, https://doi.org/10.5194/egusphere-egu23-9529, 2023.

EGU23-11889 | ECS | Posters on site | NP5.2

Data Assimilation and Subsurface Flow Modeling: Interactions between Groundwater and the Vadose Zone 

Bastian Waldowski, Insa Neuweiler, and Natascha Brandhorst

Reliable estimates of soil water content and groundwater levels are essential in evaluating water availability for plants and as drinking water and thus both subsurface components (vadose zone and groundwater) are commonly monitored. Such measurements can be used for data assimilation in order to improve predictions of numerical subsurface flow models. Within this work, we investigate to what extent measurements from one subsurface component are able to improve predictions in the other one.
For this purpose, we utilize idealized test cases at a subcatchment scale using a Localized Ensemble Kalman Filter to update the water table height and soil moisture at certain depths with measurements taken from a numerical reference model. We do joint, as well as single component updates. We test strongly coupled data assimilation, which implies utilizing correlations between the subsurface components for updating the ensemble and compare it to weakly coupled data assimilation. We also update soil hydraulic parameters and examine the role of their heterogeneity with respect to data assimilation. We run simulations with both a complex 3D model (using TSMP-PDAF) as well as a more simplified and computationally efficient 2.5D model, which consists of multiple 1D vadose-zone columns coupled iteratively with a 2D groundwater-flow model. In idealized settings, such as homogeneous subsurface structures, we find that predictions in one component consistently benefit from updating the other component.

How to cite: Waldowski, B., Neuweiler, I., and Brandhorst, N.: Data Assimilation and Subsurface Flow Modeling: Interactions between Groundwater and the Vadose Zone, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11889, https://doi.org/10.5194/egusphere-egu23-11889, 2023.

EGU23-12304 | ECS | Posters on site | NP5.2

Analysis of airborne-derived sea ice emissivities up to 340 GHz in preparation for future satellite missions 

Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell

Passive microwave radiometers onboard polar-orbiting satellites provide global information on the atmospheric state. The underlying retrievals require accurate knowledge of the surface radiative properties to distinguish atmospheric from surface contributions to the measured radiance. Polar surfaces such as sea ice contribute up to 400 GHz to the measured radiance due to the high atmospheric transmissivity under cold and dry conditions. Currently, we lack an understanding of sea ice parameters driving the variability in its radiative properties, i.e., its emissivity, at frequencies above 200 GHz due to limited field data and the heterogeneous sea ice structure. This will limit the use of future satellite missions such as the Ice Cloud Imager (ICI) onboard Metop-SG and the Arctic Weather Satellite (AWS) in polar regions.

To better understand sea ice emission, we analyze unique airborne measurements from 89 to 340 GHz obtained during the ACLOUD (summer 2017) and AFLUX (spring 2019) airborne campaigns and co-located satellite observations in the Fram Strait. The Polar 5 aircraft carried the Microwave Radar/radiometer for Arctic Clouds (MiRAC) cloud radar MiRAC-A with an 89 GHz passive channel and MiRAC-P with six double-sideband channels at 183.31 GHz and two window channels at 243 and 340 GHz. We calculate the emissivity with the non-scattering radiative transfer equation from observed upwelling radiation at 25° (MiRAC-A) and 0° (MiRAC-P) and Passive and Active Microwave radiative TRAnsfer (PAMTRA) simulations. The PAMTRA simulations are based on atmospheric profiles from dropsondes and surface temperatures from an infrared radiometer.

The airborne-derived sea ice emissivity (O(0.1km)) varies on small spatial scales (O(1km)), which align with sea ice properties identified by visual imagery. High-resolution airborne-derived emissivities vary more than emissivities from co-located overflights of the GPM constellation due to the smaller footprint size, which resolve sea ice variations. The emissivity of frozen and snow-free leads separates clearly from more compact and snow-covered ice flows at all frequencies. The comparison of summer and spring emissivities reveals an emissivity reduction due to melting. We will also conduct evaluations of emissivity parameterizations (e.g. TELSEM²) and provide insights into observations at ICI and AWS frequencies over Arctic sea ice. Findings based on the field data may be useful for the assimilation of radiances from existing and future microwave radiometers into weather prediction models in polar regions.

How to cite: Risse, N., Mech, M., Prigent, C., Spreen, G., and Crewell, S.: Analysis of airborne-derived sea ice emissivities up to 340 GHz in preparation for future satellite missions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12304, https://doi.org/10.5194/egusphere-egu23-12304, 2023.

EGU23-14227 | Orals | NP5.2

Combining sea-ice and ocean data assimilation with nudging atmospheric circulation in the AWI Coupled Prediction System 

Svetlana N. Losa, Longjiang Mu, Marylou Athanase, Jan Streffing, Miguel Andrés-Martínez, Lars Nerger, Tido Semmler, Dmitry Sidorenko, and Helge F. Goessling

Assimilation of sea ice and ocean observational data into coupled sea-ice, ocean and atmosphere models is known as an efficient approach for providing a reliable sea-ice prediction (Mu et al. 2022). However, implementations of the data assimilation in the coupled systems still remain a challenge. This challenge is partly originated from the chaoticity possessed in the atmospheric module, which leads to biases and, therefore, to divergence of predictive characteristics. An additional constrain of the atmosphere is proposed as a tool to tackle the aforementioned problem. To test this approach, we use the recently developed AWI Coupled Prediction System (AWI-CPS). The system is built upon the AWI climate model AWI-CM-3 (Streffing et al. 2022) that includes FESOM2.0 as a sea-ice ocean component and the Integrated Forecasting System (OpenIFS) as an atmospheric component. An Ensemble-type Kalman filter within the Parallel Data Assimilation Framework (PDAF; Nerger and Hiller, 2013) is used to assimilate sea ice concentration, sea ice thickness, sea ice drift, sea surface height, sea surface temperature and salinity, as well as temperature and salinity vertical profiles. The additional constrain of the atmosphere is introduced by relaxing, or “nudging”, the AWI-CPS large-scale atmospheric dynamics to the ERA5 reanalysis data. This nudging of the large scale atmospheric circulation towards reanalysis has allowed to reduce biases in the atmospheric state, and, therefore, to reduce the analysis increments. The most prominent improvement has been achieved for the predicted sea ice drift. Comprehensive analyses will be presented based upon the new system’s performance over the time period 2003 – 2022.

Mu, L., Nerger, L., Streffing, J., Tang, Q., Niraula, B., Zampieri, L., Loza, S. N. and H. F. Goessling, Sea-ice forecasts with an upgraded AWI Coupled Prediction System (Journal of Advances in Modeling Earth Systems, 14, e2022MS003176. doi: 10.1029/2022MS003176.

Nerger, L. and Hiller, W., 2013. Software for ensemble-based data assimilation systems—Implementation strategies and scalability. Computers & Geosciences, 55, pp.110-118.

Streffing, J., Sidorenko, D., Semmler, T., Zampieri, L., Scholz, P., Andrés-Martínez, M., Koldunov, N., Rackow, T., Kjellsson, J., Goessling, H., Athanase, M., Wang, Q., Sein, D., Mu, L., Fladrich, U., Barbi, D., Gierz, P., Danilov, S.,  Juricke, S., Lohmann, G. and Jung, T. (2022) AWI-CM3 coupled climate model: Description and evaluation experiments for a prototype post-CMIP6 model, EGUsphere, 2022, 1—37, doi: 10.5194/egusphere-2022-32

How to cite: Losa, S. N., Mu, L., Athanase, M., Streffing, J., Andrés-Martínez, M., Nerger, L., Semmler, T., Sidorenko, D., and Goessling, H. F.: Combining sea-ice and ocean data assimilation with nudging atmospheric circulation in the AWI Coupled Prediction System, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14227, https://doi.org/10.5194/egusphere-egu23-14227, 2023.

EGU23-14826 | Posters virtual | NP5.2 | Highlight

Inverse modelling for trace gas surface flux estimation, impact of a non-diagonal B-matrix 

Ross Bannister
One of the most appealing uses of data assimilation is to infer useful information about a dynamical system that is not observed directly. This is the case for the estimation of surface fluxes of trace gases (like methane). Such fluxes are not easy to measure directly on a global scale, but it is possible to measure the trace gas itself as it is transported around the globe. This is the purpose of INVICAT (the inverse modelling system of the chemical transport model TOMCAT), which has been developed here. INVICAT interprets observations of (e.g.) methane over a time window to estimate the initial conditions (ICs) and surface fluxes (SFs) of the TOMCAT model.
This talk will show how INVICAT has been expanded from a diagonal background error covariance matrix (B-matrix, DB) to allow an efficient representation of a non-diagonal B-matrix (NDB). The results of this process are mixed. A NDB-matrix for the SF field improves the analysis against independent data, but a NDB-matrix for the IC field appears to degrade the analysis. This paper presents these results and suggests that a possible reason for the degraded analyses is the presence of a possible bias in the system.

How to cite: Bannister, R.: Inverse modelling for trace gas surface flux estimation, impact of a non-diagonal B-matrix, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14826, https://doi.org/10.5194/egusphere-egu23-14826, 2023.

EGU23-14985 | ECS | Orals | NP5.2

Reconstructing North Atlantic Ocean Heat Content Using Convolutional Neural Networks 

Simon Lentz, Dr. Sebastian Brune, Dr. Christopher Kadow, and Prof. Dr. Johanna Baehr

Slowly varying ocean heat content is one of the most important variables when describing cli-
mate variability on interannual to decadal time scales. Since observation-based estimates of
ocean heat content require extensive observational coverage, incomplete observations are often
combined with numerical models via data assimilation to simulate the evolution of oceanic heat.
However, incomplete observations, particularly in the subsurface ocean, lead to large uncertain-
ties in the resulting model-based estimate. As an alternative approach, Kadow et al (2020) have
proven that artificial intelligence can successfully be utilized to reconstruct missing climate in-
formation for surface temperatures. In the following, we investigate the possibility to train their
three-dimensional convolutional neural network to reconstruct missing subsurface temperatures
to obtain ocean heat content estimates with a focus on the North Atlantic ocean.
The network is trained and tested to reconstruct a 16 member Ensemble Kalman Filter assimi-
lation ensemble constructed with the Max-Planck Institute Earth System Model for the period
from 1958 to 2020. Specifically, we examine whether the partial convolutional U-net represents
a valid alternative to the Ensemble Kalman Filter assimilation to estimate North Atlantic sub-
polar gyre ocean heat content.
The neural network is capable of reproducing the assimilation reduced to datapoints with ob-
servational coverages within its ensemble spread with a correlation coefficient of 0.93 over the
entire time period and of 0.99 over 2004 – 2020 (the Argo-Era). Additionally, the network is
able to reconstruct the observed ocean heat content directly from observations for 12 additional
months with a correlation of 0.97, essentially replacing the assimilation experiment by an extrap-
olation. When reconstructing the pre-Argo-Era, the network is only trained with assimilations
from the Argo-Era. The lower correlation in the resulting reconstruction indicates higher un-
certainties in the assimilation outside of its ensemble spread at times with low observational
density. These uncertainties are highlighted by inconsistencies in the assimilation’s represen-
tations of the North Atlantic Current at times and grid points without observations detected
by the neural network. Our results demonstrate that a neural network is not only capable of
reproducing the observed ocean heat content over the training period, but also before and after
making the neural network a suitable candidate to step-wise extend or replace data assimilation.

How to cite: Lentz, S., Brune, Dr. S., Kadow, Dr. C., and Baehr, P. Dr. J.: Reconstructing North Atlantic Ocean Heat Content Using Convolutional Neural Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14985, https://doi.org/10.5194/egusphere-egu23-14985, 2023.

EGU23-15189 | ECS | Orals | NP5.2

A coupled data assimilation framework with an integrated surface and subsurface hydrological model 

Qi Tang, Hugo Delottier, Oliver S. Schilling, Wolfgang Kurtz, and Philip Brunner

We developed an ensemble based data assimilation (DA) system for an integrated hydrological model to facilitate real-time operational simulations of water quantity and quality. The integrated surface and subsurface hydrologic model HydroGeoSphere (HGS) (Brunner & Simmons, 2012) which simulates surface water and variably saturated groundwater flow as well as solute transport, was coupled with the Parallel Data Assimilation Framework (PDAF) (Nerger et al., 2005). The developed DA system allows joint assimilation of multiple types of observations such as piezometric heads, streamflow, and tracer concentrations. By explicitly considering tracer and streamflow data we substantially expand the hydrologic information which can be used to constrain the simulations.    Both the model states and the parameters can be separately or jointly updated by the assimilation algorithm.  

A synthetic alluvial plain model set up by Delottier et al., (2022) was used as an example to test the performance of our DA system.  For flow simulations, piezometric head observations were assimilated, while for transport simulations, noble gas concentrations (222Rn, 37Ar, and 4He) were assimilated. Both model states (e.g., hydraulic head or noble gas concentrations) and parameters (e.g. hydraulic conductivities and porosity) are jointly updated by the DA. Results were evaluated by comparing the estimated model variables with independent observation data between the assimilation runs and the free run where no data assimilation was conducted. In a further evaluation step, a real-world, field scale model featuring realistic forcing functions and material properties was set up for a site in Switzerland and carried out for numerical simulations with the developed DA system. The synthetic and real-world examples demonstrate the significant potential in combing state of the art numerical models, data assimilation and novel tracer observations such as noble gases or Radon.

How to cite: Tang, Q., Delottier, H., Schilling, O. S., Kurtz, W., and Brunner, P.: A coupled data assimilation framework with an integrated surface and subsurface hydrological model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15189, https://doi.org/10.5194/egusphere-egu23-15189, 2023.

EGU23-16806 | Orals | NP5.2

Coupled data assimilation for numerical weather prediction at ECMWF 

Patricia de Rosnay, Phil browne, Eric de Boisséson, David Fairbairn, Sébastien Garrigues, Christoph Herbert, Kenta Ochi, Dinand Schepers, Pete Weston, and Hao Zuo

In this presentation we introduce coupled assimilation activities conducted in support of seamless Earth system approach developments for Numerical Weather Prediction and climate reanalysis at the European Centre for Medium-Range Weather Forecasts (ECMWF). For operational applications coupled assimilation requires to have reliable and timely access to observations in all the Earth system components and it relies on consistent acquisition and monitoring approaches across the components. We show recent and future infrastructure developments and implementations to support consistent observations acquisition and monitoring for land and ocean at ECMWF. We discuss challenges of surface sensitive observations assimilation and we show ongoing forward operator and coupling developments to enhance the exploitation of interface observations over land and ocean surfaces. We present plans to use new and future observation types from future observing systems such as the Copernicus Expansion missions.

How to cite: de Rosnay, P., browne, P., de Boisséson, E., Fairbairn, D., Garrigues, S., Herbert, C., Ochi, K., Schepers, D., Weston, P., and Zuo, H.: Coupled data assimilation for numerical weather prediction at ECMWF, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16806, https://doi.org/10.5194/egusphere-egu23-16806, 2023.

Reservoir simulations often require statistical predictions to quantify production uncertainty or assess potential risks. Most existing uncertainty quantification procedures aim to decompose the input random field into independent random variables if the correlation scale is small compared to the domain size. In this work, we develop a K-means-based aggregation model, for efficiently estimating multiphase flow performance in multiple geological realizations. This approach performs a number of single-phase flow simulations and uses K-means clustering to select only a few representatives on which multiphase flow simulations are performed. In addition, an empirical model is then employed to describe the relationship between the single-phase solution and the multiphase solution using these representatives. Finally, the multiphase solution in all realizations can be easily predicted using empirical models. The method is applicable to both 2D and 3D synthetic models and has been shown to perform well in the trusted interval of productivity, and probability distribution as indicated by the cumulative density function. It is able to capture a large number of ensemble statistical realizations of Monte Carlo simulation results with significantly reduced computational cost.

How to cite: Liao, Q.: Clustering aggregation model for statistical forecasting of multiphase flow problems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-973, https://doi.org/10.5194/egusphere-egu23-973, 2023.

EGU23-3891 | Posters on site | HS3.5

Estimating groundwater response time in humid climate by using spectral analysis 

Mariaines Di Dato, Timo Houben, and Sabine Attinger

During dry periods, river flow comprises baseflow, which typically generates from shallow aquifers. Understanding how such aquifers respond to climate events is key to managing environmental issues related to water supply or water quality. A typical indicator of groundwater response to climate events is the characteristic response time, which indicates the rate of depletion of shallow aquifers.

The traditional method to infer the characteristic response time analyzes the slope of the hydrograph recession curve. Such a method does not account for stormwater contribution in recession analysis, thereby assuming that the catchment is dry and the only contribution to discharge originates from groundwater. As a consequence, the recession analysis might underestimate the groundwater response time, owing to the presence of faster discharge components, i.e. surface runoff or interflow, in the falling limbs.

In this work, we propose an alternative methodology to calculate the characteristic response time, which is determined by analyzing the behavior of the baseflow time series in the frequency domain. The aquifer can be conceptualized as a low-pass filter, which smooths the high-fluctuating components in the recharge signal. Such behavior causes a cut-off frequency in the baseflow spectrum, which corresponds to the aquifer characteristic time. We applied this approach to several gauging stations in Germany, whose humid climate is ideal to compare the results with the classical recession analysis.

We observed that spectral analysis yields characteristic response times systematically larger than the ones calculated with recession analysis. On average there is a factor of two between the estimates provided by the two methods. Overall our study emphasizes careful consideration of the estimation of groundwater response times, especially in humid and sub-humid river basins.

How to cite: Di Dato, M., Houben, T., and Attinger, S.: Estimating groundwater response time in humid climate by using spectral analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3891, https://doi.org/10.5194/egusphere-egu23-3891, 2023.

EGU23-3933 | Posters on site | HS3.5

Towards identification of dominant hydrological mechanisms in ungauged catchments 

Cristina Prieto, Le Vine Nataliya, Kavetski Dmitri, Fenicia Fabrizio, Scheidegger Andreas, and Vitolo Claudia

Modelling hydrological processes in ungauged catchments is a major challenge in environmental sciences and engineering. An ungauged catchment is a catchment that lacks streamflow data suitable for traditional modelling methods. Predicting streamflow in ungauged catchments requires some form of extrapolation ("regionalisation") from other "similar" catchments, with variables of interest being flow "indices" or "signatures", such as quantiles of the flow duration curve, etc.

Another major question in hydrology is the estimation of model structure that reflects the hydrological processes relevant to the catchment of interest. This question is intimately tied to process representation. To paraphrase a common saying, all models are wrong, but some model mechanisms (process representations) might be useful. Our previous study contributed a Bayesian framework for the identification of individual model mechanisms from streamflow data.

In this study we extend the mechanism identification method to operate in ungauged basins based on regionalized flow indices. Candidate mechanisms and model structures are generated, and then the "dominant" (more a posterior probable) model mechanisms are identified using statistical hypothesis testing. As part of the derivation, it is assumed that the error in the regionalization of flow indices dominates the structural error of the hydrological model.

The proposed method is illustrated with real data and synthetic experiments based on 92 catchments from northern Spain, from which 16 catchments are treated as ungauged. We use 624 model structures from the flexible hydrological model framework FUSE. Flow indices are regionalised using random forest regression in principal component (PC) space; we select the first 4 leading indices in PC space. The case study set up includes an experiments using real data (where the true mechanisms are unknown) and a set of synthetic experiments with different error levels (where the “true” mechanisms are known).

Across the real and synthetic experiments, routing is usually among the most identifiable processes, whereas the least identifiable processes are percolation and unsaturated zone processes. The precision, i.e. the probability of making an identification (whether correct or not), remains stable at around 25%. In the synthetic experiments we can calculate the (conditional) reliability of the identification method, i.e. the probability that, when the method makes an identification, the true mechanism is identified. The conditional reliability varies from 60% to 95% depending on the magnitude of the combined regionalization and hydrological error. Our study contributes perspectives on hydrological mechanism identification under data-scarce conditions; we discus limitations and opportunities for improvement.

 

Prieto, C., N. Le Vine, D. Kavetski, F. Fenicia, A. Scheidegger, and C. Vitolo (2022) An Exploration of Bayesian Identification of Dominant Hydrological Mechanisms in Ungauged Catchments, Water Resources Research, 58(3), e2021WR030705, doi: https://doi.org/10.1029/2021WR030705.

How to cite: Prieto, C., Nataliya, L. V., Dmitri, K., Fabrizio, F., Andreas, S., and Claudia, V.: Towards identification of dominant hydrological mechanisms in ungauged catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3933, https://doi.org/10.5194/egusphere-egu23-3933, 2023.

EGU23-5100 | ECS | Orals | HS3.5

Characterising errors using satellite metadata for eco-hydrological modelling 

Hui Zou, Lucy Marshall, and Ashish Sharma

Understanding the origin of errors in model predictions is a critical element in hydrologic model calibration and uncertainty estimation. While there exist a variety of plausible error sources, only one measure of the total residual error can be ascertained when the observed response is known. Here we show that collecting extra information a priori to characterise the data error before calibration can assist in improved model calibration and uncertainty estimation. A new model calibration strategy using the satellite metadata information is proposed as a means to inform the model prior, and subsequently to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that 1) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error; 2) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance ; 3) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation.

How to cite: Zou, H., Marshall, L., and Sharma, A.: Characterising errors using satellite metadata for eco-hydrological modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5100, https://doi.org/10.5194/egusphere-egu23-5100, 2023.

EGU23-5635 | ECS | Orals | HS3.5

Spectral analysis of groundwater level time series reveals hydrogeological parameters 

Timo Houben, Mariaines Di-Dato, Christian Siebert, Thomas Kalbacher, Thomas Fischer, and Sabine Attinger

Groundwater resources are heavily exploited to supply domestic, industrial and agricultural water consumption. Climate and societal changes and associated higher abstraction will alter the subsurface storage in terms of quantity and quality in currently unpredictable ways. In order to ensure sustainable groundwater management, we must evaluate the intrinsic and spatially variable vulnerability of aquifers in terms of water quality issues and the resilience of groundwater volumes to external perturbations such as severe droughts in connection with intensive irrigation. For this purpose, physically based numerical groundwater models are of great importance, especially on the regional scale. The equations applied in these models must be fed with the hydrogeological parameters: The transmissivity T and the storativity S.

Both parameters are typically obtained through time consuming and cost intensive hydrogeological in-situ tests or by laboratory analysis of core samples from point information (drillings and wells), resulting in parameters with limited transferability to regional settings. Instead, we propose to determine the parameters by spectral analysis of groundwater level fluctuations using (semi-)analytical solutions for the frequency domain. We developed a fully automatized workflow, taking groundwater level and recharge time series together with little information about the geometry of the aquifer to derive T and S as well as tc (the characteristic response time). While the first two will be used for hydrogeological modelling, the latter can serve as an indication to assess the resilience of the groundwater system directly without additional modelling attempts. The methodology was tested with great success in simplified numerical environments and was applied to real groundwater time series in southern Germany. The response times and the storativities could be robustly estimated while the transmissivities inherit quantifiable uncertainties. Depending on the hydrogeological regime, the parameters represented effective and regional estimates.

How to cite: Houben, T., Di-Dato, M., Siebert, C., Kalbacher, T., Fischer, T., and Attinger, S.: Spectral analysis of groundwater level time series reveals hydrogeological parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5635, https://doi.org/10.5194/egusphere-egu23-5635, 2023.

EGU23-6986 | Orals | HS3.5

On the elaboration of a robust calibration strategy for the large-scale GEM-Hydro model 

Etienne Gaborit, Daniel Princz, Juliane Mai, Hongren Shen, Bryan Tolson, and Vincent Fortin

As part of the Great-Lakes Runoff Inter-comparison Project (GRIP-GL; Mai et al., 2022), which aims at comparing the performances of different hydrologic models over the Great-Lakes when calibrating them using the same meteorological inputs and geophysical databases, the GEM-Hydro hydrologic model used at Environment and Climate Change Canada (ECCC) to perform operational hydrologic forecasts was calibrated using different strategies. Following the calibration work related to GRIP-GL, progress has been achieved with regard to improving the calibration of the GEM-Hydro model.

The work presented here focuses on improvements achieved with regard to calibrating the GEM-Hydro model, compared to the default version of the model and to the performances obtained during the GRIP-GL project. For various reasons explained, the GEM-Hydro calibration performed as part of GRIP-GL was suboptimal. The general calibration framework remains the same as in GRIP-GL, for example by using the MESH-SVS-Raven model to speed-up simulation times and transferring the calibrated parameters into GEM-Hydro afterwards, by relying on global calibrations for each of the 6 Great-Lakes subdomains, etc. However, several important changes have been made compared to the work performed in GRIP-GL, like a new approach to represent the effect of Tile Drains, changing the set of flow stations used for calibration, revising the objective function, etc.

The proposed calibration methodology updates significantly improve GEM-Hydro streamflow performance across the Great-Lakes domain and in addition also improve or maintain similar performance levels as the default version of the model, with respect to auxiliary variables and surface fluxes: snow, soil moisture, evapotranspiration, 2m air temperature and dew point. Indeed, the model relies on 40m atmospheric forcings for wind speed, temperature and humidity, and simulates its own 2m atmospheric variables. To achieve this, it was necessary to constrain some parameter interval values during calibration, in order to prevent the calibration algorithm to choose physically-irrelevant parameter values that could allow to improve streamflow performances while degrading other hydrologic variables, due to equifinality.

Reference:

Mai, J., Shen, H., Tolson, B. A., Gaborit, E., Arsenault, R., Craig, J. R., Fortin, V., Fry, L. M., Gauch, M., Klotz, D., Kratzert, F., O'Brien, N., Princz, D. G., Rasiya Koya, S., Roy, T., Seglenieks, F., Shrestha, N. K., Temgoua, A. G. T., Vionnet, V., and Waddell, J. W. (2022). The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL). Hydrol. Earth Syst. Sci., 26, 3537–3572. Highlight paper. Accepted Jun 10, 2022.  https://doi.org/10.5194/hess-26-3537-2022

How to cite: Gaborit, E., Princz, D., Mai, J., Shen, H., Tolson, B., and Fortin, V.: On the elaboration of a robust calibration strategy for the large-scale GEM-Hydro model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6986, https://doi.org/10.5194/egusphere-egu23-6986, 2023.

EGU23-8423 | Orals | HS3.5

Time-varying sensitivity analysis across different hydrological model structures, variables and time scales 

Björn Guse, Anna Herzog, Stephan Thober, Diana Spieler, Lieke Melsen, Jens Kiesel, Maria Staudinger, Paul Wagner, Ralf Loritz, Sebastian Müller, Michael Stölzle, Larissa Scholz, Justine Berg, Tobias Pilz, Uwe Ehret, Doris Düthmann, Tobias Houska, Sandra Pool, and Larisa Tarasova and the other members of the DFG Scientific network IMPRO

Temporal sensitivity analyses can be used to detect dominant model parameters at different time steps (e.g. daily or monthly) providing insights on their temporal patterns and reflecting the temporal variability in dominant hydrological processes. However, hydrological processes do not only vary in time under different hydrometeorological conditions, but also the time scales of implemented processes are different. Here, the impact of different time scales (e.g. daily vs. monthly) on sensitivity patterns is investigated.

A temporal parameter sensitivity analysis is applied to three hydrological models (HBV, mHM and SWAT) for nine catchments in Germany. These catchments represent the variability of landscapes in Germany and are dominated by different runoff generation processes. In addition to discharge, further model fluxes and states such as evapotranspiration or soil moisture are used as target variables for the sensitivity analysis.

To analyse the impact of different time scales, two approaches are compared. In a first approach, daily simulated time series are used for the sensitivity analysis and aggregated then to monthly averaged sensitivities (Post-Agg). In a second approach, the simulated time series is first aggregated to a monthly time series and than used as input for the sensitivity analysis (Pre-Agg).

Our analysis shows that monthly averaged sensitivity patterns of different model outputs vary between Post- and Pre-Aggregation approach. Model parameters that are related to fast-reacting runoff processes, e.g. surface runoff or fast subsurface flow, are more sensitive when using daily time series for the sensitivity analysis (Post-Agg). In contrast, model parameters related processes with longer time scales such as snowmelt or evapotranspiration are more emphasized in monthly time series (Pre-Agg). These differences in the sensitivity results between Post-Agg and Pre-Agg are in particularly pronounced when using the integrated value of discharge as the target variable. Instead, the differences are smaller when applying the sensitivity analysis directly to represent model fluxes.

Moreover, our analysis shows changes in dominant parameters along a north-south gradient which can be explained by the physiographic characteristics of the catchments. The differences in the sensitivity results between the models can be related to the different model structures.

Based on our analysis, we recommend to either using model outputs of the major hydrological variables or different time scales for the sensitivity analysis to derive the maximum information from the diagnostic model analysis and to understand how model parameters describe hydrological systems.

How to cite: Guse, B., Herzog, A., Thober, S., Spieler, D., Melsen, L., Kiesel, J., Staudinger, M., Wagner, P., Loritz, R., Müller, S., Stölzle, M., Scholz, L., Berg, J., Pilz, T., Ehret, U., Düthmann, D., Houska, T., Pool, S., and Tarasova, L. and the other members of the DFG Scientific network IMPRO: Time-varying sensitivity analysis across different hydrological model structures, variables and time scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8423, https://doi.org/10.5194/egusphere-egu23-8423, 2023.

EGU23-10001 | Posters on site | HS3.5

Investigating the spectral analysis of groundwater level fluctuations in a numerical model of the upper Danube catchment in Germany 

Rao Ali Javed, Timo Houben, Thomas Kalbacher, and Sabine Attinger

Common in-situ methods like pumping tests, slug tests and laboratory analysis reveal aquifer parameters (that is the transmissivity and storativity) that are localized and specific to the measurement location. A need for regionally valid aquifer parameters arises when setting up regional scale physically based groundwater models. The models would help water resource managers to plan and predict the quality and quantity of groundwater resources, thus supports decision making as well as sustainable fresh water supply. A study from Houben et al. 2022 indicate that regional aquifer parameters can be obtained by analysing the frequency content of groundwater level time-series. Their work builds upon a semi-analytical solution for the groundwater head spectrum stochastically derived from the Boussinesq equation evoking the Dupuit assumptions. They found that the solution can be used to infer the transmissivity and storativity from groundwater level fluctuations and validated their hypothesis in simplified numerical environments of different complexity.

In this work, we extended the numerical experiments and applied the semi-analytical solution in homogeneous and heterogeneous 2D (x-y-plane) aquifers as well as in a complex numerical 2D (x-y-plane) model of the upper Danube catchment. We tested the hypothesis that certain locations can reveal regional aquifer parameters. In a homogeneous simulated model, the semi-analytical solution reveals effectively the model input parameters which serves as a proof-of-concept. In a heterogeneous numerical model, the obtained parameters show the complex interplay between zones of different permeability. The effects of high permeable zones can be observed on the low permeable zones which are further apart and vice versa. The obtained parameters were in the range of the model input parameters and followed the trend of the input parameters along the direction of flow. In the model of the upper Danube the obtained parameters were systematically larger than the input parameters. The shift in the obtained parameters was attributed to a violation of the assumptions of the semi-analytical solution. Thus, the complexity of model leads to a breakdown of the semi-analytical solution in some areas. Analyses on a sub-catchment scale revealed that when the assumptions of the analytical solution are met, the obtained parameters reflect the effective parameters.

How to cite: Javed, R. A., Houben, T., Kalbacher, T., and Attinger, S.: Investigating the spectral analysis of groundwater level fluctuations in a numerical model of the upper Danube catchment in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10001, https://doi.org/10.5194/egusphere-egu23-10001, 2023.

 

Hydrologic models often are used to estimate streamflows at ungauged locations for infrastructure planning. These models can contain a multitude of parameters that themselves need to be estimated through calibration. Yet multiple sets of parameter values may perform nearly equally well in simulating flows at gauged sites, making these parameters highly uncertain. Markov Chain Monte Carlo (MCMC) algorithms can quantify parameter uncertainties; however, this can be computationally expensive for hydrological models. Thus, it is important to select an MCMC algorithm that is effective (converges to the true posterior parameter distribution), efficient (fast), reliable (consistent across random seeds) and controllable (insensitive to the algorithms hyperparameters). These characteristics can be assessed through algorithm diagnostics, but current MCMC diagnostics mostly focus on evaluating convergence of an individual search process, not diagnosing general problems of the algorithms. Therefore, additional diagnostics are required to represent algorithms sensitivity to their hyperparameters and to compare their performance across problems.

Here, we propose new diagnostics to assess the effectiveness, efficiency, reliability and controllability of four MCMC algorithms: Adaptive Metropolis, Sequential Monte Carlo, Hamiltonian Monte Carlo, and DREAM(ZS). The diagnostic method builds off of diagnostics used to assess the performance of Multi-Objective Evolutionary Algorithms (MOEAs), and allows us to evaluate the sensitivity of the algorithms to their hyper-parameterization and compare their performance on multiple metrics, such as the Gelman-Rubin diagnostic and Wasserstein distance from the true posterior. We illustrate our diagnostics using the simple Hydrological Model (HYMOD) and several analytical test problems. This allows us to see which algorithms perform well on problems with different characteristics (e.g. known vs. unknown posterior shapes, uni- vs. multi-modality, low- vs. high-dimensionality). Since posterior shapes and modality are often unknown for hydrological problems, it is important to calibrate them with an MCMC algorithm that is robust across a wide variety of posterior shapes, and our new diagnostics allow for this identification.

How to cite: Kavianihamedani, H., Quinn, J., and Smith, J.: New Diagnostic Assessment of MCMC Algorithms Effectiveness, Efficiency, Reliability, and Controllability in Calibrating Hydrological Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10326, https://doi.org/10.5194/egusphere-egu23-10326, 2023.

EGU23-10510 | ECS | Posters on site | HS3.5

Uncertainty Quantification in Hydrological and Environmental Modeling based on Polynomial Chaos Expansion 

Zoe Li, Pengxiao Zhou, and Maysara Ghaith

There are significant uncertainties associated with the estimates of model parameters in hydrological and environmental modeling. Such uncertainties could propagate within a modeling framework, leading to considerable deviation of the predicted value from its real value. Quantifying the uncertainties associated with model parameters could be computationally exhaustive and is still a daunting challenge to hydrological and environmental engineers. In this study, a series of Polynomial Chaos Expansion (PCE) methods, which have a significant advantage in computational efficiency, is developed to assess the propagation of parameter uncertainty. The proposed approaches were applied to two hydrological/environmental modeling case studies. The uncertainty quantification results will be compared with those from the traditional Monte Carlo simulation technique, to demonstrate the effectiveness and efficiency of the proposed approaches. This work will provide an efficient and reliable alternative to assess the impacts of the parameter uncertainties in hydrological and environmental modeling.

How to cite: Li, Z., Zhou, P., and Ghaith, M.: Uncertainty Quantification in Hydrological and Environmental Modeling based on Polynomial Chaos Expansion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10510, https://doi.org/10.5194/egusphere-egu23-10510, 2023.

EGU23-10644 | Orals | HS3.5

Impact of Model Parameters on Runoff Sensitivities in the Community Land Model: A Study on the Upper Colorado River Basin 

Yadu Pokhrel, Ahmed Elkouk, Lifeng Luo, Liz Payton, Ben Livneh, and Yifan Cheng

Understanding how land surface models (LSMs) partition precipitation into evapotranspiration and runoff under changing climate is key to improved future hydrologic predictions. This sensitivity is rarely tuned in land models, as evidenced by prevalent biases in the sensitivity of simulated runoff to precipitation and temperature change compared to observational estimates. Here, using the Community Land Model (CLM5) over the Colorado River basin (CRB), we investigate what the informative model parameters for runoff sensitivities are and how their choices affect the sensitivities under changing temperature and precipitation. We focus on the headwater region of the CRB, motivated by inconsistent model estimates of runoff sensitivities in the region and the critical need to better understand runoff changes to address the ongoing water crises in the CRB. In each headwater basin, a set of informative parameters were identified through parameter perturbations using “one at a time” method within an adaptive surrogate-based model optimization scheme (ASMO). Results of perturbations highlight that different parameter sets with similar performance (with respect to water-year discharge) provide very different runoff sensitivities to temperature and precipitation during the 1951-2010 period. Additionally, both precipitation and temperature sensitivities of runoff show sensitivity to similar parameters across the region. The most sensitive parameters control the conductance-photosynthesis relationship, soil surface resistance for direct evaporation, the partitioning of runoff into the surface and the subsurface component, and soil hydraulic properties. We show how the importance of each parameter varies through the parameter space and derive parameter estimates by maximizing the “fit to observed sensitivities” within the ASMO scheme. Our results provide key insights regarding parameters optimization to improve long-term hydrologic sensitivities in LSMs.

How to cite: Pokhrel, Y., Elkouk, A., Luo, L., Payton, L., Livneh, B., and Cheng, Y.: Impact of Model Parameters on Runoff Sensitivities in the Community Land Model: A Study on the Upper Colorado River Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10644, https://doi.org/10.5194/egusphere-egu23-10644, 2023.

EGU23-11129 | ECS | Orals | HS3.5

Pitfalls and Opportunities in the Use of Markov-Chain Monte Carlo Ensemble Samplers for Vadose Zone Model Calibration 

Giuseppe Brunetti, Jiri Simunek, Thomas Wöhling, and Christine Stumpp

Bayesian inference has become the most popular approach to uncertainty assessment in vadose zone hydrological modeling. By combining prior information with observations and model predictions, it became popular among hydrologists as it enables them to infer parameter posterior distributions, verify model adequacy, and assess the model's predictive uncertainty. In particular, the posterior distribution is frequently the variable of interest for modelers as it describes the epistemic uncertainty of model parameters conditioned on measurements. Gradient-free Markov-Chain Monte Carlo (MCMC) ensemble samplers based on Differential Evolution (DE) or Affine Invariant (AI) strategies have been used to approximate the posterior distribution, which is frequently anisotropic and correlated in vadose zone-related problems. However, a rigorous benchmark of different MCMC algorithms to provide guidelines for their application in vadose zone hydrological model calibration is still missing. In this study, we elucidate the behavior of MCMC ensemble samplers by performing an in-depth comparison of four samplers that use AI moves or DE-based strategies to approximate the target density. Two Rosenbrock distributions, and one synthetic and one actual case study focusing on the inverse estimation of soil hydraulic parameters using HYDRUS-1D, are used to compare algorithms in different dimensions. The analysis reveals that AI-based samplers are immune to affine transformations of the target density, which instead double the autocorrelation time for DE-based samplers. This behavior is reiterated in the synthetic scenario, for which AI-based algorithms outperform DE-based strategies. However, this performance gain disappears when the number of soil parameters increases from 7 to 16, with both samplers exhibiting poor acceptance rates, which are not improved by increasing the number of chains from 50 to 200 or by mixing different strategies.

How to cite: Brunetti, G., Simunek, J., Wöhling, T., and Stumpp, C.: Pitfalls and Opportunities in the Use of Markov-Chain Monte Carlo Ensemble Samplers for Vadose Zone Model Calibration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11129, https://doi.org/10.5194/egusphere-egu23-11129, 2023.

EGU23-13104 | Posters on site | HS3.5

Combining water and pesticide data with coupled surface/subsurface hydrological modeling to reduce its uncertainty. 

Claire Lauvernet, Claudio Paniconi, Emilie Rouzies, Laura Gatel, and Antoine Caisson

In small agricultural catchments over Europe, intensive use of pesticides leads to widespread contamination of rivers and groundwater, largely due to hydraulic transfers of these reactive solutes from plots to rivers. These transfers must be better understood and described in the watershed in order to be able to propose best management practices adapted to the catchment and to reduce its contamination. The physically based model CATHY simulates interactions between surface and subsurface hydrology and reactive solute transport. However, the high sensitivity of pesticide transfers to spatially heterogeneous soil properties induces uncertainty that should be quantified and reduced. In situ data on pesticides in a catchment are usually rare and not continuous in time and space. Likewise, satellite imagery can provide spatial observations of hydrologic variables but not generally of pesticide fluxes and concentrations, and at limited scale and time frequency. The objective of this work is to combine these 3 types of information (model, in situ data, images) and their associated errors with data assimilation methods, in order to reduce pesticide and hydrological variable uncertainties. The sensitivity to spatial density and temporal frequency of the data will be evaluated, as well as the coupled data assimilation efficiency, i.e., the effect of assimilating hydrological data on pesticide-related variables. The methods will be developed using a Python package, and compared/evaluated on twin experiments using virtual data that are however generated over a real vineyard catchment, in Beaujolais, France, in order to ensure realism of the experiments, data, and associated errors.

How to cite: Lauvernet, C., Paniconi, C., Rouzies, E., Gatel, L., and Caisson, A.: Combining water and pesticide data with coupled surface/subsurface hydrological modeling to reduce its uncertainty., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13104, https://doi.org/10.5194/egusphere-egu23-13104, 2023.

EGU23-13589 | Orals | HS3.5 | Highlight

A comparison of sensitivity analysis methods and their value for comparing denitrification models 

Jesús Carrera and Jordi Petchamé

Numerous methods exist to gain insight on a model performance. Sensitivity analysis (SA) tools provide information on how a model output depends on model parameters. It is widely argued that SA is an essential tool for assessing model uncertainty. Here, I review global SA using Variogram Analysis of Response Surfaces (VARS), variance-based methods (Sobol' indices) and polynomial chaos expansion. For the comparison, we use a set of denitrification models, which are needed to assess the fate of nitrate, a global challenge. For each of the models, we assess the uncertainty and reliability of predictions, and the use of SA tools in designing experiments to reduce model uncertainty.

How to cite: Carrera, J. and Petchamé, J.: A comparison of sensitivity analysis methods and their value for comparing denitrification models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13589, https://doi.org/10.5194/egusphere-egu23-13589, 2023.

EGU23-13981 | Posters on site | HS3.5

Sensitivity analysis of water balance components under climate change in Saxony 

Niels Schuetze, Corina Hauffe, Sofie Pahner, Clara Brandes, Kan Lei, and Mellentin Udo

Catchments in Saxony differ regarding their physiographic characteristics (topography, geomorphology, geology, land use, soils, etc.) and their climatic boundaries. Both factors influence the flow behavior and the water balance components of catchments. How sensitive the water balance of catchments responds to current and future changes in the climatic boundary conditions is difficult to predict for each catchment and is associated with significant uncertainties. In Saxony, the pronounced drought in groundwater and surface water from 2018 to 2020 led to considerable regional problems in water supply and quality.

Schwarze et al. (2017) already investigated trends of the observed discharge and variables derived by hydrograph separation (e.g. baseflow) in a sensitivity study. In this presentation, we show the results of an extension of this analysis with current observation data until 2020. The following research questions are investigated: (i) Are catchments in Saxony already responding to changing climatic conditions? (ii) Which regions show the most significant changes in discharge behavior relative to other water balance components? (iii) What are the factors and drivers of changes in the water balance in Saxonian Catchments?

The study is based only on observational data for precipitation, temperature, and discharge in the period of 1961 to 2020 in Saxony. Break point analysis, hydrograph separation, and sensitivity analysis of hydrological signatures are performed for different sets of climate periods to quantify changes and elasticity of the water balance components. As a result, a decreasing trend for the mean flow can be seen for almost all 88 investigated and undisturbed catchments in Saxony. This trend is more pronounced in the mountainous regions than in the lowland of Saxony. Despite the slight increase in the mean annual precipitation, the temperature rise of about one °C from 1991-2020 compared to 1961-1990 in all catchments leads to an increasing evapotranspiration, reduced discharge, and groundwater recharge.

 

References:

Schwarze, R., Wagner, M. and Röhm, P. (2017). Adaptation strategies to climate change - Analysis of the sensitivity of water balance variables of Saxon gauge catchments with respect to the increased temperature level from 1988 onwards compared to the reference state of 1961-1987. Ed.: Saxon State Office for Environment, Agriculture and Geology (LfULG), 2017.

How to cite: Schuetze, N., Hauffe, C., Pahner, S., Brandes, C., Lei, K., and Udo, M.: Sensitivity analysis of water balance components under climate change in Saxony, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13981, https://doi.org/10.5194/egusphere-egu23-13981, 2023.

EGU23-15689 | ECS | Posters on site | HS3.5

Adaptive Surrogate Likelihood Function for Blended Hydrologic Models 

Rezgar Arabzadeh, Jonathan Romero-Cuellar, Robert Chlumsky, James Craig, and Bryan Tolson

This abstract introduces a recipe for an adaptive general likelihood function and its application in the Bayesian epistemology of model parameters and structure uncertainty. The proposed methodology focuses on a special class of likelihood function, hereinafter mentioned as adaptive general likelihood function (AGL), which require a minimum priori assumptions/knowledge about the model residuals. The goal of the AGL is to characterize the model residuals independently from the inference framework in order to avoid incorrectly posterior estimation as a result of jointly inferencing of model and error model parameters. Mathematically, AGL is structured with a mixture of gaussian distributions joined with a first order autoregressive model, account for error model shape and autocorrelation respectively. To assess the AGL application, it is benchmarked with a formal likelihood function formulated by Schoups and Vrugt (2010) and evaluated for 24 Camels basins where the blended model has been deterministically applied with success (Chlumsky et al. 2022). Both approaches are compared with the residual’s empirical distributions using various statistical tests. The model used here is a blended hydrologic model introduced by Mai et al., (2021) which is a class of hydrologic models constructed by averaging (blending) various process options at the process flux level. This blending means calibration of the model functions to identify traditionally calibrated model process parameters as well as the weights utilized to average multiple process options. The model is deployed in the Raven hydrologic framework (Craig et al., 2020) and simultaneously both processes weights and parameters were calibrated deterministically for both high flows and low flows using PADDS algorithm (Asadzadeh and Tolson, 2013). This multi-objective calibration yields a suite of sample of calibrated blended models which is then utilized for error model development and testing. The tests results indicated a statistically comparable performance for both methods for t-distributed residuals highly skewed and long-tailed residual errors which are apparent in many hydrologic model residuals. Finally, to disjoin the epistemic Bayesian inference framework from the error model parameters, an epsilon-support vector regression (eps-SVR) is deterministically trained as a surrogate model to map the structural/parametric variability to residual error model parameters. The eps-SVR calibration performance metrics indicated high quality of surrogate for training set indicating promising performance.

How to cite: Arabzadeh, R., Romero-Cuellar, J., Chlumsky, R., Craig, J., and Tolson, B.: Adaptive Surrogate Likelihood Function for Blended Hydrologic Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15689, https://doi.org/10.5194/egusphere-egu23-15689, 2023.

The applications of statistical learning theory (SLT) in hydrology have been either in the form of Support Vector Machines and other complexity regularized machine learning algorithms that learn and predict input-output patterns such as rainfall-runoff time series or of identifying optimal complexity of low order models such as k nearest neighbour models to predict hydrological time series such as streamflow. The regularization of model complexity offers a way to identify minimal complexity of a model to accurately predict a time series of interest. However such applications often assume that the modelled residual are independent of each other. This limits its application to conceptual hydrological models where residuals are often auto-correlated. This paper applies recent results of risk bounds for time series forecasting and SLT approaches to dynamical system identification to conceptual hydrological models, offering a means to identify optimal complexity of conceptual models and complexity regularised streamflow predictions based on it.

Basins from CAMELS data set are used to demonstrate the effect of regularizing the problem of hydrological model calibration on streamflow prediction over unseen data. SAC-SMA and SIXPAR (a lower order version of SACSMA) are used as model examples. Preliminary results show that prediction uncertainty bounds are narrower if regularization does not improve the performance of a calibrated model over unseen data. This effect is stronger in drier basins than in humid ones. Also, as expected, this effect is stronger when training data size is small and holds for both SACSMA and SIXPAR. 

How to cite: Pande, S. and Moayeri, M.: Complexity-based robust hydrologic prediction: extension of statistical learning theory to conceptual hydrological models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16039, https://doi.org/10.5194/egusphere-egu23-16039, 2023.

Due to the lack of accurate representation of hydrological processes and parameter measurements, physically-based hydrological models consist of many parameters requiring calibration to historical observations so that reliable hydrological inference can be obtained. With the increasing data availability from various sources (e.g., satellite remote sensing, climate model reanalysis), additional information on different water balance components (e.g., soil moisture, groundwater storage, etc.) are used to constrain and validate hydrological models, resulting in better model performance and parameter identifiability. However, given the emergence of multiple datasets for various water budget components, and their differences in temporal and spatial resolutions, the uncertainties in these datasets, when used together in driving and evaluating hydrological models, could introduce potential inconsistencies in water balance estimation and lead to a non-closure problem, which could result in potentially biased parameter and water balance component estimates in hydrological modelling.

This study addresses this issue by examining the impact of inconsistent water balance component data on model performance and exploring the importance of hydrologically consistent data for robust hydrological inference. The assessment is done using a Canadian Hydrologic-Land Surface Models named MESH in the Saskatchewan River basin, Canada over the period of 2002 to 2016. Seven precipitation datasets, seven evapotranspiration products, one source of water storage data – GRACE from three different centers using spherical harmonic and mass concentration approaches – and observed discharge data from hydrometric stations are selected as the input and evaluation data. A reference water balance dataset is developed to optimally combine all available data sources for each water balance component and to obtain water balance closure though a constrained Kalman filter data assimilation technique. The MESH model is rerun with this reference dataset and results are assessed and compared to different combinations of input and evaluation data. Preliminary results reveal great variations of model performance in the water balance components when using different combinations of input and evaluation data and results of using the reference dataset is expected to have less biased water balance component estimates. This study aims to highlight the necessity of using a set of hydrologically consistent data before any model runs and model evaluation.

How to cite: Wong, J. S., Yassin, F., and Famiglietti, J. S.: Does hydrologically consistent data improve model performance? The importance of closing the water balance of input and evaluation data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16924, https://doi.org/10.5194/egusphere-egu23-16924, 2023.

EGU23-1048 | Orals | CL1.2 | Highlight

The relative role of orbital, CO2 and ice sheet forcing on Pleistocene climate 

Charles Williams, Natalie Lord, Daniel Lunt, Alan Kennedy-Asser, David Richards, Michel Crucifix, Anne Kontula, Mike Thorne, Paul Valdes, Gavin Foster, and Erin McClymont

During the last ~2.5 million years, the Quaternary period, Earth's climate fluctuated between a series of glacials and interglacials, driven by long-term internal forcings such as those in atmospheric CO2 concentrations and ice sheet extent, and external forcings such as the orbital parameters of the Earth around the Sun.  Climate models provide a useful tool for addressing questions concerning the driving mechanisms, dynamics, feedbacks, and sensitivity of the climate system associated with these variations.  However, the structural complexity of such models means that they require significant computational resources, especially when running long (> one million year) transient simulations, and as such are not suitable for exploring orbital-scale variability on these timescales. 

 

Instead, here we use a climate model to calibrate a faster statistical model, or emulator, and use this to simulate the evolution of long-term palaeoclimate during the Quaternary period; firstly during the late Pleistocene (the last 800 thousand years) and secondly the entire Quaternary (the last 2.58 million years).  The emulator is driven by five forcing components: CO2, ice volume, and three orbital parameters.  We firstly compare the simulation with proxy records, and secondly investigate which forcing component is contributing the most to the simulation.

 

The results suggest that the emulator performs well and generally agrees with the proxy records available during the late Pleistocene, for both temperature and precipitation, especially concerning the timing and duration of the various glacial-interglacial cycles.  There are, however, some instances of discrepancies, especially concerning the minima and maxima of the cycles.  A factorial experiment shows that CO2 concentrations and ice volumes changes drive the most variability.  The efficiency of the emulator approach also allows us to carry out a quasi-transient simulation through the entire Quaternary period, and allows projections of possible future drilling results from deep Antarctic ice cores.  

How to cite: Williams, C., Lord, N., Lunt, D., Kennedy-Asser, A., Richards, D., Crucifix, M., Kontula, A., Thorne, M., Valdes, P., Foster, G., and McClymont, E.: The relative role of orbital, CO2 and ice sheet forcing on Pleistocene climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1048, https://doi.org/10.5194/egusphere-egu23-1048, 2023.

EGU23-1311 | ECS | Orals | CL1.2 | Highlight

The role of dispersal limitation in the post-glacial forest expansion of southern and central Europe 

Deborah Zani, Heike Lischke, and Veiko Lehsten

The global vegetation cover underwent strong changes during the past glacial cycle. These have been driven by climatic fluctuations but also by spatiotemporal vegetation dynamics, including migration to new climatologically suitable areas and interactions with other species. However, how much migration lag contributed to the vegetation change after the Last Glacial Maximum (LGM) is often not clear. We used the newly-implemented model LPJ-GM 2.0 to simulate the vegetation change of southern and central Europe from the end of the LGM (18.5 ka) to the preindustrial era (1.5 ka). The model couples a migration module to the dynamic global vegetation model LPJ-GUESS, thus allowing species to migrate simultaneously while interacting with each other. We compared two dispersal settings (free dispersal and dispersal limitation) against pollen data to test the reliability of the migration module to provide realistic paleo-vegetation reconstructions for biome and species distributions. Furthermore, we calculated range shifts of the leading edges and centroids to detect potential species-specific migration lags and range filling delays across simulation time. Our results show that the setting with dispersal limitation is better at capturing the initial post-glacial expansion of non-boreal forests in southern and central Europe than the scenario assuming free dispersal. Range shift analysis shows significant migration lags for most tree species at times of sudden temperature rise (start of the Bølling–Allerød warming event and following the Younger Dryas). Overall, our study suggests that it is necessary to include migration processes when simulating vegetation range expansion under rapid climate change, with implications for future vegetation projections.

How to cite: Zani, D., Lischke, H., and Lehsten, V.: The role of dispersal limitation in the post-glacial forest expansion of southern and central Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1311, https://doi.org/10.5194/egusphere-egu23-1311, 2023.

EGU23-2372 | ECS | Posters on site | CL1.2

A model-based exploration of mid-Holocene anti-phase climate variations in the Central Andes 

Ardhra Sedhu-Madhavan, Sebastian G. Mutz, Daniel Boateng, and Todd A. Ehlers

The Andes’ elevation of ~4 km and great meridional extent of ~50°S to 10°N greatly influences the spatial climate patterns across the South American continent. Apart from latitude and altitude, quasi-stable pressure systems modify the climate of the region. The Bolivian high, an upper-level anticyclonic circulation over the central part of the continent, is one such feature and has a strong impact on atmospheric moisture transport and the regional hydroclimate of the Central Andes. Orbitally forced shifts in the Bolivian High have been hypothesised to be responsible for anti-phase palaeoclimate changes in Peru in the mid-Holocene, such as the increase in humidity in the Palpa region and synchronous extreme drought near Lake Titicaca [e.g., Mächtle et al. 2013]. However, this hypothesis has not been tested, and it has not been determined how much of the mid-Holocene hydroclimate change in the Central Andes can be explained by changes in regional pressure systems. Here, we test the hypothesis that mid-Holocene orbital variations and palaeogeographical changes modified pressure fields and regional moisture transport, and lead to anti-phase changes in regional hydroclimate. We test this hypothesis using the physics-based, isotope-tracking climate model ECHAM5-wiso. More specifically, we analyse pre-industrial and mid-Holocene paleoclimate simulations [Mutz et al. 2018]  to track changes in pressure fields and moisture transport. We then assess their impacts on regional hydroclimate in the Central Andes. Results indicate that: (a) the climate models reproduce the observed synchronous anti-phase (wetter and drier) climate changes documented in different parts of Peru, and (b) these can be explained by changes in the regional pressure and wind fields. Taken together, previous proxy-based observations and model results present here indicate that orbital variations drive changes in the regional pressure systems and lead to spatially heterogenous variations in hydroclimate across the Central Andes.

How to cite: Sedhu-Madhavan, A., G. Mutz, S., Boateng, D., and A. Ehlers, T.: A model-based exploration of mid-Holocene anti-phase climate variations in the Central Andes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2372, https://doi.org/10.5194/egusphere-egu23-2372, 2023.

EGU23-2586 | ECS | Orals | CL1.2

The last glacial cycle transiently simulated with a coupled climate-ice sheet model 

Frerk Pöppelmeier, Fortunat Joos, and Thomas F. Stocker

Understanding climate variability from millennial to glacial-interglacial timescales remains challenging due to the complex and non-linear feedbacks between ice, ocean, and atmosphere. Although the ever-increasing number of reconstructions has helped to form compelling hypotheses for the evolution of ocean and atmosphere circulation or ice sheet extent over the last glacial cycle, climate models, required for systematically testing these hypotheses, struggle to dynamically and comprehensively simulate such long time periods as a result of the large computational costs. Here, we therefore coupled a dynamical ice sheet model to the Bern3D Earth system model of intermediate complexity, that allows for simulating multiple glacial-interglacial cycles in reasonable time. To test the fully-coupled model, we explore the climate evolution over the entire last glacial cycle in a transient simulation forced by the orbital configuration and greenhouse gas and aerosol concentrations. We are able to simulate Global Mean Surface Temperature (GMST) in fair agreement with reconstructions exhibiting a gradual cooling trend since the last interglacial that is interrupted by two more rapid cooling events during the early Marine Isotope Stage (MIS) 4 and Last Glacial Maximum (LGM). The glacial-interglacial GMST and mean ocean temperature differences are 5 °C and 1.6 °C, respectively. Ice volume shows pronounced variability on orbital timescales mirroring northern hemispheric summer insolation. From early MIS3 to the LGM ice volume roughly doubles in good agreement with recent sea-level reconstructions. The Atlantic overturning circulation shows larger variability during the relatively warm MIS5 than during the cooler MIS3, however we note that Dansgaard-Oeschger events are not intrinsically simulated in our setup. At the LGM the Atlantic overturning has a strength of about 14 Sv, which is a reduction by about one quarter compared to the pre-industrial. We thus demonstrate that the new coupled model is able to realistically simulate glacial-interglacial cycles, which allows as to systematically investigate the sensitivities to parameters such as equilibrium climate sensitivity or aerosol radiative forcing during the last glacial cycle.

How to cite: Pöppelmeier, F., Joos, F., and Stocker, T. F.: The last glacial cycle transiently simulated with a coupled climate-ice sheet model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2586, https://doi.org/10.5194/egusphere-egu23-2586, 2023.

EGU23-2885 | ECS | Orals | CL1.2

Atmosphere-mediated response of the Southern Hemisphere hydroclimate in simulations of spontaneous Dansgaard-Oeschger-like oscillations 

Irene Trombini, Nils Weitzel, Muriel Racky, Paul Valdes, and Kira Rehfeld

Dansgaard-Oeschger (DO) events are the most iconic mode of millennial-scale variability during the last glacial period. The manifestation of DO events outside the North Atlantic region and mechanisms responsible for the propagation of the North Atlantic signal across the globe are still little understood. Propagation of DO events to the Southern Hemisphere (SH) has first been explained by oceanic processes, that result in a muted and delayed signal in the Antarctic ice core record, known as Antarctic Isotope Maxima (AIM). Recent ice core-based reconstructions found an additional short-timescale response (years-to-decades, compared to centuries for the oceanic processes) in phase with the climate changes in Greenland. This fast response has been interpreted as the result of atmospheric transport processes. Shifts in the intertropical convergence zone and SH mid-latitude westerlies are seen as mediators of this response.

Here, we investigate the propagation of abrupt climate changes in the North Atlantic region to the SH in general circulation model simulations with spontaneous DO-like oscillations under glacial conditions. We study the relative timing of changes in temperature, hydroclimate, and atmospheric circulation and compare our results with ice core and speleothem based reconstructions. In the simulations, the timing of changes in different elements of the climate system varies on a continuum of timescales from months to centuries. This indicates the existence of more complex propagation mechanisms than the simple separation into an atmospheric and an oceanic mode. Our work emphasizes that future analysis of simulations of DO-like events should focus not just on the mechanisms responsible for the spontaneous oscillations but also on the spatio-temporal fingerprint of the oscillations across the globe.

How to cite: Trombini, I., Weitzel, N., Racky, M., Valdes, P., and Rehfeld, K.: Atmosphere-mediated response of the Southern Hemisphere hydroclimate in simulations of spontaneous Dansgaard-Oeschger-like oscillations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2885, https://doi.org/10.5194/egusphere-egu23-2885, 2023.

EGU23-4683 | ECS | Orals | CL1.2

Reduction in ENSO variability during the mid-Holocene: a multi-model perspective 

Shivangi Tiwari, Francesco S. R. Pausata, Allegra N. LeGrande, Michael L. Griffiths, Hugo Beltrami, Anne de Vernal, Clay R. Tabor, Daniel Litchmore, Deepak Chandan, and W. Richard Peltier

Paleoclimatic reconstructions have suggested a reduction inthe variability of the El Niño Southern Oscillation (ENSO) during the mid-Holocene (MH). Model simulations have largely failed to capture thisreduction, potentially due to the inadequate representation of the Green Sahara.The presence of a vegetated Sahara has been shown to have significant impacts on both regional and remote climate but remains inadequately addressed in Paleoclimate Modelling Intercomparison Project / Coupled Model Intercomparison Project (PMIP/CMIP) boundary conditions. Specifically, the incorporation of a Green Sahara has been shown to impact ENSO variability through perturbations to the Walker Circulation. In this study, we evaluate the MH (6,000 years BP) ENSO signatures of simulations from four models, namely —EC-Earth 3.1, iCESM 1.2, University of Toronto version of CCSM4 and GISS Model E2.1-G. Two simulations are considered for each model—a standard PMIP simulation (MHPMIP) with the mid-Holocene orbital parameters and greenhouse gas concentrations with vegetation prescribed to preindustrial conditions, as well as a Green Sahara simulation (MHGS) which additionally incorporates factors such as enhanced vegetation, reduced dust, presence of lakes, and land and soil feedbacks. All models show a reduction in ENSO variability due to the incorporation of Green Sahara conditions. This variability is interpreted in the context of perturbations to the Walker Circulation, triggered by the strengthening of the West African Monsoon.

How to cite: Tiwari, S., Pausata, F. S. R., LeGrande, A. N., Griffiths, M. L., Beltrami, H., de Vernal, A., Tabor, C. R., Litchmore, D., Chandan, D., and Peltier, W. R.: Reduction in ENSO variability during the mid-Holocene: a multi-model perspective, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4683, https://doi.org/10.5194/egusphere-egu23-4683, 2023.

EGU23-4963 | Orals | CL1.2

New insights of the East Asian summer monsoon variability over the past 800 kyr from a transient simulation with CLIMBER-2 

Liya Jin, Andrey Ganopolski, Matteo Willeit, Huayu Lu, Fahu Chen, and Xiaojian Zhang

The East Asian summer monsoon (EASM) is a major component of the global climate system with its variability closely associated with regional changes of rainfall, impacting the lives of over one sixth of the global population strongly. Understanding the periodicities of summer rainfall influenced by the EASM is beneficial to its future projections. However, the mechanism of the response of the EASM associated summer rainfall fluctuations to orbital-scale forcing during the late Pleistocene remains far from being well understood. Here, we provide an 800-kyr long series of EASM rainfall variations by extracting data from multiple transient simulations of CLIMBER-2 over the past 3 million years. Despite a coarse model resolution, the CLIMBER-2 captures a realistic spatial distribution and magnitude of present-day summer (June-July-August) rainfall, especially in East Asia. The CLIMBER-2 model simulates correct magnitude and timing of the last eight glacial cycles in respect to both global ice sheet volume (expressed in δ18O) and CO2 concentration. Both the simulation and reconstructions reveal predominant 100-ky and 41-ky cycles of global ice sheet volume and CO2 concentration, although precession (23- and 19-kyr) bands dominate high-latitude summer insolation. The EASM intensity is traditionally measured by the monsoonal circulation, i.e. the low-level southerly winds in summer over East Asia. Cross-spectral analysis confirms high coherence between model and proxy at 19-kyr and 41-kyr bands implying a strong low-latitude process modulated by precession. Unlike the EASM circulation from the CLIMBER-2, simulated boreal summer rainfall in East Asia, denoted as “EASM rainfall” shows pronounced 41- and 100-kyr cycles, resembling the loess record over the past 800 kyr. The simulation results reveal a decoupling between EASM rainfall and EASM circulation, which probably is a reasonable explanation for the conflicts in proxy records, and also reflects complicated mechanisms of the EASM system on glacial–interglacial timescales.

How to cite: Jin, L., Ganopolski, A., Willeit, M., Lu, H., Chen, F., and Zhang, X.: New insights of the East Asian summer monsoon variability over the past 800 kyr from a transient simulation with CLIMBER-2, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4963, https://doi.org/10.5194/egusphere-egu23-4963, 2023.

EGU23-5982 | ECS | Posters on site | CL1.2

Vegetation Simulation from the Colonization of Land Plants to the Present 

Jiaqi Guo, Yongyun Hu, and Yonggang Liu

Climate affects vegetation growth and distribution, and vegetation affects climate by modifying the exchange of carbon, water, momentum, and energy between atmosphere and land throughout evolution history. Therefore, reproducing the vegetation distribution is of great significance for understanding climate evolution, vegetation evolution, and their interaction. However, a systematic map of global vegetation distribution since the colonization of land plants (about 480 million years ago; Ma) has remained to be determined. Here, Community Earth System Model (CESM) version 1.2.2 and BIOME4 vegetation model are applied to simulate vegetation during the past 480 million years based on modern vegetation parameters. First, the simulations reveal multiple maps of global vegetation from 480 Ma to pre-industrial (PI) period with a 10-million-year interval. 28 biomes show different distribution characteristics with the evolution of climate, and parts of characteristics are supported by palaeobotanical evidence. Second, the potential biomass as a measure of plant growth is analyzed to explore causes of vegetation variations here. The results illustrate plant growth and expansion is significantly affected by terrestrial temperature and CO2 concentration, followed by terrestrial precipitation. Besides, more land area in the middle and low latitudes can be more conducive to plant flourish in geological history. The simulations provide a reference for paleo-vegetation data and some insights into the interaction between climate and vegetation evolution.

How to cite: Guo, J., Hu, Y., and Liu, Y.: Vegetation Simulation from the Colonization of Land Plants to the Present, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5982, https://doi.org/10.5194/egusphere-egu23-5982, 2023.

EGU23-6063 | Orals | CL1.2

Effects of LGM sea surface temperature and sea ice extent on the isotope-temperature slope at polar ice core sites 

Alexandre Cauquoin, Ayako Abe-Ouchi, Takashi Obase, Wing-Le Chan, André Paul, and Martin Werner

Stable water isotopes in polar ice cores are widely used to reconstruct past temperature variations over several orbital climatic cycles. One way to calibrate the isotope-temperature relationship is to apply the present-day spatial relationship as a surrogate for the temporal one. However, this method leads to large uncertainties because several factors like the sea surface conditions or the origin and the transport of water vapor influence the isotope-temperature temporal slope. In this study, we investigate how the sea surface temperature (SST), the sea ice extent and the strength of the Atlantic Meridional Overturning Circulation (AMOC) affect these temporal slopes in Greenland and Antarctica for Last Glacial Maximum (LGM, ~21 000 years ago) to preindustrial climate change. For that, we use the isotope-enabled atmosphere climate model ECHAM6-wiso [1, 2], forced with a set of sea surface boundary condition datasets based on reconstructions (GLOMAP [3] and Tierney et al. (2020) [4]) or MIROC 4m simulation outputs [5]. We found that the isotope-temperature temporal slopes in East Antarctic coastal areas are mainly controlled by the sea ice extent, while the sea surface temperature cooling affects more the temporal slope values inland. Mixed effects on isotope-temperature temporal slopes are simulated in West Antarctica with sea surface boundary conditions changes, because the transport of water vapor from the Southern Ocean to this area can dampen the influence of temperature on the changes of the isotopic composition of precipitation and snow. In the Greenland area, the isotope-temperature temporal slopes are influenced by the sea surface temperatures very near the coasts of the continent. The greater the LGM cooling off the coast of southeast Greenland, the larger the temporal slopes. The presence or absence of sea ice very near the coast has a large influence in Baffin Bay and the Greenland Sea and influences the slopes at some inland ice cores stations. We emphasize that the extent far south of the sea ice is not so important. On the other hand, the seasonal variations of sea ice distribution, especially its retreat in summer, influence the water vapor transport in this region and the modeled isotope-temperature temporal slopes in the eastern part of Greenland. A stronger LGM AMOC decreases LGM to preindustrial isotopic anomalies in precipitation in Greenland, degrading the isotopic model-data agreement. The AMOC strength does not modify the temporal slopes over inner Greenland, and only a little on the coasts along the Greenland Sea where the changes in surface temperature and sea ice distribution due to the AMOC strength mainly occur.

[1] Cauquoin and Werner, J. Adv. Model. Earth Syst., 13, https://doi.org/10.1029/2021MS002532, 2021.

[2] Cauquoin et al., Clim. Past, 15, 1913–1937, https://doi.org/10.5194/cp-15-1913-2019, 2019.

[3] Paul et al., Clim. Past, 17, 805–824, https://doi.org/10.5194/cp-17-805-2021, 2021.

[4] Tierney et al., Nature, 584, 569–573, https://doi.org/10.1038/s41586-020-2617-x, 2020.

[5] Obase and Abe-Ouchi, Geophys. Res. Lett., 46, 11 397–11 405, https://doi.org/10.1029/2019GL084675, 2019.

How to cite: Cauquoin, A., Abe-Ouchi, A., Obase, T., Chan, W.-L., Paul, A., and Werner, M.: Effects of LGM sea surface temperature and sea ice extent on the isotope-temperature slope at polar ice core sites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6063, https://doi.org/10.5194/egusphere-egu23-6063, 2023.

EGU23-6289 | Orals | CL1.2

Mid Holocene dynamic vegetation highlights unavoidable climate feedbacks 

Pascale Braconnot, Nicolas Viovy, and Olivier Marti

Green Sahara and a northern limit of forest in the northern hemisphere are key characteristics of the differences between the mid Holocene and present-day climate. However, the strength of vegetation feedback and the ability of state-of-the-art climate model to properly represent it still an issue. A reason is that vegetation lies at the critical zone between land and atmosphere. Its variations depend on interconnected factors such as light, energy, water and carbon and, in turn, affect climate and environmental factors. These interconnexions makes it difficult to disentangle the factors that affect the representation of vegetation in a fully interactive model. Dynamical vegetation introduces additional degrees of freedom in climate simulations, so that a model that produces reasonable results when vegetation is prescribed might not be able to properly reproduce the full coupled system, when feedbacks that are not dominant when the system is constraint induce first order cascading effects in coupled mode. Here we investigate the climate-vegetation feedback in mid-Holocene and pre-industrial simulation with the IPSL climate models using 3 different settings of the dynamical vegetation that combining differences in the choice of representation of photosynthesis, bare soil evaporation and parameters defining the vegetation competition and distribution. We show that whatever the set up the major differences expected between the mid-Holocene and preindustrial climates remains similar, but the realisms of the simulated climate can be very different due to cascading climate-vegetation feedbacks that trigger vegetation growth and snow-ice-temperature-soil feedbacks.  Interestingly, with this IPSLCM6 version of the IPSL model (Boucher et al., 2020) all the mid-Holocene simulations produce vegetation in the Sahara-Sahel region compatible with the green Sahara period, but the representation of boreal forests is strongly affected by the different vegetation modeling choices.

How to cite: Braconnot, P., Viovy, N., and Marti, O.: Mid Holocene dynamic vegetation highlights unavoidable climate feedbacks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6289, https://doi.org/10.5194/egusphere-egu23-6289, 2023.

EGU23-6376 | ECS | Posters on site | CL1.2

Response of East Asian summer monsoon climate to North Atlantic meltwater during the Younger Dryas 

Jie Wu, Zhengguo Shi, and Yongheng Yang

The Younger Dryas (YD) event, recognized as one of the most typical abrupt climate changes on the millennial time scale, results in striking cooling in most regions of the North Atlantic. The most acceptable hypothesis believes that this event is related to a large volume of meltwater fluxes injected into the North Atlantic. In remote Asia, various paleoclimate reconstructions have revealed that the East Asian summer monsoon (EASM) is significantly depressed during the cold YD episode. However, the effect of North Atlantic meltwater-induced cooling on the whole downstream Eurasian regions and its potential dynamics remains been not fully explored till now. In this study, the responses of Asian climate characteristics during the YD episode, especially the EASM, are evaluated based on modeling data from the Simulation of the Transient Climate of the Last 21,000 years (TraCE 21ka). The results show that the cooling signal during the YD, which is mainly caused by meltwater flux, spreads from the North Atlantic to the whole Eurasia. In agreement with the paleoclimatic proxies, the simulated EASM is obviously weakened. The summer precipitation is also suppressed over East, South, and Central Asia. Dynamically, the North Atlantic cooling produces an eastward propagated wave train across the mid-latitude Eurasia, which facilitates weaker EASM circulation. The weakened land-sea thermal contrast over East Asia also contributes to the monsoon decrease during YD cooling.

How to cite: Wu, J., Shi, Z., and Yang, Y.: Response of East Asian summer monsoon climate to North Atlantic meltwater during the Younger Dryas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6376, https://doi.org/10.5194/egusphere-egu23-6376, 2023.

EGU23-6514 | Orals | CL1.2 | Highlight

Northern Hemispheric extratropical cyclones during glacial times: impact of orbital forcing and ice sheet height 

Christoph C. Raible, Martina Messmer, Joanthan Buzan, and Emmanuele Russo

Extratropical cyclones are a major source of natural hazards in the mid latitudes as wind and precipitation extremes are associated to this weather phenomenon. Still the response of extratropical cyclones and their characteristics to strong external forcing changes is not yet fully understood. In particular, the impact of the orbital forcing as well as variations of the major ice sheets during glacial times on extratropical cyclones have not been investigated so far.  

Thus, the aim of this study is to fill this gap and to assess the impact of orbital forcing and northern hemispheric ice sheet height variations on extratropical cyclones and their characteristics during winter and summer. The main research tool is the Community Earth System Model CESM1.2. We performed a set of time slice sensitivity simulations under preindustrial (PI) conditions and for the following different glacial periods: Last Glacial Maximum (LGM), Marine Isotopic stage 4 (MIS4), MIS6, and MIS8. Additionally, we vary the northern hemispheric ice sheet height for all the different glacial periods by 33%, 66%, 100% and 125% of the ice sheet reconstructed for the LGM. For each of the simulations the extratropical cyclones are identified with a Lagrangian cyclone detection and tracking algorithm, which delivers a set of different cyclone characteristics, such as, cyclone frequency maps, cyclone area, central pressure, cyclone depth, precipitation associated to the extratropical cyclones as well as extremes in cyclone depth and extratropical cyclone-related precipitation. These cyclone characteristics are investigated for the winter and the summer season separately.

Preliminary results show that the extratropical cyclone tracks are shifted southwards on the Northern Hemisphere during the winter season. This has rather strong implication for the Mediterranean, with an increase of precipitation during glacial times over the western Mediterranean. This increase is modulated when changing the ice sheet height as extratropical cyclone tracks shift further south with increasing northern hemispheric ice sheet height. The orbital forcing shows a higher impact during the summer season, where mean precipitation is further reduced over Europe when comparing MIS4 and MIS8 with LGM. The role of the cyclones for these changes in summer needs to be assessed as well as the implication in the North Pacific.

How to cite: Raible, C. C., Messmer, M., Buzan, J., and Russo, E.: Northern Hemispheric extratropical cyclones during glacial times: impact of orbital forcing and ice sheet height, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6514, https://doi.org/10.5194/egusphere-egu23-6514, 2023.

EGU23-6932 | Orals | CL1.2 | Highlight

The Warm Winter Paradox in the mid-Pliocene Warm Period - a focus on model parameterisations. 

Julia Tindall, Alan Haywood, and Paul Valdes

Modelling results from PlioMIP2 (Pliocene Model Intercomparison Project Phase 2) are in strong disagreement with terrestrial proxy data over the high latitudes for the winter season.  This disagreement is large:  models simulate winter temperatures ~20°C cooler than the data suggests.  We term this the ‘warm winter paradox’.

We have shown that the warm winter paradox cannot be easily resolved.  For example, changing model boundary conditions to account for orbital and CO2 uncertainty have only a small effect on winter temperatures.

Here we use the Hadley Centre General Circulation Model, HadCM3, to investigate whether accounting for uncertainties in model parameterisations could improve the model data agreement for the Pliocene winter.  A new set of parameters for HadCM3, which improve model-data agreement for the Eocene, will be used to investigate the Pliocene climate.  We will show that the new parameters in HadCM3 lead to additional winter Pliocene warming at some locations, although a large model-data disagreement remains.   The new model parameters do not improve the Pliocene data-model comparison as much as they do for the Eocene.  This may indicate that finding a single set of parameters capable of producing an optimised simulation of warm climate states in general is not possible, and that further exploration of model parameter uncertainty is warranted; or that the cause of model data disagreements in the high latitudes may be time period specific.   

How to cite: Tindall, J., Haywood, A., and Valdes, P.: The Warm Winter Paradox in the mid-Pliocene Warm Period - a focus on model parameterisations., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6932, https://doi.org/10.5194/egusphere-egu23-6932, 2023.

EGU23-7448 | ECS | Orals | CL1.2

A multi-model assessment of the early last deglaciation (PMIP4 LDv1) 

Brooke Snoll, Ruza Ivanovic, Lauren Gregoire, and Sam Sherriff-Tadano and the PMIP4 Working Group

At the onset of the last deglaciation, beginning ~19 thousand years ago, ice sheets that covered the Northern Hemisphere at the Last Glacial Maximum started to melt, Earth began to warm, and sea levels rose. This time period is defined by major long-term, millennial-scale, climate transitions from the cold glacial to warm interglacial state, as well as many short-term, centennial- to decadal-scale warmings and coolings of more than 5 °C, sudden reorganisations of basin-wide circulations, and jumps in sea level of tons of meters. Long transient simulations of the deglaciation have been increasingly performed to better understand the long and short term processes, examine different possible scenarios, and compare model output to observable records. The Paleoclimate Modelling Intercomparison Project (PMIP) has provided a framework for an international coordinated effort in simulating the last deglaciation whilst encompassing a broad range of models and model complexities. This study is a multi-model intercomparison of 17 simulations of the last deglaciation from nine different climate models. Unlike other multi-model intercomparison projects, these simulations do not follow one particular experimental design but follow an intentionally flexible protocol suitable for all participants. The design of the protocol provides the opportunity to compare results from models using different forcings and examine a variety of scenarios, hence, representing the range of uncertainty in climate predictions of the time period. One particularly challenging choice to make in the experimental design is how to incorporate the resultant freshwater flux from the melting ice sheets. This research focusses on the divergence between climate trajectories in the simulations as a result of the meltwater scenario preferred by the modelling groups as well as other experimental design choices and their impact on the onset of the deglaciation. These results provide a better understanding of modelling this time period as well as model biases and uncertainty with respect to deglacial forcings and the observable proxy records. 

How to cite: Snoll, B., Ivanovic, R., Gregoire, L., and Sherriff-Tadano, S. and the PMIP4 Working Group: A multi-model assessment of the early last deglaciation (PMIP4 LDv1), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7448, https://doi.org/10.5194/egusphere-egu23-7448, 2023.

EGU23-7454 | ECS | Posters on site | CL1.2

An oscillating Atlantic Meridional Overturning Circulation during the last glacial period 

Yvan Romé, Ruza Ivanovic, and Lauren Gregoire

Abrupt climate changes over the last glacial period (~ 115 to 12 thousand years ago) are often associated with reorganisation of the Atlantic Meridional Overturning Circulation (AMOC). It has been suggested that the AMOC can exist in more than one stable mode, but the mechanisms leading to switches between different regimes are still not understood. It is also unclear how disruptions of the ocean circulation are connected to millennial-scale climate variability, such as Dansgaard-Oeschger events or abrupt transitions during the late last deglaciation. 

Most attempts at theorising glacial millennial-scale variability have involved looking at heat and salt transfers between the subtropical and subpolar gyres. This is often referred to as the ‘salt oscillator’ mechanism, which in turn controlled the intensity of the North Atlantic current. We propose that the salt oscillator is in fact part of a larger motion combining harmonic and stochastic dynamics spanning through all components of the climate system when triggered by an initial excitation. Only under certain combinations of boundary conditions and forcings can multiple stable states coexist, sometimes leading to the activation of a pseudo-oscillating regime for thousands of years. 

Based on a new set of last glacial maximum (~21 thousand years ago) simulations that oscillate when forced with snapshots of the early last deglaciation meltwater history, we propose a new way of visualising the stability of the AMOC and its shifts between different stable modes. We provide a detailed analysis of the heat and salinity tendencies in a comprehensive description of the different oscillating modes. Finally, we discuss how the freshwater forcing framework fits into the broader theory of glacial abrupt climate changes.

How to cite: Romé, Y., Ivanovic, R., and Gregoire, L.: An oscillating Atlantic Meridional Overturning Circulation during the last glacial period, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7454, https://doi.org/10.5194/egusphere-egu23-7454, 2023.

EGU23-8172 | ECS | Posters on site | CL1.2

Coupled climate-carbon simulations of the Penultimate Deglaciation and Last Interglacial in the PLASIM-GENIE model 

Tim Cutler, Philip Holden, Pallavi Anand, and Neil Edwards

Theoretical understanding of paleoclimate change such as deglaciations comes primarily from time slice simulations in state-of-the-art atmosphere-ocean general circulation models, where multimillennial transient simulations would be too computationally expensive. Such steady state runs may be missing long-timescale processes involving ocean circulation or the carbon cycle, which could be captured by long transient simulations. The PLASIM-GENIE (Planet Simulator – Grid-Enabled Integrated Earth System) model is capable of running fast, multimillennial climate-carbon cycle simulations, comprising a fully 3D spectral atmosphere and frictional geostrophic ocean with marine and terrestrial carbon cycle modules. Here, we present comparisons between steady state and pseudo-transient experiments in PLASIM-GENIE, starting from the Penultimate Glacial Maximum (140,000 years before present) through the Last Interglacial, applying the PMIP4 Penultimate Deglaciation protocol. In pseudo-transient simulations, the model is stopped at every 500 years and restarted with updated prescribed ice sheets, orbital forcings, meltwater fluxes and relaxed CO2 (with an active carbon cycle). These are compared to steady state time-slice simulations where the model is spun-up at each 500-year interval, to test for hysteresis in atmosphere, ocean and carbon cycle processes. Particular focus is on the timing of Atlantic Meridional Overturning Circulation weakening and recovery. We supplement these baseline simulations with a series of sensitivity experiments where individual forcings are varied.

How to cite: Cutler, T., Holden, P., Anand, P., and Edwards, N.: Coupled climate-carbon simulations of the Penultimate Deglaciation and Last Interglacial in the PLASIM-GENIE model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8172, https://doi.org/10.5194/egusphere-egu23-8172, 2023.

EGU23-8251 | Posters on site | CL1.2

Effects of glacial conditions on the circulation and water vapor sources of Indian monsoon precipitation 

Thejna Tharammal, Govindasamy Bala, Jesse Nusbaumer, and Andre Paul

Climate records suggest a weaker Indian monsoon circulation and drier conditions in the continent during the Last Glacial Maximum (LGM, ~19-23 ka BP). This is mainly due to circulation changes caused by high-latitude ice sheets, tropical and high-latitude SST changes, and lower atmospheric CO2 concentrations compared to pre-industrial (PI). Such changes in boundary conditions and circulation are likely to cause changes in the water vapor sources of monsoon precipitation, with implications for precipitation reconstructions using water isotope proxies. We use the water isotope/water tagging-enabled Community Earth System Model (iCESM) to study the effects of glacial conditions on the sources of water vapor and isotope ratios of precipitation for the Indian monsoon precipitation. We conduct time slice experiments for the PI and the LGM periods following the PMIP4 guidelines. iCESM was successful in identifying the water vapor sources of present-day Indian summer monsoon precipitation, namely the Indian Ocean sources and precipitation recycling. The detailed results of this study will be presented at the meeting.

How to cite: Tharammal, T., Bala, G., Nusbaumer, J., and Paul, A.: Effects of glacial conditions on the circulation and water vapor sources of Indian monsoon precipitation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8251, https://doi.org/10.5194/egusphere-egu23-8251, 2023.

EGU23-8404 | ECS | Orals | CL1.2 | Highlight

Multimodel comparison of weathering fluxes during the last deglaciation 

Fanny Lhardy, Bo Liu, Matteo Willeit, Nathaelle Bouttes, Takasumi Kurahashi-Nakamura, Stefan Hagemann, and Tatiana Ilyina

The global carbon cycle is a complex system with many drivers, including slow ones such as the chemical weathering of rocks. At long enough timescales, changes in weathering rates influence CO2 consumption, but also the river loads of carbon, nutrients, and alkalinity. In particular, the global ocean inventory of alkalinity is a critical driver of carbon sequestration into the ocean. Thus, any transitory imbalance between the sources and sinks of alkalinity can lead to changes in ocean chemistry and impact atmospheric CO2 concentration. During the last deglaciation (ca. 19-11 ka BP), the Earth’s climate transitioned from cold and arid to comparatively warmer and wetter conditions. Simultaneously, large ice sheets melted and led to a significant rise of sea level (ca. +120 m), which reduced the size of the exposed continental shelves. Loess deposits were also gradually eroded. These changes logically influenced the chemical weathering of rocks because weathering rates depend on climate variables (runoff and temperature), land-sea distribution and lithology. Some modelling studies and proxy reconstructions suggest little net changes over this period. Yet, the deglacial changes of weathering rates remain poorly constrained.

Most Earth System Models do not explicitly represent weathering and the consequent river fluxes. Moreover, the alkalinity inventory is often assumed constant in models, despite the fact that proxy data suggest an elevated total alkalinity at the Last Glacial Maximum (and the likely changes of its sources and sinks). These choices can potentially bias the model representation of the global carbon cycle, whose deglacial variations have been notoriously hard to simulate for decades. In this study, we calculate weathering fluxes of phosphorus and alkalinity (among others) using reconstructed lithological maps, and model results from transient runs of the last deglaciation and/or time-slice runs of the Last Glacial Maximum and pre-industrial period. To improve robustness, we compare the evolution and spatial distribution of weathering fluxes in different models. We demonstrate that while the increase of runoff during deglaciation enhances weathering, the rise of sea level and the erosion of loess deposits tend to have a counterbalancing effect on the river loads. Our model ensemble tends to show inconsistent deglacial changes of some river loads (e.g. for phosphorus), depending both on runoff biases and on the representation of land-sea distribution. Still, all models indicate a significant decrease of river alkalinity from the LGM to the pre-industrial. Using these findings, we discuss the implications of an explicit representation of weathering fluxes for the global carbon cycle in transient runs with Earth System Models.

How to cite: Lhardy, F., Liu, B., Willeit, M., Bouttes, N., Kurahashi-Nakamura, T., Hagemann, S., and Ilyina, T.: Multimodel comparison of weathering fluxes during the last deglaciation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8404, https://doi.org/10.5194/egusphere-egu23-8404, 2023.

EGU23-8546 | ECS | Orals | CL1.2

Transitions in the Northern Hemisphere glaciation process 

Stefanie Talento, Andrey Ganopolski, and Matteo Willeit

We use the new Earth system model of intermediate complexity CLIMBER-X to investigate pathways of Northern Hemisphere (NH) glaciation. We perform experiments in which different combinations of orbital forcing and atmospheric CO2 concentration are maintained constant in time. Each model simulation is run for 300 thousand years (kyr) starting from present-day conditions, and using an acceleration technique with asynchronous coupling between the climate and ice sheet model components.

We find that in the pathway to a NH glaciation, several bifurcations might occur. The bifurcations separate a diversity of stable configurations, which have different spatial and temporal prints. We identify four different bifurcations, separating five different equilibrium states: (i) completely ice-free conditions, (ii) present-day (ice only over Greenland), weak glaciation (with ice coverage north and west of Hudson Bay, Greenland and Scandinavia), (iv) Last Glacial Maximum – type of glaciation (with large North American and medium-size Eurasian ice sheets) and (v) mega-glaciation (full ice coverage over both North America and Eurasia).

The transitions are also clustered in terms of differential timescales. While the North-American continent full glaciation has a development timescale of ~ 100 kyr, an extensive ice coverage of the Eurasian continent involves a much longer time-frame of ~ 250 kyr. This could explain why a complete glaciation of the Eurasian continent was never observed. This result is also consistent with previous studies in the sense that one glaciation cycle is not long enough for the Eurasian ice sheet to fully grow.

How to cite: Talento, S., Ganopolski, A., and Willeit, M.: Transitions in the Northern Hemisphere glaciation process, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8546, https://doi.org/10.5194/egusphere-egu23-8546, 2023.

EGU23-8827 | Orals | CL1.2 | Highlight

Are high sensitivity models compatible with the Last Glacial Maximum? 

Navjit Sagoo and Thorsten Mauritsen

The wide range of Effective Climate Sensitivity (ECS) values in climate models are driven by inter-model spread in cloud feedbacks. The most recent generation of models (CMIP6) show an increase in both average ECS values as well as the appearance of very high ECS values (> 4.5 K) compared to the previous generation which has been attributed to an increase in the strength of total cloud feedbacks in CMIP6. Constraining ECS and in particular the high range of ECS values is paramount for reliable predictions of future climate change. The Last Glacial Maximum (LGM) is an out-of-sample climate for modern models and thus provides a valuable evaluation test for these models. This work explores whether models with high ECS values are compatible with the Last Glacial Maximum (LGM) climate and whether we can use the LGM to constrain a plausible upper boundary of ECS. We create a single model ensemble with a wide range of ECS values by modifying cloud feedbacks in the MPI-ESM1.2 model. We simulate the LGM with this ensemble and compare it with four different paleo-reconstructions. Our results indicate models with an ECS > 4 K are incompatible with the existing LGM climate reconstructions: global surface air temperature (SAT) anomalies are too cold compared to reconstructions and ultimately become unstable due to sea ice dynamics in the model. Our study indicates that models with large total cloud feedbacks and high ECS values are not plausible during the LGM. This study highlights the value of using paleoclimates to benchmark models particularly in areas where existing validation techniques are not yet sufficient i.e. constraining cloud feedbacks.

How to cite: Sagoo, N. and Mauritsen, T.: Are high sensitivity models compatible with the Last Glacial Maximum?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8827, https://doi.org/10.5194/egusphere-egu23-8827, 2023.

EGU23-9705 | ECS | Posters on site | CL1.2

On the global synchronicity of glacial vegetation changes 

Nils Weitzel, Moritz Adam, Maria Fernanda Sanchez Goñi, Marie-Pierre Ledru, Vincent Montade, Coralie Zorzi, and Kira Rehfeld

Vegetation responds to local climate and carbon dioxide changes with response times ranging from decades to millennia, depending on location, spatial scale, and vegetation characteristic. Here, we focus on orbital timescales, for which all available estimates suggest an equilibrium of vegetation and climate. Over the course of the last glacial period, global mean temperature varied between minima during Marine Isotope Stage (MIS) 4 and MIS2, and a maximum in MIS3. If orbital-scale climate changes followed this global trend across most of the globe, we would expect vegetation changes to feature a similar temporal evolution.

Leveraging a global compilation of pollen records, we quantify the synchronicity of orbital-scale vegetation changes within and across regions during the last glacial period. We use the arboreal pollen fraction, statistical mode decompositions, and key taxa as indicators for forest cover changes. Our results suggest that a globally coherent forest cover minimum occurred during MIS2. However, we do not find evidence for other periods of coherent forest cover trends across the globe or either hemisphere. This indicates that vegetation changes were more regionally confined during earlier parts of the last glacial. As chronologies become more uncertain further back in time, we examine the likelihood of dating errors to explain the absence of globally coherent vegetation changes during MIS4 and MIS3. Finally, we compare our results with simulations of climate and vegetation to assess if models capture the diagnosed forest cover trends found in the pollen records. Moreover, this comparison allows us to infer the influence of temperature, moisture availability, and carbon dioxide on vegetation variations during the last glacial period.

How to cite: Weitzel, N., Adam, M., Sanchez Goñi, M. F., Ledru, M.-P., Montade, V., Zorzi, C., and Rehfeld, K.: On the global synchronicity of glacial vegetation changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9705, https://doi.org/10.5194/egusphere-egu23-9705, 2023.

EGU23-9748 | Orals | CL1.2 | Highlight | Milutin Milankovic Medal Lecture

Milankovitch cycles and the Arctic: insights from past interglacials 

Bette L. Otto-Bliesner

The Arctic is warming at a rate greater than the global average. End-of-summer minimum sea ice extent is declining and reaching new minimums for the historical record of the last 4 decades. The Greenland ice sheet is now losing more mass than it is gaining, with increased surface melting. Earth System Models suggest that these trends will continue in the future. The geologic past can be used to inform what could happen in the future. Emiliani in his 1972 Science paper commented on the relevance of paleoclimate for understanding our future Earth.

 

Interglacials of the last 800,000 years, including the present (Holocene) period, were warm with low land ice extent. In contrast to the current observed global warming trend, which is attributed primarily to anthropogenic increases in atmospheric greenhouse gases, regional warming during these interglacials was driven by changes in Earth’s orbital configuration. Although the circumstances are different, understanding the behavior, processes, and feedbacks in the Arctic provides insights relevant to what we might expect during future global warming.

 

Data compilations suggest that despite spatial heterogeneity, Marine Isotope Stages (MIS) 5e (Last Interglacial, ~129 to 116 ka) was globally strong. The Last Interglacial (LIG) is characterized by large positive solar insolation anomalies in the Arctic during boreal summer associated with the large eccentricity of the orbit and perihelion occurring close to the boreal summer solstice. The atmospheric carbon dioxide concentration was similar to the preindustrial period.

 

Geological proxy data for the LIG indicates that Arctic latitudes were warmer than present, boreal forests extended to the Arctic Ocean in vast regions, summer sea ice in the Arctic was much reduced, and Greenland ice sheet retreat contributed to the higher global mean sea level. Model simulations provide critical complements to this data as the they can quantify the sensitivity of the climate system to the forcings, and the processes and interplay of the different parts of the Arctic system on defining these responses. As John Kutzbach explained in a briefing for science writers, "climate forecasts suffer from lack of accountability. Their moment of truth is decades in the future. But when those same computer programs are used to hindcast the past, scientists know what the correct answer to the test should be."

 

Significant attention and progress have been made in modeling the LIG in the last 2 decades. Earth System Models now capture more realism of processes in the atmosphere, ocean, and sea ice, can couple to models of the Greenland ice sheet, and include representations of the response of Arctic vegetation to the NH high-latitude summer warming. Increases in computing power has allowed these models to be run at higher spatial resolution and to perform transient simulations to examine the evolving orbital forcing during the LIG.  The international PMIP4 simulations for 127 ka illustrated the importance of positive cryosphere and ocean feedbacks for a warmer Arctic. A CESM2-Greenland ice sheet, transient LIG simulation from 127 ka to 119 ka, established a key role of vegetation feedbacks on Arctic climate change.

How to cite: Otto-Bliesner, B. L.: Milankovitch cycles and the Arctic: insights from past interglacials, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9748, https://doi.org/10.5194/egusphere-egu23-9748, 2023.

Reliable projections of future climate change are vital for mitigation and adaptation efforts. Such efforts require not only projections of mean changes but of changes in variability, too, since those directly affect the occurrence of extremes. The evaluation of climate models regarding their ability to simulate expected changes in variability of temperature and precipitation relies on the comparison of observations with simulations of past and present-day climate. As such, studying past periods of warming furthers the understanding of the climate system and its projected changes. However, the response of the climate system to forcings depends on the background state. Thus, understanding how insights from studies of the past transfer to future projections and the limitations of this transfer is vital.

Here, we present an analysis of temperature and precipitation variability in transient simulations of the Last Deglaciation and projected future climate. To this end, we analyze how the distributions of temperature and precipitation change as exemplified by the moments of the distribution, i.e. variance, skewness and kurtosis. We identify trends in the projections and compare them to results for the Last Deglaciation and present commonalities and differences between the responses in these climate states. We further present how these changes relate to differences in the background state, forcings, and the timescales on which these forcings act as well as the limitations imposed by these differences. Based on this analysis of the state-dependency of variability and its change with a warming mean state, we present conclusions on how past climates can inform and support studies of future climate change.

How to cite: Ziegler, E. and Rehfeld, K.: Past and future changes of temperature and precipitation variability in climate model projections and transient simulations of the Last Deglaciation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9813, https://doi.org/10.5194/egusphere-egu23-9813, 2023.

EGU23-10048 | Posters on site | CL1.2

From the last interglacial to the future – new insights into climate change from the PalMod Earth System modelling framework 

Kerstin Fieg, Mojib Latif, Tatjana Ilyina, and Michael Schulz

The PalMod project funded by the German Federal Ministry of Education and Research (BMBF) aims at filling gaps in our understanding of the dynamics and variability of the Earth system during the last glacial-interglacial cycle. Major goals are to enhance Earth system models (ESMs), to identify potential tipping points that could become important in a warming world, and to perform long-term projections with the advanced the ESMs. 

In PalMod Phases I and II, we focussed on three key epochs, the last glacial inception, MIS3, and the last deglaciation. In PalMod Phase III, we will use the new insights from the first two phases to perform more advanced climate projections into the next millennia. Special focus areas are rapid climate transitions, permafrost melting, and ice-sheet instability and sea level rise.  

How to cite: Fieg, K., Latif, M., Ilyina, T., and Schulz, M.: From the last interglacial to the future – new insights into climate change from the PalMod Earth System modelling framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10048, https://doi.org/10.5194/egusphere-egu23-10048, 2023.

EGU23-11127 | Orals | CL1.2 | Highlight

Rapid expansion of ice sheet area in transient simulations of the last glacial inception 

Matteo Willeit, Stefanie Talento, and Andrey Ganopolski

We present transient simulations of the last glacial inception using the Earth system model CLIMBER-X with interactive ice sheets and visco-elastic solid-Earth response. The simulations are initialized at the Eemian interglacial (125 ka) and run until 100 ka, driven by prescribed changes in orbital configuration and greenhouse gas concentrations from ice core data.
CLIMBER-X simulates a robust ice sheet expansion over North America and Scandinavia through MIS5d, in accordance with proxy data. However, we show that the crossing of a bifurcation point in the ice-covered area, which leads to a rapid (~7 million square km over a few centuries) expansion of ice sheets over North America, is critical to get a large enough ice volume to match the sea level drop of ~40 m indicated by reconstructions during the last glacial inception. As a consequence of the presence of this bifurcation point, the model results are highly sensitive to climate model biases. We also show that in the model the vegetation feedback plays an important role during glacial inception.
Further results suggest that, as long as the system responds almost linearly to insolation changes during the last glacial inception, the model results are not very sensitive to changes in the ice sheet model resolution and the acceleration factor used to speed-up the climate component. This is not valid, however, when the system response is characterized by strongly-nonlinear processes, such as a rapid increase in ice-covered area.

How to cite: Willeit, M., Talento, S., and Ganopolski, A.: Rapid expansion of ice sheet area in transient simulations of the last glacial inception, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11127, https://doi.org/10.5194/egusphere-egu23-11127, 2023.

EGU23-11206 | Orals | CL1.2

Holocene forest-cover changes in Europe - a comparison of dynamic vegetation model results and pollen-based REVEALS reconstructions 

Anne Dallmeyer, Anneli Poska, Laurent Marquer, Andrea Seim, and Marie-José Gaillard-Lemdahl

We compare Holocene forest-cover changes in Europe derived from a transient MPI-ESM1.2 simulation with high spatial resolution time-slice simulations conducted in LPJ-GUESS and pollen-based quantitative reconstructions of forest cover based on the REVEALS model (pol-RVs). The dynamic vegetation models and pol-RVs agree with respect to the general temporal trends in forest cover for most parts of Europe, with a large forest cover during the mid-Holocene and substantially smaller forest cover closer to the present time. However, the age of the start of decrease in forest cover varies between regions, and is much older in the pol-RVs than in the models. The pol-RVs suggest much earlier anthropogenic deforestation than the prescribed land-use in the models starting 2000 years ago. While LPJ-GUESS generally overestimates forest cover compared to pol-RVs, MPI-ESM indicates lower percentages of forest cover than pol-RVs, particularly in Central Europe. A comparison of the simulated climate with chironomid-based climate reconstructions reveal that model-data mismatches in forest cover are in most cases not driven by biases in the climate. Instead, sensitivity experiments show that the model results strongly depend on the models tuning regarding natural disturbance regimes (e.g. fire and wind throw). The frequency and strength of disturbances are – like most of the parameters in the vegetation models – static and calibrated to modern conditions. However, these parameter values may not be valid during climate and vegetation states totally different from today’s. In particular, the mid-Holocene natural forests were probably more stable and less sensitive to disturbances than present day forests that are heavily altered by human interventions. Our analysis highlights the fact that such model settings are inappropriate for paleo-simulations and complicate model-data comparisons with additional challenges. Moreover, our study suggests that land-use is the main driver of forest decline in Europe during the mid- and late-Holocene.

How to cite: Dallmeyer, A., Poska, A., Marquer, L., Seim, A., and Gaillard-Lemdahl, M.-J.: Holocene forest-cover changes in Europe - a comparison of dynamic vegetation model results and pollen-based REVEALS reconstructions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11206, https://doi.org/10.5194/egusphere-egu23-11206, 2023.

EGU23-12139 | ECS | Orals | CL1.2

Sensitivity of the glacial marine biological pump to particle sinking and dust deposition in MPI-ESM 

Bo Liu, Joeran Maerz, and Tatiana Ilyina

The marine biological carbon pump substantially contributes to the glacial-interglacial CO2 change. Compared to the late Holocene, proxy data for the Last Glacial Maximum (LGM) generally agree on an increased export production, associated with an enhanced marine biological carbon pump, in the subantarctic region of the Southern Ocean (SO). By contrast, global export production during the LGM is poorly constrained due to the sparseness and uncertainty of proxy data. The efficiency of the biological pump is mainly controlled by phytoplankton growth, ocean circulation and the sinking and remineralisation of organic matter. Previous modelling studies primarily focused on the sensitivity regarding the former two factors. By far, few studies have discussed the impact of marine particle sinking on glacial ocean biogeochemistry.

In this study, we examine the impact of two different sinking schemes for biogenic particles on the LGM ocean biogeochemistry in the Max Planck Institute Earth System Model (MPI-ESM). In the default sinking scheme, sinking velocities of particulate organic matter (POM), biogenic minerals (CaCO3 and opal) and dust are prescribed and kept the same between LGM and pre-industrial (PI) state. Such a scheme is also widely applied in other ocean biogeochemical models. In a new Microstructure, Multiscale, Mechanistic, Marine Aggregates in the Global Ocean (M4AGO) sinking scheme, the size, microstructure, heterogeneous composition, density and porosity of marine aggregates, consisting of POM, CaCO3, opal and dust, are explicitly represented, and the sinking speed is prognostically computed. We discuss the effect of the two particle sinking schemes under two LGM circulation states: “deep LGM AMOC” with a similar NADW/AABW boundary compared to PI, which is produced in many existing models, and “shallow LGM AMOC” with a shallower NADW/AABW boundary, which agrees better with proxy data. Furthermore, we conducted sensitivity studies regarding LGM dust deposition as the latter is subject to considerable uncertainties.

We find that for the deep LGM AMOC, the difference between the impact of the two particle sinking schemes on the ocean biogeochemical tracers is small. On the contrary, for shallow LGM AMOC, the M4AGO scheme yields more remerineralised carbon in the deep ocean and, therefore, better agreement with δ13C data, suggesting the quantitative impact of particle sinking schemes strongly depends on the background LGM circulation state. For the default sinking scheme, increased glacial dust deposition increases iron fertilisation and thus leads to a rise in both primary production and export production. For the M4AGO scheme, however, the iron fertilisation effect is surpassed by the ballasting effect that reduces the surface nutrient concentration, and LGM primary production decreases with dust deposition. This preliminary result shows that the new marine aggregate sinking scheme adds further complexities to the marine biological carbon pump response to the climate states. Our further analysis will encompass the other nutrients and dissolved oxygen, as well as the comparison to corresponding proxy data. 

How to cite: Liu, B., Maerz, J., and Ilyina, T.: Sensitivity of the glacial marine biological pump to particle sinking and dust deposition in MPI-ESM, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12139, https://doi.org/10.5194/egusphere-egu23-12139, 2023.

EGU23-12646 | ECS | Orals | CL1.2

On the sensitivity of the ocean response to LGM and MIS3-forcings 

Chetankumar Jalihal, Ute Merkel, Matthias Prange, and Uwe Mikolajewicz

The AMOC has undergone abrupt and quasi-periodic changes during the MIS-3. The prevailing background climatic conditions that produce such behavior in AMOC have yet to be fully understood. Previous studies have demonstrated that some climate models tend to have an oscillatory behavior in their AMOC under specific conditions that vary from model to model. A systematic study that compares these conditions across models is missing. Moreover, the relative impact of greenhouse gas and icesheet forcings on the mean strength of AMOC remain unresolved.

 

Here, we present our results from CMIP/PMIP style simulations with MIS-3 boundary conditions. This study has been carried out under the PalMOD project. Based on the minimum and maximum ice sheet extent and greenhouse gas radiative forcing, we carried out a set of 4 experiments. These experiments are the LGM, 38ka, LGM_38kaghg (LGM topography with 38ka greenhouse gas concentrations), and 38ka_LGMghg (38ka topography with LGM greenhouse gas concentrations). We have used two Earth system models (ESM), Viz. the MPI-ESM and the CESM. The experiments in MPI-ESM were carried out with two versions of the river run-off directions - one in which run-off directions are compatible with the topography and the other where run-off directions are set to that of the modern-day. Thus, we have three sets of simulations for each experiment.

 

A robust feature across these simulations is that during the MIS-3, the mean strength of AMOC is sensitive to changes in greenhouse gases, and the changes in ice sheets do not significantly affect the AMOC. The density of water in the North Atlantic Deep-Water formation (NADW) region does not change significantly in response to these forcings. However, the variations in the density in the Arctic and Southern Ocean deep-water formation region drive variations in AMOC strength. The AMOC in CESM undergoes Dansgaard-Oeschger (DO) like oscillations in the 38ka LGMghg simulation. No oscillations are found in any MPI-ESM experiments with the run-off adapted for topography. However, Bo-like oscillations appear in the LGM simulation with modern run-off. This highlights the importance of model parameters and the location of freshwater input into the ocean in determining the conditions that lead to oscillatory behavior in AMOC.

How to cite: Jalihal, C., Merkel, U., Prange, M., and Mikolajewicz, U.: On the sensitivity of the ocean response to LGM and MIS3-forcings, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12646, https://doi.org/10.5194/egusphere-egu23-12646, 2023.

EGU23-13276 | Orals | CL1.2 | Highlight

A multi-centennial mode of North Atlantic climate variability throughout the Last Glacial Maximum 

Matthias Prange, Lukas Jonkers, Ute Merkel, Michael Schulz, and Pepijn Bakker

Paleoclimate proxy records from the North Atlantic region reveal substantially greater multi-centennial temperature variability during the Last Glacial Maximum (LGM) compared to the current interglacial. As there was no obvious change in external forcing, causes for the increased variability remain unknown. Here we provide a mechanism for enhanced multi-centennial North Atlantic climate variability during the LGM based on experiments with the coupled climate model CESM. The model simulates an internal mode of multi-centennial variability, which is associated with variations in the Atlantic meridional overturning circulation. In accordance with high-resolution proxy records from the glacial North Atlantic, this mode induces highest surface temperature variability in subpolar and mid latitudes and almost no variance in low latitudes. Greenland surface air temperature varies by up to 4°C, which is in line with multi-centennial variability reconstructed from ice cores. We show that this mode is based on a salt-oscillator mechanism and emerges only under full LGM climate forcing. Moderate deviations from full-glacial boundary conditions lead to its disappearance. We further argue that the multi-centennial mode has to be distinguished from millennial-scale Dansgaard-Oeschger oscillations.

How to cite: Prange, M., Jonkers, L., Merkel, U., Schulz, M., and Bakker, P.: A multi-centennial mode of North Atlantic climate variability throughout the Last Glacial Maximum, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13276, https://doi.org/10.5194/egusphere-egu23-13276, 2023.

EGU23-13781 | ECS | Posters on site | CL1.2

Waterbelt states controlled by sea-ice thermodynamics 

Johannes Hörner and Aiko Voigt

Snowball Earth refers to multiple periods in the Neoproterozoic during which geological evidence indicates that Earth was largely covered in ice. A Snowball Earth results from a runaway ice-albedo feedback, but it is still under debate how the feedback stopped: with fully ice-covered oceans or with a strip of open water around the equator. 

The latter are called waterbelt states and are an attractive explanation for the Snowball Earth events because they provide a refugium for the survival of photosynthetic aquatic life, while still explaining Neoproterozoic geology. Waterbelt states can be stabilised by bare sea ice in the subtropical desert regions with lower surface albedo stopping the ice-albedo feedback. However, the sea-ice model used in climate simulations can have a significant impact on the snow cover of ice and hence the surface albedo. 

Here we investigate the robustness of waterbelt states with respect to the thermodynamical representation of sea ice. We compare two thermodynamical sea-ice models, an idealised 0-layer Semtner model and a 3-layer Winton model that takes into account the heat capacity of ice. We deploy the atmospheric part of the ICON-ESM model (ICOsahedral Nonhydrostatic - Earth System Model) in a comprehensive set of simulations to determine the extent of the waterbelt hysteresis. 

The thermodynamic representation of sea ice strongly influences snow cover on sea ice over the range of all climate states. Including heat capacity by using the 3-layer Winton model increases snow cover and enhances the ice-albedo feedback. The hysteresis of the stable waterbelt state found using the 0-layer model disappears when using the 3-layer model. This questions the relevance of a subtropical bare sea-ice edge for waterbelt states and might help explain drastically varying model results on waterbelt states in the literature.

How to cite: Hörner, J. and Voigt, A.: Waterbelt states controlled by sea-ice thermodynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13781, https://doi.org/10.5194/egusphere-egu23-13781, 2023.

EGU23-15633 | Posters on site | CL1.2 | Highlight

The impact of CO2 and ice sheet changes on the deglacial AMOC sensitivity to freshwater perturbations in three different Earth System Models 

Gregor Knorr, Marie Kapsch, Matthias Prange, Uwe Mikolajewicz, Dragan Latinovic, Ute Merkel, Lu Niu, Lars Ackermann, Xiaoxu Shi, and Gerrit Lohmann

During deglaciation disintegration of large-scale continental ice sheets represents a continuous threat to reduce the strength of the Atlantic meridional overturning circulation (AMOC) via meltwater perturbations to the northern high latitudes. Nevertheless, an abrupt AMOC recovery is detected half-way through the last deglaciation and  a growing number of studies using Earth System Models (ESMs) of varying complexity have shown that atmospheric CO2 concentrations and ice sheet volume can influence the operational mode of the AMOC, eventually including the coexistence of multiple states and associated threshold behavior for intermediate climate states between full glacial (e.g. Last Glacial Maximum, LGM) and full interglacial (e.g. pre-industrial, PD)  conditions. In this study we present results from coordinated sensitivity experiments conducted as part of the German climate modeling initiative (PalMod), using three complex ESMs (AWI-ESM, CESM and MPI-ESM). Besides differences in the impact of CO2 and ice volume changes, we also investigate how variations in these boundary conditions control the AMOC sensitivity to deglacial meltwater injections in the North Atlantic. We find that the AMOC strength responds to ice sheet and/or CO2 changes in all models, with partly opposing effects.  A similar AMOC strength for PD and LGM conditions is detected in AWI-ESM and MPI-ESM, while CESM shows a weaker LGM AMOC. This weaker LGM state is also characterized by a relatively pronounced AMOC sensitivity to freshwater perturbations. Our inter-comparison experiments suggest that this specific behavior in CESM can be detected for atmospheric concentrations between LGM and intermediate levels of ~220 ppm. This further corroborates in particular the impact of CO2 changes to modulate the trajectory of deglacial climate changes by an alteration of the AMOC susceptibility to meltwater injections as recently suggested (Sun et al., Glob. Planet. Change, 2021; Barker & Knorr, Nat. Commun., 2021).

 

 

 

References:

Sun, Y., Knorr, G., Zhang, X., Tarasov, L., Barker, S., Werner, M. and G. Lohmann (2022): Ice sheet decline and rising atmospheric CO2 control AMOC sensitivity to deglacial meltwater discharge. Global and Planetary Change 210. https://doi.org/10.1016/j.gloplacha.2022.10375

Barker, S. and G.  Knorr (2021): Millennial scale feedbacks determine the shape and rapidity of glacial termination. Nature Communications 12, 2273. https://doi.org/10.1038/s41467-021-22388-6

How to cite: Knorr, G., Kapsch, M., Prange, M., Mikolajewicz, U., Latinovic, D., Merkel, U., Niu, L., Ackermann, L., Shi, X., and Lohmann, G.: The impact of CO2 and ice sheet changes on the deglacial AMOC sensitivity to freshwater perturbations in three different Earth System Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15633, https://doi.org/10.5194/egusphere-egu23-15633, 2023.

Paleo records indicate significant variation in sea level and temperature proxies between different glacial cycles. What is unclear is the extent to which these differences are due to noise in the physical system versus a robust response to external forcings. When one considers what is happening with each individual ice sheet, variations between glacial cycles are largely unknown, given the few relevant records available to constrain ice sheet extent before the Eemian. 

To explore both the controls on past ice sheet and climate evolution and explore bounds on what the evolution might actually have looked like, we are running ensemble simulations of the last two glacial cycles with the fully coupled ice/climate model LCice. LCice is a coupled version of the Loveclim EMIC and GSM glacial systems model with hybrid shallow shelf and shallow ice flow and global visco-elastic glacio-isostatic adjustment. The current configuration includes all 4 ice sheet complexes and is subject to only orbital and greenhouse gas forcing.

To answer the above questions, we present ensemble results for the last two glacial inceptions, focusing on what key ice sheet and climate characteristics are robust across the ensemble and what are not. The role of key forcings and feedbacks are also isolated through a set of sensitivity experiments.  

How to cite: Geng, M. and Tarasov, L.: A comparison of the last two glacial inceptions via fully coupled transient ice and climate modelling., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16335, https://doi.org/10.5194/egusphere-egu23-16335, 2023.

EGU23-16377 | Orals | CL1.2 | Highlight

Angiosperms leaf evolution and the Cretaceous continental hydrological cycle : accounting for paleotraits in paleoclimate numerical simulations 

Pierre Sepulchre, Julia Bres, Quentin Pikeroen, Nicolas Viovy, and Nicolas Vuichard

Land cover, and thereby vegetation, can alter climate through biogeochemical and biogeophysical effects. Specifically, plants mediate radiative and turbulent fluxes between the surface and atmosphere and contribute to defining temperature and precipitation patterns in continental areas. In recent decades, pioneering works based both on fossil records and climate modelling have shown that vegetation parameterization is pivotal for accurately simulating past climates. Here, we focused on the Cretaceous, during which the radiation of angiosperms was accompanied by a physiological revolution characterized in the fossil record by an increase in the density of leaf veins and, ultimately, an unprecedented rise in their stomatal conductance. Emulating such an evolution of leaf traits, quantifying their consequences on plant productivity and transpiration, and identifying the associated feedbacks in the Cretaceous climate is a very challenging task. We addressed this triple problem by embedding the reconstruction of physiological paleotraits from the fossil record within the IPSL-CM5A2 earth system model, which land surface scheme allows for the interaction between stomatal conductance and carbon assimilation.

We built and evaluated vegetation parameterizations accounting for the increase in stomatal conductance during angiosperm radiation, which is consistent with the fossil record, by altering the hydraulic and photosynthetic capacities of plants in a coupled fashion. These experiments, combined with two extreme atmospheric pCO2 scenarios, show that a systematic increase in transpiration is simulated when vegetation shifts from a proto-angiosperm state to a modern-like state, and that its magnitude is related to primary productivity modulated by light, water stress, and evaporative demand. Under a high pCO2 scenario, only stomatal conductance plays a role, and the feedback of vegetation change consists mainly of more intense water recycling and rainfall over the continents. At low pCO2, the effect of the high stomatal conductance on transpiration is enhanced by the development of vegetation cover, resulting in more transpiration and higher precipitation rates at all latitudes. Enhanced turbulent fluxes lead to a surface cooling that outcompete the warming linked to the lower surface albedo. Our results suggest a larger impact of angiosperms on climate when atmospheric pCO2 is decreasing, and stresses the importance of accounting for fossil-based paleotraits in paleoclimate simulations.

How to cite: Sepulchre, P., Bres, J., Pikeroen, Q., Viovy, N., and Vuichard, N.: Angiosperms leaf evolution and the Cretaceous continental hydrological cycle : accounting for paleotraits in paleoclimate numerical simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16377, https://doi.org/10.5194/egusphere-egu23-16377, 2023.

EGU23-16871 | ECS | Orals | CL1.2

Species distribution models fail to predict paleozoological occurrences during the Holocene Green Sahara phase 

Ignacio Lazagabaster, Juliet Spedding, Irene Solano-Regadera, Chris Thomas, Salima Ikram, Severus Snape, and Jakob Bro-Jorgensen

Paleoclimatic simulations are powerful tools to investigate past faunal biogeographical patterns, but they can fail to capture complex climatic conditions at specific regional or temporal scales. Here we show that species distribution models (SDMs) do not predict the expansion of suitable habitats for mammals that were present in the Sahara during the African Humid Period (AHP) according to radiocarbon-dated paleozoological records. We illustrate this issue by modeling the current and past distribution of the hartebeest (Alcelaphus buselaphus), a typical African savanna antelope with a wide Sub-Saharan distribution. Its Holocene paleozoological record shows that its distribution during the AHP included large areas of the Sahara and the northern African Mediterranean coast, from Morocco to Egypt and the Levant. We use Bayesian additive regression trees (BARTs) with an MCMC algorithm in combination with current climate and occurrence data to generate posterior distributions of habitat suitability, evaluate variable importance, and generate variable partial-dependence plots. From these, we learn that annual precipitation is the most important climatic variable determining the hartebeest’s current distribution. We then projected habitat suitability onto various paleoclimatic scenarios during the AHP and found that the estimated precipitation did not reach the minimum required for the viability of hartebeest populations. These results highlight the potential of the fossil record to test the regional precision of paleoclimatic simulations, ultimately helping to generate more realistic past environmental scenarios.

How to cite: Lazagabaster, I., Spedding, J., Solano-Regadera, I., Thomas, C., Ikram, S., Snape, S., and Bro-Jorgensen, J.: Species distribution models fail to predict paleozoological occurrences during the Holocene Green Sahara phase, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16871, https://doi.org/10.5194/egusphere-egu23-16871, 2023.

EGU23-17590 | Orals | CL1.2

Simulating Changes in Tropical Cyclone Activity During the Deglaciation 

Clay Tabor, Marcus Lofverstrom, Isabel Montañez, Jessica Oster, and Colin Zarzycki

How tropical cyclones respond to climate change remains an open question. Due to recent increases in computing power and climate model resolution, it is now possible to explicitly simulate tropical cyclone genesis and life cycle over long temporal and spatial scales. So far, most high-resolution simulations have explored tropical cyclones under present-day and future climate conditions. There has been little work on tropical cyclone activity in past climates. Here, we help fill in this gap with high resolution simulations of the last deglaciation including the Last Glacial Maximum (LGM; 21-ka), Heinrich Stadial 1 (HS1; 16-ka), and Preindustrial (PI; 1850 CE). We use the water isotope tracer enabled version of the Community Earth System Model version 1.3 (iCESM1.3) at ~0.25° horizontal resolution to simulate climate and the TempestExtremes algorithm to track tropical cyclone features. Our preliminary results show intriguing spatial changes in tropical cyclone activity at the LGM relative to PI. The Atlantic and Indian basins produce less tropical cyclones while the Western Pacific produces more tropical cyclones at the LGM. Furthermore, tropical cyclone frequency decreases in the southern hemisphere but remains similar in the northern hemisphere. The LGM simulation also produces fewer strong storms (greater than 49 m/s). Further investigation will explore the physical mechanisms for the simulated tropical cyclone responses during the deglaciation as well as the effects of freshwater flux into the North Atlantic on tropical cyclone activity.

How to cite: Tabor, C., Lofverstrom, M., Montañez, I., Oster, J., and Zarzycki, C.: Simulating Changes in Tropical Cyclone Activity During the Deglaciation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17590, https://doi.org/10.5194/egusphere-egu23-17590, 2023.

EGU23-141 | ECS | Posters on site | CL5.3 | Highlight

Transitioning: the role of disturbances on instigating cross-overs of vegetation zones (a biome perspective) 

Bikem Ekberzade, Omer Yetemen, Omer Lutfi Sen, and H. Nuzhet Dalfes

This study considers the potential shift of biomes due to simulated changes in climatic drivers up until the end of this century, and how these changes effect the frequency of disturbances which in turn may affect the ranges of vegetation life zones. The study area is mainly the Anatolian Peninsula and its immediate surroundings, a unique location harboring high species diversity and high rates of endemism. Forcing a global to regional dynamic vegetation model with five Global Circulation Model contributions to Coupled Model Intercomparison Project (CMIP6, bias-corrected with ERA5-Land), we looked not only at the changes in the distribution and composition of key forest taxa, but the range shifts of vegetation formations from a biome perspective (classified per The International Geosphere–Biosphere Programme’s nomenclature) focusing on transition zones. Our results simulated a potential increase in the ranges of all 4 woody biomes: forest, transitional woodland, woody grassland and shrubland, with a potential retreat in grasslands. This shift is continuous throughout the simulation period of 1961-2099, with the Central Anatolian grasslands being taken over by tree taxa – comprised mostly of pines and oaks – even for the historical simulation period (1961-2021), but more significantly towards the end of the century. From a biome perspective, the increase in forest biomass and the retreat in grasslands is somewhat contrary to expectations that dryland mechanisms will become more common even in mesic environments as climate change progresses, however in line when we look at the overall picture from a taxon-specific perspective, as species that make up the composition of the simulated woody grasslands in Central Anatolia are mainly drought resistant taxa. One potential reason behind this woody plant encroachment may be the changes in fire frequency and intensity in the absence of anthropogenic interference. Our ongoing research is focusing on this curious pattern as we further analyze this phenomenon with more detailed climate input data with different time windows and with a special focus on disturbances.

How to cite: Ekberzade, B., Yetemen, O., Sen, O. L., and Dalfes, H. N.: Transitioning: the role of disturbances on instigating cross-overs of vegetation zones (a biome perspective), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-141, https://doi.org/10.5194/egusphere-egu23-141, 2023.

EGU23-1685 | Posters on site | CL5.3

CHASE: a model of human migration under environmental changes 

Rachata Muneepeerakul

This presentation focuses on migration of the most influential mammal species: humans! For humans, migration is one of the most drastic adaptation strategies against unfavorable conditions. This model is named after the factors it includes to capture migration probability by humans, namely CH = Changing mindset, A = Agglomeration, S = Social ties, and E = the Environment. Because many of these factors are not typically included in migration models of other non-human species, the CHASE model has the potential to give rise to different dynamics and patterns, which may in turn be useful for understanding and modeling migration of other species. Here we performed numerical experiments on the model by subjecting the human agents in the model to two different kinds of disturbances: sudden shocks and gradual changes. Preliminary results on the dynamics and patterns will be reported, compared, and discussed. Discussion with other presenters and comparison to other presentations in this session should lead to new ideas useful for modeling migration of humans and other species alike.

How to cite: Muneepeerakul, R.: CHASE: a model of human migration under environmental changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1685, https://doi.org/10.5194/egusphere-egu23-1685, 2023.

Initialised climate predictions demonstrate ultra long-range predictability of atmospheric angular momentum, Earth's rotation and length of day. We show how slow, poleward propagating anomalies in the atmospheric angular momentum field allow interannual 'memory', well beyond currently assumed limits of atmospheric predictability. The mechanism involves wave-mean flow interaction between transient eddies and zonal winds in the troposphere and supports the persistence and poleward migration of both positive and negative anomalies. We discuss some of the implications and opportunities this presents for multiyear prediction and show how it leads to new teleconnections that are important for interpreting the observed record of climate variability.

How to cite: Scaife, A.: Multiyear predictability of atmospheric angular momentum and its implications., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3388, https://doi.org/10.5194/egusphere-egu23-3388, 2023.

EGU23-3433 | Orals | CL5.3

The relative role of the subsurface Southern Ocean in driving negative Antarctic Sea ice extent anomalies in 2016-2021 

Liping Zhang, Thomas L. Delworth, Xiaosong Yang, Fanrong Zeng, feiyu lu, Yushi Morioka, and Mitchell Bushuk

The low Antarctic sea ice extent (SIE) following its dramatic decline in late 2016 has persisted over a multiyear period. However, it remains unclear to what extent this low SIE can be attributed to changing ocean conditions. Here, we investigate the causes of this period of low Antarctic SIE using a coupled climate model partially constrained by observations. We find that the subsurface Southern Ocean (SO) played a smaller role than the atmosphere in the extreme SIE low in 2016, but was critical for the persistence of negative anomalies over 2016-2021. Prior to 2016, the subsurface SO warmed in response to enhanced westerly winds. Decadal hindcasts show that subsurface warming has persisted and gradually destabilized the ocean from below, reducing SIE over several years. The simultaneous variations in the atmosphere and ocean after 2016 have further amplified the decline in Antarctic SIE.

How to cite: Zhang, L., Delworth, T. L., Yang, X., Zeng, F., lu, F., Morioka, Y., and Bushuk, M.: The relative role of the subsurface Southern Ocean in driving negative Antarctic Sea ice extent anomalies in 2016-2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3433, https://doi.org/10.5194/egusphere-egu23-3433, 2023.

EGU23-5446 | Orals | CL5.3

Effect of initialisation within a 20yr multi-annual climate prediction system 

André Düsterhus and Sebastian Brune

Decadal climate predictions use state-of-the-art climate models and combine them with initialisation procedures to create information about our future. Their development has proven successful in the past years and offer a wide range of applications. One of them is the option to learn about the used climate models. With predictions usually aiming at time periods up to ten lead years it is often assumed that initialisation will wear off over time and the model will regress to results comparable to uninitialised simulations.

This contribution investigates decadal predictions over lead times of up to twenty years. The decadal prediction system is based on the Max Planck Institute Earth system model (MPI-ESM), uses atmospheric nudging and an oceanic Ensemble Kalman filter for initialisation and is applied for periods from 1960 onwards. We demonstrate that the effect of initialisation within the prediction can be found for long lead years and does not necessarily regresses back to the uninitialised simulation.

We show that in some areas the prediction skill varies over time, while in others it persists or drops quickly. Examples are a consistently increased prediction skill compared to historical simulations in the North East Pacific or decreased prediction skill for lead years longer than ten in the South Atlantic. We also take a look at the Atlantic Meridional Overturning Circulation (AMOC) and its development over time. We show that the AMOC drifts on short time scales towards a new state, which is reached after about ten lead years. For decadal predictions with MPI-ESM we find that for large areas of the globe the correct determination of future developments of external forcings plays an important role. This asks the question whether the current approach to hindcasts is appropriate to determine our capability to predict the future.

How to cite: Düsterhus, A. and Brune, S.: Effect of initialisation within a 20yr multi-annual climate prediction system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5446, https://doi.org/10.5194/egusphere-egu23-5446, 2023.

EGU23-6838 | ECS | Posters on site | CL5.3 | Highlight

Changes in Arctic climate variability and extremes: effects on migratory birds 

Nomikos Skyllas and Richard Bintanja

The climate is changing most rapidly in the Arctic because of Arctic amplification, influencing migratory bird species that depend on the short, but productive Arctic summer climate. A potential increase in climate variability can lead to reduced reproductive success and even be a major source of mortality for these birds. Most studies so far, focus on mean changes in climate, telling part of the story. However, along with changes in the mean, changes in the variability of climate will occur. These two combined (changes in mean and variability) can lead to more/less frequent extreme events such as heatwaves, droughts and excessive snowfall or melt.

Here we focus on changes in variability and extremes of Arctic bird-related climatic variables, such as temperature, precipitation, snow cover, primary productivity, solar radiation, and soil moisture. We investigate how strongly these climatic variables vary on a daily, monthly, annual and decadal basis. Furthermore, we infer changes in variability between four distinct climate states (0.5x, 1x, 2x & 4x CO2 level): will the variability and probability for extreme events change in warmer or colder climates? How will this potentially affect Arctic migratory birds? For example, snowfall and ground snow cover are expected to decrease in a warmer climate, resulting in more areas available for nesting. However, snowfall variability is projected to increase, making conditions more unpredictable on an annual basis.

To this end, we carried out four long (500 years), steady-state runs (constant CO2 level), using the state-of-the-art Earth System Model EC-Earth3. We used two versions of the model (EC-Earth3-Veg & EC-Earth3-CC) and 4 CO2 levels: 0.5x, 1x, 2x & 4x CO2 concentration of the year 2022. The end result is 4,000 years of model output data, allowing us to study climate-related changes in climate variability of Arctic bird-related variables.

How to cite: Skyllas, N. and Bintanja, R.: Changes in Arctic climate variability and extremes: effects on migratory birds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6838, https://doi.org/10.5194/egusphere-egu23-6838, 2023.

EGU23-9190 | Posters on site | CL5.3

On the optimization of grand multi-model probabilistic performance and the independence of the contributing seasonal prediction systems 

Andrea Alessandri, Franco Catalano, Kristian Nielsen, and Alberto Troccoli

To optimize the performance of seasonal climate forecasts we used a Grand Multi-Model Ensemble (MME) approach. The Grand MME consists of five Seasonal Prediction Systems (SPSs) provided by the European Copernicus Climate Change Service (C3S) and of other six SPSs independently developed by centres outside Europe, five by the North American (NMME) plus the SPS by the Japan Meteorological Agency (JMA).

All the possible Grand MME combinations have been evaluated for temperature and precipitation, for different geographical regions. Results show that, in general, only a limited number of SPSs is required to maximize the skill. Although the selection of models that optimize performance is usually different depending on the region, variable and season, it is shown that the performance of the Grand-MME seasonal predictions is enhanced with the increase of the independence of the contributing SPSs.

Independence is measured by using  a novel metric developed here, named the Brier score covariance (BScov), which estimates the relative independence of the SPSs. Together with probabilistic skill metrics, BScov is used to develop a strategy for an effective identification of the combinations of SPSs that optimize the probabilistic performance of the predictions, thus avoiding the inefficient and ineffective use of all SPSs available.

How to cite: Alessandri, A., Catalano, F., Nielsen, K., and Troccoli, A.: On the optimization of grand multi-model probabilistic performance and the independence of the contributing seasonal prediction systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9190, https://doi.org/10.5194/egusphere-egu23-9190, 2023.

EGU23-10571 | Posters on site | CL5.3

Simulating hydrology and tracer dynamics in a subglacial environment underneath the Greenland ice sheet 

Ankit Pramanik, Sandra Arndt, Mauro Werder, and Frank Pattyn

The Greenland ice-sheet surface melt has increased substantially in intensity and spatial extent over the recent decades. The rapid migration of melt towards upstream areas of Greenland ice sheet is expected to incur major changes in hydrological behaviour of the ice-sheet and outlet glaciers along with changes in export fluxes of carbon, methane, and other nutrient fluxes, which, in turn, will further affect the downstream ecosystem of rivers, fjords and oceans. Subglacial environments are emerging as ecological hotspots, urging detailed understanding of interaction between subglacial-hydrology and biogeochemistry. However, due to their inaccessibility, the hydrology and biogeochemistry of subglacial environment thus far lacks a detailed understanding. Numerical models are, in combination with observational data, ideal tools to advance our understanding.

Here, we developed a novel process-based model to investigate the interplay between subglacial-hydrology and (passive and active) tracer dynamics underneath the rapidly changing Greenland ice sheet on seasonal, inter-annual and climate warming relevant timescales. We set up the subglacial-hydrology model GlaDS (Glacier Drainage System model) to simulate seasonal and interannual evolution of distributed and channelized subglacial water flow for Leverett glacier (Southwest Greenland) to explore the geometry, connectivity, and flow dynamics in the seasonally evolving drainage system.

We then use the GlaDS results to inform a reaction-transport model (RTM) of Leverett’s subglacial system following the GlaDS set-up. The RTM is run to conduct a series of idealized tracer experiments with the aim of disentangling the transport and reaction controls on subglacial tracer distribution and outflow. Tracers are injected to the system through moulins with the surface meltwater and are either passively transported (passive) or also consumed/produced (active) during their transit through the system. Model results are validated with long-term measurements in this area. Results show that the tracer transport is primarily controlled by subglacial drainage system efficiency, which is regulated by discharge magnitude, topography and moulin locations. The spatial and temporal variation in tracer concentration is further dependent on hydrological interaction between different subglacial components (cavities and channels), location and type of branching of channels, and bed properties.

In the future, we will extend the model to wider area of Greenland ice sheet and couple it to multi-component biogeochemical reaction networks with the. aim to understand the evolution of biogeochemical process along with the evolution of hydrology in warming climate.

How to cite: Pramanik, A., Arndt, S., Werder, M., and Pattyn, F.: Simulating hydrology and tracer dynamics in a subglacial environment underneath the Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10571, https://doi.org/10.5194/egusphere-egu23-10571, 2023.

EGU23-10719 | Posters virtual | CL5.3 | Highlight

Seasonal prediction and predictability of wind power potential over North America 

Xiaosong Yang, Thomas Delworth, Liwei Jia, Nathaniel Johnson, Feiyu Lu, and Colleen McHugh

The capacity factor (CF) is a critical indicator for quantifying wind turbine efficiency, and therefore has been widely used to measure the impact of interannual wind variability on wind energy production. Using the seasonal prediction products from GFDL’s Seamless System for Predicton and Earth System (SPEAR), we assess the seasonal prediction skill of CF over North America. SPEAR shows high skill in predicting winter CF over the western United States. The seasonal wind speed and CF variations associated with large-scale circulation anomalies are examined to understand the predictability mechanism of CF. The source of the skillful seasonal CF prediction can be attributed to year-to-year variations of ENSO and North Pacific Oscillation, which produce large-scale anomalous wind patterns over North America. The skillful seasonal prediction of CF is potentially beneficial to various stakeholders in the energy sector, including wind energy production, trading, and transmission.  

How to cite: Yang, X., Delworth, T., Jia, L., Johnson, N., Lu, F., and McHugh, C.: Seasonal prediction and predictability of wind power potential over North America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10719, https://doi.org/10.5194/egusphere-egu23-10719, 2023.

EGU23-11884 | Posters on site | CL5.3

Migration ecology in insects: integrative approaches to trace long-distance movements of the Painted Lady butterfly (Vanessa cardui) 

Gerard Talavera, Luise Gorki, Eric Toro-Delgado, Roger López-Mañas, Megan Reich, Mattia Menchetti, Cristina Domingo-Marimon, Llorenç Sáez, Naomi Pierce, Roger Vila, Clément Bataille, and Tomasz Suchan

Migratory insects may move in very large numbers, even surpassing migratory vertebrates in biomass. However, the extent of aerial flows of insects circulating around the planet, as well as their impact on ecosystems and biogeography, remain almost unstudied because of methodological challenges associated with tracking small, short-lived, organisms. In this presentation, I will show how a novel integrative approach allows reconstructing long-range insect movements, through a combination of tools on genetics, isotope ecology, ecological niche modelling, pollen metabarcoding, field ecology, and citizen science.

I will show the latest discoveries on the migrations of the Painted Lady butterfly (Vanessa cardui). This butterfly species is the most cosmopolitan of all butterflies, and it is known by its regular trans-Saharan migrations, that entail distances of >4000 km, similar to those of some birds. First, we track a migratory outbreak of V. cardui butterflies taking place at a continental scale in Europe, the Middle East, and Africa from March 2019 to November 2019. We use DNA metabarcoding to identify plants from pollen transported by the insects. From 265 butterflies collected in 14 countries over 7 months, we molecularly identify 398 plants. We develop a novel geolocation approach based on combining probability rasters from species distribution modelling of each identified plant, and thus trace back the location of the outbreak’s origin and the origin of each of the subsequent generations. We show a strong representation of plants of Middle Eastern distribution in butterfly swarms collected in Eastern Europe in early spring. Swarms collected in Northern Europe in late spring were highly represented by plants of Mediterranean origin, and swarms collected in the summer in the Mediterranean likely originated in central and Northern Europe.

Second, we report the first proven transatlantic crossing by individual insects, a journey of at least 4,200 km from West Africa to South America. This discovery was possible through gathering evidence from multiple sources, including coastal field surveys, wind trajectory modelling, phylogeography, pollen metabarcoding, and multi-isotope geolocation of natal origins. Wind trajectories were exceptionally favourable for the butterflies to disperse across the Atlantic from West Africa. Population genetic analyses clustered the butterflies collected in South America with the European-African population, ruling out the possibility that the migrants originated in America. Pollen metabarcoding showed highly represented plants endemic to the Sahelian region. Finally, a dual isotope analysis of hydrogen (δ2H) and strontium (87Sr/86Sr) combined with a spatio-temporal niche model of suitable reproductive habitat geolocated the natal origins of the migrants to regions in Mali, Morocco, or Portugal, and thus not discarding a journey also involving a trans-Saharan crossing.

In summary, this work contributes new methodological avenues to advance our understanding of the dispersal and migration of insects. The findings here reported suggest that we may be underestimating long-range dispersal in insects, and highlight the importance of aerial highways connecting continents by trade winds. Overall, we will discuss the scale and potential implications that insect migratory movements represent for ecosystems and nature conservation worldwide.

How to cite: Talavera, G., Gorki, L., Toro-Delgado, E., López-Mañas, R., Reich, M., Menchetti, M., Domingo-Marimon, C., Sáez, L., Pierce, N., Vila, R., Bataille, C., and Suchan, T.: Migration ecology in insects: integrative approaches to trace long-distance movements of the Painted Lady butterfly (Vanessa cardui), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11884, https://doi.org/10.5194/egusphere-egu23-11884, 2023.

EGU23-11922 | ECS | Orals | CL5.3

Is your ensemble of CMIP6 models consistent with IPCC AR6? 

Vincent Humphrey, Anna Merrifield, and Reto Knutti

The Intergovernmental Panel on Climate Change (IPCC) assesses the sensitivity of the climate system to increases in greenhouse gas concentrations using multiple lines of evidence, covering paleoclimate data, historical observations, and numerical Earth system model (ESM) simulations. Within IPCC’s latest Assessment Report (AR6), there is, for the first time, a non-negligible difference between the most likely rate of warming estimated in the report and the average warming rate simulated by the ESMs that participated in the Coupled Model Intercomparison Project (CMIP6). This discrepancy occurs because a large number of CMIP6 models have projected future warming rates that are higher than previously reported but quite unlikely according to historical observations. The consequence is that using a random selection of CMIP6 simulations is likely to overestimate historical and future warming (compared to what is assessed in the IPCC report), potentially leading to avoidable inconsistencies when compared to observations or greater projected changes compared to what could be inferred from CMIP5.

As this constitutes a wide-spread obstacle and limitation to using CMIP6 simulations ‘out of the box’, we propose here a simple model weighting method with the objective to address this problem. Our approach can be used to 1) evaluate the extent to which any given set of CMIP6 simulations is consistent with IPCC-assessed warming rates and 2) calculate the appropriate model weights so that potential inconsistencies are reduced as much as possible. The calculation of the weights is solely based on the user’s selection of a CMIP6 subset and does not require any data manipulation. The weights can then be easily implemented in existing analyses to calculate weighted (i.e. instead of just arithmetic) multi-model means, weighted quantiles, etc. We demonstrate the interest and flexibility of the method with some examples, including global to regional assessments of historical and projected changes in temperature and precipitation. We illustrate the extent to which applying model weights can reconcile otherwise divergent scientific results and provide assessments that are more robust across CMIP generations.

How to cite: Humphrey, V., Merrifield, A., and Knutti, R.: Is your ensemble of CMIP6 models consistent with IPCC AR6?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11922, https://doi.org/10.5194/egusphere-egu23-11922, 2023.

EGU23-12428 | ECS | Orals | CL5.3

Effects of the realistic vegetation cover on predictions at seasonal and decadal time scales 

Emanuele Di Carlo, Andrea Alessandri, Fransje van Oorschot, Annalisa Cherchi, Susanna Corti, Giampaolo Balsamo, Souhail Boussetta, and Timothy Stockdale

Vegetation is a relevant and highly dynamic component of the Earth System controlling, amongst others, surface roughness, albedo and evapotranspiration; its variability shows changes in seasons, interannual, decadal and longer timescales. In this study, we investigate the effects of improved representation of vegetation dynamics on climate predictions at different timescales: seasonal and decadal. To this aim, the latest generation satellite datasets of vegetation characteristics have been exploited, and a novel and improved parameterization of the effective vegetation cover has been developed. The new parameterization is implemented in the land surface scheme HTESSEL shared by two state-of-the-art Earth system models: ECMWF SEAS5 and EC-Earth3. The former model is used for sensitivity at seasonal timescale, while the latter is used for sensitivity at decadal timescale.

Both seasonal and decadal experiments show considerable sensitivity of models' surface climate bias with large effects on December-January-February (DJF) T2M, mean sea level pressure and zonal wind over middle-to-high latitudes. Consistently, a significant improvement in the skill for DJF T2M is found, especially over Euro-Asian Boreal forests. In seasonal experiments, this improvement displays a strong interannual coupling with the local surface albedo. From the region with the most considerable T2M improvement, over Siberia, originates a large-scale effect on circulation encompassing Northern Hemisphere middle-to-high latitudes from Siberia to the North Atlantic. As a result, in seasonal experiments, the correlation between the model NAO index against the ERA5 NAO index improves significantly.

These results show a non-negligible effect of the vegetation cover on the general circulation, especially for the northern hemisphere and on the prediction skill.

How to cite: Di Carlo, E., Alessandri, A., van Oorschot, F., Cherchi, A., Corti, S., Balsamo, G., Boussetta, S., and Stockdale, T.: Effects of the realistic vegetation cover on predictions at seasonal and decadal time scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12428, https://doi.org/10.5194/egusphere-egu23-12428, 2023.

EGU23-13998 | ECS | Orals | CL5.3

Variability in ENSO-induced carbon flux patterns 

István Dunkl and Tatiana Ilyina

El Niño-Southern Oscillation (ENSO) is not only a driver of global carbon cycle variability, but it also provides several mechanisms of predictability. Although most Earth system models (ESMs) can reproduce the relationship between ENSO and atmospheric CO2 concentrations, the question remains whether the ESMs agree on the origins of these ENSO-related GPP anomalies. We analyse the patterns of ENSO-induced GPP anomalies in 17 ESMs to determine from which regions these GPP anomalies come from, and whether the differences among the models are driven by climate forcing or biochemistry. While most of the GPP anomalies originate from Southeast Asia and northern South America, there are large deviations among the ESMs. The combined GPP anomaly of these two regions ranges between 26% and 75% of the global anomaly among the ESMs. To find out what causes the differences, we examined two major drivers of the GPP anomalies: the size of the ENSO-induced climate anomalies, and the sensitivity of GPP to climate. On the global average, ENSO-induced climate anomalies and GPP sensitivity have similar uncertainty among the ESMs, contributing equally to the variations in ENSO-induced GPP anomaly patterns. This analysis reveals model biases in teleconnection patterns and biochemistry. Addressing these biases is a tangible goal for model developers to decrease the uncertainty in the reproduction of the global carbon cycle, and to increase its predictability.

How to cite: Dunkl, I. and Ilyina, T.: Variability in ENSO-induced carbon flux patterns, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13998, https://doi.org/10.5194/egusphere-egu23-13998, 2023.

EGU23-14304 | ECS | Posters on site | CL5.3 | Highlight

Decadal prediction along the Western Irish Coast 

Catherine O'Beirne, Gerard McCarthy, and André Düsterhus

Over the last decade there have been vast improvements in the field of global decadal climate prediction; however, on a regional scale there is still limited confidence. Previous studies with the Max Plank Institute Earth System Model (MPI-ESM) have demonstrated that it can replicate water properties on a regional scale in the North Sea and Barents Sea.

In this study we investigate the prediction skill at depth along the Western Irish Coast using the MPI-ESM. For this we compare Hindcast simulations with Historical simulations. The employed Hindcast simulations consists of an ensemble mean of 16 members over the time frame 1961-2008 with a 2-to-5-year lead time. The Historical simulations over the same time frame also consist of an ensemble mean of 16 members.

For this contribution we investigate further the MPI-ESM predictability at depth for temperature and salinity along three transects that influence the Western Irish Coast at the Extended Ellet Line northwest, Galway Transect west, and Goban Spur southwest. A lead time analysis determines the improvement of prediction skill by initialisation. We discuss potential applications for this work in areas such as fisheries, coastal security, and marine leisure, for Ireland and its surrounding seas.

How to cite: O'Beirne, C., McCarthy, G., and Düsterhus, A.: Decadal prediction along the Western Irish Coast, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14304, https://doi.org/10.5194/egusphere-egu23-14304, 2023.

EGU23-14401 | Orals | CL5.3

A case study to investigate the role of aerosols reduction on the East Asian summer monsoon seasonal prediction 

Annalisa Cherchi, Etienne Tourigny, Juan C Acosta Navarro, Pablo Ortega, Paolo Davini, Andrea Alessandri, Franco Catalano, and Twan van Noije

In the late 20th century, both the East Asian and the South Asian summer monsoons weakened because of increased emissions of anthropogenic aerosols over Asia, counteracting the warming effect of increased greenhouse gases (GHGs). During the spring 2020, when restrictions to contain the spread of the coronavirus were implemented worldwide, reduced emissions of gases and aerosols were detected and found to be quite extended over Asia.

Following on from the above and using the EC-Earth3 coupled model, a case-study forecast for summer 2020 (May 1st start date) has been designed and produced with and without the reduced atmospheric forcing due to covid-19 related restrictions in the SSP2-4.5 baseline scenario, as estimated and adopted within CMIP6 DAMIP covidMIP experiments (hereinafter “covid-19 forcing”). The forecast ensembles (sensitivity and control experiments, meaning with and without covid-19 forcing) consist of 60 members each to better account for the internal variability (noise) and to maximize the capability to identify the effects of the reduced emissions.

The analysis focuses on the effects of the covid-19 forcing on the forecasted evolution of the monsoon, with a specific focus on the performance in predicting the summer precipitation over India and over other parts of South and East Asia. The results indicate that in 2020 a more realistic representation of the atmospheric forcing in the spring preceding the core monsoon season improves the skill of the predicted summer precipitation, mostly over East Asia. Beyond the testbed considered in this analysis, the result helps improving the understanding of the processes at work over the Asian monsoons regions, with positive implications on the usefulness of seasonal predictions products.

How to cite: Cherchi, A., Tourigny, E., Acosta Navarro, J. C., Ortega, P., Davini, P., Alessandri, A., Catalano, F., and van Noije, T.: A case study to investigate the role of aerosols reduction on the East Asian summer monsoon seasonal prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14401, https://doi.org/10.5194/egusphere-egu23-14401, 2023.

EGU23-14731 | ECS | Posters on site | CL5.3

Assessing the predictability of droughts through seasonal forecasts 

Thomas Dal Monte, Annalisa Cherchi, Andrea Alessandri, and Marco Gaetani

Atmospheric circulations at the mid-latitudes are marked by circulation regimes, structures evolving in space very slowly and persisting over time. Their persistence and duration in a context such as Europe's, could lead to weather patterns, such as heat waves and drought, that have a­­ major impact on many socio-economic sectors. Forecasts at seasonal timescale are becoming then crucial to plan or give relevant indicators for societal applications. Predictability of such events could be of great use in further applications related to energy and management of water supplies. Also, this may provide useful insights to understanding the increase in frequency and intensity of these extreme events and their location.

The late purpose of this study is to investigate the predictability of European droughts in a forecast range of 1-3 months. To this aim, drought events are firstly identified, and state-of-the-art seasonal forecast products are analysed to compute the skill for targeted drought-related climate variables and/or circulation patterns. Observational datasets, high-resolution reanalysis and latest generation satellite observations will be used for the characterization of drought events and the forecast validation.

How to cite: Dal Monte, T., Cherchi, A., Alessandri, A., and Gaetani, M.: Assessing the predictability of droughts through seasonal forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14731, https://doi.org/10.5194/egusphere-egu23-14731, 2023.

EGU23-14765 | Orals | CL5.3

Variations of the CO2 fluxes and atmospheric CO2 in multi-model predictions with an interactive carbon cycle 

Hongmei Li, Aaron Spring, istvan Dunkl, Sebastian Brune, Raffaele Bernardello, Laurent Bopp, William Merryfield, Juliette Mignot, Reinel Sospedra-Alfonso, Etienne Tourigny, Michio Watanabe, and Tatiana Ilyina

Variable fluxes of anthropogenic CO2 emissions into the land and the ocean and the remaining proportion in the atmosphere reflect on the global carbon budget variations and further modulate global climate change. A more accurate reconstruction of the global carbon budget in the past decades and a more reliable prediction of the variations in the next years are crucial for assessing the effectiveness of climate change mitigation policies and supporting global carbon stocktaking and monitoring in compliance with the goals of the Paris Agreement.

In this study, we investigate reconstructions and predictions of the CO2 fluxes and atmospheric CO2 growth from ensemble prediction simulations using 5 Earth System Model (ESM) - based decadal prediction systems. These novel prediction systems driven by CO2 emissions with an interactive carbon cycle enable prognostic atmospheric CO2 and represent atmospheric CO2 growth variations in response to the strength of CO2 fluxes into the ocean and the land, which are missing in the conventional concentration-driven decadal prediction systems with prescribed atmospheric CO2 concentration.

The reconstructions generated by assimilating physical ocean and atmosphere data products into the prediction systems are able to reproduce the annual mean historical variations of the CO2 fluxes and atmospheric CO2 growth. Multi-model ensemble means best match the assessments of CO2 fluxes and atmospheric CO2 growth rate from the Global Carbon Project with correlations of 0.79, 0.82, and 0.98 for atmospheric CO2 growth rate, air-land CO2 fluxes, and air-sea CO2 fluxes, respectively. The CO2 emission-driven prediction systems with an interactive carbon cycle still maintain the predictive skill of CO2 fluxes and atmospheric CO2 growth as found in conventional concentration-driven prediction systems, i.e., about 2 years for the air-land CO2 fluxes and atmospheric CO2 growth, the air-sea CO2 fluxes have higher skill up to 5 years. The ESM-based prediction systems are capable to reconstruct and predict the variations in the global carbon cycle and hence are powerful tools for supporting carbon budgeting and monitoring, especially in the decarbonization processes. Furthermore, we investigate the contribution of uncertainty in the predictions of CO2 fluxes and atmospheric CO2 growth rate from internal climate variability, different model responses, and emission-forcing reductions to identify the prominent challenge in limiting the skill of CO2 predictions. 

How to cite: Li, H., Spring, A., Dunkl, I., Brune, S., Bernardello, R., Bopp, L., Merryfield, W., Mignot, J., Sospedra-Alfonso, R., Tourigny, E., Watanabe, M., and Ilyina, T.: Variations of the CO2 fluxes and atmospheric CO2 in multi-model predictions with an interactive carbon cycle, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14765, https://doi.org/10.5194/egusphere-egu23-14765, 2023.

EGU23-15373 | Orals | CL5.3

DWD’s operational climate predictions – towards a seamless climate prediction website - towards a seamless climate prediction website 

Birgit Mannig, Andreas Paxian, Miriam Tivig, Klaus Pankatz, Kristina Fröhlich, Sabrina Wehring, Alexander Pasternack, Philip Lorenz, Amelie Hoff, Katharina Isensee, Saskia Buchholz, and Barbara Früh

Germany's National Meteorological Service, Deutscher Wetterdienst (DWD), is working on an operational seamless climate prediction approach: What started in 2016 with operational seasonal climate predictions, was later complemented with decadal climate predictions. Since 2022, DWD publishes decadal, seasonal, and subseasonal climate predictions on one single, comprehensive climate prediction website www.dwd.de/climatepredictions [1].

While global simulations of decadal and seasonal predictions are produced by DWD’s climate prediction systems, global subseasonal predictions are retrieved from the European Centre of Medium-Range Weather Forecast (ECMWF). The next step in the operational processing chain is the empirical-statistical downscaling EPISODES [2], which results in high-resolution climate predictions (approx. 5 km) for Germany.

Both global and regional climate predictions are evaluated using the Meteorological Analyzation and Visualization System MAVIS, a fork of the FREVA system (Free Evaluation System Framework for Earth System Modeling) [3]. We evaluate ensemble mean predictions using the Mean Squared Error Skill Score (MSESS) and the Pearson Correlation Coefficient. Probabilistic climate predictions are evaluated using the Ranked Probability Skill Score (RPSS).

Ensemble mean and probabilistic climate prediction results of global and downscaled simulations, as well as the evaluation results are jointly published on DWD’s climate prediction website. The user-friendly graphical presentation is consistent for all displayed regions (global, Europe, Germany, and German cities) and across all time scales and was developed as a co-design between DWD and various national users.

We work on several extensions of the website: multi-year seasonal predictions (e.g. 5-year summer means), the prediction of drought indices and El Nino Southern Oscillation predictions. In addition, a seamless time series combining observations, climate predictions and climate projections is in preparation.

 

[1] A. Paxian, B. Mannig, M. Tivig, K. Reinhardt, K. Isensee, A. Pasternack, A. Hoff, K. Pankatz, S. Buchholz, S. Wehring, P. Lorenz, K. Fröhlich, F. Kreienkamp, B. Früh (2023). The DWD climate predictions website: towards a seamless outlook based on subseasonal, seasonal and decadal predictions. Manuscript in review.

[2] Kreienkamp, F., Paxian, A., Früh, B., Lorenz, P., Matulla, C., 2018. Evaluation of the Empirical-Statistical Downscaling method EPISODES. Clim. Dyn. 52, 991–1026 (2019). https://doi.org/10.1007/s00382-018-4276-2.

[3] Kadow, C., Illing, S., Lucio-Eceiza, E.E., Bergemann, M., Ramadoss, M., Sommer, P.S., Kunst, O., Schartner, T., Pankatz, K., Grieger, J., Schuster, M., Richling, A., Thiemann, H., Kirchner, I., Rust, H.W., Ludwig, T., Cubasch, U. and Ulbrich, U., 2021. Introduction to Freva – A Free Evaluation System Framework for Earth System Modeling. Journal of Open Research Software, 9(1), p.13. DOI: http://doi.org/10.5334/jors.253

How to cite: Mannig, B., Paxian, A., Tivig, M., Pankatz, K., Fröhlich, K., Wehring, S., Pasternack, A., Lorenz, P., Hoff, A., Isensee, K., Buchholz, S., and Früh, B.: DWD’s operational climate predictions – towards a seamless climate prediction website - towards a seamless climate prediction website, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15373, https://doi.org/10.5194/egusphere-egu23-15373, 2023.

EGU23-16200 | Posters virtual | CL5.3

Random Forest approach to forecast onset date and duration of rainy season in Tanzania 

Kristian Nielsen, Alberto Troccoli, Indrani Roy, and Meshack Mliwa

In the SADC region of Eastern Africa the onset and duration of the rainy season is of high importance to the agriculture and general water resource management. The planting time, selection of crops and success of different crops is linked to how skillfully this date can be forecasted.  
 
As part of the Horizon 2020 project called FOCUS-Africa, in order to forecast this specific onset-date and duration for a specific location in Tanzania, we have constructed a statistical model utilizing the Random Forest algorithm. This is being trained using a mix of observation of past teleconnection indices such as IOD and ENSO3.4 from recent months that from earlier studies have shown to be connected to the onset date and dynamical seasonal forecast of precipitation with a daily temporal resolution. At this stage three dynamical models are included. Finally, the observed precipitation of the previous months is being used as predictors as well.  
 
The first results have shown an improvement of the statistical model over using climatic information such as mean onset date as the reference forecast. This can be achieved 2-3 months ahead of the onset date. Furthermore, a relatively large importance of the seasonal forecast systems and the teleconnection indices seems to be present several months ahead of the observed onset date. This also indicates the importance of mixing observations and dynamical models in order to optimize the best possible overall skill of the system in predicting the onset date of the rainy season and thereby supporting local agriculture. 

How to cite: Nielsen, K., Troccoli, A., Roy, I., and Mliwa, M.: Random Forest approach to forecast onset date and duration of rainy season in Tanzania, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16200, https://doi.org/10.5194/egusphere-egu23-16200, 2023.

EGU23-17225 | Posters virtual | CL5.3

Exploring the Role of Hybrid Energy Systems for Enhancing Green Energy Potential in Urban Areas 

Deepak Kumar and Nick P. Bassill

Hybrid energy systems for improving sustainable urban energy attempt to combine energy supply, public transport modernization, and residential/commercial energy demand reduction. Due to reduced nonrenewable resources, alternative and augmented energy sources are required everywhere. The development of science and industry has increased the energy required to achieve environmental goals with reduced gas emissions. Solar and wind energy are cleaner, more efficient alternatives to polluting energy sources, so the attention is now on large-scale hybrid energy systems. Lots of attempts have been made to show technological advancement and research has analyzed the functionality of energy systems, but urban applications have received little attention. The proposed work imitates the feasibility analysis of hybrid urban energy systems. The research acknowledged the development of research purpose, methodology, research, and data collection approach to reporting the technological, scientific, and industrial developments. This research explains a typical urban environment to determine the hourly load profile for any urban region to exhibit the role of a hybrid energy system to raise energy potential. It summarizes past, present, and future trends in energy system design, development, and implementation. The design can be enlarged to implementations with several other combinations to provide cleaner and cheaper energy.

How to cite: Kumar, D. and Bassill, N. P.: Exploring the Role of Hybrid Energy Systems for Enhancing Green Energy Potential in Urban Areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17225, https://doi.org/10.5194/egusphere-egu23-17225, 2023.

The subseasonal prediction with a lead time of 10–30 days is the gap between weather (<10 days) and climate (>30 days) predictions. Improving the forecast skill of extreme weather events at the subseasonal range is crucial for risk management of disastrous events. In this study, three deep-learning (DL) models based on the methods of convolutional neural network and gate recurrent unit are constructed to predict the rainfall anomalies and associated extreme events in East China at the lead times of 1–6 pentads. All DL models show skillful prediction of the temporal variation of rainfall anomalies (in terms of temporal correlation coefficient skill) over most regions in East China beyond 4 pentads, outperforming the dynamical models from the China Meteorological Administration (CMA) and the European Centre for Medium Range Weather Forecasts (ECMWF). The spatial distribution of the rainfall anomalies is also better predicted by the DL models than the dynamical models; and the DL models show higher pattern correlation coefficients than the dynamical models at lead times of 3 to 6 pentads. The higher skill of DL models in predicting the rainfall anomalies will help to improve the accuracy of extreme-event predictions. The Heidke skill scores of the extreme rainfall event forecast performed by the DL models are also superior to those of the dynamical models at a lead time beyond about 4 pentads. Heat map analysis for the DL models shows that the predictability sources are mainly the large-scale factors modulating the East Asian monsoon rainfall.

How to cite: Hsu, P.-C. and Xie, J.: Skillful subseasonal prediction of rainfall and extreme events in East China based on deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17300, https://doi.org/10.5194/egusphere-egu23-17300, 2023.

EGU23-17423 | Posters virtual | CL5.3

The role of multi-scale interaction on subseasonal prediction of extreme events 

June-Yi Lee, Pang-Chi Hsu, Doo-Young Lee, Young-Min Yang, and Jinhui Xie

The northward/northwestward propagation of boral summer intraseasonal oscillation (BSISO) modulates the subtropical variability ad typhoon activity and has significant impacts on the extreme weather and climate events in Asia. BSISO strongly interacts with background mean fields and tends to be stronger and longer in its northward propagation during La Nina than El Nino summers. It is further found that BSISO-related convections are stronger and more organized with northward propagation on 30-60-day timescales during El Nino developing than decaying summers over the western Pacific. Thus, for skillful subseasonal prediction of extreme events in Asia, it is crucial for climate models to well represent BSISO and its interaction with the background mean state and synoptic variability. Our case study shows that the rare extreme flooding event in Henan Province, China, during July 2021 (referred to as the “21.7” flooding event) was a result of scale interactions between the background mean field associated with the weak La Nina condition, intraseasonal oscillations, and synoptic disturbances. The two distinct modes of the BSISO (10-30- and 30-90-day modes) unusually had a crucial combined role in moisture convergence, aided by the increased seasonal-mean moisture content, maintaining persistent rainfall during the 21.7 event. Synoptic-scale moisture convergence was also contributed to the extreme values in the peak day of the event. The five state-of-the art subseasonal-to-seasonal prediction models showed limited skills in predicting this extreme event one to two weeks in advance, partly because of their biases in representing the BSISO and multiscale interactions. Our results highlight that an accurate prediction of subseasonal perturbations and their interactions with the background moisture content is crucial for improving the extended-range forecast skill of extreme precipitation events.

How to cite: Lee, J.-Y., Hsu, P.-C., Lee, D.-Y., Yang, Y.-M., and Xie, J.: The role of multi-scale interaction on subseasonal prediction of extreme events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17423, https://doi.org/10.5194/egusphere-egu23-17423, 2023.

EGU23-604 | ECS | Posters on site | CL4.3

Boreal Spring Southern Hemisphere Climate Mode and Global Monsoon 

Shikhar Srivastava, Arindam Chakraborty, and Raghu Murtugudde

The global climatic pattern is governed by the dominant mode of variability in the tropics and the extratropic and their interaction. The extratropical atmosphere is much more vigorous than the tropics owing to sharp meridional temperature gradients in the mid-latitude. Especially on the decadal timescales, large signals are seen over the extratropical region than in the tropics. Here, we propose that during boreal spring, the second leading mode of climate variability in the Southern Hemisphere, has a decadal pattern. This mode is independent of the Southern Annular Mode (SAM), which represents the most dominant mode of climate variability in the Southern Hemisphere. The boreal spring climate of the Southern Hemisphere interacts with the tropics and significantly impacts the global climate, which is reflected in the global Monsoon rainfall. During the positive phase of the decadal mode, the global Monsoon rainfall is coherently suppressed. We propose a new finding highlighting that the Southern Hemisphere's extratropical forcing can significantly impact the tropical Pacific through subtropical pathways on the decadal to multidecadal timescale. The impact of such decadal climate variability is enormous and global and can add a new paradigm to the pursuit of improving decadal predictions of the global climate.

How to cite: Srivastava, S., Chakraborty, A., and Murtugudde, R.: Boreal Spring Southern Hemisphere Climate Mode and Global Monsoon, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-604, https://doi.org/10.5194/egusphere-egu23-604, 2023.

As a dominant pattern of the North Pacific sea surface temperature decadal variability, the Pacific Decadal Oscillation (PDO) has remarkable influences on the marine and terrestrial ecosystems. However, the PDO is highly unpredictable. Here, we assess the performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the PDO, with an emphasis on the evaluation of CMIP6 models in reproducing a recently detected early warning signal based on climate network analysis for the PDO regime shift. Results show that the skill of CMIP6 historical simulations remains at a low level, with a skill limited in reproducing PDO’s spatial pattern and nearly no skill in reproducing the PDO index. However, if the warning signal for the PDO regime shift by climate network analysis is considered as a test-bed, we find that even in historical simulations, a few models can represent the corresponding relationship between the warning signal and the PDO regime shift, regardless of the chronological accuracy. By further conducting initialization, the performance of the model simulations is improved according to the evaluation of the hindcasts from two ensemble members of the Decadal Climate Prediction Project (NorCPM1 and BCC-CSM2-MR). Particularly, we find that the NorCPM1 model can capture the early warning signals for the late-1970s and late-1990s regime shifts 5–7 years in advance, indicating that the early warning sig- nal somewhat can be captured by some CMIP6 models. A further investigation on the underlying mechanisms of the early warning signal would be crucial for the improvement of model simulations in the North Pacific.

How to cite: Ma, Y.: On the Pacific Decadal Oscillation Simulations in CMIP6 Models: A New Test‐Bed from Climate Network Analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5325, https://doi.org/10.5194/egusphere-egu23-5325, 2023.

Climate extremes can impact societies in various ways: from nuances in daily lives to full humanitarian crises. Droughts  are usually slow onset extremes but can be highly disruptive and affect millions of people every year. Warm temperature extremes (e.g. heat waves) can exacerbate droughts and their impacts and trigger a faster drought evolution. Combined drought and heat waves can lead to devastating consequences. For example, 2022 was a very active year in terms of drought or combined drought and heat waves, affecting particularly hard several regions of the world (e.g. Europe, China, southern South America and East Africa). In a context of risk management and civil protection, the use of operationally available seasonal climate forecasts can provide actionable information to reduce the risks and the impacts of these events on societies with different levels of development and adaptive capacities. 

 

Within the Copernicus Emergency Management Service (CEMS), the European and Global Drought Observatories (EDO and GDO, respectively) provide real time drought and temperature extreme monitoring products freely available and displayed through two dedicated web services. Recent efforts have been targeting the optimal integration and use of multi-system forecasting products to enhance the early warning component of the service. This contribution provides an overview of first results in terms of  initial multi-model skill assessment of forecasts available through the Copernicus Climate Change Service (C3S). It also discusses future avenues to improve skill in regions with limited predictability, for example by applying physically-based sampling techniques.    

How to cite: Acosta Navarro, J. C. and Toreti, A.: Seasonal forecasting of drought and temperature extremes as part of the Copernicus Emergency Management Service (CEMS), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5602, https://doi.org/10.5194/egusphere-egu23-5602, 2023.

EGU23-6000 | ECS | Orals | CL4.3

Seasonal forecasting of the European North West Shelf: Quantifying the persistence of the physical marine environment 

Jamie Atkins, Jonathan Tinker, Jennifer Graham, Adam Scaife, and Paul Halloran

The European North West shelf seas (NWS) support economic and environmental interests of several adjacent populous countries. Forecasts of physical marine variables on the NWS for upcoming months – an important decision-making timescale – would be useful for many industries. However, currently there is no operational seasonal forecasting product deemed sufficient for capturing the high variability associated with shallow, dynamic shelf waters. Here, we identify the dominant sources of seasonal predictability on the shelf and quantify the extent to which empirical persistence relationships can produce skilful seasonal forecasts of the NWS at the lowest level complexity. We find that relatively skilful forecasts of the typically well-mixed Winter and Spring seasons are achievable via persistence methods at a one-month lead time. In addition, incorporating observed climate modes of variability, such as the North Atlantic Oscillation (NAO), can significantly boost persistence for some locations and seasons, but this is dependent on the strength of the climate mode index. However, even where high persistence skill is demonstrated, there are sizeable regions exhibiting poor predictability and skilful persistence forecasts are typically limited to ≈ one-month lead times. Summer and Autumn forecasts are generally less skilful owing largely to the effects of seasonal stratification which emphasises the influence of atmospheric variability on sea surface conditions. As such, we also begin incorporating knowledge of future atmospheric conditions to forecasting strategies. We assess the ability of an existing global coupled ocean-atmosphere seasonal forecasting system to exceed persistence skill and highlight areas where additional downscaling efforts may be needed.

How to cite: Atkins, J., Tinker, J., Graham, J., Scaife, A., and Halloran, P.: Seasonal forecasting of the European North West Shelf: Quantifying the persistence of the physical marine environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6000, https://doi.org/10.5194/egusphere-egu23-6000, 2023.

EGU23-7676 | Orals | CL4.3

Decadal Climate Variability and Predictability with a High-resolution Eddy-resolving Model 

Wei Zhang, Ben Kirtman, Leo Siqueira, and Amy Clement

Current global climate models typically fail to fully resolve mesoscale ocean features (with length scales on the order of 10 km), such as the western boundary currents, potentially limiting climate predictability over decadal timescales. This study incorporates high-resolution eddy-resolving ocean (HR: 0.1°) in a suite of CESM model experiments that capture these important mesoscale ocean features with increased fidelity. Compared with eddy-parametrized ocean (LR: 1°) experiments, HR experiments show more realistic climatology and variability of sea surface temperature (SST) over the western boundary currents and eddy-rich regions. In the North Atlantic, the inclusion of mesoscale ocean processes produces a more realistic Gulf Stream and improves both localized rainfall patterns and large-scale teleconnections. We identify enhanced decadal SST predictability in HR over the western North Atlantic, which can be explained by the strong vertical connectivity between SST and sub-surface ocean temperature. SST is better connected with slower processes deep down in HR, making SST more persistent (and predictable). Moreover, we detect a better representation of the air-sea interactions between SST and low-level atmosphere over the Gulf Stream, thus improving low-frequency rainfall variations and extremes over the Southeast US. The results further imply that high-resolution GCMs with increased ocean model resolution may be needed in future climate prediction systems.

How to cite: Zhang, W., Kirtman, B., Siqueira, L., and Clement, A.: Decadal Climate Variability and Predictability with a High-resolution Eddy-resolving Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7676, https://doi.org/10.5194/egusphere-egu23-7676, 2023.

Earth system predictability on decadal timescales can arise from both low frequency internal variability as well as from anthropogenically forced long-term changes. However, on these timescales, the chaotic nature of the climate system makes skillful predictions difficult to achieve even if we include information from climate change projections. Furthermore, it is difficult to separate the contributions from internal variability and external forcing to predictability. One way to improve skill is through identifying and harnessing initial conditions with more predictable evolution, so-called state-dependent predictability. We explore a neural network approach to identify these opportunistic initial states in the CESM2 large ensemble and subsequently explore how predictability may manifest in a future climate, influenced by both forced warming and internal variability. We use an interpretable neural network to demonstrate that internal variability will continue to play an important role in future climate predictions, especially for states of increased predictability.

How to cite: Gordon, E. and Barnes, E.: An interpretable neural network approach to identifying sources of predictability in the future climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8000, https://doi.org/10.5194/egusphere-egu23-8000, 2023.

EGU23-8296 | ECS | Orals | CL4.3 | Highlight

Better late than never: arrival of decadal predictions to the climate services arena 

Balakrishnan Solaraju-Murali, Dragana Bojovic, Nube Gonzalez-Reviriego, Andria Nicodemou, Marta Terrado, Louis-Philippe Caron, and Francisco J. Doblas-Reyes

Decadal prediction represents a source of near-term climate information that has the potential to support climate-related decisions in key socio-economic sectors that are influenced by climate variability and change. While the research to illustrate the ability of decadal predictions in forecasting the varying climate conditions on a multi-annual timescale is rapidly evolving, the development of climate services based on such forecasts is still in its early stages. This study showcases the potential value of decadal predictions in the development of climate services. We summarize the lessons learnt from coproducing a forecast product that provides tailored and user-friendly information about multi-year drought conditions for the coming five years over global wheat harvesting regions. The interaction between the user and climate service provider that was established at an early stage and lasted throughout the forecast product development process proved fundamental to provide useful and ultimately actionable information to the stakeholders concerned with food production and security. This study also provides insights on the potential reasons behind the delayed entry of decadal predictions in the climate services discourse and practice, which were obtained from surveying climate scientists and discussing with decadal prediction experts.

How to cite: Solaraju-Murali, B., Bojovic, D., Gonzalez-Reviriego, N., Nicodemou, A., Terrado, M., Caron, L.-P., and Doblas-Reyes, F. J.: Better late than never: arrival of decadal predictions to the climate services arena, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8296, https://doi.org/10.5194/egusphere-egu23-8296, 2023.

EGU23-8750 | Orals | CL4.3

A simple coupled assimilation approach for improved initialization of decadal climate predictions 

Tim Kruschke, Mehdi Pasha Karami, David Docquier, Frederik Schenk, Ramon Fuentes Franco, Ulrika Willén, Shiyu Wang, Klaus Wyser, Uwe Fladrich, and Torben Koenigk

We introduce a simple data assimilation approach applied to the coupled global climate model EC-Earth3.3.1, aiming at producing initial conditions for decadal climate hindcasts and forecasts. We rely on a small selection of assimilated variables, which are available in a consistent manner for a long period, providing good spatial coverage for large parts of the globe, that is sea-surface temperatures (SST) and near-surface winds.

Given that these variables play a role directly at or very close to the ocean-atmosphere interface, we assume a comparably strong cross-component impact of the data assimilation. Starting from five different free-running CMIP6-historical simulations in 1900, we first apply surface restoring in the model’s ocean component towards monthly means of HadISST1. After integrating this five-member ensemble with only assimilating SST for the period 1900-1949, we start additionally assimilating (nudging) 6-hourly near-surface winds (vorticity and divergence) taken from the ERA5 reanalysis from 1950 onwards. To mitigate the risk of model drifts after initializing the decadal predictions and to account for known instationary biases of the model, we assimilate anomalies of all variables that are calculated based on a 30-year running mean.

By assimilating near-surface data over several decades before entering the actual period targeted by the decadal hindcasts/forecasts for CMIP6-DCPP, we expect the coupled model to be able to ingest a significant share of observed climate evolution also in deeper ocean layers. This would then potentially serve as a source of predictive skill on interannual-to-decadal timescales.

We show that the presented assimilation approach is able to force the coupled model’s evolution well in phase with observed climate variability, positively affecting not only near-surface levels of the atmosphere and ocean but also deeper layers of the ocean and higher levels of the atmosphere as well as Arctic sea-ice variability. However, we also present certain problematic features of our approach. Two examples are significantly strengthened low-frequency variability of the AMOC and a wind bias resulting into generally reduced evaporation over ocean areas.

How to cite: Kruschke, T., Karami, M. P., Docquier, D., Schenk, F., Fuentes Franco, R., Willén, U., Wang, S., Wyser, K., Fladrich, U., and Koenigk, T.: A simple coupled assimilation approach for improved initialization of decadal climate predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8750, https://doi.org/10.5194/egusphere-egu23-8750, 2023.

The interdisciplinary research project "BayTreeNet" examines the reactions of forest ecosystems to climate dynamics. To establish a relationship between tree growth and climate, it is important to know that in the mid-latitudes, local climate phenomena often show a strong dependence on the large-scale climate weather types (WT), which significantly determine the climate of a region through frequency and intensity. Different WT show various weather conditions at different locations, especially in the topographically diverse region of Bavaria. The meaning of every WT is the physical basis for the climate-growth relationships established in the dendroecology sub-project to investigate the response of forests to individual WT at different forest sites. Complementary steps allow interpretation of results for the past (20th century) and projection into the future (21st century). One hypothesis is that forest sites in Bavaria are affected by a significant influence of climate change in the 21st century and the associated change in WT.

The automated classification of large-scale weather patterns is presented by Self-Organizing-Maps (SOM) developed by Kohonen, which enables visualization and reduction of high-dimensional data. The poster presents the SOM-setting which was used to classify the WT and the results of past environmental conditions (1990-2019) for different WT in Europe based on ERA5 data. Morover, it shows a future projection until 2100 for European WT and their respective environmental conditions. The projections are based on a novel GCM selection technique for two scenarios (ssp1-2.6 and ssp5-8.5) to obtain a range of the most likely conditions.

How to cite: Wehrmann, S. and Mölg, T.: GCM-based future projections of European weather types obtained by Self‑Organizing-Maps and a novel GCM selection technique, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8934, https://doi.org/10.5194/egusphere-egu23-8934, 2023.

EGU23-9520 | Orals | CL4.3

Estimating the significance of the added skill from initializations: The case of decadal predictions 

Bo Christiansen, Shuting Yang, and Dominic Matte

A considerable part of the skill in decadal forecasts often come from the forcings which are present in both initialized and un-initialized model experiments. This makes the added value from initialization difficult to assess. We investigate statistical tests to quantify if initialized forecasts provide skill over the un-initialized experiments. We consider three correlation based statistics previous used in the literature. The distributions of these statistics under the null-hypothesis that initialization has no added values are calculated by a surrogate data method. We present some simple examples and study the statistical power of the tests. We find that there can be large differences in both the values and the power for the different statistics. In general the simple statistic defined as the difference between the skill of the initialized and uninitialized experiments behaves best. However, for all statistics the risk of rejecting the true null-hypothesis is too high compared to the nominal value.

We compare the three tests on initialized decadal predictions (hindcasts) of near-surface temperature performed with a climate model and find evidence for a significant effect of initializations for small lead-times. In contrast, we find only little evidence for a significant effect of initializations for lead-times larger than 3 years when the experience from the simple experiments is included in the estimation.

How to cite: Christiansen, B., Yang, S., and Matte, D.: Estimating the significance of the added skill from initializations: The case of decadal predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9520, https://doi.org/10.5194/egusphere-egu23-9520, 2023.

EGU23-9986 | Posters on site | CL4.3

Probabilistic nonlinear lagged teleconnections of the sea surface temperature field 

Carlos Pires and Abdel Hannachi

The monthly anomaly sea surface temperature field over the global ocean exhibit probabilistic dependencies between remote points and lagged times, which are explained eventually by some oceanic or atmospheric bridge of information transfer. Despite much of the bivariate SST dependencies appear to be linear, others are characterized by robust and statistically significant nonlinear correlations. In order to enhance that, we present a general method of extracting bivariate (X,Y) dependencies, seeking for pairs of polynomials P(X) and Q(Y) which are maximally correlated. The method relies on a Canonical correlation Analysis (CCA) between sets of standardized monomials of X and Y, up to a certain (low) degree (e.g. 4). Polynomial coefficients are obtained from the leading CCA eigenvector. Polynomials are calibrated and validated over independent periods, being afterwards subjected to marginal Gaussian anamorphoses. The bivariate non-Gaussianity in the space of marginally Gaussianized polynomials remains residual because of the correlation concentration and maximization. Consequently, the bivariate Gaussian pdf or in alternative, a copula pdf in the space of maximally correlated polynomials can accurately approximate the bivariate dependency. That probabilistic model is then used to determine conditional pdfs, cdfs and probabilities of extremes.

The method is applied to various (X,Y) pairs. In the first example, X is an optimized polynomial of the El-Niño 3.4 index while Y is that index lagged to the future. For lags between 6 and 18 months, the nonlinear El-Niño forecast clearly surpasses the linear one, contributing to lower the El-Niño seasonal predictability barrier. In the second example, we relate El-Niño (X) with the lagged Atlantic multidecadal oscillation index (Y). Nonlinear, robust correlations appear, both for positive and negative lags up to 5 years putting in evidence Pacific-Atlantic basin oceanic teleconnections.

The above probabilistic (polynomial based) model appears to be a good candidate tool for the statistical (seasonal up to decadal) forecast of regime probabilities (e.g. dry/wet) and extremes, given certain antecedent precursors.

This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020- IDL and the project JPIOCEANS/0001/2019 (ROADMAP: ’The Role of ocean dynamics and Ocean–Atmosphere interactions in Driving cliMAte variations and future Projections of impact–relevant extreme events’). Acknowledgements are also due to the International Meteorological Institute (IMI) at Stockholm University.

How to cite: Pires, C. and Hannachi, A.: Probabilistic nonlinear lagged teleconnections of the sea surface temperature field, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9986, https://doi.org/10.5194/egusphere-egu23-9986, 2023.

EGU23-13375 | ECS | Orals | CL4.3

Role of the subpolar North Atlantic region in skillful climate predictions for high northern latitudes: A pacemaker experiment 

Annika Drews, Torben Schmith, Shuting Yang, Steffen Olsen, Tian Tian, Marion Devilliers, Yiguo Wang, and Noel Keenlyside
Recent studies have suggested that the Atlantic water pathway connecting the subpolar North Atlantic (SPNA) with the Nordic Seas and Arctic Ocean may lead to skillful predictions of sea surface temperature and salinity anomalies in the eastern Nordic Seas. To investigate the role of the SPNA for such anomalies downstream, we designed a pacemaker experiment, using two decadal climate prediction systems based on EC-Earth3 and NorCPM. We focus on the subpolar extreme cold anomaly in 2015 and its subsequent development, a feature not well captured and predicted. The pacemaker experiment follows the protocol of the CMIP6 DCPP-A retrospective forecasts or hindcasts initialized November 1, 2014, but the models are forced to follow the observed ocean temperature and salinity anomalies in the SPNA from ocean reanalysis from November 2014 through to December 2019. Two sets of 10-year hindcasts are performed with 10 members for EC-Earth3 and 30 members for NorCPM. We here detail and discuss the design of this pacemaker experiment and present results, comparing with the initialized CMIP6 DCPP-A experiment assessing differences in decadal prediction skill outside the SPNA. We conclude that the pacemaker experiments show improved skill compared to the standard decadal predictions for the eastern Norwegian Sea, and therefore the SPNA is key for successful decadal predictions in the region.

How to cite: Drews, A., Schmith, T., Yang, S., Olsen, S., Tian, T., Devilliers, M., Wang, Y., and Keenlyside, N.: Role of the subpolar North Atlantic region in skillful climate predictions for high northern latitudes: A pacemaker experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13375, https://doi.org/10.5194/egusphere-egu23-13375, 2023.

EGU23-13639 | Orals | CL4.3

Seasonal prediction of UK mean and extreme winds 

Julia Lockwood, Nicky Stringer, Katie Hodge, Philip Bett, Jeff Knight, Doug Smith, Adam Scaife, Matthew Patterson, Nick Dunstone, and Hazel Thornton

For several years the Met Office has produced a seasonal outlook for the UK every month, which is issued to the UK Government and contingency planners.  The outlook gives predictions of the probability of having average, low, or high seasonal mean UK temperature and precipitation for the coming three-months.  In recent years, there has been increasing demand from sectors such as energy and insurance to include similar probabilistic predictions of UK wind speed: both for the seasonal mean and for measures of extreme winds such as storm numbers.  In this presentation we show the skill of the Met Office’s GloSea system in predicting seasonal (three-month average) UK mean wind and a measure of UK storminess throughout the year, and discuss the drivers of predictability.  Skill in predicting the UK mean wind speed and storminess peaks in winter (December–February), owing to predictability of the North Atlantic oscillation.  In summer (June–August), there is evidence that a significant proportion of variability in UK winds is driven by a Rossby wave train which the model has little skill in predicting. Nevertheless there are signs that the wave is potentially predictable and skill may be improved by reducing model errors.

How to cite: Lockwood, J., Stringer, N., Hodge, K., Bett, P., Knight, J., Smith, D., Scaife, A., Patterson, M., Dunstone, N., and Thornton, H.: Seasonal prediction of UK mean and extreme winds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13639, https://doi.org/10.5194/egusphere-egu23-13639, 2023.

EGU23-13736 | ECS | Posters on site | CL4.3

Decadal predictability of European temperature extremes. 

Eirini Tsartsali, Panos Athanasiadis, Stefano Tibaldi, and Silvio Gualdi

Accurate predictions of climate variations at the decadal timescale are of great interest for decision-making, planning and adaptation strategies for different socio-economic sectors. Notably, decadal predictions have rapidly evolved during the last 15 years and are now produced operationally worldwide. The majority of the studies assessing the skill of decadal prediction systems focus on time-mean anomalies of standard meteorological variables, such as annual mean near-surface air temperature and precipitation. However, the predictability of extreme events frequency may differ substantially from the predictability of multi-year annual or seasonal means. Predicting the frequency of extreme events at different timescales is of major importance, since they are associated with severe impacts on various natural and human systems. In the current study we evaluate the capability of state-of-the-art decadal prediction systems to predict the frequency of temperature extremes in Europe. More specifically, we assess the skill of a multi-model ensemble from the Decadal Climate Prediction Project (DCPP, 163 ensemble members from 12 models in total) to forecast the number of days belonging to heatwaves episodes during summer (June–August). We find statistically significant predictive skill over Europe, except for the United Kingdom and a large part of the Scandinavian Peninsula, most of which is associated with the long-term warming trend. We are progressing with the evaluation of other statistical aspects of extreme events, including warm and cold episodes during winter, and we are also investigating whether there is predictive skill beyond that stemming from the external forcing.  

How to cite: Tsartsali, E., Athanasiadis, P., Tibaldi, S., and Gualdi, S.: Decadal predictability of European temperature extremes., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13736, https://doi.org/10.5194/egusphere-egu23-13736, 2023.

EGU23-13789 | Posters on site | CL4.3

Do oceanic observations (still) matter in initializing decadal climate predictions over the North Atlantic ocean? 

Sebastian Brune, Vimal Koul, and Johanna Baehr

Earth system models are now regularly being used in inter-annual to decadal climate prediction. Such prediction systems based on CMIP5-generation Earth system models had demonstrated an overall positive impact of initialization, i.e. deriving initial conditions of retrospective forecasts from a separate data assimilation experiment, on decadal prediction skill. This view is now being increasingly challenged in the context of improvements both in CMIP6-generation Earth system models and CMIP6-evaluation of external forcing as well as in the context of ongoing transient climate change. In this study we re-evaluate the impact of atmospheric and oceanic initialization on decadal prediction skill of North Atlantic upper ocean heat content (0-700m) in a CMIP6-generation decadal prediction system based on the Max Planck Institute Earth system model (MPI-ESM). We compare the impact of initial conditions derived through full-field atmospheric nudging with those derived through an additional assimilation of observed oceanic temperature and salinity profiles using an ensemble Kalman filter. Our experiments suggest that assimilation of observed oceanic temperature and salinity profiles into the model reduces the warm bias in the subpolar North Atlantic heat content, and improves the modelled variability of the Atlantic meridional overturning circulation and ocean heat transport. These improvements enable a proper initialization of model variables which leads to an improved decadal prediction of surface temperatures. Our results reveal an important role of subsurface oceanic observations in decadal prediction of surface temperatures in the subpolar North Atlantic even in CMIP6-generation decadal prediction system.

How to cite: Brune, S., Koul, V., and Baehr, J.: Do oceanic observations (still) matter in initializing decadal climate predictions over the North Atlantic ocean?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13789, https://doi.org/10.5194/egusphere-egu23-13789, 2023.

EGU23-14755 | ECS | Posters on site | CL4.3

A low-dimensional dynamical systems approach to climate ensemble design and interpretation 

Francisco de Melo Viríssimo and David Stainforth

Earth System Models (ESMs) are complex, highly nonlinear, multi-component systems described by large number of differential equations. They are used to study the evolution of climate and its dynamics, and to make projection of future climate at both regional and global levels – which underpins climate change impact assessments such as the IPCC report. These projections are subject to several sorts of uncertainty due to high internal variability in the system dynamics, which are usually quantified via ensembles of simulations.

Due to their multi component nature of such ESMs, the emerging dynamics also contain different temporal scales, meaning that climate ensembles come in a variety of shapes and sizes. However, our ability to run such ensembles is usually constrained by the computational resources available, as they are very expensive to run. Hence, choices on the ensemble design must be made, which conciliate the computational capability with the sort of information one is looking for.

One alternative to gain information is to use low-dimensional climate-like systems, which consists of simplified, coupled versions of atmosphere, ocean, and other components, and hence capture some of the different time scales present in ESMs. This approach allows one to run very large ensembles, and hence to explore all sorts of model uncertainty with only modest computational usage.

In this talk, we discuss this approach in detail, and illustrate its applicability with a few results. Particular attention will be given to the issues of micro and macro initial condition uncertainty, and parametric uncertainty – including external, anthropogenic-like forcing. The ability of large ensembles to constrain decadal to centennial projections will be also explored.

How to cite: de Melo Viríssimo, F. and Stainforth, D.: A low-dimensional dynamical systems approach to climate ensemble design and interpretation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14755, https://doi.org/10.5194/egusphere-egu23-14755, 2023.

EGU23-15829 | ECS | Posters on site | CL4.3

Near term climate change in Emilia-Romagna (Italy) using CMIP6 decadal climate predictions 

Valeria Todaro, Marco D'Oria, Daniele Secci, Andrea Zanini, and Maria Giovanna Tanda

Ongoing climate change makes both short- and long-term adaptation and mitigation strategies urgently needed. While many long-term climate models have been developed and investigated in recent years, little attention has been paid to short-term simulations. The first attempts to perform multi-model initialized decadal forecasts were presented in the fifth Coupled Model Intercomparison Project 5 (CMIP5). Near-term climate prediction models are new socially relevant tools to support the decision makers delivering climate adaptation solutions on an annual or decadal scale. Recent improvements in decadal models were coordinated in CMIP6 and the World Climate Research Program (WCRP) Grand Challenge on Near Term Climate Prediction, as part of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6, IPCC). The Decadal Climate Prediction Project (DCPP) provides decadal climate forecasts based on advanced techniques for the reanalysis of climate data, initialization methods, ensemble generation and data analysis. The initialization allows to consider the predictability of the internal climate variability reducing the prediction errors compared to those of the long-term projections, whose simulations do not take into account the phasing between the internal variability of the model and the observations. The aim of this work is to assess the near-future climate change in the Emilia-Romagna region in northern Italy until 2031. The hydrological variables analyzed are the daily precipitation and maximum and minimum temperature. An ensemble of models, with the highest resolution available, is used to handle the uncertainty in the predictions. Initially, to assess the reliability of the selected climate models, the hindcast data of the DCPP are checked against observations. Then, the DCPP predictions are used to investigate the variability of precipitation and temperature in the near future over the investigated area. Some climate features that are referenced to have an important impact on human health and activities are evaluated, such as drought indices and heat waves.

How to cite: Todaro, V., D'Oria, M., Secci, D., Zanini, A., and Tanda, M. G.: Near term climate change in Emilia-Romagna (Italy) using CMIP6 decadal climate predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15829, https://doi.org/10.5194/egusphere-egu23-15829, 2023.

EGU23-16034 | ECS | Posters on site | CL4.3

Seasonal forecast of the Sudden Stratospheric Warming occurrence 

Mikhail Vokhmyanin, Timo Asikainen, Antti Salminen, and Kalevi Mursula

The polar vortex in the wintertime Northern Hemisphere can sometimes experience a dramatic breakdown after an associated warming of the stratosphere during so-called Sudden Stratospheric Warmings (SSWs). These events are known to influence the ground weather in Northern Eurasia and large parts of North America. SSWs are primarily generated by enhanced planetary waves propagating from the troposphere to the stratosphere where they decelerate the vortex and lead to its breakdown. According to the Holton-Tan mechanism, the easterly direction of equatorial stratospheric QBO (Quasi-Biennial Oscillation) winds weakens the northern polar vortex by guiding more waves poleward. Recently, we found that during easterly QBO the occurrence rate of SSWs is modulated by the geomagnetic activity. We used the aa-index which is a good proxy for the energetic electron precipitations (EEP) responsible for the indirect effect on ozone. Our model shows that the breaking of the polar vortex is very likely to occur if the geomagnetic activity is weak. On the other hand, during westerly QBO, solar irradiance modulates the SSW occurrence: more SSWs happen under high solar activity.

How to cite: Vokhmyanin, M., Asikainen, T., Salminen, A., and Mursula, K.: Seasonal forecast of the Sudden Stratospheric Warming occurrence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16034, https://doi.org/10.5194/egusphere-egu23-16034, 2023.

The state-of-the-art climate models suffer from significant sea surface temperature (SST) biases in the tropical Indian Ocean (TIO), greatly damaging the climate prediction and projection. In this study, we investigate the multidecadal atmospheric bias teleconnections caused by the TIO SST biases and their impacts on the simulated atmospheric variability. A set of century long simulations forced with idealized SST perturbations, resembling various persistent TIO SST biases in coupled climate models, are conducted with an intermediate complexity climate model. Bias analysis is performed using the normal-mode function decomposition which can differentiate between balanced and unbalanced flow regimes across spatial scales. The results show that the long-term atmospheric circulation biases caused by the TIO SST biases have the Matsuno-Gill-type pattern in the tropics and Rossby wavetrain distribution in the extratropics, similar to the steady state response to tropical heating. The teleconnection between the tropical and extratropical biases is set up by the Rossby wavetrain emanating from the subtropics. Over 90% of the total bias energy is stored in the zonal modes k≤6, and the Kelvin modes take 50-65% of the total unbalanced bias energy. The spatial and temporal variabilities have different responses to positive SST biases. In the unbalanced regime, variability changes are confined in the tropics, but the spatial variability increases whereas the temporal variability decreases. In the balanced regime, the spatial variability generally increases in the tropics and decreases in the extratropics, whereas the temporal variability decreases globally. Variability responses in the tropics are confined in the Indo-west Pacific region, and those in the extratropics are strong in the Pacific-North America region and the Europe. In the experiment with only negative SST biases, spatial and temporal variabilities increase in both regimes. In addition, the comparison between experiments indicates that the responses of the circulation and its variability are not sensitive to the structure and location of the TIO SST biases.

How to cite: Zhao, Y.-B., Žagar, N., Lunkeit, F., and Blender, R.: Long-term atmospheric bias teleconnection and the associated spatio-temporal variability originating from the tropical Indian Ocean sea surface temperature errors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16899, https://doi.org/10.5194/egusphere-egu23-16899, 2023.

NP6 – Turbulence, Transport and Diffusion

EGU23-132 | ECS | Orals | NP6.1

Airborne observations of shoaling and breaking internal waves 

Teodor Vrecica, Nick Pizzo, and Luc Lenain

Internal waves are crucial contributors to the transport of sediment, heat, and nutrients in coastal areas. While internal waves have been extensively studied using point measurements, their spatial variability is less well understood. Here, we present a unique set of high-resolution infrared imagery collected from a helicopter, hovering over very energetic shoaling and breaking internal waves. We compute surface velocities by tracking the evolution of thermal structures at the ocean surface and find horizontal velocity gradients with magnitudes that are more than 100 times the Coriolis frequency. Under the assumption of no vertical shear we determine vertical velocities from the obtained horizontal divergence estimates and identify areas of the wave undergoing breaking. The spatial variability of the internal wave occurs on scales from a few to a few hundred meters. These results highlight the need to collect spatio-temporal observations of the evolution of internal waves in coastal areas.

How to cite: Vrecica, T., Pizzo, N., and Lenain, L.: Airborne observations of shoaling and breaking internal waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-132, https://doi.org/10.5194/egusphere-egu23-132, 2023.

This work addresses the effects of time-dependent, mesoscale turbulence on the wind-driven ocean circulation in a closed basin with variable topography. The main results concern the so-called Neptune effect, which involves the generation of persistent flows correlated with topography, but in this case, such currents are formed in the presence of a continuous, stochastic forcing. Numerical simulations of a single-layer fluid with sloping bottom topography near the boundaries are performed. The forcing is a suitable combination of a steady, basin-scale wind that generates the classical western-intensified anticyclonic gyre, plus a shorter, time-dependent forcing that injects energy at a narrow range of scales. Two contrasting situations are considered. First, in the absence of large-scale forcing, the turbulence generates a cyclonic flow that follows the geostrophic contours around the basin. This configuration corresponds to the most probable state equivalent to that expected in statistical equilibrium. Second, the resulting mean circulation is studied when the large and small-scale forcing terms are considered together. The main consequence is the alteration of the anticyclonic gyre due to the turbulent-induced cyclonic circulation. This result implies that large-scale, semi-steady circulations might be altered according to the turbulence characteristics.

How to cite: Zavala Sanson, L.: Effects of mesoscale turbulence on the wind-driven circulation in a closed basin with topography, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-623, https://doi.org/10.5194/egusphere-egu23-623, 2023.

EGU23-1200 | Orals | NP6.1

Light-limited dynamics of sinking phytoplankton in a convective flow model with ice-covered waters 

Stefano Berti, Vinicius Beltram Tergolina, Enrico Calzavarini, and Gilmar Mompean

Plankton dynamics are controlled by an often subtle interplay between biological and physical processes. Among the latter, fluid transport is known to play a prominent role. Field studies have, e.g., provided evidence of the effects of turbulent-convection upwelling and downwelling motions on phytoplankton survival. Recent numerical investigations have emphasized, in addition, that relatively large-scale coherent flow features on the vertical can considerably hinder survival and thus negatively impact plankton blooms.

In nutrient-rich polar marine environments phytoplankton growth is critically limited by light availability, especially in waters that are partially covered by ice. In these conditions, the heterogeneity of the light intensity distribution, in association with a large-scale coherent fluid flow, can give rise to complex biological dynamics. In the Arctic ocean, several studies reported under-ice phytoplankton blooms that were initiated by the onset of ice melt. Nevertheless, it is still only partially known how such blooms are controlled by the interaction between different factors, such as the increase of light transmittance, leads (openings in the ice), convective mixing, and biological processes. Under-ice blooms are expected to become more common in the future, due to increasingly thinner and dynamic ice coverage, and thus more frequent lead formation. This could significantly alter primary production, and have important consequences on local marine food webs.

In this work we consider an advection-reaction-diffusion model of phytoplankton light-limited vertical dynamics in the presence of convective transport, intended as an idealized representation of nonuniformly ice-covered polar waters. Specifically, we assume that the incident light intensity at the surface is horizontally modulated by the presence of opaque obstacles, giving rise to regions of the water column that are characterized by different production regimes. We focus on the impact of advection, and more generally of the different transport processes occurring in the fluid, on the average biomass. By means of numerical simulations we show that convective motions may be harmful to under-ice blooms, in agreement with previous findings. In the present setup, such effect depends on the positions of the surface obstacles with respect to the upwelling and downwelling flow regions. We further find, however, that the sinking speed, due to the density difference between phytoplankton organisms and water, also plays an important role, which depends on how it adds to the flow. While small, the sinking speed has a measurable impact on the growth dynamics of the population and can even be critical for its survival, which may have ecological relevance, as different phytoplankton species have different densities and, hence, different settling velocities.

How to cite: Berti, S., Tergolina, V. B., Calzavarini, E., and Mompean, G.: Light-limited dynamics of sinking phytoplankton in a convective flow model with ice-covered waters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1200, https://doi.org/10.5194/egusphere-egu23-1200, 2023.

In late winter many lakes are iced over, and hence remain cut off from the mechanical forcing due to wind.  At the same time, strong radiative forcing modifies the inverse stratification associated with wintertime conditions.  The inverse stratification occurs due to the fact that freshwater has a temperature of maximum density (around 4 degrees Centigrade) and the equation state of freshwater is thus nonlinear.  In this talk I will demonstrate that this nonlinearity has a profound influence on the characteristics of nonlinear internal solitary-like waves in the cold water regime.  In particular, predcitions of waves made using a piecewise linear density profile yield waves with the opposite polarity to those calculated using temperature profiles and the full nonlinear equation of state.  I will present results based on the Dubreil-Jacotin Long theory, but similar conclusions can be made based on weakly nonlinear (KdV) theory.  Time permitting I will discuss implications of these results for shoaling.

How to cite: Stastna, M.: Nonlinearity of the equation of state effects dynamics of nonlinear internal waves in late winter lakes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1470, https://doi.org/10.5194/egusphere-egu23-1470, 2023.

EGU23-1554 | Posters on site | NP6.1

Interaction of Fully-Nonlinear Internal Solitary Waves with Cores 

Kevin Lamb

Under appropriate background conditions internal solitary waves may have surface or subsurface cores. Both types of waves have been observed in the ocean. Solutions of the Dubreil-Jacotin-Long equation predict cores with closed isopycnals and, in a reference frame moving with the wave, closed streamlines. In numerical simulations of a time-evolving field these cores are unsteady and leaky: fluid is continually being entrained into the core and leaking out of the rear of the core. In this talk I will present results of the interaction of two internal solitary waves, one with a core over-taking a smaller wave without a core. In general, during the interaction the large ISW decrease in amplitude while transferring energy to the smaller ISW. During this process the large ISW loses its core and the fluid inside the core is left behind. The smaller wave grows in amplitude and forms a new core. In many cases the final small ISW is considerably smaller than the initial small ISW while the larger ISW may be larger than the iniitial ISW. ISW energy is also transferred to small amplitude internal waves. 

How to cite: Lamb, K.: Interaction of Fully-Nonlinear Internal Solitary Waves with Cores, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1554, https://doi.org/10.5194/egusphere-egu23-1554, 2023.

EGU23-1766 | Posters on site | NP6.1

Numerical analysis of breather interactions 

Keisuke Nakayama and Kevin Lamb

While the existence of breathers in the ocean is not clearly revealed, Rouvinskaya et al. (2015) suggested the possibility that breathers occurred in the Baltic Sea. In three-layer symmetric stratifications with the same density difference across each interface, the modified KdV equation (the Gardner equation with the quadratic nonlinear coefficient equal to zero) predicts that breathers exist. Therefore, the soliton-like characteristics of fully nonlinear breathers must be better understood. Thus, this study used fully nonlinear numerical simulations to investigate breather interactions by analysing overtaking collisions of two breathers in a three-layer fluid. As a result, an overtaking collision of two breathers is almost elastic when the ratio of the breather amplitude to the upper and lower layer thickness is smaller. Furthermore, the collision is found to remove the mode-2 structure, resulting in a significant role in forming breathers.

How to cite: Nakayama, K. and Lamb, K.: Numerical analysis of breather interactions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1766, https://doi.org/10.5194/egusphere-egu23-1766, 2023.

EGU23-2594 | ECS | Orals | NP6.1

Laminar transport and turbulent cascades in downslope rotating gravity currents. 

Sévan Rétif, Maria-Eletta Negretti, Achim Wirth, and Axel Tassigny

We present experimental results from large-scale laboratory experiments of rotating downslope gravity currents intruding into a two-layer stratified ambient performed in the Coriolis Rotating Platform in Grenoble. By means of PIV velocity and conductivity data for the density measurement, we show that mixing occurs mostly on the slope area during the descent rather than once the current has penetrated the stratified ambient, where the Richardson number remains above the stability threshold of 1/4. Looking at the time evolution of the vertical density profile in the stratified receiving ambient, two distinct mixing regimes can be identified, the first issued by laminar transport through Ekman dynamics, the second by turbulent transport due to intermittent cascading events. Vertical density gradients reveal a linear piece-wise dependence on the density anomaly, highlighting an advection-diffusion process as proposed by the theoretical model of Munk & Wunsch (1998). If the gravity current flow is laminar on the slope, the structure shows a linear variation of the density with depth ; For the turbulent transport regime characterized by intermittent cascades, an exponential shape is rather observed. The shape of the density structure allows to estimate bulk mixing coefficients and entrainment velocities at the top and the bottom of the intruding gravity current, which can be further compared to oceanographic observational data.

How to cite: Rétif, S., Negretti, M.-E., Wirth, A., and Tassigny, A.: Laminar transport and turbulent cascades in downslope rotating gravity currents., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2594, https://doi.org/10.5194/egusphere-egu23-2594, 2023.

EGU23-3642 | ECS | Orals | NP6.1

The sensitivity of internal solitary waves to localized patches of mixing 

Nicolas Castro-Folker and Marek Stastna

While most theoretical work on internal waves idealizes the stratification, geophysical stratifications are typically much more complicated.  We build on recent work on nearly linear stratifications by adopting perturbations that take the form of a localized patch of mixing. We present a data-centric framework that seeks to identify which locations and widths of a mixing patch yield the largest effect on the structure of exact waves (computed via the Dubreil-Jacotin-Long equation), linear waves (computed via the longwave Taylor-Goldstein equation), and evolving nonlinear waves (via time-dependent simulations using the incompressible Navier-Stokes equations). We find that the vertical structure functions of linear waves are most sensitive to perturbations below (above) the pycnocline when the pycnocline is above (below) mid-depth; furthermore, as the pycnocline approaches mid-depth, the depth of the perturbation layer with the greatest impact approaches the depth of the pycnocline. In contrast, the centre streamwise velocity profile of a DJL wave is perturbed most by layers above (below) the pycnocline when the pycnocline is above (below) mid-depth. Finally, we present the results of simulations of evolving nonlinear waves, where we compare pairs of cases with and without a perturbation layer. Despite the presence of an initial patch of unstable fluid, the perturbation layer is sustained during the simulation; nevertheless, slight Rayleigh-Taylor instabilities are observed within and about the perturbation layer. Modulations in the horizontal velocity field about the leading solitary wave are compared with the results of the linear and DJL analyses.

How to cite: Castro-Folker, N. and Stastna, M.: The sensitivity of internal solitary waves to localized patches of mixing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3642, https://doi.org/10.5194/egusphere-egu23-3642, 2023.

EGU23-4236 | Orals | NP6.1

New method of directional spectrum estimation accounting for ambient shearing currents 

Yaron Toledo, Rotem Soffer, and Eliezer Kit

Realistic currents in seas and oceans are almost always changing in depth thus indicating on the presence of shear in the mean ambient flow. However, analysis methodologies interpreting directional wave data gathered by in-situ measurement devices such as: buoys, pressure gauges and Acoustic Doppler Current Profilers (ADCPs) utilize potential irrotational flow theory which cannot account for the rotational shearing currents. The effects of shearing currents on the wave direction estimations were studied on synthetic ADCP data of waves propagating in a predetermined spread. The synthetic data was generated employing the Rayleigh Boundary Value Problem (BVP) and a selected ambient current profile. The potential data processing led to significant errors in wave directional spread estimation for common shearing currents (~10°  in mean wave direction for the presented example). This finding is of great importance, as it addresses the influence of an ambient current profile on wave propagation direction. The obtained results suggest that there is an uncertainty with the confidence of any wave directional spread ever presented by in-situ wave measurement devices.

A new methodology was developed for estimating directional wave spectra based on rotational flow physics by acquiring new terms emanate from wave-shearing current interaction governing equations. This included a derivation of new numerical transfer functions for the fluid’s physical properties based on the Rayleigh BVP. Then, by applying classical cross- and auto-spectral analysis on time-series data sets, the directional spread function was numerically reconstructed. The newly derived data processing methodology was applied to the same synthetic ADCP data sets. It was found to be capable of reconstructing the spread with great accuracy (0.4° in mean wave direction for the presented example). In addition, to modeling and synthetic data, field measurement data from several campaigns were also analyzed showing the importance of accounting for the vertical shear. This makes it a prominent methodology for estimating directional wave spectra in realistic oceanic conditions.

How to cite: Toledo, Y., Soffer, R., and Kit, E.: New method of directional spectrum estimation accounting for ambient shearing currents, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4236, https://doi.org/10.5194/egusphere-egu23-4236, 2023.

EGU23-5170 | ECS | Posters virtual | NP6.1

Scaling analysis of wave profiles 

Yang Gao, Francois Schmitt, Jianyu Hu, and Yongxiang Huang

In the field of wind-wave interaction, scaling features for both wind and waves are often found experimentally. Several theoretical explanations of the scaling law for wind speed and sea surface wave height have been advocated, while a theoretical consideration for the significant wave height (Hs) is still lacking. In this work, we considered a long-term (more than 20 years) and high sampling frequency (about 0.78 Hz) wave profile data collected by buoys provided by Coastal Data Information Program (CDIP). The scaling features for Hs and for the absolute value of the wave profile are evident in the sense of the Fourier power spectrum. The same scaling features were obtained for frequencies below 10-4 Hz, with a scaling exponent close to 3. While the spectrum for wave profile shows a plain-like distribution under the frequency around 0.02 Hz due to the band pass filter. Furthermore, measured Hs is well overlapped with the absolute value of the wave profiles, which indicates that the amplitude modulation is still preserved after band pass filtering, and that might be the reason for the existence for the scaling features for Hs.

How to cite: Gao, Y., Schmitt, F., Hu, J., and Huang, Y.: Scaling analysis of wave profiles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5170, https://doi.org/10.5194/egusphere-egu23-5170, 2023.

EGU23-5470 | ECS | Posters on site | NP6.1

Bassin study of vetically sheared currents from the dispersion of surface gravity wave 

Alexandra Cuevas, Vincent Rey, Julien Touboul, and Fabrice Ardhuin

Wave conditions from the open sea to the coast, provide necessary information, the good understanding and modeling of coastal dynamics, the design of coastal engineering structures coastal engineering structures, for navigation or the flooding risks evaluation. It is also a potential way to access information present below the surface. Refraction, diffraction and reflection of waves are not only forced by variations in bathymetry but also by the presence of currents. However, the effects of current in propagation models have long been limited to the consideration of homogeneous current in the water column. Nevertheless, currents are usually observed to be sheared vertically by wind, tides or waves. When wave propagate in the presence of currents their celerity is modified. It is also affected by the vertical structure of the current.

This work proposes and discusses methods for reconstructing current fields from wave data based on synchronous analysis of wave spectra at different points in space. We consider here the 2D case of progressive or partially stationary waves in the presence of homogeneous currents or with a vertical sheared profile. The study is based on data from experiments carried out at the Bassin de Génie Océanique FIRST(BGO), for progressive or partially stationary waves in the framework of the ANR project MORHOC'H 2. These test campaigns allowed us to test the sensitivity of the wave’s phase evolution during its propagation in order to estimate the feasibility of reconstructing either constant or sheared currents in the water column. Under the assumption of a progressive wave, the study of the phase evolution shows a significant influence of the current, allowing to reach the intensity of a uniform current. The calculated phase evolution in the presence of sheared current is consistent with the theory, but for small values of shear, the phase velocity changes are much smaller, making the method more sensitive to "noise". Furthermore, for a partially stationary wave, a significant impact of its phase evolution is observed in the propagation direction of the incident wave even for weak reflections, making it necessary to include this parameter in the reconstruction of currents from synchronous wave measurement data.

Acknowledgements:

The DGA is thanked for funding the AID thesis grant of Alexandra CUEVAS, as well as for the ANR grant: ANR-21-ASM1-003.

How to cite: Cuevas, A., Rey, V., Touboul, J., and Ardhuin, F.: Bassin study of vetically sheared currents from the dispersion of surface gravity wave, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5470, https://doi.org/10.5194/egusphere-egu23-5470, 2023.

EGU23-5790 | Orals | NP6.1

Droplet dynamics in homogeneous and isotropic turbulence 

Sergio Chibbaro, Marco Crialesi-Esposito, and Luca Brandt

Emulsions are a major class of multiphase flows, crucial in industrial process (e.g. food and drug production) and ubiquitous in environmental flows (e.g. oil spilling in maritime environment). Already at volume fractions of few precents, the dispersed phase interacts with pre-existing turbulence created at large scale, yet the interaction between phases and the turbulent energy transport across scales is not yet fully understood.

In this work, we use Direct Numerical Simulation to study emulsions in homogeneous and isotropic turbulence, where the Volume of Fluid (VoF) method is used to represent the complex features of the liquid-liquid interface.

We consider a mixture of two matching-density phases, where we vary volume fraction, viscosity ratio and large-scale Weber number aiming at understanding the turbulence modulation and the observed droplet size distributions.  The analysis, based on the spectral scale-by-scale analysis, reveals that energy is consistently transported from large to small scales by the interface, and no inverse cascade is observed. We find that the total surface is directly proportional to the amount of energy transported, and that the energy transfer in the inertial range provides information about the droplet dynamics. We observe the -10/3 and -3/2 scaling on droplet size distributions, suggesting that the dimensional arguments which led to their derivation are verified in HIT conditions and denser conditions. Finally, we discuss the highly intermittent behaviour of the multiphase flow, which can be directly related to the polydisperse nature of the flow.

The study provides some significant observations towards a more comprehensive understanding of multiphase turbulence, opening new questions for future studies. 

How to cite: Chibbaro, S., Crialesi-Esposito, M., and Brandt, L.: Droplet dynamics in homogeneous and isotropic turbulence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5790, https://doi.org/10.5194/egusphere-egu23-5790, 2023.

EGU23-6336 | ECS | Orals | NP6.1

Use of HDG oceanic models to study eddy formation in coastal upwelling areas 

Inés Hernández García, Albert Oliver Serra, and Francisco Machín Jiménez

The Canary Islands region is located in the North-East Atlantic Ocean, next to the African coast. It is situated within the equatorward travelling Canary Current.

This area has a high mesoscale activity. Some important features of this area are the African Upwelling System, the filaments originated by the upwelling, and long-lived cyclonic and anticyclonic mesoscale eddies. The generation of these mesoscale eddies, by the perturbation of the Canary Current caused by the islands, has been largely studied.

The aim of this work is to use a novel Hybridisable Discontinuous Galerkin (HDG) oceanic model, based on Finite Elements, in addition to real in situ and satellite data, in order to study different generation mechanisms and the evolution of the mesoscale eddies south of the Canary Islands.

How to cite: Hernández García, I., Oliver Serra, A., and Machín Jiménez, F.: Use of HDG oceanic models to study eddy formation in coastal upwelling areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6336, https://doi.org/10.5194/egusphere-egu23-6336, 2023.

EGU23-6774 | ECS | Orals | NP6.1

Prolate microswimmer in surface gravity waves 

Francesco Michele Ventrella, Filippo De Lillo, Guido Boffetta, Massimo Cencini, Jean-Luc Thiffeault, and Nimish Pujara

Planktonic microorganisms immersed in a fluid interact with the ambient flow, altering their trajectories. In surface gravity waves, a common goal for microswimmers is vertical migration. By modeling phytoplankton as spheroidal bodies with a certain swimming velocity, we investigate how the combination of swimmer's dynamical characteristics and fluid velocity gradients affect the motion. We investigate the case of prolate, negative buoyant swimmers. We consider also the case of gyrotactic swimmers. We find that it is possible for microswimmers to be trapped at a finite depth below the sea level. This phenomenon is due to the coupling between swimming, gyrotaxis and flow-induced reorientations. The trajectories obtained by numerical simulations, indicate that the dynamics consist of fast oscillations at the surface wavelength superposed with a slower trend at a longer timescale. This suggests using a multiple time-scale expansion to remove the fast oscillations. The presence of stable fixed points for the slow dynamics allows the trapping behaviour.

How to cite: Ventrella, F. M., De Lillo, F., Boffetta, G., Cencini, M., Thiffeault, J.-L., and Pujara, N.: Prolate microswimmer in surface gravity waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6774, https://doi.org/10.5194/egusphere-egu23-6774, 2023.

EGU23-8518 | Posters virtual | NP6.1

Dynamics of small-scale turbulence in the upper ocean under the action of currents and internal waves 

Lev Ostrovsky, Daria Gladskikh, Irina Soustova, and Yuliya Troitskaya

We study the evolution of a turbulent layer in a stratified ocean layer using the theory of unsteady turbulent flows in a stratified fluid developed in [1] and subsequent works. The theory starts from a kinetic equation for turbulence parameters and results in the set of equations involving the mutual transformation of the kinetic and potential energies of turbulence that is shown to significantly affect the overall dynamics of energy exchange between small-scale turbulence and mesoscopic motions and the formation of the upper mixed layer. Besides, this approach allows an account for some important but usually neglected effects such as the dependence of vertical anisotropy of turbulence on stratification. Notably, the transformation between kinetic and potential energies eliminates the restriction on the existence of turbulence at large Richardson numbers.  The results are applied to the analysis of in situ data for turbulence evolution under the action of shear flows and internal waves, obtained in different regions that are significant for climate research, including the upper equatorial ocean. The fundamental role of potential energy in the formation of a turbulent flow is demonstrated.

The work was supported by RSF project No. 23-27-00002.

[1] Ostrovsky L.A., Troitskaya Yu.I. (1987) A model of turbulent transfer and dynamics of turbulence in a stratified shear flow. Izvestiya, Atm. and Oceanic Phys., 23(10), 767-773 (1987).

How to cite: Ostrovsky, L., Gladskikh, D., Soustova, I., and Troitskaya, Y.: Dynamics of small-scale turbulence in the upper ocean under the action of currents and internal waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8518, https://doi.org/10.5194/egusphere-egu23-8518, 2023.

EGU23-9604 | Posters on site | NP6.1

Transformation of internal solitary waves under ice cover edge 

Kateryna Terletska, Vladimir Maderich, and Elena Tobisch

Internal wave-driven mixing is an important factor in the balance of heat and salt fluxes in the Polar Regions. The interaction between internal waves and ice cover in these areas of the ocean is complex and depends on both the characteristics of the ice and the characteristics of internal waves. Harsh environment in Arctic Ocean obstructs direct field observations of internal solitary waves thus, numerical modellings are an essential tool to overcome this shortcoming. The numerical three dimensional, free-surface, non-hydrostatic model for stratified flows using the Navier-Stokes equations in the Boussinesq approximation so called NH-POM was used for simulations of transformation of internal solitary waves under ice cover edge. As the result of the research it was shown, that propagation of internal solitary waves under edge of the ice cover may lead to their destabilization through overturning and breaking events. Such parameters as ice cover depth and internal waves amplitudes were responsible for the evolution and disintegration of an ISW beneath the ice cover while the boundary friction beneath the ice cover had little effect. During the interaction, maximum energy loss could reach about 60% near the ice edge. Interaction of ISWs with the ice edge significantly enhanced the turbulent dissipation and consequentially could potentially accelerated melting of the ice. It was suggested that the blocking parameter B, that is ratio of incident amplitude to the depth of the upper layer beneath the ice, controls the transfer of energy across the ice edge, that is, more energy is reflected if the ratio increases. When the ice depth decreased, the ice-ISW interaction and resultant dissipation weakened.

How to cite: Terletska, K., Maderich, V., and Tobisch, E.: Transformation of internal solitary waves under ice cover edge, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9604, https://doi.org/10.5194/egusphere-egu23-9604, 2023.

EGU23-9614 | Orals | NP6.1

Diapycnal Mixing in the Bransfield Strait 

Ángel Rodríguez-Santana, Borja Aguiar-González, Ángeles Marrero-Díaz, Luis Pablo Valencia, and Francisco Machín

During the austral midsummer near the South Shetland Islands, an interdisciplinary cruise (COUPLING) was carried out in January 2010 (Sangrà et al, 2014). For this study we selected one transect of 12 stations across the Central Bransfield Strait with vertical profiles of Conductivity, Temperature and Depth (CTD) and Acoustic Doppler Current Profiler (ADCP). Vertical profiles of microstructure turbulence were measured at stations of the transect located in specific dynamic features (two fronts: Bransfield Front and Peninsula Front; and an anticyclonic eddy) from a free-fall turbulence profiler. Using CTD and ADCP data, we computed the Thorpe scales, gradient Richardson numbers and density ratios that were compared with microstructure data.

We found that the most active turbulent layer was observed within the upper mixed layer (UML) of the anticyclonic eddy between stations 3 and 6 of the transect. However, intense inversions below the UML were found at the axis of the Peninsula Front (station 9). In the region of the Bransfield Front, it is noteworthy that there were obtained relative high values of kinetic energy dissipation rate (ε) with mixing processes due to vertical shear instabilities and double diffusion.  With this work, we have a deeper understanding of the mixing processes in the Bransfield Strait, which will allow a better estimation of the vertical fluxes of heat, salt and nutrients for this region.

Key words:

Bransfield Strait, Diapycnal Mixing, Microstructure Turbulence.

References:

Sangrà, P., C. García-Muñoz, C.M. García, A. Marrero-Díaz, C. Sobrino, B. Mouriño-Carballido, B. Aguiar-González, C. Henríquez-Pastene, A. Rodríguez-Santana, L. M. Lubián, M. Hernández-Arencibia, S. Hernández-León, E. Vázquez, S.N. Estrada-Allis (2014). Coupling between upper ocean layer variability and size-fractionated phytoplankton in a non-nutrient-limited environment. Marine Ecology Progress Series, 499, 35-46.

How to cite: Rodríguez-Santana, Á., Aguiar-González, B., Marrero-Díaz, Á., Valencia, L. P., and Machín, F.: Diapycnal Mixing in the Bransfield Strait, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9614, https://doi.org/10.5194/egusphere-egu23-9614, 2023.

EGU23-10079 | ECS | Posters on site | NP6.1

The role of turbulence and double-diffusion in the exchange of central waters at the Cape Verde Frontal Zone 

Luis P. Valencia, Ángel Rodríguez-Santana, Antonio Martínez-Marrero, Nadia Burgoa, Carmen Gordo, Diana Grisolía, and Ángeles Marrero-Díaz

The Cape Verde Frontal Zone (CVFZ) separates North and South Atlantic Central Waters (NCAW and SACW, respectively) in the eastern North Atlantic Subtropical Gyre. This front is described as a strong meandering thermohaline front near Cape Blanc at latitudes close to 20ºN. It shows sharp gradients in temperature and salinity in the upper 600 m with the presence of large lateral intrusions. One important aspect of the CVFZ is the compensating character of the temperature and salinity fields, which cause horizontal density gradients to be relatively small across the front. This frontal feature is an important factor in reducing vertical shear of horizontal velocity in some parts of the frontal region, allowing double diffusion processes to be one of the main causes of the observed diapycnal mixing. However, the presence of large lateral intrusions could favor diapycnal mixing induced by vertical shear instabilities which could overcome double diffusion effects. Despite its importance, studies in the CVFZ with direct turbulence measurements focused on diapycnal mixing and its relation with lateral thermohaline intrusions are scarce. In this study, we use microstructure measurements from a vertical free-falling profiler together with CTD-O and SADCP records of two high spatial resolution (each oceanographic stations ~9 km apart) oceanographic transects along and across the CVFZ (~300 and 100 km, respectively) during November of 2017. An assessment of the turbulent and double-diffusive mixing related to the lateral intrusions was made, identifying the latter through the diapycnal spiciness curvature method. Lateral intrusions ranging from ~20-100 km at subsurface and central levels of the water column showed relative increments in dissipation and diapycnal diffusivity. Therefore, at their boundaries occur the exchange of properties between the NACW and SACW.

How to cite: Valencia, L. P., Rodríguez-Santana, Á., Martínez-Marrero, A., Burgoa, N., Gordo, C., Grisolía, D., and Marrero-Díaz, Á.: The role of turbulence and double-diffusion in the exchange of central waters at the Cape Verde Frontal Zone, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10079, https://doi.org/10.5194/egusphere-egu23-10079, 2023.

EGU23-10130 | Orals | NP6.1

The stability of an asymmetric slice of the Gulf Stream 

Francis Poulin

The Gulf Stream plays an important role in the meridional overturning circulation in the North Atlantic, one of the primary mechanism by which the warm, salty water can move from low to high latitudes. It also provides closure to the North Atlantic subtropical gyre circulation as a western boundary current and makes Western European countries much warmer by transporting warm water across the ocean.

Observational data of the Gulf Stream (along the Oleander line, between New Jersey and Bermuda) has found that its stream-wise velocity skews to the right with increasing depth. This has motivated our development of an idealized model of a laterally skewed Gulf Stream jet that is surface trapped overlying a flat bottom. The nonlinear evolution of this unstable asymmetric jet is investigated using the Oceananigans.jl library for multiple values of a skewness parameter. The results show that the maximum growth rate has a nonlinear dependency on the skewness parameter, though weak and strong skew tend to be stabilizing and destabilizing, respectively. 

How to cite: Poulin, F.: The stability of an asymmetric slice of the Gulf Stream, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10130, https://doi.org/10.5194/egusphere-egu23-10130, 2023.

EGU23-10892 | ECS | Posters on site | NP6.1

Triadic resonance of internal wave modes with background shear 

Ramana Patibandla, Anubhab Roy, and Manikandan Mathur

In this work we study resonant triad interactions among discrete internal wave modes in a finite-depth, two dimensional uniformly stratified shear flow. The primary wave-field is considered to be a linear superposition of various internal wave modes. The weakly-nonlinear solution of the primary wave-field consists of a superharmonic (2ω) part and a mean-flow part (ω=0).  For a given modal interaction, we study the location in the frequency (ω) -Richardson number (Ri) parameter space where the amplitude of the superharmonic part attains a maximum i.e, where two primary internal wave modes of modenumbers 'm' and 'n' resonantly excite a secondary wave mode of modenumber 'q'. Using asymptotic theory we show that, unlike the case of no-shear, the presence of weak-shear, doesn't require the vertical wavenumber condition to be satisfied for resonance. This entails an activation of several new resonances in the presence of arbitrarily weak shear, where only the frequency and the horizontal wavenumber conditions are satisfied. This also leads to the possibility of self-interaction and resonances close to ω = 0. A similar asymptotic theory can be extended to other inhomogeneities (eg: non-uniform stratification) as well. For an exponential background shear flow, we track the location of these resonances in the (ω, Ri) parameter space and present their behaviour.

How to cite: Patibandla, R., Roy, A., and Mathur, M.: Triadic resonance of internal wave modes with background shear, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10892, https://doi.org/10.5194/egusphere-egu23-10892, 2023.

EGU23-12895 | Posters on site | NP6.1

Growth and chain formations of diatoms (Pseudo-nitzschia) under different turbulent conditions: a laboratory analysis 

Vasileios Bampouris, Emilie Houliez, Francois G. Schmitt, Muriel Crouvoiser, Kostas Kormas, and Urania Christaki
Diatoms have high productivity and are highly influenced by turbulent conditions. We consider here diatoms of the species Pseudo-nitzschia,  which are chain forming. The objective of this work was to show how the turbulent environment affects the growth and the chain forming of these species. For this, cultures of the species Pseudo-nitzschia multiseries and Pseudo-nitzschia fraudulenta were performed in the laboratory and submitted to stationary turbulent conditions, using the Agiturb system developed in the LOG at Wimereux (Le Quiniou et al. 2022). 
In the Agiturb system, the turbulent flow is produced using four contra-rotating agitators that are placed under a cubic tank, generating a statistically stationary, spatially inhomogeneous flow with compression and stretching. The injection of the energy in the flow is produced by 4 stirring bars activated by 4 magnetic stirrers situated at symmetric positions. The cubic tank is almost half-full with 15 liters of sea water. For each experiment, the magnitude of the rotation rate of each agitator was identical, with two agitators rotating clockwise and two anti-clockwise, the same directions being along the diagonal. Different values of the rotation rate were chosen to reach different turbulence levels, characterized by the microscale Reynolds number Rλ going from 130 to 360. These Reynolds numbers correspond to typical values found in the ocean, from the epicontinental zone, to coastal, surf zones and even storm conditions. 
In the experiments, all the other parameters that affect the diatoms’ proliferation were kept the same. Formation and growth of the chains were assessed through microscopy.  P. fraudulenta displayed higher growth than P. multiseries in all turbulence levels except from the control condition (Rλ=0) where the growth was approximately the same. The level of turbulence that was more beneficial for the growth of P. multiseries was the agitated (Rλ= 240) whereas for P. fraudulenta it was for a smaller Reynolds number (Rλ = 160). The chain length were also considered in relation with turbulence level, by considering the probability density of single chains, small chains (2 or 3 cells) and long chains (more than 4 cells). The result was that the predominant form of the cells for both species was the single cells. However, P. multiseries presented higher variations in chain forming throughout the whole experiment than P. fraudulenta. Within this approach, the optimal turbulence level, for growth as well as chain formation, can be assessed for each phytoplankton species.

How to cite: Bampouris, V., Houliez, E., Schmitt, F. G., Crouvoiser, M., Kormas, K., and Christaki, U.: Growth and chain formations of diatoms (Pseudo-nitzschia) under different turbulent conditions: a laboratory analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12895, https://doi.org/10.5194/egusphere-egu23-12895, 2023.

EGU23-12946 | ECS | Orals | NP6.1

A Semi-Lagrangian solver for the free surface Euler system with application to rotational wave flows 

Andreas Alexandris-Galanopoulos and Kostas Belibassakis

Even though the majority of the classical water wave theory is restricted to potential flows, vortical flows are abundant in nature. This necessitates the need for the development of accurate and efficient methods for the simulation of rotational phenomena, such as the propagation of waves over bathymetry in the presence of a sheared current [1, 2].

In the present work, a numerical method for the free surface Euler system with constant density and general bathymetry is developed within the framework
of classical Computational Fluid Dynamics (CFD). Specifically, using the well known σ coordinate system, a layer-wise integration followed by an operator
splitting is performed. The resulting horizontal advection component is, treated as a multilayered Shallow Water Equations (mSWE) system (see, e.g. [3]) 
and it is solved with a conventional Finite Volume solver. The vertical counterpart (that works similar to remeshing operator) regulates if the system is treated with a Lagrangian or an Eulerian approach. Finally, the dynamic pressure component coupled with the incompressibility constraint is treated using the well-known projection of Chorin [4].

The method’s main advantages stem from its highly modular character that makes it both robust and easy to implement. The method’s performance is tested in the case of waves propagating on top of a sheared current. Results concerning the dispersion and propagation characteristics for general current profiles are presented and compared with other models [1,2,5].

References
[1] Julien Touboul and Kostas Belibassakis. A novel method for water waves propagating in the presence of vortical mean flows over variable bathymetry. Journal of Ocean Engineering and Marine Energy, 5(4):333–350, 2019
[2] Kostas Belibassakis and Julien Touboul. A nonlinear coupled-mode model for waves propagating in vertically sheared currents in variable
bathymetry—collinear waves and currents. Fluids, 4(2):61, 2019.
[3] Fracois Bouchut and Vladimir Zeitlin. A robust well-balanced scheme for multi-layer shallow water equations. Discrete and Continuous Dynamical
Systems-Series B, 13(4):739–758, 2010.
[4] Zhe Liu, Lei Lin, Lian Xie, and Huiwang Gao. Partially implicit finite difference scheme for calculating dynamic pressure in a terrain-following coordinate
non-hydrostatic ocean model. Ocean Modelling, 106:44–57, 2016. [5] Ellingsen SA, Li Y (2017) Approximate dispersion relations for waves on arbitrary shear flows. J Geophys Res Oceans 122(12):9889–9905
[5] Ellingsen SA, Li Y (2017) Approximate dispersion relations for waves on arbitrary shear flows. J Geophys Res Oceans 122(12):9889–9905

How to cite: Alexandris-Galanopoulos, A. and Belibassakis, K.: A Semi-Lagrangian solver for the free surface Euler system with application to rotational wave flows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12946, https://doi.org/10.5194/egusphere-egu23-12946, 2023.

EGU23-13078 | ECS | Orals | NP6.1

In situ observation of mode 1 nonlinear internal waves of opposite polarity in a changing environment 

Adèle Moncuquet, Nicole Jones, Lucie Bordois, Andrew Zulberti, François Dufois, Florent Grasso, and Pascal Lazure

The Bay of Biscay (Bob) is a hot spot for the generation of internal tides and nonlinear internal waves (NLIW). However, no studies have focused on internal waves on the continental shelf of the Bob. Here, we present 22 days of collocated temperature, velocity and backscatter profiles within a water depth H of 65 m. The background stratification evolved from two pycnoclines, with the strongest one near the sea bed, to a continuous profile due to wind-driven upwelling.

Under the double pycnocline situation, we observed trains of elevation emerging from each internal tidal front with amplitude reaching up to H/4 and propagating at speeds between 0.1 and 0.35 m/s. Sporadically depression waves were measured within the train and can propagate substantially faster (between 0.36 and 0.54 m/s). With the continuous stratification, the trains of NLIWs of elevation and containing opposite polarities were no longer observed.

These observations suggest that depression waves can cross the train of elevation waves. Resulting interactions could have significant impacts on sediment dynamics over the shelf. The double pycnocline regime and the impact of the stratification modification due to wind will be investigated numerically in future work.

How to cite: Moncuquet, A., Jones, N., Bordois, L., Zulberti, A., Dufois, F., Grasso, F., and Lazure, P.: In situ observation of mode 1 nonlinear internal waves of opposite polarity in a changing environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13078, https://doi.org/10.5194/egusphere-egu23-13078, 2023.

EGU23-13754 | ECS | Posters on site | NP6.1

Multi-scale analysis of atmospheric and oceanic pCO2 time series and of their difference 

Kévin Robache, François G. Schmitt, and Yongxiang Huang
The oceans play an important role in the carbon cycle by exchanging CO2 with the atmosphere. These exchanges correspond to the biological pump, where the ocean can be sink or source of atmospheric CO2. Our hypothesis is that CO2 concentration, either atmospheric or oceanic, are chemical tracers being strongly influence by turbulence: we thus study separately their dynamics, and also their difference which is giving indication of the direction of the air-sea CO2 flux.
For this we use a publicly available data set of pCO2 simultaneous measurements at high frequency (typically 3 hours time step) at 40 difference places around the globe, from surface buoys (Sutton et al. 2019). We consider here the scaling properties of these quantities in order to characterize their multi-scale fluctuations, which are considered in the framework of passive or active scalars in turbulence.  For each site, this is done by analyzing temperature, salinity, oceanic (pCO2sw), atmospheric (pCO2air) pCO2 and their difference $\delta = pCO2sw - pCO2air$. Power spectral density are estimated in Fourier space and using Hilbert spectral analysis, with adapted methodologies to take into account the missing data problem. Spectral slopes are recorded and are interpreted in relation with the local climatology, depth and other factors.

How to cite: Robache, K., Schmitt, F. G., and Huang, Y.: Multi-scale analysis of atmospheric and oceanic pCO2 time series and of their difference, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13754, https://doi.org/10.5194/egusphere-egu23-13754, 2023.

We report on results from laboratory experiments performed in a quasi-two-layer system of cold and warm water in a rectangular laboratory tank. Warm front propagation is initiated by removing a vertical barrier from between the two prepared sections of the tank containing cold and warm water filled up to the same level. The warm front propagation in the vicinity of the free water surface is monitored using a high precision infrared camera from above, and with dye visualisation from the side simultaneously. After the warm front reaches the sidewall of the tank, its "head" is reflected, and hence an internal bore emerges along the interface separating the two layers. Following further reflections the bore splits to a train of internal solitary waves, resembling the solutions of the KdV equation. We find that, interestingly, although the waves propagate along the internal interface, certain surface signatures of the bore and wave dynamics can be detected from the water surface temperature fields due to secondary convective flows. This result may have certain applicability for the detection of internal waves using infrared sea-surface temperature data from satellites.

How to cite: Vincze, M. and Kiss, Z. Á.: Laboratory experiments on internal solitary wave reflections and their detectability via water surface infrared thermography, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14424, https://doi.org/10.5194/egusphere-egu23-14424, 2023.

EGU23-14457 | Orals | NP6.1

Weakly nonlinear wave energy flux and radiation stress 

Paolo Pezzutto

It is known that the wave action propagated in spectral wave models is a small steepness approximation of the observable wave action. For relevant steepness, we need higher order corrections to get a proper representation of the sea states [Janssen, 2009]. For the same reasons, other diagnostic variables should be corrected. Based on the fifth order Stokes solution obtained by Fenton [1985], Jonsson and Arneborg [1995] showed the importance of higher order corrections for determining the energy properties of long crested waves.

Proceeding from Longuet-Higgins and Stewart [1960], assuming a mean stream velocity, we see that how, using Krasitskii [1994] canonical transformations, we can derive general 2D weakly non linear corrections to the rate of transfer of energy across a surface fixed in space. For a monochromatic wave, the resulting equations are compared with truncated expressions given by Jonsson and Arneborg [1995], confirming that second order contributions (in terms of wave energy) can be relevant, depending on steepness and relative water depth.
After applying a proper statistical closure, the derived equations can be used to correct the wave energy properties of wave models spectra, for example to refine the informations transferred to a coupled circulation model.

John D. Fenton. A Fifth-Order Stokes Theory for Steady Waves. Journal of Waterway, Port, Coastal, and Ocean Engineering, 111(2):216–234, 1985. ISSN 0733-950X.
Peter a. E. M. Janssen. On some consequences of the canonical transformation in the Hamiltonian theory of water waves. J. Fluid Mech., 637(November):1–44, 2009. ISSN 1469-7645.
Ivar G. Jonsson and Lars Arneborg. Energy properties and shoaling of higher-order stokes waves on a current. Ocean Engineering, 22(8):819–857, 1995. ISSN 00298018.
Vladimir P. Krasitskii. On reduced equations in the Hamiltonian theory of weakly nonlinear surface waves. J. Fluid Mech., 272(-1):1–20, 1994. ISSN 0022-1120.
M. S. Longuet-Higgins and R W Stewart. Changes in the form of short gravity waves on long waves and tidal currents. Journal of Fluid Mechanics, 8(04): 565–583, 1960.

 

How to cite: Pezzutto, P.: Weakly nonlinear wave energy flux and radiation stress, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14457, https://doi.org/10.5194/egusphere-egu23-14457, 2023.

EGU23-15036 | Orals | NP6.1

Resonant water-waves in a circular channel: forced KdV solutions 

Uwe Harlander, Franz-Theo Schön, Ion D. Borcia, Sebastian Richter, Rodica Borcia, and Michael Bestehorn

Tidal bores are natural phenomena observed in at least 450 river estuaries all around the world from Europe to America and Asia. Tidal bores manifest as a series of waves propagating over long distances upstream in the estuarine zone of a river. Bores can be studied experimentally using sloshing water tanks where sloshing itself is a process with many applications, not only relevant for environmental flows. In a remarkable paper, Cox and Mortell (1986) showed that for an oscillating water tank, the evolution of small-amplitude, long-wavelength, resonantly forced waves follow a forced Korteweg-de Vries (fKdV) equation. The solutions of this model agree well with experimental results by Chester and Bones (1968). At first glance this is surprising since their experimental setup is in conflict with a number of assumptions made for deriving the fKdV equation. It is hence worth to repeat the experiment by Chester and Bones but using a long narrow channel setup.

We use a long circular channel and repeat the experiments by Chester and Bones. We compare the results with solutions from the fKdV equation but also with the one from a full nonlinear model solving the Navier-Stokes equations. Under resonance conditions, depending on the parameters, we find a range of nonlinear localized wave types from single and multiple solitons to undular bores. As shown by Cox and Mortell, when the fluid is considered to be inviscid a kind of Fermi-Pasta-Ulam recurrence is observed for the fKdV model. Stationarity is reached by including a weak damping to the fKdV equation. 

References
A.A. Cox, M.P. Mortell 1986. J. Fluid Mech. 162, pp. 99-116.
W. Chester and J.A. Bones 1968. Proc. Roy. Soc. A, 306, 23 (Part II).

How to cite: Harlander, U., Schön, F.-T., Borcia, I. D., Richter, S., Borcia, R., and Bestehorn, M.: Resonant water-waves in a circular channel: forced KdV solutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15036, https://doi.org/10.5194/egusphere-egu23-15036, 2023.

EGU23-15066 | Orals | NP6.1

Breaking threshold and energy dissipation in solitary waves in a depth transition 

Wouter Mostert, Hunter Boswell, and Guirong Yan

Energy dissipation due to the breaking of surface waves remains an important open topic in both the open ocean and in coastal waters. Here we will discuss similarities between the deep- and shallow-water regimes. To do this, we first present data from direct numerical simulations of shoaling and breaking solitary waves in bathymetric depth transition. In an abrupt depth transition, we investigate the influence of the severity of the depth transition on whether the incident wave will break, finding good agreement with experimental data of Losada et al. (1988). We next investigate the energy dissipation rate in a gradual, linear depth transition. The resulting dataset is compared with an array of existing physics-based scaling arguments, and finds especially good agreement with an inertial model of Mostert & Deike (2020). We then discuss possible scaling approaches for understanding breaker dissipation in shallow water and draw comparisons with deep-water data and models. We will conclude with some insights towards a potential universal breaking parametrisation.

How to cite: Mostert, W., Boswell, H., and Yan, G.: Breaking threshold and energy dissipation in solitary waves in a depth transition, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15066, https://doi.org/10.5194/egusphere-egu23-15066, 2023.

EGU23-16032 | ECS | Orals | NP6.1

Imprint of ocean currents on signicant wave height 

Han Wang, Bia Villas Bôas, Jacques Vanneste, and William Young

Ocean currents have been observed to impact the spatial distribution of significant wave height (hereafter "Hs") of surface gravity waves profoundly, with implications for air-sea fluxes, extreme waves, and error budget in satellite observations. In this work, we derive analytic formulas that relate Hs to current velocities under the weak-current approximation, cross-validate the results with WAVEWATCH III, and find implications potentially useful for observational and modelling studies. 

First, we show that when swell-like surface waves interact with a localized current, caustics, where rays cross in real space, do not lead to singularities in Hs if the wave energy spectra have a realistic directional spread in wavenumber space. This has implications for understanding the origin of freak waves, where caustics have been postulated as a possible source. Then, we consider another regime where weak turbulent flows are considered. Analytic formulas are found that deterministically link the patterns of Hs to currents. The formulas' statistical counterparts are applied to study how the spectral slopes, amplitudes and directionality of Hs are related to currents. Our results demonstrate that the variations of Hs are controlled by the rotational component of the currents, suggesting the potential of using information from surface wave to infer current properties in real observations, or vice versa.

How to cite: Wang, H., Villas Bôas, B., Vanneste, J., and Young, W.: Imprint of ocean currents on signicant wave height, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16032, https://doi.org/10.5194/egusphere-egu23-16032, 2023.

EGU23-16131 | ECS | Posters on site | NP6.1

Lagrangian flow networks for passive dispersal: tracers versus finite-size particles 

Deoclécio Valente, Ksenia Guseva, and Ulrike Feudel

Passive dispersal of different materials in ocean flows has gotten considerable attention over the last decade to increase our knowledge about the distribution of seeds plants among islands and coastal areas, the transport of larvae of different organisms between habitats and the transport of litter. Most studies have treated these objects as tracers to investigate distribution patterns and connectivity between different areas. We compare this approach with a study that considers the objects' size and density and discusses the deviation from the tracer approach. To this end, we introduce a two-dimensional kinematic velocity field which allows us to study the connectivity between an arbitrary number of islands located at arbitrary but prescribed positions in an open flow of a given direction. First, the mixing induced by the islands, which act as obstacles in the flow, was accounted for with the inclusion of a von K\'arm\'an vortex street in the wake of each island. Furthermore, we accounted for the size and density of particles approximated as spheres. Finally, we treated the particles as inertial particles experiencing various forces in the flow and computed their trajectories in a given flow field by solving the Maxey-Riley equations. In this way, we have constructed a Lagrangian flow network reflecting the connectivity between islands depending on the properties of the finite-size particles and comparing them with the motion of tracers. We show that the density differences, the flow properties, and the islands' position geometry substantially change the connectivity between islands. That change leads to segregating inertial particles according to their size and density. Nevertheless, the most striking observation is how the tracer transport (independently of geometry) overestimates the probabilities for specific pathways. In fact, the connectivity for inertial particles is much sparser than for tracers, such that certain pathways have extremely low probabilities; they practically do not exist. These results suggest that the transport probabilities can be highly under or overestimated by tracers' often-used approximation of inertial particles.

How to cite: Valente, D., Guseva, K., and Feudel, U.: Lagrangian flow networks for passive dispersal: tracers versus finite-size particles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16131, https://doi.org/10.5194/egusphere-egu23-16131, 2023.

EGU23-16217 | Posters on site | NP6.1

A cumulant analysis of ocean waves fluctuations over the global ocean, using CFOSAT data 

Amine Benbelkacem, François Schmitt, and Yongxiang Huang

The China France Oceanography Satellite (CFOSAT) was launched in October, 2018 and records over the oceans the wind field as well as the ocean waves. We consider here the ocean wave data, which are given through the significant wave height (Hs). We analyse along-track fluctuations of $Hs$ by considering its fluctuations of the form $y_r = \Delta_r Hs = Hs(x+r)-Hs(x) $, with values of the spatial scale $r$ between 12.5 km and a global and large scale of 2000 km. For this we consider a cumulant approach: we estimate the cumulant generating function (of $\log y_r$) $\Psi(q) = \log <y_r^q>$. This function is considered in a log-stable framework, where its development is non-analytical of the form $\Psi(q)= C_1 q + C_{\alpha} q^{\alpha}$, where $C_1$ is the first cumulant ($C_1 = < \log y_r>$), $0< \alpha \leq 2$ is the non-analycity parameter and $C_{\alpha}$ a parameter. The analysis is done by partionning the global ocean into several oceans: Indian ocean, South and North Pacific, South and North Atlantic. The statistics of the three parameters are considered over the scale $r$ and for each ocean. This provides a global view of the significant wave height multi-scale fluctuations and is complementary to a previous analysis done using Fourier spectral analysis (Gao et al 2021).

How to cite: Benbelkacem, A., Schmitt, F., and Huang, Y.: A cumulant analysis of ocean waves fluctuations over the global ocean, using CFOSAT data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16217, https://doi.org/10.5194/egusphere-egu23-16217, 2023.

EGU23-16425 | ECS | Posters on site | NP6.1

Comparison of wavelet- and FFT-based bathymetry retrieval methods and its application to nearshore X-band radar image sequenses 

Pavel Chernyshov, Michael Stresser, Ruben Carrasco, and Jochen Horstmann

Wavelet- and Fast Fourier Transform (FFT)-based methods for bathymetry retrieval from X-band radar image sequences are compared and analyzed. Both methods utilize the similar idea of the waves' phase shift estimation using cross-spectral analysis. Within the FFT-based approach the corresponding technique is used to determine wave vector's components from the image sequence frequency decomposition. The last means that the time FFT is applied to the original image sequence. Then for each frequency slice the corresponding wavenumber is derived applying the cross-spectrum analysis of the one pixel shifted images in the corresponding spatial direction. In such a way a set of wavevector-frequency (k, ω) pairs are formed and filtered according to a confidence criterion that reflects the stability of the local phase pattern. In the case of a wavelet-based method the corresponding cross-spectral analysis is applied to the 2D Continuous Wavelet Transform (CWT) directional complex spectra for pairs of successive images, resulting in a set of wavevector-celerity (k, c) pairs. Further, the corresponding set of pairs are fitted to the unknown depth using nonlinear least-square method and finite water depth linear dispersion relationship as a model. Weights proportional to the spectral power density and confidence values are used in the fitting process for the wavelet- and FFT-based methods correspondingly. Furthermore, both methods are verified by applying it to stochastic simulations of corresponding shoaling sea elevation image sequences and real X-band radar image sequences collected near the Hofn tidal inlet (Iceland). For the wave simulations, a linear solution of a mild slope equation is utilized. In order to accout for the effects ofthe ambient currents, a ray-tracing technique is applied. As a testing case, the shoaling of an incident JONSWAP spectrum-based wavefields are evaluated both on the following and opposing currents. A radar image model including tilt and shadowing modulations together with speckle noise is further applied to the modeled surface elevations. Both methods are able to reconstruct the original bathymetry for intermediate to shallow water depths (kph<1.2) with plausible accuracy both for all the synthetic cases (with varied probing geometries, bottom topography, ambient current, and sea state conditions) and real radar data case. In the last case, the accuracy of the FFT-based method is on the level 0.7-0.9 m in terms of the mean absolute error value with fairly small bias the standard deviation of the error is also less than 1 m in the whole area studied except the tidal channel, where the depth gradients are significantly larger. The wavelet-based method showes a higher bias with comparable mean absolute error and standard deviation.

How to cite: Chernyshov, P., Stresser, M., Carrasco, R., and Horstmann, J.: Comparison of wavelet- and FFT-based bathymetry retrieval methods and its application to nearshore X-band radar image sequenses, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16425, https://doi.org/10.5194/egusphere-egu23-16425, 2023.

EGU23-16723 | Orals | NP6.1

Three-dimensionnal Wave-Current Interactions can significantly affect a Strong Tidal Current in a Complex Environment: Application to Alderney Race 

Anne-Claire Bennis, Lucille Furgerot, Pascal Bailly du Bois, Emmanuel Poizot, Yann Méar, and Franck Dumas

Due to the climate change, it is necessary to modify the energy modes of production. The mix energetic, based on renewable energies as tidal currents, is one of the solutions to decrease the energy production carbon footprint. This study focuses on hydrodynamic interactions in Alderney Race (France), which is the most energetic tidal site in Western Europe. The impact of a winter storm occurring during spring tide is assessed thanks to numerical modeling with a 3D fully-coupled wave-current model and in-situ data. Firstly, an analysis of the impacts of the storm on the wave field and the current effects on waves is performed. Then, the modifications of the current and tidal stream energy caused by waves are discussed. After a successful validation step with excellent PBIAS and R2 scores, the main finding are : i) although the current intensity is strong (around 3-4m/s), wave effectssignificantly change the vertical profile of the current, with a reduction of the PBIAS by a factor of 1.78 between simulations with and without wave effects, ii) ocean waves affect the tidal assymmetry, with a flood current whose intensity is 13% higher than for the ebb current, inducing a decrease of 30% in the tidal stream energy, iii) the flow is very sensitive to the angle between the directions of propagation of waves and current, with an acceleration or a reduction of the velocity, as observed in the presence of a 3D turbulent structure, iv) current effects on waves cause a wavenumber shift, changes in significant wave height (modulated by tide), wave direction due to refraction and an increase of the energy transfer from waves to ocean ascribed to the wave breaking. By a feedback mechanism, the modifications of the wave field by current and water level significantly alter the flow with a decrease of its velocity when waves propagate against current. This study shows that the 3D wave-current interactions need to be considered during a storm even during a spring tide event where currents are the strongest.

How to cite: Bennis, A.-C., Furgerot, L., Bailly du Bois, P., Poizot, E., Méar, Y., and Dumas, F.: Three-dimensionnal Wave-Current Interactions can significantly affect a Strong Tidal Current in a Complex Environment: Application to Alderney Race, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16723, https://doi.org/10.5194/egusphere-egu23-16723, 2023.

EGU23-16817 | ECS | Posters virtual | NP6.1

Comparison of CPU and GPU parallelization approaches between two programming languages in copepod model simulations 

Varshani Brabaharan, Sachithma Edirisinghe, and Kanchana Bandara

This study presents a comparative assessment to evaluate between two high performance computing languages, Java and FORTRAN for the computation vs. communication trade-off observed during a strategy-oriented copepod model simulation. Here we compared the computational time of (i) sequential processing, (ii) latency (CPU) and (iii) throughput (GPU) oriented designs. CPU based parallelization was accomplished on a 4-core Intel i7 processor with a clock speed of 1.99 GHz. On this CPU, we implemented a (i) fork/join framework design based on work-stealing algorithm in Java and (ii) Open Multi- Processing (OpenMP), a directive-based application programming interface (API) with shared memory architecture on FORTRAN 95. The GPU processing power was leveraged using the CUDA framework in Java and OpenACC API on FORTRAN on a NVIDIA GeForce MX230 with 256 unified pipelines. The simulation time for sequential CPU execution was ca. 41% lower in FORTRAN compared to Java (18 s vs. 25 s). Furthermore, the FORTRAN simulation was ca. 43% lower in execution time in latency-oriented CPU design compared to Java (10s vs. 13s). In the simulation regarding GPU-approach with unified memory space accessibility, Java computation consumed ca. 38% less time than FORTRAN (5s vs. 8s). Unlike FORTRAN, Java is purely an object-oriented language and therefore, object handling is not optimized in GNU compliers of FORTRAN. Nevertheless, memory consumption of FORTRAN can be fine-tuned thereby, decreasing latency unlike in Java. OpenMP API is based on self-consistency, shared memory architecture and its temporary view memory allows threads to cache variables and thereby reduce latency by avoid accessing the memory for each reference of variables unlike the fork/join framework in Java. Furthermore, OpenMP has a thread private memory, which allows efficient synchronization within the code. OpenACC is designed as a high-level platform, which is an independent abstract programming accelerator that offers a pragmatic alternative for accessing GPU programming without much programming effort. Nevertheless, some uses of unified memory space accessibility on NVIDIA GPU’s are better represented in CUDA despite OpenACC having a cache directive. Therefore, its best to investigate the performances of different accelerator models and different programming languages depending on the simulation needs and efficiency targets desired by the model.

Keywords: FORTRAN, Java, OpenMP, OpenACC, high-performance computing, copepods, modelling

How to cite: Brabaharan, V., Edirisinghe, S., and Bandara, K.: Comparison of CPU and GPU parallelization approaches between two programming languages in copepod model simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16817, https://doi.org/10.5194/egusphere-egu23-16817, 2023.

Phytoplankton populations have been in a steep decline in the elbe estuary since several decades. Previous studies using concentration based biochemical models helped to further the understanding of the ecosystem in general but fail to pinpoint specific reasons due to their high complexity.

We approach this problem with a novel langrangian model. By explicitly simulating phytoplankton trajectories, we are able to examine bathymetry-related effects. These effects can play a big role in the Elbe estuary due the high average depth in the navigational channel of Hamburg’s harbor.  In detail, or model represent processes like turbulent dispersion, vertical migration and stranding mechanics to study this problem. To our knowledge this is the first time that this problem is tackled with such a method that includes biological processes in an estuarine context.

We will present results from experiments looking at Phytoplankton retention mechanics to avoid outwashing and depth related mortality in the navigational channel.

How to cite: Steidle, L.: Phytoplankton trajectories in the Elbe estuaries - examinig retention and die-off in the Hamburg harbor, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16913, https://doi.org/10.5194/egusphere-egu23-16913, 2023.

EGU23-17262 | ECS | Posters on site | NP6.1

Internal tides off the Amazon shelf: importance to structure ocean's temperature during two contrasted seasons 

Fernand Assene, Ariane Koch-Larrouy, Isabelle Dadou, Michel Tchilibou, Guillaume Morvan, Jérôme Chanut, Vincent Vantrepotte, Damien Allain, and Trung-Kien Tran

Tides and internal tides (IT) in the ocean can significantly affect local to regional ocean temperature and even sea surface temperature (SST), via processes such as vertical mixing, vertical advection and transport of water masses. Offshore of the Amazon River, IT have already been detected and studied; however, their impact on temperature, SST and associated processes are not known in this region. In this work, we use high resolution (1/36°) numerical simulations with and without the tides from an ocean circulation model (NEMO) which explicitly resolves the internal tides (IT), to assess how they can affect ocean temperature in the studied area. We distinguish the analysis for two contrasted seasons, from April to June (AMJ) and from August to October (ASO), since the seasonal stratification off the Amazon River modulates the IT’s response and their influence in temperature.  

The IT are well reproduced by the model, and are in good agreement with observations, for both their generation and their propagation. The simulation with tides is in better agreement with satellite SST data compared to the simulation without tides. During ASO season, stronger meso-scale currents, deeper and weaker pycnocline are observed in contrast to the AMJ season. Results show that the observed coastal upwelling during ASO season is well reproduced by the model including tides, whereas the no-tide simulation is too warm by +0.3 °C at sea surface. In the subsurface above the thermocline, the tide simulation is cooler by -1.2 °C, and warmer below the thermocline by +1.2 °C compared to the simulation without the tides. The study further highlights that the IT induce vertical mixing on their generation site along the shelf break and on their propagation pathways towards the open ocean. This process explains the cooler temperature at the ocean surface and in the subsurface water above the thermocline and a warming in the deeper layers (below the thermocline). The surface cooling induced in turn an increase of the net heat flux from the atmosphere to the ocean surface, which could induce significant changes in the local and even for the regional tropical Atlantic atmospheric circulation and precipitation. We therefore demonstrate that IT, mainly via vertical diffusivity along their propagation pathways of approximately 700 km offshore, and tides over the continental shelf, play a key role on the temperature structure off the Amazon River mouth, particularly in the coastal cooling enhanced by IT.  

How to cite: Assene, F., Koch-Larrouy, A., Dadou, I., Tchilibou, M., Morvan, G., Chanut, J., Vantrepotte, V., Allain, D., and Tran, T.-K.: Internal tides off the Amazon shelf: importance to structure ocean's temperature during two contrasted seasons, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17262, https://doi.org/10.5194/egusphere-egu23-17262, 2023.

EGU23-17324 | Orals | NP6.1

Inertial torque on a squirmer 

Bernhard Mehlig, Fabien Candelier, Jingran Qiu, Lihao Zhao, and Greg Voth

A small spheroid settling in a quiescent fluid experiences an inertial torque that aligns it so that it settles with its broad side first. Here we show that an active particle experiences such a torque too, as it settles in a fluid at rest. For a spherical squirmer, the torque is T = -9/8  mf (vs(0)  x vg(0)), where vs(0)  is the swimming velocity, vg(0) the settling velocity in the Stokes approximation, and mf the equivalent fluid mass. This torque aligns the swimming direction against gravity: swimming up is stable, swimming down is unstable. This talk is based on Candelier, F., Qiu, J., Zhao, L., Voth, G., & Mehlig, B. (2022). Inertial torque on a squirmer. Journal of Fluid Mechanics, 953, R1.

How to cite: Mehlig, B., Candelier, F., Qiu, J., Zhao, L., and Voth, G.: Inertial torque on a squirmer, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17324, https://doi.org/10.5194/egusphere-egu23-17324, 2023.

EGU23-3017 | ECS | Orals | NP6.2 | Highlight

Energy Conversion and Partition in Plasma Turbulence Driven by Magnetotail Reconnection 

Xinmin Li, Rongsheng Wang, Can Huang, Quanming Lu, and San Lu

A long-outstanding issue in fundamental plasma physics is how magnetic energy is finally dissipated in kinetic scale in the turbulent plasma. Based on the Magnetospheric Multiscale mission data in the plasma turbulence driven by magnetotail reconnection, we establish the quantitative relation between energy conversion (J·E , J is current density and E is electric field) and current density (J). The results show that the magnetic energy is primarily released in the perpendicular directions (up to 90%), in the region with current density less than 2.3 Jrms, where Jrms  is the root mean square value of the total current density J. In the relatively weak current region (< 1.0 Jrms ), the ions get most of the released energy while the largely negative energy conversion rate of the electrons means a dynamo action. In the strong currents (>1.0 Jrms), the ion energization was negligible and the electrons are significantly energized. Moreover, a linearly increasing relationship was established between J·E and J. The observations indicate that ions overall dominate energy conversion in turbulence, but the electron dynamics are crucial for energy conversion in intense currents and the turbulence evolution.

How to cite: Li, X., Wang, R., Huang, C., Lu, Q., and Lu, S.: Energy Conversion and Partition in Plasma Turbulence Driven by Magnetotail Reconnection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3017, https://doi.org/10.5194/egusphere-egu23-3017, 2023.

EGU23-3604 | ECS | Orals | NP6.2

Swift generator for 3D magnetohydrodynamic turbulence 

Daniela Maci, Rony Keppens, and Fabio Bacchini

Turbulent states of motion are almost unavoidable in fluids, gases, and plasmas. The ubiquitous presence of turbulence largely contributes to the central role that its study holds in many research fields. This work focuses on space and astrophysical plasmas, where magnetohydrodynamic turbulence is observed nearly everywhere. However, it builds on an issue that is shared by all turbulence-related field of studies: direct numerical simulations (DNS), required to verify turbulent states properties such as scaling law behaviors, require substantial computing resources.

The presentation will introduce the audience to BxC[1], an analytic generator of realistic-looking turbulent magnetic fields, that computes 3D O(10003 grid points) solenoidal vector fields in minutes to hours on desktops. The model is inspired by recent developments in 3D incompressible fluid turbulence theory: intermittent, multifractal random fields are generated through non-linear transformations of a Gaussian white noise vector, combined to specifically designed geometrical constructions. Furthermore, the model is implemented starting from a modified Biot-Savart law, which allows for a clear interpretation of the BxC parameters.

The turbulent magnetic field realized with BxC is then compared and validated against a much more computationally expensive DNS in terms of: (i) characteristic sheet-like structures of current density, (ii) volume-filling aspects across current intensity, (iii) power-spectral behaviour, (iv) probability distribution functions of increments for magnetic field and current density, structure functions, spectra of exponents, and (v) partial variance of increments.

 

[1] Durrive, J.-B., Changmai, M., Keppens, R., Lesaffre, P., Maci, D., and Momferatos, G. (2022). Swift generator for three-dimensional magnetohydrodynamic turbulence. Phys. Rev. E, 106:025307

How to cite: Maci, D., Keppens, R., and Bacchini, F.: Swift generator for 3D magnetohydrodynamic turbulence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3604, https://doi.org/10.5194/egusphere-egu23-3604, 2023.

EGU23-5887 | ECS | Posters on site | NP6.2

Ion Temperature Anisotropy in Plasma Jets 

Louis Richard, Yuri V. Khotyaintsev, Daniel B. Graham, Andris Vaivads, Daniel J. Gershman, and Christopher T. Russell

Magnetotail magnetic reconnection results in fast plasma flows referred to as jets. Reconnection jets are populated with complex non-Maxwellian ion distributions providing a source of free energy for the micro-instabilities, which contribute to the ion heating in the reconnection region. We present a statistical analysis of the ion temperature anisotropy in magnetic reconnection jets using data from the Magnetospheric Multiscale spacecraft. Compared with the quiet plasma in which the jet propagates, we often find anisotropic and non-Maxwellian ion distributions in the plasma jets. We observe magnetic field fluctuations associated with unstable ion distributions, but the wave amplitude is not large enough to scatter ions during the observed lifetime of the jet. Our estimate of the phase-space diffusion due to chaotic and quasi-adiabatic ion motion in the current sheet shows that the diffusion is sufficiently fast to be the main process leading to isotropization.

How to cite: Richard, L., Khotyaintsev, Y. V., Graham, D. B., Vaivads, A., Gershman, D. J., and Russell, C. T.: Ion Temperature Anisotropy in Plasma Jets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5887, https://doi.org/10.5194/egusphere-egu23-5887, 2023.

Magnetic reconnection interlinks different regions of plasmas by converting magnetic energy into plasma heating and energization of particles. It causes abrupt changes in the temperature, density, field strength and flow speed. The physical mechanism behind charge particle energization during magnetic reconnection is best explained by the concept of double layers (DLs) and associated parallel electric fields. In-situ observations of reconnection sites by Magnetospheric Multi Scale (MMS), THEMIS and FAST have confirmed that charge particle energization in these regions is associated with large parallel electric fields in auroral regions, Earth’s plasma sheet and separatrix region of Earth’s magnetosphere. The reported literature motivated us to investigate double layers and associated electric field at the reported sites by using multi-fluid theory for electron-ion plasma and employing fully nonlinear Sagdeev potential approach. We have considered the ion inertial effect whereas electrons are assumed to be non-Maxwellian following (r, q) distribution function. In particular, parallel electric fields associated with Alfvenic double layer have been investigated at non-Maxwellian effective temperature scales and then compared with the observations. We have seen that the characteristics of DLs associated with the kinetic Alfvén waves are significantly modified due to the nonthermal parameters r and q, propagation angle 𝜃, and Alfvénic Mach number 𝑀A. Our current study supports both the compressive and rarefactive double layer structures.

How to cite: Khalid, S. and Qureshi, M. N. S.: Parallel Electric Field and Double Layers at Non-Maxwellian Effective Temperature Scales in Near Earth Space Plasmas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5905, https://doi.org/10.5194/egusphere-egu23-5905, 2023.

EGU23-6253 | Orals | NP6.2 | Highlight

Turbulent energy transfer and dissipation in the terrestrial magnetosheath 

Zoltan Vörös, Owen Wyn Roberts, Luca Sorriso-Valvo, Emiliya Yordanova, Yasuhito Narita, Rumi Nakamura, and Ferdinand Plaschke

The terrestrial magnetosheath (MS) represents a turbulent, high-beta, compressional, sporadically Alfvenic environment which contains the shocked solar wind (SW) magnetized plasma permeated with waves, instabilities and structures of various origins. In the processes of interaction of the structured SW with the shock and the MS, the electromagnetic, kinetic and thermal energies are transported between locations,  transferred between scales, conversed between each other and finally dissipated. Similarly to the SW case the energy transfer in MS is expected to be manifested in typical scalings seen in power spectral densities of various field and plasma parameters  over the fluid (inertial-range) and kinetic ion-electron scales. However, near the sub-solar dayside MS the inertial-range turbulent cascade is usually absent, while the kinetic range scaling roughly remains the same as in the SW. Observations of short magnetic correlation lengths near the sub-solar MS also confirm the absence of large-scale magnetic fluctuations which could populate the inertial-range of scales. Without the inertial range energy cascade the kinetic range turbulence should exhibit a fast decay downstream of the shock, but it is not observed. We argue that to understand the spectral scalings in the MS the whole energy budget has to be considered including possible nonlocal energy transfer terms. By using MMS data in the MS we show that, when the inertial range is present, the turbulent energy dissipation rate can be estimated by the energy transfer rate from both the Yaglom law and from the pressure-strain interaction term. When the inertial range is absent and the Yaglom law cannot be used,  the dissipation rate can still be estimated by using the pressure-strain term.

How to cite: Vörös, Z., Roberts, O. W., Sorriso-Valvo, L., Yordanova, E., Narita, Y., Nakamura, R., and Plaschke, F.: Turbulent energy transfer and dissipation in the terrestrial magnetosheath, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6253, https://doi.org/10.5194/egusphere-egu23-6253, 2023.

EGU23-6702 | ECS | Orals | NP6.2 | Highlight

Electron-scale reconnecting current sheet formed within the lower hybrid wave-active region of Kelvin-Helmholtz waves 

Kevin Alexander Blasl, Takuma Nakamura, Rumi Nakamura, Adriana Settino, Zoltan Vörös, Martin Hosner, Daniel Schmid, Martin Volwerk, Owen Wyn Roberts, Evgeny Panov, Yi-Hsin Liu, Ferdinand Plaschke, Hiroshi Hasegawa, Julia Stawarz, and Justin Craig Holmes

The Kelvin-Helmholtz instability (KHI) excited at the Earth’s magnetopause has been considered responsible for causing efficient mass and energy transfer across the magnetopause. Theoretical, numerical and observational studies have revealed that the evolution of the KHI and the resulting nonlinear vortex flow involve secondary processes. As a unique case of such multi-scale and inter-process couplings, we recently reported observations of the MHD-scale KH waves and embedded smaller-scale phenomena in data from NASA’s Magnetospheric Multiscale (MMS) mission at the dusk- flank magnetopause during southward interplanetary magnetic field (IMF) conditions. Given quantitative consistencies with corresponding fully-kinetic particle-in-cell (PIC) simulations designed for this event, the MMS observations demonstrate the onset of the Lower-Hybrid Drift Instability (LHDI) during the nonlinear phase of the KHI and the subsequent turbulence and mixing of plasmas near the boundary layer.

In this study, we further explored this southward IMF KHI event and found signatures of magnetic reconnection in an electron-scale current sheet observed in the KH vortex-driven LHDI turbulence. This reconnection event was observed under high guide field conditions and features a super-Alfvénic electron outflow, a Hall perturbation of the magnetic field and enhanced energy conversion. Results from a high-resolution PIC simulation designed for this reconnecting current sheet suggest a highly dynamical current sheet evolution, quantitatively consistent with the observations made by MMS.

In addition, results from statistical studies utilizing data from several KH wave/vortex edge crossings throughout this southward IMF KH event show that the formation of electron-scale current sheets due to the interplay of the KHI and LHDI would be a ubiquitous phenomenon at least under the observed conditions of this magnetopause event and thus an important factor in the study of cross-scale energy transfer of the KHI.

How to cite: Blasl, K. A., Nakamura, T., Nakamura, R., Settino, A., Vörös, Z., Hosner, M., Schmid, D., Volwerk, M., Roberts, O. W., Panov, E., Liu, Y.-H., Plaschke, F., Hasegawa, H., Stawarz, J., and Holmes, J. C.: Electron-scale reconnecting current sheet formed within the lower hybrid wave-active region of Kelvin-Helmholtz waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6702, https://doi.org/10.5194/egusphere-egu23-6702, 2023.

EGU23-8086 | ECS | Posters on site | NP6.2 | Highlight

Energy conversion by magnetic reconnection in multiple ion temperature plasmas 

Jeremy Dargent, Sergio Toledo-Redondo, Andrey Divin, and Maria Elena Innoncenti

This work investigates the energy transfer in the process of collisionless antiparallel magnetic reconnection and its dependance to the velocity distribution function of the inflowing plasma. We realised two two-dimensional semi-implicit PIC simulations of symmetric reconnection with exactly the same global parameters, but with different distributions of plasma: one simulation is loaded using Maxwellian distributions, while the other is the sum of two Maxwellian distributions, a hot one and a cold one, resulting in a very peaked distribution with large tails. We measure the increase of the bulk and thermal kinetic energies in both simulation for each population and compare it to the loss of magnetic energy through a contour surrounding the ion diffusion region. We show that the global energy budget for ions and electrons does not change depending on the distribution function of the plasma, but also that, when focusing on sub-populations, the hot ion population (i.e. the tail of the distribution) get more thermal energy than the cold ion population (i.e. the core of the distribution).

How to cite: Dargent, J., Toledo-Redondo, S., Divin, A., and Innoncenti, M. E.: Energy conversion by magnetic reconnection in multiple ion temperature plasmas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8086, https://doi.org/10.5194/egusphere-egu23-8086, 2023.

EGU23-9773 | Posters virtual | NP6.2

Flow Crossover during Collisionless Magnetic Reconnection: A Particle-Labelling Particle-in-Cell Study 

Kittipat Malakit, Theerasarn Pianpanit, Pakkapawn Prapan, David Ruffolo, Peera Pongkitiwanichakul, Michael Shay, Paul Cassak, and Piyawat Suetrong

During 2D magnetic reconnection, plasma is normally understood to flow from one of the inflow sides into the diffusion region and then turn sharply and join the outflow on the same side. Using particle-in-cell simulations with a modification to allow us to label ions and electrons by their initial locations, we find that inflowing plasma does not join the outflow on the same side; instead, plasma crosses to the other inflow side before changing direction to produce an outflow jet. Furthermore, we find that ions and electrons undergo different crossover mechanisms leading to different crossing patterns. The ion crossover occurs more locally within the ion diffusion region whereas the electron crossover occurs over a wider region as its mechanism does not require electrons to pass through the electron diffusion region. This flow crossover occurs both in symmetric reconnection and in a more complex scenario such as a guide-feld asymmetric reconnection, suggesting that it is a general feature of collisionless magnetic reconnection. Recognizing the existence of the flow crossover can be important in improving our understanding of reconnection in many situations. This research has been partially supported by Thailand's National Science and Technology Development Agency (NSTDA): High-Potential Research Team Grant Program (N42A650868), grant MRG6180176 from Thailand Science Research and Innovation, and by a grant from Kasetsart University Research and Development Institute.

How to cite: Malakit, K., Pianpanit, T., Prapan, P., Ruffolo, D., Pongkitiwanichakul, P., Shay, M., Cassak, P., and Suetrong, P.: Flow Crossover during Collisionless Magnetic Reconnection: A Particle-Labelling Particle-in-Cell Study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9773, https://doi.org/10.5194/egusphere-egu23-9773, 2023.

EGU23-10328 | ECS | Posters virtual | NP6.2

Analysis on magnetic field gradients in turbulent magnetosheath by using MMS data 

Yong Ji, Chao Shen, Lan Ma, Nian Ren, and Nisar Ahmad

Magnetic field gradients determine magnetic topological structure and current density in space plasma turbulence. This study uses multi-point method analyze high-quality field and plasma data measured by the Magnetospheric Multiscale (MMS) mission in the turbulent magnetosheath. The statistical properties of the curvature of the magnetic field line and the geometric invariant of the magnetic field gradient tensor are further investigated. The results show that the probability distribution function of curvature has two scaling laws. There is a correlation between large curvatures and pressure anisotropy, indicating the acceleration due to curvature drifts. During strong magnetic field, flux ropes and tubes are the most possible magnetic structures. Statistics in the plane formed by geometrical invariants show that about 23% are force free structures consist of 20.5% flux tubes and 79.5% flux ropes. The remaining actively evolved structures are comprised of 30% flux tubes and 70% flux ropes. Moreover, the conditional average of current density and Lorentz force decomposition in geometrical invariants plane are conducted. Results show that flux ropes carried more current density than flux tubes for same geometrical invariants, and flux ropes tend to associate with magnetic pressure force and flux tubes tend to associate with magnetic tension.

How to cite: Ji, Y., Shen, C., Ma, L., Ren, N., and Ahmad, N.: Analysis on magnetic field gradients in turbulent magnetosheath by using MMS data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10328, https://doi.org/10.5194/egusphere-egu23-10328, 2023.

EGU23-10735 | Orals | NP6.2

Statistics of the high-speed electron flows in the magnetotail 

Huijie Liu, Wenya Li, Binbin Tang, Cecilia Norgren, Daniel Graham, Yuri Khotyaintsev, Daniel Gershman, James Burch, and Chi Wang

High-speed electron flows play an important role in the energy dissipation and conversion in the terrestrial magnetosphere and are widely observed in regions related to magnetic reconnection, e.g., the vicinity of electron diffusion regions (EDRs), and separatrix layers. NASA’s Magnetospheric Multiscale mission was designed to resolve the electron-scale kinetic processes of Earth’s magnetosphere. Here, we perform a systematic survey of high-speed electron flows in the terrestrial magnetotail using the MMS observations from 2017 to 2021. The high-speed electron flows are characterized by electron bulk speeds larger than 5000 km/s. We identified 649 events. Those events demonstrate unambiguous dawn-dusk asymmetry, and 73% of them locate in the dusk magnetotail. The selected events are found in EDRs, the reconnection separatrix boundary layer, and the lobe region. More than 70% of the events are identified in the separatrix boundary layer and the lobe region and are aligned with the ambient magnetic field. 75 cases, with magnetic field magnitude smaller than 5 nT, locate near the plasma-sheet neutral line. Approximately 20 cases among them have EDR signatures, and those high-speed electron flows are directed arbitrarily with respect to the ambient magnetic field. We also show other statistical properties of the events, including electron bulk speed, electron number density, and temperature anisotropy. 

How to cite: Liu, H., Li, W., Tang, B., Norgren, C., Graham, D., Khotyaintsev, Y., Gershman, D., Burch, J., and Wang, C.: Statistics of the high-speed electron flows in the magnetotail, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10735, https://doi.org/10.5194/egusphere-egu23-10735, 2023.

EGU23-11279 | Orals | NP6.2

Electron dynamics in guide-field magnetic reconnection 

Binbin Tang, Hanwen Wang, Wenya Li, Yongcun Zhang, Daniel Graham, Yuri Khotyaintsev, Chunhui Gao, Xiaocheng Guo, and Chi Wang

Magnetic reconnection is a fundamental process that rapidly converts energy from the magnetic field to plasma. Recent studies have shown that a large parallel electric field (E) can appear in guide-field reconnection, and its magnitude can be several times larger than the reconnection electric field. However, the generation of this large E is still not fully understood, and the reaction of electrons to this E has not been fully investigated. In this study, we focus on these issues in a strong guide-field reconnection event (the normalized guide field is ~ 1.5) from Magnetospheric Multiscale (MMS) observations. With the presence of a large E in the electron current sheet, electrons are accelerated when streaming into this E region from one direction, and decelerated from the other direction. Some decelerated electrons can reduce the parallel speed to ~ 0 to form relatively isotropic electron distributions at one side of the electron current sheet, as the estimated acceleration potential (Φ ~ 2 kV) satisfies the relation eΦ ≥ kT, where T is the electron temperature parallel to the magnetic field. Therefore, a large E is generated to balance the parallel electron pressure gradient across the electron current sheet, since electrons at the other side of the current sheet are still anisotropic. Based on these observations, we further show that the electron beta is an important parameter in guide-field reconnection, providing a new perspective to solve the large parallel electric field puzzle in guide-field reconnection.

How to cite: Tang, B., Wang, H., Li, W., Zhang, Y., Graham, D., Khotyaintsev, Y., Gao, C., Guo, X., and Wang, C.: Electron dynamics in guide-field magnetic reconnection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11279, https://doi.org/10.5194/egusphere-egu23-11279, 2023.

EGU23-11615 | Posters on site | NP6.2

Electron-scale dynamics and generalized Ohm’s law of an MMS X-line encounter on 27 August 2018 

Wenya Li, Binbin Tang, and Chi Wang

Magnetic reconnection is a fundamental process in collisionless space plasma, and the electron-scale kinetic physics at the X line controls how the magnetic field lines break and reconnect. The four spacecraft of the Magnetospheric Multiscale (MMS) mission encountered an X line of symmetric reconnection in the terrestrial magnetotail on 27 August 2018. Here, we present the electron-scale dynamics and the generalized Ohm’s law (GOL) analysis of this case. Its two-dimensional structure, magnetic topology, and electron streamline map are reconstructed based on a time-independent and inertialess form of electron magnetohydrodynamic (eMHD) equation. We map the electron velocity distribution functions (VDFs) along the MMS trajectories through the X line, covering the two-side inflow and reconnected regions, and the typical electron motions for forming the observed VDFs are also presented. The observed reconnection electric field EM is approximately 2-3 mV/m and predominantly balanced by the spatial gradient of the electron pressure off-diagonal term PeMN, which is mostly contributed by the electron meandering motion at the X line. Our results show the electron-scale dynamics and the associated electron VDFs at an X line and their role in the electron force balance.

How to cite: Li, W., Tang, B., and Wang, C.: Electron-scale dynamics and generalized Ohm’s law of an MMS X-line encounter on 27 August 2018, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11615, https://doi.org/10.5194/egusphere-egu23-11615, 2023.

EGU23-11637 | Posters on site | NP6.2

A numerical study of tearing instability growth rate as a function of current sheet thickness in the kinetic regime 

Maria Elena Innocenti, Fulvia Pucci, Elisabetta Boella, Anna Tenerani, and Jeremy Dargent

In this study, we use fully kinetic Particle In Cell (PIC) simulations to investigate numerically the dispersion relation of the tearing instability in the kinetic regime, which is at the moment rather poorly explored by theoretical investigations. To reduce the computational cost of the simulations, we use the the semi-implicit, energy conserving ECsim code (Lapenta et al, 2017), that allows us to step over the smaller scales and fastest frequencies and focus on characteristic scales of interest, with excellent energy conservation.

We run several simulations with current sheets of fixed length. The current sheet half-thickness is progressively increased from $\delta \sim d_i$ to significantly larger. The other simulation parameters are kept identical.

In our simulations, the tearing instability grows without external perturbation from the particle noise of PIC simulations. Later onset times are (predictably) observed when the number of particles per cell is increased.

Several modes grow unstable in each simulation. We plot the growth rates of the unstable modes as a function of the current sheet thickness. We obtain a spread around a curve decreasing with increasing current sheet thickness. 

How to cite: Innocenti, M. E., Pucci, F., Boella, E., Tenerani, A., and Dargent, J.: A numerical study of tearing instability growth rate as a function of current sheet thickness in the kinetic regime, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11637, https://doi.org/10.5194/egusphere-egu23-11637, 2023.

EGU23-12262 | ECS | Orals | NP6.2

Plasma mixing during active Kelvin-Helmholtz instability at the Earth’s magnetopause under different interplanetary magnetic field configurations 

Adriana Settino, Rumi Nakamura, Kevin A. Blasl, Takuma Nakamura, Denise Perrone, Francesco Valentini, Owen Wyn Roberts, Evgeny Panov, Zoltan Vörös, Martin Volwerk, Daniel Schmid, Martin Hosner, Daniel B. Graham, and Yuri V. Khotyaintsev

The Kelvin-Helmholtz (KH) instability is a shear-driven instability commonly observed at the Earth’s magnetopause under different solar wind conditions. The evolution of the KH instability is characterised by the nonlinear coupling of different modes, which tend to generate smaller and smaller vortices along the shear layer. Such a process leads to the conversion of energy due to the large-scale motion of the shear flow into heat contributing to the local heating and the generation of a turbulent environment. On the other hand, it allows the entry of the dense and cold solar wind plasma into the tenuous and hot magnetosphere, thus favoring the mixing of these two different regions.

In this context, we introduce a new quantity, the so-called mixing parameter, which can identify the vortex boundaries and distinguish among different types of KH structures crossed by the spacecraft. The mixing parameter exploits the well distinct particle energies which characterise the magnetosphere and magnetosheath plasmas by using only single-spacecraft measurements [1]. The mixing parameter is therefore used to conduct a statistical analysis of the evolution of KH structures observed by the Magnetospheric Multiscale mission in the near Earth’s environment for two specific interplanetary magnetic field configurations: northward and southward. Moreover, in situ measurements are compared with kinetic KH instability simulations modeling realistic conditions observed by the satellites. The good agreement between synthetic data and in situ observations further strengthen our interpretation of the mixing parameter features and results.

 

[1] Settino, A., et al. (2022) Journal of Geophysical Research: Space Physics, 127, e2021JA029758.

How to cite: Settino, A., Nakamura, R., Blasl, K. A., Nakamura, T., Perrone, D., Valentini, F., Roberts, O. W., Panov, E., Vörös, Z., Volwerk, M., Schmid, D., Hosner, M., Graham, D. B., and Khotyaintsev, Y. V.: Plasma mixing during active Kelvin-Helmholtz instability at the Earth’s magnetopause under different interplanetary magnetic field configurations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12262, https://doi.org/10.5194/egusphere-egu23-12262, 2023.

EGU23-13090 | ECS | Posters on site | NP6.2

The influence of multiscale fractal geometry on the generation of turbulence 

Otman Ben Mahjoub and Aziz Ouadoud

The present research aims to study the influence of multiscale fractal geometry on the generation and decay of turbulence by spaced fractal square grid (SFSG) in order to understand how the turbulent flow is modified when it is generated at different scales. Velocity measurements were made in an open-circuit suction wind tunnel at various positions downstream of the grid in the streamwise and spanwise direction for three different inlet velocities using a constant temperature hot wire anemometer. The SFSG pattern producing a multiscale forcing of velocity is new and is the one used as the basis for this project. It was found that this space-filling grid model with relatively low solidity has the ability to generate turbulence with high turbulence intensity and high Reynolds numbers compared to the turbulence generated by fractal square grid (FSG) and regular grids at the same flow velocity. A more comprehensive understanding of this type of multiple length scales in momentum and energy transport has a key role to understand the analysis of structural implications due to the pollutant dispersion in the atmosphere.

How to cite: Ben Mahjoub, O. and Ouadoud, A.: The influence of multiscale fractal geometry on the generation of turbulence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13090, https://doi.org/10.5194/egusphere-egu23-13090, 2023.

EGU23-14458 | ECS | Posters on site | NP6.2

Modelling magnetic turbulence with log-normal intermittency by continuous cascades 

Jeremiah Lübke, Frederic Effenberger, Horst Fichtner, and Rainer Grauer

The transport of cosmic rays in turbulent magnetic fields is commonly investigated by solving the Newton-Lorentz equation of test particles in synthetic turbulence fields. These fields are typically generated from superpositions of Fourier modes with prescribed power spectrum and uncorrelated random phases, bringing the advantage of covering a wide range of turbulence scales at manageable computational effort. However, almost all of these models to date only account for second-order Gaussian statistics and thus fail to include intermittent features. Recent observations of the solar wind suggest that astrophysical magnetic fields are strongly non-Gaussian, and the question of how such higher-order statistics impact cosmic ray transport has only received limited attention. To address this, we present an algorithm for generating synthetic turbulence based on Kolmogorov’s log-normal model of intermittency. It generates a divergence-free magnetic field by computing the curl of a vector potential, which in turn is obtained from an inverse wavelet transform of a continuous log-normal cascade process. We investigate the statistics of the generated fields, show that anomalous scaling properties are accurately reproduced and discuss implications on cosmic ray transport. *Supported by DFG (SFB 1491)

How to cite: Lübke, J., Effenberger, F., Fichtner, H., and Grauer, R.: Modelling magnetic turbulence with log-normal intermittency by continuous cascades, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14458, https://doi.org/10.5194/egusphere-egu23-14458, 2023.

EGU23-15616 | Orals | NP6.2

On the nature of electric field fluctuations in the near-Sun solar wind and its implication for the turbulent energy transfer at ion and electron scales 

Luca Franci, Emanuele Papini, Daniele Del Sarto, Alfredo Micera, Julia Stawarz, Tim Horbury, Giovanni Lapenta, Harry Lewis, Chadi Salem, Simone Landi, Petr Hellinger, Lorenzo Matteini, Antonio Cicone, Mirko Piersanti, Maria Elena Innocenti, Milan Maksimovic, and David Burgess

We model plasma turbulence in the near-Sun solar wind by means of a high-resolution fully kinetic simulation initialised with average plasma conditions measured by Parker Solar Probe during its first solar encounter. Once turbulence is fully developed, the power spectra of the plasma and electromagnetic fluctuations exhibit clear power-law intervals down to sub-electron scales. Our simulation models the electron-scale electric field fluctuations with unprecedented accuracy. This allows us to perform the first detailed analysis of the different terms of the electric field in the generalised Ohm's law (MHD, Hall, and electron pressure terms) at ion and electron scales, both in physical space and in Fourier space. Such analysis suggests rewriting the Ohm’s law in a different form, which disentangles the contribution of different underlying plasma mechanisms, characterising the nature of the electric field fluctuations in the different range of scales. This provides a new insight on how energy in the turbulent electromagnetic fields is transferred through ion and electron scales and seems to favour the role of pressure-balanced structures versus waves. We finally test our assumptions and numerical results by means of a statistical analysis using magnetic field, electric field, and electron density data from Solar Orbiter and Parker Solar Probe. Preliminary results show good agreement with our theoretical expectations inspired by our simulation.

How to cite: Franci, L., Papini, E., Del Sarto, D., Micera, A., Stawarz, J., Horbury, T., Lapenta, G., Lewis, H., Salem, C., Landi, S., Hellinger, P., Matteini, L., Cicone, A., Piersanti, M., Innocenti, M. E., Maksimovic, M., and Burgess, D.: On the nature of electric field fluctuations in the near-Sun solar wind and its implication for the turbulent energy transfer at ion and electron scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15616, https://doi.org/10.5194/egusphere-egu23-15616, 2023.

EGU23-15797 | Posters on site | NP6.2 | Highlight

Stability properties of a new anisotropic current sheet equilibrium for planetary magnetotails 

Patricio A. Munoz, Xiaowei Zhou, and Jörg Büchner

Current sheets are the fundamental structures that store magnetic energy in astrophysical plasmas, such as planetary magnetospheres and solar flares. This free energy can then be explosively released by magnetic reconnection. This process has been traditionally modeled by highly idealized models such as the so-called Harris current sheet equilibrium. But recently, a new class of current sheet equilibrium has been analytically  developed, which takes into account several features of recently observed current sheets in planetary magnetotails. Those features include an embedded multi-layer structure, electron temperature anisotropy and a non-linear magnetic field profile in the (inner) electron inner layer which also includes a normal magnetic field component.
Here we present the analysis of the so-far unknown stability properties of this new current sheet equilibrium by means of fully kinetic Particle-in-Cell (PIC) numerical simulations. We used parameters appropriate for the current sheets in diverse planetary magnetotails.
Our results allow us to make more realistic predictions concerning the development of magnetic reconnection in those magnetotails compared to the standard Harris current sheet models.

How to cite: Munoz, P. A., Zhou, X., and Büchner, J.: Stability properties of a new anisotropic current sheet equilibrium for planetary magnetotails, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15797, https://doi.org/10.5194/egusphere-egu23-15797, 2023.

EGU23-16725 | Orals | NP6.2

Investigating the interactions of alpha particles in collisionless oblique heliospheric shocks 

Leon Ofman, Lynn B Wilson, Teresa Nieves-Chinchilla, Lan Jian, and Adam Szabo

Heliospheric shocks associated with interplanetary coronal mass ejections (ICMEs) were observed by Wind, and DSCOVR at L1, STEREO spacecraft at ~1AU, and recently by the Parker Solar Probe in the inner heliosphere. The magnetic structure and the downstream magnetic oscillations were detected by Wind with 10.9 samples/s and DSCOVR with 50 samples/s. However, the velocity distributions of the protons are available at much lower cadence, and the potentially important interaction between  the alpha particles and  the heliospheric shocks are difficult to obtain directly from present data. Since the alpha particles in the solar wind are the second most abundant ion that can carry significant energy, momentum and mass flux of the solar wind, the alphas can significantly affect the propagation of these shocks. Recently, using hybrid-(PIC) models we studied the effects of alpha particles on the structure and magnetic oscillations of oblique high Mach number heliospheric shocks, and found that the magnetic and density structures of these shocks are significantly affected by the alpha particles with typical solar wind relative abundances. Here, we extend the study and report the results of new hybrid models of oblique shocks guided by observations. We investigate the typical observed relative solar wind abundances of alphas, Mach numbers, and shock normal directions, and compare the results for the various shock parameters. We model the effects of alpha particles properties on the shock ramp, wake, and downstream oscillations and study the properties of proton and alpha particle velocity distribution functions (VDFs) and the kinetic waves downstream of the shocks in the inner heliosphere. We expand the model and study for the first time the effects of relative streaming of proton-alpha ion populations as well as the ion anisotropies on the shock propagation. We investigate the effects of the ion kinetic properties on the heliospheric shock structures and discuss how the modeling results can improve the interpretation of spacecraft observations of these shocks. 

How to cite: Ofman, L., Wilson, L. B., Nieves-Chinchilla, T., Jian, L., and Szabo, A.: Investigating the interactions of alpha particles in collisionless oblique heliospheric shocks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16725, https://doi.org/10.5194/egusphere-egu23-16725, 2023.

EGU23-16954 | ECS | Posters virtual | NP6.2 | Highlight

On the Effect of Driving Turbulence on Magnetic Reconnection: A Particle-In-Cell Simulation Study 

Jeffersson Andres Agudelo Rueda, Yi-Hsin Liu, and Kai Germaschewski

Energy dissipation in collisionless plasmas is one of the most outstanding open questions in plasma physics. Magnetic reconnection and turbulence are two phenomena that can produce the right conditions for energy dissipation. These two phenomena are closely related to each other in a wide range of plasmas. Turbulent fluctuations can emerge in critical regions of reconnection events, and magnetic reconnection can occur as a product of the turbulent cascade. Moreover, the presence of a turbulent field can affect the onset and evolution of magnetic reconnection. In this study, we perform 2D and 3D particle-in-cell simulations of a reconnecting Harris current sheet in the presence of turbulent fluctuations to explore the effect of turbulence on the reconnection process in collisionless plasmas. We use the Langevin antenna method to drive turbulence in the reconnecting magnetic field. We compare our results with existing theories.

How to cite: Agudelo Rueda, J. A., Liu, Y.-H., and Germaschewski, K.: On the Effect of Driving Turbulence on Magnetic Reconnection: A Particle-In-Cell Simulation Study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16954, https://doi.org/10.5194/egusphere-egu23-16954, 2023.

EGU23-17214 | Posters on site | NP6.2 | Highlight

Magnetic reconnection in the solar wind: Filamentary currents in a multi-layered exhaust region at an ICME sheath—ejecta boundary 

Matti Ala-Lahti, Tuija Pulkkinen, Julia Ruohotie, Mojtaba Akhavan-Tafti, Simon Good, and Emilia Kilpua

In the solar wind, a bifurcated current sheet is often observed in a reconnection outflow region as predicted by the original Petchek reconnection model, with the detailed exhaust structure becoming more complex when asymmetries between reconnecting plasmas are present. Here we present the first multi-spacecraft mission in-situ observations of a solar wind reconnection exhaust populated with filamentary (Hall) currents at an interplanetary coronal mass ejection (ICME) sheath—ejecta boundary. At the ICME sheath—ejecta boundary, asymmetric inflow conditions control reconnection, a relatively hot and dense plasma of the sheath coupling with the sparse low-beta ejecta plasma. These novel high-resolution observations demonstrate a multi- layered exhaust, and speak for the opportunities that future missions, such as HelioSwarm, and Parker Solar Probe and Solar Orbiter open for investigating magnetic reconnection in the solar wind.

How to cite: Ala-Lahti, M., Pulkkinen, T., Ruohotie, J., Akhavan-Tafti, M., Good, S., and Kilpua, E.: Magnetic reconnection in the solar wind: Filamentary currents in a multi-layered exhaust region at an ICME sheath—ejecta boundary, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17214, https://doi.org/10.5194/egusphere-egu23-17214, 2023.

The Salish Sea is a semi-enclosed coastal sea between Vancouver Island and the coast of British Columbia and Washington State, invaluable from both an economic and ecologic perspective. Pacific inflow to the Sea is the main contributor of many biologically important constituents. The contribution of Pacific water masses to the flow through Juan de Fuca Strait (JdF), the Salish Sea’s primary connection to the Pacific Ocean, is explored. Quantitative Lagrangian particle tracking using Ariane was applied to two numerical ocean models (CIOPS-W in the shelf region, and SalishSeaCast in the Salish Sea) matched together within JdF. Water parcels seeded near the entrance of JdF were integrated forwards and backwards in time to assess water mass path (and properties while on this path) from the shelf region and once within the Salish Sea in more detail than previously possible. During summer upwelling, intermediate flow from the north shelf and offshore dominate inflow, while during winter downwelling, intermediate flow from the south shelf and surface flow from the Columbia River plume are the dominant sources. A weaker and less consistent estuarine flow regime in the winter led to less Pacific inflow overall and a smaller percentage of said inflow reaching the Salish Sea's inner basins than in the summer. Nevertheless, it was found that winter dynamics are the main driver of interannual variability, in part due to the strongly anti-correlated behaviour and distinct properties of the two dominant winter sources. This analysis extends the knowledge on the dynamics of Pacific inflow to the Salish Sea and highlights the importance of winter inflow to the interannual variability in biogeochemical conditions in the region.

How to cite: Beutel, B. and Allen, S.: Interannual and seasonal water mass analysis in the Salish Sea using Lagrangian particle tracking, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-316, https://doi.org/10.5194/egusphere-egu23-316, 2023.

EGU23-2725 | ECS | Posters on site | NP6.3

Lagrangian Spatiotemporal Fingerprints of Dissolved Inorganic Carbon in Eighteen Degree Water Formation 

Daan Reijnders, Dorothee Bakker, and Erik van Sebille

Mode waters are defined as thick, weakly stratified layers with homogeneous properties. They have the ability to store these properties, such as heat, carbon and nutrients, and exchange these with the surface or atmosphere during outcropping events or with other layers via mixing processes. Eighteen Degree Water (EDW) is the subtropical mode water of the western North Atlantic. Its yearly outcropping events in late winter makes it an important regulator of ocean heat, nutrients and carbon in the North Atlantic on annual timescales.

Previous studies have given insight into the formation and destruction of Eighteen Degree Water. These have largely focused on physical aspects such as EDW formation rates. Due to the importance of EDW formation in setting the biogeochemical environment in the North Atlantic, it is instructive to investigate how biogeochemical tracers are altered along EDW formation routes. This study investigates in particular how dissolved inorganic carbon (DIC) is altered along ocean water parcel trajectories as EDW is formed. To do so, we compute Lagrangian trajectories of subducted EDW backwards in time using a coupled hydrodynamic and biogeochemical model. By sampling biogeochemical tracer values along Lagrangian pathways, we construct timeseries which we use to map the dominant locations at which DIC concentrations are altered in space and time to identify the Lagrangian fingerprint of DIC in Eighteen Degree Water.

How to cite: Reijnders, D., Bakker, D., and van Sebille, E.: Lagrangian Spatiotemporal Fingerprints of Dissolved Inorganic Carbon in Eighteen Degree Water Formation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2725, https://doi.org/10.5194/egusphere-egu23-2725, 2023.

EGU23-3970 | ECS | Posters on site | NP6.3 | Highlight

Lagrangian trajectories to assess marine plastic pollution distribution in the Canary Islands 

Marcos Cividanes García, Borja Aguiar González, May Gómez Cabrera, Alicia Herrera Ulibarri, Ico Martínez Sánchez, Ángel Rodríguez Santana, and Francisco José Machín Jiménez

The increasing presence of plastics in the ocean is a harmful problem for marine ecosystems and the socio-economic sector. A recurrent type of debris gathered in waters of the Canary Islands are the identification tags employed at lobster traps deployed at the north-eastern coast of North America. Since 2016 to the present, these debris have been routinely collected and classified by the EOMAR group (MICROTROFIC Project) through coastal sampling focused on the eastern part of the Canary archipelago. In order to address this problem, a further understanding of the distribution and dynamics of these debris in the ocean is demanding. In this work, a pre-existing tool in Matlab has been upgraded to produce Lagrangian trajectories based on Marine Copernicus surface current velocity (GLORYS12V1). The main goal is to assess the trajectories that floating particles might follow in the North Atlantic subtropical gyre when released over a grid in the north-eastern coast of North America (Gulf of Maine). Our results provide a quantitative basis about the link between the North American north-eastern coast and the Canary Islands, where the presence of these and other debris is of increasing concern.

How to cite: Cividanes García, M., Aguiar González, B., Gómez Cabrera, M., Herrera Ulibarri, A., Martínez Sánchez, I., Rodríguez Santana, Á., and Machín Jiménez, F. J.: Lagrangian trajectories to assess marine plastic pollution distribution in the Canary Islands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3970, https://doi.org/10.5194/egusphere-egu23-3970, 2023.

EGU23-4003 | ECS | Orals | NP6.3

Quasi-Objective Eddy Visualization from Sparse Drifter Data 

Alex Pablo Encinas Bartos, Nikolas O. Aksamit, and George Haller

Lagrangian eddies, generally referred to as elliptic Lagrangian coherent structures (LCS) in the dynamical systems literature, are material objects that trap and transport floating particles over large distances in the ocean in a coherent fashion. In order to expand our understanding of the transport of marine tracers, we need to accurately and reliably track the evolution of vortical flow structures. Drifter trajectories represent a valuable but sparse source of information for this purpose. We employ a recently developed single-trajectory Lagrangian diagnostic tool, the trajectory rotation average (TRA), to visualize oceanic vortices (or eddies) from sparse drifter data in a quasi-objective fashion. We apply the TRA to two drifter data sets that cover various oceanographic scales: the Grand Lagrangian Deployment (GLAD) and the Global Drifter Program (GDP). Based on the TRA, we develop a general algorithm that extracts approximate eddy boundaries. We find that the TRA outperforms other available single-trajectory-based eddy detection methodologies on sparse drifter data and identifies eddies on scales that are unresolved by satellite-altimetry.

How to cite: Encinas Bartos, A. P., O. Aksamit, N., and Haller, G.: Quasi-Objective Eddy Visualization from Sparse Drifter Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4003, https://doi.org/10.5194/egusphere-egu23-4003, 2023.

EGU23-6036 | Orals | NP6.3

A versatile Lagrangian-data aggregation framework for marine biological dispersal studies 

Willi Rath, Lara Schmittmann, Carola Trahms, Felix Kirch, Leon Mock, and Arne Biastoch

Lagrangian particle dispersal simulations are widely used for studying directed connectivity between different locations in the ocean. They are used, both, for the understanding of ocean physics and for interdisciplinary questions. One biological example is the dispersal of passively drifting marine organisms.

The typical modus operandi of such “bio-physical” studies is to design an underlying Lagrangian simulation in close synchronisation with a specific biological research question. This leads to a conflation of concerns between physical and biological aspects of the study. This conflation might result in repeated and slow development cycles of re-calculation for different scenarios and hence inhibit scientific progress.

We aim at improving the separation of concerns between biological and physical components for bio-physical Lagrangian studies, by aggregating physical Lagrangian data into directed multigraphs encoding locations as nodes and multiple parallel pathways as directed edges. Those graphs condense the physics-based information on directed oceanic relations and thus serve as a basis for simultaneously answering various biological questions on connectivity. As the proposed aggregation retains the distinction of different pathways between locations, it can, to some extent, also provide information of underway environmental conditions. This greatly enhances the range of applications of our approach over existing aggregations of Lagrangian data as connectivity probability graphs.

We present a specific set of biological case studies — the multi-year spreading of two oyster diseases in the North Sea — and develop a framework that facilitates efficiently and simultaneously testing multiple biological hypotheses for marine diseases of various species based on the same processed physical data set.

How to cite: Rath, W., Schmittmann, L., Trahms, C., Kirch, F., Mock, L., and Biastoch, A.: A versatile Lagrangian-data aggregation framework for marine biological dispersal studies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6036, https://doi.org/10.5194/egusphere-egu23-6036, 2023.

EGU23-6537 | ECS | Orals | NP6.3

Forecast of Particle Spreading Using Machine Learning in a Complex Multiple-Inlet Coastal System 

Jeancarlo M. Fajardo-Urbina, Yang Liu, Ulf Gräwe, Sonja Georgievska, Meiert W. Grootes, Herman J.H. Clercx, Theo Gerkema, and Matias Duran-Matute

The implementation of continuous operational forecast systems using numerical models for coastal environments are scarce, computationally expensive, and difficult to maintain. As an alternative, computationally cheaper tools such as machine learning models can be employed. This is especially relevant when the time to produce a forecast is paramount like in oil spills, marine litter spread due to container-ship accidents, and search and rescue operations. Working in this direction, we tested the skill of an advanced deep learning model, namely a convolutional long short-term memory network (ConvLSTM), to predict the Lagrangian advection (the displacement vector of the center of mass) and the dispersion (the spread described by a covariance matrix) of patches of passive tracers. This model was trained with data from a realistic numerical simulation of the Dutch Wadden Sea: a multiple-inlet system of great ecological importance. Using the relevant drivers (wind, tidal amplitude, and atmospheric pressure), the model was set to learn the advection and dispersion after one tidal period of clouds of particles released on a 200 x 200 m grid, covering the entire DWS. Our results show that the model learned the system-wide temporal variability of both advection and dispersion, while the local spatial features were better reproduced for advection than for dispersion. We use the predicted advection and dispersion as inputs to a set of stochastic differential equations for the reconstruction of particle trajectories, as it is commonly done in particle tracking applications that employ diffusion instead of dispersion. We were able to predict the temporal evolution over several tidal periods of particle patches released from specific locations under contrasting cases like calm and stormy conditions. Our method was employed to predict only the horizontal spreading, but it can be extended to predict the 3D evolution of the particle clouds. Finally, our approach requires simulation data and relevant drivers (e.g. atmospheric forcing and tidal amplitudes) for training and the same drivers from any typical forecast systems for forecasting the evolution of particle patches, which makes it a promising operational tool.

How to cite: Fajardo-Urbina, J. M., Liu, Y., Gräwe, U., Georgievska, S., Grootes, M. W., Clercx, H. J. H., Gerkema, T., and Duran-Matute, M.: Forecast of Particle Spreading Using Machine Learning in a Complex Multiple-Inlet Coastal System, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6537, https://doi.org/10.5194/egusphere-egu23-6537, 2023.

EGU23-7212 | ECS | Orals | NP6.3

Lagrangian tracer spreading in surface ocean turbulence with ageostrophic dynamics 

Michael Maalouly, Gilmar Mompean, and Stefano Berti

Ocean submesoscales are characterized by horizontal scales smaller than approximately 10 km that evolve with timescales of O(1) day. Due to their small size and rapid temporal evolution, they are notoriously difficult to measure. In particular, the associated velocity field is not resolved in current satellite altimetry products. At these scales, surface ocean flows are populated by small eddies, and filaments linked with strong gradients of physical properties, such as temperature. Several recent studies indicate that submesoscale fronts are associated with important vertical velocities, thus playing a significant role in vertical transport. On that account, these fine-scale flows are key to the dynamical coupling between the interior and the surface of the ocean, as well as to plankton dynamics and marine ecology. In spite of their importance, the understanding of submesoscale ocean dynamics is still incomplete. In particular, a relevant open question concerns the role played by the ageostrophic components of the surface velocity field that manifest at these scales.

By means of numerical simulations, we investigate ocean submesoscale turbulence in the SQG+1 model, which accounts for ageostrophic motions generated at fronts, and which is obtained as a small-Rossby-number approximation of the primitive equations. In the limit of vanishing Rossby number, this system gives surface quasi-geostrophic (SQG) dynamics. In this study, we explore the effect of the ageostrophic flow components on the spreading process of Lagrangian tracer particles on the horizontal. We particularly focus on the characterization of pair-dispersion regimes and particle clustering, as a function of the Rossby number, using different indicators. The observed Lagrangian behaviours are further related to the structure of the underlying turbulent flow. We find that relative dispersion is essentially unaffected by the ageostrophic flow components. However, these components are found to be responsible for (temporary) particle aggregation in cyclonic frontal regions. These results appear interesting for the modelling of submesoscale dynamics and for comparison purposes with the new high-resolution surface current data that will be soon provided by the satellite SWOT.

How to cite: Maalouly, M., Mompean, G., and Berti, S.: Lagrangian tracer spreading in surface ocean turbulence with ageostrophic dynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7212, https://doi.org/10.5194/egusphere-egu23-7212, 2023.

EGU23-7220 | ECS | Orals | NP6.3

Impact of Model Resolution on Mixing and Dispersion in the Gulf of Mexico 

Nektaria Ntaganou, Eric Chassignet, and Alexandra Bozec

We investigate the importance of model resolution in identifying the nature of mixing and dispersion in the Gulf of Mexico, by comparing two data-assimilative, high-resolution simulations, one of which is submesoscale-resolving. By employing both Eulerian and Lagrangian metrics, upper-ocean differences between the mesoscale- and submesoscale-resolving simulations are examined. Focusing on regions characterized by high submesoscale activity, we approach the notion of mixing by tracking the generation of Lagrangian Coherent Structures (LCSs) and transport barriers. Finite-time Lyapunov exponents (FTLE) fields reveal higher separation rates of fluid particles in the submesoscale-resolving case which indicates more vigorous mixing. Using probability density functions (PDFs), the extent of mixing homogeneity is also explored, with preliminary results suggesting that mixing is more homogeneous in the submesosclae-resolving case. Finally, we aim to identify regions of convergence in the areas of interest by advecting passive tracers that tend to organize themselves along attracting LCSs. Applications of passive tracer advection are then translated to extreme event situations, such as the Deepwater Horizon.  

How to cite: Ntaganou, N., Chassignet, E., and Bozec, A.: Impact of Model Resolution on Mixing and Dispersion in the Gulf of Mexico, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7220, https://doi.org/10.5194/egusphere-egu23-7220, 2023.

EGU23-8646 | Orals | NP6.3

Mixing and transport across the Atlantic Meridional Overturning Circulation: a 3D geometrical perspective 

Ana M. Mancho, Renzo Bruera, Jezabel Curbelo, and Guillermo Garcia-Sanchez

Vertical motions across the ocean are central to processes, like CO2 fixation, heat removal or pollutant transport, which are essential to the Earth’s climate. This presentation describes 3D conveyor routes across the Atlantic Meridional Overturning Circulation (AMOC), with the support of Lagrangian Coherent Structures. Our findings show the geometry of mixing structures in the upper and deep ocean layers. We identify among others, zones linked to vertical transport and characterize vertical transport time scales.

 

Acknowledgments: RB acknowledges support of a CSIC JAE intro fellowship.  AMM and GGS acknowledge the support of a CSIC PIE project Ref. 202250E001 and MICINN grants PID2021-123348OB-I00 and EIN2020-112235. AMM is an active member of the CSIC Interdisciplinary Thematic Platforms POLARCSIC. JC acknowledges the support of the RyC project RYC2018-025169, the Spanish grant PID2020-114043GB-I00 and the Catalan Grant No. 2017SGR1049 and the ``Beca Leonardo a Investigadores y Creadores Culturales 2022 de la Fundación BBVA''.

How to cite: Mancho, A. M., Bruera, R., Curbelo, J., and Garcia-Sanchez, G.: Mixing and transport across the Atlantic Meridional Overturning Circulation: a 3D geometrical perspective, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8646, https://doi.org/10.5194/egusphere-egu23-8646, 2023.

On 13 August 2021, the Fukutoku-Okanoba submarine volcano in the North Pacific Ocean was erupted. Satellites detected many pumice rafts that drifted westward to reach southern Japan in about two months. To cope with potential danger due to the pumice rafts, it is crucial to predict their trajectories. Using a Lagrangian particle tracking model, the trajectories of the rafts were investigated. The model results showed strong sensitivity to the windage coefficient of pumice rafts, which is uncertain and could cause large errors. By comparing the model results with satellite images using a skill score, the distance between a simulated particle and the nearest observed raft divided by the travel distance of the particle, an optimal windage coefficient was estimated. The optimal windage coefficients ranging between 2 to 3% produced pathways comparable to the obervation using satellites. The pumice rafts  moved from Fukutoku-Okanoba, toward the Ryukyu Islands for approximately two months before being pushed toward Taiwan by the intensified wind. The techniques presented here may become helpful in managing coastal hazards due to diverse marine debris.

How to cite: Park, Y.-G., Iskandar, M. R., kim, K., and Jin, H.: Tracking the pumice rafts from the recent eruption of the submarine volcano Fukutoku-Okanoba, Japan using Satellites and Lagrangian Particles tracking, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10296, https://doi.org/10.5194/egusphere-egu23-10296, 2023.

EGU23-11091 | Posters virtual | NP6.3

Detection of materially coherent eddies in the Bay of Bengal 

Lijin Jayan, Jishad Mandayi, Neeraj Agarwal, Rashmi Sharma, and Manikandan Mathur

Eddies are prominent features in the ocean and these energetic circulatory motions influence lateral and vertical transport of heat, mass and momentum. Ability of these eddies to coherently transport various scalar species is an important consideration in understanding freshwater transport, locating regions of harmful algal blooms, oxygen deficient zones and potential fishing zones. In this study, we present an implementation of Lagrangian Averaged Vorticity Deviation (LAVD) technique to detect materially coherent eddies from satellite derived sea surface currents in the Bay of Bengal (BoB). We also evaluate the efficacy of a Eulerian method based on sea surface height (SSH) in capturing materially coherent eddies in the BoB. Parameter values for robust detection of eddies are determined by performing a systematic sensitivity analysis in both the methods. Finite time material behaviour of eddies detected using both the methods are evaluated by numerical particle advection experiments. We then focus on material coherence of Sri Lanka Dome (SLD), an annually occurring cyclonic eddy of dynamical relevance in the BoB. SLD characteristics including its spatio-temporal evolution is discussed by analysing ocean surface currents data spanning 27 years from 1993 to 2019.

How to cite: Jayan, L., Mandayi, J., Agarwal, N., Sharma, R., and Mathur, M.: Detection of materially coherent eddies in the Bay of Bengal, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11091, https://doi.org/10.5194/egusphere-egu23-11091, 2023.

EGU23-13939 | ECS | Orals | NP6.3

Inertial effects on the transport of an anisotropic particle in surface gravity waves 

Himanshu Mishra and Anubhab Roy

We study the transportation and rotational dynamics of a finite-sized spheroidal particle in a linear monochromatic surface gravity wave to better understand the transport dynamics of microplastics in oceanic flows. A spheroidal particle, modeled as an anisotropic tracer, attains preferential alignment in a linear wavy flow. We analyze the drift of a finite-size anisotropic particle and find that the horizontal drift of such particles can either increase or decrease depending on the initial orientation and the ratio of the size of the particle to the wavelength of the background wave field. Next, we derive the finite-size modification to the preferred alignment of the spheroidal particle with the flow propagation direction of the wave. In most scenarios, particles in the ocean can have a wide range of densities and are classified into positively and negatively buoyant particles. Negatively buoyant particles settle in a wavy flow with complex trajectories. We study the effect of the orientation and size of such particles on settling and show that the aspect ratio of the particle could alter the trajectory in the wave propagation direction. We also obtain a non-zero vertical Stokes drift. Finally, we consider the effects of fluid and particle inertia in our coupled evolution equations and study the drift and the orientation of an anisotropic particle in a wavy flow field. We demonstrate that considering such an effect could provide a complete picture of the transport and dynamics of microplastics in the upper part of the ocean that can be described more accurately. 

How to cite: Mishra, H. and Roy, A.: Inertial effects on the transport of an anisotropic particle in surface gravity waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13939, https://doi.org/10.5194/egusphere-egu23-13939, 2023.

EGU23-14947 | Posters on site | NP6.3

Small-scale Lagrangian modelling of air bubbles and oil droplets under breaking waves 

Tor Nordam and Arsalan Mostaani

Lagrangian transport modelling is commonly applied for marine environmental transport problems. When applied to problems on a timescale of days to weeks, such as marine oil spills, Lagrangian models are often forced with environmental data from operational models for atmosphere, waves and ocean currents. These models typically have a temporal resolution of around 1 hour. Effects that take place on shorter timescale, such as entrainment of oil droplets and air bubbles due to breaking waves, must therefore be parametereised.

On short timescales, the random flight approach is clearly more realistic than a random walk, since the particles have a well-defined and realistic velocity, regardless of the length of the timestep, and since particles in real turbulence do not instantaneously change their direction by arbitrarily large amounts. A consequence of this is that in random flight, particles exhibit superdiffusion on short timescales, and normal diffusion on long timescales, compared to the de-correlation time of the turbulent motion. Random walk methods, on the other hand, always behave as diffusion. Hence, random flight methods are expected to be more relevant for small-scale modelling of transport on short timescale under breaking waves.

Here, we consider small-scale modelling of oil droplet and air bubble entrainment, modelling the transport close to the surface, and at high temporal resolution. We use two different Lagrangian methods: random walk (AR0) and random flight (AR1), and compare the two modelling approaches to each other, as well as to pre-existing parameterisations of the average effects of entrainment. Input parameters to the Lagrangian models are informed by experimental turbulence measurements in a wave flume, and RANS-modelling of the breaking wave. Comparison of particle transport to observations in experimental flume work is ongoing.

How to cite: Nordam, T. and Mostaani, A.: Small-scale Lagrangian modelling of air bubbles and oil droplets under breaking waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14947, https://doi.org/10.5194/egusphere-egu23-14947, 2023.

EGU23-15421 | ECS | Posters on site | NP6.3

Lagrangian dynamics of heavy inertial particles on vortical flows 

Anu Viswanathan Sreekumari Nath and Anubhab Roy

We study the dynamics of dust particles in various vortical flows which is relevant to geophysical context. The inertial particles are advected by the background vortex flow. The dynamics is tracked using the Maxey-Riley equation. The finite inertia of the particles make their dynamics different from passive fluid parcels, which is interesting. The dust particles may show periodic dynamics or chaotic diffusion depending on parametric variations. The result contradicts the earlier predictions that only density matched inertial particles can have chaotic dynamics, which we justify through our explanation. In addition, the heavy inertial particles in a self rotating vortex patch is observed to be attracted near the vortical region, which is contrary to the physics where they should ideally centrifuged out. The reason behind this phenomena also we explore in detail here.

How to cite: Viswanathan Sreekumari Nath, A. and Roy, A.: Lagrangian dynamics of heavy inertial particles on vortical flows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15421, https://doi.org/10.5194/egusphere-egu23-15421, 2023.

EGU23-15441 | ECS | Posters on site | NP6.3

Measurements of bubble size distribution underneath breaking waves 

Arsalan Mostaani, Tor Nordam, and Emlyn Davies

Entrainment of particles by breaking waves are an important process for several applications. For example, entrainment of air bubbles is relevant for air-sea gas exchange, which in turn is relevant for climate modelling. Entrainment of oil droplets in a marine oil spill will have an effect on the fate of the oil, and help determine environmental effects. Hence, being able to measure and model these entrainment effects are important.

We are conducting experiments in a linear wave flume, with piston-type wave maker, looking at entrainment of air bubbles under breaking waves. Using a camera system with a uniform backlight and a telecentric lens, the SINTEF SilCam, we can image bubbles ranging in size from tens of micrometers, to cm scale. By accurately constraining the measurement volume, we can determine concentration of bubbles of different sizes. Taking images at high frequency, and repeating the same breaking wave many times, we are able to measure the time-development of the ensemble-average bubble size distribution.

In this poster, we describe the camera system and the image analysis pipeline, and we present some preliminary results and discuss some of the inherent challenges in measuring bubble size distributions close to the surface underneath breaking waves.

How to cite: Mostaani, A., Nordam, T., and Davies, E.: Measurements of bubble size distribution underneath breaking waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15441, https://doi.org/10.5194/egusphere-egu23-15441, 2023.

EGU23-3207 | ECS | Posters on site | ST1.11

The change of properties of solar wind turbulence across different types of interplanetary shocks 

Byeongseon Park, Alexander Pitňa, Jana Šafránková, Zdeněk Němeček, Oksana Krupařová, and Vratislav Krupař

The interaction between interplanetary (IP) shocks and solar wind has been studied for the understanding of energy dissipation mechanisms and the properties of turbulence (e.g., cross helicity, residual energy, proton temperature anisotropy, magnetic compressibility, etc.) within collisionless plasmas. Compared to the study of the interaction with fast shocks, less attention has been directed to the interaction with other types of IP shocks including slow mode shocks (i.e., fast forward, fast reverse, slow forward and slow reverse). We analyze IP shocks observed by the Wind spacecraft from 1995 to 2021. Spectral indices in the ion inertial and kinetic ranges for the upstream and downstream magnetic field fluctuations are estimated by continuous wavelet transform. The changes of the plasma turbulence properties and the distributions of characteristic proton length scales are presented. We preliminarily found that spectral indices in both inertial and kinetic ranges and the distributions of characteristic proton length scales are statistically conserved across the investigated shocks. Mechanisms associated with the energy dissipation can be seen unaffected by shock. Other turbulence properties—cross helicity, residual energy and proton temperature anisotropy—evolve without a significant modification as well.

How to cite: Park, B., Pitňa, A., Šafránková, J., Němeček, Z., Krupařová, O., and Krupař, V.: The change of properties of solar wind turbulence across different types of interplanetary shocks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3207, https://doi.org/10.5194/egusphere-egu23-3207, 2023.

EGU23-3214 | Posters on site | ST1.11

Evolution of magnetic field fluctuations and their spectral properties within the heliosphere: Statistical approach 

Jana Safrankova, Zdeněk Němeček, František Němec, Daniel Verscharen, Lubomír Přech, Timothy S. Horbury, and Stuart D. Bale

The contribution presents the first comprehensive statistical study of the evolution of both compressive and non-compressive magnetic field fluctuations in the inner heliosphere. Based on Parker Solar Probe and Solar Orbiter data in various distances from the Sun, we address the general trends and compare them with Wind observations near 1 AU. We analyze solar wind power spectra of magnetic field fluctuations in the inertial and kinetic ranges of frequencies. We found a systematic steepening of the spectrum in the inertial range with the spectral index of around –3/2 at closest approach to the Sun toward –5/3 at larger distances (above 0.4 AU), the spectrum of the magnetic field component perpendicular to the background field being steeper at all distances. In the kinetic range, spectral indices increase from –4.5 at closest PSP approach to –3 at ≈0.4 AU and this value remains constant toward 1 AU. We show that the radial profiles of spectral slopes, fluctuation amplitudes, spectral breaks and their mutual relation rapidly change near 0.4 AU.

How to cite: Safrankova, J., Němeček, Z., Němec, F., Verscharen, D., Přech, L., Horbury, T. S., and Bale, S. D.: Evolution of magnetic field fluctuations and their spectral properties within the heliosphere: Statistical approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3214, https://doi.org/10.5194/egusphere-egu23-3214, 2023.

EGU23-6538 | Posters on site | ST1.11

A detailed analysis of ion-acoustic waves observed in the solar wind by the Solar Orbiter 

David Pisa, Jan Soucek, Ondrej Santolik, Tomas Formanek, Milan Maksimovic, Thomas Chust, Yuri Khotyaintsev, Matthieu Kretzschmar, Christopher Owen, Philippe Louarn, and Andrei Fedorov

Ion-acoustic waves are often observed in the solar wind along the Solar Orbiter’s orbit. These electrostatic waves are generated via ion-ion or current-driven instabilities below the local proton plasma frequency. Due to the Doppler shift, they are typically observed in the frequency range between the local electron and proton plasma frequency in the spacecraft frame. Ion-acoustic waves often accompany large-scale solar wind structures and play a role in the energy dissipation in the propagating solar wind. Time Domain Sampler (TDS) receiver, a part of the Radio and Plasma Waves (RPW) instrument, is sampling wave emissions at frequencies below 200 kHz almost continuously from the beginning of the mission. Almost three years of observations allow us to perform a detailed study of ion-acoustic waves in the solar wind under variable plasma conditions. The emission tends to be observed when proton density and temperature are highly perturbed. A detailed analysis of the proton velocity distribution and wave generation using solar wind data from a Proton and Alpha particle Sensor (PAS) of the Solar Wind Analyzer (SWA) is shown.

How to cite: Pisa, D., Soucek, J., Santolik, O., Formanek, T., Maksimovic, M., Chust, T., Khotyaintsev, Y., Kretzschmar, M., Owen, C., Louarn, P., and Fedorov, A.: A detailed analysis of ion-acoustic waves observed in the solar wind by the Solar Orbiter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6538, https://doi.org/10.5194/egusphere-egu23-6538, 2023.

EGU23-6669 | Posters on site | ST1.11

Wave turbulence in inertial electron magnetohydrodynamics 

Vincent David and Sébastien Galtier

A wave turbulence theory is developed for inertial electron magnetohydrodynamics (IEMHD) in the presence of a relatively strong and uniform external magnetic field B0 = B0e. This regime is relevant for scales smaller than the electron inertial length de. We derive the kinetic equations that describe the three-wave interactions between inertial whistler or kinetic Alfvén waves. We show that for both invariants, energy and momentum, the transfer is anisotropic (axisymmetric) with a direct cascade mainly in the direction perpendicular (⊥) to B0. The exact stationary solutions (Kolmogorov–Zakharov spectra) are obtained for which we prove the locality. We also found the Kolmogorov constant CK ≃ 8.474. In the simplest case, the study reveals an energy spectrum in k−5/2k−1/2 (with k the wavenumber) and a momentum spectrum enslaved to the energy dynamics in k−3/2k−1/2. These solutions correspond to a magnetic energy spectrum ∼k−9/2, which is steeper than the EMHD prediction made for scales larger than de.

How to cite: David, V. and Galtier, S.: Wave turbulence in inertial electron magnetohydrodynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6669, https://doi.org/10.5194/egusphere-egu23-6669, 2023.

Turbulent plasmas such as the solar wind and the magnetosheath exhibit an energy cascade which is present across a broad range of scales, from the stirring scale at which energy is injected, down to the smallest scales where energy is dissipated through processes such as reconnection and wave-particle interactions. Recent observations of Earth’s bow shock reveal the presence of a disordered or turbulent transition region which exhibits some features of turbulent dissipation, such as reconnecting current sheets. Understanding the variations in the origin and character of these disordered fluctuations addresses open questions such as how disordered or turbulent fluctuations in the bow shock and magnetosheath are related, and how quickly magnetosheath turbulence arises from bow shock processes. Here, we present two case studies of bow shock crossings observed by Magnetospheric Multiscale (MMS), one quasi-perpendicular and one quasi-parallel. Using high-cadence, combined search-coil and fluxgate magnetometer data, we measure changes in correlation lengths of the magnetic field through three regions: the upstream (solar wind), shock transition region, and downstream (magnetosheath). The influence of the discontinuous shock ramp is reduced using high-pass filters with variable cut-off frequencies. We find that correlation lengths are higher on the solar wind side of the shock, reducing to around 20 ion inertial lengths in the magnetosheath for both the quasi-parallel and the quasi-perpendicular shocks. We also discuss implications of the observed evolution of the correlation length to bow shock and magnetosheath processes.

How to cite: Plank, J. and Gingell, I.: Measures of correlation length at Earth’s quasi-parallel and quasi-perpendicular bow shock, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6971, https://doi.org/10.5194/egusphere-egu23-6971, 2023.

EGU23-7895 | Posters on site | ST1.11

The ‘Inverse-Gaussian’ SW proton populations: Do they tell something about heating/acceleration by turbulence ? 

Philippe Louarn and the SWA Team + RPW and MAG

The solar wind proton distributions measured by PAS (Proton Alfa Sensor -Solar Orbiter)  are often constituted by a core and a beam. If the core is generally gaussian, the beam is asymmetric and non-gaussian with a more populated high-energy side (‘heavy tail’) in the magnetic field direction. It appears that the ‘Inverse Gaussian Distribution’ (IG), a type of hyperbolic statistical distributions, provides good fits of these skewed distributions. Then, excellent models of the whole proton distribution are obtained by superposing a gaussian (or almost gaussian distribution) for the core and an IG for the beam.  This modelling (Gaussian + Inverse Gaussian) applies to different situations: relatively slow and fast winds, single and double-bump populations, low or high level of turbulence. An interpretation is given, inspired by the ‘Normal-Inverse Gaussian’ (NIG) process, common in finance applications. Our ‘toy’ model assumes that the acceleration/heating is modelled as a drifting gaussian process in velocity space controlled (or subordinated) by an independent time-control process that follows an IG distribution. It is proposed that this control process is linked to the time of residence of the particles within accelerating structures of finite size, the relative motion between the particles and the structures being a drifting random walk (problem of the 'first passage time' of a random walk). Some applications of the model are discussed, as the estimate of the relative importance of heating and acceleration or the possible role of ambipolar fields.

How to cite: Louarn, P. and the SWA Team + RPW and MAG: The ‘Inverse-Gaussian’ SW proton populations: Do they tell something about heating/acceleration by turbulence ?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7895, https://doi.org/10.5194/egusphere-egu23-7895, 2023.

EGU23-7919 | Posters virtual | ST1.11

Mechanical and Total Pressure Statistics in Vlasov-Maxwell Plasmas 

Subash Adhikari, Paul A. Cassak, Tulasi N. Parashar, William H. Matthaeus, and Michael A. Shay

Pressure is an important parameter in plasma turbulence. Historically, pressure fluctuations have been studied extensively via density in the nearly incompressible (NI) magnetohydrodynamic (MHD) framework1-3. However, the statistics of mechanical and total pressure in kinetic plasmas have not been explored much. In this study, we examine the statistics of mechanical and total pressure using a 2.5D particle-in-cell (PIC) simulation of plasma turbulence4. As turbulence is fully developed in the system, it is found that the magnetic and thermal pressure display a negative correlation keeping the total pressure about constant, consistent with MHD behavior. This negative correlation is observed locally in regions near the current sheets and justified by the nature of the joint probability distribution of the two5. Further, pressure spectra are calculated for magnetic, thermal and total pressure. The thermal and magnetic pressure spectra exhibit a slope of -5/3 in the inertial range, while the total pressure spectrum exhibits a slope of -7/3 in agreement with hydrodynamic scaling, influenced by the cross-spectral contribution of the individual pressures. Finally, the implications of the local structures of pressure to intermittency are discussed using probability distribution functions and scale dependent kurtosis.

1. Montgomery, D., Brown M. R., and Matthaeus W. H. "Density fluctuation spectra in magnetohydrodynamic turbulence"JGR: Space Physics A1 (1987): 282-284.

2. Matthaeus, W. H., Brown M. R., "Nearly incompressible magnetohydrodynamics at low Mach number"The Physics of Fluids 12 (1988): 3634-3644.

3. Matthaeus, W. H., et al. "Nearly incompressible magnetohydrodynamics, pseudosound, and solar wind fluctuations" JGR: Space Physics A4 (1991): 5421-5435.

4. Adhikari, S., et al. "Energy transfer in reconnection and turbulence" Physical Review E 6 (2021): 065206.

5. Adhikari S., et al. “Statistics of Total Pressure in Kinetic Plasma Turbulence" ESS Open Archive (2023).

How to cite: Adhikari, S., Cassak, P. A., Parashar, T. N., Matthaeus, W. H., and Shay, M. A.: Mechanical and Total Pressure Statistics in Vlasov-Maxwell Plasmas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7919, https://doi.org/10.5194/egusphere-egu23-7919, 2023.

EGU23-8164 | Posters on site | ST1.11

The location of the spectral break in compressible fluctuations in the solar wind. 

Owen Roberts, Rumi Nakamura, Yasuhito Narita, and Zoltan Voros

We use density deduced from spacecraft potential to study the power spectral density (PSD) of fluctuations in the solar wind. Typically plasma measurements do not have high enough time resolutions to resolve ion kinetic scales. However, calibrated spacecraft potential allows much higher time resolutions to resolve the spectral break between ion inertial and kinetic ranges. Fast Survey mode data from Magnetospheric MultiScale data are used when the spacecraft were in the pristine solar wind. We find that the density spectra' morphology differs from the magnetic field fluctuations, with a flattening often occurring between inertial and kinetic ranges. We find that the spectral break of the trace magnetic field fluctuations occurs near the expected frequency for cyclotron resonance or magnetic reconnection. Meanwhile, the spectral break at the start of the ion kinetic range for density fluctuations is often at a higher frequency when compared to the magnetic field. We discuss possible interpretations for these observations. Two plausible scenarios are presented; 1. the compressive fluctuations consist of a slow wave cascade at large scales before kinetic Alfven waves become dominant at smaller scales 2. charge separation begins to occur at these scales, and the Hall electric field starts to play a role. 

How to cite: Roberts, O., Nakamura, R., Narita, Y., and Voros, Z.: The location of the spectral break in compressible fluctuations in the solar wind., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8164, https://doi.org/10.5194/egusphere-egu23-8164, 2023.

EGU23-8601 | ECS | Orals | ST1.11

Whistler wave occurrence in the magnetosheath: comparing the quasi-parallel and quasi-perpendicular geometries 

Ida Svenningsson, Emiliya Yordanova, Yuri V. Khotyaintsev, Mats André, and Giulia Cozzani

​​The Earth’s magnetosheath is a turbulent plasma region where the interplay between coherent structures and various plasma waves affect the particle dynamics and energy transfer. The properties of the magnetosheath are controlled by the upstream conditions. Magnetosheath plasma downstream of a quasi-parallel bow shock (the angle between the shock normal and the interplanetary magnetic field being less than 45°) tends to have stronger fluctuations while a quasi-perpendicular shock leads to a more stationary magnetosheath. These two geometries create different environments for processes such as wave generation. One example is whistler waves that can be excited by non-Maxwellian electron velocity distributions formed in local magnetic structures. Whistler waves have been observed throughout the magnetosheath. As previous statistical studies have considered the region as a whole, it is yet unexplored which magnetosheath geometry creates more favorable conditions for whistler wave generation.

In this work, we address this issue and investigate how the occurrence and properties of whistler waves depend on the magnetosheath configuration. We detect whistler waves using data from the Magnetospheric Multiscale (MMS) mission. We compare whistler wave occurrence to the shock normal angle estimated from upstream conditions, as well as local conditions which are typically different between the quasi-parallel and quasi-perpendicular geometries. The results give an indication of the conditions needed for the whistler waves to efficiently dissipate energy in the turbulent magnetosheath.

How to cite: Svenningsson, I., Yordanova, E., Khotyaintsev, Y. V., André, M., and Cozzani, G.: Whistler wave occurrence in the magnetosheath: comparing the quasi-parallel and quasi-perpendicular geometries, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8601, https://doi.org/10.5194/egusphere-egu23-8601, 2023.

The magnetometer onboard the NOAA DSCOVR spacecraft samples the interplanetary magnetic field at 50 samples/second, presenting unique opportunities to study turbulence and plasma waves in the solar wind up to the instruments 25 Hz Nyquist. In this study, we present example observations by DSCOVR of turbulence and Alfven waves during periods of solar flares, coronal mass ejections, and solar energetic particle (SEP)events. We present the turbulence structures, including spectral indices at different frequencies, and discuss how it relates to coherence waves observed during cascade and dissipation. We also present wave properties, including frequency range, wave power and polarization. In addition, by comparing DSCOVR to ACE and Wind results, we discuss the dependency of solar wind parameters on spacecraft separation and the implications for studying the evolution of cascading turbulence. Finally, we explain how users can access this distinctive DSCOVR full high-resolution magnetic field dataset through the NOAA-NCEI DSCOVR portal.

How to cite: Loto'aniu, P.: DSCOVR Turbulence and Plasma Wave Observations at L1, and Correlation of Solar Wind Parameters With Spacecraft Separation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8757, https://doi.org/10.5194/egusphere-egu23-8757, 2023.

EGU23-8810 | Orals | ST1.11

Transmission of turbulent structures and energetic particles dynamics in the interaction between collisionless shocks and plasma turbulence. 

Domenico Trotta, Francesco Pecora, Adriana Settino, Denise Perrone, David Burgess, David Lario, Heli Hietala, Timothy Horbury, Rami Vainio, Luis Preisser, William Matthaeus, Oreste Pezzi, Sergio Servidio, and Francesco Valentini

The interaction between shock and turbulence is an important pathway to energy conversion and particle acceleration in a large variety of astrophysical systems. Novel insights of such an interaction will be presented.

Using a combination of in-situ observations (using the Wind spacecraft and Magnetospheric Multiscale mission, MMS) and self-consistent kinetic simulations, the transmission of turbulent structures across the Earth’s bow shock will be discussed first. Then, the role of turbulence strength for efficient particle diffusion in phase space will be discussed using novel kinetic simulations and will be put in the context of observations of very-long lasting field aligned beams in interplanetary space. Finally, novel three-dimensional simulations of the shock turbulence interplay will be presented, with a focus on the shock front behaviour and irregular proton heating in presence of pre-existing fluctuations. In this scenario, the importance of novel multi-spacecraft missions will be discussed.

How to cite: Trotta, D., Pecora, F., Settino, A., Perrone, D., Burgess, D., Lario, D., Hietala, H., Horbury, T., Vainio, R., Preisser, L., Matthaeus, W., Pezzi, O., Servidio, S., and Valentini, F.: Transmission of turbulent structures and energetic particles dynamics in the interaction between collisionless shocks and plasma turbulence., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8810, https://doi.org/10.5194/egusphere-egu23-8810, 2023.

EGU23-8814 | Posters virtual | ST1.11

Comparative MMS analysis of Markov turbulence in the magnetosheath on kinetic scales 

Wiesław M. Macek and Dariusz Wójcik

We apply Fokker-Planck equation to investigate processes responsible for turbulence in space plasma. In our previous studies, we have shown that turbulence in the inertial range of hydromagnetic scales exhibits Markov properties [1,2]. We have also extended this statistical approach on much smaller scales, where kinetic theory should be applied. Namely, we have already obtained the results of the statistical analysis of magnetic field fluctuations in the Earth’s magnetosheath based on the Magnetospheric Multiscale (MMS) mission [3]. Here we compare the characteristics of turbulence behind the bow shock, inside the magnetosheath, and near the magnetopause. We check whether the second order approximation of the Fokker-Planck equation leads to kappa distribution of the probability density function provided that the first Kramers-Moyal coefficient is linear and the second term is quadratic, describing drift and diffusion correspondingly, which is a generalization of Ornstein-Uhlenbeck process. In some cases the power-law distributions are recovered. For moderate scales we have the kappa distributions described by various peaked shapes with heavy tails. In particular, for large values of the kappa parameter this is reduced to the normal Maxellian distribution. The obtained results on kinetic scales could be important for a better understanding of the physical mechanism governing turbulent systems in laboratory and space.

Keywords: Kinetic scales, Markov processes, MMS probe, Plasmas, Solar wind, Turbulence.

Acknowledgments. This work has been supported by the National Science Centre, Poland (NCN), through grant No. 2021/41/B/ST10/00823.

References

1. Strumik, M., & Macek, W. M. 2008a, Testing for Markovian character and modeling of intermittency in solar wind turbulence, Physical Review E, 78, 026414, doi=10.1103/PhysRevE.78.026414.
2. Strumik, M., & Macek, W. M. 2008b, Statistical analysis of transfer of fluctuations in solar wind turbulence, Nonlinear Processes in Geophysics, 15, 607-613, doi=10.5194/npg-15-607-2008.
3. Macek, W. M., Wójcik, D. & Burch, J. L. 2023, Magnetospheric Multiscale observations of Markov turbulence on kinetic scales, arXiv=2211.05098, Astrophysical Journal, https://doi.org/10.3847/1538-4357/aca0a0.

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How to cite: Macek, W. M. and Wójcik, D.: Comparative MMS analysis of Markov turbulence in the magnetosheath on kinetic scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8814, https://doi.org/10.5194/egusphere-egu23-8814, 2023.

EGU23-8938 | Orals | ST1.11

Theoretical Developments on Energy Conversion via the Pressure-Strain Interaction 

Paul Cassak and M. Hasan Barbhuiya

Energy conversion between bulk kinetic and thermal energy in weakly collisional and collisionless plasma processes such as magnetic reconnection and plasma turbulence has recently been the subject of intense scrutiny. This channel of energy conversion is described by the pressure-strain interaction. In a closed system, this quantity accounts for all the net change of the thermal energy. It is common to decompose it into pressure dilatation and «Pi-D», which isolates energy conversion via compressible and incompressible physics, respectively. Here, we propose an alternative decomposition of pressure-strain interaction that instead isolates flow convergence/divergence and bulk flow shear. We furnish a simple example to illustrate how Pi-D can be counterintuitive and the new decomposition is intuitive. Moreover, for applications to magnetized plasmas, we derive the pressure-strain interaction in a magnetic field-aligned coordinate system. This results in its decomposition into eight terms, each with a different physical mechanism that changes the thermal energy. Results from particle-in-cell simulations of two-dimensional magnetic reconnection plotting the decompositions in both Cartesian and magnetic field-aligned coordinates are shown. We identify the mechanisms contributing to heating and cooling during reconnection. The results of this study are readily applicable for interpreting numerical and observational data of pressure-strain interaction in both Cartesian and field-aligned coordinates in fundamental plasma processes such as reconnection, turbulence and collisionless shocks.

How to cite: Cassak, P. and Barbhuiya, M. H.: Theoretical Developments on Energy Conversion via the Pressure-Strain Interaction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8938, https://doi.org/10.5194/egusphere-egu23-8938, 2023.

EGU23-9803 | ECS | Posters on site | ST1.11

Measurement of the rate of change of the electron heat flux due to the whistler instability with Solar Orbiter observations 

Jesse Coburn, Daniel Verscharen, Christopher Owen, Timothy Horbury, Milan Maksimovic, Christopher Chen, Fan Guo, and Xiangrong Fu

Non-Maxwellian features of the coronal electron population are important for some models of solar wind acceleration processes. Remnants of these features are detectable in spacecraft observations, in particular in the form of field-aligned beams (strahl) and anti-sunward deficits in the electron distribution function. These features are shaped by expansion, collisions, and kinetic effects. Therefore, determining how these processes alter the distribution is important for our understanding of how the solar wind accelerates and evolves. The strahl and deficit contribute to the overall electron heat flux. If the heat flux crosses the threshold for instabilities, the plasma will generate waves which in turn reduce the heat flux via pitch-angle scattering of electrons out of the strahl and/or into the deficit. The work presented here examines an interval observed by Solar Orbiter during which short bandwidth whistler waves are observed by the Radio and Plasma Waves instrument. We apply a method to measure the pitch-angle gradient to high cadence pitch angle distribution (PAD) functions measured by the Electrostatic Analyser System to quantify the rate of change of heat flux from quasilinear theory. The primary part of the measurement technique is based on low-pass filtering of the PAD function with a Hermite-Laguerre transform providing a measurement of the pitch-angle gradient. We compare our quantification of the rate of change of the heat flux with other timescales and processes relevant in the solar wind. We show the potential of our technique to further our understanding of the role of wave-particle interactions in the evolution of the solar wind electrons.

How to cite: Coburn, J., Verscharen, D., Owen, C., Horbury, T., Maksimovic, M., Chen, C., Guo, F., and Fu, X.: Measurement of the rate of change of the electron heat flux due to the whistler instability with Solar Orbiter observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9803, https://doi.org/10.5194/egusphere-egu23-9803, 2023.

EGU23-11491 | Posters on site | ST1.11

On the Nature of Ion-to-Electron Scale Field Fluctuations in the Solar Wind: Insight from Artemis Observations, Simulations and Linear Theory 

Chadi Salem, John Bonnell, Christopher Chaston, Kristopher Klein, Luca Franci, and Vadim Roytershteyn

Recent observational and theoretical work on solar wind turbulence and dissipation suggests that kinetic-scale fluctuations are both heating and isotropizing the solar wind during transit to 1 AU.  The nature of these fluctuations and associated heating processes are poorly understood. Whatever the dissipative process that links the fields and particles - Landau damping, cyclotron damping, stochastic heating, or energization through coherent structures - heating and acceleration of ions and electrons occurs because of electric field fluctuations. The dissipation due to the fluctuations depends intimately upon the temporal and spatial variations of those fluctuations in the plasma frame.  In order to derive that distribution in the plasma frame, one must also use magnetic field and density fluctuations, in addition to electric field fluctuations, as measured in the spacecraft frame (s/c) to help constrain the type of fluctuation and dissipation mechanisms that are at play.

We present here an analysis of electromagnetic fluctuations in the solar wind from MHD scales down to electron scales based on data from the Artemis spacecraft at 1 AU. We focus on a few time intervals of pristine solar wind, covering a reasonable range of solar wind properties (temperature ratios and anisotropies; plasma beta; and solar wind speed). We analyze magnetic, electric field, and density fluctuations from the 0.01 Hz (well in the inertial range) up to 1 kHz. We compute parameters such as the electric to magnetic field ratio, the magnetic compressibility, magnetic helicity, compressibility and other relevant quantities in order to diagnose the nature of the fluctuations at those scales between the ion and electron cyclotron frequencies, extracting information on the dominant modes composing the fluctuations. We also use the linear Vlasov-Maxwell solver PLUME to determine the various relevant modes of the plasma with parameters from the observed solar wind intervals. These results are supplemented by analysis of fully nonlinear kinetic simulations of decaying turbulence at small scales. We discuss the results and highlight  the relevant modes as well as the major differences between our results in the solar wind and results in the magnetosheath.

How to cite: Salem, C., Bonnell, J., Chaston, C., Klein, K., Franci, L., and Roytershteyn, V.: On the Nature of Ion-to-Electron Scale Field Fluctuations in the Solar Wind: Insight from Artemis Observations, Simulations and Linear Theory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11491, https://doi.org/10.5194/egusphere-egu23-11491, 2023.

EGU23-12689 | ECS | Orals | ST1.11

Turbulence and whistlers in magnetic clouds observed by Solar Orbiter 

A. L. Elisabeth Werner, Emiliya Yordanova, Andrew P. Dimmock, and Ida Svenningsson

Kinetic processes control the cross-scale energy transfer between large-scale dynamics and dissipation in the solar wind. Large-scale magnetic flux ropes, also known as magnetic clouds (MCs), inside interplanetary coronal mass ejections (ICMEs) have been shown to effectively drive magnetospheric disturbances, but little is known about the turbulence properties and wave-particle interactions inside the MCs.

Here, we study the properties of the turbulence inside MCs between 0.3-1 AU observed by Solar Orbiter. We find that the spectral index in the inertial range fits Kolmogorov’s power law, but in the high-frequency regime we find a spectral bump at the beginning of a steeper power law regime. This is likely due to the presence of a significant number of whistler waves inside the MCs.

We have developed an automated search algorithm to find and record the properties of whistler waves inside MCs observed by Solar Orbiter. We find that MCs contain a significant number of whistler wave events with high magnetic field wave power (>0.5 nT2), which we do not find in the ICME sheath regions. We study these waves and attempt to determine their generation mechanism. In order to explore possible relations between the turbulence and the presence of whistler waves, we also determine the mean energy transfer rate, the magnetic field intermittency and the turbulent properties of the MCs and compare with the sheaths.  

How to cite: Werner, A. L. E., Yordanova, E., Dimmock, A. P., and Svenningsson, I.: Turbulence and whistlers in magnetic clouds observed by Solar Orbiter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12689, https://doi.org/10.5194/egusphere-egu23-12689, 2023.

EGU23-13050 | ECS | Orals | ST1.11

Cross helicity modified by large-scale velocity shears in the solar wind 

Juska Soljento, Simon Good, Adnane Osmane, and Emilia Kilpua

Cross helicity quantifies the balance between counterpropagating Alfvénic fluctuations, which interact nonlinearly to generate turbulence in the solar wind. We have investigated how cross helicity is modified by large-scale velocity shears in the solar wind plasma. Using the linear Kelvin–Helmholtz (KH) instability threshold, we identified velocity shears at a 30-min timescale. The shears were associated with 74 interplanetary coronal mass ejection (ICME) sheaths observed by the Wind spacecraft at 1 au between 1997 and 2018. Typically weaker shears upstream of the sheaths and downstream in the ICME ejecta were also analyzed. Below the KH threshold, cross helicity was approximately invariant or weakly rising with shear amplitude. Above the KH threshold, fluctuations tended toward a balanced state with increasing shear amplitude. These findings are consistent with velocity shears being local sources of sunward fluctuations that act to reduce net imbalances in the antisunward direction, and suggest that the KH instability plays a role this process.

How to cite: Soljento, J., Good, S., Osmane, A., and Kilpua, E.: Cross helicity modified by large-scale velocity shears in the solar wind, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13050, https://doi.org/10.5194/egusphere-egu23-13050, 2023.

EGU23-13102 | ECS | Orals | ST1.11

Wavelet determination of scaling exponents and intermittency seen by Solar Orbiter 

Alina Bendt, Sandra Chapman, and Bogdan Hnat

The solar wind provides a natural laboratory for plasma turbulence at high Reynolds number. We use Solar Orbiter (SO) observations from the Magnetometer (MAG) and the Solar Wind Analyser Suite (SWA) to study extended intervals of homogeneous turbulence. Intervals which exhibit a clear scaling range of magnetohydrodynamic (MHD) turbulence, and transitions to both the ‘1/f’ range at low frequencies, and the kinetic range at frequencies where MHD is no longer valid, are selected. We ensure that all intervals are of steady solar wind flow and do not contain isolated structures such as shocks, pressure pulses and discontinuities.

Solar wind turbulence is anisotropic due to the presence of a background magnetic field. We first rotate the magnetic field into orthogonal coordinate systems with one coordinate parallel to the average direction of the magnetic field B0, a second coordinate perpendicular to both B0 and average solar wind flow direction U, and the third in the plane of both B0 and U. We then perform a Haar wavelet decomposition to obtain the timeseries of magnetic field fluctuations on multiple temporal scales. The Haar wavelet decomposition is by linearly spaced intervals in logarithmic frequency space and hence provides a precise determination of the power spectral exponents, discriminating between 5/3, 3/2 and other relevant values. It also directly estimates the Local Intermittency Measure, which characterizes localized coherent turbulent structures, and the structure functions, which quantify higher order scaling exponents.

We apply these methods to SO intervals in order to test for systematic dependencies on the properties of the turbulence with different plasma conditions and at different distances from the sun.

How to cite: Bendt, A., Chapman, S., and Hnat, B.: Wavelet determination of scaling exponents and intermittency seen by Solar Orbiter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13102, https://doi.org/10.5194/egusphere-egu23-13102, 2023.

EGU23-13543 | ECS | Orals | ST1.11

Quasi-Lagrangian studies of spatio-temporal correlation in incompressible MHD turbulence 

Raquel Mäusle and Wolf-Christian Müller

Turbulence is of fundamental importance for many physical systems on Earth and throughout the universe. A turbulent flow can be described as the superposition of turbulent fluctuations of various length scales, which interact with each other non-linearly, leading to a transfer of energy across scales. We aim at a better understanding of the temporal and spatial properties of this energy transfer process in plasma turbulence, by studying the spatio-temporal correlation between turbulent structures in magnetohydrodynamic (MHD) turbulence.

To this end we perform three-dimensional direct numerical simulations with a pseudo-spectral method. We employ the quasi-Lagrangian reference frame, in which tracer particles are followed in the flow each carrying with it a set of probes at fixed distances across which the fluctuations are computed. This avoids the large-scale sweeping effect, which in the case of fixed-grid (Eulerian) measurements would obscure the small-scale temporal dynamics. This approach is based on previous studies in Navier-Stokes turbulence [Physics of Fluids 23.8 (2011): 085107] and has been extended to account for the magnetic field.

We investigate systems with different mean magnetic field strength. The spatio-temporal correlation functions yield insight into the nature of the cross-scale transfer of energy in terms of the direction, strength, and time scale of the transfer process. In particular, the scaling of the correlation times perpendicular and parallel to the local magnetic field, the influence of the mean magnetic field and the implications for the current understanding of the cross-scale transfer process are discussed.

How to cite: Mäusle, R. and Müller, W.-C.: Quasi-Lagrangian studies of spatio-temporal correlation in incompressible MHD turbulence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13543, https://doi.org/10.5194/egusphere-egu23-13543, 2023.

EGU23-13782 | ECS | Orals | ST1.11

Generalised Ohm’s Law in the Magnetosheath: How do plasma conditions impact turbulent electric fields? 

Harry Lewis, Julia Stawarz, Luca Franci, Lorenzo Matteini, Kristopher Klein, and Chadi Salem

Turbulence is a complex phenomenon whereby fluctuation energy is transferred between different scale sizes as a result of nonlinear interactions. Electromagnetic turbulence is ubiquitous within space plasmas, wherein it is associated with numerous nonlinear interactions. The dynamics of the magnetic field, which are widely studied in turbulence theory, are intimately linked to the electric field, which controls the exchange of energy between the magnetic field and the particles. Magnetospheric Multiscale (MMS) provides the unique opportunity to decompose electric field dynamics into contributions from different linear and nonlinear processes. The evolution of the electric field is described by generalised Ohm’s law, which breaks down the dynamics into components arising from different physical effects. Using high-resolution multipoint measurements, we compute the MHD, Hall and Electron Pressure terms of generalised Ohm’s law for 60 turbulent magnetosheath intervals. These terms, which have varying contributions to the dynamics as a function of scale, arise as a result of different physical effects related to a range of underlying turbulent phenomena. We examine how two characteristics of the turbulent electric field spectra depend on plasma conditions: the transition scale between MHD and Hall dominance (the ‘Hall scale’, kHall) and the relative amplitude of Hall and Electron Pressure contributions. Motivated by dimensional analysis arguments which appeal to characteristics of the plasma and the turbulence that can be quantified in a number of ways by MMS, we demonstrate the necessary refinements required to reproduce measured values. The scalar isotropic kinetic Alfven wave prediction for the ratio of Electron Pressure to Hall terms as a function of plasma beta is not consistent with measurements. We observe that the MHD and Hall terms are dominated by either nonlinear or linear dynamics, depending on the interval, while the Electron Pressure term is dominated by linear components only. Our work shows how contributions to turbulent dynamics change in different plasma conditions, which may provide insight into other turbulent plasma environments.  

How to cite: Lewis, H., Stawarz, J., Franci, L., Matteini, L., Klein, K., and Salem, C.: Generalised Ohm’s Law in the Magnetosheath: How do plasma conditions impact turbulent electric fields?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13782, https://doi.org/10.5194/egusphere-egu23-13782, 2023.

The solar wind plasma environment in the outer heliosphere is different from the inner heliosphere where has been widely studied. An important factor influencing the turbulence evolution in the outer heliosphere is the pickup ions, primarily originated from neutral atoms from the interstellar medium. Pickup ions are not readily assimilated by the background solar wind plasma and thus provide extra free energies which can drive ion-scale instabilities. The unstable growing waves will end up taking part in the turbulent energy transport. However, how these pickup-ion-associated energies involve in turbulent cascade and influence turbulence evolution have yet to be studied. In this work, we study the solar wind turbulence evolution from 1 au to 33 au based on Voyager 2 magnetic field measurements. We study 305 time intervals listed in Pine et al. (2020). In all these time intervals, no ion-scale bumps are present in the turbulent spectra. We find that: (1) The perpendicular and trace power spectra (and ) still follow a Kolmogorov-like spectrum until 33 au while the parallel power spectrum transits from -2 to -5/3 at heliocentric distance R~10 au; (2) At periods 10 s <τ< 500 s, quasi-parallel propagation dominates in 1 au<R<7 au, with quasi-perpendicular propagation gradually emerging at R>5au. For R > 7 au, oblique propagation becomes a primary component. (3) At larger periods of τ>100 s, increases with propagation angle in 1 au<R<5 au, and in contrast decreases with propagation angle at R>5 au due to the enhanced power level at propagation angles smaller than . We suggest that such enhancement may derive from the injection of the wave energy from the pickup ion source into the background tubulent cascade , and the injected wave energy is transferred across scales withou leaving bumps in or .

How to cite: Zhu, X., He, J., Duan, D., and Lin, R.: Evolution of Turbulence Anisotropy in the Outer Heliosphere and Transport of Pickup-ion-associated Energy in Turbulence Channel : Voyager 2 Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15757, https://doi.org/10.5194/egusphere-egu23-15757, 2023.

EGU23-15770 | Posters on site | ST1.11

Magnetic helicity dynamics in compressible isothermal MHD turbulence 

Jean-Mathieu Teissier and Wolf-Christian Müller

Magnetic helicity dynamics are important in the context of the generation of large scale magnetic structures from small scale fluctuations. Up to the present day, these dynamics have remained largely unexplored in compressible plasmas. We present new results from direct numerical simulations of isothermal magnetohydrodynamic turbulence, with Mach numbers ranging from 0.1 to 10 by employing higher-order numerics. A mechanical driving injects kinetic energy at the largest scales, while a small scale electromotive driving injects helical magnetic fluctuations. A large-scale sink of magnetic energy leads to the formation of a turbulent statistically stationary state, which is analyzed, extending the results on nonlinear cross-scale transfer presented in doi:10.1017/jfm.2021.32 and doi:10.1017/jfm.2021.496.

How to cite: Teissier, J.-M. and Müller, W.-C.: Magnetic helicity dynamics in compressible isothermal MHD turbulence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15770, https://doi.org/10.5194/egusphere-egu23-15770, 2023.

EGU23-15845 | Posters on site | ST1.11

Stochasticity and fractalityin irregular space plasmas 

Massimo Materassi and Giuseppe Consolini

One of the most relevant feature of turbulent fluids, included Space Plasmas, is the irregularity of the fields defining their local state (for example $\vec{B}$, $\vec{j}$ and $\vec{v}$ in the Solar Wind). In particular, time series of local quantities collected by \emph{in situ} measurements, e.g. by satellites, as well as remote sensing data, e.g. those from trans-medium communications, show scale-dependent statistical behaviour suggesting the local state fields to be better represented as \emph{fractal} or \emph{multi-fractal measures} rather than smooth functions of time and position.
In this presentation, the relationship between those measured fractal properties and the stochastic generalizations of fluid models describing the plasma is traced, suggesting a possible future development of Space Plasma turbulence theory.

How to cite: Materassi, M. and Consolini, G.: Stochasticity and fractalityin irregular space plasmas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15845, https://doi.org/10.5194/egusphere-egu23-15845, 2023.

EGU23-16937 | ECS | Posters on site | ST1.11

Analysis of magnetic helicity generation in MHD-shell model 

Ilyas Abushzada, Egor Yushkov, and Dmitry Sokoloff

The mechanism of stellar large-scale magnetic field formation, including the eleven-year solar cycle, is currently generally understood. In particular, its linear mode, in which the reverse effect of the magnetic field on the velocity field can be neglected. However, the non-linear reverse influence, which stabilize the growing average magnetic field, is not completely clear. The most possible reason of the nonlinear stabilization of this process is assumed the hydrodynamic helicity, but the balance of hydrodynamic and magnetic helicity and its transport along the spectrum remains to be studied. The present report is devoted to this problem. An exponential growth of magnetic energy at sufficiently high magnetic Reynolds numbers can be observed in a random short-correlated plasma flow at small-scales relative the velocity correlation length. Magnetic helicity is generated in this case together with the small-scale energy of magnetic field. And despite the fact that this phenomenon is traditionally studied by using the Kazantsev’s approach, we are trying to recreate this process of small-scale generation by a mhd shell approach, which is more convenient for the subsequent study of the balance and energy/helicity transport from small scales to large ones. To do this, in the complex shell model we add a small magnetic field to the well-established Kolmogorov spectrum and, by observing the exponential growth of magnetic energy on small scales, we compare the generation process with the magnetic small-scale Kazantsev dynamo. We select the correlation time for the velocity field and the working spectral regions to show that, in general, both approaches describe the same process with the same generation rates and scales. Thus, we show that the shell approach can be used for the future study of small-scale energy/helicity transport along the spectrum and for the problems of large-scale stellar dynamo processes stabilization. This work was supported by the BASIS Foundation grant no. 21-1-3-63-1.

How to cite: Abushzada, I., Yushkov, E., and Sokoloff, D.: Analysis of magnetic helicity generation in MHD-shell model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16937, https://doi.org/10.5194/egusphere-egu23-16937, 2023.

NP7 – Nonlinear Waves

EGU23-4009 | ECS | Posters on site | NP7.1

Size distributions reveal regime transition of dominant driving force in lake systems 

Shengjie Hu, Zhenlei Yang, Sergio Torres, Zipeng Wang, and Ling Li

Power law size distribution, associated with important system behaviors including scale-invariance, critical tipping and self-organization, has been observed in many complex systems. Such distribution also emerges from natural lakes, with potentially important links to the dynamics of lake systems. But the driving mechanism that generates and shapes this feature in lake systems remains unclear. Moreover, the power law itself was found inadequate for fully describing the size distribution of lakes, due to deviations at the two ends of size range. Based on observed and simulated lakes in China’s 11 hydro-climatic zones, we established a conceptual model for lake systems, which covers the whole size range of lake size distribution and reveals the underlying driving mechanism. The full lake size distribution is composed of three components featured by exponential, stretched-exponential and power law distribution. These three distributions are referred to as three phases which represent system (size) states with successively increasing degrees of heterogeneity and orderliness, and more importantly, indicate the dominance of exogenic and endogenic forces in lake systems, respectively. As the dominant driving force changes from endogenic to exogenic, a phase transition occurs with lake size distribution shifted from power law to stretched-exponential and further to exponential distribution. Apart from compressing the power law phase, exogenic force also increases its scaling exponent, driving the corresponding lake size power spectrum into the regime of “blue noise” with reduced system resilience. Besides, the change may also lead to a rising proportion of small lakes in the whole size distribution, which would increase the overall greenhouse gas emissions from natural lakes.

How to cite: Hu, S., Yang, Z., Torres, S., Wang, Z., and Li, L.: Size distributions reveal regime transition of dominant driving force in lake systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4009, https://doi.org/10.5194/egusphere-egu23-4009, 2023.

EGU23-4271 | ECS | Orals | NP7.1

Study on the rotation of blocks in two-dimensional block-rock mass 

Kuan Jiang and Cheng-zhi Qi

Rock mass has complex block-hierarchical structure involving various scale levels, which should be considered both in dynamic and static conditions. Because the interlayer has weak mechanical properties compared with rock blocks, the deformation of rock mass mainly concentrates at the interlayers both in dynamic and static conditions, which provides the possibility of translation and rotation for rock blocks. The basic carriers of pendulum-type wave in rock mass are geoblocks with translational and rotational degrees of freedom involving various hierarchical levels. The major part of the energy of a blast is spent to fragmentation of rocks and is transferred to rock blocks of the stressed geomedium in the form of kinetic energy (including translational kinetic energy and rotational kinetic energy). The in-situ experimental data has shown that the block-rock mass has significant angular deformation under dynamic impact, and the rotation of blocks can deeply affect the wave propagation and dynamic behavior of rock mass. Previous research on 1D dynamic model of block-rock mass cannot reflect the rotation effect of blocks, and the new 2D dynamic model should take into account the rotation of blocks and energy transfer. Consequently, aiming at the investigation of rotation of blocks, the 2D dynamic model of block-rock mass is established based on the accurate consideration of rotation effect. The research based on this model reveals the mechanism of the rotation of blocks, and determines the characteristics of energy transfer and the influence of the rotation of blocks on the inhomogeneous deformation of interlayers. Research shows that the rotation of blocks is not directly related to whether the structure of rock mass is symmetrical, or whether the interlayer is deformed or not, or the form of external loads, but is caused by the non-equilibrium shear between interfaces in the absence of the external torque. The rotation of blocks results in the inhomogeneous deformation of interlayers, and has a significant influence on the shear deformation of interlayers. At some local positions, in addition to the deformation of the interlayer caused by translation, the block-rock mass also produces additional tension and compression deformation caused by the rotation of blocks, which may lead to the phenomenon of rock crushing. This study theoretically solves the problems of wave propagation in block medium under arbitrary loads and torque, and is helpful for the research of seismic wave propagation in block medium with inhomogeneous complex structures.

How to cite: Jiang, K. and Qi, C.: Study on the rotation of blocks in two-dimensional block-rock mass, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4271, https://doi.org/10.5194/egusphere-egu23-4271, 2023.

EGU23-6080 | ECS | Posters virtual | NP7.1

Rocks with rotating blocks: 1D displacement, rotation and wave propagation 

Maoqian Zhang, Elena Pasternak, and Arcady Dyskin

Fragmentation of rocks, e.g. splitting into blocks, is a common occurrence at a range of scales from rock fragmentation caused by rockbursts or blasting to blocky rock mass produced by systems of fractures to rubble-pile asteroids. Common in these diverse objects is the ability of blocks (fragments) to assume relatively independent displacement and/or rotation.

 

Modelling deformation of blocky/fragmented rocks is complicated by the phenomenon of elbowing [1] whereby the rotating block pushes away the neighbouring blocks. The direction of the push can be independent of the direction of block rotation making the problem strongly non-linear (the “absolute value” type non-linearity). In order to investigate elbowing we constructed a simple 1D physical model of a chain of blocks with one translational and one rotational degrees of freedom. It is found that when one block (the initial block) is rotated, the neighbouring blocks may not rotate, only displace, depending on the magnitude of friction and the number of blocks in the chain. A discrete element (3DEC) model of the chain is developed. It shows the conditions of rotation of the blocks and the rotational wave propagation following a pulse rotation of the initial block.

 

  • Pasternak, E., Dyskin, A.V., Estrin, Y. (2006) Deformations in transform Faults with rotating crustal blocks. Pure Appl. Geophys. 163 2011–2030.

 

Acknowledgement. The authors are grateful to Dr I. Shufrin and School of Engineering workshop for help with designing and manufacturing of the physical model. EP and AVD acknowledge support from the Australian Research Council through project DP210102224.

How to cite: Zhang, M., Pasternak, E., and Dyskin, A.: Rocks with rotating blocks: 1D displacement, rotation and wave propagation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6080, https://doi.org/10.5194/egusphere-egu23-6080, 2023.

EGU23-6358 | ECS | Posters virtual | NP7.1

Thermal spallation and fracturing of rocks produced by surface heating 

Yide Guo, Elena Pasternak, and Arcady Dyskin

Heating of rock surface (e.g., flame heating) induces compressive stresses in the surface layer and tensile stresses of lower magnitude in the layer beneath. If the heating temperature is large enough (around 900 deg for shales), the compressive stresses initiate spallation produced by pre-existing cracks that and extensively grow parallel to the surface under compression. The extensive cracks separate thin layers from different parts of the heated surface which eventually buckle opening a new surface which starts being subjected to flame heating. Then the spallation process repeats itself producing a cavity of approximately cylindrical shape growing into the rock normal to the surface.

 

The presentation reports the results of tests on flame heating of shales, which demonstrate that the spallation process is accompanied by emergence of a large tensile fracture normal to the surface. In order to check whether the fracture can be produced by tensile thermal stresses induced in the layer situated under the compressed layer we conducted a series of finite element simulations of thermal stresses for different spallation depths (depths of the cavity). The modelling shows that: (1) as the spallation cavity deepens the magnitudes of maximum compressive and tensile stresses remain approximately the same except of two peaks at the spallation depths of about 6% and 30% of the diameter of the heating flame; (2) the magnitude of the maximum tensile stresses is about half of the compressive stress. Given that the spallation strength is about half of the UCS (e.g., [1]) and that the tensile strength is often up to an order of magnitude lower than the UCS, the induced tensile thermal stresses can be considered as sufficient to produce the tensile fracture.

 

The experiment and computer modelling suggest that the production of tensile fractures is an intrinsic feature of the spallation process. These results can assist in understanding large scale spallation-like processes in the Earth’s crust and design rock cutting based on thermal spallation.

 

  • Wang, H., A.V. Dyskin, Pasternak, P. Dight and B. Jeffcoat-Sacco, 2021. Fracture mechanics of in-situ spallation. Engineering Fracture Mechanics, 260, 108186.

How to cite: Guo, Y., Pasternak, E., and Dyskin, A.: Thermal spallation and fracturing of rocks produced by surface heating, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6358, https://doi.org/10.5194/egusphere-egu23-6358, 2023.

Spallation is a type of surface rock failure under uniaxial and biaxial compression manifested by successive production and ejection of spalls/fragments. This type of failure is observed in laboratory experiments on uniaxial/biaxial compression of rocks and mortar as well as in rock masses. In the latter case spallation is seen in slopes and in the walls of underground openings. In its unstable phase the spallation can lead to such a dangerous phenomenon as strain rockburst.

Spallation is caused by formation and extensive growth of wing cracks parallel to a free surface (e.g., excavation wall) under the applied compressive load. Their growth amplified by the strong interaction with the surface leads to separation of thin layers whose subsequent buckling produces the spalls and opens a new surface. This produces new wing cracks extensively growing parallel to the new surface, thus enabling the process that repeats itself, e.g. [1].

A critical role in this mechanism is played by the interaction of the wing crack with the free surface. The interaction is the stronger the closer the wing crack to the free surface. The closeness to the free surface is limited by the sizes of the largest pre-existing defects seeding the wing cracks. Therefore, the wing cracks inducing each step of spallation are approximately coplanar. Subsequently, the layer separated from the bulk of the rock can be considered as a plate connected to the main part of the rock by bridges formed by intact rock sections remaining between the wing cracks. In the first approximation the effect of bridges can be modelled by Winkler layer [2]. The cracks are assumed to be disc-like; the interaction with the free surface is computed using the beam asymptotics [3].

The velocity of flexural wave propagation depends upon the Winkler layer stiffness and the frequency of oscillations. There exists a minimum frequency, below which the wave does not propagate.  Both parameters depend upon the average crack radius and the number of wing cracks. If the monitoring of the wave velocities and the minimum frequency is complemented by monitoring of the average surface deformation (for instance using non-contact methods such as the digital image correlation) the parameters of the spallation process can be determined, and the approaching buckling phase identified. Results of this research will be instrumental in developing methods of monitoring and predicting strain rockbursts.

1. Wang H, A.V. Dyskin, E. Pasternak, P. Dight and B. Jeffcoat-Sacco, 2022. Fracture mechanics of spallation. Engineering Fracture Mechanics, 260:108186.

2. He, J., Pasternak, E. and A.V. Dyskin, 2020. Bridges outside fracture process zone: Their existence and effect. Engineering Fracture Mechanics, 225, 106453.

3. Dyskin, A.V., L.N. Germanovich and K.B. Ustinov, 2000. Asymptotic analysis of crack interaction with free boundary. J. Solids Structures, 37, 857-886.

4. Lloyd J.R. and Miklowitz, 1962. Wave Propagation in an Elastic Beam or Plate on an Elastic Foundation. J. Applied Mechanics, 459-464.

Acknowledgement. The authors acknowledge support from the Australian Research Council through project DP210102224.

How to cite: Dyskin, A. and Pasternak, E.: Monitoring of spallation processes in rocks by continuous measurements of surface deformation and wave parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6427, https://doi.org/10.5194/egusphere-egu23-6427, 2023.

EGU23-6464 | Orals | NP7.1

Cracking properties of shale influenced by bedding layers and a pre-existing slot 

Yuxin Ban, Jun Duan, Qiang Xie, Xiang Fu, and Weichen Sun

The key to increasing shale gas production is to construct fracture networks in shale reservoir to provide channels for shale gas. Understanding the cracking characteristics of shale is necessary for oil and gas exploitation engineering. Given this, uniaxial compression tests were conducted on Longmaxi shale in China to study the mechanical properties and cracking behaviors affected by bedding layers and pre-existing slot. Sandstone specimens with different pre-existing slot angles were also tested as a comparison. A mechanical-optical-acoustical comprehensive data acquisition system consisting of a rigid hydraulic machine, high-speed industrial camera and acoustic emission acquisition instrument was established to monitor the cracking behaviors in real time. The results show that the cracking behaviors of shale specimens are quite different from sandstone specimens in the uniaxial compression tests. Crack initiation is predominantly controlled by the pre-existing slot and is also affected by bedding layers. Crack propagation is mainly controlled by bedding layers and stress field distribution. When the bedding layers are vertical, the cracks are most likely to propagate along the direction of the bedding and tensile cracks are observed. When the bedding is 30°, the shale specimens are most likely to be controlled by the bedding layers, resulting in shear slip failure along the bedding layers. The experimental results contribute to the understanding of cracking properties in layered anisotropic materials.

How to cite: Ban, Y., Duan, J., Xie, Q., Fu, X., and Sun, W.: Cracking properties of shale influenced by bedding layers and a pre-existing slot, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6464, https://doi.org/10.5194/egusphere-egu23-6464, 2023.

EGU23-6515 | Posters virtual | NP7.1

Harmonics multiple to the driving frequency of damped bilinear oscillators 

Elena Pasternak, Arcady Dyskin, Roman Pevzner, and Boris Gurevich

Field observations show that power spectra of the response to high amplitude harmonic excitation contain peaks at frequencies multiple to the driving frequency, e.g. [1]. This phenomenon is conventionally attributed to the effect of mechanical non-linearity of the Earth’s crust. Given that there exist various types of non-linearity it is important to identify the types of non-linearities that can produce multiple harmonics in response to high power excitation and thus ensure the correct interpretation of the monitoring data.

One type of non-linearity capable of producing multiple resonances is bilinearity of stiffness, the simplest representation of which is a bilinear oscillator – the oscillator with different stiffnesses for compression and tension. In the Earth’s crust the role of bilinear oscillators can be played by pre-existing fractures initially closed by the in-situ compression but capable of being opened by the tensile phase of the applied high amplitude harmonic excitation.

Bilinear oscillators possess multiple resonances, e.g. [2], however these are multiples of the natural frequency. We note that extreme damping effected by the presence of fluids in fractures and porous rocks can quickly eliminate the effect of the natural frequency leaving only the stationary oscillations with the driving frequency in each linear (tensile or compressive) stage of oscillations. The transition from one stage to another is characterised by short transients, which gives rise to multiple spectral peaks. This mechanism is investigated in asymptotics of high damping ratio. It is shown the existence of the following spectral peaks: if f0 is the driving frequency, the peaks will be observed at 2f0, 3f0, 5f0 and further at all odd multiples of f0.

The theory developed is essential for identifying the prevailing mechanisms of non-linearity in the Earth’s crust and determining their parameters.

1. Yurikov, A., B. Gurevich, K. Tertyshnikov, M. Lebedev, R. Isaenkov, E. Sidenko, S. Yavuz, S. Glubokovskikh, V. Shulakova, B. Freifeld, J. Correa, T.J. Wood, I.A. Beresnev and R. Pevzner, 2022. Evidence of nonlinear seismic effects in the earth from downhole distributed acoustic sensors. Sensors 2022, 22, 9382.

2. Dyskin, A.V., E. Pasternak and E. Pelinovsky, 2012. Periodic motions and resonances of impact oscillators. Journal of Sound and Vibration 331(12) 2856-2873.

Acknowledgement. EP, AVD and BG acknowledge support from the Australian Research Council through project DP190103260. RP and BG acknowledge financial support from the Australian Department of Industry, Science and Resources for the 2021 Global Innovation Linkage (GILIII000114) grant and the Sponsors of the Curtin Reservoir Geophysics Consortium.

How to cite: Pasternak, E., Dyskin, A., Pevzner, R., and Gurevich, B.: Harmonics multiple to the driving frequency of damped bilinear oscillators, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6515, https://doi.org/10.5194/egusphere-egu23-6515, 2023.

EGU23-6565 | Posters virtual | NP7.1

Study of deformation and fracture behavior of shale by a novel anisotropic regular lattice spring model 

Qiang Xie, Weichen Sun, Kai Wu, Zhilin Cao, Xiang Fu, Alessio Fumagalli, and Yuxin Ban

This research aims to study the deformation and fracture behavior of shale by a novel anisotropic regular lattice spring model (ARLSM). The novel ARLSM applies the normal and tangential coupling spring to release the Poisson's ratio limitation in the traditional regular lattice spring model. Meanwhile, a nonlinear strength criterion is introduced into ARLSM to simulate the fracture failure of shale. Two benchmark problems are tested to implement the research. The study shows that ARLSM has larger range of Poisson's ratio and better effects comparing with the existing anisotropic lattice spring model. Moreover, ARLSM can accurately predict the deformation and fracture behavior of shale under different conditions.

How to cite: Xie, Q., Sun, W., Wu, K., Cao, Z., Fu, X., Fumagalli, A., and Ban, Y.: Study of deformation and fracture behavior of shale by a novel anisotropic regular lattice spring model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6565, https://doi.org/10.5194/egusphere-egu23-6565, 2023.

After the impoundment of a high dam reservoir, the water pressure environment of the rock masses in dam base and reservoir bank changes, which may easily induce engineering problems such as bank slope instability and dam collapse. In order to investigate the differences and mechanisms of different constant water pressures on the rock mass of dam base, triaxial loading tests were conducted on sandstone with initial damage under different high constant porewater pressures, and the multidirectional fracture mechanism was analyzed by combining CT and electron scans. The test results show that:(1) Under the confining pressure of 80 MPa, the greater the pore water pressure, the more brittle the sandstone is, the lower the peak strength, the smaller the volume expansion stress, the pore water pressure increases from 10 MPa to 50 MPa, and the peak strength decreases by 33%.  (2) For different pore water pressure, there are significant differences in sandstone internal deterioration range and deterioration effect  as the fracture surfaces of sandstone specimens have various forms and directions. Due to CT scaning results, with the pore water pressure increases, the deterioration effect spreads from specimen middle to both ends. When the water pressure-confining pressure ratio is less than 25.0%, the deterioration of pore water pressure is mainly concentrated in the middle 1/3 of the specimen. When the water pressure-confining pressure ratio is bigger than 62.5%, the pore water pressure has obvious deterioration effect on the whole specimen. (3) Electron microscopy scanning reveals that with the increase of pore water pressure: the microgranular structure of sandstone changes from shear slip failure to shear fracture failure, and the microcrystalline structure of sandstone changes from cauliflower to rice granular. The macroscopic failure mode changes from plastic failure to brittle failure, and multidirectional fracture plane is formed, which is related to the migration of fine particles and the fracture of large particles in the meso-particle structure under pore water pressure. The formation of the multidirectional fracture plane is directly related to the shear strength of the microscopic crystal structure.

How to cite: Fu, X., Ban, Y., Xie, Q., and Sun, W.: Triaxial compression mechanical properties and multidirectional fracture mechanism of sandstone under different pore pressure, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6852, https://doi.org/10.5194/egusphere-egu23-6852, 2023.

EGU23-7495 | ECS | Orals | NP7.1

Joint multifratcal analysis of available wind power and rain intensity from an operational wind farm 

Jerry Jose, Auguste Gires, Ernani Schnorenberger, Ioulia Tchiguirinskaia, and Daniel Schertzer

Wind power production plays an important role in achieving UN’s (United nations) Sustainable development goal (SDG) 7 - affordable and clean energy for all; and in the increasing global transition towards renewable and carbon neutral energy, understanding the uncertainties associated with wind and turbulence is extremely important. Characterization of wind is not straightforward due to its intrinsic intermittency: activity of the field becomes increasingly concentrated at smaller and smaller supports as the scale decreases. When it comes to power production by wind turbines, another complexity arises from the influence of rainfall, which only a limited number of studies have addressed so far suggesting short term as well as long term effects. To understand this, the project RW-Turb (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency, ANR-19-CE05-0022) employs multiple 3D sonic anemometers (manufactured by Thies), mini meteorological stations (manufactured by Thies), and disdrometers (Parsivel2, manufactured by OTT) on a meteorological mast in the wind farm of Pays d’Othe (110 km south-east of Paris, France; operated by Boralex). With this simultaneously measured data, it is possible to study wind power and associated atmospheric fields under various rain conditions.

Variations of wind velocity, power available at the wind farm, power produced by wind turbines and air density are examined here during rain and dry conditions using the framework of Universal Multifractals (UM). UM is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over wide range of scales which accounts for the intermittency in the field. While statistically analysing the power produced by turbine, rated power acts like an upper threshold resulting in biased estimators. This is identified and quantified here using the theoretical framework of UM along with the actual sampling resolution of instruments under study. Further, from event based analysis, differences in UM parameters were observed between rain and dry conditions for the fields illustrating the influence of rain. This is further explored using joint multifractal analysis and an increase in correlation exponent was observed between various fields with increase in rain rate.

How to cite: Jose, J., Gires, A., Schnorenberger, E., Tchiguirinskaia, I., and Schertzer, D.: Joint multifratcal analysis of available wind power and rain intensity from an operational wind farm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7495, https://doi.org/10.5194/egusphere-egu23-7495, 2023.

EGU23-7726 | Orals | NP7.1

Dynamic response of an elliptic cylinder inclusion with imperfect interfaces subjected to plane SH wave 

Tao Ming, Luo Hao, Zhao Rui, and Xiang Gongliang

Underground chambers or tunnels often contain inclusions, the interface between the inclusion and the surrounding rock is not always perfect, which influences stress wave propagation. In this study, the spring model and Ricker wavelet were adopted to represent the imperfect interface and transient seismic wave. Based on the wave function expansion method and Fourier transform, an analytical formula for the dynamic stress concentration factor (DSCF) for an elliptical inclusion with imperfect interfaces in infinite space subjected to a plane SH-wave was determined. The theoretical solution was verified via numerical simulations using the LS-DYNA software, and the results were analyzed. The effects of the wave number (k), radial coordinate (ξ), stiffness parameter (β), and differences in material properties on the dynamic response were evaluated. The numerical results revealed that the maximum DSCF always occurred at both ends of the elliptical minor axis, and the transient DSCF was generally a factor of 2-3 greater than the steady-state DSCF. Changes in k and ξ led to variations in the DSCF value and spatial distribution, changes in β resulted only in variations in the DSCF value, and lower values of ωp and β led to a greater DSCF under the same parameter conditions. In addition, the differences in material properties between the medium and inclusion significantly affected the variation characteristics of the DSCF with k and ξ.

How to cite: Ming, T., Hao, L., Rui, Z., and Gongliang, X.: Dynamic response of an elliptic cylinder inclusion with imperfect interfaces subjected to plane SH wave, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7726, https://doi.org/10.5194/egusphere-egu23-7726, 2023.

EGU23-7767 | ECS | Orals | NP7.1

Simulating Evapotranspiration in Green roofs using a Multifractal approach 

Arun Ramanathan S, Pierre-Antoine Versini, Daniel Schertzer, Ioulia Tchiguirinskaia, Remi Perrin, and Lionel Sindt

Abstract

Several equations and their simplified versions already exist for estimating evapotranspiration. Still, the practical difficulty in using them is that they contain too many variables and empirical parameters including some non-atmospheric vegetation-based ones which may not be appropriate for all plant types. Therefore, a simple empirical equation is suggested here to approximately estimate evapotranspiration loss in a deterministic manner as a function of the green roof’s water content, ambient air temperature, wind speed, relative humidity, and total net radiation flux. For nonlinear processes such as evapotranspiration clearly, such deterministic estimates are not representative of the extreme values observed in evapotranspiration losses. Therefore, a universal multifractal-based simulation procedure is proposed here to improve such deterministic estimates, so that the simulated evapotranspiration loss has realistic intermittency and temporal scaling behaviour, while preserving its diurnal variability.

 

Keywords

Multifractals, Non-linear geophysical systems, Cascade dynamics, Scaling, Hydrology, Meteorology.

 

How to cite: Ramanathan S, A., Versini, P.-A., Schertzer, D., Tchiguirinskaia, I., Perrin, R., and Sindt, L.: Simulating Evapotranspiration in Green roofs using a Multifractal approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7767, https://doi.org/10.5194/egusphere-egu23-7767, 2023.

EGU23-8426 | ECS | Posters on site | NP7.1

Effect of Rainfall Fractal Behaviour on that of Recharge and Groundwater Levels 

Abrar Habib, Athanasios Paschalis, Adrian P. Butler, Christian Onof, John P. Bloomfield, and James P. R. Sorensen

Using a physically based recharge-groundwater flow model, a multiplicative random cascade rainfall model and robust detrended fluctuation analysis (r-DFA), the effect of the fractal behaviour of rainfall on recharge and groundwater levels is investigated. The study site selected for this work is in Wallingford, United Kingdom, where groundwater levels in a shallow riparian aquifer and meteorological data of high temporal resolution are monitored.

The rainfall model is calibrated to the observed rainfall and used to simulate 40 synthetic rainfall series exhibiting different scaling behaviour (with r-DFA scaling exponents between 0.6 and 1.05). The scaling behaviour of the rainfall series are then objectively quantified using r-DFA. The synthetic rainfall is used as forcing to run the recharge-groundwater flow model which is calibrated to the observed groundwater levels.

It is found that small changes in the fractal behaviour of rainfall has a significant effect on the fractal behaviour of recharge and this in turn results in a small change in the fractal behaviour of groundwater levels. The significant effect on the fractal behaviour of drainage is attributed to the extended recharge periods which correspond to more frequent rain events in rainfall with higher scaling exponents. This effect is more subdued in groundwater level fluctuations due to attenuation of the recharge signal as it percolates through the unsaturated zone.

How to cite: Habib, A., Paschalis, A., Butler, A. P., Onof, C., Bloomfield, J. P., and Sorensen, J. P. R.: Effect of Rainfall Fractal Behaviour on that of Recharge and Groundwater Levels, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8426, https://doi.org/10.5194/egusphere-egu23-8426, 2023.

Rainfall fields exhibit extreme variability over wide range of space-time scales which make them complex to characterize, model and even measure. Furthermore, rainfall, as most geophysical fields, is strongly anisotropic. Fortunately, scaling anisotropy has been developed for a few decades to generalise scaling in an anisotropic framework, e.g., in the simplest case iso-surfaces become self-affines ellipsoids instead of self-similar spheres. This is particularly straightforward for continuous in scale cascades. For them, as well as for discrete in scale cascades, Universal Multifractals (UM) have been widely used to analyse and simulate such geophysical fields with the help of a very limited number of physically meaningful parameters. Recently blunt cascades have been introduced. They enable to remain in the simple framework of discrete cascades while partly overcoming their well known strong limitations such as non-stationnarity. It basically consists in geometrically interpolating over moving windows the multiplicative increments at each cascade steps.

Here we suggest to incorporate observed features in blunt 2D and 3D (space-time) blunt discrete cascade simulations. The data analysis corresponds to a 1D analysis along various directions ,considering each lof them as a different “sample” of the process. Analysing how the UM parameters change with the angle of the chosen direction enables to unveil underlying rainfall anisotropy features. Impacts, and notably potential biases, of these features on standard spatial analysis in 2D are also explored and discussed. For this purpose high resolution space-time rainfall data collected with help of a dual polarisation X-band radar operated by HM&Co-ENPC is used .

To simulate anisotropy features with the help of blunt extension of discrete UM cascades, we tentatively suggest to use moving window shaped as ellipses instead of squares. Tuning the eccentricity and orientation of the ellipses enables to introduce various levels of anisotropy within the simulated fields. First, multifractal expected behaviour is theoretically established and then it is numerically confirmed with the help of ensembles of stochastic simulations and the previously developed analysis approach.

Authors acknowledge the RW-Turb project (supported by the French National Research Agency - ANR-19-CE05-0022), for partial financial support.

How to cite: Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Characterizing and simulating with blunt extension of discrete cascades rainfall anisotropy in a Universal Multifractals framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9020, https://doi.org/10.5194/egusphere-egu23-9020, 2023.

Over the past years in the development of oil and gas fields, there has been a trend towards an increase in the development of unconventional low-permeability reservoirs. In this regard, it is becoming increasingly important to study the problems associated with the use of hydraulic fracturing technology (HF) in rocks with a complex internal structure. To achieve the maximum oil and gas production rate and increase the drainage zone in the near-wellbore space, it is necessary to carry out hydraulic fracturing with the most extensive system of fractures.

In this work the authors investigate the propagation of a hydraulically driven fracture in a fully saturated, permeable, and porous medium at the pore scale. To achieve a goal, at the first stage, we set a system of determining ratios and a crack propagation criterion. At the next stage, a three-dimensional numerical poroelastic model of a rock sample is prepared based on a three-dimensional image of the pore space of rock samples. Then numerical poroelastic modeling of the processes of one- and two-phase filtration and rock destruction using the extended finite element method is performed. For a more accurate description of filtration processes, the authors have prepared a physico-mathematical model that takes into account the flow rate and leakage of fluid into the rock during fracture growth at the pore scale. The obtained numerical results are compared with the previously conducted results of laboratory studies.

As a result of the numerical simulation, the authors prepared a digital rock model (DRM) based on microCT data, performed numerical simulation of the filtration process in the DRM and numerical simulation of fracture propagation in a fully saturated, permeable, and porous medium at the pore scale. Then, the dependences of filtration, initiation and fracture propagation were investigated depending on various conditions of HF fluid injection.

How to cite: Taurenis, D. and Nachev, V.: Three-dimensional numerical simulation of multiphase filtration and fracture propagation at the pore scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9483, https://doi.org/10.5194/egusphere-egu23-9483, 2023.

EGU23-10989 | ECS | Posters virtual | NP7.1

Using Equivalent Horton-Strahler Ratios to Predict Extreme Events in Colombian Andes Catchments 

Juan Mauricio Bedoya-Soto and Heli Steven Ocampo-Zapata

The frequency and severity of extreme hydrometeorological events in the Colombian Andes have increased due to the combined effects of climate change and climate variability, with the El Niño-Southern Oscillation (ENSO) being the main contributor. To address this issue and improve hydrologic and hydraulic infrastructure designs, it is necessary to develop better tools for accurately predicting the impact of these events. Hydrological scaling and similarity, based on relatively simple mathematical laws, can synthetically translate the high heterogeneity of hydrological processes into equations that are particularly useful in ungauged catchments, a widespread problem in the Colombian Andes. This research proposes the use of specific hydrological scaling tools, including the Geomorphological Instantaneous Unit Hydrograph (GIUH) and the equivalent Horton-Strahler (H-S) ratios, to calculate peak flows. These ratios express the self-similarity of channels and basins, independent of the threshold area for channel initiation, which the classical bifurcation ratio (RB), length ratio (RL), and area ratio (RA) depend on. Using digital elevation model (DEM) data from NASA's ALOS-PALSAR mission, which provides terrain elevation at a resolution of 12.5m x 12.5m, we analyzed regional patterns of extreme event scaling on various slopes of the Andes Mountains (Colombia) using the GIUH/equivalent H-S theory. With this DEM data, we developed a methodology for automatically extracting the equivalent H-S (RBe, RLe, RAe) in several catchments of the Western, Central, and Eastern ranges that compose the Colombian Andes, while simultaneously validating the self-similarity assumption of their channel networks. Our results highlight the importance of the equivalent H-S ratios as self-similarity indices and regional indicators of the intrinsic relationship between geomorphology and hydrology in the Colombian Andes and their usefulness for hydrological design engineering purposes.

How to cite: Bedoya-Soto, J. M. and Ocampo-Zapata, H. S.: Using Equivalent Horton-Strahler Ratios to Predict Extreme Events in Colombian Andes Catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10989, https://doi.org/10.5194/egusphere-egu23-10989, 2023.

EGU23-11210 | ECS | Posters on site | NP7.1

Power law Scaling in Drainage Basin Areas of Independent landscapes 

Dnyanesh Borse and Basudev Biswal

Power-law distributions occur in a diverse range of phenomena. Natural drainage networks also exhibit distinctive fractal properties and certain power-law scaling relationships irrespective of the underlined controls, such as geology, topography, and climate. Here we study the distribution of basin areas of continents as well as some islands. We used area-fraction vs. rank distribution, where the area fraction represents the area of a basin with respect to the total landscape area. To obtain the basin area distribution, we used HydroRivers data for the nine continent regions and performed DEM analysis for 12 islands. The results show that basin area distribution follows a power law in the case of all continents with scaling exponent ranging from -1.15 to -1.4. In the case of islands, the majority of them followed power law scaling with exponent ranging from -1.2 to around -2.5; however, distributions of some islands deviated from the power laws.

We also looked at the basin area distribution with the optimal channel network model with all boundary pixels modelled as outlets. We got the scaling exponent around -1.8. Our recently proposed probabilistic model for drainage network evolution (Borse & Biswal, 2023) shows the capability to produce networks with different distributions. This model can capture the varying range of exponents with its flexible parameters. Further studies would be needed to understand the significance of this basin area distribution scaling exponent and whether it could be used as a metric to characterize landscapes.

How to cite: Borse, D. and Biswal, B.: Power law Scaling in Drainage Basin Areas of Independent landscapes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11210, https://doi.org/10.5194/egusphere-egu23-11210, 2023.

EGU23-11767 | ECS | Orals | NP7.1

Small Scales Space-Time Variability of Wind Fields: Simulations with Vector Fields and Transfer to Turbine Torque Computation 

Ángel García Gago, Auguste Gires, Paul Veers, Ioulia Tchiguirinskaia, and Daniel Schertzer

Wind fields are extremely variable in space and time over a wide range of scales. This extreme variability is transferred to the wind turbine torque and ultimately to wind energy production. The Universal Multifractal (UM) framework is a powerful tool that allows to characterise and simulate the extreme variability of geophysical fields across scales with the help of only three parameters (α, C1 and H) with physical interpretation; while the 4th, the power a of a conservative flux, is absorbed by the empirical estimation of the mean singularity over a non-conservative field.

The main challenge is to simulate over 2D space plus time vector fields which realistically reproduce observed spatial and temporal variability of wind fields. The outer scale of the simulated fields should basically correspond to the size of the wind turbine in space and ten minutes in time. To achieve that, we combine two broad classes of stochastic processes: stable Levy processes and Clifford algebra. We use as input characteristic parameters obtained from the multifractal analysis of the data collected by two high-resolution 3D anemometers with approx. 33 m vertical distance on a meteorological mast. The data is collected as part of the RW-Turb measurement campaign (https://hmco.enpc.fr/portfolio-archive/rw-turb/), supported by the French National Research Agency (ANR-19-CE05-0022). The expected behaviour of the simulated field is confirmed by multifractal analysis. 

In the second step, we investigate the effect of small-scale wind variability on the wind turbine torque computation by imputing the simulated vector fields to three modelling chains with increasing complexity. The first one only considers the temporal variability, averaging the wind field and considering it at hub height. The second one is based on the angular moment definition and allows us to consider both spatial and temporal variability by computing the torque at each blade point and integrating it along the radius for each time step. Finally, the third one uses the realistic software OpenFAST developed by the US National Renewable Energy Laboratory (NREL). To analyse and physically interpret wind variability's effect, we compared the torque obtained by the three modelling chains focused on the small scales. As we expected, we found pronounced differences on small scales with stronger fluctuations exhibited in the second modelling chain, followed by OpenFAST and the first one. 

How to cite: García Gago, Á., Gires, A., Veers, P., Tchiguirinskaia, I., and Schertzer, D.: Small Scales Space-Time Variability of Wind Fields: Simulations with Vector Fields and Transfer to Turbine Torque Computation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11767, https://doi.org/10.5194/egusphere-egu23-11767, 2023.

We propose a theory for preventing instabilities in frictionally unstable systems such as earthquakes are. We exploit the dependence of friction on fluid pressure and use it as a backdoor for provoking controlled, slow-slip over a single mature seismic fault. We use the mathematical Theory of Control and notions from passivity in order to (a) stabilize and restricting chaos, (b) impose slow frictional dissipation and (c) tune the system toward desirable global asymptotic equilibria of lower energy. Our control approach is robust and does not require exact knowledge of the frictional behavior of the system and its fluid diffusion properties (e.g. permeability, viscosity, compressibility) or of other parameters related to complex physical processes that are hard to determine in practice. We expect our methodology to inspire earthquake mitigation strategies regarding anthropogenic and/or natural seismicity.

References

[1] Stefanou, I. (2019). Controlling Anthropogenic and Natural Seismicity: Insights From Active Stabilization of the Spring‐Slider Model. Journal of Geophysical Research: Solid Earth, 124(8), 8786–8802. https://doi.org/10.1029/2019JB017847
[2] Tzortzopoulos G., Braun P., Stefanou I. (2021), Absorbent Porous Paper Reveals How Earthquakes Could be Mitigated, Geophysical Research Letters 48. https://doi.org/10.1029/2020GL090792.
[3] Stefanou, I., Tzortzopoulos, G. (2022). Preventing instabilities and inducing controlled, slow-slip in frictionally unstable systems. Journal of Geophysical Research: Solid Earth. https://doi.org/10.1029/2021JB023410
[4] Gutiérrez-Oribio D., Tzortzopoulos G., Stefanou I., Plestan F. (2022). Earthquake Control: An Emerging Application for Robust Control. Theory and Experimental Tests. http://arxiv.org/abs/2203.00296
[5] Papachristos, E., Stefanou, I. (2022), Controlling earthquake-like instabilities using artificial intelligence. http://arxiv.org/abs/2104.13180.
[6] Gutiérrez-Oribio D., Stefanou I., Plestan F. (2022). Passivity-based Control of a Frictional Underactuated Mechanical System: Application to Earthquake Prevention. https://arxiv.org/abs/2207.0718

How to cite: Stefanou, I., Tzortzopoulos, G., and Gutierrez-Oribio, D.: Preventing earthquake instabilities and inducing controlled, slow-slip by active fluid pressure control in the vicinity of a single seismic fault, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14939, https://doi.org/10.5194/egusphere-egu23-14939, 2023.

EGU23-16812 | Posters virtual | NP7.1

Study of tectonic fault transition from aseismic to seismic slip due to fluid injection 

Sergey Turuntaev and Vasily Riga

The conditions for the transition from slow slip to seismic generation motion along a tectonic fault as a result of fluid injection through a well located near the fault are studied.

Movements along the fault caused by fluid injection can occur in the form of slow slips or lead to earthquakes. The implementation of a particular type of movement is dependent on the injection parameters and the fault friction and stress conditions. Numerical calculations were performed in which the consequences of fluid injection lasting from 1.5 months to 6 years were modeled. The calculations varied the total volume of the injected fluid, the flow rate during injection, the rate-state friction law properties of the fault, tangential stresses on the fault. It was found that under certain combinations of fault parameters and fluid flow, seismic generations occur. The transition to such a mode within the framework of the considered model occurs abruptly, a further increase in the injection rate does not lead to an increase in the rate of seismic movement, reaching values of 0.1-1 m/sec, depending on tectonic tangential stresses.

With fixed parameters of the rate-state friction law, the magnitude of the maximum displacement velocity depends on the rate of the pressure perturbation on the fault. Until the sliding velocity reaches a value of the order of 10-6 m/sec, the dependence of the logarithm of the sliding velocity on the rate of the pressure perturbation is linear or close to it, then there is a significant more dramatic increase in sliding velocity depending on the rate of the perturbation growth. The influence of the rate-state friction law parameters on the movements along the fault is not so unambiguous. However, it can be said that the sliding is determined by a combination of the following parameters: the critical length at which the stiffness of the fault section reaches the value of critical stiffness, and the characteristic response time determined by the parameters of the friction law.

How to cite: Turuntaev, S. and Riga, V.: Study of tectonic fault transition from aseismic to seismic slip due to fluid injection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16812, https://doi.org/10.5194/egusphere-egu23-16812, 2023.

EGU23-1058 | ECS | Orals | OS4.3

Climate Change Trends in The Eastern Mediterranean Hotspot 

Sagi Knobler, Gil Rilov, and Dan Liberzon

The sea surface temperature increase due to global warming is causing rapid iceberg melting and increased condensation of clouds, each project to a global consequence in the form of sea surface temperature drop during storms, marine heatwaves, sea level rise, and increase in intensification and rate of recurrence of storm weather events.

Here we present the analysis of 30-year-long measurements of sea surface temperature and instantaneous water surface elevation, measured by two buoys moored in separate locations in the climate hotspot area in the Eastern Mediterranean Sea at the depth of 24 meters, two kilometers off the Israeli coastline. Additional long-term measurements of sea level rise from several stations along the Israeli coastline are also integrated into the analysis. The increase in storm weather events was examined in terms of storms’ significant wave height statistics, using peak-over-threshold analysis over the historic data. 

The results showed occurrences of sea surface temperature drop events following storms and of marine heatwaves, positive trends were observed in sea level and in sea surface temperature rise. The last two decades are shown to be characterized by storm intensification. The sea surface rise was correlated against the measured sea surface temperature trends as obtained by the buoys and compared to Copernicus satellite data with remarkable conclusions.

How to cite: Knobler, S., Rilov, G., and Liberzon, D.: Climate Change Trends in The Eastern Mediterranean Hotspot, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1058, https://doi.org/10.5194/egusphere-egu23-1058, 2023.

EGU23-1090 | Posters on site | OS4.3

Wave Breaking Statistics Under Wind in Sea and Laboratory Conditions 

Dan Liberzon, Sagi Knobler, Ewelina Winiarska, and Alexander Babanin

The hydrodynamical process of breaking water waves is still a source of many unsolved questions. An extensive research work has been carried out during the last decades in order to quantify and define the associated energy redistribution, which directly influences a wide range of climate processes, maritime applications, and oceanic phenomena.

Naturally, waves become steeper toward the inception of breaking; however, there is still a lack of unanimity regarding the relationship between breaking probability statistics and wave steepness. Here we present a detailed analysis of different sea states from the Black Sea measurements and from a closed wind-wave flume experiments. Together with the wind-derived parameters, the water wave statistics were gathered using an innovative breaking wave detection algorithm. The algorithm was recently developed to allow accurate detection of breaking waves based on the phase-time approach and wavelet analysis to identify breaking-associated patterns in the instantaneous frequency variations of surface elevation fluctuations. The in-depth analysis of breaking and non-breaking wave statistics included wave-by-wave calculations resulting in steepness and celerities of the local wave, derived from the local wave frequency and wavenumber. Finally, the findings, after investigation and validation, presented a skewed Gaussian-like steepness histogram, revealing that both non-breaking and breaking waves can reach steep profiles, above the Stokes limit. 

How to cite: Liberzon, D., Knobler, S., Winiarska, E., and Babanin, A.: Wave Breaking Statistics Under Wind in Sea and Laboratory Conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1090, https://doi.org/10.5194/egusphere-egu23-1090, 2023.

EGU23-1173 | ECS | Posters on site | OS4.3

Multiscale analysis of typhoon-induced oceanic responses: A Case Study of Typhoon Kalmaegi in the South China Sea 

Gang Li, Yijun He, Yang Yang, Guoqiang Liu, Xiaojie Lu, and William Perrie

A localized multiscale energetics framework is used to study the multiscale typhoon-induced upper oceanic responses, in the case of Typhoon Kalmaegi in the South China Sea. A diagnostic methodology of the time-varying energetics, on the basis of the multiscale window transform (MWT) —namely, localized multiscale energy and vorticity analysis (MS-EVA) decomposes HYCOM variable fields into a low-frequency background flow window, a mid-frequency flow window and a high-frequency process window. The background window represents mesoscale processes and Kuroshio currents well and the mid-frequency window captures near-inertial processes influenced by typhoon-induced wind stresses. The scale-scale kinetic energy transfers from the near-inertial window to the background window, mainly on the right-hand side of the typhoon track. Advection and pressure work redistribute energy contribute to the accumulation of kinetic energy in the mid-frequency flow window and enhances ocean mixing. Negative vorticity has a significant impact on the distribution and downward propagation of the near-inertial energy, leading to heterogeneity in the mixing of the upper ocean. We offer new insights into understanding the multiscale interactions between typhoons and the upper ocean.

How to cite: Li, G., He, Y., Yang, Y., Liu, G., Lu, X., and Perrie, W.: Multiscale analysis of typhoon-induced oceanic responses: A Case Study of Typhoon Kalmaegi in the South China Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1173, https://doi.org/10.5194/egusphere-egu23-1173, 2023.

EGU23-2435 | Posters on site | OS4.3

Ordering of small parameters in nonlinear wave problems 

Georgy Burde
It is a common situation when asymptotic methods are applied to nonlinear wave problems which involve several parameters assumed to be small. As a canonical example, the classical problem of shallow water waves in ideal fluid may be mentioned. In particular, the famous Korteweg–de Vries (KdV) equation, which is the prototypical example of an exactly solvable soliton equation, was first introduced in the context of that problem. The system of equations describing the long, small-amplitude wave motion in shallow water with a free surface involves two independent small parameters: the amplitude parameter α and the wave length parameter β. No relationship between orders of magnitude of α and β follows from the statement of the problem. In the derivation of model equations, the question of ordering is usually not discussed and it is tacitly assumed that the two small parameters are of the same order of magnitude (the derivation of the KdV equation is the case). However, it is evident that there are no grounds for that assumption and that, in general, the parameters α and β can be not of the same order of magnitude. It is indicated in [1], that, in such a case, a consistent truncation of the asymptotic expansion can be made only on the basis of a prescribed relationship between orders of magnitude of α and β, and a systematic procedure for deriving an equation for surface elevation is developed. The results of the analysis provide a set of consistent model equations for unidirectional water waves which replace the KdV equation in the cases of the nonstandard ordering. The problem of shallow water waves over a slowly varying bottom [2], [3] provides an example of the problem which involves three independent small parameters. As other examples of the problems involving several small parameters, the nonlinear interactions among internal oceanic gravity waves and nonlinear instability of (weakly) nonparallel flows are to be considered.
[1] G. I. Burde and A. Sergyeyev, J. Phys. A: Math. Theor. 46, 075501 (2013).
[2] A. Karczewska, P. Rozmej, and E. Infeld, Phys. Rev. E 90, 012907 (2014).
[3] G. I. Burde, Phys. Rev. E 101, 036201 (2020).

How to cite: Burde, G.: Ordering of small parameters in nonlinear wave problems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2435, https://doi.org/10.5194/egusphere-egu23-2435, 2023.

EGU23-2437 | Orals | OS4.3

Wave boundary layer at the ice–water interface: theory and experiment 

Jie Yu, Mark Orzech, David Wang, Blake Landry, Carlo Zuniga-Zamalloa, and Kathryn Trubac

Marginal ice zones (MIZs) are distinguished by the highly heterogeneous condition of sea ice, e.g., floes of various sizes, pancake, brash and frazil ice, ice ages, brine content, ice thickness and concentration, etc. This makes it challenging to model wave propagation in MIZs, either theoretically or numerically, since there remain similar limitations to mathematically describing such an ice cover on the ocean surface. In this study, we re-consider the problem of linear gravity waves in two layers of fluids with a viscous ice layer overlaying water of deep depth, giving a comprehensive analysis of the fluid velocities, velocity shear, and Reynolds stress associated with wave fluctuations in both the ice layer and the wave boundary layer just beneath the ice. For the turbulent wave boundary layer, water eddy viscosity is used. Speculation of the Eulerian steady streaming is made based on the Reynolds stress distribution, offering a preliminary insight into the wave-induced mean drifts in both the ice layer and wave boundary layer in the water. For wave attenuation, the results using a typical ice viscosity and a reasonable water eddy viscosity show good agreement with data over the range of frequencies for both field and lab waves, significantly outperforming those results assuming an inviscid water. Also discussed are the PIV (particle imaging velocimetry) measurements from the experiment of wave propagation through broken surface ice in a salt water tank in a temperature-controlled facility at the US Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL). Preliminary analysis of the PIV data has provided strong evidence of such a wave boundary layer at the water–ice interface. The measured vertical profiles of fluid velocities and wave-induced Reynolds stress have trends similar to the theoretical predictions, despite the quantitative discrepancies in terms of numerical values. To our knowledge, this is only the second such experiment to measure the three-dimensional fluid velocity fields due to the wave motion under surface ice. This is to be followed by the phase II experiment (scheduled in 2023) in which the ice thickness and other properties will be configured to improve the similitude with field applications. 

How to cite: Yu, J., Orzech, M., Wang, D., Landry, B., Zuniga-Zamalloa, C., and Trubac, K.: Wave boundary layer at the ice–water interface: theory and experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2437, https://doi.org/10.5194/egusphere-egu23-2437, 2023.

EGU23-3573 | Orals | OS4.3 | Highlight

A probabilistic prediction of rogue waves 

Johannes Gemmrich, Leah Cicon, Benoit Pouliot, and Natacha Bernier

Rogue waves are individual ocean surface waves with a height greater than 2.2 times the significant wave height.  They can pose a danger to marine operations, onshore and offshore structures, and beachgoers, especially when encountered in high sea states. The prediction of bulk sea state parameters like significant wave height, period, direction, and swell components is satisfactorily addressed in current operational wave models. Individual wave heights cannot be predicted by those spectral models, and the prediction of rogue wave occurrence has to be in a probabilistic sense.

Previous attempts on such a prediction are based on the Benjamin Feir Index (BFI), which reflects the nonlinear process of modulation instability as the dominant generation mechanism for rogue waves. However, there is increasing evidence that BFI has limited predictive power in the real ocean. Recent studies established the average crest-trough correlation as the strongest single variable to correlate with rogue wave probability.

We demonstrate that crest-trough correlation can be forecast by an operational WAVEWATCHIII wave model with moderate accuracy. Using multi-year wave buoy observations from the northeast Pacific we establish the functional relation between crest-trough correlation and rogue wave occurrence rate, thus calibrating predicted crest-trough correlations into probabilistic rogue wave predictions. Combined with the predicted significant wave heights we can identify regions of enhanced rogue wave risk. Results from a case study of a large storm off Canada’s west coast are presented to evaluate the regional wave model at high seas, and to present the rogue wave probability forecast based on crest-trough correlation.

How to cite: Gemmrich, J., Cicon, L., Pouliot, B., and Bernier, N.: A probabilistic prediction of rogue waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3573, https://doi.org/10.5194/egusphere-egu23-3573, 2023.

EGU23-4788 | Posters on site | OS4.3

Observation-Based Physics in Spectral Wave-Forecast Models 

Alexander Babanin

Major update of the physics of the third generation models will be presented. The new source terms for wind input, whitecapping dissipation, interaction of waves with adverse winds (negative input) and swell attenuation have been developed and implemented in WAVEWATCH-III, SWAN and WAM models. Physics and parameterisations for the new source functions are based on observations, which allowed us to reveal features and processes previously unknown and not accounted for. For extreme conditions, physics of the wind input and whitecapping dissipation terms exhibit additional features irrelevant or inactive at moderate weather.

In particular, the wave growth term was shown to be a nonlinear function of wave steepness (spectral density). Additionally, the wave breaking was found to enhance the wind input. Relative reduction of the wind input at strong-wind/steep-wave conditions was observed, due to full flow separation found at such circumstances. At strong wind forcing, this causes saturation of the sea drag.

Spectral distribution of the whitecapping dissipation is the most elusive function to measure. Breaking of waves, and hence such dissipation exhibits a clear threshold behaviour in terms of wave steepness (or saturation spectrum). Other novel observed features are cumulative effect away from the spectral peak (dissipation is not local in wavenumber space), directional bimodality. It was found that at moderate winds the dissipation is fully determined by the wave spectrum whereas at strong winds it is a function of the wind speed.

In absence of breaking (swell or other circumstances when the spectral density is below the threshold), other energy sink has to be invoked. It is based on observations of wave-turbulence interactions, and dependence of such interactions on wave steepness.

Interaction of the waves with adverse wind is a necessary additional term if the above-mentioned wind input function is employed, since this function only describes forcing of waves by the following wind. These dependences are calibrated by means of observations in tropical cyclones.

In order to test the source functions independently, and control the flux balance in the model, additional observation-based constraints are implemented. At each time step, the total momentum input is verified to match an independently known wind stress.

Qualitative and quantitative effects and properties of the observation-based source terms are parameterised, and the parameterisations are presented in forms suitable for spectral wave models. The new versions of the models have undergone extensive testing by means of academic tests, regional and global wave hindcast, modelling extreme conditions ranging from tropical cyclones to the marginal ice zone.

How to cite: Babanin, A.: Observation-Based Physics in Spectral Wave-Forecast Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4788, https://doi.org/10.5194/egusphere-egu23-4788, 2023.

FIO-ESM (First Institute of Oceanography-Earth System Model) developed by the First Institute of Oceanography of the Ministry of Natural Resources, is an earth system model with surface gravity wave models and composed of a physical climate model and a global carbon cycle model. The Earth system model has developed from FIO-ESM v1.0, to FIO-ESM v2.0, which has been improved in both its physical climate model and the global carbon cycle model. The marine carbon cycle model of FIO-ESM v2.0 global carbon cycle model has been upgraded from the nutrient-driven model of v1.0 to the NPZD (Nutrient Phytoplankton Zooplankton Detritus) type ocean ecological carbon cycle model, and the terrestrial carbon cycle model has been upgraded from the simple light energy utilization model of v1.0 to the carbon-nitrogen coupling model considering carbon-nitrogen interaction. The atmospheric carbon cycle model is still the CO2 transport processes, with the anthropogenic carbon emissions from the fossil fuel and land use change. In terms of effects of physical process parameterization schemes on the global carbon cycle, the FIO-ESM v2.0 global carbon cycle considers not only the role of non-breaking wave induced mixing on biogeochemical variables, but also the effects of SST diurnal cycle on air-sea CO2 flux. Primary analysis shows that FIO-ESM v2.0 can simulate the global carbon cycle fairly well after considering more complex carbon cycle processes.

How to cite: Bao, Y.: Global Carbon Cycle of Earth System Model FIO-ESM with Surface Waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4824, https://doi.org/10.5194/egusphere-egu23-4824, 2023.

EGU23-4850 | Posters virtual | OS4.3

Development and validation of a global 1/32° surface wave-tide-circulation coupled ocean model: FIO-COM32 

Bin Xiao, Fangli Qiao, Qi Shu, Xunqiang Yin, Guansuo Wang, and Shihong Wang

Model resolution and the included physical processes are two of the most important factors those determine the realism or performance of ocean model simulations. In this study, a new global surface wave-tide-circulation coupled ocean model FIO-COM32 with a resolution of 1/32°×1/32° is developed and validated. Promotion of the horizontal resolution from 1/10° to 1/32° leads to significant improvements in the simulations of surface eddy kinetic energy (EKE), main paths of the Kuroshio and Gulf Stream, and the global tides. We propose the Integrated Circulation Route Error (ICRE) as a quantitative criteria to evaluate the simulated main paths of Kuroshio and Gulf Stream. The non-breaking surface wave-induced mixing (Bv) is proven to still be an important contributor that improves the agreement of the simulated summer mixed layer depth (MLD) against the Argo observations even with a very high horizontal resolution of 1/32°. The mean error of the simulated mid-latitude summer MLD is reduced from -4.8 m in the numerical experiment without Bv to -0.6 m in experiment with Bv. By including the global tide, the global distributions of internal tide can be explicitly simulated in this new model and are comparable to the satellite observations. Based on Jason3 along-track sea surface height (SSH), wave number spectral slopes of mesoscale ranges and wave number-frequency analysis show that the unbalanced motions, mainly internal tides and inertia-gravity waves, induced SSH undulation is a key factor for the substantially improved agreement between model and satellite observations in the low latitudes and low EKE regions. For ocean model community, surface waves, tidal currents and ocean general circulations have been separated artificially into different streams for more than half a century. This paper demonstrates that it should be the time to merge these three streams for new generation ocean model development.

How to cite: Xiao, B., Qiao, F., Shu, Q., Yin, X., Wang, G., and Wang, S.: Development and validation of a global 1/32° surface wave-tide-circulation coupled ocean model: FIO-COM32, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4850, https://doi.org/10.5194/egusphere-egu23-4850, 2023.

EGU23-6067 | ECS | Orals | OS4.3

Observations of the ocean waves directional spreading during the HIGHWAVE project and SUMOS campaign. 

Daniel Santiago Peláez Zapata, Vikram Pakrashi, and Frederic Dias

The directional distribution of ocean waves is of great importance for a better understanding of air-sea interactions. Countless applications in science and engineering, such as, offshore energy production, microseisms prediction, wave climate modelling, coastal erosion, among many others, require precise information about the wave directionality. However, in spite of its importance, this quantity is poorly understood and difficult to accurately model. This study presents observations of the directional spreading parameters obtained from a set of low-cost GPS-based buoys during highly energetic conditions. One of the buoys was anchored off the west coast of Ireland during the HIGHWAVE project. These observations are compared with the measurements of 20 freely drifting buoys deployed in the Bay of Biscay during the SUMOS campaign. Spreading parameters were compared in the framework of widely used parameterisation for the directional distribution. The directional spreading is narrower at the spectral peak and broadens as the frequency moves away towards higher and lower scales. There is a particularly sharp increase in the spreading for f < fp. The results showed that buoy-based observations significantly differ from spatial-based measurements for frequencies around half the spectral peak. The measruements obtained by the drifting buoys show that for 2 < f/fp < 6, the spreading appears to be approximately constant with the frequency and tends to increase again for f > 6fp. The results showed that the directional spreading seems to be independent of the wave age, roughly across the entire range of frequencies.  This may imply that the shape of the directional spectrum is primarily controlled by the non-linear wave-wave interactions rather by the wind forcing.  In the vicinity of the spectral peak, a weakly linear relationship between the directional spreading and the significant wave height was observed.  The results show that as the significant wave height increases by one meter, the spreading decreases by about 4.5°. The preliminary results presented here contribute to the understanding of the directional distribution of ocean waves. However, further observations and comparisons are needed to fully capture the complexity of this phenomenon. Despite being preliminary, these results provide valuable insights and add to the ongoing discussion on this topic. This work was funded by the European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (grant agreement no. 833125-HIGHWAVE). We are very grateful to the scientific team behind the SUMOS campaign for providing the drifting buoys data.

How to cite: Peláez Zapata, D. S., Pakrashi, V., and Dias, F.: Observations of the ocean waves directional spreading during the HIGHWAVE project and SUMOS campaign., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6067, https://doi.org/10.5194/egusphere-egu23-6067, 2023.

EGU23-6244 | Posters virtual | OS4.3 | Highlight

Surface waves can much improve ocean to climate models 

Fangli Qiao

As the time and spatial scales of surface waves are several seconds and hundreds meters, which are much smaller than those of ocean circulation and climate, months and thousands kilometers or even bigger. As a result, ocean surface wave models are separated from ocean circulation models and climate models as different streams. During the past 2 decades, we find that surface waves play dominant role in the vertical mixing of the upper ocean, and heavily modulate the air-sea momentum and heat fluxes. (1) By including surface waves into ocean general circulation models (OGCMs), the ever-standing simulated shallow mixed layer and over-estimated sea surface temperature (SST) especially in summer faced by nearly all OGCMs are dramatically reduced, 80-90% common errors can be removed from OGCMs; (2) Although the forecasting error of Tropical Cyclone (TC) track is reduced by about half during the past 3 decades, the forecasting of TC intensity has no much progress. By including surface waves, the TC intensity error is reduced by about 40%; (3) SST is a crucial parameter in climate system. All climate models have huge SST simulation bias which has last for half century. By including surface wave, the SST bias can be reduced by about half. All above suggests that surface waves should be included in ocean, TC and climate models for improving our forecasting ability on ocean, TC and climate.

How to cite: Qiao, F.: Surface waves can much improve ocean to climate models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6244, https://doi.org/10.5194/egusphere-egu23-6244, 2023.

EGU23-9740 | ECS | Posters on site | OS4.3

Deterministic directional wave forecasting in deep water 

Eytan Meisner, Dan Liberzon, Mariano Galvagno, David Andrade, and Raphael Stuhlmeier

Recent years have seen an extensive increase in maritime activity, including new coastal and offshore infrastructure, increased cargo transport, and research on wave energy converters. While long-term macro-scale wave forecasting has been extensively researched (e.g. Günter & Hasselmann, 1991), with several forecasting models available today, there is a noticeable gap in local-scale deterministic wave forecasting models. Such models are needed to improve the efficiency of the design and operation of offshore installations and vessels, providing close-to-real-time data and short-term predictions of waves and wave-induced forcing.

We will report on the development of a new, computationally efficient model, allowing for weak nonlinearities in directional wavefields, based on previous studies on the unidirectional case (Stuhlmeier & Stiassnie, 2021). The model is capable of providing a deterministic forecast of the wavefield inside the prediction domain in time and space, based on measurements conducted over an initial region (Figure 1).

The mathematical framework used is the Zakharov equation, which determines the nonlinear cross-corrections to the frequencies between the various modes in the spectrum (Stuhlmeier & Stiassnie, 2019), used to derive the actual velocities at which the various wave field components are propagating.

The presentation will elaborate the full mathematical framework, alongside explanations of its benefits with respect to linear predictions. The model’s performance is validated using numerical data of nonlinear directional wavefields, generated using the higher order spectral (HOS) method.

Figure 1 – Predictable region in time (vertical axis) based on measurements at initial domain η0(x,y)

References

​​Günter, H. & Hasselmann, S., 1991. Wamodel cycle 4, Hamburg: German Climate Computing Centre.

Raphael Stuhlmeier and Michael Stiassnie. Deterministic wave forecasting with the Zakharov equation. J. Fluid Mech., 913:1–22, 2021.

Raphael Stuhlmeier and Michael Stiassnie. Nonlinear dispersion for ocean surface waves. J. Fluid Mech., 859:49–58, 2019.

How to cite: Meisner, E., Liberzon, D., Galvagno, M., Andrade, D., and Stuhlmeier, R.: Deterministic directional wave forecasting in deep water, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9740, https://doi.org/10.5194/egusphere-egu23-9740, 2023.

Langmuir circulations arise through the interaction between the Lagrangian drift of the surface waves and the wind-driven shear layer. The high shear rate alone is sufficient for generating quasi-streamwise vortices within the shear layer. Despite the different formation mechanisms, both vortical structures manifest themselves by inducing wind-aligned streaks on the surface. Numerical simulations of a stress-driven turbulent shear layer bounded by monochromatic surface waves are conducted to reveal the mutual interaction between the large-scale vortical structures of Langmuir circulations and the small-scale quasi-streamwise vortices in Langmuir turbulence. The averaged structure of Langmuir circulations is educed from conditional averaging guided by the signatures of predominant surface streaks obtained from empirical mode decomposition. The width of the averaged vortex pair of Langmuir circulations is found to be comparable to the most unstable wavelength of the wave-averaged Craik–Leibovich equation. Small-scale coherent vortical structures are identified using a detection criterion based on local analysis of the velocity-gradient tensor and their topological geometry. Quasi-streamwise vortices accumulated beneath the windward surface are found to dominate the distribution of small-scale coherent vortical structures. Employing the variable-interval spatial average to the identified quasi-streamwise vortices reveals that they tend to form in the edge vicinity of the high-speed surface jets induced by the Langmuir cells. The tilting of vertical vorticity at the outer edges of surface jets by shear current and wave drift enhances the formation of quasi-streamwise vortices. The results highlight the differences in the coherent vortical structures between the Langmuir turbulence and the turbulent wall layer.

How to cite: Tsai, W. and Lu, G.: Interaction between large-scale vortical structures and quasi-streamwise vortices in Langmuir turbulence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10245, https://doi.org/10.5194/egusphere-egu23-10245, 2023.

EGU23-10740 | Orals | OS4.3 | Highlight

Computation of Nonlinear Wave Motion Using a Quantum Algorithm 

Alfred Osborne

           

The development of quantum computers over the next decade or so suggests that the geophysical sciences may benefit from very rapid computations from “quantum supremacy.” I have developed a pilot project which would help orient researchers to the use of quantum computers. The first step, and the main topic of my talk, would be to quantize a nonlinear wave equation in order that quantum algorithms might be developed. I focus on the nonlinear Schroedinger equation (NLS). The main emphasis is to show that the NLS equation for spatially periodic boundary conditions is a Hamiltonian system: Thus, I derive the solution and the coordinates and momenta in terms of quasiperiodic Fourier series. Then I apply the method of Heisenberg to develop the matrix mechanics of the NLS equation. Quantization arises as the lack of commutation for the product of the coordinate and the momenta matrices of the equation. I also discuss other equations due to the Dysthe, Trulsen and Dysthe, Yan Li and the Zakharov equations. I discuss how the method of matrix mechanics as applied to nonlinear wave equations might be programmed on a quantum computer.

How to cite: Osborne, A.: Computation of Nonlinear Wave Motion Using a Quantum Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10740, https://doi.org/10.5194/egusphere-egu23-10740, 2023.

EGU23-10993 | ECS | Posters on site | OS4.3

Impact of the representation of waves on simulated particle dispersal in the surface ocean 

Siren Rühs, Erik van Sebille, Aimie Moulin, and Emanuela Clementi

The knowledge of how seawater moves around in the global ocean and transports tracers and particulates, is crucial for solving many outstanding issues in physical oceanography and climate science. Due to limited available observations, seawater pathways are often estimated by evaluating virtual particle trajectories inferred from velocity fields computed with ocean models. The quality of these Lagrangian analyses strongly depends on how well the underlying ocean model represents the ocean circulation features of interest.
Here, we investigate how simulated surface particle dispersal changes, if the – often omitted or only approximated – impact of surface waves is considered. Specifically, we test the impact of new representations of wave-current interactions for the ocean model NEMO in a case study for the Mediterranean Sea. We are using velocity output from a high-resolution (1/24°) ocean-only model simulation as well as a complementary coupled ocean-wave model simulation, to answer the following questions: How do waves impact the simulated surface particle dispersal, and what is the relative impact of Stokes drift and wave-driven Eulerian currents? How well can the wave impact be approximated by the superposition of Eulerian mean and Stokes drift velocity fields obtained from independently run ocean and wave models?
We find that the wave coupling leads to a decrease in the mean surface current speed in summer dominated by wave-driven Eulerian currents, and an increase in the mean surface current speed in winter dominated by Stokes drift. We further show that Lagrangian simulations with superimposed Eulerian currents and Stokes drift from independent ocean-only and wave models do not necessarily yield more realistic results for surface dispersal patterns than simulations that do not include any wave effect. This implies that – whenever possible – velocity fields from a coupled ocean-wave model should be used for surface particle dispersal simulations.

How to cite: Rühs, S., van Sebille, E., Moulin, A., and Clementi, E.: Impact of the representation of waves on simulated particle dispersal in the surface ocean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10993, https://doi.org/10.5194/egusphere-egu23-10993, 2023.

EGU23-11149 | Posters on site | OS4.3

A direct numerical simulation of nonbreaking-surface-waves induced mixing 

Yutaka Yoshikawa, Haruka Imamura, and Yasushi Fujiwara

Nonbreaking surface waves (NBSWs) induce vertical mixing even under the windless condition (WLC).  Recent laboratory experiments (e.g., Dai et al. 2010) demonstrated this mixing clearly; stratified water was vertically mixed by the NBSWs under the WLC.  The estimated vertical diffusivity amounts to O(10-5 m2/s), two orders of magnitude lager than the molecular diffusivity.  Yet, the mechanism of the mixing was not clarified in this laboratory experiment.   Recent numerical studies (e.g., Tsai et al. 2017; Fujiwara et al. 2020) on the other hand showed that the NBSWs under the WLC formed streamwise vortices  beneath the water surface through the CL2 mechanism like Langmuir circulations.  However, the intensity of the mixing was not evaluated in their numerical studies due to short integration time or artificially large eddy viscosity/diffusivity.  As a consequence, how the NBSWs under the WLC could induce the vertical mixing remains to be investigated.  In fact, local generation of turbulence by the wave orbital velocity is proposed as another mechanism of the NBSW-induced turbulence (e.g., Dai et al. 2010; Qiao et al. 2016).  Here, in order to investigate whether and how the NBSW alone could induce such the large vertical mixing, we performed a direct numerical simulation (DNS) of the NBSW under the WLC as in Dai et al. (2010).  The DNS with a sigma-coordinate free-surface nonhydrostatic model reveals that streamwise vortices like Langmuir circulations, developed exponentially at first, grow to be finite amplitude and keep slowly increasing in size and intensity.  At the finite-amplitude stage, the simulated water temperature was vertically mixed from near the surface.  The vertical eddy diffusivity was O(10-5 m2/s) very near the surface, which is overall similar to the previous estimation (Dai et al.  2010), but its vertical profile was different.  Enstrophy analysys reveals that CL2 mechanism, the same as for Langmuir circulations, kept working even in the finite-amplitude stage to induce the intense mixing near the surface.

How to cite: Yoshikawa, Y., Imamura, H., and Fujiwara, Y.: A direct numerical simulation of nonbreaking-surface-waves induced mixing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11149, https://doi.org/10.5194/egusphere-egu23-11149, 2023.

EGU23-11366 | Posters virtual | OS4.3

Observed Drag Coefficient Asymmetry in a Tropical Cyclone 

Sheng Chen

The behavior of drag coefficient (CD) in two different motion-relative quadrants of Typhoon Mujigae (2015) is investigated through the flux observations conducted on a fixed platform over the coastal region in the northern South China Sea. Observations reveal that the variation of CD is closely related to the location relative to the tropical cyclone (TC) center. The CD  presents an enhancement when the typhoon is away from the observational site. The spatial distribution of CD on the periphery of a TC is asymmetric, and the CD in the right rear quadrant is much larger than that in the right front quadrant for the same wind speed range. This asymmetric distribution of CD can be explained by the differences in wave properties between the two quadrants. CD is smaller in cross-swell conditions than that in the along-wind wave conditions. Observations also confirm that CD tends to level off and even attenuate with the increase of wind speed, and the critical wind speed for CD saturation over the coastal region (~20 m/s) is much lower than that over the open ocean (~30 m/s). The observational spatial distribution of CD in TC quadrants not only improves our understanding on the air-sea momentum flux but also provides a potential solution for the long-standing scientific bottleneck on TC intensity forecasting.

How to cite: Chen, S.: Observed Drag Coefficient Asymmetry in a Tropical Cyclone, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11366, https://doi.org/10.5194/egusphere-egu23-11366, 2023.

EGU23-13339 | Posters on site | OS4.3

Mutual interactions between waves and turbulence: an experiment 

Simen Å Ellingsen, Benjamin K Smeltzer, Olav Rømcke, and R Jason Hearst

 

The mutual interaction between waves and turbulent currents plays a key role in the energy budget, mixing and mass transfer in the upper layer of the ocean. Turbulence is ubiquitous in the uppermost layer of the ocean, where it interacts with surface waves. Theoretical, numerical, and experimental works (e.g. [1 - 3] and others) predict that motion of non-breaking waves will increase turbulent energy, in turn leading to a dissipation of waves and, potentially, increased mixing and gas transfer between ocean and atmosphere. Conversely, waves encountering a turbulent currents will be scattered and directional seas can suffer a broadening of the directional spectrum [4,5].

In this work we study how the mutual interaction of waves and turbulent flow depends on the properties of the ambient turbulence. The measurements were performed in the water channel laboratory at NTNU Trondheim [6], able to mimic the water-side flow in the ocean surface layer under a range of conditions. An active grid at the inlet allowed the turbulence intensity and length scale to be varied for the same mean flow. The flow field was measured in the spanwise-vertical plane by stereo particle image velocimetry for various background turbulence cases with waves propagating against the current. Scattering was measured with pairs of wave probes at increasing distances from the wave-maker.

A strong increase in streamwise enstrophy (mean-square streamwise vorticity) is observed after vs before the passage of a long, Gaussian wave group. Enstrophy is intensified under troughs and reduced under crests. Scattering is observed, increasing linearly with propagation distance. The scattering rate is found to depend primarily on the energy content at the largest turbulent scales larger than a wavelength, whereas the intensification of turbulence by waves occur at length scales smaller than a wavelength.

[1] Teixeira M. and Belcher S. 2002 “On the distortion of turbulence by a progressive surface wave” J. Fluid Mechanics 458 229-267.
[2] McWilliams J. C., Sullivan P. P. and Moeng C-H. 1997 “Langmuir turbulence in the ocean” J. Fluid Mechanics 334 1-30.
[3] Thais L. and Magnaudet J. 1996 “Turbulent structure beneath surface gravity waves sheared by the wind” J. Fluid Mechanics 328 313-344.
[4] Phillips O. M. 1959 "The scattering of gravity waves by turbulence"  J. Fluid Mech. 5 177-194.
[5] Fabrikant and Raevsky 1994 "The influence of drift flow turbulence on surface gravity wave propagation" J. Fluid Mech. 262 141-156.
[6] Jooss Y., Li L., Bracchi T. and Hearst R.J. 2021 “Spatial development of a turbulent boundary layer subjected to freestream turbulence” Journal of Fluid Mechanics 911 A4.

How to cite: Ellingsen, S. Å., Smeltzer, B. K., Rømcke, O., and Hearst, R. J.: Mutual interactions between waves and turbulence: an experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13339, https://doi.org/10.5194/egusphere-egu23-13339, 2023.

EGU23-14060 | Orals | OS4.3 | Highlight

A fractal approach to document the Wave-Influenced Boundary Layer in the Large Air-Sea Interaction Facility of Luminy, Marseille 

Denis Bourras, Christopher Luneau, Rémi Chemin, William Bruch, Saïd Benjeddou, and Philippe Fraunié

The study of the relationship between wind speed, altitude, and the geometric properties of the ocean surface (characteristics of dominant waves, surface roughness, see the wave-breaking rate) is a central topic both (1) for the representation of the transfer of momentum at the ocean-atmosphere interface in weather, ocean, wave growth forecasting models, and in coupled models, from sub-meso-scale to climate and paleoclimatic scales, and (2) for spaceborne remote sensing of the wind speed at the surface of the oceans, either in microwaves (mainly scatterometers and radiometers) or in visible wavelengths (observation of foam lines). Our study is focused on the surface air layer that is directly influenced by the presence of waves, which is so-called Wave-influenced Bounday Layer (WBL). After a survey of the existing relations found between wind, momentum, and the surface geometry in gradually increasing wind conditions, we will attempt to relate the results of ongoing fractal analyses based on (1) wind field deduced from PIV technique, (2) horizontal wave slope images from light refraction technique, and (3) Laser slice, in a controlled environment.

How to cite: Bourras, D., Luneau, C., Chemin, R., Bruch, W., Benjeddou, S., and Fraunié, P.: A fractal approach to document the Wave-Influenced Boundary Layer in the Large Air-Sea Interaction Facility of Luminy, Marseille, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14060, https://doi.org/10.5194/egusphere-egu23-14060, 2023.

EGU23-14900 | Orals | OS4.3 | Highlight

On the impact of ocean/wave coupling in tropical cyclones conditions in the Indian Ocean 

Lotfi Aouf, Stephane Law-Chune, Daniele Hauser, and Bertrand Chapron

Ocean waves play a key role in the exchange of heat and momentum fluxes between the ocean and atmosphere, expecially in extreme wind conditions. The availability of directional wave spectra from SWIM lead to a better description of wave systems nearby the trajectories of tropical cyclones as shown recently by Le Merle et al. 2022. Also the assimilation of these directional observations induced an improved forecast of integrated wave parameters and initial conditions from wind-sea to swell propagation. The objective of this work is to examine the impact of the wave-ocean coupling under cyclonic conditions in the Indian Ocean. Coupled simulations between the MFWAM wave model and the NEMO ocean model have performed over the 2020 and 2021 cyclonic seasons in indian ocean. We used an improved wave forcing by assimilating the directional wave spectra and the corresponding significant wave heights provided by the instrument SWIM of CFOSAT satellite. The impact of this enhanced wave forcing on the ocean circulation was compared with the one without CFOSAT data assimilation. The main coupling processes are wave-modified stress, Stokes drift and wave breaking induced turbulence. The results show that wave/ocean coupling leads to a significant increase of the ocean mixed layer along the trajectories of cyclones. This clearly induces a cooling of the upper ocean layers at the rear of the cyclones. The validation of key ocean parameters indicates an improvement in sea surface temperature compared to satellite data (OSTIA). We investigated the currents variability in the upper ocean following the trajectory of cyclone HEROLD. We also examined the impact of the coupling process driven by the wave breaking induced turbulence and investigated a better parametrization than the used one from Craig and Banner (1992). Further conclusions and comments will be discussed in the final presentation of this work.

How to cite: Aouf, L., Law-Chune, S., Hauser, D., and Chapron, B.: On the impact of ocean/wave coupling in tropical cyclones conditions in the Indian Ocean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14900, https://doi.org/10.5194/egusphere-egu23-14900, 2023.

EGU23-16204 | Orals | OS4.3 | Highlight

Influence of wind-wave/swell interactions on the air-water momentum flux 

Marc Buckley, Janina Tenhaus, Silvia Matt, and Ivan Savelyev

The ocean surface is, more often than not, riddled with locally generated, growing wind-waves interacting with remotely generated swells. In moderate to high wind speeds, these complex interactions may strongly influence the occurrence of wave breaking as well as airflow separation events, which, in turn, control air-sea fluxes of momentum and scalars.

We present laboratory measurements of air and water dynamics in the vicinity of wind-modulated mechanically generated waves, at a 10 m fetch, using Particle Image Velocimetry. Using flow vorticity and turbulence estimates above and below the waves, we are able to quantify airflow separation and wave breaking events.

We observe modulations of the airflow by locally generated wind waves, including small sheltering events downwind of sharp wave crests. We will discuss the influence of local vs peak wind-wave conditions (e.g., wave age, slope), on wind-wave momentum and energy flux mechanisms.

How to cite: Buckley, M., Tenhaus, J., Matt, S., and Savelyev, I.: Influence of wind-wave/swell interactions on the air-water momentum flux, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16204, https://doi.org/10.5194/egusphere-egu23-16204, 2023.

EGU23-16788 | ECS | Orals | OS4.3

Mass transport induced by nonlinear surface gravity waves 

Laura Grzonka and Witold Cieślikiewicz

As waves pass, fluid elements experience not only periodic motion but also a movement in a direction of wave propagation (Stokes, G.G. (1847) On the Theory of Oscillatory Waves. Transactions of the Cambridge Philosophical Society, 8, 441-455). Defined as a difference between the average Lagrangian flow velocity of a particle and the average Eulerian flow velocity of the fluid, the Stokes drift entails, amongst others, the existence of wave-induced mass transport (van den Bremer TS, Breivik Ø. 2017 Stokes drift. Phil. Trans. R. Soc. A 376:20170104. http://dx.doi.org/10.1098/rsta.2017.0104). Knowledge of it is of high significance since it allows one to calculate tracer transport, for instance, plastic or oil pollution.
While operating in the Eulerian frame of reference, one should recognize that a fixed point in space in the vicinity of a free surface emerges and submerges under the water during wave motion. This phenomenon is called the emergence effect and it does impact the particle kinematics properties. Cieślikiewicz & Gudmestad developed a method of calculating the wave-induced mass transport for deterministic and random waves taking into account the emergence effect (Cieślikiewicz, W. & Gudmestad, O. T. (1994). Mass transport within the free surface zone of water waves. Wave Motion, 19(2), 145–158. https://doi.org/10.1016/0165-2125(94)90063-9).
The goal of the study was to introduce numerical examples and verification of both deterministic and random wave cases presented by Cieślikiewicz & Gudmestad (1994) depending on wind wave parameters. Wolfram Mathematica software was used to carry out the calculations and draw figures. The wave energy spectrum was determined using the JONSWAP formula (Hasselmann, K., Barnett, T. P., Bouws, E., Carlson, H., Hasselmann, D. E., Kruseman, P., Meerburg, A., Mûller, P., Olbers, D. J., Richter, K., Sell, W., & Walden, H. (1973). Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Ergaenzungsheft Zur Deutschen Hydrographischen Zeitschrift, Reihe A., 12(A8), 1–95). The results show that the mass transport values for a representative deterministic wave agree with values for random waves. Therefore, the deterministic wave formulas may be used to initial estimate mass transport induced by random water wave field.

How to cite: Grzonka, L. and Cieślikiewicz, W.: Mass transport induced by nonlinear surface gravity waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16788, https://doi.org/10.5194/egusphere-egu23-16788, 2023.

EGU23-17107 | ECS | Posters on site | OS4.3

Ocean surface wave measurements from a phase array high-frequency radar system in the coastal area of Northwest of Mexico Pacific waters 

Juan Carlos Guevara Aguirre, Reginaldo Durazo, Héctor García-Nava, Bernardo Esquivel-Trava, Roberto Gomez, and Francisco J. Ocampo-Torres

High frequency radars (HFR) are systems that allow us to monitor some oceanographic variables through the backscatter signal from the ocean surface.  Typically, they provide us with a relatively high space-time resolution of surface currents and the wave field, very important local information to be used for maritime operation applications, such as search and rescue, safety at sea and transportation, and marine energy resources assessment.  Although the main product from HFR is ocean surface currents, they can in addition, provide useful information to derive important characteristics of the wave field, such as significant wave height (Hs) and even the directional spectrum. We, nevertheless, focus our attention in this work in the wave field, and specifically Hs values. A HFR (WERA system) is in operation in Todos Santos Bay, Ensenada, Mexico, since March 2021. Maps of significant wave height are estimated every hour over an area of approximately 250 km2 with spatial resolution of 800 m. These measurements have been compared with wave data derived from three moored instruments (ADCP), the results yielded correlations greater than 0.7 and RMSE values less than 40 cm. In the last two decades this technology has been implemented throughout the world, although there is very limited detail on calibration and validation of the instrument with local ocean wave conditions, especially with respect to the presence of swell. In this study, an empirical calibration is performed using an algorithm provided by the manufacturer in which a correction parameter  is obtained according to the operating frequency of the radar, in particular a WERA system. This study takes into consideration some particular characteristics of the area of interest and the performance of the correction parameter is determined as a function of the wave height and direction of travel relative to the radial direction from the WERA site.

How to cite: Guevara Aguirre, J. C., Durazo, R., García-Nava, H., Esquivel-Trava, B., Gomez, R., and Ocampo-Torres, F. J.: Ocean surface wave measurements from a phase array high-frequency radar system in the coastal area of Northwest of Mexico Pacific waters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17107, https://doi.org/10.5194/egusphere-egu23-17107, 2023.

EGU23-17299 | Posters on site | OS4.3

On the effect of ocean surface waves on air-sea interactions: results from in-situ and remote measurements in the Gulf of Mexico. 

Francisco J. Ocampo-Torres, Pedro Osuna, Nicolas G. Rascle, Héctor García-Nava, Guillermo Díaz Méndez, Bernardo Esquivel-Trava, Carlos E. Villarreal-Olivarrieta, and Rodney E. Mora-Escalante

Ocean surface wave full directional spectrum is estimated directly from measurements obtained with a spar buoy and from synthetic aperture radar images of the sea surface. These two techniques complement each other to provide us with a rather comprehensive view of the dynamical behaviour of surface waves. We focus our study in sea state conditions under varying winds, when frequently mixed sea and swell systems are encountered. These conditions are characterized by non-equilibrium wind-wave systems. Direct measurements of ocean-atmosphere momentum fluxes obtained from dedicated air-sea interaction spar buoy are also analyzed. The aim is to better understand the ocean-atmosphere momentum transfer behaviour and uppermost ocean currents under rapidly varying wind field. Atmospheric cold front passage through the measuring buoy imposed a unique wind-wave system information, especially under the occurrence of cases when swell propagation opposes locally generated wind-waves. Of particular importance is the analysis of the wave field making use of synthetic aperture radar images of the sea surface. The wave and wind fields to both sides of the atmospheric front are analyzed. From the buoy measurements fetch-limited wind sea growth is also determined, where slanting fetch is to be considered as very relevant. In particular, wind acceleration effect on wave growth is addressed, during specific cases when wind direction prevailed relatively constant. Wind-wave growth rate is somewhat greater than stationary conditions, as it can be observed also in some laboratory experiments at least for the early stages of the growth process. 

How to cite: Ocampo-Torres, F. J., Osuna, P., Rascle, N. G., García-Nava, H., Díaz Méndez, G., Esquivel-Trava, B., Villarreal-Olivarrieta, C. E., and Mora-Escalante, R. E.: On the effect of ocean surface waves on air-sea interactions: results from in-situ and remote measurements in the Gulf of Mexico., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17299, https://doi.org/10.5194/egusphere-egu23-17299, 2023.

The sea surface under tropical cyclone conditions is covered by whitecaps and whiteout material. The whitecap areas are formed by large breaking waves and occupy ~4% of the sea surface (Holthuijsen et al. 2012). These areas produce large amounts of bubble and spray but occupy only a relatively small faction of the sea surface. The whiteout material that covers the rest of the sea surface can be caused by shear-induced instabilities of the Kelvin-Helmholtz (KH) type (Soloviev et al. 2017). The KH type instabilities at the gas-liquid interface have been intensively studied in engineering applications such as atomization of the fuel in combustion and cryogenic rocket engines, food processing, and inkjet printing. KH at the air-water interface can take on different forms like ‘fingers’, ‘bags’, ‘mushrooms’, etc. At the air-sea interface KH is additionally modulated by surface waves. In addition, the KH wave at an interface with a large density difference, like the air-water interface, evolves into a strongly asymmetrical shape with all action on the gas side in the form of relatively large spray particles - spume (Hopfner et al. 2011). The sea spray generation function (SSGF) is an input parameter in spray-resolving tropical cyclone forecasting models; however, it is still a major unknown under tropical cyclone conditions. Most of the information on the SSGF for the spume size range comes from the theoretical estimates based on laboratory experiments. The lab measurements are typically conducted above the wave crests and require extrapolation to water surfaces using additional assumptions (Vernon 2015, Ortiz-Suslow et al. 2016, Troitskaya et al. 2018). In this work, we have implemented a computational fluid dynamics (CFD) approach involving a combination of Eulerian and Lagrangian multiphase physics. We have calculated the SSGF function using the ANSYS Fluent Volume of Fluid to Discrete Phase Model (VOF to DPM) including dynamic remeshing. Similar to the laboratory experiments conducted in high-wind speed facilities at Kyoto University, University of Delaware, and University of Miami, the SSGF size distribution of spray particles obtained with VOF to DPM, shows the presence of a significant number of large particles (spume) in major tropical cyclones, which is in contrast to traditional parameterizations. Spume appears to provide the main contribution into the mass, momentum, and energy exchanges at the air-sea interface (Sroka and Emanuel 2022). This is also an indication that spume production is substantially underpredicted by traditional SSGF parameterizations. Importantly, the VOF to DPM extends the SSGF into the range of category 5 tropical cyclone winds, which is still impossible to evaluate even in laboratory conditions. Furthermore, the CFD model provides the “true” SSGF that represents sea spray generation at the air-sea surface and does not require any assumptions as in traditional parameterizations. Implementation of the new SSGF is expected to significantly improve momentum flux, enthalpy flux, and gas flux treatments in tropical cyclone forecasting models in extreme wind speed conditions.

How to cite: Soloviev, A. and Vanderplow, B.: Sea Spray Generation Function Due to Shear-Induced Instabilities of the Air-Sea Interface Under Tropical Cyclone Conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17304, https://doi.org/10.5194/egusphere-egu23-17304, 2023.

NP8 – Emergent Phenomena in the Geosciences

Since the settlement of the São Miguel Island (Azores-Portugal), in the middle of the fifteenth century, there is a record of occurrence of landslides, some with high socio-economic impact. In this work, we carried out a spatial, temporal and impact analysis of landslide events that were registered in the NATHA (Natural Hazards in Azores) database for the period 1900-2020, based on newspapers descriptions. A total of 236 landslide events (a day with one or more landslides identified) that caused human losses, damage to houses or obstruction of roads on São Miguel Island were catalogued. Based on the recorded events, it is verified that there is not a regular increment and/or pattern in the distribution of the events over time, although two main periods can be distinguished: 1900–1994 (1.0 events per year) and 1995–2020 (5.3 events per year). The events were responsible for 82 fatalities, 41 injuries, 66 houses partially or totally destroyed and 305 homeless people. The municipality of Povoação registered 76 landslide events, followed by the municipalities of Ribeira Grande (71 events), Ponta Delgada (69 events), Vila Franca do Campo (47 events), Nordeste (26 events) and Lagoa (21 events). Although there is a relative homogeneity on the distribution of landslide events in the municipalities of Povoação, Ribeira Grande and Ponta Delgada, the same does not apply to the impact caused. In the municipality of Povoação were counted 48 fatalities, 20 injuries, 17 houses destroyed and 109 homeless people, in Ponta Delgada 14 fatalities, 14 injuries, 24 houses destroyed and 173 homeless people and in Ribeira Grande 8 fatalities, 5 injuries, 16 houses destroyed and 21 homeless people. In the municipality of Vila Franca do Campo were counted 7 fatalities and 2 houses destroyed, in Nordeste 3 fatalities and 2 injuries, and in Lagoa 2 fatalities, 7 houses destroyed and 2 were homeless people. Rainfall was the triggering factor responsible for 70% of the catalogued landslide events, followed by sea erosion (8%), anthropogenic actions (4%) and earthquakes (2%). The triggering factor was not possible to identify in 16% of the landslide events. Landslides occurred mostly during the rainiest season (from November to March), which comprise about 78% of the catalogued landslide events.

How to cite: Silva, R. F., Marques, R., and Zêzere, J. L.: Landslides on São Miguel Island (Azores-Portugal) in the period 1900-2020: Analysis of the spatio-temporal distribution, triggering factors and impact based on newspapers press articles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-445, https://doi.org/10.5194/egusphere-egu23-445, 2023.

EGU23-1542 | ECS | Orals | HS7.5

Triggering rainfall conditions of post-fire debris flows in Campania, Southern Italy 

Stefano Luigi Gariano, Giuseppe Esposito, Rocco Masi, Stefano Alfano, and Gaetano Giannatiempo

The Campania region, in Southern Italy, is affected by hundreds of wildfires every year, mainly during the summer season. Starting from the month of September, mountain watersheds including those hit by wildfires are impacted by even more frequent intense rainstorms. In such conditions, the high sediment availability, lack of recovered vegetation and a likely stronger soil water repellency increase the likelihood of surface runoff and soil erosion, leading to potential post-fire debris flows downstream.

This work provides information on more than 100 post-fire debris flows (PFDFs) that occurred in Campania between 2001 and 2021, with a particular focus on the triggering rainfall conditions. Rainfall measurements at a high temporal resolution (10 min) were gathered from a dense rain gauge network, with an average distance between sensors and PFDFs initiation areas of 2.6 km. Information on the occurrence of PFDFs was obtained from web news, social networks, and reports produced by the Fire Brigades. The collection of accurate information related to the debris flow timing and location allowed retrieving and analyzing properties of the triggering rainfall inputs, by identifying the minimum triggering conditions with rainfall thresholds. Moreover, to evaluate the temporal structure and type of the storms associated with the PFDFs (e.g., convective or frontal systems), the standardized rainfall profiles of the triggering events were defined. The return times of the peak cumulative rainfall of the bursts in 10, 20, and 30 minutes were also calculated.

Results show that the triggering rainfall events are very short (37 minutes on average), with high average intensity (73.2 mm/h and 49 mm/h in 10 and 30 minutes, respectively), and mostly associated with severe convective systems (i.e., thunderstorms). The estimated return times are quite low, with 75° percentiles of the related distribution ranging from 2.7 to 3.2 years, indicating that these rainfall events are neither rare nor extreme, as also observed by other authors worldwide. Differences are observed in return times and the spatial distribution of the events that occurred in July-September (higher rainfall magnitudes and longer return times) rather than in October-December. The time window in which PFDFs are more likely to occur in the study area has an extension of four months, from September to December. According to the defined triggering rainfall threshold, a rainfall of 11.4 mm in 30 minutes (corresponding to an average intensity of 22.8 mm/h) is likely sufficient to trigger a PFDF in the study area.

These research outcomes provide reliable and effective support to inform decision-makers engaged in hazard assessment and risk management, in order to implement suitable countermeasures in terms of monitoring and early warning systems. It is worth noting that PFDFs often occur in small-scale watersheds characterized by very short concentration times, in response to intense bursts of less than 60 minutes. This means insufficient lead time to fully develop an effective emergency response. This and other criticalities represent serious challenges requiring additional work.

How to cite: Gariano, S. L., Esposito, G., Masi, R., Alfano, S., and Giannatiempo, G.: Triggering rainfall conditions of post-fire debris flows in Campania, Southern Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1542, https://doi.org/10.5194/egusphere-egu23-1542, 2023.

EGU23-2000 | ECS | Orals | HS7.5

Foreseeing the propensity of rivers to extreme floods 

Stefano Basso, Ralf Merz, Larisa Tarasova, and Arianna Miniussi

Notwithstanding hundreds of years of efforts, flooding is still the most common natural disaster. A reliable assessment of the impending flood hazard is indeed an outstanding challenge with severe consequences. Mistaken estimates of the odds and magnitude of extreme floods especially result in huge economic losses due to widespread destruction of infrastructure and properties.

We show here that we can infer the propensity of rivers to generate extreme floods by means of two hydroclimatic and geomorphic descriptors of watersheds, which embody the spatial organization of the stream network and the characteristic streamflow dynamics of the river basin. These features are main determinants of a sharp increase of the magnitude of the rarer floods and of the flood value for which this marked growth of magnitude occurs, which we term flood divide as it separates ordinary from extreme floods. Their relevance is suggested by a novel ecohydrological approach to flood hazard assessment and confirmed by observations from hundreds of watersheds in the USA and Germany.

We first ascertained the capability of the method to distinguish between basins which do not and exhibit a flood divide, and its ability to dependably estimate its magnitude. We then applied a dimensional reduction tool to pinpoint key physioclimatic controls of the occurrence of flood divides, verifying our results against data. Finally, we utilized descriptors of these controls (namely the hydrograph recession exponent and streamflow variability) within binary logistic regression to predict the possible occurrence of flood divides and extreme floods in river basins. Repeated analyses for independent realizations of subsets of data indicate good prediction accuracy.

The identified controls of the propensity of rivers to generate extreme floods are readily estimated from primary hydroclimatic variables. The tool thus allows for inferring cases where extreme events shall be expected from short records of ordinary events, providing valuable information to raise awareness of the peril of floods in river basins.

This study summarizes results of the DFG-funded project "Propensity of rivers to extreme floods: climate-landscape controls and early detection - PREDICTED" (Deutsche Forschungsgemeinschaft - German Research Foundation, Project Number 421396820).

How to cite: Basso, S., Merz, R., Tarasova, L., and Miniussi, A.: Foreseeing the propensity of rivers to extreme floods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2000, https://doi.org/10.5194/egusphere-egu23-2000, 2023.

EGU23-2240 | ECS | Posters on site | HS7.5

Large-scale dynamical drivers associated with sub-daily extreme rainfall in Europe 

Anna Whitford, Hayley Fowler, Stephen Blenkinsop, and Rachel White

Short-duration (3hr) extreme rainfall events can cause significant socioeconomic and structural damage, alongside loss of life, due to their ability to generate dangerous flash floods, particularly in urban areas and small catchments. With the projected future increase in the frequency and intensity of these events due to global warming, it is imperative to improve our ability to provide warning to communities that may be impacted by these floods. Large-scale atmospheric dynamics play a role in generating the conditions conducive to the development of local-scale sub-daily extremes, but our current understanding of these processes is limited. Additionally, large-scale circulations are inherently more forecastable than small-scale features such as convection, therefore, this project focuses on finding connections between the large-scale dynamics and sub-daily extremes.

This study uses the quality-controlled Global Sub-Daily Rainfall dataset to identify past extreme events in western Europe. The atmospheric circulation pattern present on the day of each event is extracted from the UK Met Office’s set of 30 weather patterns (WPs) based on mean sea level pressure. This information is then used to examine the intensity and frequency of extreme events under each WP, leading to analysis of the spatial connections between the WPs and sub-daily extremes.

Results indicate just 5 of the 30 WPs account for 53% of recorded 3hr events above the 99.9th percentile in Europe in summer. The important WPs are a mixture of those showing a cyclonic system (cut-off low) close to or over western Europe and those representing a transitional environment. There are also distinct spatial patterns to the relationships in some cases, for example WP11 (isolated low pressure centred over the south-west UK), is associated with very high frequency of extremes over the UK and Portugal but much lower frequencies elsewhere in Europe. The identification of a select group of WPs as important for the generation of sub-daily extremes has implications for forecasting these events at longer lead times, as the large-scale WPs can be predicted further ahead than local conditions.

The WP-based analysis is supplemented by investigation of the links between the sub-daily rainfall extremes and synoptic scale Rossby wave patterns. The Local Finite Amplitude Wave Activity (LWA) metric is used to identify regions of anomalous cyclonic or anticyclonic wave activity both prior to and during the extreme events. This analysis indicates anomalous cyclonic wave activity at certain locations, including over Alaska, to the west of the British Isles and over northern Siberia, is significantly correlated with extreme rainfall over Europe. It is also possible to trace the LWA in days leading up to the extreme events, enabling identification of wave patterns that evolve into conditions associated with the extremes.

These results offer new evidence on the role of large-scale dynamics associated with sub-daily extreme rainfall, whilst also providing powerful information that could be used in the forecasting of these events.

How to cite: Whitford, A., Fowler, H., Blenkinsop, S., and White, R.: Large-scale dynamical drivers associated with sub-daily extreme rainfall in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2240, https://doi.org/10.5194/egusphere-egu23-2240, 2023.

EGU23-2462 | ECS | Orals | HS7.5

Towards a method of rapid flood scenario mapping using hybrid approaches of hydraulic modelling and machine learning 

Andrea Pozo, Matthew Wilson, Emily Lane, Fernando Méndez, and Marwan Katurji

Floods are the most common hazard in New Zealand, the second most costly and they will change rapidly in frequency and intensity, become more extreme as the impacts of climate change become realized. At the same time, we are undergoing an intense urban development and growing population lives in floodplains, increasing the risk for people’s households and wellbeing. Additionally, computers have limited power and capacity, so there is a limitation in the number of flood scenarios that can be assessed and in the complexity of the hydrodynamic modelling process. This research project, which is part of the 5-year multi-stakeholder research programme “Reducing flood inundation hazard and risk across Aotearoa/New Zealand”, supported by the New Zealand Government and led by the National Institute of Water and Atmospheric Research (NIWA); investigates the feasibility of using a hybrid hydrodynamic/machine learning model to reduce the numerical modelling load and enable probabilistic modelling. The study site is the Wairewa catchment (Little River, Canterbury, New Zealand), working closely with the Wairewa Rūnanga based there. A sample of flooding scenarios is constructed based on the characteristics of the main inundation driver (spatial and temporal characteristics of rainfall extreme events) and other inundation drivers (lake level and antecedent conditions in the catchment). Selected scenarios from this sample will be modelled through a previously calibrated hydrodynamic model and the resultant inundation maps (maximum water depth map for each event) will be used to train a machine learning algorithm to produce the maps for the remaining events. The hybrid model would provide for any flooding scenario (defined by a specific number of variables) the corresponding inundation map in a fast and accurate way, avoiding the hydrodynamic modeling process (very time and computationally expensive). Results from this research will be used to develop a Mātauranga Māori approach to flood resilience and flood related policies by the local and central governments.

How to cite: Pozo, A., Wilson, M., Lane, E., Méndez, F., and Katurji, M.: Towards a method of rapid flood scenario mapping using hybrid approaches of hydraulic modelling and machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2462, https://doi.org/10.5194/egusphere-egu23-2462, 2023.

EGU23-3005 | ECS | Posters virtual | HS7.5

Variations in floods associated with Tropical Cyclones over Mexico under ENSO conditions 

Christian Dominguez and Alejandro Jaramillo

Tropical cyclones (TCs) are among the most hazardous hydrometeorological phenomena. Mexico is affected by TCs from the North Atlantic and Eastern Pacific oceans, and they originate 86.5% of domestic disasters. The natural hazards associated with TCs are extreme precipitation events, floods, storm surges, and landslides. In the present preliminary study, we focus on exploring how El Niño-Southern Oscillation (ENSO) modulates the frequency and magnitude of extreme precipitation events and floods caused by TCs. We use the CHIRPS dataset for determining the extreme precipitation events (defined by the 95th percentile of daily precipitation) and Mexican rain gauge stations from May to November during the 1981-2013 period. We find that TCs are responsible for ~60% of floods in coastal regions, but this percentage decreases inland. Under El Niño conditions, most floods occur over southwestern Mexico. During neutral conditions, the western coast of Mexico is mainly affected. Under La Niña conditions, most floods occur over the eastern coast of Mexico. Additionally, trends in floods are explored. We conclude that local decision-makers need this information to decrease the hydrometeorological risk before the tropical cyclone season begins. Implementing this information on Early Warning Systems for TCs is also discussed.

How to cite: Dominguez, C. and Jaramillo, A.: Variations in floods associated with Tropical Cyclones over Mexico under ENSO conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3005, https://doi.org/10.5194/egusphere-egu23-3005, 2023.

EGU23-3073 | Posters on site | HS7.5

Flooding Hazard of Union Station and Impact of Ridership due to Climate Change-an Example of Banqiao Main Station 

Yong-Jun Lin, Hsiang-Kuan Chang, Kai-Yuan Ke, Jihn-Sung Lai, and Yih-Chi Tan

This study adopts the rainfall scenario generated by TCCIP (The Taiwan Climate Change Projection Information and Adaptation Knowledge Platform) based on IPCC AR5, which provides the 95th percentile of Taipei’s maximum 24-hour cumulative rainfall due to climate change. The baseline of this scenario is 404 mm for 1979-2008, and the projected rainfall is 517 mm for the future mid-century (2039-2065).

The flooding potentials of the Taipei Mass Rapid Transit (MRT) stations are obtained by applying the scenarios of rainfalls and the corresponding rainfall patterns of each rainfall station to a two-dimensional flood model. The flooding simulations of baseline and future scenarios show that Jingan Station and Fu-Jen University Station have the highest flooding potential, with a maximum flooding depth of 2 meters. The flooding hazard factors include flooding depth, flow velocity, and rising rate of water surface level. We adopted those factors to analyze the flooding hazard at Banqiao Main Station, which unites Banqiao Railway Station, a high-speed rail station, and Banqiao MRT station. It has a severe flooding potential and a large traffic volume. Because the mid-century rainfall is 1.43 times that of the baseline, the corresponding flooded area of the future scenario is also increased. As a result, the flooding hazards around the exits of Banqiao Main Station are high within the 300 m buffer for the baseline. In contrast, the very high flood hazard was found in a 200m-300m buffer for the future scenario.  

MRT Banqiao Station has 5 entrances/exits, while Banqiao Railway Station has 6 entrances/exits, a total of 11. The average daily ridership at this union station before Covid-19 is 159,239 people/day. The impact ratio of the ridership is set by the degree of flood hazard for each entrance/exit. In the future scenario, the number of affected people is roughly estimated to be 11,611 people/day, which is about 7% of daily ridership before Covid-19.

How to cite: Lin, Y.-J., Chang, H.-K., Ke, K.-Y., Lai, J.-S., and Tan, Y.-C.: Flooding Hazard of Union Station and Impact of Ridership due to Climate Change-an Example of Banqiao Main Station, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3073, https://doi.org/10.5194/egusphere-egu23-3073, 2023.

EGU23-3734 | ECS | Orals | HS7.5

Modeling risk to infrastructure due to episodic debris fan aggradation 

Yuan-Hung Chiu, Colin P. Stark, and Hervé Capart

In many mountain valleys, communities and infrastructure are exposed to high risks of damage due to debris fan aggradation. To assess such risks, two questions must be addressed: (1) What will be the extent and thickness of deposition over the fan for a given volume of debris delivered from the upstream catchment? (2) How large could debris flow volumes be for a single event or a sequence of events? In this contribution, we propose a methodology to address both questions. Its first component is a simplified model of debris fan morphology, based on assuming a fan-slope-distance relationship along paths affected by topographic obstacles like steep valley sides. Using a computationally efficient algorithm, this model can be used to reconstruct past fan volumes from terrace remnants resolved on high resolution DEM topography, and to simulate large numbers of possible future events. Its second component is a stochastic model for the evolution of fan volume framed as a form of random walk. To take into account the episodicity of debris delivery, we model this random walk as a gamma-subordinated Wiener process aka a variance-gamma process. To calibrate the model parameters, we exploit both short-term and long-term data: for the slope-distance relationship, topographic data from recent and Holocene debris-fan remnants; for the stochastic process parameters, reconstructed fan-volume changes associated with recent flood events and with older radiocarbon-dated fan surfaces. We illustrate the approach with an application to the Laonong River in southern Taiwan. In this valley, an important roadway link has been repeatedly damaged by debris-flow aggradation. To guide road and bridge reconstruction, it is essential to assess fan aggradation risk for different design alternatives on a decadal time scale or more. The model provides a basis for optimizing the layout and height of such infrastructure.

How to cite: Chiu, Y.-H., Stark, C. P., and Capart, H.: Modeling risk to infrastructure due to episodic debris fan aggradation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3734, https://doi.org/10.5194/egusphere-egu23-3734, 2023.

EGU23-4243 | ECS | Posters on site | HS7.5

Do CMIP6 climate models capture rapid shifts between dry and wet extremes? 

Rong Gan and Yuting Yang

Do CMIP6 climate models capture rapid shifts between dry and wet extremes?

Authors: Rong Gan1, Yuting Yang1,*

Affiliations: 1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China

*Correspondence to: Yuting Yang (yuting_yang@tsinghua.edu.cn)

Keywords: CMIP6, climate extremes, compound events

Abstract:

Rapid shifts between dry and wet extremes may impose higher socioeconomic and environmental pressure than single extremes. Whether the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models are capable of capturing the abrupt alternations between dry and wet periods remain elusive. Here we examine such compound events simulated by CMIP6 models based on the state-of-the art reanalysis datasets, namely ERA5, NCEP-NCAR and MERRA-2. The 1-month Standard Precipitation-Evapotranspiration Index (SPEI) were first calculated to identify dry spells (SPEI≤1) followed by wet spells (SPEI≥1), and vice versa. Event characters including frequency, duration and intensity were then evaluated across all CMIP6 models and reanalysis datasets spanning 1980-2014. We find the following:

  • CMIP6 multimodel-ensemble median and reanalysis ensemble give close estimates of event characters on global average, with frequency being about 4.1 and 3.67 (No. events/20-year), duration of 2.50 and 2.55 (months), and intensity around 3 (SPEI mean) for dry-wet events, respectively. Similar values were found for wet-dry events.
  • During 1980-2014, CMIP6 and reanalysis indicate roughly 10% increase in event frequency comparing the first and last 20-year periods, and less than 1% increase in duration and intensity for both dry-wet and wet-dry events.
  • Spatial distribution for event frequency tends to overlap for dry-wet and wet-dry events, as shown by both CMIP6 models and reanalysis. Hot spots were found in North-eastern America, Europe, Eastern Asia, South-western America, and Middle Africa. Higher latitude regions were shown to experience more events. Despite general spatial agreement between CMIP6 and reanalysis, discrepancies can be seen on finer scales within each region.
  • Common spatial patterns for duration were also found between the two types of events based on CMIP6 models, where the events tend to last longer in middle and southern Eurasia, Eastern Africa, northwest of South America and west of Northern and Central America. However, reanalysis indicates longer events also happened in Middle Africa and eastern Australia. Both CMIP6 models and reanalysis indicate longer event duration roughly around the equator.
  • CMIP6 models give much higher dry-wet intensity compared to wet-dry, especially in Australia and Southern and Western Asia. Reanalysis agrees well on this pattern, yet greater magnitude differences were found in eastern South America.

Overall, CMIP6 models are capturing the variations of abrupt dry and wet alternations well when compared to reanalysis. The models are more skilful in simulating event frequency than duration and intensity in general. Caution should be paid assessing such compound events especially on smaller spatial scales and sensitive regions such as Africa for frequency and Australia for duration and intensity. Our results can be further employed to support climate risk adaptation and mitigation.

How to cite: Gan, R. and Yang, Y.: Do CMIP6 climate models capture rapid shifts between dry and wet extremes?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4243, https://doi.org/10.5194/egusphere-egu23-4243, 2023.

EGU23-4417 | ECS | Posters on site | HS7.5

Hazard index applied to natural rivers – Preliminary result from a case study of mountain trails in southern Brazil 

Marina Refatti Fagundes, Fernando Mainardi Fan, Gean Paulo Michel, Karla Campagnolo, Masato Kobiyama, Ronald Pöppl, and Bruno Henrique Abatti

Trails are one of the main places for ecotourism practitioners’ activities. Many of them are located close to watercourses, and it is often necessary for practitioners to cross them. This often leads to dangerous situations, since critical conditions of water stages and flow velocity can make people lose their walking stability. One way to quantify these hazards is the hazard index (HI) which, in general, is defined as the product of the flow velocity by its depth (Stephenson, 2002). Although many studies have been carried out to determine the HI values as safety limits for people exposed to water flows, none of them analyzed the natural river conditions like those encountered during an ecotourism trail. In these environments, locomotion is hampered due to the surface which is usually highly irregular and often contains slippery rocks and sediments. Thus, that there is a gap related to the HI analysis in natural rivers, and more research becomes necessary, since more people have sought to carry out activities related to ecotourism. The main objective of this research is to apply HI approach in natural rivers so that its results can be utilized in the management of trails containing watercourses crossing. Initially, a bibliographic review was carried out, where some important concerns related to people's loss of stability were analyzed. The results of the bibliographic review were organized within a summary table which permits verifying variables with stronger influence on people's stability, during these walks. After this first stage, three mountain trails located in the Aparados da Serra National Park, in southern Brazil, were selected for field measurements. In all of these trails, measurements of flow depth and velocity were carried out using a small current meter and the granulometry of the river sediments was measured through an adaptation of the Pebble Count method. The measurements were taken at all points where tourists cross the riverbed during the trails, i.e., 23 measurement sites in total. The analysis of these data resulted in preliminar information: (i) an easy-to-interpret diagram that indicates the thresholds values of HI in natural rivers, named Hazard Index Diagram of Natural River (HIDNR); and (ii) list of the main variables responsible for people's loss of stability, in order to contribute to the safety of ecotourism practitioners. One of the next steps of the work is to analyze how the sediment transport and connectivity behaviour could give us insights about hazard levels.

REFERENCES

STEPHENSON, D. (2002). Integrated flood plain management strategy for the Vaal. Urban Water, v. 4, n. 4, p. 423-428.

How to cite: Refatti Fagundes, M., Mainardi Fan, F., Michel, G. P., Campagnolo, K., Kobiyama, M., Pöppl, R., and Abatti, B. H.: Hazard index applied to natural rivers – Preliminary result from a case study of mountain trails in southern Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4417, https://doi.org/10.5194/egusphere-egu23-4417, 2023.

EGU23-4537 | ECS | Orals | HS7.5

What controls physical vulnerability to geo-hydrological hazards? A contribution to quantitative assessment of landslide and flood risk in western Uganda 

John Sekajugo, Grace Kagoro-Rugunda, Rodgers Mutyebere, Clovis Kabaseke, David Mubiru, Esther Namara, Violet Kanyiginya, Bosco Bwambale, Liesbet Jacobs Jacobs, Olivier Dewitte, and Matthieu Kervyn

Geo-hydrological hazards (landslides and floods) are often associated with significant damages on physical infrastructure like buildings and roads. Understanding the factors controlling the extent of damage is a prerequisite for quantitatively estimating risk and its spatial distribution, and advising on measures to reduce vulnerability. In this study we document the impact of 64 landslide and six flood events in four selected districts in western Uganda for the period May 2019 - March 2021 through extensive fieldwork. We quantify in economic value the physical damage of landslide and flood hazards on exposed buildings, roads and bridges. We then analyse the physical vulnerability based on damage ratios and determine the factors  (building material, hazard characteristics and age of the building) that control the degree of damage using fractional logistic regression. Out of the 91 buildings affected by landslides, 54% were totally destroyed, and only 10% not or minorly damaged, for an average damage cost of 3,179 USD/building. For the 212 documented buildings affected by floods, 35% were totally destroyed, 28% had severe to moderate damage and the rest were minorly or not affected, with an average damage costs of 1,755 USD/building. The physical vulnerability of buildings to landslides depends on the size of the landslide, age of the building, type of building wall material and the steepness of the slope cut to establish an artificial foundation platform. On the other hand, the physical vulnerability of buildings to flood hazards is largely controlled by the flood depth, the distance from the river channel, slope, size of flooded area and type of floor material. The physical vulnerability functions developed in this study are being used as a new inputs into a regional quantitative model of geo-hydrological risks. Combining the hazard estimates with the most accurate information on exposure of physical infrastructure, will facilitate the identification of the types of events and the locations that require most attention for risk reduction.

How to cite: Sekajugo, J., Kagoro-Rugunda, G., Mutyebere, R., Kabaseke, C., Mubiru, D., Namara, E., Kanyiginya, V., Bwambale, B., Jacobs, L. J., Dewitte, O., and Kervyn, M.: What controls physical vulnerability to geo-hydrological hazards? A contribution to quantitative assessment of landslide and flood risk in western Uganda, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4537, https://doi.org/10.5194/egusphere-egu23-4537, 2023.

EGU23-5513 | ECS | Posters on site | HS7.5

Global IDF curves created from local observations using machine learning 

Jannis Hoch, Izzy Probyn, Joe Bates, Oliver Wing, and Christopher Sampson

Intensity–duration–frequency (IDF) curves are representations of the probability that a given rainfall intensity will occur within a given period. At the global scale, however, only for a few locations sub-daily rain gauge data is available from which global IDF curves could be derived. This poses a major challenge for simulations of global pluvial flood hazard and risk which require information of intensity, duration, and probability as boundary conditions. Therefore, efficient yet accurate means for scaling the locally available data to the global extent need to be found.

Consequently, we use available quality-controlled sub-daily precipitation data from the GSDR data set to derive growth curve parameters at around 10,000 locations world-wide. After combining these scale and shape parameters with globally available data of main precipitation drivers, a regionalized machine learning model is first trained and tested and then applied to produce global IDF maps.

Finally, we evaluated these maps against an ensemble of openly available local IDF curves found in literature. By selecting locations spread across the globe, we try to ensure to include as much variability as possible in the evaluation. Additionally, the global IDF curves were benchmarked against available more bespoke IDF data in the USA and UK.

While such data-driven approaches clearly depend on the quality and quantity of available sub-daily rainfall observations, the method still shows to capabilities of current data-driven modelling approaches to scale local data to global data applicable in both flood risk research and practice.

How to cite: Hoch, J., Probyn, I., Bates, J., Wing, O., and Sampson, C.: Global IDF curves created from local observations using machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5513, https://doi.org/10.5194/egusphere-egu23-5513, 2023.

EGU23-6689 | ECS | Orals | HS7.5 | Highlight

Global analysis of emergency service provision to vulnerable populations during floods of various magnitude under climate change 

Sarah Johnson, Robert Wilby, Dapeng Yu, and Tom Matthews

In a world of increasing global flood hazards, vulnerable populations (very young and elderly) are disproportionately affected by flooding due to their low self-reliance, weak political voice and insufficient inclusion in climate adaptation and emergency response plans. These individuals account for most flood casualties and often rely on emergency services due to flood-induced injuries, exacerbated medical conditions, and requiring evacuative assistance. However, emergency service demand often exceeds the potential capacity whilst flooded roads and short emergency response timeframes decrease accessibility, service area, and population coverage; but how does this compare across the globe and what will the future hold?

To answer this question, a global analytical framework has been created to determine the spatial, temporal, and demographic variability of emergency service provision during floods. This is based on global fluvial and coastal flooding (at 10-year and 100-year return periods), and present and future flood conditions (present-day and 2050, under RCP 4.5 and RCP 8.5 climate scenarios). The framework includes an accessibility analysis to identify emergency service accessibility to vulnerable populations based on restrictions of flood barriers and response-time frameworks, a vulnerability analysis to compare the difference in emergency service provision between key demographic groups, and a hotspot analysis to identify the extent and distribution of flood hazards and at-risk vulnerable populations.

Research findings include the identification that (based on the scenario of 2050 riverine flooding at a 100-yr return period under RCP8.5 and a 30-minute response time):

  • Globally, 64% of schools are always accessible to the ambulance service and 56% of schools are always accessible to the fire service
  • Globally, 29% of schools are never accessible to the ambulance service and 38% of schools are never accessible to the fire service.
  • Globally, approximately 20% fewer people are accessible to emergency services than under non-flood conditions.
  • Africa and Asia experience the greatest accessible population reductions (14-27% and 24-25%) whilst Europe experiences the least accessible population reductions (8-9%).
  • Priority hotspot countries are primarily located in central North America (e.g., Belize), northern South America (e.g., Guyana) and west-central Africa (e.g., Liberia).

The highlighted geographical and temporal differences in emergency service provision globally and between regions, in addition to the framework itself, can be used by national and international organisations to inform strategic planning of emergency response operations and major investments of infrastructure, services, and facilities to maximise the benefit to the disproportionately affected vulnerable populations. This includes the production of more detailed flood hazard and evacuation maps that highlight vulnerability hotspots, the prioritisation of vulnerable population groups in emergency response plans to minimise geographic and population disparities of flood injuries and fatalities, and the allocation of emergency service hubs in regions of high vulnerability but low emergency response provision.

How to cite: Johnson, S., Wilby, R., Yu, D., and Matthews, T.: Global analysis of emergency service provision to vulnerable populations during floods of various magnitude under climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6689, https://doi.org/10.5194/egusphere-egu23-6689, 2023.

EGU23-7000 | Orals | HS7.5

Areal reduction factor assessment for extreme rainfalls through a new empirical fixed-area formulation 

Alessia Flammini, Jacopo Dari, Carla Saltalippi, and Renato Morbidelli

In the hydraulic structures design against extreme events a proper estimate of the areal reduction factor (ARF) is required. Specifically, rainfall-runoff models widely used need to be fed with information on areal-average rainfall over a watershed surface, while rainfall data is typically available at a point scale. The ARF allows to convert rainfall data from point to areal scale.

In this work, a new fixed-area and deterministic approach for estimating the ARF is proposed; it involves ratios between observed annual maxima with specific duration of average rainfall occurring in a specific area and those referring to all the available point rainfalls in the same area. The approach was applied to the Umbria region in Central Italy where, using high-quality and validated rainfall data (with a temporal resolution of 1 minute), a parametric relation expressing ARFs as function of duration and area was found. The outcomes were then compared with those of the most widespread empirical approaches available in literature, often applied when rainfall data are lacking, obtaining substantial over- or underestimation of empirical ARFs. This confirms that the transposition of ARF relations from a geographic area to another could have not-negligible impacts on the design of hydraulic structures. In addition, indications aimed at selecting the most suitable method to be applied for ARF estimation are provided. Specifically, the proposed approach is suitable when a limited number of years of rainfall observations is available. In this regard, the robustness of the methodology was tested by varying the length of the rainfall observation period; a minimum number of about 6 years was found to make the derived empirical formulation sufficiently accurate in a specific area.

How to cite: Flammini, A., Dari, J., Saltalippi, C., and Morbidelli, R.: Areal reduction factor assessment for extreme rainfalls through a new empirical fixed-area formulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7000, https://doi.org/10.5194/egusphere-egu23-7000, 2023.

EGU23-7194 | ECS | Posters on site | HS7.5

Urban Flood Risk in Dhaka, Bangladesh 

Farzana Mohuya, Claire Walsh, and Hayley Fowler

Dhaka, the capital city of Bangladesh, is one of the most densely populated cities in South Asia. Urban flooding from extreme rainfall is a recurring phenomenon, with historic floods in 1988, 1998, and 2004 amongst the most catastrophic events in Dhaka. Prolonged urban flooding or water logging is a major concern for both Dhaka North City Corporation (DNCC) and Dhaka South City Corporation (DSCC) areas. This research investigates how “Citizen Science (CS)” could help individuals, communities, and stakeholders understand and manage the risk of current and future urban flooding, integrating formal flood risk management along with the affected area’s respondents’ self-perceived perception, concerns, experience, awareness, and opinions about flood risk management, and ability to cope with the flood risk. Fieldwork data were collected through the administration of a purposely designed questionnaire to 500 respondents in the water logging affected wards of the two city corporations’ areas in Dhaka. Preliminary findings from the fieldwork revealed that every year approximately 45.6% and 29.4% respondents in the study area experienced 1-3 days of urban flooding/water logging, mostly during the monsoon season (June – September), with a work time loss of 3-4 hours respectively. Respondents in the study area are aware and concerned about flooding and its associated risk, and approximately 36.9% respondents think that the frequency of urban flooding will increase in Dhaka in the next 10 years. In terms of the vulnerability, approximately 51.5% respondents mentioned that they are vulnerable to urban flooding and small business holders (Entrepreneurs) are most affected (61.5% respondents) by flooding. Although almost 61.2% respondents were not familiar with the “Citizen Science” concept, but approximately 42.8% of respondents expressed an eagerness to involve themselves in any Citizen Science based project to promote awareness and mitigation of urban flood risk/water logging issues in their community or in Dhaka City. In addition, preliminary findings from Key Informant Interviews (KII) and Focus Group Discussion (FGD) Meetings suggested that unplanned urbanisation, poor and inadequate drainage system management, and recent extreme rainfall events were the major drivers behind the urban flooding/water logging situation in Dhaka.

The study also explored annual and seasonal trends of rainfall in Dhaka (using observed datasets from the Bangladesh Meteorological Department) over the period from 1953-2019 using extreme precipitation indices [Climate Change Detection and Indices (ETCCDI)]. It is revealed that over these 67 years, Annual Maximum Daily Rainfall has increased during winter (0.021 mm/year) but statistically significantly decreased during the monsoon (-0.636 mm/year). The overall annual rainfall has significantly decreased (-0.718 mm/year). Trends in Consecutive Dry Days, heavy, and very heavy precipitation days indicate an annual increasing rate of 0.158 days/year for CDD, 0.077 days/year with >= 10 mm rainfall and 0.019 days/year with >= 20 mm rainfall, respectively. Results from the rainfall datasets are now being integrated with the fieldwork findings and other secondary datasets to set up a Hydrodynamic Model (CityCAT) to investigate current and future flood risk in Dhaka in more detail.

How to cite: Mohuya, F., Walsh, C., and Fowler, H.: Urban Flood Risk in Dhaka, Bangladesh, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7194, https://doi.org/10.5194/egusphere-egu23-7194, 2023.

EGU23-7772 | ECS | Orals | HS7.5 | Highlight

Societal Flood Risk in Italy 

Mina Yazdani, Paola Salvati, Mauro Rossi, Cinzia Bianchi, and Fausto Guzzetti

Flood events are among the most damaging natural disasters, with billions of people being directly exposed to the risk of intense flooding worldwide. The economic and societal consequences of these events are expected to increase in the coming years. Flood societal risk can be determined by analyzing the relationship between the frequency of fatal flood events and the magnitude of the resulting consequences to the population (evaluated by the number of fatalities due to the event). Here, we test an approach previously proposed for landslides to estimate the flood societal risk in Italy, using historical sparse data on flood fatalities, available through national catalogues. Such an approach is based on the use of the Zipf distribution, which has previously been widely adopted for the modeling of societal risk for different natural hazards. The model allowed the evaluation of the spatial and temporal distribution of societal flood risk over the Italian territory over a regularly spaced grid. Different risk scenarios are presented and discussed.  

How to cite: Yazdani, M., Salvati, P., Rossi, M., Bianchi, C., and Guzzetti, F.: Societal Flood Risk in Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7772, https://doi.org/10.5194/egusphere-egu23-7772, 2023.

EGU23-10096 * | ECS | Orals | HS7.5 | Highlight

Could the 2019-20 Australia bushfires or 2020-22 floods be predicted using CMIP decadal prediction? 

Ze Jiang, Dipayan Choudhury, and Ashish Sharma

Over the past six years, Australia has experienced significant fluctuations in rainfall, including prolonged dry conditions and extensive bushfires, followed by two consecutive years of heavy rainfall in the east. Could such anomalies be predicted many years in advance is the question this study hopes to answer. A prediction framework that combines empirical and physically-based approaches using CMIP decadal prediction, and a novel spectral transformation approach is presented. When tested in a hindcast experiment, this framework shows significant prediction skill for rainfall up to five years in the future across all regions and climate zones in Australia. This framework was used to project from 2018 to 2022, covering the years of bushfires and extreme floods in Australia, as an added blindfolded validation of the prediction approach used. Following this, a blind projection of the precipitation anomalies over the continent for the coming five years is presented, to assess whether the anomalies for the past five years were, indeed, anomalies, or part of a pattern of what can be expected into the future. It is shown that this decadal framework has great potential for predicting whether the next few years will be wetter or drier, extending the predictive accuracy beyond a few months into the future. This can be valuable for managing water resources, prioritizing demands, protecting vulnerable systems, and reducing uncertainty in hydrological decision-making.

How to cite: Jiang, Z., Choudhury, D., and Sharma, A.: Could the 2019-20 Australia bushfires or 2020-22 floods be predicted using CMIP decadal prediction?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10096, https://doi.org/10.5194/egusphere-egu23-10096, 2023.

EGU23-10255 | Orals | HS7.5

Cascading flood hazards: the role of large wood transport 

Virginia Ruiz-Villanueva

Floods are one of the most relevant natural hazards, causing significant socio-economic damage every year globally. They will likely continue to increase for various reasons: the climate and global changes, two relevant ones. More importantly, our still limited capability to predict river response to flooding and anticipate the consequences by designing proper and sustainable risk mitigation measures. A recent example was Europe's floods in July 2021, the highest recorded. They led to many casualties and economic losses (i.e., 180 fatalities and billions of Euros). Extreme long, high-intensity rainfall resulted in extreme flows, particularly in small tributaries, but this could not solely explain the devastating impacts. Geomorphological changes, bank erosion and channel widening, sediment erosion and transport, and uprooted and transported trees and instream large wood accumulated at bridges played a significant role. However, these cascade processes are rarely quantified or considered in flood hazard and risk analysis. This is the focus of this talk. Case studies showing a combination of modelling approaches will illustrate how quantifying the supply and transport of instream large wood is essential in river reaches crossing infrastructures like bridges to assess flood-related hazards and risks.

How to cite: Ruiz-Villanueva, V.: Cascading flood hazards: the role of large wood transport, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10255, https://doi.org/10.5194/egusphere-egu23-10255, 2023.

Characterizing the upper tail of flood peak distributions remains a challenge due to the elusive nature of extreme floods, particularly the key elements of flood-producing storms that are responsible for them. Here I examine the upper tail of flood peaks over China based on a comprehensive flood dataset that integrates systematic observations from 1759 stream gaging stations and 14,779 historical flood surveys. I show that flood peak distributions over China are associated with a mixture of rainfall-generation processes. The storms responsible for the upper-tail floods (with the recurrence intervals beyond 50 years) are characterized with anomalous moisture transport and/or synoptic configurations, with respect to those responsible for annual flood peaks. Anomalous moisture transport (in terms of intensity, pathways, and durations) dictates the space-time rainfall dynamics (relative to the drainage networks) that subsequently lead to anomalous basin-scale flood response. I provide physical insights into extreme flood processes based on downscaling simulations using the Weather Research and Forecasting model driven by the 20th Century Reanalysis fields. Modeling analyses for a collective of extreme flood events highlight the role of interactions between complex terrain and large-scale environment in determining the spatial and temporal variability of extreme rainfall. My analyses contribute to improved understanding of the unprecedented flood hazards over China by establishing the nexus between atmospheric processes and basin-scale flood response. These knowledge gains can be potentially used to the upper tail of flood peak distributions.

How to cite: Yang, L.: Hydrometeorological processes and controls of the upper-tail floods over China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10453, https://doi.org/10.5194/egusphere-egu23-10453, 2023.

EGU23-10474 | ECS | Posters on site | HS7.5

A hydrological and socioeconomic risk assessment of tropical cyclone disasters by leveraging space-based Earth observations 

Gigi Pavur, Venkataraman Lakshmi, and James H Lambert

On September 28, 2022, Hurricane Ian made landfall in Florida as the 5th strongest tropical cyclone on record for the United States of America. Preliminary damage assessments conducted by the National Oceanic and Atmospheric Administration (NOAA) estimated over $50 billion USD in insured and uninsured losses from the event. The extensive environmental and socioeconomic consequences of recent hydrometeorological extremes in Florida indicate an urgent need to improve understanding of hydrological and socioeconomic vulnerability in the region to inform future investments to increase resilience to events like Hurricane Ian. This study conducts an interdisciplinary risk analysis of both hydrological and socioeconomic variables before and after Hurricane Ian to improve understanding of the region’s hydrological and socioeconomic vulnerability to hydrometeorological extremes. A variety of publicly available satellite-based remote sensing data are leveraged for the hydrological analysis, specifically precipitation data from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), soil moisture data from Soil Moisture Active Passive (SMAP), synthetic aperture radar data from Sentinel-1, optical imagery from Landsat 8, and Global Navigation Satellite System Reflectometry (GNSS-R) data from the Cyclone Global Navigation Satellite System (CYGNSS) are utilized. Additionally, high-resolution commercial satellite data from Planet, Maxar, and Capella are used to further identify infrastructure damages from Hurricane Ian. To support the socioeconomic risk analysis, publicly available demographic and economic data are used from the U.S. Census Bureau and State of Florida. Results from this work can be used to improve understanding of hydrological and socioeconomic risk in Florida due to hydrometeorological extremes. Additionally, this work can be used to inform priorities and strategy aimed to decrease risk and increase resilience in this region towards major tropical cyclones. 

How to cite: Pavur, G., Lakshmi, V., and Lambert, J. H.: A hydrological and socioeconomic risk assessment of tropical cyclone disasters by leveraging space-based Earth observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10474, https://doi.org/10.5194/egusphere-egu23-10474, 2023.

EGU23-11439 | ECS | Posters on site | HS7.5 | Highlight

Assessing floods impacts on population displacement in Sudan 

Eleonora Panizza, Yared Abayneh Abebe, and Roberto Rudari

The frequency and intensity of floods in the Intergovernmental Authority on Development (IGAD) region in Eastern Africa have increased over the years because of climate variability and change. Sudan is one of the IGAD countries most affected by these extreme events. In August 2022, the country experienced the fourth consecutive year of major flooding, which extensively damaged buildings and impacted people’s livelihoods. Floods also cause the displacement of thousands of people every year in Sudan due to direct damage to houses and impacts on livelihoods, critical services, and infrastructure. The effects of these events on people’s lives are worsened by contextual socio-economic, political, and individual vulnerabilities. In this regard, assessing flood impacts on displacement is crucial to increase people’s resilience and risk reduction capacities.

In this poster, we present the design, execution, and results of a data collection campaign focused on a pilot area in the Khartoum State of Sudan. These data will support the next phase of research, which is an agent-based modeling (ABM) study. The aims of the broader study are to better understand the nexus between flood events and displacement patterns in the area, including flood perception, preparedness, and displacement duration, and to evaluate the impact of different risk reduction policies. The overall goal of the effort is to strengthen local resilience and capacity, and to support policymakers in identifying effective mitigation and management strategies.

Considering that there could not be a one-size-fits-all solution for different contexts, first-hand data were collected at the local level to capture specific information about the area and its population. Questionnaires were administered to a statistically significant sample of residents in the pilot area, focusing on household characteristics, their experience regarding floods and displacement, and their risk perception. Among the results, it was found that 67% of the surveyed population was displaced due to flooding at least once, most of them for a period ranging from 1 to 5 months. The main reason for the decision to move was the damage to the house, followed by flood impacting livelihood. Displacements occurred most often during the event itself, showing a lack of preparedness. Data showed that 81% of the respondents perceived that they lived in a flood-prone area, while 56% of them believed they were at high risk of being displaced due to flood events. To gain a broader understanding of flood risk reduction policies and implementation contexts, representatives of Sudanese institutions and relevant organizations were interviewed. Policy options were explored, including housing policy and Early Warning Systems.  Both questionnaires and interviews are being used to inform the construction of the ABM.

The research is therefore relevant to understand the main elements that affect displacement decisions and to support the design of strategies for mitigating the risk of involuntary mobility in the area, and for increasing people’s resilience and capacity to cope with flood events and displacement risks.

How to cite: Panizza, E., Abebe, Y. A., and Rudari, R.: Assessing floods impacts on population displacement in Sudan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11439, https://doi.org/10.5194/egusphere-egu23-11439, 2023.

EGU23-11966 | Orals | HS7.5

Spatially consistent flood risk assessment for Germany 

Bruno Merz, Mostafa Farrag, Xiaoxiang Guan, Björn Guse, Li Han, Heidi Kreibich, Dung Nguyen, Nivedita Sairam, Kai Schröter, and Sergiy Vorogushyn

Flood risk assessments are an important basis for risk management. For larger regions, these assessments are often based on small-scale modelling, which is subsequently compiled into a large-scale picture. However, this approach neglects spatial interactions, such as decreasing risk due to upstream dike breaches, and does not provide realistic risk statements for larger regions. This paper presents the ‘derived flood risk analysis’ as an alternative approach and its implementation for Germany. A model chain consisting of hydrological, hydraulic, and damage models simulates the occurrence of extreme runoff, inundation, and direct economic damages. This model chain is driven by a weather generator that provides spatially consistent fields of climate variables. The generation of very long (several thousand years) time series with daily resolution allows the estimation of extreme runoff and corresponding damages. The consideration of the spatial relations in all model components, from the weather generator to the damage model, is able to provide consistent large-scale risk statements. This avoids the significant overestimates typical of many large-scale flood risk assessments.

How to cite: Merz, B., Farrag, M., Guan, X., Guse, B., Han, L., Kreibich, H., Nguyen, D., Sairam, N., Schröter, K., and Vorogushyn, S.: Spatially consistent flood risk assessment for Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11966, https://doi.org/10.5194/egusphere-egu23-11966, 2023.

EGU23-12932 | ECS | Orals | HS7.5 | Highlight

Communicating the return period of extremes 

Elisa Ragno and Amir AghaKouchak

The concept of return period (recurrence interval) of extreme events is widely used in engineering practice and in the media. In engineering design and risk assessment, the concept of return period is used to determine the expected magnitude(s) of one or more extreme weather events – i.e., the expected magnitude of an event that, if occurred, might cause the failure of a structure. In the media, the concept of return period is used to communicate to the general public the severity of an event. For example, the 2021 summer flood in Northwestern Europe was reported in the news as a one-in-400-year event – an event expected on average once in 400 years. The strength of return period as a metric (in years) to describe the severity of events resides in the straightforward comparison between the average occurrence in years of an event with the average number of years a person can experience and recollect events.

Generally, the return period of a rare event and its magnitude (known as return level) is inferred from limited observations - often derived by extrapolating from a distribution function fitted to the available observations. The distribution is often greatly influenced by the length of observations. These factors make the concept of return period prone to misinterpretation as extreme events are rarely observed in existing records.

Here we provide a new perspective on the return period of extremes determined not only by its exceedance probability but also in relation to the observations used to describe the underlying distribution. Our method offers a straightforward metric, independent of the type of statistical distribution adopted, to quantify and communicate the likelihood of having observed the event of interest in the available observations, ranging from unlikely to very likely. This metric can provide a measure of confidence in the statistical inference of return periods based on the length of record used for inference. We argue that this additional information on likelihood offers important information for designers, planners, and decision-makers.

How to cite: Ragno, E. and AghaKouchak, A.: Communicating the return period of extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12932, https://doi.org/10.5194/egusphere-egu23-12932, 2023.

EGU23-14062 | Orals | HS7.5

Suitability of near-real time precipitation products for Flood Risk Forecasting 

Jose Luis Salinas Illarena, Ludovico Nicotina, Shuangcai Li, and Arno Hilberts

Accurate real and near-real time forecasting of extreme flood events has lately become more and more important for the insurance and re-insurance industry (e.g., for claims allocations, Insurance Linked Securities and Catastrophe Bonds…). Examples of such events triggering significant losses in recent years are low-pressure system Bernd (July 2021, eastern Belgium, western Germany, and north-eastern France), hurricane Ida (August-September 2021, Louisiana and Northeastern United States), or hurricane Ian (September 2022, Florida). In order to estimate overall flood risk and flood losses in near-real time, a precipitation product released with a short latency is necessary.

This study analyses the use of the near-real time precipitation products NOAA’s Climate Prediction Center (CPC) and Multi-Radar/Multi-Sensor System (MRMS) for flood forecasting, the latter having a higher spatial and temporal resolution than the former. We investigate and compare their different rainfall characteristics in terms of their ability to capture rainfall extremes, their suitability as input for hydrological/inundation models, and the effect that they have on overall economic losses for a series of selected historical events over the Conterminous United States. Finally, we include in the comparison the more stablished, long-latency dataset North American Land Data Assimilation System (NLDAS), more frequently used for event reconstruction c.a. 1 week after the event.

How to cite: Salinas Illarena, J. L., Nicotina, L., Li, S., and Hilberts, A.: Suitability of near-real time precipitation products for Flood Risk Forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14062, https://doi.org/10.5194/egusphere-egu23-14062, 2023.

EGU23-14903 | ECS | Posters on site | HS7.5

Modelling severe hail events over Austria using the metastatistical extreme value distribution 

Marc-André Falkensteiner, Gregor Ehrensperger, Thorsten Simon, and Tobias Hell

Knowledge about extreme values of severe hail plays an important role in engineering and insurance. The estimation of return levels of severe hail events is challenging, as hail is locally rare and documentation about hail events is not available in a unified way. For instance for the state of Austria GeoSphere provides radar based probabilities of hail (POH) and maxima of expected hail size (MEHS) that only span a period from 2010 onward.

Based on this sparse data the application of classical extreme value theory, such as Block-Maxima or Peak over Threshold might be invalid. Instead we use a version of the metastatistical extreme value distribution (MEVD), which was shown to work reasonably well in the context of extreme precipitation events, even with a rather small number of available years used for the estimation in comparison to the recurrence time. More precisely we make an assumption about the underlying probability distribution of the daily maximum POH values. The parameters of the distribution are then modeled as smooth functions of the day of the year and the year of observation, thus employing the framework of generalized additive models for location, scale and shape (GAMLSS). Furthermore we add topographic information (longitude, latitude, altitude) to our model, resulting in a full spatiotemporal model across the whole domain of Austria, from which the return values of the POH, respectively MEHS are calculated.

This framework allows for the incorporation of an arbitrary number of additional covariables, as long as they are available on the same grid as the desired output. To illustrate this we use the information of daily precipitation extremes to enrich the model with additional atmospheric information.

How to cite: Falkensteiner, M.-A., Ehrensperger, G., Simon, T., and Hell, T.: Modelling severe hail events over Austria using the metastatistical extreme value distribution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14903, https://doi.org/10.5194/egusphere-egu23-14903, 2023.

In Indian Himalayas, many hydroelectric projects are now under construction due to the availability of a perennial water source and a natural head for hydropower generation. Hydropower plants often require significant investments, design lifetimes, and lengthy repayment. Indian Himalayan states are now developing State Action Plans on Climate Change, with policies for climate change mitigation and adaptation at the subnational level. These plans recognize GLOFs as a significant climate change-related flood to be considered for the safety of River Valley Projects. The snow-fed catchment area of these projects has many glacial lakes, and there is a high likelihood of breaching for lakes located at the glacier's snout. In general, potentially dangerous lakes are located near the end of a glacier in the lower part of the ablation area. A large mother glacier can create potentially hazardous lakes. These moraine dams could likely breach due   to   piping   or   overtopping   due   to   their porous soil content inside dam body. A sudden discharge of significant magnitude could endanger the safety of the downstream HE hydroelectric project. It is suggested, the glacial lake outburst flood (GLOF) and the design flood be simultaneously considered while assessing the spillway capacity of new hydropower projects to ensure that they are hydrologically secure.

Bajoli-Holi Hydroelectric Project, located on river Ravi in the Himachal Pradesh state of India, is studied, to analyze its spillway capacity considering both GLOF and Inflow Design flood. BIS published the guidelines for fixing spillway capacity. As per the codal provisions, the Bajoli-Holi dam qualifies for PMF as its Inflow design flood.

The hydrology of a particular basin or project undergoes certain changes due to factors such as climate change, urbanization, deforestation, soil erosion, a heavy spell of short-duration rainfall, etc. With the aid of the most recent methods, including hydrodynamic modeling and a hydro meteorological approach, the design flood and GLOF for the dam have been evaluated in this study.

There are a total of 83 glacial lakes identified and mapped in this catchment area. It is further critically analysed to find the effect of the most critical glacial lake which is glacial Lake-52 having an area of 14.5 ha at a distance of 26.5km from the project location. River cross sections spaced 400 m apart has been considered. The upper envelope of the PMF is calculated to be 15,303 cumecs, average envelope is 6247cumecs and the lower envelope value is 2551 cumecs. The combined GLOF peak attenuated after hydrodynamic channel routing at the project site and the PMF analysed, will be taken as the inflow flood for analyzing the spillway requirements for the Bajoli-Holi project. The study results can be applied to similar hydro-meteorologically similar basins of the Himalayas in India which are under the influence of glacial lake outbursts and PMF.

How to cite: Issac, I., Goel, D. N. K., and Rai, N.: Approach and methodology for estimating combined glacial lake outburst flood (GLOF) and PMF design flood for Bajoli Holi hydro-electric project in the Indian Himalayas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15819, https://doi.org/10.5194/egusphere-egu23-15819, 2023.

Flash droughts are generally considered a subset of seasonal drought events. In the present study, we have characterized the flash drought events based on soil moisture index (SMI) using daily ERA5 reanalysis data having a spatial resolution of 0.250 * 0.250 from 1960 till 2021. Flash drought events were identified when SMI drops below the 20th percentile within less than 3 next pentads, and it terminates when SMI goes above the 20th percentile and stays there for the next 2 pentads. Flash drought time series was prepared and the Mann-Kendall trend test was applied to investigate the evidence of the statistically significant trends. To assess the atmospheric drivers (precipitation, PET) of flash drought, standardized precipitation index (SPI), and standardized precipitation evapotranspiration index (SPEI) were calculated during the occurrence of each flash drought event at each grid pixel. For calculating SPI and SPEI, ERA5 reanalysis data of precipitation and PET (potential evapotranspiration) was used. Seasonal analysis of results showed that the flash drought frequency observed during the pre-monsoon season (March-April-May) shows considerable variation when compared to the monsoon (July-August-September) and post-monsoon (October-November-December) seasons. Results of Mann-Kendall statistics show the increasing trend of flash drought over semi-arid regions like Marathwada and Vidarbha. Both SPI and SPEI shows spatially varying similarity with the flash drought events. When observed on a seasonal scale, it is observed that SPEI shows a higher degree of similarity with flash drought events during pre-monsoon season as compared to SPI as evaporative demand is high during this period.  

How to cite: Remesan, R. and Pachore, A.: Analysis of Spatio-temporal variability and atmospheric drivers of the flash drought over Godavari river basin., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15836, https://doi.org/10.5194/egusphere-egu23-15836, 2023.

EGU23-16630 | ECS | Orals | HS7.5

Projection of future rainfall erosivity over China under global warming 

Wenting Wang, Shuiqing Yin, Zeng He, Deliang Chen, Hao Wang, and Andreas Klik

Five CMIP6 models were selected to project changes in rainfall erosivity of China for two future periods (the near-term in 2041-2065, the long-term in 2076-2100) under SSP1-RCP2.6 and SSP5-RCP8.5 scenarios. Models’ capacity in estimating two erosivity indices, annual average rainfall erosivity (R-factor) and the storm erosivity at 10-year return level (10-year storm EI) were evaluated by comparing the model derived indices for the historical period with the state-of-the-art reference erosivity maps of China interpolated with hourly observations. Results show that GFDL-ESM4, IPSL-CM6A-LR, and UKESM1-0-LL outperform the other two models with higher NSEs and better spatial correlation, especially in the water erosion regions. R-factor and 10-year storm EI estimated using MMEs (the arithmetic means of the aforementioned three models) for the historical period are generally underestimated, and the median biases are 0.80 and 0.66, respectively. Biases for each grid were determined as the bias-correction factors for future erosivity projection. Generally, most areas in eastern and central China are expected to experience larger rainfall erosivity. Under SSP1-RCP2.6 and SSP5-RCP8.5 scenarios, R-factor over mainland China is projected to increase by 18.9% and 19.8% for the near-term and 26.0% and 46.5% for the long-term, respectively; and 10-year storm EI is projected to increase by 14.2% and 17.4% for the near-term, and 14.9% and 45.0% for the long-term, respectively. The projected increases in rainfall erosivity are mainly due to the increasing probability of extreme precipitation. This implies that soil and water conservation measures in China need to be further strengthened to meet the challenges brought by the increasing number and magnitude of extreme events in the context of global warming.

How to cite: Wang, W., Yin, S., He, Z., Chen, D., Wang, H., and Klik, A.: Projection of future rainfall erosivity over China under global warming, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16630, https://doi.org/10.5194/egusphere-egu23-16630, 2023.

EGU23-16753 | Orals | HS7.5

Dry and wet climatic change and its driving factors in China 

Jie Tang, Wenting Wang, and Yun Xie

Evaluating the characteristics of long-term dry and wet climate changes under the background of global climate change is important for regional water resources security, ecosystem security and socio-economic development. Based on the daily meteorological data of 1680 meteorological stations in China from 1971 to 2019, the reference evapotranspiration (ET0) was estimated with the FAO-56 Penman–Monteith equation. Based on which, the temporal and spatial variations of humidity index (HI), precipitation (P), reference evapotranspiration (ET0) and the driving factors of which were further analyzed. Results showed that HI significantly increased in the northwest China of arid area, the northeast China of subhumid area and the Huang-Huai region of humid area, while it significantly decreased in the southwest and southeast China of humid areas. The change of HI can be mainly attributed to the change of ET0 while no significant trends has been detected for P for most regions of China. During the past 50 years, the increasing rate of ET0 was 3.76 mm/10a. But the temporal variation of ET0 are different from regions, and the increasing and decreasing area were mainly dominated by climate different factors. For region of Huang-huai and northern Northeast China, ET0 showed significant downward trend. Among factors that relating to ET0, wind speed contributes most to the significant decrease of ET0. For all rest regions of China, ET0 showed significant upward trends, and relative humidity contribute most to the increase.

 

Key words: Dry and wet climatic change, humidity index, reference evapotranspiration, contribution, climatic factors.

How to cite: Tang, J., Wang, W., and Xie, Y.: Dry and wet climatic change and its driving factors in China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16753, https://doi.org/10.5194/egusphere-egu23-16753, 2023.

EGU23-17047 | Orals | HS7.5 | Highlight

A just map: community and fluvial science working together for flood hazard vulnerability mapping in Massachusetts 

Christine Hatch, Seda Salap-Ayca, Christian Guzman, and Eve Vogel

In the Northeastern U.S., the most costly damages from intense storm events were impacts to road-stream crossings.  In steep post-glacial terrain, erosion by floodwater and entrained sediment is the largest destructive force during intense storms, and the most likely driver of major morphological changes to riverbanks and channels.  Steam power analysis is a tool that can successfully quantify floodwater energy that caused damages, however, prediction of which reaches or watersheds may experience future impacts remains uncertain. Downstream, in urban areas, floodwaters increasingly occupy larger geographic extents that spill well beyond traditionally mapped flood and hazard zones. Limiting these maps are critical biases: Often more information is available for coastal and urban areas (missing steeper terrain geomorphic hazard zones), base functional assumptions (that flood risk is dominantly inundation risk from a specific depth of water, ignoring the force of moving water, sediment or erosion), their concentration around the highest-value infrastructure (lower-value and lower-density development or undeveloped areas have little or no map coverage) and how these maps are utilized for regulatory purposes (e.g. mortgage and insurance requirements). Compounding the physical destruction of flooding is the unequal distribution of these impacts on socially vulnerable populations that are least able to recover from them.  We strive to improve the co-generated mapping of social vulnerability and flood risk by (1) utilizing measures of social vulnerability with greater social and geographical insight and nuance, including self-organizing maps (SOM) that cluster overlapping metrics, (2) applying modified flood hazard maps that accurately represent fluvial geomorphic hazards, urban flooding hazards, and climate change considerations, and (3) overlapping these to understand what factors influence current maps and policy practice; what populations and places may be overlooked or under-resourced relative to vulnerability; and use this collective insight to help inform and develop improved map products and policy approaches.  Integration of this information directly with practitioners’ resources allows communities to prioritize and make land-use decisions and flood-response and preparedness decisions that are informed by the specific vulnerabilities of their populations as well as the fluvial geomorphic workings of the larger watershed, and that have powerful local implications.  Outreach and educational programs focused on social vulnerability and fluvial systems for river practitioners and politicians at all levels align communities’ attitudes about flooding and rivers can ultimately result in ecologically sound, socially just, and more flood resilient policies and practices.

How to cite: Hatch, C., Salap-Ayca, S., Guzman, C., and Vogel, E.: A just map: community and fluvial science working together for flood hazard vulnerability mapping in Massachusetts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17047, https://doi.org/10.5194/egusphere-egu23-17047, 2023.

The Sikkim Himalaya, similar to other mountain regions, has lost considerable ice cover over the years owing to the changing climatic factors leading to enlargement of glacier-fed lakes, and thus posing a potential threat to downstream communities in the mountain and Tarai (foothills) region in case of breach anytime in the future. The Chhombo Chhu Watershed (CCW) of the Tista Basin in the Sikkim Himalaya, located between the Greater Himalayan Range and the Tethyan Sedimentary Sequence, is the storehouse of number of glacial lakes with large areas and volumes. In this study, we mapped the glacial lakes' changes between 1975–2018 and assessed their dynamics based on manual analysis of optical satellite images using KeyHole-9 Hexagon (∼4 m), Landsat Series (∼15-30 m), and Sentinel 2A-MSI (∼10-20 m) imagery and verified during field surveys. The results show that the number of lakes has increased from 62 to 98, and its total area expanded significantly by 34.6 ± 5.4%, i.e., from 8.5 ± 0.2 km2 in 1975 to 11.4 ± 0.6 km2 by 2018, at an expansion rate of 0.8 ± 0.1% a–1. Lake outburst susceptibility result reveals that a total of twenty-seven potentially dangerous glacial lakes exist in the watershed; 5 have a status of ‘high’ outburst probability, 17 ‘medium’ and 5 ‘low’. The majority of the proglacial lakes in the watershed have significantly enlarged due to the faster melting and calving processes as a result of accelerating increasing long term average annual trend of temperature (+0.283° Ca–1; 95% confidence level) and homogeneous or slightly declining precipitation.

How to cite: De, S. K., Chowdhury, A., and Sharma, M. C.: Inventory, Classification, Evolution, and Potential Outburst Flood Assessment of Glacial Lakes in the Chhombo Chhu Watershed (Sikkim Himalaya, India), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17459, https://doi.org/10.5194/egusphere-egu23-17459, 2023.

Of all the natural resources available to humankind, water holds a prominent place, particularly because of its importance for human livelihood. Savelugu district in northern Ghana is characterized by unpredictable rainfall patterns with periodic and perennial water shortages. The distance people travel to fetch water and the person-hours spent in search for water affect productivity, economic livelihood, and health and education benefits. Provision of potable water supply to these communities is expected to bring not only health, education benefits but also increase in sanitation and hygiene practices. Static water levels (SWLs) of 19 wells in the study area were collected, analyzed and compared to the initial SWLs measured when the wells were immediately drilled and constructed. The SWL data was subjected to paired samples T-test (with α = 0.05). From the results, there was significant difference in the SWL immediately after drilling and construction (µ = 12.15, σ = 7.50) and SWL after at least 10 years (µ = 17.81, σ = 10.29); t (18) = -3.7, P = 0.002. Lowered groundwater levels were recorded in all wells measured. This can lead to drying up of some of the wells whose difference between the current SWL and well depth is close. There must be strong advocacy, development and implementation of IWRM plans to help address the problem of inadequate WASH in the study area.

How to cite: Acheampong, A.: Lowering of groundwater levels and their effect on Water, Sanitation and Hygiene services in the Savelugu District, Northern Region of Ghana, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-139, https://doi.org/10.5194/egusphere-egu23-139, 2023.

Agriculture, in general, has a long production cycle and is affected by many endogenous and exogenous uncertainty factors. Changes in rainfall patterns, maximum or minimum temperature, types and amounts of fertilizer input, timing, availability of irrigation water, and soil quality can drastically change the agricultural yield. In developing countries such as India, where more than half of countries population is engaged in agriculture, and the whims of nature may affect the agricultural output, it is essential to check how the entire agricultural system reacts to the changes in climatic parameters and anthropogenic practices. This study analyses agricultural trends in four primary staple crops, trends in climatic parameters, and anthropogenic inputs in Indian districts. Significant trends were detected and quantified using the non-parametric Mann-Kendall (MK) test, modified MK test, and Theil-Sen estimator at a 5% significance level. Spearman’s correlation test is used to determine the contributing factors to the changes in agricultural yield. Rice, Wheat, Pearl Millet, and Maize yields have shown significant increasing trends in a large number of the districts. Despite decreases in the gross cropped area in the majority of the districts, the trends in production are mostly positive. According to Spearman’s Rho correlation test, the increase in fertilizer consumption in most districts and the increase in crop-wise irrigated land in many districts are the significant reasons for the increase in yields. The rainfall did not change much compared to maximum and minimum temperatures at both the annual and seasonal levels. Although there were significant climatic changes in the last three decades, the correlation with agricultural yield is mostly insignificant.

How to cite: Sarkar, N. and Ray, S.: Analysis of Agricultural and Climatic trends in Indian Districts and finding the contributing factors in recent Indian Agricultural Outputs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-653, https://doi.org/10.5194/egusphere-egu23-653, 2023.

EGU23-1916 | ECS | PICO | HS7.3

Modeling the potential of management options to reduce irrigation demand in Western Switzerland 

Malve Heinz, Christoph Raible, Bettina Schaefli, and Annelie Holzkämper

European Agriculture is experiencing the consequences of summer droughts and heatwaves in form of quality and quantity losses for numerous crops and feed production. Water availability for irrigation in the vital summer and fall months is decreasing and therefore, irrigation will most likely not be able to sufficiently mitigate the effects of droughts and heat in the future. Thus, approaches that reduce the need for irrigation are required. We investigate potential water-use reduction strategies based on a modelling framework applied to a selected case study in Western Switzerland, the Broye catchment. The region is characterized by intensive agricultural use and drought-related irrigation bans in summer. In the first step of our project, we quantify the total irrigation demand under current and future climate conditions using the soil-water-atmosphere-plant model SWAP. SWAP mainly simulates water and solute flow in soil as well as vegetation growth by solving a set of equations such as the Richards equations. Irrigation demand is quantified by applying this 1D model to the full range of climatic, soil and land use conditions prevailing in the selected catchment. The model calculates both the irrigation requirements and the yield of various irrigation-intensive crops currently grown in the region, such as potatoes, maize, or sugar beet. In a second step, we use the model to assess the efficiency of different management options to reduce the water demand, such as mulching, organic amendments, biochar application, different tillage methods or the cultivation of better-adapted crops. In future work, we will couple the field-scale model to a catchment-scale rainfall-runoff model to assess the impact of a large-scale application of such measures on the water balance of the catchment.

How to cite: Heinz, M., Raible, C., Schaefli, B., and Holzkämper, A.: Modeling the potential of management options to reduce irrigation demand in Western Switzerland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1916, https://doi.org/10.5194/egusphere-egu23-1916, 2023.

EGU23-2603 | PICO | HS7.3

Rainwater harvesting as climate change adaptation strategy for durum wheat production in Sardinia 

Francesco Viola, Roberto Deidda, Salvatore Urru, and Elena Cristiano

The Mediterranean region is widely recognized as a climate change hotspot, where, mainly due to the increase of CO2 concentration, both historical records and future climate models’ projections reveal an increase of the daily average temperature and a reduction of the mean annual precipitation, with less frequent but more intense rainfall events. These changes could have strong impacts on the durum wheat production, and consequently to the food chain that derives from it. Water availability is expected to be the main limiting factor in the durum wheat growth, which is usually rainfed in Mediterranean region. On the other hand, CO2 increase may act as a counterbalance factor, by increasing the water use efficiency. In this work, within the framework of the H2020 European Union project ARSINOE (“Climate-resilient regions through systemic solutions and innovations”), we investigated the possibility to adapt durum wheat production to climate changes, compensating the rainfall reduction with emergency irrigation derived from a rainwater harvesting system, with the aim to keep constant the durum wheat production or alleviate the yield reduction. The Aquacrop model, a crop growth model developed by FAO’s Land and Water Division, has been calibrated to reproduce the actual durum wheat production in the Campidano region in Sardinia (Italy), implementing the local climate and soil characteristics. The model has been then used to simulate the crop production in correspondence of different bias corrected future climate scenarios, which foreseen an average rainfall reduction and increase of average temperature and CO2 concentration in the atmosphere. A rainwater harvesting system to collect rainfall from the rooftops or impervious surface within the cultivated area (100m2/ha) has been designed and the volume for potential emergency irrigation has been estimated year by year. Preliminary results show the importance of implementing rainwater harvesting systems to provide emergency irrigation and sustain durum wheat production in a context of climate changes.

Acknowledgments

This project has received funding from the European Union’s Horizon H2020 innovation action programme under grant agreement 101037424.

How to cite: Viola, F., Deidda, R., Urru, S., and Cristiano, E.: Rainwater harvesting as climate change adaptation strategy for durum wheat production in Sardinia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2603, https://doi.org/10.5194/egusphere-egu23-2603, 2023.

With the impact of climate change and the main rainfall seasons in Taiwan are concentrated in the plum rain season from May to June and the typhoon season from July to September each year.There are significant differences in rainfall and spatial and temporal distribution between the wet season and the dry season,the droughts will occur and even lead to severe water shortages, such as the worst drought in half a century in 2021.From a macroscopic spatial scale, for example, the El Niño phenomenon and solar activity may have a certain impact on the overall climate and water resources of the earth.Therefore, this study analyzes the correlation between rainfall and large-scale influencing factors such as sunspots, El Niño-Southern Oscillation,and uses machine learning models to predict and classify rainfall under different conditions,the prediction accuracy rate through historical data can reach 89.9% , with sunspots as the most significant factor. It is hoped that relevant units can provide reference for water resources management and planning.

How to cite: Weng, Z.-H. and Lin, Y.-C.: Establishing a macroscopic-scale rainfall climate and water resources estimation model by machine learning method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3008, https://doi.org/10.5194/egusphere-egu23-3008, 2023.

EGU23-3528 | ECS | PICO | HS7.3

Effects of heat and drought stress and their co-occurrence on winter wheat yields in Germany under climate change 

Rike Becker, Bernhard Schauberger, Ralf Merz, Stephan Schulz, and Christoph Gornott

In our changing climate, heatwaves and droughts and their spatio-temporal co-occurrences are likely to intensify. This will inevitably challenge future agricultural production and calls for adaptation strategies to protect future yields. To find suitable climate adaptation strategies for Germany’s major staple crop - winter wheat - it is important to know how heat stress, drought stress or their compound effects drive wheat yield failures. The principal aim of this study is, therefore, to quantify the impacts of heat, drought, and their compound effects on winter wheat yields in Germany, in a spatially and temporally discrete manner.

To address our aim, we develop a statistical crop-climate model for the time period 1991-2019 at the county level. We first create agroclimatic proxies for heat stress, drought stress and their compound effects and use these to construct a separate time series model with the addition of time-dependent interaction terms. Our approach constructs separate regression models for each county, based on common elements that allow for comparing and jointly interpreting individual models.

Preliminary results show that more than 50% of Germany’s wheat yield variability can be explained by climate effects. Compound effects of heat and drought stresses are responsible for approx. 42% of the variability in Germany’s winter wheat yields. Drought stress alone explains approx. 7%, with higher impacts in the east of the country, and heat stress alone explains approx. 3% of the year-to-year yield variability, with higher impacts in the north-west of Germany. The results confirm the importance of compound effects and underline their dominating impacts on winter wheat yields, when compared to individual heat and water stress impacts – a finding which should guide future adaptation strategies. Furthermore, our study shows that heat stress is becoming increasingly important for wheat yield failure in Germany – alone and in conjunction with moisture stress.

In conclusion, we suggest that climate change adaptation strategies for winter wheat in Germany should focus on combined measures against drought and heat extremes. While the increase of multi-stress resilience should be the main goal for entire Germany, north-western areas should prioritize strategies to increase heat resilience and eastern areas should prioritize strategies to increase drought resilience.

How to cite: Becker, R., Schauberger, B., Merz, R., Schulz, S., and Gornott, C.: Effects of heat and drought stress and their co-occurrence on winter wheat yields in Germany under climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3528, https://doi.org/10.5194/egusphere-egu23-3528, 2023.

current legislation requires the inspection and calibration of operational survey radiation monitoring instruments used in nuclear medicine and radiotherapy departments as well as in any field that uses ionizing radiation sources. As a result, Morocco's national secondary standard dosimetry laboratory provides reliable calibration results with high accuracy while adhering to national and international radiation protection standards and covering the various measurement ranges, using the attenuators offered by the automated Gamma G10 irradiator or the validated beam qualities produced by the X-ray irradiator type X80-320kV as required. The measurements’ reliability was demonstrated by participation in a comparison program launched by the International Atomic Energy Agency (IAEA).

This work aims to develop a digital graphical user interface designed for the calibration of measuring instruments in radiation protection through the programming language Python, which serves to facilitate the establishment of all operations and calculations related to the determination of calibration factors and measurement uncertainties according to the ISO 4037 standard in a minimum time that allows to process several instruments during the day with high accuracy, while minimizing the sources of errors, this interface allows the recording of calculations as well as the establishment and electronic archiving of the calibration certificate in pdf format ported from PHP FPDF.

How to cite: Belhaj, O. E., Boukhal, H., and Belhaj, S.: Digital graphical user interface as a facilitator for the calibration of radiation monitoring instruments according to ISO 4037:2019 at the national secondary standard dosimetry laboratory of morocco, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4503, https://doi.org/10.5194/egusphere-egu23-4503, 2023.

Groundwater is an essential source of water in Taiwan, and its long-term overuse has resulted in water resource problems that have become a potential crisis in the Zhuoshui River Basin. This overuse of groundwater may also lead to subsidence, which can have significant consequences for the area and its infrastructure. The lack of complete observations of groundwater extraction in Taiwan due to historical factors has made it difficult to accurately understand and manage the amount of water being taken, particularly for agricultural purposes.In view of this, this study uses time series data from 87 agricultural groundwater wells in Huwei Town, Yunlin County from January 2016 to July 2017, and time series data on agricultural well electricity usage in the Changshui River Basin, combined with other attribute data, to understand farmers' water pumping behavior using data mining methods and to estimate the amount of water taken in the Huwei area using machine learning.This study obtained the spatial and temporal distribution of groundwater withdrawals in the Huwei area in 2018.

How to cite: Tseng, Y. K. and Yu, H. L.: Using Time Series Data and Machine Learning Estimating Agricultural Groundwater Extraction in Huwei Town, Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5173, https://doi.org/10.5194/egusphere-egu23-5173, 2023.

EGU23-5551 | ECS | PICO | HS7.3

Probabilistic modelling of water distribution networks and resilient reduction of leakages: Large scale application to the city of Patras in western Greece 

Athanasios V. Serafeim, George Kokosalakis, Roberto Deidda, Nikolaos Th. Fourniotis, Irene Karathanasi, and Andreas Langousis

Modeling of leakages in Water Distribution Networks (WDNs) is a vital task for all water related professionals and experts towards the development of management practices and strategies, which aim at the reduction of water losses (leakages) and the associated financial cost and environmental footprint. In the current work we develop an integrated, theoretically founded, and easily applicable probabilistic framework for resilient reduction of leakages in WDNs, which combines: a) a set of conceptually and methodologically different probabilistic approaches for minimum night flow (MNF) estimation in WDNs based on statistical metrics (Serafeim et al., 2021 and 2022a), and b) a combination of statistical clustering and hydraulic modeling techniques for the rigorous and user unbiased partitioning of WDNs into pressure management areas (PMAs) or district metered areas (DMAs), which seeks for minimization of leakages while maintaining an acceptable level of the network’s hydraulic resilience (Serafeim et al., 2022b). The efficiency of the introduced framework is tested via a large-scale real-world application to the water distribution network of the City of Patras, the largest smart water network (SWN) in Greece, which covers an area of approximately 27 km2 and serves more than 213000 consumers (based on data from the Hellenic Statistical Authority and the Municipality of Patras), with more than 700 km of pipeline grid (mainly HDPE and PVC pipes).

Acknowledgements

The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 1162).

References

Serafeim, A.V., Kokosalakis, G., Deidda, R., Karathanasi I. and Langousis A (2021) Probabilistic estimation of minimum night flow in water distribution networks: large-scale application to the city of Patras in western Greece, Stoch. Environ. Res. Risk. Assess., https://doi.org/10.1007/s00477-021-02042-9.

Serafeim, A.V., G. Kokosalakis, R. Deidda, I. Karathanasi and A. Langousis (2022) Probabilistic Minimum Night Flow Estimation in Water Distribution Networks and Comparison with the Water Balance Approach: Large-Scale Application to the City Center of Patras in Western Greece, Water, 14, 98, https://doi.org/10.3390/w14010098.

Serafeim, A.V., G. Kokosalakis, R. Deidda, N. Th. Fourniotis and A. Langousis (2022) Combining statistical clustering with hydraulic modeling for resilient reduction of water loses in water distribution networks: Large scale application to the city of Patras in Western Greece, Water, 14(21), 3493. https://doi.org/10.3390/w14213493.

 

How to cite: Serafeim, A. V., Kokosalakis, G., Deidda, R., Fourniotis, N. Th., Karathanasi, I., and Langousis, A.: Probabilistic modelling of water distribution networks and resilient reduction of leakages: Large scale application to the city of Patras in western Greece, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5551, https://doi.org/10.5194/egusphere-egu23-5551, 2023.

EGU23-5567 | PICO | HS7.3

A probabilistic approach for detection and classification of PRV malfunctions in the water distribution network of the city of Patras in western Greece 

Anastasios Perdios, George Kokosalakis, Nikolaos Th. Fourniotis, Demetris Pantzalis, and Andreas Langousis

Effective management of water losses in water distribution networks (WDNs) still remains a demanding task, as the temporal and spatial variability of water resources under changing climatic conditions and the increasing needs for drinking water may lead to freshwater shortages. In this context, pressure management strategies are widely adopted in an effort to reduce the water losses in the supply and distribution parts of water networks and, consequently, deescalate their environmental footprint. Installation of pressure reducing valves (PRVs) at critical locations of WDNs plays a central role in pressure regulation strategies, as PRVs reduce the upstream pressure to a set outlet pressure (i.e., downstream of the PRV), usually referred to as set point. Perdios et al. (2022) developed a novel statistical framework and applied it to an existing pressure management area (PMA) of the city of Patras in western Greece, aiming at early detection of PRV malfunctions that may significantly influence network’s operation and the corresponding lifetime of related infrastructure. The results showed that the suggested methodology allows reliable detection of critical malfunctions at least 2 days prior to flow disruptions. Ιn this study, we calibrate and implement Perdios et al. (2022) statistical framework, using pressure data for a 4-year period from 01/Jan./2017 to 26/Nov./2020 from several important PMAs of the WDN of the city of Patras, aiming towards better understanding of the causes of the malfunctions, by decomposing the observed pressure deviations from the set point to systematic and random error components.

Acknowledgements

The research work has been conducted within the project PerManeNt, which has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation under the call RESEARCH – CREATE – INNOVATE (project code: T2EDK-04177).

Reference

Perdios A., G. Kokosalakis, N. Th. Fourniotis, I. Karathanasi and A. Langousis (2022) Statistical framework for the detection of pressure regulation malfunctions and issuance of alerts in water distribution networks, Stoch. Env. Res. Risk Asses., https://doi.org/10.1007/s00477-022-02256-5

How to cite: Perdios, A., Kokosalakis, G., Fourniotis, N. Th., Pantzalis, D., and Langousis, A.: A probabilistic approach for detection and classification of PRV malfunctions in the water distribution network of the city of Patras in western Greece, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5567, https://doi.org/10.5194/egusphere-egu23-5567, 2023.

EGU23-10057 | ECS | PICO | HS7.3

Building a smart green system to control water leakage and monitor drinking water quality in the water supply system of Paramythia city, Greece: the case of SMASH project 

Angelos Chasiotis, Stavroula Tsitsifli, Konstantinos Panytsidis, Vegard Nilsen, Nikolaos Mantas, Dimitrios Theodorou, Thomas Kyriakidis, Stefanos Chasiotis, Maria Bousdeki, Elissavet Feloni, Harsha Ratnaweera, Panagiotis Nastos, and Malamati Louta

Water leakage is acknowledged as one of the most important issues that drinking water supply systems are facing worldwide. Non-Revenue Water is estimated to 346 million m3 per day and its cost/value is estimated to 39 billion USD per year. At the same time drinking water quality is jeopardized from the water intake points to the consumer’s tap, even during normal operating conditions.

ICT support water utility operators to improve the operational capacity of their water supply system. A smart green system to control water leakage and monitor drinking water quality in the water supply system of Paramythia city will be built in the context of SMASH project. It consists of: (a) IoT system comprising three local control stations, installed in selected parts of the water supply network, monitoring water quantity&quality parameters in real time; (b) the hydraulic simulation model of the water supply system of Paramythia; (c) a virtual sensors system, which will be used for water quality prediction; (d) a Decision Support System (DSS) for leakage detection and optimal management of water supply system parameters in an automated manner.

The DSS will detect and locate water leakages within the DMA zone and inform the operators for excessive values in drinking water quality parameters. The DSS will use as inputs the data from the IoT system, will interact with the hydraulic simulation model, and obtain the water quality data from the virtual sensors. All these data will be processed by the DSS logic in the backend subsystem. The IoT and the hydraulic simulation data, based on the digital twin of the water supply system, are used for the calculation of specific performance indicators related to water leakage, such as well-known IWA indicators: water losses, ILI, etc. Calculating the divergences between the PI values observed & the ones representing the optimal operation of the water network without leakages and setting appropriate thresholds, the DSS will detect the leakage, while several different scenarios will run in hydraulic simulation. The frontend subsystem of the DSS will be able to visualize the water distribution network, statistical values of water quantitative & qualitative parameters. It will provide alarms in case of leakage or exceedance of water quality parameters’ values and it will show the leakage location in a map. The architecture of the smart green system, currently under development, is depicted in Fig.1.

Figure 1. The DSS for the water parameters management in the water supply system

Keywords: Drinking water; water quality; leakage; virtual sensors; smart system; decision support.

Acknowledgement: This work is co-financed by EEA Grants 2014 – 2021 and Greek Public Investments Program.

  • Liemberger, R., & Wyatt, A. (2019). Quantifying the global non-revenue water problem. Water Supply19(3), 831-837.
  • Antzoulatos G., Mourtzios C., et al (2020), Making urban water smart: the SMART-WATER solution. Water Science & Technology, 82(12), 2691–2710.
  • Alegre, H., Baptista, et al (2016). Performance indicators for water supply services. 3rd IWA publishing.

How to cite: Chasiotis, A., Tsitsifli, S., Panytsidis, K., Nilsen, V., Mantas, N., Theodorou, D., Kyriakidis, T., Chasiotis, S., Bousdeki, M., Feloni, E., Ratnaweera, H., Nastos, P., and Louta, M.: Building a smart green system to control water leakage and monitor drinking water quality in the water supply system of Paramythia city, Greece: the case of SMASH project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10057, https://doi.org/10.5194/egusphere-egu23-10057, 2023.

By calculating the water demand and programming a fine irrigation project, the management and cultivating efficiency of traditional agriculture can be greatly improved. Taking rotational irrigation for example, the efficiency of irrigation can be maximized by adjusting water distribution routes, irrigation area allocation, and irrigation schedule planning. However, in actual operation, some problems are often encountered, such as how to persuade farmers and promote the designed irrigation project, and the negotiation of various stakeholders. Generally, due to the complexity of the irrigation design model, it is impossible to have an effective and immediate communication or presentation. Therefore, this study introduces the Bayesian network to presents the key points of the irrigation project after simplifying the relationship. In addition to being simpler for stakeholders to understand, it is also possible to adjust various parameters in time to obtain rough estimation results.

The research area of this study is a 100-hectare farmland, which is located in Kinmen County, Taiwan. For many years, local farmers have only relied on precipitation to cultivate sorghum, wheat and other crops. However, the precipitation in Kinmen is semiarid and unstable. In the past five years, the annual rainfall has been lower than the average in previous years, which directly led to a very bleak crop harvest. Therefore, we hope to establish an irrigation project in Kinmen, using recycled water as the water source to provide local farmers with a reliable water source.

The Bayesian network used in this study is a directed acyclic graphical (DAG) model based on conditional probability and Bayesian theorem to express the possible relationship between variables. In terms of operation, the different influencing factors in the research topic are converted into nodes, and the relationship between nodes is given by different conditional probabilities. This study uses GeNIe to establish a Bayesian network that can be used to estimate water profit and loss and other results. This Bayesian network can be divided into four sub-blocks, which are the relevant data of the irrigation area, the water demand, the water supply, and the final result calculation. Therefore, when the stakeholders are negotiating the irrigation project, they can discuss the different estimation results by adjusting each node of the first three sub-blocks.

How to cite: Su, Y. and Yu, H.-L.: Application of Bayesian Network in Analysis and Management of Agricultural Water - Taking Kinmen for Example, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10515, https://doi.org/10.5194/egusphere-egu23-10515, 2023.

Assessing the Sustainability in Water Use under
Different Agricultural Management Planning
in Yeongsan-River Basin, South Korea

 

Yujong Jeong1, Hyun-woo Jo1, YanYan1, Minwoo Noh1, Woo-Kyun Lee1*

 

1 Department of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea

*E-mail: leewk@korea.ac.kr

(Address: Korea University, Anamro 145, Seongbukgu, Seoul 02841, Republic of Korea)

 

Abstract:

From the past, South Korea has been experiencing high level of water stress as reported by WRI, in 2013, and chronically imbalanced spatiotemporal water allocation. Yeongsan-river basin, where the biggest national breadbasket is located, is facing unequal water allocation among different water uses and inefficient water management under episodic water shortage conditions. Therefore, the main objective of this study was to analyse current water management and allocation scheme, and to evaluate 3 different agricultural management plans in terms of efficiency and equity. The Soil and Water Assessment Tool(SWAT) was applied to simulate the hydrological process and crop yield in the basin. The model was calibrated and validated using observed outflows to set adequate system parameters for the entire watershed. Crop water productivity and spatial-temporal-sectoral water distribution are utilized as the indices to evaluate different agricultural strategies. The results suggested that there was potential to improve both crop productivity and water allocation at the same time with the suggested plannings. Crop water productivity increased in all three strategies in order of on-farm management measures (precise agriculture), crop diversification (replacing rice to beans) and agroforestry (mixing trees and crops). The crop water productivity of on-farm measurement ranges from 5t/L to 13t/L and rises about 20% on average. In addition, it is found that applying the combination of different agricultural management measures could achieve better water allocation in terms of space and time, and between agriculture and ecosystem. The outcomes of this study can serve scientific-evidence policy and decision-making systems for sustainable agricultural society and ecosystem.

KeywordsHydrological Modelling, SWAT, Crop water productivity, Water allocation, Agricultural Management Planning, Yeongsan-River Basin

Acknowledgements: This work was supported under the framework of international cooperation program managed by the National Research Foundation of Korea (No. 2021K2A9A1A02101519).

 

 

How to cite: Jeong, Y.: Assessing the Sustainability in Water Use under Different Agricultural Management Planning in Yeongsan-River Basin, South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10943, https://doi.org/10.5194/egusphere-egu23-10943, 2023.

EGU23-10953 | ECS | PICO | HS7.3

Leveraging Hydroclimate and Earth Observation to Predict Grain Production in Sub-Saharan Africa 

Donghoon Lee, Frank Davenport, Shraddhanand Shukla, Laura Harrison, Greg Husak, Chris Funk, Michael Budde, James Rowland, Amy McNally, and James Verdin

The importance of forecasting agricultural production in Sub-Saharan Africa (SSA) is increasing for the management of agricultural supply chains, market forecasting, and targeting of food aid. In particular, agricultural forecasts enable governments and humanitarian organizations to respond more effectively to shocks in food production and price spikes resulting from extreme droughts. In this study, we use hydroclimate, earth observations (EO) and machine learning to develop an operational, sub-national grain production forecast system for a number of SSA countries, including food-insecure regions where rapid response is critical. Before creating the forecast, we collect and organize crop production data from the Famine Early Warning Systems Network in order to identify trends and variability in agricultural technology, climate, and vegetation. In addition, we demonstrate the capability of hydroclimate and EO data to capture favorable or unfavorable crop development conditions during the growing season. In addition, we demonstrate a unique capability that explains how EO characteristics influence current grain production forecasts, thereby enhancing the forecasts' reliability and efficacy. This research lays the groundwork for the development of a large-scale, operational crop yield forecasting system that will provide actionable predictions of food shocks for famine early warning and guide advanced preparedness and response strategies.

How to cite: Lee, D., Davenport, F., Shukla, S., Harrison, L., Husak, G., Funk, C., Budde, M., Rowland, J., McNally, A., and Verdin, J.: Leveraging Hydroclimate and Earth Observation to Predict Grain Production in Sub-Saharan Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10953, https://doi.org/10.5194/egusphere-egu23-10953, 2023.

EGU23-11183 | ECS | PICO | HS7.3

Implications of 1.50C global warming for agricultural productivity over a global rice exporting region in Central India 

Shoobhangi Tyagi, Sandeep Sahany, Dharmendra Saraswat, Saroj Kanta Mishra, Amlendu Dubey, and Dev Niyogi

Water, food, and energy security are the major climate risks of global warming. The Paris Agreement proposed an ambitious target of limiting the rise in global mean surface temperature to well below 20C, and preferably to 1.50C, compared to the pre-industrial era. However, the implication of this policy discourse on the agricultural system is imperative for ensuring food security in the face of global warming. This research focuses on understanding the changes in water availability and rice productivity under 1.50C global warming over a global rice-exporting semi-arid watershed in Central India. Towards this goal, the mean climate under 1.50C of global warming was computed for 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate models (GCMs). For each GCM, the corresponding changes in blue-green water availability and rice productivity at 1.50C warming period were estimated under two global warming scenarios (SSP2-4.5 and SSP5-8.5) based on the semi-distributed Soil and Water Assessment Tool (SWAT). Results suggest that the green and blue water is projected to change by ~ -20% to 10 and ~ -50 to 20%, respectively. The rice yield is projected to reduce in the range of 5% to 50%, with an increase in local temperature (~10C) and a decrease in local precipitation (~20%) being the limiting factor. This study provides useful information on when the 1.50C global warming could reach and how it can affect the agricultural productivity of semi-arid watersheds across different global warming scenarios. This study will help develop appropriate strategies to reduce/alleviate the impacts of global warming and foster food security at the watershed-scale.   

How to cite: Tyagi, S., Sahany, S., Saraswat, D., Mishra, S. K., Dubey, A., and Niyogi, D.: Implications of 1.50C global warming for agricultural productivity over a global rice exporting region in Central India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11183, https://doi.org/10.5194/egusphere-egu23-11183, 2023.

        Due to climate change, Taiwan's rainfall has become unstable in recent years, leading to short rainy seasons and low rainfall. In 2021, a severe drought occurred due to the lowest rainfall on record. Groundwater is essential for agricultural development, but less than 10% of wells are legal. Improper or excessive use of groundwater resources can cause serious disasters, such as sea intrusion and land subsidence. However, if the government and farmers extract groundwater effectively and sustainably, it will bring more flexibility to water management.

        In this study, a land subsidence model was conducted based on geological conditions and groundwater level. This study analyzes multi layer compaction monitoring well profiles, and further finds the correlation among the two main factors and subsidence. The goal of this study is to visualize which areas are more suitable for using groundwater and assist the government in water resource management. This study focuses on the Choshui river alluvial fan in Taiwan. A risk map of land subsidence for this area is made by evaluating two main factors, geological conditions and groundwater level.

How to cite: Su, S.-H. and Yu, H.-L.: Assessment of Land Subsidence based on Geological Conditions, Groundwater Levels in the Choshui River Alluvial Fan, Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11879, https://doi.org/10.5194/egusphere-egu23-11879, 2023.

EGU23-12693 | ECS | PICO | HS7.3

Photo-driven processes for the removal of biotoxins derived from Harmful Microalgal Blooms 

Javier Moreno-Andrés, Sandra Lage, Ana Catarina Braga, Leonardo Romero-Martínez, Asunción Acevedo-Merino, Enrique Nebot, and Pedro R Costa

Harmful Algal Blooms (HABs) are increasing in frequency and magnitude globally. These episodes are associated with the generation of biotoxins, which pose a potential risk to human and animal health. Biotoxins notably affect aquaculture activities and shellfish production, which has a clear impact on food and human health. Consequently, it is sometimes necessary to close the harvesting areas until the organisms are decontaminated. These natural detoxification mechanisms depend largely on the type of toxin and physiology of the organism, resulting in lengthy processes that can cause severe economic losses to aquaculture activities. As the main goal of this communication, we propose a technological alternative for the degradation of marine biotoxins through the implementation of UV technology as a treatment for agricultural, environmental, and health-related purposes. Therefore, advanced photochemical processes should be evaluated for the efficient degradation of marine biotoxins. The toxin selected was okadaic acid (OA), which is a very stable diarrheal toxin (DSP) and has a great impact on shellfish production areas, e.g. on the Portuguese coast. First, irradiation experiments were performed under UV-A, UV-B, and UV-C irradiation. In general, the concentration remained similar after different UV exposures, indicating that there was no observable photodegradation of OA after 3 h of UV irradiation, detecting a maximum degradation of 19.5% (± 0.95) in the UV-C region, suggesting that OA is clearly resistant to UV photodegradation. Second, the combined UV/H2O2, UV/HSO5, and UV/S2O82 − processes were tested. Two different UV sources were evaluated: LED and low-pressure lamps (LP), performing OA exposure in distilled water and seawater, with a maximum UV exposure of 3 h. In general, a clear degradation of OA is observed in photochemical processes in distilled water, with a slight decrease in efficiency in the UV/H2O2 process with an LED irradiation source. In the case of UV/S2O82 − and UV/HSO5, both the LP lamp and LED achieved a total degradation of OA. In the case of the marine matrix, the effect is clearly inhibited for the UV/H2O2 process; however, for UV/ HSO5, salinity does not seem to affect OA degradation, obtaining practically 100% removal. The study of new UV-LEDs would favor aquaculture activities by increasing sustainability and health safety. Likewise, the results obtained might provide the basis for a possible scale-up of technological processes specifically designed for the minimization of marine biotoxins.

How to cite: Moreno-Andrés, J., Lage, S., Braga, A. C., Romero-Martínez, L., Acevedo-Merino, A., Nebot, E., and Costa, P. R.: Photo-driven processes for the removal of biotoxins derived from Harmful Microalgal Blooms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12693, https://doi.org/10.5194/egusphere-egu23-12693, 2023.

EGU23-15429 | ECS | PICO | HS7.3

Effect of distance of crop canopy temperature observations on Crop Water Stress Index 

Aditi Yadav, Hitesh Upreti, and Gopal Singhal

The need for water management in the agriculture sector, which is a 70% consumer of global water resources, is imperative. For the same, a plant-based index called crop water stress index (CWSI) is widely being adopted for irrigation scheduling. An empirically derived CWSI is dependent on three parameters of canopy temperature (Tc), air temperature (Ta), and relative humidity (RH).This study was conducted by performing controlled crop experiments in the arid region of Uttar Pradesh state of India, which aims to evaluate the significance of height of Tc observations, taken from March to April 2022, on CWSI calculations for the wheat crop.This has been done by observing theTc by aiming the wheat crop from the top of the crown at two distances of 10 cm and 100 cm, respectively. Handheld remote sensingdevice known as infrared thermometeris used for the observation of canopy temperature. Variation in the height from 10 cm to 100 cm leads to a variation in the field of view from 51.28 sq. cm to 5128 sq. cm. The effect of enhanced area and the involvement of extra soiland vegetation pixels can be understood by this work. Five different irrigation regimes have been provided to study the effect of change in height for Tc observations. The regimes consist of five plots 1,2,3,4, and 5 with soil moisture depletion by the following percentage respectively: 50% in drip irrigation, 25% in drip irrigation, unregulated flood irrigation, 50% in flood irrigation, and no irrigation plot.Plot 2 has been used to formulatea lower baselinefor CWSI calculations. A lower baseline represents a non-water-stressed condition of the crop where the crop is provided with sufficient irrigation treatment leading towards negligible stress conditions. The lower baseline equations used for CWSI assessment for 10 cm and 100 cm height are -1.287(VPD) -2.19 and -1.214(VPD)-1.738, respectively. VPD represents vapor pressure deficit which is a function of Ta and RH. Upon increasing the height from 10 cm to 100 cm, Tc increased by 2.1%, 2.7%, 0.6%, 0.9%, and 1.3% for plots 1,2,3,4, and 5, respectively. This change in temperature led to a decrease in CWSI by 21.8%,36.4 %,9.2%, and 12.2% in plots 1, 2, 3, and 4 respectively. An increase in CWSI by 5.8% for a rise of 1.3% in Tc for plot 5 was also noted. Further coefficient of determination R2 was observed between CWSI at 10 cm height and CWSI at 100 cm height for all plots. It was observed to be 0.65, 0.50, 0.93, 0.93, and 0.87 for plots 1, 2, 3, 4, and 5, respectively. This study shows the effect of observation distance of crop canopy temperature on CWSI that can lead to the development of sampling procedures meant for CWSI studies.

How to cite: Yadav, A., Upreti, H., and Singhal, G.: Effect of distance of crop canopy temperature observations on Crop Water Stress Index, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15429, https://doi.org/10.5194/egusphere-egu23-15429, 2023.

Agricultural water use comprises the major part of the total water consumption in many countries, and Taiwan is no exception. However, urbanization and industrialization have triggered the competition for water among different sectors. Water is transferred to satisfy the daily need and industrial need, especially the need of high-tech industries, from the agricultural sector. Groundwater hence becomes an alternative water resource for agriculture, but the over-exploitation of groundwater resources also leads to some problems such as environmental degradation and land subsidence, and climate change has worsened the situation in the recent years.

In Taiwan, groundwater is one of the vital water resources for irrigation, especially when the first crop rice begins being cultivated in the late dry season in central Taiwan. Yunlin County located in central Taiwan is chosen as the study area, which is now facing severe issues about groundwater over-exploitation and suffering from land subsidence threatening the safety of Taiwan High Speed Rail. Because of the high water consumption, groundwater extraction from agriculture is deemed to be the major cause of the land subsidence and should be well monitored and reduced. However, farmers’ pumping behaviors are highly related to the national water allocation policy, food policy and the socioeconomic factors in the rural area. The top-down agricultural water management might not be sufficient and sustainable. Hence, in this study, we propose a participatory framework for agricultural water management using a Bayesian network. The framework tries to incorporate the main factors that affect decision making among different stakeholders, including the Water Resources Agency, Irrigation Agency, Agriculture and Food Agency, farmers, etc., and represent the causal relationship among factors through Bayes’ theorem, or the conditional probability tables (CPTs). The CPTs are constructed based on data, literature reviews and interviews with stakeholders. The key issues concerning different stakeholders are considered in the framework as well, such as surface water shortage for agriculture, land subsidence, and sustainability of agriculture in Yunlin. The network can be used to hold discussions with stakeholders and show the interactions of their decisions among others. The aim of this framework is to facilitate the discussions and formulate the strategies for sustainable agricultural water management with the aid of the intuitive and transparent structure of the Bayesian network.

How to cite: Lee, S.-Y. and Yu, H.-L.: Using Bayesian network to build a participatory framework for sustainable agriculture water management in Yunlin, Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15459, https://doi.org/10.5194/egusphere-egu23-15459, 2023.

Irrigation plays a crucial role in alleviating the negative effects of drought on crop production. However, increasing competition for water by other sectors, such as industry and domestic use, increases the pressure on available water supplies. Under these circumstances, agricultural producers must be able to manage their available supplies efficiently to optimize irrigation water use. The objective of this research is to develop a decision support system (DSS) for optimizing irrigation scheduling for cotton production using Deep Reinforcement learning (DRL). Our approach uses multiple DRL algorithms that enable an intelligent agent to learn cotton irrigation needs in an interactive environment by trial and error using feedback from its past actions and experiences. Aquacrop is used as an environment (cotton field) simulator and is coupled with a DRL model to simulate crop yield for different actions taken by the agent. Our proposed software estimates the daily irrigation needs of a 7-acre crop field irrigated by a center pivot system located at Clemson University's Edisto Research and Education Center (REC), near Blackville, South Carolina. This new system enables a closed-loop control scheme to adapt the DSS to local perturbations such as soil moisture and rainfall variabilities.

How to cite: Umutoni, L.: An Intelligent Irrigation Decision Support System for Optimizing Cotton Water Use, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16787, https://doi.org/10.5194/egusphere-egu23-16787, 2023.

Previous studies with coarse-resolution global climate models (GCMs) have widely shown that extensive deforestation in the Amazon leads to a reduction in precipitation, with a potential irremediable loss of the rainforest past a critical threshold. However, precipitation in the Amazon region is of convective nature and thus has to be parameterized in coarse-resolution GCMs, limiting confidence in the results of such studies. To bypass this limitation, this study aims to investigate the impact of Amazon deforestation on precipitation in global climate simulations that can explicitly represent convection. The simulations are conducted with the ICON-Sapphire atmosphere-only model configuration run with a grid spacing of 5 km for two years. To understand the impacts of Amazon deforestation, we compare the results of a complete deforestation simulation with a control simulation. Results show no significant change in precipitation during the wet season and a slight decrease of precipitation during the dry season in the deforested simulation. Precipitation decreases due to decreased evapotranspiration are compensated by enhanced moisture convergence.

How to cite: Yoon, A.: The impact of Amazon deforestation on rain system using a storm-resolving global climate model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1304, https://doi.org/10.5194/egusphere-egu23-1304, 2023.

The current crisis state of the planet, commonly called the Anthropocene, emerged as the result of the Great Acceleration in human consumption and environmental impact which followed the Second World War in the middle of the 20th c. There is growing evidence suggesting that similar acceleration dynamics, characterised by exponential growth in human environmental impact, occurred locally or regionally at earlier stages in human history. It is, however, difficult to identify, quantify, and confirm such cases without high-resolution, well-dated historical or paleoenvironmental data. In this presentation, I review three cases of well-documented Anthropocene-like accelerations, from Roman Anatolia, medieval Poland, and early modern Greece. In all of these cases, it was political consolidation, even if short-lived, as well as economic integration, that created the social tipping point triggering exponential acceleration of human environmental impact. All of these acceleration phases also collapsed once the underlying social dynamics was no longer present.

How to cite: Izdebski, A.: Social tipping points of Anthropocene acceleration dynamics in European history, from Roman times to the Little Ice Age, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3151, https://doi.org/10.5194/egusphere-egu23-3151, 2023.

Many aspects of anthropogenic global change, such as land cover change, biodiversity loss, and the intensification of agricultural production, threaten the natural biosphere. Implications of these specific aspects of environmental conditions are not immediately obvious, so it is hard to obtain a bigger picture of what these changes imply and distinguish beneficial from detrimental human impacts.  Here I describe a holistic approach that provides a bigger picture and use it to understand how the terrestrial biosphere can be sustained in the presence of increased human activities.  This approach focuses on the free energy generated by photosynthesis, the energy needed to sustain both the dissipative metabolic activity of ecosystems and human activities, with the generation rate being set by the physical constraints of the environment.  One can then distinguish two kinds of human impacts on the biosphere: detrimental effects caused by enhanced human consumption of this free energy, and empowering effects that allow for more photosynthetic activity and, therefore, more dissipative activity of the biosphere.  I use examples from the terrestrial biosphere to illustrate this view and global datasets to show how this can be estimated.  I then discuss how certain aspects of modern technology can enhance the free energy generation of the terrestrial biosphere, which can then safeguard its sustenance even as human activity increasingly shapes the functioning of the Earth system.

Note: Presentation is based on this manuscript (https://arxiv.org/abs/2210.09164), accepted for publication in the INSEE journal.

How to cite: Kleidon, A.: How to sustain the terrestrial biosphere in the Anthropocene? A thermodynamic Earth system perspective, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3251, https://doi.org/10.5194/egusphere-egu23-3251, 2023.

EGU23-3443 | Orals | CL3.2.6 | Highlight

Regional Climate Expected to Continue to Change Significantly After Net-Zero CO2 Emissions Reached 

Andrew H. MacDougall, Josie Mallett, David Hohn, and Nadine Mengis

The Zero Emissions Commitment (ZEC) is the expected temperature change following the cessation of anthropogenic emissions of climate altering gases and aerosols. Recent model intercomparison work has suggested that global average ZEC for CO2 is close to zero. However there has thus far been no effort to explore how temperature is expected to change at spatial scales smaller than the global average. Here we analyze the output of nine full complexity Earth System Models which carried out standardized ZEC experiments to quantify the ZEC from CO2. The models suggest that substantial temperature change following cessation of emissions of CO2 can be expected at large and regional spatial scales. Large scale patterns of change closely follow long established patterns seen during modern climate change, while at the regional scale patterns of change are far more complex and show little consistency between different models. Analysis of model output suggest that for most models these changes far exceed pre-industrial internal variability, suggesting either higher climate variability, continuing changes to climate dynamics or both. Thus it appears likely that at the regional scale, where climate change is directly experienced, climate disruption will not end even as global temperature stabilizes. Such indefinite continued climate changes will test the resilience of local ecosystem and human societies long after economic decarbonization is complete. Overall substantial regional changes in climate are expected following cessation of CO2 emissions but the pattern, magnitude and sign of these changes remains highly uncertain.

How to cite: MacDougall, A. H., Mallett, J., Hohn, D., and Mengis, N.: Regional Climate Expected to Continue to Change Significantly After Net-Zero CO2 Emissions Reached, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3443, https://doi.org/10.5194/egusphere-egu23-3443, 2023.

EGU23-5233 | Posters on site | CL3.2.6

Association for Trans-Eurasia Exchange and Silk-Road Civilization Development 

Likun Ai, Juzhi Hou, Haichao Xie, Yanbo Yu, and Fahu Chen

Spanning more than 6,400 kilometers across Eurasia, the Silk Road played a key role in facilitating exchanges in economy, culture, politics, and religions between East and West. The ancient Silk Road was one of the most important passages for trans-Eurasia exchange and human migrations, which could be traced back to 5000-4000 years before present. To deepen understanding of the effects of environmental changes in shaping the long-term trans-Eurasia exchanges and Silk Road civilization, the Trans-Eurasia Exchange and Silk-Road Civilization Development (ATES) was launched by a group of scientists with background of climate, hydrology, environment, archaeology in 2019. There are about 118 scientists from 10 countries that with different background have joined the ATES so far. ATES now has a President, and three coordinators in the secretariat, and all the alliance members are allocated to the 5 Working Groups (WG) based on their background and research interests. The main scientific issues for the ATES are: 1) Routes and driving forces of ancient human migrations across Eurasia in the Paleolithic; 2) Relationship between the food globalization, development of agro-pastoralism in Eurasia and human migration in the Neolithic; 3) Mechanisms of establishment, shift and demise of routes and key towns along the ancient Silk Road; 4) Effects of environmental changes on the rise and fall of the Silk Road civilization as to the trans-Eurasia exchanges in terms of economy, technology and culture. What does it tell us about the future of ongoing climate change? ATES aims to set an international platform to exchange multi-discipline knowledge and the latest research achievement on the ancient Silk Road, including exchanges of culture, science, and technology along the roads, perceptions of climate change, and socio-economic development in different historical periods along the Silk Road, and effects of environmental changes on the rise and fall of the Silk Road civilization.

ATES welcomes institutes and scientists worldwide to initiate and launch relevant research programs and projects with the ATES community. By establishing several joint research and education centers with partners, ATES facilitates and supports field observations, research, and capacity building. Training of Young Scientists is one of the main tasks for ATES capacity building, which includes the training workshops and field learnings organized by ATES and its partners. In order to strengthen the interaction of the ATES community, and to enhance the exchange of new achievements and insights of the interdisciplinary study on the evolution of trans-Eurasia exchanges and Silk Road civilization, the ATES Silk Road Civilization Forum invites a world-renowned scientist to give a special lecture on the focused topic every 3 months. ATES will organize parallel sessions and side meetings in the big events such as AGU, EGU, Conference of the Parties of the UNFCCC, UNCBD, ANSO conference, et al. ATES partners and other institutes are welcome to join in organizing the above meetings.

How to cite: Ai, L., Hou, J., Xie, H., Yu, Y., and Chen, F.: Association for Trans-Eurasia Exchange and Silk-Road Civilization Development, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5233, https://doi.org/10.5194/egusphere-egu23-5233, 2023.

EGU23-5722 | ECS | Orals | CL3.2.6 | Highlight

Recurrent droughts increase risk of cascading tipping events by outpacing adaptive capacities in the Amazon rainforest 

Nico Wunderling, Arie Staal, Frederik Wolf, Boris Sakschewski, Marina Hirota, Obbe A. Tuinenburg, Jonathan F. Donges, Henrique M.J. Barbosa, and Ricarda Winkelmann

Since the foundational paper by Lenton et al. (2008, PNAS), tipping elements in the climate system have attracted great attention within the scientific community and beyond. One of the most important tipping elements is the Amazon rainforest. Under ongoing global warming, it is suspected that extreme droughts such as those in 2005 and 2010 occur significantly more often, up to nine out of ten years from the mid to late 21st century onwards (e.g. Cox et al., 2008, Nature; Cook et al., 2020, Earth’s Future).

In this work, we quantify how climates ranging from normal rainfall conditions to extreme droughts may generate cascading tipping events through the coupled forest-climate system. For that purpose, we make use of methods from nonlinear dynamical systems theory and complex networks to create a conceptual model of the Amazon rainforest, which is dependent on itself through atmospheric moisture recycling.

We reveal that, even when the rainforest is adapted to past local conditions of rainfall and evaporation, parts of the rainforest may still tip when droughts intensify. We uncover that forest-induced moisture recycling exacerbates tipping events by causing tipping cascades that make up to one-third (mean+-s.d. = 35.9+-4.9%) of all tipping events. Our results imply that if the speed of climate change might exceed the adaptation capacity of the forest, knock-on effects through moisture recycling impede further adaptation to climate change.

Further, we use a network analysis method to compare the four main terrestrial moisture recycling hubs: the Amazon Basin, the Congo Rainforest, South Asia and the Indonesian Archipelago. By evaluating so-called network motifs, i.e. local-scale network structures, we quantify the fundamentally different functioning of these regions. Our results indicate that the moisture recycling streams in the Amazon Basin are more vulnerable to disturbances than in the three other main moisture recycling hubs.

How to cite: Wunderling, N., Staal, A., Wolf, F., Sakschewski, B., Hirota, M., Tuinenburg, O. A., Donges, J. F., Barbosa, H. M. J., and Winkelmann, R.: Recurrent droughts increase risk of cascading tipping events by outpacing adaptive capacities in the Amazon rainforest, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5722, https://doi.org/10.5194/egusphere-egu23-5722, 2023.

EGU23-7871 | Posters on site | CL3.2.6 | Highlight

Is the current methane growth event comparable to a glacial/interglacial Termination event? 

Euan Nisbet, Martin Manning, David Lowry, Rebecca Fisher, and James France

Atmospheric methane shows very sharp growth since 2006. Growing evidence for methane's main sink, atmospheric OH, being relatively stable implies a major increase in methane emissions is occurring. Methane's synchronous isotopic shift to more negative d13C(CH4) values means the increase is primarily driven by rapid growth in emissions from biogenic sources, such as natural wetlands and agriculture. Recent acceleration in the increase is also strong evidence that it is too large to be caused primarily by anthropogenic sources. Instead, much of the growth may come from large-scale climate-change feedbacks affecting the productivity and balance between methanogenic and methanotrophic processes in tropical and boreal wetlands. Emissions from tropical wetlands in particular may be larger and more influenced by climate shifts than hitherto realised. If so, even despite the Global Methane Pledge, achieving the goals of the UN Paris Agreement may be much harder than previously anticipated.

Modelling indicates that, for scale and speed, the biogenic feedback component of methane's growth and isotopic shift in the 16 years from 2006-2022 is comparable to (or greater than) phases of abrupt growth and isotopic shift during glacial/interglacial terminations, from Termination V (about 430 ka BP) to Termination I that initiated the Holocene. These were rapid global-scale climate shifts when the Earth system reorganised from cold glacial to warmer interglacial conditions.  Methane's recent 2006-2022 growth in biogenic sources may be within Holocene variability, but it is also a possibility that methane may be providing the first indication that a very large-scale end-of-Holocene reorganisation of the climate system is already under way: Termination Zero.

How to cite: Nisbet, E., Manning, M., Lowry, D., Fisher, R., and France, J.: Is the current methane growth event comparable to a glacial/interglacial Termination event?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7871, https://doi.org/10.5194/egusphere-egu23-7871, 2023.

EGU23-9387 | ECS | Posters on site | CL3.2.6

Robustness of critical slowing down indicators to power-law extremes in an Amazon rainforest model 

Vitus Benson, Jonathan F. Donges, Jürgen Vollmer, and Nico Wunderling

Critical slowing down has recently been detected as an indicator of reduced resilience in remotely sensed data of the Amazon rainforest [1]. Tropical rainforests are frequently hit by disturbances such as fire, windthrow, deforestation or drought, which are known to follow a heavy-tailed amplitude distribution. Early warning signals based on critical slowing down are theoretically grounded for systems under the influence of weak, Gaussian noise. Hence, it is not imminent that they are applicable also for systems like the Amazon rainforest, which are influenced by heavy-tailed noise. Here, we extended a conceptual model of the Amazon rainforest [2] to study the robustness of critical slowing down indicators to power-law extremes. These indicators are expected to increase before a critical transition. 

We find the way by which such an increase is detected is decisive for the recall of the early warning indicator (i.e. the proportion of critical transitions detected by the indicator). If a linear slope is taken, the recall of the early warning signal is reduced under power-law extremes. Instead, the Kendall-Tau rank correlation coefficient should be used because the recall remains high in this case. Other approaches to increase robustness, like a high-pass filter or the interquartile range, are less effective. In [1], reduced resilience of the Amazon rainforest was determined through an increase in the lag-1 autocorrelation measured by the Kendall-tau rank correlation. Hence, if there was a resilience loss, they can correctly detect it even in the presence of relatively strong power-law disturbances. However, we also quantify the false positive rate, that is, how often a resilience loss is measured if the model represents a stable rainforest. At a significance level of 5% (1%, 10%) for the early warning signal detection, the false positive rate is approximately 10% (5%, 15%). For strong heavy-tailed noise, this false positive rate can deteriorate to as high as 25% (15%, 35%). This indicates, that increasing critical slowing down may not always be caused by an approaching critical transition, a false positive detection is possible.

 

[1] Boulton, C.,  Lenton, T.  and Boers, N.: “Pronounced Loss of Amazon Rainforest Resilience since the Early 2000s”. Nature Climate Change 12-3 (2022).

[2] Van Nes, E., Hirota, M., Holmgren, M. and Scheffer, M.: “Tipping Points in Tropical Tree Cover”. Global Change Biology 20-3 (2014).

How to cite: Benson, V., Donges, J. F., Vollmer, J., and Wunderling, N.: Robustness of critical slowing down indicators to power-law extremes in an Amazon rainforest model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9387, https://doi.org/10.5194/egusphere-egu23-9387, 2023.

EGU23-9954 | ECS | Posters on site | CL3.2.6

Climate tipping risks under policy-relevant overshoot temperature pathways 

Tessa Möller, Ernest Annika Högner, Samuel Bien, Carl-Friedrich Schleussner, Johan Rockström, Jonathan F. Donges, and Nico Wunderling

The risk of triggering multiple climate tipping points if global warming levels were to exceed 1.5°C has been heavily discussed in recent literature. Current climate policies are projected to result in 2.7°C warming above pre-industrial levels by the end of this century and will thereby at least temporarily overshoot the Paris Agreement temperature goal.

Here, we assess the risk of triggering climate tipping points under overshoot pathways derived from emission pathways and their uncertainties from the PROVIDE ensemble using PyCascades, a stylised network model of four interacting tipping elements including the Greenland Ice Sheet, the West Antarctic Ice Sheet, the Atlantic Meridional Overturning Circulation, and the Amazon Rainforest.

We show that up until 2300, when overshoots are limited to 2°C, the upper range of the Paris Agreement goal, the median risk of triggering at least one element would be less than 5%, although some critical thresholds may have been crossed temporarily. However, the risk of triggering at least one tipping element increases significantly for scenarios that peak above the Paris Agreement temperature range. For instance, we find a median tipping risk in 2300 of 46% for an emission scenario following current policies. Even if temperatures would stabilize at 1.5°C after having peaked at temperatures projected under current policies, the long-term median tipping risks would approach three-quarters.

To limit tipping risks beyond centennial scales, we find that it is crucial to constrain any temperature overshoot to 2°C of global warming and to stabilize global temperatures at 1.0°C or below in the long-term.

How to cite: Möller, T., Högner, E. A., Bien, S., Schleussner, C.-F., Rockström, J., Donges, J. F., and Wunderling, N.: Climate tipping risks under policy-relevant overshoot temperature pathways, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9954, https://doi.org/10.5194/egusphere-egu23-9954, 2023.

EGU23-10044 | ECS | Orals | CL3.2.6 | Highlight

The Impact of Solar Radiation Modification on Earth System Tipping Points and Threshold Free Feedbacks 

Gideon Futerman and Claudia Wieners

The modification of the climate by Solar Radiation Modification (SRM) could be a potentially important human-Earth System interaction in the Anthropocene, having potentially beneficial and adverse impacts across climatic and human indices. SRM would likely interact with Earth system resilience in many ways, with our paper exploring SRM’s interaction with Earth System tipping point which has been extremely underexplored in the literature thus far.

SRM would likely be able to reduce global mean surface temperature quickly, although its broader climate imprint, especially on precipitation and local climatic conditions, is not the same as reversing greenhouse gas emissions. Its cooling effect suggests that SRM can help stop us from hitting those tipping elements that are most temperature-dependent, while the situation is more complex for tipping elements which strongly depend on other factors such as precipitation or regional climate changes. This more complex picture could have important implications for the role (or lack of) that SRM could and ought to play in improving Earth system resilience in the Anthropocene.

We review the available literature about the influence of SRM on the tipping elements and threshold free-feedbacks identified by McKay et al. (2022), as well as reviewing the impact of SRM on relevant climatic conditions that could contribute to tipping of each element, to give an assessment of the potential beneficial or adverse impact of SRM and identify key uncertainties and knowledge gaps. We will also briefly assess how these impacts may differ with different methods of deployment and with the termination of SRM.

How to cite: Futerman, G. and Wieners, C.: The Impact of Solar Radiation Modification on Earth System Tipping Points and Threshold Free Feedbacks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10044, https://doi.org/10.5194/egusphere-egu23-10044, 2023.

EGU23-10864 | Posters on site | CL3.2.6

Towards the Anthropocene peatlands and forests – old-growth forest loss in Western Poland initiated peat growth and peatland state shifts 

Mariusz Lamentowicz, Sambor Czerwiński, Monika Karpińska-Kołaczek, Piotr Kołaczek, Mariusz Gałka, Piotr Guzowski, and Katarzyna Marcisz

During European states’ development, various past societies utilized natural resources, but their impact was not uniformly spatially and temporally distributed. Considerable changes resulted in landscape fragmentation, especially during the Middle Ages. Changes in state advances that affected the local economy significantly drove the trajectories of ecosystems’ development. The legacy of significant changes from pristine forests to farming is visible in natural archives as novel ecosystems. Here, we present two high‑resolution, densely dated multi‑proxy studies covering the last 1000 years from peatlands in CE Europe. In that case, the economic activity of medieval societies was related to the emerging Polish state and new rulers, the Piasts (in Greater Poland) and the Joannites (the Order of St. John of Jerusalem, Knights Hospitaller). Our research revealed rapid deforestation and subsequent critical land-use transition in the high and late Middle Ages and its consequences on the peatland ecosystem development. The shift from the old-growth forests correlates well with raising the local economy, deforestation and enhanced peat initiation. Along with the emerging landscape openness, the wetlands switched from wet fen with open water to terrestrial habitats. Both sites possess a different timing of the shift, but they also show that the catchment deforestation caused accelerated terrestrialization. Our data show how closely the ecological state of wetlands relates to forest microclimate. We identified a significant impact of economic development and the onset of intensive agriculture processes near the study sites. Our results revealed a surprisingly fast rate at which the feudal economy eliminated pristine nature from the studied area and led to intensive nature exploitation in the Anthropocene. In consequence, its activities led to the creation of novel peatlands types.

How to cite: Lamentowicz, M., Czerwiński, S., Karpińska-Kołaczek, M., Kołaczek, P., Gałka, M., Guzowski, P., and Marcisz, K.: Towards the Anthropocene peatlands and forests – old-growth forest loss in Western Poland initiated peat growth and peatland state shifts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10864, https://doi.org/10.5194/egusphere-egu23-10864, 2023.

EGU23-13587 | ECS | Posters virtual | CL3.2.6

Model hierarchies and bifurcations in QE monsoon models 

Krishna Kumar S and Ashwin K Seshadri

The convective quasi-equilibrium (CQE) framework has been successfully employed in the past to build intermediate complexity models accounting for the interaction of convection and large-scale dynamics (Neelin and Zeng, 1999, JAS). As a consequence, these models find use in the study of monsoon circulations, which also experience abrupt onset among several other intriguing features. While some low-order simplifications of CQE based Quasi-equilibrium tropical circulation model (QTCM) yields insights into the mechanisms of monsoon dynamics, they are restricted in the range of processes accounted for. A hierarchy of models, on the other hand, would serve well to study monsoon dynamics and various influences. While the existence of bifurcations or 'tipping-points' in monsoon dynamics has been studied for certain simple models, a thorough investigation of this possibility across a hierarchy of models is absent. Such a hierarchy of models would provide an understanding of effects of different simplifying assumptions on dominant balances in the momentum and thermodynamic equations and resulting nonlinear dynamics, including the choice of precipitation parameterizations. This study explores a hierarchy of such models of varying complexity, based on the QTCM equations. The potential occurrence of bifurcation phenomena are considered, along with their sensitivity to various parameter changes, in the context of the role of different nonlinearities present in these models. The study builds on recent results interpreting the suppression of bifurcation phenomena in these models, as a result of shifts in equilibrium branches and consequently their physical relevance. The hierarchy of models approach, in this context, reconciles apparent contradictions between bifurcations being observed in the simplest models and the evidence from more complex models as well as observations, while identifying robust phenomena.

How to cite: Kumar S, K. and Seshadri, A. K.: Model hierarchies and bifurcations in QE monsoon models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13587, https://doi.org/10.5194/egusphere-egu23-13587, 2023.

EGU23-13620 | Orals | CL3.2.6

The Western Amazon social-ecological system at risk of tipping: A transdisciplinary modelling approach 

Benjamin Stuch, Rüdiger Schaldach, Regine Schönenberg, Katharina Meurer, Merel Jansen, Claudia Pinzon Cuellar, Shabeh Ul Hasson, Christopher Jung, Ellen Kynast, Jürgen Böhner, and Hermann Jungkunst

The Amazon rainforest is a tipping element of the global climate system due to its high carbon storage potential and its flying rivers providing rain for South America. Studies suggest that land use and land cover change (LUCC) in the Amazon, i.e. deforestation, strongly disturb regional convectional rain pattern, which could lead to an increase of drought frequencies and intensities. Under increasing drought stress, the evergreen tropical rainforest may transform into a seasonal forest or even a savannah ecosystem. Such a transformation would likely activate the Amazon tipping element and may affect global climate change by triggering other critical tipping elements of the global climate system.  

Here we present our transdisciplinary research approach in the Western Amazon rainforest developed in context of the PRODIGY research project. We apply a social-ecological system approach to account for the dynamic interactions and feedbacks between people and nature, which could either stabilize or self-enforce regional tipping cascades. For example, regional land users may suffer declining yield and net primary production from decreasing precipitation. Land users may compensate the drop in production/income e.g. by cultivating more land or seeking for other income sources. As a response, deforestation could increase which may drive a self-enforcing feedback loop that further decrease precipitation.

In a participatory process, together with regional stakeholders we develop land use related explorative scenarios. Preliminary results from the scenario exercise show that future agricultural production increases in all scenarios (crops between 20% and 200% and livestock between 0% and 300%). In the first modelling step, these  changes drive the regionally adjusted spatial land system model LandSHIFT. Simulation results indicate that deforestation increases in all scenarios depending on the production technology and the reflexivity of institutions establishing appropriate management options.

In an integrated modelling step, the calculated LUCC maps serve as input to a regional climate model (WRF), which simulates respective changes in regional temperature and precipitation. Then, temperature and precipitation changes are applied to the biogeochemical model CANDY to simulate the impact (of regional deforestation) on crop yields, Net Primary Production (NPP) and changes in soil C and N cycling. In an iterative process, the yield and NPP responses are fed back to the land-use change model to simulate the required land use adaptations, accordingly. By closing the feedback loop between deforestation, climate, yield and NPP as well as respective land use adaptation, we are able to simulate a cascade of endogenous key process in the regions social ecological system. The integrated modelling results will support the stakeholders in identifying key measures/options/policies that could increase resilience of the regional social-ecological system to prevent crossing destructive regional tipping points.

How to cite: Stuch, B., Schaldach, R., Schönenberg, R., Meurer, K., Jansen, M., Pinzon Cuellar, C., Ul Hasson, S., Jung, C., Kynast, E., Böhner, J., and Jungkunst, H.: The Western Amazon social-ecological system at risk of tipping: A transdisciplinary modelling approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13620, https://doi.org/10.5194/egusphere-egu23-13620, 2023.

Microbial communities in freshwater lake sediments play a crucial role in regulating geochemical cycles and controlling greenhouse gas emissions. Many of them exhibit a highly ordered structure along depth profile. Besides redox effect, sediment stratification could also reflect historical transition. Dam construction dramatically increased in the mid-20th century and is considered one of the most far-reaching anthropogenic modifications of aquatic ecosystems. Here we attempted to identify the effect of historical dam construction on sediment microbial zonation in Lake Chaohu, one of the major freshwater lakes in China. The damming event in AD 1962 was coincidentally labeled by the 137Cs peak. Physiochemical and sequencing analyses (16S amplicon and shotgun metagenomics) jointly showed a sharp transition occurred at the damming-labeled horizon which overlapped with the nitrate-methane transition zone (NMTZ) and controlled the depth of methane sequestration. At the transition zone, we observed significant taxonomic differentiation. Random forest algorithm identified Bathyarchaeota, Spirochaetes, and Patescibacteria as the damming-sensitive phyla, and Dehalococcoidia, Bathyarchaeia, Marine Benthic Group A, Spirochaetia, and Holophagae as the damming-sensitive classes. Phylogenetic null model analysis also revealed a pronounced shift in microbial community assembly process, from a selection-oriented deterministic community assembly down to a more stochastic, dispersal-limited one. These findings delineate a picture in which dam-induced changes to the lake trophic level and sedimentation rate generate great changes in sediment microbial community structure, energy metabolism, and assembly process.

How to cite: Zhou, X. and Ruan, A.: Dam construction as an important anthropogenic modification triggers abrupt shifts in microbial community assembly in freshwater lake sediments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14360, https://doi.org/10.5194/egusphere-egu23-14360, 2023.

EGU23-14772 | Posters on site | CL3.2.6

Sustainable Pathways under Climate Variability 

Kira Rehfeld and the SPACY research group members

External forcings and feedback processes of the Earth system lead to timescale and state-dependent climate variability, causing substantial surface climate fluctuations in the past. Particularly relevant for future livelihoods, changing variability patterns could also modify the occurrence of extreme events. However, spatiotemporal mechanisms of climate variability are poorly understood. Likewise, the societal implications are weakly constrained, particularly variability’s potential to drive sustainable transformation. The SPACY project investigates climate variability from past cold and warm periods to future scenarios. One research focus is how forcing mediates climate fluctuations. Bridging the gap between Earth system models and palaeoclimate proxies, we study vegetation and water isotope changes. A second focus is exploring sustainable pathways under climate variability, addressing potential interactions between artificial carbon dioxide removal and surface climate, among others.

 

In particular, we validate the ability of climate models to represent potential climate variability changes. Here, we focus on isotope-enabled simulations with dynamic vegetation. We find that models exhibit less local temperature and water isotope variability than paleoclimate proxies on decadal and longer timescales. Simulations with natural forcing agree much better with proxy records than unforced ones. The mean local temperature variability decreases with warming. Furthermore, we analyze potentials and limitations of terrestrial hydroclimate proxies. This includes water isotopes in speleothems and ice cores and vegetation indicators derived from pollen assemblages.

Transferring our understanding to the future, we contribute to mitigation and sustainable transitions. Weather and climate extremes determine losses and damages, but their impact on socioeconomic development is poorly examined. We scrutinize damage parametrization of economic models regarding the ability to consider variability. While large-scale sequestration of atmospheric carbon dioxide is paramount to mitigation targets, its representation in climate models is insufficient. Accounting for feedbacks of carbon dioxide removal (CDR) requires model experiments with modified land surfaces. We develop CDR representations of “artificial photosynthesis” in Earth system models. Pollen records benchmark the simulated climate–carbon dioxide–vegetation interactions. This supports modeling endogenous societal land use decisions in the future.

Our work continues to improve the understanding of long-term climate predictability. The combined knowledge from past climate studies and comprehensive modeling for future scenarios underlines the relevance of changing boundary conditions for a future within planetary boundaries.

 

 

How to cite: Rehfeld, K. and the SPACY research group members: Sustainable Pathways under Climate Variability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14772, https://doi.org/10.5194/egusphere-egu23-14772, 2023.

EGU23-16944 | ECS | Orals | CL3.2.6

Socio-Political Feedback on the Path to Net Zero 

Saverio Perri, Simon Levin, Lars Hedin, Nico Wunderling, and Amilcare Porporato

Anthropogenic emissions of CO2 must soon approach net zero to stabilize the global mean temperature. Although several international agreements have advocated for coordinated climate actions, their implementation has remained below expectations. One of the main challenges of international cooperation is the different degrees of socio-political acceptance of decarbonization.

In this contribution, we interrogate a minimalistic model of the coupled human-natural system representing the impact of such socio-political acceptance on investments in clean energy and the path to net-zero emissions. Despite its simplicity, the model can reproduce complex interactions between human and natural systems, and it can disentangle the effects of climate policies from those of socio-political acceptance on the path to net zero. Although perfect coordination remains unlikely, as clean energy investments are limited by myopic economic strategies and a policy system that promotes free-riding, more realistic decentralized cooperation with partial efforts from each actor could still lead to significant emissions cuts.

Since the socio-political feedback on the path to net zero could influence the trajectories of the Earth System for decades to centuries and beyond, climate models need to incorporate better the dynamical bi-directional interactions between socio-political groups and the environment. Our model represents a first step for incorporating this feedback in describing complex coupled human and natural systems.

How to cite: Perri, S., Levin, S., Hedin, L., Wunderling, N., and Porporato, A.: Socio-Political Feedback on the Path to Net Zero, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16944, https://doi.org/10.5194/egusphere-egu23-16944, 2023.

EGU23-17342 | ECS | Orals | CL3.2.6

Systematic assessment of climate tipping points 

Sina Loriani, Boris Sakschewski, Jonathan Donges, and Ricarda Winkelmann

Tipping elements constitute one high-risk aspect of anthropogenic climate change - after their critical thresholds are passed, self-amplifying feedbacks can drive parts of the Earth system into a different state, potentially abruptly and/or irreversibly. A variety of models of different complexity shows these dynamics in many systems, ranging from vegetation over ocean circulations to ice sheets. This growing body of evidence supports our understanding of  potential climate tipping points, their interactions and impacts.

However, a systematic assessment of Earth system tipping points and their uncertainties in a dedicated model intercomparison project is of yet missing. Here we illustrate the steps towards automatically detecting abrupt shifts and tipping points in model simulations, as well as a standardised evaluation scheme for the Tipping Point Model Intercomparison Project (TIPMIP). To this end, the model outputs of taylored numerical experiments are screened for potential tipping dynamics and spatially clustered in a bottom-up approach. The methodology is guided by the anticipated setup of the intercomparison project, and in turn contributes to the design of the TIPMIP protocol.

How to cite: Loriani, S., Sakschewski, B., Donges, J., and Winkelmann, R.: Systematic assessment of climate tipping points, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17342, https://doi.org/10.5194/egusphere-egu23-17342, 2023.

EGU23-17397 | ECS | Posters virtual | CL3.2.6

Is Arctic Permafrost a Climate Tipping Element? – Potentials for Rapid Permafrost Loss Across Spatial Scales 

Jan Nitzbon, Thomas Schneider von Deimling, Sarah Chadburn, Guido Grosse, Sebastian Laboor, Hanna Lee, Norman Julius Steinert, Simone Maria Stuenzi, Sebastian Westermann, and Moritz Langer

Arctic permafrost is yet the largest non-seasonal component of Earth's cryosphere and has been proposed as a climate tipping element. Already today, permafrost thaw and ground ice loss have detrimental consequences for Arctic communities and are affecting the global climate via carbon-cycle–feedbacks. However, it is an open question whether climatic changes drive permafrost loss in a way that gives rise to a tipping point, crossing of which would imply abrupt acceleration of thaw and disproportional unfolding of its impacts.

Here, we address this question by geospatial analyses and a comprehensive literature review of the mechanisms and feedbacks driving permafrost thaw across spatial scales. We find that neither observation-constrained nor model-based projections of permafrost loss provide evidence for the existence of a global-scale tipping point, and instead suggest a quasi-linear response to global warming. We identify a range of processes that drive rapid permafrost thaw and irreversible ground ice loss on a local scale, but these do not accumulate to a non-linear response beyond regional scales.

We emphasize that it is precisely because of this overall linear response, that there is no „safe space“ for Arctic permafrost where its loss could be acceptable. Every additional amount of global warming will proportionally subject additional land areas underlain by permafrost to thaw, implying further local impacts and carbon emissions.

How to cite: Nitzbon, J., Schneider von Deimling, T., Chadburn, S., Grosse, G., Laboor, S., Lee, H., Steinert, N. J., Stuenzi, S. M., Westermann, S., and Langer, M.: Is Arctic Permafrost a Climate Tipping Element? – Potentials for Rapid Permafrost Loss Across Spatial Scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17397, https://doi.org/10.5194/egusphere-egu23-17397, 2023.

EGU23-17457 | ECS | Orals | CL3.2.6 | Highlight

Indicators of changing resilience and potential tipping points in the automotive industry 

Joshua E Buxton, Chris A Boulton, Jean-Francois Mercure, Aileen Lam, and Timothy M Lenton

Through innovation and wider socio-economic processes, large sections of the economy have been known to rapidly (and often irreversibly) transition to alternative states. One such sector currently undergoing a transition is the automotive industry, which is moving from a state dominated by internal combustion engines to one characterised by low-emission vehicles. While much research has focused on early warning signals of climate and ecological tipping points, there is much to be done on assessing the applicability of these methods to social systems. Here we focus on the potential for tipping points to occur in the sale of electrical vehicles in various markets, including Norway and the UK. Early indicators that this new state is being approached are considered through the use of novel data sources such as car sales, infrastructure announcements and online advert engagement. We then map out the socio-technical feedback loops which may drive these tipping points. Consideration is also given to the resilience of the wider automotive industry to previous economic shocks. 

How to cite: Buxton, J. E., Boulton, C. A., Mercure, J.-F., Lam, A., and Lenton, T. M.: Indicators of changing resilience and potential tipping points in the automotive industry, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17457, https://doi.org/10.5194/egusphere-egu23-17457, 2023.

EGU23-3351 | ECS | Posters on site | G3.3

Validation of Modelled Uplift Rates with Space Geodetic Data 

Meike Bagge, Eva Boergens, Kyriakos Balidakis, Volker Klemann, and Henryk Dobslaw

Models of glacial isostatic adjustment (GIA) simulate the time-delayed viscoelastic response of the solid Earth to surface loading induced mainly by mass redistribution between ice and ocean during the last glacial cycle considering for rotational feedback, floating ice and moving coastlines. These models predict relative sea level change and surface deformation. The GIA component of present-day uplift is responsible for crustal uplift rates of more than 10 mm/year in areas such as Churchill (Canada) and Angermanland (Sweden). As GIA models have several uncertainties, the model output needs to be validated against observational data. Here, we validate displacements predicted by a GIA model code, VILMA-3D, by using space geodetically observed vertical land motion. We have created a GIA model ensemble using geodynamically constrained 3D Earth structures derived from seismic tomography to consider more realistic lateral variations in the GIA response. To validate the modelled uplift rates, we employ a multi-analysis-centre ensemble of GNSS station and geocentre motion coordinate solutions that have been assimilated into the latest international terrestrial reference frame (ITRF2020). Tectonic and weather signatures were reduced in estimating GNSS-derived velocities, and the trend signal is extracted from these GNSS time series with the STL method (seasonal-trend decomposition based on Loess).  Additionally, uplift rates observed within the ITRF2020 of VLBI, DORIS, and SLR are employed in this study. Because the geodetic stations are unevenly distributed, we employ a weighting scheme that involves the network density and the cross-correlation of the stations’ displacement time series. As measures of agreement for global and regional cases, we employ weighted root mean square error (RMSE) and weighted mean absolute error (MAE). With this validation, we determine the GIA model parameters that are most suitable for modelling present-day uplift rates and identify regions with the best and worst agreement.

The results show an agreement between RMSE and MAE for the global case (all stations are considered) and the majority of regional cases, except for the farfield (away from formerly glaciated regions) and for North America. For the global case and for separate regions covered by the major ice sheets during glaciation (North America, Fennoscandia, Antarctica, Greenland), the best fit is performed by the GIA models with 3D Earth structures which show largest lateral variability in viscosity. For the GIA model with the best global fit, the MAE ranges between 0.03 and 0.98 for the respective regions British Isles, Antarctica, farfield, Fennoscandia and North America. In contrast, for the three regions with the lowest amount of observational data, Patagonia, Alaska and Greenland, the MAE is increased to values between 2.07 and 8.63. In general, the MAE ranges between 0.83 and 0.78 for the different GIA models when all stations are considered. Both the RMSE and the MAE show a larger spread between the regions than between the considered GIA models indicating the relevance of also evaluating regional differences in the model performance.

How to cite: Bagge, M., Boergens, E., Balidakis, K., Klemann, V., and Dobslaw, H.: Validation of Modelled Uplift Rates with Space Geodetic Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3351, https://doi.org/10.5194/egusphere-egu23-3351, 2023.

EGU23-4604 | ECS | Posters virtual | G3.3

The importance of underestimated local vertical land motion component in sea-level projections: A case study from the Oka estuary, northern Spain 

Tanghua Li, Ane García-Artola, Jennifer Walker, Alejandro Cearreta, and Benjamin Horton

Vertical land motion (VLM) is an important component in relative sea-level (RSL) projections, especially at regional to local scales and over the short to medium term. However, VLM is difficult to derive because of a lack of long-term instrumental records (e.g., GPS, tide gauge). Geological data offer an alternative, revealing RSL histories over thousands of years that can be compared with glacial isostatic adjustment (GIA) models to isolate VLM.

Here, we present a case study from the Oka estuary, northern Spain. We apply two GIA models for the Atlantic coast of Europe with different ice model inputs (ICE-6G_C and ANU-ICE) but the same 3D Earth model. Both models fit well with the late Holocene RSL data along the Atlantic coast of Europe, with misfit statistics < 1.5, except the Oka estuary region, where both models show notable misfits with misfit statistics > 4.5. The significant misfits of both models in the Oka estuary region are indicative of local subsidence. The nearby GPS (station SOPU) with 15 years records shows a VLM rate of -0.96 ± 0.57 mm/yr (subsiding) compared to -0.15 ± 0.40 mm/yr to -2.48 ± 0.37 mm/yr elsewhere along the Atlantic coast of Europe. The VLM rate of SOPU accounts for the misfit between the GIA models and late Holocene RSL data, which decreases by ~90% from > 4.5 to ~0.5 after the subsidence correction of the late Holocene RSL data. The VLM rate incorporated in IPCC AR6 projections in Oka estuary is ~0.18 mm/yr (uplifting), which is contradictory in direction. Therefore, the projected sea-level rise rate is underestimated by 19 - 25% by 2030, 14 - 20% by 2050 and 9 - 26% by 2100 under the five Shared Socioeconomic Pathway (SSP) scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Our study indicates the importance of considering local/regional VLM component in sea-level projections.

How to cite: Li, T., García-Artola, A., Walker, J., Cearreta, A., and Horton, B.: The importance of underestimated local vertical land motion component in sea-level projections: A case study from the Oka estuary, northern Spain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4604, https://doi.org/10.5194/egusphere-egu23-4604, 2023.

EGU23-6911 | ECS | Posters on site | G3.3

Study of the impact of rheologies on GIA modeling 

alexandre boughanemi and anthony mémin

The Antarctic Ice Sheet (AIS) is the largest ice sheet on Earth that has known important mass changes during the last 26 kyrs. These changes deform the Earth and modify its gravity field, a process known as Glacial Isostatic Adjustment (GIA). GIA is directly influenced by the mechanical properties and internal structure of the Earth and is monitored using Global Navigation Satellite System positioning or gravity measurements. However, GIA in Antarctica remains poorly constrained due to the cumulative effect of past and present ice-mass changes, the unknown history of the past ice-mass change, and the uncertainties of the mechanical properties of the Earth. The viscous deformation due to GIA is usually modeled using a Maxwell rheology. However, other geophysical processes employ the Andrade rheology for tidal deformation or Burgers for post-seismic deformation which could result in a more rapid response of the Earth. We investigate the effect of using these different rheologies to model GIA-induced deformation in Antarctica.
We use the Love number and Green functions formalism to compute the radial surface displacements and the gravity changes induced by the past and present day ice-mass changes. We use the elastic properties and the radial structure of the Preliminary Reference Earth Model (PREM) and the viscosity profile VM5a given by Peltier et al., 2015 and a modified version of it to account for the recent results published regarding the present-day ice-mass changes. Deformations are computed for each rheological laws mentioned above using ICE6g deglaciation model and altimetry data from various satellite missions over the period 2002 to 2017 to represent the past and present changes of the AIS, respectively.
We find that the three rheological laws lead to significant discrepancies in the Earth response. The differences are the largest between Maxwell and Burgers rheologies during the 100 -1000 years following the beginning of the surface-mass change. First using a simple deglaciation model, we find that the deformations rates can be 3 times and 1.5 times greater using the Burgers and Andrade rheologies. However, the ratio between the gravity change rate and the displacement rate are similar for all rheologies (less than 5% difference). Results show that using the Andrade and Burgers rheologies can lead to a 5 and 10m difference in the radial displacement with regards to the Maxwell rheology, on a 200 year period after deglaciation using the ICE6g model. Regarding the response to present changes in Antarctica, the largest discrepancies are obtained in regions with the greatest current melting rates, namely Thwaites and Pine Island Glacier in West Antarctica. Using the Burgers and Andrade rheologies lead to deformations rates respectively 6 times and 2 times greater with respect to Maxwell rheology.

How to cite: boughanemi, A. and mémin, A.: Study of the impact of rheologies on GIA modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6911, https://doi.org/10.5194/egusphere-egu23-6911, 2023.

EGU23-7921 | ECS | Orals | G3.3

Emulating the influence of laterally variable Earth structure in a model of glacial isostatic adjustment 

Ryan Love, Parviz Ajourlou, Soran Parang, Glenn A. Milne, Lev Tarasov, and Konstantin Latychev

At present, exploring the space of rheological parameters in models of glacial isostatic adjustment (GIA) and relative sea level (RSL) which incorporate laterally variable Earth structure is computationally expensive. A single simulation using the Seakon model (Latychev et al., 2005), using contemporary high-performance computing hardware, requires several wall-days & ≈ 1 core-year for one RSL simulation from late Marine Isotope Stage 3 to present day. However, it is well established that the impact from laterally variable mantle viscosity and lithospheric thickness on RSL and GIA is significant (Whitehouse, 2018). We present initial results from using the Tensorflow (Abadi et al.) framework to construct artificial neural networks that emulate the difference in the rate of change of relative sea level and relative radial displacement between model configurations using spherically symmetric (SS) and laterally variable (LV) Earth structures. Using this emulator we can accurately sample the parameter space (≈ 360 realisations of the background (SS) structure) for a given realization of lateral Earth structure (e.g. viscosity variations derived from shear-wave tomographic models) using ≈ 1/10th the amount of parameter vectors as a training set. Average misfits are O(0.1-1%) of the total RSL signal when using the emulator to adjust SS GIA model output to incorporate the impact from LV. We shall report on two case studies which allow us to examine the influence of lateral Earth structure on inferences of background (i.e. global-mean) viscosity. For these case studies, the emulator, in conjunction with a fast SS GIA/RSL model, is used to determine optimal Earth model parameters (elastic lithosphere thickness, upper and lower mantle viscosities) by calculating the model misfits across the parameter space. The first case study uses the regional RSL database of Vacchi et al. (2018) which spans the Canadian Arctic and East Coast with several hundred sea level index points and limiting points for the early to late Holocene. The second case study uses a global database of several thousand contemporary uplift rates derived from GPS data (Schumacher et al., 2018). For the first case study we find two main features from incorporating LV structures compared to the SS configuration: a decrease in the best scoring misfit and a shift of the misfit distribution in the parameter space to favour a reduced upper mantle viscosity and reduced sensitivity to the lower mantle viscosity.

References
Abadi, M., Agarwal, A., Barham, P., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow. org/.
Latychev, K., Mitrovica, J. X., Tromp, J., et al.: Glacial isostatic adjustment on 3-D Earth models: a finite-volume formulation, GJI, 161, 421–444, https://doi.org/10.1111/j.1365-246x.2005.02536.x, 2005.
Schumacher, M., King, M. A., Rougier, J., et al.: A new global GPS data set for testing and improving modelled GIA uplift rates, GJI, 214, 2164–2176, https://doi.org/10.1093/gji/ggy235, 2018.
Vacchi, M., Engelhart, S. E., Nikitina, D., et al.: Postglacial relative sea-level histories along the eastern Canadian coastline, QSR, 201, 124–146, https://doi.org/10.1016/j.quascirev.2018.09.043, 2018.
Whitehouse, P. L.: Glacial isostatic adjustment modelling: historical perspectives, recent advances, and future directions, Earth Surface Dynamics, 6, 401–429, https://doi.org/10.5194/esurf-6-401-2018, 2018.

How to cite: Love, R., Ajourlou, P., Parang, S., Milne, G. A., Tarasov, L., and Latychev, K.: Emulating the influence of laterally variable Earth structure in a model of glacial isostatic adjustment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7921, https://doi.org/10.5194/egusphere-egu23-7921, 2023.

EGU23-9405 | ECS | Orals | G3.3

Quantifying the Impact of Modern Ice Mass Loss on Crustal Strain and Seismicity across Greenland and the European Arctic 

Sophie Coulson, Matthew Hoffman, Kelian Dascher-Cousineau, Brent Delbridge, Roland Bürgmann, and Joshua Carmichael

Ice mass loss from the Greenland Ice Sheet and Arctic glaciers has accelerated over the last three decades due to rapid changes in Arctic climate. This loss of ice from glaciated areas and redistribution of water across the global oceans creates a complex spatio-temporal pattern of crustal deformation due to the load changes on Earth’s surface. We test whether the resulting strain perturbations from this deformation are large enough to influence seismic activity in the Arctic on decade to century timescales.

 

Using new ice-mass-loss estimates from radar altimetry for the Greenland Ice Sheet and model reconstructions of glaciers across the European Arctic, we predict gravitationally self-consistent sea level changes across the Arctic over the last three decades. These surface loads are then used as input for our deformation model, developed to calculate strain at depth within the crust, using a Love number formulation for a spherically symmetric Earth. Our global model captures both the near-field effects directly beneath ice centers and deformation across the sea floor, allowing us to fully quantify the spatio-temporal perturbations to the regional strain field created by glacial isostatic adjustment (GIA) processes. Using declustered earthquake catalogs of Arctic earthquake activity over the last three decades, we search for correlation between the earthquake record and our modelled strain perturbations. In particular, we focus our search along the Mid Atlantic Ridge and beneath Greenland. In the former, small magnitude GIA-related strains enhance or counteract rapid tectonic background loading, while in the latter intra-plate setting, GIA processes likely dominate the crustal strain field.

 

While correlations over the last three decades may not be statistically definitive, this framework also allows for prediction of crustal strain patterns for future ice sheet scenarios, as ice mass loss from Greenland accelerates, and therefore predictions of the likelihood and potential geographic variability of climate-change-induced seismicity in the future.

How to cite: Coulson, S., Hoffman, M., Dascher-Cousineau, K., Delbridge, B., Bürgmann, R., and Carmichael, J.: Quantifying the Impact of Modern Ice Mass Loss on Crustal Strain and Seismicity across Greenland and the European Arctic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9405, https://doi.org/10.5194/egusphere-egu23-9405, 2023.

EGU23-9697 | ECS | Orals | G3.3

Constraints of Relative Sea Level Change on the Late Pleistocene Deglaciation History 

Kaixuan Kang and Shijie Zhong

In this study, we examine the relationships among mantle viscosity, ice models and RSL data. We analyzed two widely used ice models, the ANU and ICE-6G ice models, and found significant difference between these two models, suggesting that significant uncertainties exist in ice models. For six RSL datasets covered both the near- and far-field from published works [Peltier et al., 2015; Lambeck et al., 2014, 2017; Vacchi et al., 2018; Engelhart et al., 2012, 2015], we performed forward GIA modelling using a 1-D compressible Earth model to seek the preferred upper and lower mantle viscosities that fit each of the six RSL datasets, for each of these two ice models. Our calculations show that viscosity in the lower mantle is significantly larger than the upper mantle for almost all the pairs of RSL datasets and ice models, but the RSL datasets for North America and Fennoscandia by Peltier et al., [2015] can be matched similarly well with a large parameter space of upper and lower mantle viscosities, both relatively uniform mantle viscosity and with large increase with depth. The preferred mantle viscosity using the ANU ice model and Lambeck et al. [2017] RSL data for North America is in a good agreement with that by Lambeck et al. [2017].    By using the GIA model with the preferred viscosity structures, we constructed the spatial and temporal distributions of misfit to different RSL datasets, for both the ICE-6G and ANU ice models. The misfit patterns for the ANU and ICE-6G ice models do not differ significantly in North America, although these two ice models differ greatly in North America. However, due to relatively small ice volume in ICE-6G, it fails to explain the far-field RSL data, reflecting the so-called “missing ice” problem. Guided by the spatial and temporal misfit patterns, we made initial attempts to modify ICE-6G by adding more ice to the ice model to improve the fit to far-field RSL data. The three modified ICE-6G ice models we consider all significantly improve far-field RSL data, while maintaining or even improving misfit for near field RSL data. This shows the promise with our method in improving ice models and fit to RSL data.

How to cite: Kang, K. and Zhong, S.: Constraints of Relative Sea Level Change on the Late Pleistocene Deglaciation History, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9697, https://doi.org/10.5194/egusphere-egu23-9697, 2023.

EGU23-10493 | Orals | G3.3 | Highlight

New GNSS Observations of Crustal Deformation due to Ice Mass Loss in the Amundsen Sea Region, Antarctica 

Terry Wilson, Demián Gómez, Peter Matheny, Michael Bevis, William J. Durkin, Eric Kendrick, Stephanie Konfal, and David Saddler

Twelve continuous GNSS systems are deployed on bedrock across the Amundsen Embayment region, spanning the Pine Island, Thwaites and Pope-Smith-Kohler (PSK) glacial drainage network of the West Antarctic Ice Sheet.  Continuous daily position time series for these sites range from 4 to 12 years, yielding reliable crustal motion velocity solutions at these fast-moving bedrock sites. Remarkably, multiple stations record sustained uplift of 40-50 mm/yr.  Maximum uplift defined by the current distribution of sites is centered on the Pope-Smith-Kohler glaciers, where rapid thinning and grounding line retreat is well documented. Horizontal bedrock displacements, which are particularly sensitive to the location of changing surface mass loads, show a clear radial pattern with motion outward away from upstream portions of the Pope/Smith glaciers. Several modeling studies suggest there is a viscous deformation response to this decadal mass loss. Our modeling, however, shows that elastic deformation response explains nearly the entire measured signal at the PSK region sites. We will present new modeling results and discuss implications for ongoing cryosphere-solid Earth interactions.

How to cite: Wilson, T., Gómez, D., Matheny, P., Bevis, M., Durkin, W. J., Kendrick, E., Konfal, S., and Saddler, D.: New GNSS Observations of Crustal Deformation due to Ice Mass Loss in the Amundsen Sea Region, Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10493, https://doi.org/10.5194/egusphere-egu23-10493, 2023.

EGU23-10574 | Orals | G3.3

GLAC3: Joint glaciological model and visco-elastic earth model history matching of the last glacial cycle: Greenland and Antarctica components 

Lev Tarasov, Benoit Lecavalier, Greg Balco, Claus-Dieter Hillenbrand, Glenn Milne, Dave Roberts, and Sarah Woodroffe

We present the Antarctic and Greenland components of an extensive
history matching for last glacial cycle evolution and regional earth
rheology from glaciological modelling with fully coupled regional
visco-elastic glacio-isostatic adjustment.  Of further distinction is
the accounting for model structural uncertainty. The product is a high
variance set of joint chronologies and earth model parameter vectors
that are not inconsistent with available constraints given
observational and model uncertainties.

Ensemble parameters are from Markov Chain Monte Carlo sampling with
Bayesian artificial neural network emulators.  The glaciological model
is the Glacial Systems Model with hybrid shallow shelf and shallow ice
physics and a coupled energy balance climate model. It includes a much
larger set of ensemble parameters (34 and 38 respectively for
Greenland and Antarctica) than other paleo ice sheet models to
facilitate more complete assessment of past ice sheet evolution
uncertainty. The history matching is against a large curated set of
relative sealevel, vertical velocity, cosmogenic age, and marine
constraints as well as the present-day physical and thermal
configuration of the ice sheet.

The careful assessment of uncertainties, breadth of modelled
processes, and sampling approach has resulted in NROY (not ruled out
yet) chronologies and rheological inferences that contradict previous
more limited model-based reconstructions.  For instance, in contrast
to most previous inferences for the Antarctic contribution to the last
glacial maximum (LGM) low-stand (with inferred values of about 10 m ice
equivalent sea-level (mESL), our NROY set includes chronologies with
LGM contributions of up to 23 mESL.  This result represents a
potentially significant contribution towards addressing the challenge
of LGM missing ice.

How to cite: Tarasov, L., Lecavalier, B., Balco, G., Hillenbrand, C.-D., Milne, G., Roberts, D., and Woodroffe, S.: GLAC3: Joint glaciological model and visco-elastic earth model history matching of the last glacial cycle: Greenland and Antarctica components, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10574, https://doi.org/10.5194/egusphere-egu23-10574, 2023.

EGU23-10729 | Orals | G3.3

Observations and modelling of GIA in the Ross Sea region, Antarctica 

Stephanie Konfal, Terry Wilson, Pippa Whitehouse, Grace Nield, Tim Hermans, Wouter van der Wal, Michael Bevis, Demián Gómez, and Eric Kendrick

ANET-POLENET (Antarctic Network of the Polar Earth Observing Network) bedrock GNSS sites in the Ross Sea region of Antarctica surround an LGM load center in the Siple region of the Ross Embayment and record crustal motion due to GIA.  Rather than a radial pattern of horizontal motion away from the former load, we instead observe three primary patterns of deformation; 1) motions are reversed towards the load in the southern region of the Transantarctic Mountains (TAM), 2) motions are radially away from the load in the Marie Byrd Land (MBL) region, and 3) an overall gradient in motion is present, with magnitudes progressively increasing from East to West Antarctica.  We investigate the effects of alternative Earth model and ice loading scenarios, with the goal of understanding these distinct patterns of horizontal bedrock motion and their drivers. Using GIA models with a range of 1D Earth models, alternative ice loading scenarios for the Wilkes Subglacial Basin (LGM time scale) and the Siple Coast (centennial and millennial time scales) are explored.  We find that no 1D model, regardless of the Earth model and ice loading scenario used, reproduces all three distinct patterns of observed motion at the same time.  For select ice loading scenarios we also examine the influence of more complex rheology by invoking a boundary in Earth properties beneath the Transantarctic Mountains.  This approach accounts for the strong lateral gradient in Earth properties across the continent by effectively separating East and West Antarctica into two different Earth model profiles.  Some of our GIA models utilizing 3D Earth structure reproduce predicted motions that match all three observed patterns of deformation, and we find that a multiple order magnitude of change in upper mantle viscosity between East and West Antarctica is required to fit the observations. 

How to cite: Konfal, S., Wilson, T., Whitehouse, P., Nield, G., Hermans, T., van der Wal, W., Bevis, M., Gómez, D., and Kendrick, E.: Observations and modelling of GIA in the Ross Sea region, Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10729, https://doi.org/10.5194/egusphere-egu23-10729, 2023.

EGU23-13583 | ECS | Orals | G3.3

A generalised Fourier collocation for fast computation of glacial isostatic adjustment 

Jan Swierczek-Jereczek, Marisa Montoya, Javier Blasco, Jorge Alvarez-Solas, and Alexander Robinson

Glacial isostatic adjustment (GIA) represents an important negative feedback on ice-sheet dynamics. The magnitude and time scale of GIA primarily depend on the upper mantle viscosity and the lithosphere thickness. These parameters have been found to vary strongly over the Antarctic continent, showing ranges of 1018 - 1023 Pa s for the viscosity and 30 - 250 km for the lithospheric thickness. Recent studies show that coupling ice-sheet models to 3D GIA models capturing these spatial dependencies results in substantial differences in the evolution of the Antarctic Ice Sheet compared to the use of 1D GIA models, where the solid-Earth parameters are assumed to depend on the latitude but not on the longitude and the depth. However, 3D GIA models are computationally expensive and sometimes require an iterative coupling for the ice sheet and the solid-Earth solutions to converge. As a consequence, their use remains limited, potentially leading to errors in the simulated ice-sheet response and associated sea-level rise projections. Here, we propose to tackle this problem by generalising the Fourier collocation method for solving GIA proposed by Lingle and Clark (1985) and implemented by Bueler et al. (2007). The method allows for an explicit accounting of the effects of spatially heterogeneous viscosity and lithospheric thicknesses and is computationally very efficient. Thus, for a continental domain at relatively high spatial resolution (256 x 256 grid points) and a 1-year time step, the model runs with speeds of ca. 200 simulation years per second on a single CPU, while keeping the error low compared to 3D GIA models. As the time step is small enough, the need of an iterative coupling method is avoided, thus making the model easy to couple with ice-sheet models.

How to cite: Swierczek-Jereczek, J., Montoya, M., Blasco, J., Alvarez-Solas, J., and Robinson, A.: A generalised Fourier collocation for fast computation of glacial isostatic adjustment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13583, https://doi.org/10.5194/egusphere-egu23-13583, 2023.

EGU23-14958 | Posters virtual | G3.3

Effect of transient deformation in southeast Greenland 

Valentina R. Barletta, Andrea Bordoni, and Shfaqat Abbas Khan

Recent studies have shown that in the area of the Kangerlussuaq glacier, a large GPS velocities residual after removing predicted purely elastic deformations caused by present-day ice loss suggests the possibility of a fast rebound to little ice age (LIA) deglaciation. We previously investigated this area with a Maxwell viscoelastic rheology Earth model and compared the model predictions with GPS residual. We found a match for a rather thick lithospheric thickness and a rather low mantle viscosity structure beneath SE-Greenland. In this study we are going to examine the effect of a Burger model: 1) we compare the results with those from the Maxwell model and 2) we estimate if and where the differences can be discriminated with observational data.
Maxwell models describe a steady state mantle deformation and they are the most commonly model used in post glacial rebound problems. Burgers models, instead, describe a time-varying mantle deformation, which include an initial fast transient components followed by a steady-state phase of mantle deformation. This kind of transient deformation would allow to reconcile the Earth rebound caused by the Pleistocene deglaciation and the faster rebound caused by the recent LIA deglaciation.
We then analyze several scenarios of ice retreat in the last 2000 years in the fiord in front of Kangerlussuaq glacier, in view of the difference between the two rheologies.

How to cite: Barletta, V. R., Bordoni, A., and Khan, S. A.: Effect of transient deformation in southeast Greenland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14958, https://doi.org/10.5194/egusphere-egu23-14958, 2023.

EGU23-15597 | ECS | Orals | G3.3

Lateral and radial viscosity variations beneath Fennoscandia inferred from seismic and MT observations 

Florence Ramirez, Kate Selway, Clinton Conrad, Maxim Smirnov, and Valerie Maupin

Fennoscandia is continuously uplifting in response to past deglaciation, a process known as glacial isostatic adjustment or GIA. One of the factors that controls the uplift rates is the viscosity of the upper mantle, which is difficult to constrain. Here, we reconstruct the upper mantle viscosity structure of Fennoscandia by inferring temperature and water content from seismic and magnetotelluric (MT) data. Using a 1-D MT model for Fennoscandian cratons together with a global seismic model, we infer an upper mantle viscosity range of ~1019 - 1024 Pa·s for 1 – 10 mm grain size, which encompasses the GIA-constrained viscosities of 1020 - 1021 Pa·s. The associated viscosity uncertainties of our calculation are attributed to the uncertainties associated with the geophysical data and unknown grain size. We can obtain tighter constraints if we assume that the Fennoscandian upper mantle is either a wet harzburgite (1019.2 - 1023.5 Pa·s) or a dry pyrolite (1020.0 - 1023.6 Pa·s) below 250 km, where pyrolite is ~10 times more viscous than harzburgite. Furthermore, assuming a constant grain size of either 1 mm or 10 mm reduces the viscosity range by approximately 2 orders of magnitude. In northwestern Fennoscandia, where a high-resolution 2-D resistivity model is available, the calculated viscosities are ~10 - 100  times lower than those for the Fennoscandian craton because the mantle has a higher water content, and both pyrolite and harzburgite must be wet. Overall, our calculated viscosities for Fennoscandia that are constrained from seismic and MT observations agree with the mantle viscosities constrained from GIA. This suggests that geophysical observations can usefully constrain upper mantle viscosity, and its lateral variations, for other parts of the world without GIA constraints.

How to cite: Ramirez, F., Selway, K., Conrad, C., Smirnov, M., and Maupin, V.: Lateral and radial viscosity variations beneath Fennoscandia inferred from seismic and MT observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15597, https://doi.org/10.5194/egusphere-egu23-15597, 2023.

EGU23-17095 | Posters on site | G3.3

Glaciations of the East Siberian Sea 

Aleksey Amantov, Marina Amantova, Lawrence Cathles, and Willy Fjeldskaar

The existence and nature of Quaternary glaciations of the eastern part of the Arctic basin is very far from being solved, and many think glaciations there may been absent or very local, even at the Last Glacial Maximum.  It is unlikely under the conditions of permafrost and low precipitation during MIS 2, that the glaciers would have produced significant topographic relief.  However, significant ice loads will produce a significant isostatic response.  In the area of the Novosibirsk Islands, Holocene changes in sea level and transitions from continental to marine sedimentation indicate differences in emergence over the course of the transgression  that suggest the melting of significant grounded ice masses (e.g. Anisimov et al., 2009). Shorelines deviate from those expected from the hydroisostatic component. The best-fit isostatic model suggests significant LGM ice accumulation close to the ocean in the area of the Henrietta and Jeannette islands of the De Long archipelago in the East Siberian Sea. The uplift deviations in the Zhokhov island district are best matched for an effective elastic lithosphere thickness Te ~40 km. The ice accumulations close to the shelf-ocean margin in the last glaciation seem to also have occurred in earlier glaciations of the region.

Anisimov, M.A., Ivanova, V.V., Pushina, Z.V., Pitulko, V.V. 2009. Lagoon deposits of Zhokhov Island: age, conditions of formation and significance for paleogeographic reconstructions of the Novosibirsk Islands region // Izvestiya RAS, Geographical Series. No. 5. pp. 107-119.

How to cite: Amantov, A., Amantova, M., Cathles, L., and Fjeldskaar, W.: Glaciations of the East Siberian Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17095, https://doi.org/10.5194/egusphere-egu23-17095, 2023.

EGU23-17255 | Posters virtual | G3.3

Sensitivity of Antarctic GIA correction for GRACE data to viscoelastic Earth structure 

Yoshiya Irie and Jun'ichi Okuno

Changes in Antarctic ice mass have been observed as gravity changes by the Gravity Recovery and Climate Experiment (GRACE) satellites. The gravity signal includes both the component of the ice mass change and the component of the solid Earth response to surface mass change (Glacial Isostatic Adjustment, GIA). Therefore, estimates of the ice mass change from GRACE data require subtraction of the gravity rates predicted by the GIA model (GIA correction).

Antarctica is characterized by lateral heterogeneity in seismic velocity structure. West Antarctica shows relatively low seismic velocities, suggesting low viscosity regions in the upper mantle. On the other hand, East Antarctica shows relatively high seismic velocities, suggesting a thick lithosphere. Here we investigate the dependence of the GIA correction on lithospheric thickness and upper mantle viscosity.

The GIA correction for the average viscoelastic structure of West Antarctica is nearly identical to that for the average viscoelastic structure of East Antarctica. There is a trade-off between the lithospheric thickness and the upper mantle viscosity. This trade-off may reduce the effect of the lateral variations in the Earth’s viscoelastic structure beneath Antarctica on estimates of Antarctic ice mass change.

How to cite: Irie, Y. and Okuno, J.: Sensitivity of Antarctic GIA correction for GRACE data to viscoelastic Earth structure, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17255, https://doi.org/10.5194/egusphere-egu23-17255, 2023.

The GRACE (Gravity Recovery and Climate Experiment) satellites measure the Earth’s geopotential, and we can use this data to monitor spatiotemporal mass load changes in Earth's ice sheets. The geopotential measurements are both resolution-limited by the orbital configurations and subject to the complexities of present-day sea level change; for example, when an ice sheet melts, the accompanying migration of water should lead to a systematic bias in GRACE estimates of ice mass loss (Sterenborg et al., 2013). Indeed, using mascons and an iterative approach, Sutterley et al. (2020) found that variations in regional sea level affect ice sheet mass balance estimates in Greenland and in Antarctica by approximately 5%. Here, we use the sea level equation in our inferences of ice-mass loss both to increase the resolution of those inferences and to include the sea-level response in the analysis of GRACE data. We will test the resolution, implementation, accuracy, and impacts of a constrained least squares inversion of GRACE data. We will then investigate how deformation associated with our estimates of ongoing global surface mass change affects Earth-model inferences from geodetic data and Glacial Isostatic Adjustment modeling, with a focus region of Fennoscandia.

How to cite: Powell, E. and Davis, J.: Using the sea level equation to increase the resolution of GRACE inferences: Implications for studies of Fennoscandian GIA, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17418, https://doi.org/10.5194/egusphere-egu23-17418, 2023.

EGU23-1094 | Orals | ST1.4

Magnetotail plasmoid eruption: Interplay of instabilities and reconnection 

Minna Palmroth and the Team

Rapid plasma eruptions explosively release energy in the Earth’s magnetosphere, at the Sun, and solar system planets. At Earth, these eruptions, termed plasmoids, occur in the magnetospheric nightside, and are associated with the sudden brightening of the aurora. The chain of events leading to the plasmoid is one of the longest-standing unresolved questions in space physics. Two competing paradigms, based on magnetic reconnection or kinetic instabilities, are proposed to explain the course of events. We report results of a major technological achievement modelling the Earth’s magnetosphere at realistic scales, with sufficient spatiotemporal resolution, and resolving ion-kinetic physics, and thereby capturing physics essential to both paradigms. We show that both magnetic reconnection and kinetic instabilities are required to induce a global topological reconfiguration of the magnetotail, thereby combining the seemingly contradictory paradigms. Our results show that magnetic reconnection creates local plasmoids that are combined into a tail-wide structure by a current sheet disruption in the center tail. Large-scale current sheet flapping, caused by a drift kink instability and driven by reconnection-generated ions, leads to the current disruption. Our results help to understand plasma eruptions ubiquitous in space plasmas, guide spacecraft constellation mission design, and lead to improved understanding of space weather. We also contemplate the future direction of models within the solar system plasma physics and heliophysics discipline.

How to cite: Palmroth, M. and the Team: Magnetotail plasmoid eruption: Interplay of instabilities and reconnection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1094, https://doi.org/10.5194/egusphere-egu23-1094, 2023.

EGU23-2060 | Posters on site | ST1.4

Turbulent magnetic reconnection in the solar wind 

Rongsheng Wang, Xinmin Li, Shimou Wang, Quanming Lu, San Lu, and Walter Gonzalez

Turbulent magnetic reconnection was observed in the magnetotail and the magnetopause. In turbulent magnetic reconnection, the diffusion region is filled with a number of filamentary currents primarily carried by the electrons and some flux ropes. These dynamic filamentary currents constitute a kind of three-dimensional network in the diffusion region and lead the reconnection into turbulence. The electrons are trapped and sufficiently accelerated inside such a complicated current network.

According to the previous observations, magnetic reconnection generally displays a quasi-steady state in the solar wind, where the energy is dissipated via slow-mode shocks. It is elusive why the reconnection in the solar wind is quasi-steady. Here we present a direct observation of bursty and turbulent magnetic reconnection in the solar wind, with its associated exhausts bounded by a pair of slow-mode shocks. We infer that the plasma is more efficiently heated in the magnetic reconnection diffusion region than across the shocks and that the flow enhancement is much higher in the exhausts than in the area around the diffusion region. We detected 75 other, similar diffusion-region events in solar wind data between October 2017 and May 2019, suggesting that bursty reconnection in the solar wind is more common than previously thought and actively contributes to solar wind acceleration and heating.

How to cite: Wang, R., Li, X., Wang, S., Lu, Q., Lu, S., and Gonzalez, W.: Turbulent magnetic reconnection in the solar wind, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2060, https://doi.org/10.5194/egusphere-egu23-2060, 2023.

EGU23-2179 | Orals | ST1.4

Open questions in heliophysics: terrestrial laboratory 

Elena Kronberg

The heliosphere is a part of the Universe in which we can do in situ measurements of plasma dynamics under diverse conditions. The terrestrial magnetosphere is especially well accessible for studying universal plasma processes, in particular those associated with the conversion and flow of electromagnetic and plasma energy. To fully understand phenomena such as shocks, instabilities at plasma boundaries, and magnetic reconnection it is crucial to consider the coupling between physical processes at small and large scales. Planetary magnetospheric systems are not completely understood, because kinetic and global scales are rarely measured simultaneously. In most observations, the energy range and composition are not resolved for all important contributors.  The mentioned aspects are essential for an assessment of the magnetosphere-ionosphere-atmosphere-subsurface coupling and for the prediction of space weather. The influence of ionospheric charged particles on the magnetospheric dynamics will be used as an illustrative example. 

How to cite: Kronberg, E.: Open questions in heliophysics: terrestrial laboratory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2179, https://doi.org/10.5194/egusphere-egu23-2179, 2023.

EGU23-3258 | ECS | Orals | ST1.4

Radial Diffusion Benchmarking: Initial Conditions 

Sarah Bentley, Jen Stout, Daniel Ratliff, Rhys Thomspon, and Clare Watt

Earth’s radiation belts are a hazardous environment containing trapped charged particles. Radial diffusion is one of the major processes driving radiation belt physics, accounting for energisation, transport and loss of electrons in the outer belt. The outer radiation belt is highly variable in energy and location, resulting in behaviour which is difficult to model accurately.

 

Ensemble modelling is needed to characterise this variability. Ensembles can be constructed by varying physical parameters (capturing the uncertainty in our knowledge across many scales) and considering the spread of the final model outputs. However, it is unclear what proportion of the subsequent variability comes from physics versus the numerical methods used. We investigate the effect of varying initial conditions for typical radial diffusion coefficients.

 

We present two methods of establishing the timescale over which initial conditions affect the subsequent radial diffusion; time to monotonicity (the time taken for the particle distribution to reach a state where radial diffusion effects become uninteresting) and dimensional analysis. Both are needed to capture the processes we are interested in as well as the inherent timescales from diffusion. Our measures are often domain dependent, indicating that the choice of where we perform our radial diffusion simulations is significant.

 

How to cite: Bentley, S., Stout, J., Ratliff, D., Thomspon, R., and Watt, C.: Radial Diffusion Benchmarking: Initial Conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3258, https://doi.org/10.5194/egusphere-egu23-3258, 2023.

EGU23-3517 | ECS | Orals | ST1.4

Cometary Plasma Science - Open questions and implications for heliophysics 

Charlotte Goetz and the Cometary Plasma Science White Paper Team

Comets hold the key to the understanding of our Solar System, its formation and its evolution, and to the fundamental plasma processes at work both in it and beyond it. A comet nucleus emits gas as it is heated by the sunlight. The gas forms the coma, where it is ionised, becomes a plasma, and eventually interacts with the solar wind. Besides these neutral and ionised gases, the coma also contains dust grains, released from the comet nucleus. As a cometary atmosphere develops when the comet travels through the Solar System, large-scale structures, such as the plasma boundaries, develop and disappear, while at planets such large-scale structures are only accessible in their fully grown, quasi-steady state. In situ measurements at comets enable us to learn both how such large-scale structures are formed or reformed and how small-scale processes in the plasma affect the formation and properties of these large scale structures. Furthermore, a comet goes through a wide range of parameter regimes during its life cycle, where either collisional processes, involving neutrals and charged particles, or collisionless processes are at play, and might even compete in complicated transitional regimes. Thus a comet presents a unique opportunity to study this parameter space, from an asteroid-like to a Mars- and Venus-like interaction. The Rosetta mission and previous fast flybys of comets have together made many new discoveries, but the most important breakthroughs in the understanding of cometary plasmas are yet to come. The Comet Interceptor mission will provide a sample of multi-point measurements at a comet, setting the stage for a multi-spacecraft mission to accompany a comet on its journey through the Solar System. We will review the present-day knowledge of cometary plasmas, discuss the many questions that remain unanswered, and outline a multi-spacecraft European Space Agency mission to accompany a comet that will answer these questions by combining both multi-spacecraft observations and a rendezvous mission, and at the same time advance our understanding of fundamental plasma physics and its role in planetary systems.

How to cite: Goetz, C. and the Cometary Plasma Science White Paper Team: Cometary Plasma Science - Open questions and implications for heliophysics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3517, https://doi.org/10.5194/egusphere-egu23-3517, 2023.

EGU23-4065 | Orals | ST1.4

STELLA—Potential European  contributions to a NASA-led interstellar probe 

Robert F. Wimmer-Schweingruber, Nicolas André, Stas Barabash, Pontus C. Brandt, Timothy S. Horbury, Luciano Iess, Benoit Lavraud, Ralph L. McNutt, Jr., Elena A. Provornikova, Eric Quémerais, Robert Wicks, Martin Wieser, and Peter Wurz

Stella is a proposed European contribution to NASA’s Interstellar Probe (ISP), a large-strategic mission candidate. ESA’s call for M-class mission proposals was the best and only currently available option for the European science community to contribute to the astronomically constrained ISP launch window in 2036 – 2037. Traveling with a speed of ~ 7.0 au/year ISP would reach 350 au during its nominal 50-year life-time. The proposed Stella contribution to ISP includes two core and two optional elements for the full complement:

• Core: Provision of European scientific instruments;

• Core: Provision of the European ISP communication system including the spacecraft’s 5-m high gain antenna;

• Full complement: ESA deep space communication facility: an extension of ESA’s DSA with a new antenna array;

• Full complement: Contribution to ISP operations to increase drastically the ISP and European payloads science return.

How to cite: Wimmer-Schweingruber, R. F., André, N., Barabash, S., Brandt, P. C., Horbury, T. S., Iess, L., Lavraud, B., McNutt, Jr., R. L., Provornikova, E. A., Quémerais, E., Wicks, R., Wieser, M., and Wurz, P.: STELLA—Potential European  contributions to a NASA-led interstellar probe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4065, https://doi.org/10.5194/egusphere-egu23-4065, 2023.

Radial diffusion in planetary radiation belts is a dominant transport mechanism resulting in the energisation and loss processes of charged particles by ultra-low frequency (ULF) fluctuations in the Pc4-Pc5 range. The theoretical framework upon which radial diffusion coefficients have been analytically derived in the past 60 years belongs to various types of quasi-linear theories. In quasi-linear theories, the evolution equation for the distribution function experiencing radial diffusion is only valid on slow timescales longer than the characteristic period of the ULF waves and the azimuthal drift period of the particles, ranging from tens of minutes to a few hours for electrons with energies between tens of keV to several hundreds of keV. Therefore, radiation belts’ dynamical processes occurring on fast timescales comparable to ULF wave periods or azimuthal drift periods, such as fast magnetopause losses localised in magnetic local time (MLT), cannot self-consistently be quantified in terms of radial diffusion models. In this communication, we present a new theoretical framework based on drift kinetic (Hazeltine, 1973) to distinguish between the fast and slow response of energetic electrons to ULF waves. We conclude our talk with two examples to demonstrate the benefits of the drift kinetics approach: 1) fast electron losses due to MLT localised compression of the magnetopause, and 2) non-diffusive acceleration associated with symmetric ULF fluctuation.  

How to cite: Osmane, A.: A new theoretical framework to model radial transport of energetic particles by ULF waves in the Earth's magnetosphere., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5242, https://doi.org/10.5194/egusphere-egu23-5242, 2023.

EGU23-5715 | Orals | ST1.4

Plasma-neutral gas interactions in various space environments beyond simplified approximations 

Masatoshi Yamauchi, Johan De Keyser, George Parks, Shin-ichiro Oyama, Peter Wurz, Takumi Abe, Arnaud Beth, Malcolm Dunlop, Pierre Henri, Harald Kucharek, Octav Marghitu, Georgios Nicolaou, Manabu Shimoyama, Joachim Saur, Satoshi Taguchi, Takuo Tsuda, and Bruce Tsurutani

The majority of the atmospheres of solar system bodies are composed of neutral gas, and hence their upper atmosphere are always partially ionized by the solar UV and collisions, allowing a complex nonlinear interaction with interplanetary plasma.  Thus, ion-neutral and electron-neutral interaction plays a key role in this transition regions (ionosphere for planets and moons). However, our current understanding of plasma-neutral gas interactions is very limited due to lack of observations with proper instrumentation and to the difficulty in making laboratory experiments (almost impossible to reproduce the ionosphere with low energy plasma).  Particularly the effect of small amount of neutral species in space above the exobase and the effects of electric charges on neutrals have been underestimated.  

To advance our knowledge of these basic but still poorly understood interactions between plasma and neutral gas at key regions of energy, momentum, and mass exchange between the space and the atmosphere, we evaluate what kind of measurement package is needed for different solar system objects in a cost-effective manner.  We particularly focus on understanding the re-distribution of externally provided energy to the composing species through this interaction.  

The presentation is based on a white paper submitted to ESA's Voyage 2050 (Experimental Astronomy, 2022), and related mission proposals to space agencies.  Here we skip the chemical aspect that is also mentioned in the white paper.

Reference: https://doi.org/10.1007/s10686-022-09846-9

How to cite: Yamauchi, M., De Keyser, J., Parks, G., Oyama, S., Wurz, P., Abe, T., Beth, A., Dunlop, M., Henri, P., Kucharek, H., Marghitu, O., Nicolaou, G., Shimoyama, M., Saur, J., Taguchi, S., Tsuda, T., and Tsurutani, B.: Plasma-neutral gas interactions in various space environments beyond simplified approximations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5715, https://doi.org/10.5194/egusphere-egu23-5715, 2023.

EGU23-5939 | Orals | ST1.4

Plasma-Neutral Interactions in the Lower Thermosphere-Ionosphere: The need for in situ measurements to address outstanding questions 

Theodoros Sarris and the co-authors of the white paper for the decadal survey for solar and space physics 2024-2033

The lower thermosphere-ionosphere (LTI) is a key transition region between the Earth’s neutral atmosphere and plasma-dominated space. Interactions between ions and neutrals maximize within the LTI and in particular at altitudes from 100 to 200 km, which is the least visited region of the near-Earth environment due to enhanced atmospheric drag. The lack of in situ co-temporal and co-spatial measurements of all relevant parameters and their elusiveness to most remote-sensing methods means that the complex interactions between neutral and charged constituents in the LTI remain poorly characterized to this date. This lack of measurements, together with the ambiguity in the quantification of key processes in the 100 to 200 km altitude range, affect current modeling efforts to expand atmospheric models upward to include the LTI and limit current space weather prediction capabilities. In this talk, fundamental science themes in ionosphere-thermosphere physics and related societal and operational needs are outlined; past proposed implementation schemes to sample this transition region are reviewed; and recent efforts by ESA and NASA to highlight outstanding science questions in the LTI and the need for in situ measurements to address them are presented.

How to cite: Sarris, T. and the co-authors of the white paper for the decadal survey for solar and space physics 2024-2033: Plasma-Neutral Interactions in the Lower Thermosphere-Ionosphere: The need for in situ measurements to address outstanding questions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5939, https://doi.org/10.5194/egusphere-egu23-5939, 2023.

EGU23-6626 | ECS | Orals | ST1.4

Global coronal structure and possible new insights in the upcoming perihelia of Parker Solar Probe 

Samuel Badman, Yeimy Rivera, Stuart Bale, and Michael Stevens

 The global structure of the Sun’s extended corona is governed by the physical processes which represent some of the biggest outstanding questions in heliophysics. These include the nature of coronal heating and solar wind acceleration. NASA’s Parker Solar Probe (PSP) offers unique new opportunities to probe this structure directly through its unprecedented orbit which takes it closer to the Sun than any prior spacecraft. As of Spring 2023, PSP has achieved perihelia of 13.3 Rs, but will continue to dive deeper to an eventual closest approach of 9.8Rs at the end of 2024. Already PSP is starting to offer tantalizing glimpses into the sub-alfvenic corona. The most recent orbits exhibit hints of an imminent global plasma regime change on multiple fronts: As well as unambiguous crossings of the Alfven critical surface, PSP sees significant solar wind deceleration, possible global magnetic field reorganization, and proton core temperatures hot enough to be comparable to isothermal solar wind models. In this talk, we will discuss how these exciting initial measurements may become decisive constraints in the latter orbits of the PSP mission. We trace the implications of making such direct measurements of the corona-solar wind transition to the science questions of coronal heating and solar wind acceleration.

How to cite: Badman, S., Rivera, Y., Bale, S., and Stevens, M.: Global coronal structure and possible new insights in the upcoming perihelia of Parker Solar Probe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6626, https://doi.org/10.5194/egusphere-egu23-6626, 2023.

EGU23-6791 | Orals | ST1.4

Physics of plasma–surface–exosphere–dust coupling at the lunar surface for future exploration programmes 

Yoshifumi Futaana and the ESA Topical Team : Physics Of Plasma-Surface-Exosphere-Dust Coupling At The Lunar Surface For Future Exploration Programmes

Exploration of the Moon provides opportunities to investigate the deep space environment upstream of the geospace and the terrestrial magnetosphere and associated space weather phenomena. Moreover, the Moon interaction with the solar wind adds novel, interdisciplinary aspects to fundamental space research: a complex coupling between the solar wind/magnetospheric plasma – energetic particles – exosphere – dust – solid-surface – mini-magnetosphere. As the Moon is the next step in space exploration, characterizing the environment provides vital support to this endeavor. We note that investigations in this area of science are invaluable in providing a characterization of the environment for the needs of human exploration. On the other hand,  the lunar environment is fragile against human activities. For example, the total mass of the lunar atmosphere is of the order of 10 tons. Therefore, the environment will change drastically once human activity starts on the lunar surface. It is significantly essential to characterize the environment before the fragile lunar atmosphere is “contaminated” by human activities at the surface.
With these aspects as a background, we formed an ESA topical team to formulate scientific questions in space plasma physics that can be uniquely investigated on or near the lunar surface. We also derived the required measurements, which can be addressed by lunar missions in the short and long term, including the EL3 (European Logistic Lunar Lander) mission. This presentation introduces the background scientific context and describes the derived scientific concepts. 

How to cite: Futaana, Y. and the ESA Topical Team : Physics Of Plasma-Surface-Exosphere-Dust Coupling At The Lunar Surface For Future Exploration Programmes: Physics of plasma–surface–exosphere–dust coupling at the lunar surface for future exploration programmes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6791, https://doi.org/10.5194/egusphere-egu23-6791, 2023.

EGU23-7030 | Posters virtual | ST1.4

Measuring the Net Charge Density of Space Plasmas 

Chao Shen

Space plasmas are composed of charged particles that play a key role in electromagnetic dynamics. However, to date, there has been no direct measurement of the distribution of such charges in space. In this study, three schemes for measuring charge densities in space are presented. The first scheme is based on electric field measurements by multiple spacecraft. This method is applied to deduce the charge density distribution within Earth’s magnetopause boundary layer using Magnetospheric MultiScale constellation (MMS) 4-point measurements, and indicates the existence of a charge separation there. The second and third schemes proposed are both based on electric potential measurements from multiple electric probes. The second scheme, which requires 10 or more electric potential probes, can yield the net charge density to first-order accuracy, while the third scheme, which makes use of seven to eight specifically distributed probes, can give the net charge density with second-order accuracy. The feasibility, reliability, and accuracy of these three schemes are successfully verified for a charged-ball model. These charge density measurement schemes could potentially be applied in both space exploration and ground-based laboratory experiments.

 

How to cite: Shen, C.: Measuring the Net Charge Density of Space Plasmas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7030, https://doi.org/10.5194/egusphere-egu23-7030, 2023.

EGU23-7436 | Posters virtual | ST1.4

Exploring solar-terrestrial interactions via multiple imaging observers 

Graziella Branduardi-Raymont

How does solar wind energy flow through the Earths magnetosphere, how is it converted and distributed? This are the questions we want to address. We need to understand how geomagnetic storms and substorms start and grow, not just as a matter of scientific curiosity, but to address a clear and pressing practical problem: space weather, which can influence the performance and reliability of our technological systems, in space and on the ground, and can endanger human life and health.

Much knowledge has already been acquired over the past decades, particularly by making use of multiple spacecraft measuring conditions in situ, but the infant stage of space weather forecasting demonstrates that we still have a vast amount of learning to do. A novel global approach is now being taken by a number of space imaging missions which are under development and the first tantalising results of their exploration will be available in the next decade. In a White Paper, submitted to ESA in response to the Voyage 2050 Call, we propose the next step in the quest for a complete understanding of how the Sun controls the Earth’s plasma environment: a tomographic imaging approach comprising two spacecraft in highly inclined polar orbits, enabling global imaging of magnetopause and cusps in soft X-rays, of auroral regions in FUV, of plasmasphere and ring current in EUV and ENA (Energetic Neutral Atoms), alongside in situ measurements. Such a mission, encompassing the variety of physical processes determining the conditions of geospace, will be crucial on the way to achieving scientific closure on the question of solar-terrestrial interactions.

The White Paper was published on 16 August 2021 (G. Branduardi-Raymont et al., Experimental Astronomy, https://doi.org/10.1007/s10686-021-09784-y) and full co-author details are at the end of the article.

How to cite: Branduardi-Raymont, G.: Exploring solar-terrestrial interactions via multiple imaging observers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7436, https://doi.org/10.5194/egusphere-egu23-7436, 2023.

EGU23-8410 | Posters virtual | ST1.4

Future Heliospheric System Science Exploration in Japan 

Yoshifumi Saito, Yoshizumi Miyoshi, Kanako Seki, and Shinsuke Imada

Toward the inner Heliospheric system science exploration in the late 2020s, ISAS/JAXA is currently operating the Arase, BepiColombo/Mio, Hinode, and Akatsuki satellites, and Solar-C EUVST is scheduled for launch in the near future. These missions will be linked together with other satellite missions such as Solar-Orbiter, Solar Parker Probe, Cluster, THEMIS, MMS etc. to realize exploration of the inner Heliosphere with unprecedented scale.

In the early 2030s, Japanese Solar Terrestrial Physics group is considering the FACTORS formation-flight satellite mission in order to reveal the energy coupling mechanisms and mass transport between the space and Earth’s atmosphere. In the late 2030s, another formation-flight magnetospheric satellite mission the science target of which includes understanding the cross-scale / cross-region coupling is also under consideration hopefully on orbit at the same time with European future mission Plasma Observatory. These future missions will closely collaborate with NASA’s future GDC and Magnetospheric Constellation missions.

The future Heliospheric system science exploration will be conducted by multiple satellite missions further expanding their observation area while improving the quality of each individual satellite mission. Japanese Solar Terrestrial Physics group will conduct in-situ observation of space plasmas with MMX (Martian Moons Exploration) and MIM(Mars Ice Mapper) in the Martian system and with JUICE (Jupiter Icy Moons Explorer) in the Jovian system. Collaboration between Japanese Solar Physics and Solar Terrestrial Physics groups for considering the future out-of-ecliptic-plane mission is also about to start.

In order to realize the future Heliospheric system science exploration, significant technological development is mandatory. Current status of the technological development in Japan for enabling the future Heliospheric system science exploration will also be presented.

How to cite: Saito, Y., Miyoshi, Y., Seki, K., and Imada, S.: Future Heliospheric System Science Exploration in Japan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8410, https://doi.org/10.5194/egusphere-egu23-8410, 2023.

EGU23-8879 | Posters on site | ST1.4

On the Contribution of Coronal Mass Ejections to the Heliospheric Magnetic Flux Budget on Different Time Scales 

Reka Winslow, Camilla Scolini, Noé Lugaz, Nathan Schwadron, and Antoinette Galvin

Coronal mass ejections (CMEs) contribute closed magnetic flux to the heliosphere while they are connected at both ends to the Sun and play a key role in adding magnetic flux to the heliosphere. Here, we discuss an outstanding question in heliophysics: how the type of magnetic reconnection that opens CME field lines in the inner heliosphere, i.e. interchange (IC) reconnection (below the Alfvén surface) and/or interplanetary (IP) reconnection (above the Alfvén surface), determines the length of time CMEs contribute to the heliospheric flux budget. Although IP reconnection does not alter the total amount of magnetic flux in the heliosphere, it matters in this context because it prevents the efficient opening of CME closed magnetic flux through IC reconnection, thereby prolonging the length of time that CMEs contribute closed magnetic flux to the heliosphere. We suggest that there is a varying timescale of contribution of individual CMEs to the heliospheric flux budget, with some CMEs contributing for considerably longer than others, depending on their interactions in IP space (i.e., depending on the fraction of the CME magnetic field lines opened up through IP reconnection vs. IC reconnection, or both). Such a distinction has not been taken into account in past studies that estimate the CME flux opening timescale. We outline key criteria to aid in distinguishing IC reconnection from IP reconnection based on in situ spacecraft data and highlight these through two example events. Studying the manner in which CMEs reconnect and open in the inner heliosphere has implications for a broad range of solar and heliospheric physics research areas and yields important insights not only into CMEs' role in the heliospheric flux budget but also the evolution of CME complexity, connectivity, and topology.

How to cite: Winslow, R., Scolini, C., Lugaz, N., Schwadron, N., and Galvin, A.: On the Contribution of Coronal Mass Ejections to the Heliospheric Magnetic Flux Budget on Different Time Scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8879, https://doi.org/10.5194/egusphere-egu23-8879, 2023.

EGU23-9043 | Posters on site | ST1.4

Plasma Observatory ESA M7 candidate mission: unveiling plasma energization and energy transport through multiscale observations 

Maria Federica Marcucci, Alessandro Retinò, Malcolm Dunlop, Colin Forsyth, Yuri Khotyaintsev, Olivier Le Contel, Ian Mann, Rumi Nakamura, Minna Palmroth, Ferdinand Plaschke, Jan Soucek, Masatoshi Yamauchi, Andris Vaivads, and Francesco Valentini and the Plasma Observatory Team

The Earth's Magnetospheric System is the complex and highly dynamic environment in near-Earth space where plasma gets actively energized and transport of large amounts of energy occurs, due to the interaction of the solar wind with the Earth's magnetic field. Understanding plasma energization and energy transport is an open challenge of space plasma physics, with important implications for space weather science as well as for the understanding of distant astrophysical plasmas. Plasma energization and energy transport are related to fundamental processes such as shocks, magnetic reconnection, turbulence and waves, plasma jets and instabilities, which are at the core of the current space plasma physics research. ESA/Cluster and NASA/MMS four-point constellations, as well as the large-scale multipoint mission NASA/THEMIS, have greatly improved over the last two decades our understanding of plasma processes at individual scales compared to earlier single-point measurements. Despite the large amount of available observations, we still do not fully understand the physical mechanisms which give rise to plasma energization and energy transport. The reason is that the fundamental physical processes governing plasma energization and energy transport operate across multiple scales ranging from the large fluid to the smaller kinetic scales. Here we present the Plasma Observatory (PO) multiscale mission concept which is tailored to study plasma energization and energy transport within the Earth's Magnetospheric System. PO baseline is comprised of one mothercraft (MSC) and six identical smallsat daughtercraft (DSC) in an HEO 8 RE X 18 RE orbit, covering all the key regions of the Magnetospheric System where strong energization and transport occur: the foreshock, bow shock, magnetosheath, magnetopause, magnetotail current sheet, and the transition region. MSC payload provides a complete characterization of electromagnetic fields and plasma particles in a single point with time resolution sufficient to resolve kinetic physics at sub-ion scales. The DSCs have identical payload which is much simpler than on the MSC, yet giving a full characterization of the plasma at the ion and fluid scales. Going beyond Cluster, THEMIS and MMS, PO will permit us to resolve for the first time the coupling between ion and fluid scales as well as the non-planarity and non-stationarity of plasma structures at those scales.  PO is one of the five ESA M7 candidates to be launched around 2037 and is currently undergoing a competitive Phase 0 at ESA for further downselection to Phase A at the end of 2023.

How to cite: Marcucci, M. F., Retinò, A., Dunlop, M., Forsyth, C., Khotyaintsev, Y., Le Contel, O., Mann, I., Nakamura, R., Palmroth, M., Plaschke, F., Soucek, J., Yamauchi, M., Vaivads, A., and Valentini, F. and the Plasma Observatory Team: Plasma Observatory ESA M7 candidate mission: unveiling plasma energization and energy transport through multiscale observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9043, https://doi.org/10.5194/egusphere-egu23-9043, 2023.

EGU23-9223 | Orals | ST1.4

The ESA Heliophysics Working Group: building cross-discipline bridges to better serve the European Heliophysics  community 

Matthew Taylor, Piers Jiggins, Juha-Pekka Luntama, Astrid Orr, and Anja Strømme

Heliophysics, the science of understanding the Sun and its interaction with the Earth and the solar system, has a large and active international community, with significant expertise and heritage in the European Space Agency and Europe. Several ESA directorates have activities directly connected with this topic, including ongoing and/ or planned missions and instrumentation, comprising a ESA Heliophysics observatory or more musically, a Heliophysics Orchestra. More specifically in ESA: The Directorate of Science with mission such as Ulysses, SOHO, Cluster, Solar Orbiter, SMILE etc, as well as hosting the Heliophysics archive; The Directorate of Earth Observation with Swarm and other Earth Explorer missions, as well as the ongoing ESA-NASA Lower Thermosphere-Ionosphere Science Working Group (EN-LoTIS-WG); The Directorate of Operations with the Vigil mission, the Distributed Space Weather Sensor System (D3S) and the Space Weather Service Network; The Directorate of Human and Robotic Exploration with many ISS and LOP-Gateway payloads and the Directorate of Technology, Engineering Quality with expertise in developing instrumentation and models for measuring and simulating environments throughout the heliosphere.

An ESA Heliophysics Working group has been appointed by several ESA Directors, under the direction of the ESA Director General, to work on optimizing synergies across directorates, and to act as a focus for discussion, inside ESA, of the scientific interests of the Heliophysics community, including the European ground-based community and data archiving activities. 

How to cite: Taylor, M., Jiggins, P., Luntama, J.-P., Orr, A., and Strømme, A.: The ESA Heliophysics Working Group: building cross-discipline bridges to better serve the European Heliophysics  community, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9223, https://doi.org/10.5194/egusphere-egu23-9223, 2023.

EGU23-9239 | Orals | ST1.4

ISTPNext and the ITM Great Observatory: The need for international coordination in Heliophysics 

Emil Kepko and the COSPAR Task Group on Establishing an International Geospace Systems Program

Heliophysics is the study of the Sun and its effects throughout the solar system. It covers an incredible range of scales, from plasma physics at the electron scale to the boundary that separates our solar system from interstellar space. It also includes a diverse array of sub disciplines and expertise, with measurements spanning in situ particles and fields from the ionosphere out to the Sun’s corona, to remote sensing of the Sun, heliosphere, and near-Earth environment at multiple wavelengths and in energetic neutral atom observations. Many of the biggest unanswered science questions that remain across Heliophysics center around the interconnectivity of the different physical systems that comprise the Heliosphere, and the role of mesoscale dynamics in modulating, regulating, and controlling that interconnected behavior. These are complex, yet ultimately fundamental questions of how the Sun-Heliosphere and Geospace interact, and answers are needed to more accurately predict and model space weather impacts on and around Earth, the moon, and Mars. To answer these long-standing questions on the Sun-Heliosphere and Geospace as system-of-systems, we believe that Heliophysics requires a coordinated, deliberate, worldwide scientific effort. We suggest that the worldwide Heliophysics discipline should embark on a grand program to study these system-of-systems holistically, with coordinated, multipoint measurements, with particular emphasis on resolving mesoscale dynamics, and a whole-of-science approach that includes ground-based measurements and advanced numerical modeling. Without such a unified, next generation ISTP-type program, these questions will remain largely unanswered. In this paper we lay out the case for such an approach, and discuss how the ITM community is using the upcoming NASA GDC mission as a cornerstone to develop the ITM Great Observatory, a grass-roots, holistic approach modeled after ISTP to study the ITM system.

How to cite: Kepko, E. and the COSPAR Task Group on Establishing an International Geospace Systems Program: ISTPNext and the ITM Great Observatory: The need for international coordination in Heliophysics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9239, https://doi.org/10.5194/egusphere-egu23-9239, 2023.

EGU23-9872 | Orals | ST1.4

Our Heliosphere in the Very Local Interstellar Medium: Exploration by New Horizons, Voyager, IBEX, IMAP and a Future Interstellar Probe 

Pontus Brandt, Alan Stern, Linda Spilker, Heather Elliott, Matt Hill, Peter Kollmann, Ralph McNutt, Parisa Mostafavi, Dave McComas, Randy Gladstone, Mihaly Horanyi, Andrew Poppe, Elena Provornikova, Jeff Linsky, Seth Redfield, Tod Lauer, Kelsi Singer, John Spencer, Anne Verbiscer, and Merav Opher

Our solar system has evolved through accretion of dust and gas as the Sun and its protective magnetic bubble – “the heliosphere” - have plowed through interstellar space on its journey through the galaxy. Over the course of its evolution, the solar system has encountered dramatically different interstellar properties resulting in a severely compressed heliosphere with periods of full exposures of interstellar gas, plasma, dust and galactic cosmic rays (GCRs) that all have helped shaped the system we live in today. Our current knowledge lacks the direct measurements necessary to understand how our star upholds its vast heliosphere and its potentially game-changing role in the evolution of our galactic home.

Voyager 1 and 2 are now in the Very Local Interstellar Medium (VLISM), where they are expected to operate until the mid-2030’s having uncovered many unexpected discoveries and mysteries. After its paradigm-shifting discoveries at Pluto and Arrokoth, New Horizons is currently the only spacecraft in the outer heliosphere and is following the same heliospheric longitude as Voyager 2, but in the ecliptic plane – a trajectory that intersects the IBEX ribbon. It is projected to operate across the heliospheric termination shock and possible the heliopause with new measurements that will shed light on many of the mysteries of our heliosphere. Now passing 55 au, New Horizons is uniquely positioned to investigate the evolution of the solar wind, energetic particles, GCRs, and, in particular interstellar Pick-Up Ions (PUIs) that Voyager was not equipped to measure, to help constrain the structure and dynamics of the heliosphere. Observations of GCRs offers an opportunity to understand how these scatter strongly in the wavy structure of the “ballerina skirt” of the solar magnetic field leading to the strong modulation as part of the overall heliospheric shielding.

As New Horizons continues to travel outward, dust measurements may reveal an interstellar component that will provide the strongest constraint to date on how interstellar dust grains interact with the heliosphere. Now beyond the infrared and UV haze of the circumsolar dust and hydrogen gas, the Alice UV camera holds promise to search for signatures of the hydrogen wall and perhaps even signatures of our neighboring interstellar clouds.

New Horizons continues to break new ground in understanding the formation of our solar system by revealing the properties of multiple distant Kuiper Belt Objects and provide critical constraints on the structure of the Sun’s enormous dust disk. Because of its distant position, New Horizons is also providing the unprecedented estimates of the cosmic background.

In this presentation we provide an overview of New Horizons’ heliophysics observations in the context of the exploration by Voyager, IBEX, and IMAP. We conclude by providing a status of the future Interstellar Probe mission concept that is now under consideration in the Solar and Space Physics Decadal Survey.

How to cite: Brandt, P., Stern, A., Spilker, L., Elliott, H., Hill, M., Kollmann, P., McNutt, R., Mostafavi, P., McComas, D., Gladstone, R., Horanyi, M., Poppe, A., Provornikova, E., Linsky, J., Redfield, S., Lauer, T., Singer, K., Spencer, J., Verbiscer, A., and Opher, M.: Our Heliosphere in the Very Local Interstellar Medium: Exploration by New Horizons, Voyager, IBEX, IMAP and a Future Interstellar Probe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9872, https://doi.org/10.5194/egusphere-egu23-9872, 2023.

EGU23-10279 | ECS | Posters on site | ST1.4

Energetic Particles in the Outer Heliosphere 

Parisa Mostafavi, Matthew Hill, Peter Kollmann, Pontus Brandt, Ralph McNutt, Alan Stern, Bishwas Shrestha, Fran Bagenal, Kelsi Singer, Anne Verbiscer, and John Spencer

Nonthermal energetic pickup ions (PUIs), created in the heliosphere by charge exchange between solar wind ions and interstellar neutral atoms, play an essential role in understanding solar wind evolution in the outer heliosphere and the structure and dynamics of the global heliosphere. New Horizons spacecraft, launched in 2006, is now located at about 55 au from the Sun, exploring the outer heliosphere, and is the only spacecraft equipped with proper instruments to measure nonthermal energetic pickup ions (PUIs) in the outer heliosphere for the first time. Its observations showed that energetic PUIs dominate the internal pressure of the outer heliosphere, with PUI pressures larger than the thermal solar wind and magnetic pressures outside ~ 20 au. At these distances, PUIs contribute substantially to heating and slowing down the solar wind. Moreover, New Horizons observations showed that PUIs mediate shock waves in the outer heliosphere. Here, we give an overview of the energetic particles in the outer heliosphere and their effect on shocks. We present the in situ observations of the hydrogen and Helium PUIs made by New Horizons' SWAP and PEPSSI instruments. Finally, we present some of the most important open questions related to the outer heliosphere that future studies and space missions should address.

How to cite: Mostafavi, P., Hill, M., Kollmann, P., Brandt, P., McNutt, R., Stern, A., Shrestha, B., Bagenal, F., Singer, K., Verbiscer, A., and Spencer, J.: Energetic Particles in the Outer Heliosphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10279, https://doi.org/10.5194/egusphere-egu23-10279, 2023.

In the 2020s we are entering a golden age of inner heliosphere science. International Mercury exploration mission BepiColombo was launched in 2018 and will arrive at Mercury in 2025. During the interplanetary cruise phase, BepiColombo will range from 1.2 AU to 0.3 AU, and will stay in the inner heliosphere for long time. BepiColombo started its science observations during the interplanetary cruise phase in 2020. The initial results showed its enough performance to observe solar wind electrons, IMF, and solar energetic particles (SEPs) even in the composite spacecraft configuration. Especially in 2021 two spacecraft of BepiColombo, Mercury Planetary Orbiter (MPO) and Mercury Magnetosphere Orbiter (Mio), successfully detected many SEP events. BepiColombo can contribute to leading and expanding the heliospheric system science. In addition to BepiColombo, NASA’s Parker Solar Probe and ESA’s Solar Orbiter are also exploring the inner heliosphere. Coordinated observations between these multi spacecraft have been planned and performed. In March 2021, we also coordinated a joint observation campaign of the solar corona and solar wind with BepiColombo, Akatsuki, and Hinode. These coordinated observations/analysis with multi spacecraft, ground-based observations, and numerical simulations can give us great opportunities to address outstanding questions in heliophysics. Here we Here we present the overview and updated status of BepiColombo and the coordinated science observations.

How to cite: Murakami, G. and Benkhoff, J.: Coordinated observations for inner heliospheric science: contribution by the BepiColombo mission, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11768, https://doi.org/10.5194/egusphere-egu23-11768, 2023.

EGU23-11849 | Posters on site | ST1.4

3D evolution of localized plasma flow and its interaction with ambient field 

Rumi Nakamura, Yoshizumi Miyoshi, and Evgeny Panov

A major part of the transport of the magnetic flux and energy in the midtail and the near-Earth tail region is accomplished by local fast plasma jets, called bursty bulk flows (BBF) or flow bursts.  The interaction between BBF and ambient field plays an important role in the complex chain of solar wind-magnetosphere-ionosphere coupling processes. Furthermore, near-Earth flow braking/bouncing processes and associated magnetic and pressure disturbances in the transition region of the magnetic field configuration from tail-like to dipolar field  lead to complex localized current sheet restructuring. Associated energetic particle injection further effects the inner magnetosphere bringing in the source population of the plasma waves that cause electron accelerations as well as seed populations of the radiation belts.

 

In this presentation we stress the importance of observations of BBF and dipolarization by covering extensive region, both near the equator and off-equator simultaneously, for understanding the energy transport processes by including both the field-aligned and perpendicular evolution of the flux tube.  By showing several examples of observations with fortuitous multi-spacecraft configuration, 3D nature of the interaction between BBF and ambient plasma will be discussed. 

 

 

How to cite: Nakamura, R., Miyoshi, Y., and Panov, E.: 3D evolution of localized plasma flow and its interaction with ambient field, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11849, https://doi.org/10.5194/egusphere-egu23-11849, 2023.

EGU23-12289 | ECS | Orals | ST1.4

Citizen science and the exploration of solar data 

Sophie Musset, Lindsay Glesener, Ramana Sankar, Lucy Fortson, Paloma Jol, Kekoa Lasko, Yixian Zhang, Navdeep Panesar, Gregory Fleishman, Mariana Jeunon, Neal Hurlburt, and Yuping Zheng

Citizen science provides a way to analyze large and complex data sets, complementary to contemporary tools such as machine learning. Indeed, while trained algorithms excel in the task they are trained for, humans can spot outliers and make serendipitous discoveries. With recent and new instruments, we are able to observe the Sun and the heliosphere at high cadence and high resolution, providing large amounts of data, revealing complexity in the observed features, and leading to the discovery of new features on small scales. We will present how citizen science, while still under-utilized in solar and heliospheric physics, is particularly adapted to explore, and analyze solar data sets. The “Solar Jet Hunter”, a citizen science project launched one year ago to build a catalog of coronal jets, will be presented as an example, and other science cases for which citizen science is the most adequate tool will be highlighted. Finally, the opportunities raised by citizen science to create strong relationships between academia and society will be discussed.

How to cite: Musset, S., Glesener, L., Sankar, R., Fortson, L., Jol, P., Lasko, K., Zhang, Y., Panesar, N., Fleishman, G., Jeunon, M., Hurlburt, N., and Zheng, Y.: Citizen science and the exploration of solar data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12289, https://doi.org/10.5194/egusphere-egu23-12289, 2023.

The “Mars Magnetosphere ATmosphere Ionosphere and Space-weather SciencE (M-MATISSE)” mission is an ESA Medium class (M7) candidate currently in Phase 0 study by ESA. M-MATISSE’s main scientific goal is to unravel the complex and dynamic couplings of the Martian magnetosphere, ionosphere and thermosphere (MIT coupling) with relation to the Solar Wind (i.e. space weather) and the lower atmosphere. It will provide the first global characterisation of the dynamics of the Martian system at all altitudes, to understand how the atmosphere dissipates the incoming energy from the solar wind, including radiation, as well as how different surface processes are affected by Space Weather activity.

M-MATISSE consists of two orbiters with focused, tailored, high-heritage payloads to observe the plasma environment from the surface to space through coordinated simultaneous observations. It will utilize a unique 3-vantage point observational perspective, with the combination of in-situ measurements by both orbiters and remote observations of the lower atmosphere and ionosphere by radio crosstalk between them.

M-MATISSE is the product of a large organized and experienced international consortium. It has the unique capability to track solar perturbations from the Solar Wind down to the surface, being the first mission fully dedicated to understand planetary space weather at Mars. It will revolutionize our understanding and ability to forecast potential global hazard situations at Mars, an essential precursor to any future robotic & human exploration.

How to cite: Sanchez-Cano, B. and the the M-MATISSE team: The M-MATISSE mission: Mars Magnetosphere ATmosphere Ionosphere and Space weather SciencEAn ESA Medium class (M7) candidate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13687, https://doi.org/10.5194/egusphere-egu23-13687, 2023.

ESA F-Class missions offer a new opportunity to do space science with smaller, cheaper spacecraft. The mission format presents a tough challenge for plasma physics missions, can we make simple, small and cost-effective spacecraft for a topic that will make breakthrough discoveries? This presentation will discuss the past two proposals of the Debye mission, the lessons learned and the challenges ahead to make a similar mission feasible. Debye addresses a grand-challenge problem at the forefront of physics: to understand how energy is transported and transformed in plasmas. The smallest characteristic scales, at which electron dynamics determines the behaviour of energy, are the next frontier in space and astrophysical plasma research. Debye will be the first electron-astrophysics mission. Electron-kinetic processes operate at very small scales (< 10 km) but define the behaviour of the plasma at system-size scales. Debye will use the solar wind as a natural plasma laboratory to measure these electron-scale processes. Understanding the heating, acceleration, thermalisation, and heat flux of electrons is fundamental to our understanding of the dynamics and thermodynamics of plasmas throughout the Universe and thus to the entire field of astrophysics. Debye will answer the fundamental science question "How are electrons heated in astrophysical plasmas?" The mission will make the highest-resolution measurements of electrons ever made in space in terms of energy, angle, time, and space, coupled with two-point high-cadence field measurements to identify the plasma fluctuations responsible for electron energisation. This mission concept will provide ground-breaking and transformative physics results since the combination of rapid particle and field measurements over distances of less than 10 km is completely unprecedented. We believe Debye is a fast, feasible, and focussed mission, tailored to achieve these science objectives, but there are some technical challenges that are assessed to be problematic, how can we address data transmission, formation flying, the cost of multi-item payloads and multi-spacecraft missions?

How to cite: Wicks, R. and Verscharen, D.: Electron-astrophysics in the solar wind: plasma physics at F-Class, lessons learned and things to do., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13772, https://doi.org/10.5194/egusphere-egu23-13772, 2023.

EGU23-14278 | ECS | Posters on site | ST1.4

Sub-Alfvénic solar wind streams near the earth: characteristics and their origin 

Rong Lin, Jiansen He, and Chuanpeng Hou

The PSP observation of a long-lived sub-Alfvénic solar wind, along with its magnetic-dominant character, marks a milestone that a human spacecraft has entered the solar corona for the first time (Kasper et al. 2021 PRL). In fact, sub-Alfvénic solar wind streams have also been observed multiple times near the earth by WIND spacecraft. What are the difference and possible connections between the sub-Alfvénic streams very close to the sun and the sub-Alfvénic streams 1 au from the sun? What process generates and sustains the near-earth sub-Alfvénic streams as they propagate outwards? Why the yearly occurrence frequency of them strongly correlates with solar activity? We study several sub-Alfvénic streams, which can be categorized into two groups: sub-Alfvénic background solar wind and sub-Alfvénic ICMEs. In-situ observations, remote observations, and connecting tools are used in our study. We find the sub-Alfvénic background streams are magnetic enhancements embedded in rarefactions. Their origin can be the boundary of the expanding coronal holes and shrinking active regions. A sub-Alfvénic ICME is generally a low-density part of the whole ICME, whose solar origin tends to be elusive in the coronagraph but still geomagnetically effective because of the ICME magnetic field.

How to cite: Lin, R., He, J., and Hou, C.: Sub-Alfvénic solar wind streams near the earth: characteristics and their origin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14278, https://doi.org/10.5194/egusphere-egu23-14278, 2023.

EGU23-15055 | Posters virtual | ST1.4

Cool Multiphase Plasma in Hot Environments 

Patrick Antolin and Clara Froment

Cool plasmas (≈ 104 K) embedded in a larger, much hotter (>106 K) medium are ubiquitous in different astrophysical systems such as solar & stellar coronae, the circumgalactic (CGM), interstellar (ISM) and intra-cluster (ICM) media. The role of these multiphase plasmas has been highlighted in mass-energy cycles at all such scales, from thermal non-equilibrium (TNE) cycles in the solar atmosphere to precipitation-regulated feedback cycles that drive star and galaxy formation. The properties of the cool plasmas across these multiple scales is strikingly similar, intimately linked to the yet unclear but fundamental mechanisms of coronal and ICM heating and instabilities of thermal or other nature. The solar corona constitutes a formidable and unique astrophysics laboratory where we can spatially and temporally resolve the physics of such multiphase plasma. The multi-faceted and measured response of the solar atmosphere to the heating is exemplified by TNE cycles that manifest through EUV intensity pulsations and through the generation of cool coronal rain and prominences whose mysterious properties are like that of multiphase filamentary structure in the ISM and ICM or to molecular loops in the Galactic centre. Coronal rain also occurs across a wide energetic scale extending to flares, whose features seem recurrent in active stars but remains poorly investigated due to lack of multi-temperature coverage at appropriate resolution. The formation and stability-loss of prominences is of major importance to space weather and their ‘slingshot’ counterparts provide unique diagnostic capabilities to the wind mass-loss rate. These exciting new cross-disciplinary possibilities are part of a Heliophysics Decadal Survey white paper and call for a high-resolution multi-wavelength imaging and spectroscopic solar instrument able to capture the multithermal, dynamic and pervasive nature of the multiphase plasma in the hot solar corona.

How to cite: Antolin, P. and Froment, C.: Cool Multiphase Plasma in Hot Environments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15055, https://doi.org/10.5194/egusphere-egu23-15055, 2023.

EGU23-15289 | Posters virtual | ST1.4

Space Weather with Radio Telescopes in Australia 

Mark Cheung, Ron Ekers, John Morgan, Rajan Chhetri, Angelica Waszewski, George Hobbs, Dilpreet Kaur, Andrew Zic, Ramesh Bhat, and Meng Jin

CSIRO, Australia's national science agency, operates a number of world-class radio astronomy observatories that are collectively known as the Australia Telescope National Facility (ATNF). The facility offers a powerful view of the southern hemisphere radio spectrum and supports world-leading research by Australian and international astronomers. Decades after the Culgoora Radioheliograph made fundamental discoveries about solar radio bursts, a new generation of radio telescopes in Australia are providing unique measurement capabilities to address outstanding questions in Heliophysics. Inyarrimanha Ilgari Bundara (“Sharing the Sky and Stars”), the CSIRO Murchison Radio-astronomy Observatory in Western Australia, is home to the Murchison Widefield Array (operated by a consortium led by Curtin University), the Australian Square Kilometre Array Pathfinder (ASKAP), and the future home of the Square Kilometre Array (SKA)-Low Telescope. Interplanetary scintillation (IPS) measurements by these radio telescope arrays will provide important observational constraints of the solar wind and interplanetary coronal mass ejections (ICMEs). This is enabled by simultaneous detections of a high density of scintillating sources over a wide field of view. Complementarily, Parkes Radio Telescope observations towards pulsars may provide density and magnetic field diagnostics of the corona and solar wind. In addition, radio observations toward exoplanet host stars give important constraints on the habitability of exoplanets. In this presentation, we will introduce the facilities, relevant radio astronomical diagnostics, early results, and plans for using the observations for data assimilation. 

How to cite: Cheung, M., Ekers, R., Morgan, J., Chhetri, R., Waszewski, A., Hobbs, G., Kaur, D., Zic, A., Bhat, R., and Jin, M.: Space Weather with Radio Telescopes in Australia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15289, https://doi.org/10.5194/egusphere-egu23-15289, 2023.

EGU23-16422 | Posters virtual | ST1.4

Venus Dynamics Tracer (VdT) - a mission dedicated for in-situ measurements of the Venus atmosphere. 

Gabriella Stenberg Wieser, Masatoshi Yamauchi, and Moa Persson

Recent Venus missions (Venus Express and Akatsuki) provided a large-scale view of Venus atmosphere and discovered new phenomena, such as high-altitude extension of the mountain wave to the cloud layer and a dawn-dusk asymmetry in the ionospheric motion.  The superrotation of the cloud layer is assumed to be driven by the thermal tide but its relation to any meridional convection or waves is still unknown.  The key to understand all these phenomena is to determine the multi-step re-distribution of the absorbed solar radiation to other forms of energy: (1) internal energy; including temperature, latent heat, and chemical energy (2) kinetic energy both in large scale flows/waves and in minor deviations of convection motions, (3) electric energy including ionization.

To understand how the motion of Venus atmosphere is driven by the energy originating from the absorption of solar radiation, we proposed Venus Dynamics Tracer (VdT), a mission for in-situ measurements, as a response to the ESA call for new M-class missions.  Specific targets were two major energy absorption regions: the cloud layer and the ionized upper atmosphere. The scientific goals were to investigate (a) the roles of the vertical and meridional circulation in maintaining major atmospheric dynamics near the cloud layer where visible light is absorbed and drives the vertical motions of the air, and to understand the (b) global dynamics of ions and neutrals in the upper atmosphere where EUV is absorbed both by neutrals and ions and where energy and momentum are transferred between them. 

For the first target, multiple-balloons are deployed for in situ observations with supporting camera/s on an orbiter giving global context. For the second target, the motions of ions and neutrals are directly measured.  This presentation discusses required measurements to answer the scientific goals.  

How to cite: Stenberg Wieser, G., Yamauchi, M., and Persson, M.: Venus Dynamics Tracer (VdT) - a mission dedicated for in-situ measurements of the Venus atmosphere., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16422, https://doi.org/10.5194/egusphere-egu23-16422, 2023.

EGU23-16967 | Posters on site | ST1.4

A new approach to modeling galactic cosmic rays in the heliosphere using arbitrary data driven MHD backgrounds 

Vladimir Florinski, Juan Alonso Guzman, Merav Opher, and Keyvan Ghanbari

The Voyager space probes provided us with a global perspective on galactic cosmic ray transport through the heliosphere at low to moderate heliographic latitudes, as well as their behavior at the boundary with the very local interstellar medium (VLISM). There remain, however, multiple interesting region the Voyagers have not visited, including high latitudes and the distant flanks of the heliopause where long-term trapping of charged particles is though to take place. We attempt to fill the gaps in our understanding of the distant heliosphere using computer simulations. The Space Plasma and Energetic Charged particle TRansport on Unstructured Meshes (SPECTRUM) code is a versatile software platform to perform tracing of particle trajectories using multiple physics models and internal or externally provided MHD background data. We apply the model to the problem of galactic cosmic ray transport in the outer heliosphere and the surrounding very local interstellar medium (VLISM) using the MHD background provided on a adaptive block mesh from the Space Weather Modeling Framework (SWMF). We compare the guiding center and nearly isotropic (Parker) physics models and elucidate the role of perpendicular diffusion in cosmic-ray penetration through the heliospheric boundary.

How to cite: Florinski, V., Alonso Guzman, J., Opher, M., and Ghanbari, K.: A new approach to modeling galactic cosmic rays in the heliosphere using arbitrary data driven MHD backgrounds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16967, https://doi.org/10.5194/egusphere-egu23-16967, 2023.

EGU23-17230 | Orals | ST1.4

The Firefly Constellation: The Need for a Wholistic View of the Sun and its Environment 

Nour E. Raouafi and the The Firefly Constellation Team

Firefly is an innovative mission concept study for the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033 to fill long-standing knowledge gaps in Heliophysics. A constellation of spacecraft will provide both remote sensing and in situ observations of the Sun and heliosphere from a whole 4π-steradian field of view. The concept implements a holistic observational philosophy that extends from the Sun’s interior, to the photosphere, through the corona, and into the solar wind simultaneously with multiple spacecraft at multiple vantage points optimized for continual global coverage over much of a solar cycle. The mission constellation includes two spacecraft in the ecliptic and two flying as high as ~70º solar latitude. The ecliptic spacecraft will orbit the Sun at fixed angular distances of ±120º from the Earth. Firefly will provide new insights into the fundamental processes that shape the whole heliosphere. The overarching goals of the Firefly concept are to understand the global structure and dynamics of the Sun’s interior, the generation of solar magnetic fields, the origin of the solar cycle, the causes of solar activity, and the structure and dynamics of the corona as it creates the heliosphere. We will provide an overview of the Firefly mission science and architecture and how it will revolutionize our understanding of long-standing heliospheric phenomena such as the solar dynamo, solar cycle, magnetic fields, solar activity, space weather, the solar wind, and energetic particles

How to cite: Raouafi, N. E. and the The Firefly Constellation Team: The Firefly Constellation: The Need for a Wholistic View of the Sun and its Environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17230, https://doi.org/10.5194/egusphere-egu23-17230, 2023.

EGU23-798 | ECS | Posters on site | ST2.4

Investigating the acceleration efficiency of VLF and ULF waves on different electron populations in the outer radiation belt through multi-point observations and modeling 

Afroditi Nasi, Christos Katsavrias, Sigiava Aminalragia-Giamini, Nour Dahmen, Antoine Brunet, Constantinos Papadimitriou, Ingmar Sandberg, Sébastien Bourdarie, Viviane Pierrard, Edith Botek, Fabien Darrouzet, Ondrej Santolik, Benjamin Grison, Ivana Kolmasova, David Pisa, Yoshizumi Miyoshi, Wen Li, Hugh Evans, and Ioannis A. Daglis and the Arase Team

During the second half of 2019, the Earth’s magnetosphere was impacted by a sequence of Corotating Interaction Regions (CIRs) during four consecutive solar rotations. Based on the solar wind properties, the CIRs can be divided in four groups, with the 3rd group, which arrived on August-September 2019, resulting in significant multi-MeV electron enhancements, up to ultra-relativistic energies of 9.9 MeV.

Each CIR group has a different effect on the outer radiation belt electron populations; we investigate them by exploiting combined measurements from the Van Allen Probes, THEMIS, and Arase satellites. We produce Phase Space Density (PSD) radial profiles and inspect their dependence on the values of the first and second adiabatic invariants (μ,K), ranging from seed to ultra-relativistic electrons and from near-equatorial to off equatorial mirroring populations, respectively.

Focusing on the 3rd CIR group, and in order to assess the relative contribution of radial diffusion and gyro-resonant acceleration, we perform numerical simulations of the radiation belt environment, combining several relevant models: EMERALD (NKUA), GEO model (NKUA), Salammbô (ONERA), VLF model (IAP), Plasmaspheric model (BIRA-IASB), FARWEST (ONERA). We further compare the temporal evolution of the simulated electron PSD with the above observations.

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870437 for the SafeSpace project.

How to cite: Nasi, A., Katsavrias, C., Aminalragia-Giamini, S., Dahmen, N., Brunet, A., Papadimitriou, C., Sandberg, I., Bourdarie, S., Pierrard, V., Botek, E., Darrouzet, F., Santolik, O., Grison, B., Kolmasova, I., Pisa, D., Miyoshi, Y., Li, W., Evans, H., and Daglis, I. A. and the Arase Team: Investigating the acceleration efficiency of VLF and ULF waves on different electron populations in the outer radiation belt through multi-point observations and modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-798, https://doi.org/10.5194/egusphere-egu23-798, 2023.

Atmospheric precipitation of radiation belt electrons plays an important role in the magnetosphere-ionosphere-atmosphere coupling system, which can trigger chemical and electric effects in the upper atmosphere and meanwhile generate aurorae of various types. In the regime of the quasi-linear theory, it is commonly accepted that the population of trapped electrons is no smaller than the precipitated population. However, such a concept has been proved to break down due to the nonlinear wave-particle interactions, which can drive the superfast electron precipitation. Therefore, on basis of the long-term MEPED datasets of POES satellites, we perform a comprehensive analysis of the spatiotemporal characteristics and geomagnetic dependence of superfast radiation belt electron precipitation. Our results demonstrate that superfast atmospheric precipitation of energetic electrons occurs with a non-negligible percentage with respect to the overall electron precipitation observations, and has the geomagnetic dependence similar to that of whistler-mode chorus waves.

How to cite: Guo, D., Xiang, Z., and Ni, B.: A statistical study of superfast atmospheric precipitation of radiation belt electrons observed by POES satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3069, https://doi.org/10.5194/egusphere-egu23-3069, 2023.

EGU23-3134 | ECS | Posters on site | ST2.4

Simultaneous observations of whistler mode waves by the DEMETER spacecraft and the Kannuslehto station 

Kristyna Drastichova, František Němec, Jyrki Manninen, and Michel Parrot

We use conjugate observations of magnetospheric whistler mode electromagnetic waves at frequencies up to 16 kHz to determine their typical spatial scales and propagation to the ground. For this purpose, we use data obtained by the DEMETER spacecraft at an altitude of about 700 km and by the ground-based Kannuslehto station in Finland. The overlap between the two data sets corresponds to more than 500 DEMETER half-orbits between November 2006 and March 2008. Two different approaches are used. First, specific wave events observed simultaneously by both the spacecraft and the ground station are analyzed in detail. Second, the correlations of the power spectral densities of measured signals are calculated as a function of the frequency and geomagnetic longitude/L-shell separation. These are used to determine typical longitudinal/L-shell correlation lengths and to discuss wave propagation to the ground.

How to cite: Drastichova, K., Němec, F., Manninen, J., and Parrot, M.: Simultaneous observations of whistler mode waves by the DEMETER spacecraft and the Kannuslehto station, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3134, https://doi.org/10.5194/egusphere-egu23-3134, 2023.

EGU23-3188 | Orals | ST2.4

Line radiation events: Properties, generation, and propagation 

Frantisek Nemec, Ondřej Santolík, Jyrki Manninen, George B. Hospodarsky, and William S. Kurth

Whistler-mode waves propagating in the Earth’s inner magnetosphere sometimes appear as a set of nearly constant frequency elements separated by a fixed frequency. Such events are typically called line radiation, and they can have two distinct origins. First, events with narrow spectral lines and the frequency spacing corresponding to the base power system frequency (50/100 or 60/120 Hz) are generated by electromagnetic radiation from electric power systems on the ground (power line harmonic radiation, PLHR). Second, waves with broader spectral lines, whose frequency spacing does not correspond to the power system frequency, are believed to be generated by plasma instabilities in the magnetosphere (magnetospheric line radiation, MLR).

Frequencies of line radiation events are typically on the order of a few kHz, while their frequency spacing is on the order of a hundred Hz. Relevant spacecraft observations at larger radial distances are thus very sparse due to the typically low frequency resolution of available measurements, not sufficient to distinguish the line structure. We use high-resolution multicomponent wave measurements performed by the EMFISIS instrument on board the Van Allen Probes during the burst mode to fill this observational gap. We systematically identify the line radiation events and analyze their occurrence and properties. Detailed wave propagation analysis allows us to reveal wave propagation throughout the magnetosphere. We further show that the frequency spacing of MLR events appears to be related to an electrostatic wave observed at the corresponding frequency (≈100 Hz). Finally, conjugate observations performed by the Kannuslehto station in Finland are used to estimate the spatial extent of the events.

How to cite: Nemec, F., Santolík, O., Manninen, J., Hospodarsky, G. B., and Kurth, W. S.: Line radiation events: Properties, generation, and propagation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3188, https://doi.org/10.5194/egusphere-egu23-3188, 2023.

Whistler-Mode Chorus (WMC) waves are an important contributor to the dynamics of the magnetosphere, not only for their prevalence in measured observations of near-Earth space but also for their dominant role in transporting energy and particles throughout it. It is therefore of key importance to space weather modelling that we understand how WMC waves are generated, how they subsequently evolve and how they interact with the particle populations that they transport. There are also fundamental physics question to answer as WMC waves display nonlinear phenomena rarely seen in other fields, including their ability to raise and lower their frequency repeatedly and rapidly leading to rising and falling tone waves respectively. Are the interactions between the wave and the particles driving such phenomena, and if so to what degree are they doing so?

 

In this talk, we revisit the nonlinear evolution of WMC waves from a theoretical perspective.  Wave-particle interactions are shown to be a key driver of the modulational instabilities that lead to element and subelement formation which are well represented by an extension of the well-known Nonlinear Schrodinger equation. Simulations of this yields power spectrum reminiscent of the rising and falling tone emissions observed in mission data from the Van Allen probes, THEMIS, MMS and Cluster and determines that that wave-particle interactions are the primary cause of this effect. As a result, this nonlinear theory indicates regimes in which these frequency sweeps can be enhanced or dampened, and suggests why the WMC band gap at half the gyrofrequency exists.

How to cite: Ratliff, D. and Allanson, O.: Nonlinear wave-particle interactions in Whistler-Mode Chorus waves: modulation as a route to rising and falling tones, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3375, https://doi.org/10.5194/egusphere-egu23-3375, 2023.

EGU23-3445 | Orals | ST2.4

Global modeling of the mesoscale buildup of the ring current and its role in magnetosphere-ionosphere coupling 

Kareem Sorathia, Adam Michael, Anthony Sciola, Shanshan Bao, Dong Lin, Slava Merkin, Sasha Ukhorskiy, Constanze Roedig, and Jeffrey Garretson

During geomagnetically active periods plasma is transported from the magnetotail into the inner magnetosphere to become the ring current. The transpot of plasma into the ring current occurs at different spatial and temporal scales, from global quasi-steady convection to bursty bulk flows (BBFs), with typical cross-tail extents of 1-3 Earth radii. During its enhancement, the ring current plays a critical role in magnetosphere-ionosphere coupling. Ring current ions build up plasma pressure in the inner magnetosphere and will drive field-aligned currents which must close in the ionosphere, while electrons will lead to diffuse precipitation and enhanced ionospheric conductance which shape the ionospheric path of current closure. Current closure in the ionosphere will couple to the thermospheric neutral population, via Joule heating, and alter the dynamics of the plasmasphere, via the penetration electric field in the inner magnetosphere. 

Understanding the relative role of convection at different spatial scales in both the buildup of the ring current and its broader effects on geospace coupling is an area of active interest and one of the core science questions of the Center for Geospace Storms. In this talk I will describe how addressing this question has informed the development of the Multiscale Atmosphere Geospace Environment (MAGE) model and highlight several recent modeling studies which illustrate the central role of mesoscale processes.

How to cite: Sorathia, K., Michael, A., Sciola, A., Bao, S., Lin, D., Merkin, S., Ukhorskiy, S., Roedig, C., and Garretson, J.: Global modeling of the mesoscale buildup of the ring current and its role in magnetosphere-ionosphere coupling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3445, https://doi.org/10.5194/egusphere-egu23-3445, 2023.

EGU23-3482 | Orals | ST2.4

The controlling effect of the cold plasma density over the acceleration and loss of ultra‐relativistic electrons 

Yuri Shprits, Hayley Allison, Alexander Drozdov, and Dedong Wang

Novel analysis of phase space densities at multiple energies allows for differentiation between various acceleration mechanisms at ultra‐relativistic energies. This method allows us to trace how particles are being accelerated at different energies and show how long it takes for acceleration to reach particular energy. This method clearly demonstrates the importance of local acceleration and also demonstrates the importance of outward radial diffusion in transporting electrons to GEO.

Acceleration to such high energies occurs only when cold plasma in the trough region is extremely depleted, down to the values typical for the plasma sheet. We perform event and statistical analysis of these depletions and show that the ultra‐relativistic energies are reached for each such depletion that is accompanied by the intensification of ~2MeV. VERB‐2D simulations are then used to explain these observations. There is also a clear difference between the loss mechanisms at MeV and multi‐MeV energies due to EMIC waves that can very efficiently scatter ultra‐relativistic electrons but leave MeV electrons unaffected.

Modelling and observations clearly show that cold plasma has a controlling effect over the ultra‐ relativistic electrons that are 10^6‐10^7 times more energetic. We also present how the new understanding gained from the Van Allen Probes mission can be used to produce the most accurate data assimilative forecast. Under the recently funded EU Horizon 2020 Project Prediction of Adverse effects of Geomagnetic storms and Energetic Radiation (PAGER) we study how ensemble forecasting from the Sun can produce long‐term probabilistic forecasts of the radiation environment in the inner magnetosphere.

How to cite: Shprits, Y., Allison, H., Drozdov, A., and Wang, D.: The controlling effect of the cold plasma density over the acceleration and loss of ultra‐relativistic electrons, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3482, https://doi.org/10.5194/egusphere-egu23-3482, 2023.

EGU23-4064 | ECS | Orals | ST2.4

Archimedean Spiral Distribution of Electrons in Earth Inner Magnetosphere 

Weiqin Sun, Jian Yang, Wenrui Wang, and Jun Cui

We present an analytic theory to demonstrate that electrons with an initially asymmetric spatial distribution would form an Archimedean spiral distribution in the inner magnetosphere. Such evolution is a result of the gradient/curvature drift, whose angular velocity decreases with radial distance. It has been known for a long time that spectrograms of energetic electrons in Earth's inner radiation belt exhibit time-varying organized peaks and valleys. Recent observations from Van Allen Probes have shown that such regular patterns are ubiquitous and are referred to as “zebra stripes”. Our theory can predict zebra stripes accurately. We also use the Rice Convection Model (RCM) to simulate zebra stripes. For the simplest situation with the dipolar magnetic field model, the analytic theory perfectly matches with the RCM simulation. In a realistic simulation, the RCM reproduces the time-dependent structures and evolution of the zebra stripes, which are in good consistency with Van Allen Probes observations.

How to cite: Sun, W., Yang, J., Wang, W., and Cui, J.: Archimedean Spiral Distribution of Electrons in Earth Inner Magnetosphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4064, https://doi.org/10.5194/egusphere-egu23-4064, 2023.

EGU23-4471 | Orals | ST2.4

Quantifying the Contribution of Nonlinear Resonant Effects to Diffusion Rates 

Dmitri Vainchtein, Anton Artemyev, Didier Mourenas, and Xiaojia Zhang

The wave-particle resonant interaction is a key process controlling energetic electron flux dynamics in the Earth’s radiation belts. All existing radiation belt codes are Fokker-Planck models relying on the quasi-linear diffusion theory to describe the impact of wave-particle interactions. However, in the outer radiation belt, spacecraft often detect waves sufficiently intense to interact resonantly with electrons in the nonlinear regime.

We propose an approach to (1) estimate the contribution of such nonlinear resonant interactions, and (2) include them into diffusion-based radiation belt models. Using statistics of chorus wave-packet amplitudes and sizes (number of wave periods within one packet), we provide a rescaling factor for the quasi-linear diffusion rates to account for the contribution of nonlinear interactions in long-term electron flux dynamics. Such nonlinear effects may speed up 0.1-1 MeV electron diffusive acceleration by a factor of x2-3 during disturbed periods.

How to cite: Vainchtein, D., Artemyev, A., Mourenas, D., and Zhang, X.: Quantifying the Contribution of Nonlinear Resonant Effects to Diffusion Rates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4471, https://doi.org/10.5194/egusphere-egu23-4471, 2023.

Space plasmas are often characterized by non-thermal particle distributions that are generally characterized by a high-energy tail that follows a power law for large velocity arguments. For modelling purposes, these are often described by kappa-type distributions (Livadiotis, 2017). Over the past few decades, the kappa distribution has been adopted in interpretations of observations in various space plasma contexts including the solar wind (Chotoo et al., 2000), planetary magnetospheres (Collier and Hamilton, 1995), the outer heliosphere (Decker and Krimigis, 2003) and the inner heliosheath (Livadiotis and McComas, 2012) and also in theoretical models (Hellberg et al., 2009). An abundance of data from the Cassini and Voyager missions has established in Saturn's magnetosphere the coexistence of non-thermal electron populations (of different characteristics). Schippers et al. (2008) analysed the radial distribution of electron populations in Saturn's magnetosphere by using an ad hoc two-kappa model, thus establishing the relevance of multi-kappa models with respect to electron populations in Saturn's magnetosphere. This coexistence of electron clouds (at distinct temperatures) is a key element in our work.

Electrostatic Solitary Waves (ESWs), generally associated with bipolar electric field waveforms observed alongside propagating density disturbances, are known to occur in Saturn's magnetosphere (Pickett et al., 2015). In this study, we have relied on a multi-fluid plasma model to investigate the significance of suprathermal electron populations in determining the characteristics of different types of solitary wave solutions. Our investigation reveals that the spectral index (i.e. the  parameter value related to the cold electron population mainly) is crucial in explaining the difference among different types of nonlinear structures. A comparison with spacecraft observations suggests that our theoretical estimations may be relevant in the interpretation of ESW observations in Saturn's magnetosphere.

References

Chotoo, K., Schwadron, N.A., Mason, G.M., Zurbuchen, T.H., Gloeckler, G., Posner, A., Fisk, L.A., Galvin, A.B., Hamilton, D.C., Collier, M.R., 2000. J. Geophys. Res. Space Phys. 105, 23107–23122. https://doi.org/10.1029/1998JA000015

 

Collier, M.R., Hamilton, D.C., 1995. Geophys. Res. Lett. 22, 303–306. https://doi.org/10.1029/94GL02997

 

Decker, R.B., Krimigis, S.M., 2003. Adv. Space Res. 32, 597–602. https://doi.org/10.1016/S0273-1177(03)00356-9

 

Hellberg, M.A., Mace, R.L., Baluku, T.K., Kourakis, I. and Saini, N.S., 2009. Physics of Plasmas, 16(9), p.094701

 

Livadiotis, G., 2017. Kappa Distributions - Theory and Applications in Plasmas (Elsevier).

 

Livadiotis, G., McComas, D.J., 2012. Astrophys. J. 749, 11. https://doi.org/10.1088/0004-637X/749/1/11

 

Pickett, J.S., Kurth, W.S., Gurnett, D.A., Huff, R.L., Faden, J.B., Averkamp, T.F., Píša, D. and Jones, G.H., 2015. Journal of Geophysical Research: Space Physics120(8), pp.6569-6580.

 

Schippers, P., Blanc, M., André, N., Dandouras, I., Lewis, G.R., Gilbert, L.K., Persoon, A.M., Krupp, N., Gurnett, D.A., Coates, A.J., Krimigis, S.M., Young, D.T., Dougherty, M.K., 2008. J. Geophys. Res. Space Phys. 113, https://doi.org/10.1029/2008JA013098

 

 

How to cite: Varghese, S. S. and Kourakis, I.: On the role of suprathermal electrons on the characteristics of electrostatic solitary waves in Saturn’s magnetosphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4797, https://doi.org/10.5194/egusphere-egu23-4797, 2023.

EGU23-5059 | ECS | Orals | ST2.4

Nightside plasmaspheric plume-to-core migration of whistler-mode hiss waves 

Zhiyong Wu, Zhenpeng Su, Jerry Goldstein, Nigang Liu, Zhaoguo He, Huinan Zheng, and Yuming Wang

Whistler-mode hiss waves play an important role in the radiation belt electron depletion. Whether the hiss waves with significant differences in amplitude and propagation direction within the plasmaspheric core and plume are related to each other remains unclear. We here show that the plasmaspheric plume facilitates the energy conversion from energetic electrons to hiss waves and then guides hiss waves into the plasmaspheric core. Three ground and space missions captured the initial formation and subsequent rotation of the plasmaspheric plume in the noon-dusk-midnight sector following a strong substorm. The observed hiss waves in the nightside plasmaspheric plume and core propagated oppositely but highly correlated with each other at a time lag of 4-10 s. The linear instability of energetic electrons in the plasmaspheric plume qualitatively explains the frequency-dependence of hiss waves, and the ray-tracing modeling reproduces the propagation direction and timing of hiss waves.

How to cite: Wu, Z., Su, Z., Goldstein, J., Liu, N., He, Z., Zheng, H., and Wang, Y.: Nightside plasmaspheric plume-to-core migration of whistler-mode hiss waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5059, https://doi.org/10.5194/egusphere-egu23-5059, 2023.

EGU23-7338 | ECS | Posters on site | ST2.4

A number density/temperature description of the Earth’s outer radiation belt 

Dovile Rasinskaite

Substorms can inject electrons of energies ranging from 10s to 100s keV (often called source and seed populations) into the magnetosphere which can be accelerated to relativistic energies and be harmful to space-based infrastructure. Here we present a number density/temperature description of the Earths outer radiation belt obtained by using omni-directional flux and energy measurements from the HOPE and MagEIS instruments from the Van Allen Probe mission. This dataset provides a comprehensive statistical study of the whole Van Allen probe era. Values of number density and temperature are extracted by fitting energy and phase space density in log space to find the distribution function. Zeroth and second moments are taken respectively of the distribution function to find the number density and temperature. A number density/ temperature description is advantageous over an energy/flux description as it allows to differentiate between the transport and heating of electrons. The shape and variation of plasma distributions is also discussed, and general statistical properties presented. The relative importance of transport and heating is also discussed. We will explore the classification of substorm injections (i.e., is the injection a heating or transport of electrons, or a combination of both) and this technique can be extended across more energy ranges. 

How to cite: Rasinskaite, D.: A number density/temperature description of the Earth’s outer radiation belt, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7338, https://doi.org/10.5194/egusphere-egu23-7338, 2023.

EGU23-7524 | Orals | ST2.4

An Empirical Model of Whistler Mode Waves in the Radiation Belt Region 

Ondrej Santolik, Ivana Kolmasova, Ulrich Taubenschuss, Marie Turcicova, and Miroslav Hanzelka

Whistler mode waves interact with different magnetospheric particle populations in the inner magnetosphere and significantly influence particle fluxes in the Earth's radiation belts. Using recently acquired large databases of spacecraft measurements from the Van Allen Probes and Cluster missions we construct new empirical models of whistler mode waves in the inner magnetosphere. We pay special attention to the off-equatorial region, which is often under-sampled in the currently existing models, and to the inter-calibration of data from different spacecraft missions. We take into account the effects of instrumental noise and other artifacts which influence the quality of data at the input of the modeling procedure. Our results show that dawn chorus occurs most often around noon, while its peak average amplitudes are observed during the local night. We also show that off-equatorial plasmaspheric hiss has a strong obliquely propagating component. We further confirm the influence of low plasma density regions on the intensity of chorus.

How to cite: Santolik, O., Kolmasova, I., Taubenschuss, U., Turcicova, M., and Hanzelka, M.: An Empirical Model of Whistler Mode Waves in the Radiation Belt Region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7524, https://doi.org/10.5194/egusphere-egu23-7524, 2023.

EGU23-7785 | Orals | ST2.4

The Angular Distribution of Whistler-Mode Chorus Wave Vector Directions from Van Allen Probes and MMS Observations 

David P. Hartley, Ivar Christopher, Lunjin Chen, Ondrej Santolik, Craig Kletzing, Matthew Argall, and Narges Ahmadi

The dynamics of Earth's outer electron radiation belt is, in part, driven by interactions with whistler-mode chorus waves.  Chorus can cause rapid acceleration of electrons up to relativistic energies, as well as drive precipitation of particles into the atmosphere causing both microbursts and diffuse aurora.  Chorus can propagate in such a way that it crosses the plasmapause boundary and may contribute to the possible sources of plasmaspheric hiss, which itself can cause atmospheric losses of particles and the formation of the slot region between the inner and outer radiation belts.  The direction of the wave vector relative to the background magnetic field is a key parameter for quantifying these processes, since it determines the propagation trajectory of the wave, and is required for calculating the resonance condition of the wave-particle interaction.

The orientation of the wave vector is investigated using both survey mode data and high-resolution burst mode observations from the EMFISIS Waves instrument on the Van Allen Probes spacecraft.  Spatial coverage beyond the Van Allen Probes orbit is provided by burst-mode observations from the FIELDS instrument suite on Magnetospheric Multiscale (MMS).  The polar and azimuthal wave vector angles are considered using both spectral analysis, where the frequency-time structure can be resolved, and instantaneous values, which can be used to identify variations within individual chorus subpackets.  We compare the results from each of these different timescales.  Near strong plasma density gradients, such as those which occur on the boundaries of plasmaspheric plumes, we identify that the wave vector becomes more oblique than the general case where no density gradients are present.  The obliquity of the wave vector is shown to directly relate to both the magnitude of the density gradient, and its proximity to the spacecraft.  

How to cite: Hartley, D. P., Christopher, I., Chen, L., Santolik, O., Kletzing, C., Argall, M., and Ahmadi, N.: The Angular Distribution of Whistler-Mode Chorus Wave Vector Directions from Van Allen Probes and MMS Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7785, https://doi.org/10.5194/egusphere-egu23-7785, 2023.

EGU23-8042 | ECS | Posters on site | ST2.4

Impact of interplanetary shocks on the radiation belt environment measured by a low altitude satellite 

Stefan Gohl and František Němec

We use electron flux data measured by the Energetic Particle Telescope (EPT) onboard the Proba-V satellite in a Low Earth Orbit (LEO) to investigate the radiation belt response to the interplanetary shock arrival. Altogether, as many as 31 interplanetary shocks selected from the OMNI data during 2013-2018 are investigated. While the radiation belt fluxes are nearly unaffected by the shock arrival in some cases, other events reveal a sudden drop of energetic electron fluxes spanning over a broad range of L-shells. Electron flux changes at various L-shells and energies are evaluated and compared with the solar wind dynamic pressure change across the shock front, magnetopause location, and z-component of the interplanetary magnetic field. The aim is to identify parameters governing the radiation belt response to the interplanetary shock passage and to understand the strikingly different responses to the seemingly similar solar wind variations.

How to cite: Gohl, S. and Němec, F.: Impact of interplanetary shocks on the radiation belt environment measured by a low altitude satellite, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8042, https://doi.org/10.5194/egusphere-egu23-8042, 2023.

EGU23-8693 | Orals | ST2.4

Observations and Analysis of Deep Penetrations of MeV Electrons from REPT PHA Data 

Xinlin Li, Declan O'Brien, and Daniel Baker

The Relativistic Electron and Proton Telescope (REPT), consisting of a stack of nine aligned silicon detectors onboard Van Allen Probes, has contributed a great number of discoveries based its nominal data. However, the REPT Pulse Height Analysis (PHA) data set, which was taken every 12 milliseconds (ms), including the pulse height that is proportional to the energy deposit of each individual particle from all nine REPT detectors, has been seldom-tapped. Here we show that this data set actually provides higher energy resolution particle measurements than the typical binned data from REPT. Geant4 simulations are used to extend and improve the electron detecting capabilities of REPT using the PHA data. After replicating the nominal characteristics of REPT in the Geant4 toolbox, new channels for REPT, going from 12 electron channels to 47 and lowering the minimum energy to ~1 MeV, have been formulated. The deep storm-time penetration of MeV electrons into the slot region (2<L<3) and inner belt (L<2) has been investigated. Clear dynamic variations of MeV electrons in these regions are revealed and substantiated by quantitative analysis. This is only an example of how the REPT PHA data will enable us to quantitatively address many more various science questions.

How to cite: Li, X., O'Brien, D., and Baker, D.: Observations and Analysis of Deep Penetrations of MeV Electrons from REPT PHA Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8693, https://doi.org/10.5194/egusphere-egu23-8693, 2023.

EGU23-8906 | ECS | Orals | ST2.4

Impact of the solar activity on the non-linearity of the statistical dependency between solar wind and the inner magnetosphere 

Sanni Hoilijoki, Veera Lipsanen, Adnane Osmane, Milla Kalliokoski, Harriet George, Lucile Turc, and Emilia Kilpua

Solar wind variations and transients are the main driver of the dynamics of the Earth’s magnetosphere. Interplanetary coronal mass ejections (ICME) cause the largest variations in the near-Earth space, but significant geomagnetic activity can also be driven by high-speed streams (HSSs) and stream interaction regions (SIRs). Solar wind – magnetosphere interactions drive fluctuations in the inner magnetosphere and impact the electrons in the outer radiation belt. Ultra low frequency (ULF) waves in the Pc5 range (2-7mHz) can accelerate electrons in the inner magnetosphere via drift resonance and cause changes in the electron flux up to several orders of magnitude. The different solar wind structures, ICMEs and HSSs/SIRs have been found to have different impact on the ULF waves and electrons in the inner magnetosphere. In this study we use mutual information from information theory to study the statistical dependency of the ULF waves and radiation belt electrons on the solar wind parameters and fluctuations over the solar cycle 23. Unlike Pearson correlation coefficient mutual information can also be used to investigate non-linear statistical dependencies between different parameters. We calculate correlation coefficients separately for each year and find that the non-linearity between the solar wind parameters and some magnetospheric parameters is higher during solar maximum when most of the geomagnetic activity is driven by ICMEs, while the non-linearity decreases during the declining phase, as larger portion of the geomagnetic activity is driven by HSSs and SIRs. To investigate further if the change of the ratio of ICMEs and HSSs is the possible cause of the changes in the non-linearity during the solar cycle, we calculate the correlation coefficients separately during ICMEs, HSSs/SIRs and quiet solar wind.

How to cite: Hoilijoki, S., Lipsanen, V., Osmane, A., Kalliokoski, M., George, H., Turc, L., and Kilpua, E.: Impact of the solar activity on the non-linearity of the statistical dependency between solar wind and the inner magnetosphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8906, https://doi.org/10.5194/egusphere-egu23-8906, 2023.

EGU23-8925 | ECS | Orals | ST2.4

Radiation belt particle diffusion, drift and advection via cyclotron interactions 

Oliver Allanson, Jacob Bortnik, Donglai Ma, Adnane Osmane, and Jay Albert

There is a growing body of observational, theoretical and experimental evidence to indicate that a proper description of radiation belt charged particle transport will require new mathematical models, i.e. new partial differential equations. One leading candidate is to extend the ‘standard diffusion equation’ to a more general Fokker-Planck equation in order to include advection coefficients. Ideally, these advection (first-order transport) coefficients should be parameterized by plasma and VLF/ELF electromagnetic wave parameters in a similar manner to that used for the diffusion coefficients. To the authors' knowledge, this goal has not yet been achieved - at least not to obtain an equation that can be/has been implemented into operational global scale numerical models.

In general, advection coefficients are in fact a combination of both ‘drift coefficients’ and derivatives of the diffusion coefficients. In the standard quasilinear formalism, this combination produces advection coefficients that are identically zero because of specific constraints imposed via the Hamiltonian structure, with a derivation often attributed to Landau/Lichtenberg & Lieberman [1].

In this paper [2] we present a new theory that incorporates and builds upon the ‘weak turbulence/quasilinear results’ of [3,4] and demonstrates the breaking of the ‘Landau-Lichtenberg-Liebermann condition’ for the case of high wave amplitudes, or equivalently small timescales.

We therefore obtain:
(i) the standard quasilinear results for small wave amplitudes and long timescales;
(ii) and non-zero advection coefficients - as well as diffusion coefficients - that are valid for short timescales (high wave amplitudes).

These limiting timescales are determined by the electromagnetic wave amplitude. This also demonstrates that one can use what may be considered ‘quasilinear methods’ to obtain interesting new results for ‘nonlinear/high-amplitude’ waves in radiation belt modelling. We verify the results using high-performance test-particle experiments.

References

[1] A.J. Lichtenberg, and M.A. Lieberman, “Regular and Chaotic Dynamics”, 2nd Ed., Springer, 1991

[2] O. Allanson et al (in prep)

[3] D.S. Lemons, PoP, 19, 012306, 2012

[4] O. Allanson, T. Elsden, C. Watt, and T. Neukirch, Frontiers Aston. Space Sci., 8:805699, 2022

How to cite: Allanson, O., Bortnik, J., Ma, D., Osmane, A., and Albert, J.: Radiation belt particle diffusion, drift and advection via cyclotron interactions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8925, https://doi.org/10.5194/egusphere-egu23-8925, 2023.

EGU23-9976 | ECS | Orals | ST2.4

Using pitch angle index to quantify anisotropies in the outer radiation belt 

Ashley Greeley, Shrikanth Kanekal, and Quintin Schiller

Changes in pitch angle distributions can be a useful indicator of various changes in the radiation belts. Many methods of observing pitch angle distributions are qualitative. We present a method of studying pitch angle distributions that allows for a quantitative analysis of pitch angle distributions over time and energy channels, which allows for closer monitoring of spatial and temporal changes in the radiation belts. We use Van Allen Probes data from both spacecraft in fit pitch angle distributions with the form J0sinnα, tracking ‘n’ over time. We use this method of tracking pitch angle distributions to establish a connection between very localized wave particle interactions and particle scattering.

How to cite: Greeley, A., Kanekal, S., and Schiller, Q.: Using pitch angle index to quantify anisotropies in the outer radiation belt, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9976, https://doi.org/10.5194/egusphere-egu23-9976, 2023.

EGU23-10180 | Orals | ST2.4

Characteristic Times for Radiation Belt Drift Phase Mixing 

Solène Lejosne and Jay M. Albert

One of the key assumptions of radiation belt modeling based on a three-dimensional Fokker-Planck equation is that trapped particle fluxes do not depend on the drift phase (i.e., the azimuthal angle, or magnetic local time, MLT). It is usually considered that MLT-dependent structures (such as particle injection signatures and subsequent drift echoes) are rapidly smoothed out by drift phase mixing. Yet, the characteristic times for radiation belt drift phase mixing are not well known.

In this presentation, we show the existence of a naturally occurring phase mixing process in the presence of field fluctuations. This process complements the observational phase mixing due to the finite resolution of the measuring instrument.

We present a first quantification for the characteristic time of natural phase mixing and we discuss the implications in terms of radiation belt modeling.

How to cite: Lejosne, S. and Albert, J. M.: Characteristic Times for Radiation Belt Drift Phase Mixing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10180, https://doi.org/10.5194/egusphere-egu23-10180, 2023.

EGU23-10216 | Orals | ST2.4

Study of Ion Injection into the Inner Magnetosphere Using an Implicit Particle in Cell Simulation Driven by A Global MHD simulation 

Mostafa El Alaoui, Giovanni Lapenta, Liutauras Rusaitis, and Raymond Walker

Observations and magnetohydrodynamic simulations show that not all plasma injections from reconnection in the tail reach the inner magnetosphere to populate the ring current. We have used a self-consistent three-dimension particle-in-cell (PIC) simulation one way coupled to a global magnetohydrodynamic (MHD) simulation of the solar wind-magnetosphere-ionosphere system to investigate the population of the ring current during storm time substorms. This model includes a large fraction of the inner magnetosphere and the near-Earth tail. It allows us to study of the injection of particles from the tail and the interaction of the particles with plasma waves. The calculation begins with electrons and ions propagating earthward from the tail reconnection region. The particle distributions that enter the inner magnetosphere (R < 10 RE) from the magnetotail have a suprathermal component which acts as a seed population for the ring current. We imposed a steady southward IMF with a magnitude of 8 nT at the upstream boundary of the MHD simulation domain for more than three hours. The solar wind number density was 6 cm-3, the thermal pressure was 16 pPa, and the velocity was 530 km/s in the X direction toward Earth.  After we ran the MHD simulation, we chose an interval to examine during which there were several earthward flow channels and dipolarization fronts. Then, we used the output from this time to populate a large PIC simulation domain in the inner magnetosphere. In GSM coordinates, this domain extends over -22 RE <X < 12.5 RE, -13 RE < Y <13 RE, -5 RE < Z < 5 RE. The mass ratio was 256 with realistic ions and more massive electrons. In an initial simulation, we ran the code for 16,000 cycles and found that a ring current developed. We will discuss the reasons why some particles from the tail reach the inner magnetosphere, and some do not by examining how the particles are accelerated and lost.    

How to cite: El Alaoui, M., Lapenta, G., Rusaitis, L., and Walker, R.: Study of Ion Injection into the Inner Magnetosphere Using an Implicit Particle in Cell Simulation Driven by A Global MHD simulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10216, https://doi.org/10.5194/egusphere-egu23-10216, 2023.

EGU23-10513 | ECS | Posters virtual | ST2.4

EMIC wave induced proton precipitation during the 27-28 May 2017 storm:Comparison of BATSRUS+RAM-SCB simulations with ground/space based observations 

Shreedevi Porunakatu Radhakrishna, Yiqun Yu, Yoshizumi Miyoshi, Xingbin Tian, Minghui Zhu, Sandeep Kumar, Satoko Nakamura, Chae-Woo Jun, Masafumi Shoji, Kazuo Shiokawa, Vania Jordanova, Tomoaki Hori, Kazushi Asamura, Iku Shinohara, Shoichiro Yokota, Satoshi Kasahara, Kunihiro Keika, Ayako Matsuoka, Martin Connors, and Akira Kadokura

Recent studies have shown that the ion precipitation induced by EMIC waves can contribute significantly to the total energy flux deposited into the ionosphere and severely affect the magnetosphere-ionosphere coupling. During the geomagnetic storm of 27-28 May 2017, the ARASE and the RBSPa satellites observed typical signatures of EMIC waves in the inner magnetosphere. The DMSP and MetOp satellites observed enhanced proton precipitation during the main phase of the storm. In order to understand the evolution of proton precipitation into the ionosphere, its correspondence to the time and location of excitation of the EMIC waves and its relation to the source and distribution of proton temperature anisotropy, we conducted two simulations of the BATSRUS+RAMSCBE model with and without EMIC waves included. Simulation results suggest that the H- and He-band EMIC waves are excited within regions of strong temperature anisotropy of protons in the vicinity of the plasmapause. In regions where the Arase/RBSPa satellite measurements recorded EMIC wave activity, an increase in the simulated growth rates of H- and He-band EMIC waves is observed indicating that the model is able to capture the EMIC wave activity. The RAM-SCBE simulation with EMIC waves reproduces the precipitating fluxes in the premidnight sector fairly well, and is found to be in good agreement with the DMSP and MetOp satellite observations. The results suggest that the EMIC wave scattering of ring current ions gives rise to the proton precipitation in the premidnight sector at subauroral latitudes during the main phase of the 27 May 2017 storm.

How to cite: Porunakatu Radhakrishna, S., Yu, Y., Miyoshi, Y., Tian, X., Zhu, M., Kumar, S., Nakamura, S., Jun, C.-W., Shoji, M., Shiokawa, K., Jordanova, V., Hori, T., Asamura, K., Shinohara, I., Yokota, S., Kasahara, S., Keika, K., Matsuoka, A., Connors, M., and Kadokura, A.: EMIC wave induced proton precipitation during the 27-28 May 2017 storm:Comparison of BATSRUS+RAM-SCB simulations with ground/space based observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10513, https://doi.org/10.5194/egusphere-egu23-10513, 2023.

EGU23-10543 | ECS | Posters on site | ST2.4

Outer radiation belt electron flux and phase space density changes during sheath regions of coronal mass ejections from Van Allen Probes and GPS data 

Milla Kalliokoski, Michael Henderson, Steven Morley, Emilia Kilpua, Adnane Osmane, Leonid Olifer, Drew Turner, Allison Jaynes, Harriet George, Sanni Hoilijoki, Lucile Turc, and Minna Palmroth

Turbulent and compressed sheath regions ahead of interplanetary coronal mass ejections are key drivers of dramatic changes in the electron fluxes in the Earth’s outer radiation belt. They are also associated with elevated wave activity in the inner magnetosphere. These changes in electron fluxes can occur on timescales of tens of minutes that are not readily captured by a two-satellite mission such as the Van Allen Probes due to long revisit times. The recently released Global Positioning System (GPS) data set, on the other hand, provides a larger number of measurements at a given location within a given amount of time, owing to the many satellites in the constellation. In our statistical study on the impact of sheath regions on the outer radiation belt, we investigated events in 2012-2018 at timescales of 6 hours (Van Allen Probes data) and 30 minutes (GPS data). The study showed that the flux response to sheaths as reported from Van Allen Probes observations is reproduced by GPS data.  We highlight that the shorter timescale allowed by GPS data further confirms that the energy and L-shell dependent flux changes are associated with the sheaths rather than the following ejecta. Additionally, we studied the electron phase space density, which is a key quantity for identifying non-adiabatic electron dynamics. This showed that electrons are effectively accelerated only during geoeffective sheaths (SYM-H < -30 nT). Outer belt losses are common for all sheaths, and the lost electrons are replenished during the early ejecta.

How to cite: Kalliokoski, M., Henderson, M., Morley, S., Kilpua, E., Osmane, A., Olifer, L., Turner, D., Jaynes, A., George, H., Hoilijoki, S., Turc, L., and Palmroth, M.: Outer radiation belt electron flux and phase space density changes during sheath regions of coronal mass ejections from Van Allen Probes and GPS data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10543, https://doi.org/10.5194/egusphere-egu23-10543, 2023.

EGU23-10778 | ECS | Posters virtual | ST2.4

Plasma pressure distribution of ions and electrons in the inner magnetosphere during CIR driven storms observed during Arase satellite era 

Sandeep Kumar, Yoshizumi Miyoshi, Vania Koleva Jordanova, Lynn M Kistler, Inchun Park, Porunakatu Radhakrishna Shreedevi, Kazushi Asamura, Shoichiro Yokota, Satoshi Kasahara, Yoichi Kazama, Shiang -Yu Wang, Sunny W. Y. Tam, Takefumi Mitani, Nana Higashio, Kunihiro Keika, Tomo Hori, Chae-Woo Jun, Ayako Matsuoka, Shun Imajo, and Iku Shinohara

Geomagnetic storms are the main component of space weather. Enhancement of the ring current is a typical feature of the geomagnetic storm and a global decrease in the H component of the geomagnetic field is observed during the main phase of the geomagnetic storm.  The ring current represents a diamagnetic current driven by the plasma pressure in the inner magnetosphere. The plasma pressure is mainly dominated by protons in an energy range of a few to a few hundred keVs during quiet times. The O+ contribution is also important, and sometimes dominates more than H+ during intense geomagnetic storms. However, electron contribution to the ring current is not studied well. Recently, we showed that the electron pressure also contributes to the depression of ground magnetic field during the November 2017 CIR-driven storm by comparing Ring current Atmosphere interactions Model with Self Consistent magnetic field (RAM-SCB) simulation, Arase in-situ plasma/particle data, and ground-based magnetometer data [Kumar et al., 2021]. Arase satellite observed 26 geomagnetic storms driven by Corotating Interaction Regions (CIR) during 2017-2021. In this study, we examine statistically the spatial and temporal distribution of ions (H+, He+, O+) and electrons pressure as a function of magnetic local time, L shell and wide range of energies during prestorm, main phase, early recovery and late recovery phase for 26 CIR storms using in situ plasma/particle data obtained by Arase. The results indicate that the electrons (20-50 keV) contribution to the ring current pressure is non-negligible.

How to cite: Kumar, S., Miyoshi, Y., Jordanova, V. K., Kistler, L. M., Park, I., Shreedevi, P. R., Asamura, K., Yokota, S., Kasahara, S., Kazama, Y., Wang, S.-Y., Tam, S. W. Y., Mitani, T., Higashio, N., Keika, K., Hori, T., Jun, C.-W., Matsuoka, A., Imajo, S., and Shinohara, I.: Plasma pressure distribution of ions and electrons in the inner magnetosphere during CIR driven storms observed during Arase satellite era, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10778, https://doi.org/10.5194/egusphere-egu23-10778, 2023.

The polar wind, consisting of low-energy ions and electrons, is an outflow along the open magnetic field lines from the polar cap ionosphere to the magnetosphere. Previous studies found that both solar radiation and solar wind electromagnetic energy are the two main energy sources for the polar wind. The polar rain, being field-aligned precipitating electrons from the solar wind to the polar cap, may provide additional energies for the polar wind. This scenario is complicated as simulation studies show that polar rain changes the electric potential structures over the polar cap ionosphere. It is unclear how the polar rain affects the polar wind ion outflow. In this study, we show a positive correlation between the polar wind and the polar rain. Meanwhile, the polar wind is generally diminished in regions with strong Earth’s magnetic field, suggesting the B modulates the penetration depth of the polar rain through the magnetic mirror force and thus the energy dissipation of the polar rain. Therefore, the polar rain can be an additional energy source for the polar wind although the polar rain has generally smaller energies and intensities than the particle precipitations in the auroral regions.

How to cite: Li, K.: The effects of the polar rain on the polar wind ion outflow from the nightside ionosphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11056, https://doi.org/10.5194/egusphere-egu23-11056, 2023.

EGU23-11084 | ECS | Posters on site | ST2.4

A Missing Dusk-side Loss Process in the Electron Ring Current 

Bernhard Haas, Yuri Y. Shprits, Michael Wutzig, Dedong Wang, and Mátyás Szabó-Roberts

The Earth’s magnetic field traps charged particles which are transported longitudinally around Earth, generating a near-circular current, known as the ring current. While the ring current has been measured on the ground and space for many decades, the enhancement of the ring current during geomagnetic storms is still not well understood, due to many processes contributing to its dynamics on different time scales. The low energy part of the ring current of 10-50 keV is responsible for surface charging effects on spacecraft, potentially causing satellite anomalies.

Here, we show that existing ring current models systematically overestimate the in-situ satellite measurements of the Earth’s night side electron ring current during geomagnetic storms. By analyzing electron drift trajectories during the storm onset, we show that this systematic overestimation of flux can be explained through a missing loss process which operates in the pre-midnight sector. Quantifying this loss reveals that the theoretical upper limit of strong diffusion has to be reached in a broad region of space in order to reproduce the observed flux. We include this missing loss process and show that predictions of electron flux can be significantly improved. Identifying missing loss processes in ring current models is vital to accurately predict storm time dynamics and the associated hazards, that result from a delicate balance of source and loss processes.

How to cite: Haas, B., Shprits, Y. Y., Wutzig, M., Wang, D., and Szabó-Roberts, M.: A Missing Dusk-side Loss Process in the Electron Ring Current, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11084, https://doi.org/10.5194/egusphere-egu23-11084, 2023.

EGU23-12704 | ECS | Posters on site | ST2.4

Studying the South Atlantic Anomaly temporal evolutionfrom 1998 to 2022 using the SEM-2 proton flux 

François Ginisty, Frédéric Wrobel, Robert Ecoffet, Mioara Mandea, Alain Michez, Nicolas Balcon, Marine Ruffenach, and Julien Mekki

The SEM-2 (Space Environment Monitor-2) instrument embedded on the NOAA-15 Low Earth Orbit satellite provides measurements of trapped protons in the Van Allen inner belt from 1998 to nowadays. This continuous amount of measurements enables us to study the temporal evolution of the dynamics of the South Atlantic Anomaly (SAA) over more than two solar cycles.
In particular, we study the temporal evolution of the area of the SAA. We observe that the area of the SAA is anti-correlated with the solar activity. Two physical process explain this anticorrelation.
First, the more the Sun is active the more it disables the cosmic rays to reach the Earth Magnetosphere and to fill the inner radiation belt with protons. Then, when the Sun in more active, the upper atmosphere is warmer and therefore absorbs more protons from the radiation belt.
Then, we investigate the protons flux centroid of the SAA. The temporal evolution of its position, latitude and, longitude is studied over the same time interval (1998-2022). We notice the latitude of the centroid is also anti-correlated with the solar activity whereas the longitude seems absolutely
independent. Some explanations are given for these observations.
The temporal evolution of the position of the centroid shows a drift of the SAA. Indeed from 1998 to 2022 the SAA drifted of about 7 degrees West.
The SEM-2 instrument measures flux for protons of different energies (16, 36, 70 and, 140 MeV). For each energy, the SAA dynamic has a similar trend but with different values. These differences are investigated and the results discussed.

How to cite: Ginisty, F., Wrobel, F., Ecoffet, R., Mandea, M., Michez, A., Balcon, N., Ruffenach, M., and Mekki, J.: Studying the South Atlantic Anomaly temporal evolutionfrom 1998 to 2022 using the SEM-2 proton flux, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12704, https://doi.org/10.5194/egusphere-egu23-12704, 2023.

EGU23-13125 | ECS | Posters on site | ST2.4

Investigating Solar Wind Drivers of Ultrarelativistic Electron Enhancements in the Outer Radiation Belt 

Matyas Szabo-Roberts, Yuri Shprits, and Hayley Allison

A distinct population of ultrarelativistic electrons has been observed in the outer radiation belt after several geomagnetic storms, and recent modeling results indicate that an existing seed population, and depletions in plasmasphere electron density, are a necessary condition for the appearance of this electron population. In order to similarly deepen our understanding of the solar wind drivers behind the appearance of these electrons with extreme energy, we catalog storms corresponding to ultrarelativistic enhancements by origin, and begin to establish necessary and sufficient solar wind conditions for these enhancement events. To do so, we perform superposed epoch analysis on a 6 year period from 2012 to 2018, using solar wind data from the Omniweb service, as well as electron flux and electron density data products from the Van Allen Probes mission. We also provide an overview of further modeling objectives and open questions for continued investigation of this electron population.

How to cite: Szabo-Roberts, M., Shprits, Y., and Allison, H.: Investigating Solar Wind Drivers of Ultrarelativistic Electron Enhancements in the Outer Radiation Belt, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13125, https://doi.org/10.5194/egusphere-egu23-13125, 2023.

EGU23-13189 | ECS | Posters virtual | ST2.4

Impact of Interplanetary Coronal Mass Ejections and High Speed Streams on the dynamic variations of the electron population in the outer Van Allen belt 

Adamantia Dimitrakoula, Alexandra Triantopoulou, Afroditi Nasi, Christos Katsavrias, Ioannis A. Daglis, and Ingmar Sandberg

The outer Van Allen radiation belt stands out for its intense variability, due to the complex mechanisms that take place due to the Sun – Earth coupling. One fundamentally important effect is the acceleration of seed electrons to relativistic and ultra – relativistic energies, through different mechanisms, namely radial diffusion and local acceleration.

In our work, we examine 46 events from the Van Allen Probes era (2012 – 2018), which we categorize according to the interplanetary driver of the geomagnetic disturbance. In particular, we study 16 events caused by Interplanetary Coronal Mass Ejections (ICMEs) and 30 events caused by High Speed Streams (HSS), following Stream Interaction Regions (SIRs), for which we calculate the electron Phase Space Density (PSD) for distinct values of the first adiabatic invariant (μ = 100, 1000, 5000 MeV/G) corresponding to seed, relativistic and ultra – relativistic electrons in the outer radiation belt. Furthermore, we perform a Superposed Epoch Analysis (SEA) of the geomagnetic disturbance events, which lead to either enhancements or depletions of the electron PSD, taking into consideration the parameters of solar wind activity, the state of the magnetosphere and the values of the second adiabatic invariant (K = 0.03, 0.09, 0.15 G1/2RE). We discuss the effects of the drivers on the variability of the outer radiation belt and how the different electron populations are affected, by comparing the time and radial profiles of the PSD. Our results lead to a clear difference between the two drivers, as far as it concerns the acceleration mechanisms.

How to cite: Dimitrakoula, A., Triantopoulou, A., Nasi, A., Katsavrias, C., Daglis, I. A., and Sandberg, I.: Impact of Interplanetary Coronal Mass Ejections and High Speed Streams on the dynamic variations of the electron population in the outer Van Allen belt, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13189, https://doi.org/10.5194/egusphere-egu23-13189, 2023.

EGU23-14289 | Posters on site | ST2.4

Developing Chorus Wave Model Using Van Allen Probe and Arase Data 

Dedong Wang, Yuri Shprits, Ting Feng, Thea Lepage, Ingo Michaelis, Yoshizumi Miyoshi, Yoshiya Kasahara, Atsushi Kumamoto, Shoya Matsud, Ayako Matsuoka, Satoko Nakamura, Iku Shinohara, and Fuminori Tsuchiiya

Chorus waves play an important role in the dynamic evolution of energetic electrons in the Earth’s radiation belts and ring current. Due to the orbit limitation of Van Allen Probes, our previous chorus wave model developed using Van Allen Probe data is limited to low latitude. In this study, we extend the chorus wave model to higher latitudes by combining measurements from the Van Allen Probes and Arase satellite. As a first step, we intercalibrate chorus wave measurements by comparing statistical features of chorus wave observations from Van Allen Probes and Arase missions. We first investigate the measurements in the same latitude range during the two years of overlap between the Van Allen Probe data and the Arase data. We find that the statistical intensity of chorus waves from Van Allen Probes is stronger than those from Arase observations. After the intercalibration, we combine the chorus wave measurements from the two satellite missions and develop an analytical chorus wave model which covers all magnetic local time and extends to higher latitudes. This chorus wave model will be further used in radiation belt and ring current simulations.

How to cite: Wang, D., Shprits, Y., Feng, T., Lepage, T., Michaelis, I., Miyoshi, Y., Kasahara, Y., Kumamoto, A., Matsud, S., Matsuoka, A., Nakamura, S., Shinohara, I., and Tsuchiiya, F.: Developing Chorus Wave Model Using Van Allen Probe and Arase Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14289, https://doi.org/10.5194/egusphere-egu23-14289, 2023.

EGU23-15611 | Orals | ST2.4

Observations of Off-Equatorial ULF Waves and Simulations of their effects on Radial Diffusion in the Radiation Belts 

Theodore Sarris, Xinlin Li, Hong Zhao, Kostis Papadakis, Wenlong Liu, Weichao Tu, Vassilis Angelopoulos, Karl-Heinz Glassmeier, Yoshizumi Miyoshi, Ayako Matsuoka, Iku Shinohara, and Shun Imajo

Magnetospheric ultra-low frequency (ULF) waves are known to cause radial diffusion and transport of hundreds-keV to few-MeV electrons in the radiation belts, as the range of drift frequencies of such electrons overlaps with the frequencies of the waves, leading to resonant interactions. Numerous expressions have been derived to quantitatively describe radial diffusion, so that they can be incorporated in global models of radiation belt electrons; however, most expressions of the radial diffusion rates are derived only for equatorially mirroring electrons, and are based on estimates of the power of ULF waves that are obtained either from spacecraft close to the equatorial plane or from the ground. Recent studies using the Van Allen Probes and Arase have shown that the wave power in magnetic fluctuations is significantly enhanced away from the magnetic equator, consistent with models simulating the natural modes of oscillation of magnetospheric field lines. This has significant implications for the estimation of radial diffusion rates, as higher pitch angle electrons will experience considerably higher ULF wave fluctuations than equatorial electrons. In this talk, we present recent results on the distribution of the magnetic field wave power as a function of magnetic latitude in different local time sectors and under different solar and geomagnetic conditions. Furthermore, using analytic functions of wave amplitudes in 3D test particle simulations, we simulate the change in L over time for particles of different pitch angles; this change in L can be translated to novel analytic diffusion coefficients with pitch-angle, L and energy dependence. In this talk we discuss the potential implications for the radial diffusion rates as currently estimated.

How to cite: Sarris, T., Li, X., Zhao, H., Papadakis, K., Liu, W., Tu, W., Angelopoulos, V., Glassmeier, K.-H., Miyoshi, Y., Matsuoka, A., Shinohara, I., and Imajo, S.: Observations of Off-Equatorial ULF Waves and Simulations of their effects on Radial Diffusion in the Radiation Belts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15611, https://doi.org/10.5194/egusphere-egu23-15611, 2023.

EGU23-15928 | Posters on site | ST2.4

Proton precipitation from EMIC waves at high latitudes: A casestudy from 29 March 2021 

Patrizia Francia, Marcello DE Lauretis, Mirko Piersanti, Giulia D'Angelo, and Alexandra Parmentier

Electron precipitation driven by electromagnetic ion cyclotron (EMIC) waves in the Pc1 range (0.1–5Hz) has been suggested as a significant loss mechanism for outer radiation belt fluxes of electrons in the 1–5 MeV energy range. Moreover, EMIC waves have been also observed to cause significant precipitation of ring current protons during geomagnetic storms.
In this study we report the concurrent observations of electromagnetic ion cyclotron Pc1 waves in both ionospheric F-region and at ground. Key event on March 29, 2021 shows that high latitude ground magnetometers in Antarctica and CSES LEO satellite detected concurrent Pc1 wave and energetic proton precipitation. In the ionospheric F-layer above the Auroral zone, the CSES satellites observed transverse Pc1 waves and localized plasma density enhancement, which is occasionally surrounded by wide/shallow depletion. This might indicate that EMIC wave-induced proton precipitation contributes to the energy transfer from the magnetosphere to the ionosphere and to the ionization of the F-layer.

How to cite: Francia, P., DE Lauretis, M., Piersanti, M., D'Angelo, G., and Parmentier, A.: Proton precipitation from EMIC waves at high latitudes: A casestudy from 29 March 2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15928, https://doi.org/10.5194/egusphere-egu23-15928, 2023.

EGU23-15979 | ECS | Posters on site | ST2.4

Auroral oval identification based on Swarm magnetometer data 

Margot Decotte, Spencer Hatch, Karl Laundal, and Jone Reistad

Following the work done with the DMSP spectrometer data to derive the auroral occurrence probability in all covered MLat-MLT sectors above 50 degrees MLat (Decotte et al. 2023), here we use the Swarm magnetometer data to extract the probability to detect magnetic field perturbations in the East--West direction. We derive the integrated spectral density from the magnetic field data in a given frequency band, and we define a minimum power threshold above which fluctuations would indicate field-aligned currents. We obtain MLat-MLT distributions of magnetic field fluctuations for various geomagnetic conditions. We find strong similarities between the preferred region of magnetic perturbations and the Xiong and Lühr auroral boundaries (2014), suggesting that the auroral oval morphology could be investigated through magnetic field spectral power estimates. We compare the magnetic field fluctuation probability with the auroral occurrence probability (DMSP particle data) and we find a recurrent dawn-dusk asymmetric pattern in both distributions.  

How to cite: Decotte, M., Hatch, S., Laundal, K., and Reistad, J.: Auroral oval identification based on Swarm magnetometer data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15979, https://doi.org/10.5194/egusphere-egu23-15979, 2023.

EGU23-16056 | ECS | Posters on site | ST2.4

VLF banded structured events observed in the 5–39 kHz frequency range in Finland 

Liliana Macotela, Jyrki Manninen, and Martin Fullekrug

Analysis of very low frequency (VLF) radio waves provides us the remarkable possibility of investigating the response of both the lower ionosphere and magnetosphere to a diversity of transient and long-term physical phenomena originating on Earth (e.g., atmospheric waves) or in space (e.g., CMEs). In this work, broadband VLF data measured at Kannuslehto, in northern Finland, is used to characterize a new type of VLF emissions displaying a strip-like structure observed in the 5–39 kHz frequency range. Analyzing campaigns from 2006 to 2022, we found that this emission can be observed either in the high VLF frequency ranges or spanning from low to high frequency ranges. We also found that the events last usually less than 1 hour, occur during evening hours, and during quiet geomagnetic conditions. We discuss the seasonal dependence of this kind of events by analyzing a complete year during 2022. We also discuss whether their origin might be due to plasma instabilities in the magnetosphere, as in the case of auroral hiss.

How to cite: Macotela, L., Manninen, J., and Fullekrug, M.: VLF banded structured events observed in the 5–39 kHz frequency range in Finland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16056, https://doi.org/10.5194/egusphere-egu23-16056, 2023.

EGU23-16202 | ECS | Posters virtual | ST2.4

Observation of VLF transmitter induced electron precipitation of up to 400keV 

Coralie Neubüser, Roberto Battiston, Francesco Maria Follega, William Jerome Burger, Mirko Piersanti, and Dario Recchiuti

Ground-based very low frequency (VLF; 10-30kHz) transmitters have been found in previous studies to emit whistler waves that can re- sonate with high-energy particles (>100keV) in the radiation belt, causing energetic electron precipitation via pitch angle scattering. In the attempt to find a similar mechanism responsible for electron precipitation due to EM waves emitted during seismic events, we ha- ve analysed three years of data (2019-2021) from the China Seismo- Electromagnetic Satellite (CSES) and the NOAA POES satellites. We found enhanced electron fluxes due to the 19.8kHz waves of the NWC transmitter in Australia at L-shell values of about 1.5 and 1.8 at energies up to 400keV in the data of the CSES and NOAA POES-19 sa- tellite, respectively. The enhanced fluxes can be followed along the drift shells from Australia eastwards, and are observed to be lost in the the South Atlantic Anomaly (SAA) due to the interaction with the atmosphere. The high energy resolution of the HEPP-L detector on board CSES of 11keV from 0.1 to 3MeV allows a detailed study of the signal and we will present the expected energy-dispersed wisp struc- ture in L-shell. Finally, we will present our latest results on the identification of isolated electron bursts and the assignment to dif- ferent VLF transmitters, which includes the correlation of VLF wave measurements from ground and space-based instruments to determined on/off periods of the transmitters.

How to cite: Neubüser, C., Battiston, R., Follega, F. M., Burger, W. J., Piersanti, M., and Recchiuti, D.: Observation of VLF transmitter induced electron precipitation of up to 400keV, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16202, https://doi.org/10.5194/egusphere-egu23-16202, 2023.

EGU23-17001 | Orals | ST2.4

A new mechanism for early time plasmaspheric refilling 

Raluca Ilie, Jianghuai Liu, Michael Liemohn, and Joseph Borovky

We present a robust assessment of the formation and evolution of the cold H+ population produced via charge-exchange processes between ring current ions and exospheric neutral hydrogen in the inner magnetosphere, inferred via numerical simulations of the near-Earth plasma using a drift kinetic model of the ring current-plasmasphere system.

We evaluate the flow of mass and energy through the inner magnetospheric system and show that the production and evolution of the cold H+ population can be primarily driven by the plasma sheet conditions and dynamics and has the potential to reshape the plasmasphere and enhance the early-stage plasmaspheric refilling. We present evidence that the plasma sheet heavy ion composition is the primary controlling factor in the formation of the cold H+ via charge exchange with the geocorona, while the neutral density plays a much smaller role.

How to cite: Ilie, R., Liu, J., Liemohn, M., and Borovky, J.: A new mechanism for early time plasmaspheric refilling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17001, https://doi.org/10.5194/egusphere-egu23-17001, 2023.

EGU23-17213 | Orals | ST2.4

New pathways for EMIC wave propagation within the ionosphere: SWARM observations and modelling 

Robert Rankin, Dmytro Sydorenko, and Ian R Mann

Electromagnetic ion cyclotron (EMIC) waves are important because of their essential role in reducing the amount of radiation in the Earth's radiation belts under geomagnetic storm conditions. In this presentation, we show results from a new simulation model of EMIC waves and compare them with SWARM satellite data and ground-based observations [I. P. Pakhotin et al., Geophys. Res. Lett., 2022, doi:10.1029/2022GL098249]. The EMIC wave model is a first-of-a-kind in accounting for wave propagation in the magnetosphere and a realistic ionosphere specified using the IRI and MSIS empirical models. The inclusion of a realistic ionosphere in the model enables new pathways to the upper atmosphere to be identified, which is crucial for understanding the waves detected on the ground. We show using a model-data comparison that EMIC wave energy is reflected at different locations in the ionosphere toward the equator to form standing waves. This is a new resonance phenomenoncreated by interference of waves that produces an amplitude peak in the upper atmosphere at lower latitudes, far from the location of the initial source. Understanding such pathways is crucial for correctly diagnosing the location of EMIC wave populations in space, and assessing their role in radiation belt loss.

How to cite: Rankin, R., Sydorenko, D., and Mann, I. R.: New pathways for EMIC wave propagation within the ionosphere: SWARM observations and modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17213, https://doi.org/10.5194/egusphere-egu23-17213, 2023.

EGU23-17286 | ECS | Orals | ST2.4

Nonlinear Scattering of Relativistic Electrons by Oblique EMIC Waves 

Miroslav Hanzelka, Wen Li, Qianli Ma, and Luisa Capannolo

Electrons in the Earth’s outer radiation belt can experience rapid energization and pitch angle scattering through interactions with naturally generated electromagnetic waves. Cyclotron resonant interactions with large amplitude electromagnetic ion cyclotron (EMIC) emissions cause scattering and major atmospheric losses of relativistic electrons in the sub-MeV and MeV energy range. While theory and simulations in the past focused mostly on parallel propagating waves, in-situ spacecraft observations of EMIC waves commonly show quasi-parallel or moderately oblique propagation.

Here we present the results of test-particle analysis of electron interaction with helium band and hydrogen band EMIC waves parametrized by wave normal angle (WNA) and wave amplitude. It is shown that nonlinear phase trapping and the associated transport of electrons to low-pitch angles become efficient only at very large amplitudes (> 1% of the background magnetic field), especially in the helium band frequency range, making the nonlinear effects less important than in the whistler-electron interaction case. Harmonic resonant interactions with oblique waves further increase the probability of detrapping, pushing the pitch angle evolution closer to pure diffusion. We also analyze the pitch angle behavior near the loss cone and study the evolution of phase space density (PSD) through the Liouville mapping method. Despite the significant advection effects caused by force-bunching of resonant electrons at low pitch angles, the PSD in the loss cone exhibits behavior similar to strong diffusion. We argue that this is expected to be the case for any bursty precipitation caused by cyclotron resonant interactions.

The wave normal angle has only minor impact on the precipitation rate in the energy range affected by the off-equatorial fundamental resonance, except for the case of very oblique waves (WNA > 70 deg). However, since oblique EMIC waves are elliptically polarized and interact with both co-streaming and counter-streaming electrons, they can enhance the changes in the pitch angle of mirrored (trapped) relativistic electrons. The scattering efficiency for counter-streaming electrons strongly depends on the wave ellipticity, and in turn, on wave frequency, wave normal angle, and ion composition. Our simulation results support the need for accurate wave normal angle and amplitude distribution to quantify the relativistic electron precipitation to the Earth’s atmosphere.

How to cite: Hanzelka, M., Li, W., Ma, Q., and Capannolo, L.: Nonlinear Scattering of Relativistic Electrons by Oblique EMIC Waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17286, https://doi.org/10.5194/egusphere-egu23-17286, 2023.

EGU23-71 | ECS | Orals | ERE4.3

Spectroscopic Studies and Confirmatory Geochemical Analyses of Rare Earth Element Bearing Rocks from the Neoproterozoic Siwana Ring Complex, Rajasthan, India 

Saraah Imran, Ajanta Goswami, Angana Saikia, Hrishikesh Kumar Rai, and Bijan Jyoti Barman

Abstract:

Rare earth elements (REEs) are of high economic value owing to their electronic, magnetic, optical, catalytic, and phosphorescent properties, thereby making them an important part of the development of green technology. They exhibit characteristic sharp absorption features in reflectance spectra in the visible-near infrared (VNIR) to short-wave infrared (SWIR) region due to their 4f-4f orbital intra-configurational electronic transitions.

In this study laboratory based close-range imaging spectroscopy techniques are used along with confirmatory geochemical analytical techniques (petrography, ICPMS, SEM and EPMA) to study 20 samples collected from REE-bearing rocks of the Neoproterozoic Siwana Ring Complex (SRC), a collapsed caldera structure situated in Barmer District, Rajasthan (India).

The SRC is an anorogenic, rift-related bimodal volcano-plutonic rock association belonging to the Malani Igneous Suite. It comprises of felsic and basic volcanic lava flows, rhyolite, peralkaline granite, pyroclastics, tuff and later microgranite, aplite and felsite dykes.

The spectral reflectance curves of the samples collected using an ASD FieldSpec4 (350-2500 nm) exhibit characteristic absorption dips at 439, 491, 580, 740 and 800 nm indicating the presence of Nd3+. Other major absorption dips are attributed to the presence of Sm3+, U4+, etc. Various combinations of absorption features in the VIS-SWIR region indicate the presence of minerals like biotite, epidote, chlorite, nontronite, goethite, and REE fluorocarbonates. The Fourier Transform Infrared (FTIR) spectra of the samples collected using a Thermo Fisher Scientific Nicolet 6700 (400-4000 cm-1) show symmetric and asymmetric bending and stretching vibration features of Si-O, P-O and O-H bonds, which are diagnostic of minerals like aegirine, riebeckite, and REE minerals like monazite apart from other major silicate minerals like quartz and feldspar. The presence of these minerals is confirmed by mineral chemistry, bulk and trace element data.

The observations from the spectroscopic studies seem to correlate well with data obtained from various geochemical analyses. This study provides spectroscopic information on the rocks from SRC for the first time. It shows the proficiency of spectroscopic studies as a cost-effective and non-destructive technique for the identification of REE minerals which can be used before detailed geochemical and mineralogical studies as well as future exploration.

Keywords: Siwana Ring Complex, Spectroscopy, REE

Abbreviations:

ASD – Analytical Spectral Devices, Inc.

EPMA – Electron Probe Micro Analyzer

FTIR – Fourier Transform Infrared

ICPMS – Inductively Coupled Plasma Mass Spectrometry

REE – Rare Earth Elements

SRC – Siwana Ring Complex

SWIR – Short Wave Infrared

VNIR – Visible Near Infrared

How to cite: Imran, S., Goswami, A., Saikia, A., Kumar Rai, H., and Jyoti Barman, B.: Spectroscopic Studies and Confirmatory Geochemical Analyses of Rare Earth Element Bearing Rocks from the Neoproterozoic Siwana Ring Complex, Rajasthan, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-71, https://doi.org/10.5194/egusphere-egu23-71, 2023.

EGU23-1642 | ECS | Orals | ERE4.3

HyMap airborne imaging spectroscopy for mineral potential mapping of cupriferous mineralization in a semi-arid region based on pixel/sub-pixel hydrothermal alteration minerals mapping – A case study 

Soufiane Hajaj, Abderrazak El Harti, Amine Jellouli, Amin Beiranvand Pour, Saloua Mnissar Himyari, Abderrazak Hamzaoui, Mohamed Khalil Bensalah, Naima Benaouis, and Mazlan Hashim

Recently, hyperspectral datasets recognized a great interest in mineral exploration studies due to their high accuracy in detecting and mapping hydrothermal alteration minerals. Remote and mountainous regions are hardly accessible by geologists, while the spectral richness of imaging spectroscopy could provide detailed information about geology/mineralogy without having a direct contact with the ground surface. The Kerdous inlier in the Anti-Atlas belt of Morocco is recognized by several occurrences of Cu, Pb, Zn Au, Ag, and Mn mineral deposits. This study is carried out in Eastern Kerdous where the abandoned Idikel mine occurs in order to perform a high-resolution mineral potential map using Gamma-Fuzzy logic approach with twenty HyMap-derived layers. The HyMap-based thematic layers were generated using Directed Principal Component Analysis (DPCA), Relative Absorption Band Depth (RBD), and the Mixture Tuned Matched Filtering (MTMF) for pixel/sub-pixel mineral mapping. The hydrothermally altered regions within the study area reveal several Minerals/Mineral mixtures of hematite, illite, kaolinite, montmorillonite, muscovite, topaz, dolomite, and pyrophyllite. Then, the line density map extracted automatically from the HyMap data image was also integrated. The findings of the image processing were validated using field investigation, petrographic, and XRD analysis. This study demonstrates the great potential of the present research methodology and HyMap as a tool for mineral exploitation in similar areas in Morocco's western Anti-Atlas belt.

How to cite: Hajaj, S., El Harti, A., Jellouli, A., Beiranvand Pour, A., Mnissar Himyari, S., Hamzaoui, A., Khalil Bensalah, M., Benaouis, N., and Hashim, M.: HyMap airborne imaging spectroscopy for mineral potential mapping of cupriferous mineralization in a semi-arid region based on pixel/sub-pixel hydrothermal alteration minerals mapping – A case study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1642, https://doi.org/10.5194/egusphere-egu23-1642, 2023.

Underground mining is increasing in Korea, primarily due to the depletion of high quality mineral resources from surface open pit mining, and also due to the fact that environmental regulations are gradually tightened and strengthened. For sustainable mine design, safety and environmental issues are the most important factors forcing more specified and systematic guidelines to secure the stability of the mine openings and adits. However, with complex geological settings and various types of rock discontinuities, a geological mapping process to analyze the behavior of fractured rockmass is generally time-consuming. Information on the geologic structures are often collected by visual observation and analyzed based on two-dimensional drawings. Even worse, very limited and unrepresentative data are collected specially at operating mines leading to unreliable conclusions. Hence, construction of three-dimensional hydrogeological models adopting sophisticated surveying techniques has become a routine site investigation process. Laser scanners of high-end specifications are widely used in Korea. In this study, the Trimble X7 with automatic calibration and in-field registration capability has been used to collect accurate geospatial information at an underground limestone mine adopting the room-and-pillar method, with three drifts 9~12m wide and 6m high. For the two pillars of major stability concern, laser scanning was performed to obtain point-cloud data from which a total of 581 discontinuities were extracted. A discrete fracture network was simulated and the stability was evaluated based on the safety factor and displacement using a numerical model.

 

How to cite: Baek, H. and Kim, D.: Application of the 3-D laser scanning method for assessing the stability of fractured rockmass at an underground limestone mine in Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1750, https://doi.org/10.5194/egusphere-egu23-1750, 2023.

Rare earth elements (REE) have been a focus of global interest because of their irreplaceable role in developing “low carbon” technologies. The Bayan Obo is the world’s largest REE deposit, but its genesis is still highly debated. It is considered to have a close genetic association with carbonatite due to the presence of the carbonatite dykes around the orefield, as well as the geochemical similarities between these dykes and the orebody. However, the evolution of the carbonatite dykes and their REE mineralization are still poorly understood, hindering the interpretation of the genesis of the deposit. More than 100 carbonatite dykes have been found within the area of 0-3.5km nearby the orebodies of the deposit. These dykes show significant variations in mineralogy and geochemistry and were classified into dolomite (DC) and calcite carbonatite (CC). The rocks show an evolutionary sequence from DC to CC, and their corresponding REE contents increased remarkably, with the latter having very high REE content (REE2O3 up to 20 wt. %). The DC is composed of coarse-grained dolomite, magnetite, calcite, and apatite without apparent REE mineralization. The medium-grained calcites, and significant amounts of REE minerals, such as monazite, bastnäsite, and synchysite, make up CC. The REE minerals have a close relationship with barite, quartz, and aegirine. The REE patterns of dolomite and calcite in DC showed a steep negative slope with a strong LREE enrichment. In contrast, the calcite from CC has a near-flat REE pattern enriched in both LREE and HREE. Besides, apatite and magnetite in CC are characterized by strong REE enrichment compared to those from DC. Based on detailed petrology, mineralogy, and element geochemistry, we propose that strong fractional crystallization of initial carbonatitic melts led the REE enriched in the residual melt/fluid to form REE mineralization. In addition, sulfate, alkalis, and silica components play an important role in REE transportation and precipitation.

How to cite: Yang, J. and Song, W.: Mineralogy, major and trace element geochemistry of rock-forming and rare earth minerals in the Bayan Obo (China) carbonatite dykes: implications for REE mineralization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2318, https://doi.org/10.5194/egusphere-egu23-2318, 2023.

EGU23-3180 | ECS | Posters on site | ERE4.3

Radiogenic and stable Sr isotope geochemistry of regolith hosted REE deposits: a preliminary report 

Hamed Pourkhorsandi, Vinciane Debaille, Sophie Decrée, Jeroen de Jong, Ali Yaraghi, Georges Ndzana, Martin Smith, Kathryn Goodenough, and Jindřich Kynický

The increasing global demand for the rare earth elements (REE), that are critical for green energy production, justifies the necessity of understanding REE ore formation processes [1]. The main type of REE mineralization is mostly found in association with carbonatites and alkaline rocks [1,2]. In addition, in some cases the REE can also reach economical levels in secondary products called supergene REE resources [3]. Primary ore mineralizations mostly are composed of mineral phases that are highly unstable and easily soluble in the near-surface conditions in time. The secondary concentration of the REE in weathering regolith into economic deposits is more favourable than those in primary igneous rocks. As the main source of global heavy-REE, weathering deposits in southern China are the most studied ores of this type [4]. Recently, because of the recent surge in REE deposit exploration and their geological importance, other potentially similar deposits are being studied worldwide. Most of these works focus on mineralogical and elemental aspects of these systems. However, those weathering (in cooperation with alteration) systems are complex and a lot of questions on their formation remain unanswered.

In this work, we focus on the isotopic characterization of regolith hosted REE deposits. To better understand their formation, we utilize stable 88Sr/86Sr and radiogenic 87Sr/86Sr ratios, which have been used widely in understanding chemical weathering [5]. Mainly controlled by the incongruent weathering of primary minerals, Sr isotopes can help to identify the sources involved and the main factors affecting regolith hosted REE deposit formation. Strontium is especially important because, as Ca and K, it occurs in different REE-bearing primary and secondary minerals such as carbonates, ancylite, apatite, clays etc.

We will present different regolith profiles’ Sr isotopic data from Asia and Africa. Combining with the elemental and mineralogical data, we will devise a formation model for regolith hosted REE deposits.

References: [1] Goodenough et al. (2016) Ore Geo. Rev., 72, 838. [2] Chakhmouradian & Zaitsev (2012) Elements 8, 347. [3] Estrade et al. (2019) Ore Geo. Rev., 112, 103027. [4] Li et al. (2019) Econ. Geol., 114, 541. [5] Pett-Ridge et al. (2009) GCA, 73, 25.

 

How to cite: Pourkhorsandi, H., Debaille, V., Decrée, S., de Jong, J., Yaraghi, A., Ndzana, G., Smith, M., Goodenough, K., and Kynický, J.: Radiogenic and stable Sr isotope geochemistry of regolith hosted REE deposits: a preliminary report, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3180, https://doi.org/10.5194/egusphere-egu23-3180, 2023.

EGU23-4661 | Posters on site | ERE4.3

Gamma radiation for rare earth elements (REEs) in deep-sea sediments 

Changyoon Lee, Yuri Kim, Yoon-Mi Kim, Sung Kyung Hong, and Seok-Hwi Hong

Gamma ray is routinely used for correlation, evaluation or classification of minerals and rocks on continent and ocean. Using natural gamma radiation (NGR) derived from Integrated Ocean Drilling Program (IODP) and Ocean Drilling Program (ODP), this study focuses on the correlation between lithology and REE (Rare Earth Element)-bearing sediments in two deep-sea areas, IODP Expedition 329 in the Southwest Pacific and ODP Leg 199 Sites in the Northeast Pacific basins, where values of the REEs are abundant. Deep-sea sediments are consisting mainly of clays, calcareous oozes and siliceous oozes. As a result of the correlation, the REEs prefer to the clays rather than oozes and high values of the REEs correspond with intervals of the clays where the upper sediments (0–70 mbsf) are. The clays show relatively high values of the gamma radiation and the differences between significant elements (Th, U and K) for gamma radiation, derived from geochemical analysis at every site, show two trends reflecting characteristics of regions. Therefore we suggest that the gamma radiation is fully useful for detecting REEs in the deep-sea sediments and plays a role as a predictable tool for finding quantitative REEs. 

How to cite: Lee, C., Kim, Y., Kim, Y.-M., Hong, S. K., and Hong, S.-H.: Gamma radiation for rare earth elements (REEs) in deep-sea sediments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4661, https://doi.org/10.5194/egusphere-egu23-4661, 2023.

Carbonatites are known to host over 95% of light rare earth element (REE) resource, and the REEs are commonly hosted in minerals with well-established extraction methods. Most REE mineralized carbonatites are associated with hydrothermal alteration/recrystallization. Identifying the source composition and role of recrystallization is crucial for understanding the formation of the giant carbonatite-associated REE deposit. Here we report the first in-situ carbon and magnesium isotopic compositions for the hosting dolomite in the Bayan Obo deposit.

In-situ carbon isotope analyses of dolomite from the coarse-grained (CM), fine-grained (FM) and heterogeneous-grained (HM) samples show a wide range of δ13C values (-5.19‰ to 2.08‰), which is distinct from the common mantle-derived carbonatite and slightly overlaps the range of sedimentary carbonate. CM dolomite displays almost homogeneous carbon isotope compositions (δ13C=-1.29‰ to 0.16‰) with the average δ13C of -0.82‰. Recrystallized dolomites from both FM and HM samples vary greatly, and FM dolomite generally displays a heavier δ13C range (-3.94‰ to 2.08‰) compared to that for HM dolomite (-5.19‰ to 0.64‰). CM dolomite also shows relative consistent Mg isotope compositions in the range of -0.27‰ to 0.05‰ with an average of -0.10‰, which is similar to the mantle value. δ26Mg values of FM and HM dolomites vary greatly from -1.18‰ to 0.06% with averages of -0.40‰ and -0.32‰, which are lighter compared to that of CM dolomite. The recrystallized dolomites (FM and HM) are characterized by depleted light REE (LREE) and increased Pb/CeN features compared to the pristine dolomite (CM). Moreover, the LREE depletion and Pb/CeN increase correlate with the lighter Mg isotope compositions. The highly variable C isotopes recorded by FM and HM dolomites (lighter or heavier compared to the pristine dolomite) involve both recrystallization and degassing. The combined in-situ Mg and C isotope compositions of the pristine dolomite suggest the Bayan Obo carbonatite sourced from the mantle previously fertilized by fluids derived from the carbonate-bearing subduction slab.

How to cite: Chen, W., Yang, F., and Lu, J.: In-situ C and Mg isotopes of dolomite from the giant Bayan Obo REE deposit: Implications for recrystallization and recycled carbonate in the source, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4823, https://doi.org/10.5194/egusphere-egu23-4823, 2023.

As the world's largest rare earth elements (REEs) deposit, the giant Bayan Obo deposit accounts for more than one third of the world's REEs resources. Fenitization is an alkali metasomatism that widely occurs around the carbonatite dykes at Bayan Obo and recent studies reveal huge quantities of REEs could be transferred from the alkaline magma to fenite (Sokół et al., 2022). However, the contribution of fenitization to REE mineralization at Bayan Obo remains unclear. Here, we present bulk rock chemical compositions, in-situ chemical and C-Sr isotopic investigations of calcite and apatite together with Th-Pb ages of monazite, aiming to provide new constraints on REE mineralization during fenitization.

Carbonatite at Wu dyke is mainly composed of calcite, aegirine and barite associated with REE minerals dominated by bastnasite and parisite, which intruded into the surrounding wall rocks of quartz conglomerate. The associated fenites include the close Na-fenite and faraway K-fenite. Na-fenite contains calcite, riebeckite, aegirine and apatite with minor monazite and bastnasite in association with barite. K-fenite consists of K-feldspar and quartz with accessory riebeckite and albite. Both REE and SO3 contents decrease from the center to the wall rocks. REE are most enriched in the centered carbonatites (up to 7.39 wt%), and Na-fenites also display strong REE enrichment (9876-22492 ppm). Of note, high-grade Na-fenite is characterized by the highest LREE concentrations among fenites, whereas HREE is most enriched in medium-grade Na-fenite. The latter is dominantly controlled by apatite, which hosts abundant HREE (118-677 ppm). Calcite from fenites displays flat REE patterns with more depleted LREE (La/YbN=0.28-3.02) compared to that within carbonatite (La/YbN=1.66-6.52). Th-Pb ages of monazite from fenites cover a wide range from 420 Ma to 1.27 Ga, which suggests these fenites have also undergone the early Paleozoic hydrothermal alteration. In-situ Sr and C isotope analyses of calcite from carbonatite define a limited range (87Sr/86Sr=0.70344 to 0.70358 and δ13C=-4.36 to -5.1 ‰), which are consistent with a mantle origin . 87Sr/86Sr and δ13C values for calcite within Na-fenite show larger variations of 0.70358 to 0.70620 and -4.92 to -9.87 ‰, respectively. Negative shift in δ13C values suggest degassing through the fenitizing reaction of 18CO32-+2Na++3(Mg2+,Fe2+)+2Fe2++8SiO2+24H++0.5O2= Na2(Mg,Fe2+)3Fe3+2Si8O22(OH)2+18CO2+11H2O. More radiogenic Sr isotopic compositions of fenites result from both assimilation of wall rocks during fenitization and the redistribution of Sr isotopes among minerals during the Paleozoic hydrothermal alteration.

Carbonatite-exsolved fenitizing fluids result in predominant REE enrichment within Na-fenite accompanying with light and heavy REE mineralization. LREE mineralization is dominated by monazite precipitation, and HREE enrichment is mostly controlled by apatite. Sulfate is an important ligand for REE transportation and mineralization during fenitization. Barite crystallization and simultaneous precipitation of LREE-bearing minerals lead to fenitizing fluids abundant in HREE, promoting the further formation of HREE-rich apatite.

Reference:

Sokół K., Finch A.A., Hutchison W., et al., 2022. Quantifying metasomatic high-feld-strength and rare-earth element transport from alkaline magmas. Geology, https://doi.org/10.1130/G49471.1.

 

 

How to cite: Yang, F. and Chen, W.: Fenitization associated with the Wu carbonatite dyke at Bayan Obo (Inner Mongolia, China): Implications for REE mineralization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5183, https://doi.org/10.5194/egusphere-egu23-5183, 2023.

EGU23-8008 | Posters on site | ERE4.3

The metasomatism affecting karstic bauxites from the south-central Pyrenees, Catalonia (NE Spain) and its implications on the REE geochemistry in similar geological settings. 

Josep Roqué-Rosell, Pablo Granado, Juan Diego Martín-Martín, Jordi Ibáñez-Insa,, Ivanna Pérez Bustos, Roger Roca-Miró, and Abigail Jiménez Franco

Karstic bauxite deposits are the main resource of aluminum in Europe and are formed through a combination of weathering, leaching, and deposition processes known as bauxitization. Bauxites have recently been proposed as unconventional resources of rare-earth elements (REE) as well. The studied karstic bauxite deposits are located on the salt-detached Serres Marginals thrust sheet, at the external most unit of the south-central Pyrenees (Catalonia, NE Spain). The Pyrenean bauxites are found overlaying and filling karstic surfaces forming aligned pockets up to several meters thick. These deposits have been mined for more than 20 years and present high variability in SiO2, Al2O3 and Fe2O3 contents. Here, we characterize these deposits for the first time by a combination of field geology, XRD, FTIR and XRF to determine their formation, mineralogy, and geochemistry and to understand the causes affecting their compositional variations. Field data indicate that the bauxite deposits fill a paleokarst system affecting Dogger dolostones and/or Tithonian-Berriasian limestones. XRD data indicate that the studied karstic bauxites are mainly composed of Al-rich minerals kaolinite and boehmite, in addition to the Fe-oxide hematite, and lesser amounts of the Ti-oxides rutile and anatase. The detailed study of the FTIR spectra also confirmed the presence of diaspore and dickite. XRF data confirm the presence of varying amounts of Al, Fe and Si in addition to varying low contents of REE. These results suggest that boehmite was formed first during bauxitization and later transformed to diaspore, kaolinite and finally to dickite upon metasomatism. The presence of dickite in faults and fractures provides a direct proof for such fluid circulation. Our results suggest that the mechanisms responsible of the compositional variations in karstic bauxites are rather complex and fall beyond the standard bauxitization processes. The observed metasomatism should be further assessed, since the inferred fluid-rock interactions are susceptible to affect and mobilize REE not only in the south-central Pyrenees karstic bauxites but elsewhere in similar geological settings.

How to cite: Roqué-Rosell, J., Granado, P., Martín-Martín, J. D., Ibáñez-Insa,, J., Pérez Bustos, I., Roca-Miró, R., and Jiménez Franco, A.: The metasomatism affecting karstic bauxites from the south-central Pyrenees, Catalonia (NE Spain) and its implications on the REE geochemistry in similar geological settings., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8008, https://doi.org/10.5194/egusphere-egu23-8008, 2023.

EGU23-9090 | Orals | ERE4.3

Blast Hole Rock Cuttings analysis: Design and Implementation of an open Architecture LIBS System 

Ad Maas, Jorgina Akushika, and Federico Arboleda

This paper presents the development and implementation of a LIBS (Laser-Induced Breakdown Spectroscopy) system based on a robotic arm for fast chemical characterization of blast hole rock cuttings in open pit mining. The system is designed with an open architecture, allowing for the easy integration of additional sensors such as a spectrophotometer and a magnetic susceptibility meter. The use of the LIBS system significantly reduces the time required to characterize the raw material and obtain a broader characterization, including geological characterization. The preliminary results of this development demonstrate the potential of the LIBS system in improving the efficiency and accuracy of rock characterization in open pit mining operations.

How to cite: Maas, A., Akushika, J., and Arboleda, F.: Blast Hole Rock Cuttings analysis: Design and Implementation of an open Architecture LIBS System, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9090, https://doi.org/10.5194/egusphere-egu23-9090, 2023.

Recently, due to the active spread of electric vehicles, the demand for batteries is increasing fast, and for this reason, the exploration for lithium that is an essential mineral for battery production, is increasing. In Korea, lithium exploration is also being conducted around deposits where lithium was identified in the past. However, most lithium mines are located in very rough terrain, so it is not easy to conduct a surface geological and geophysical exploration. Without considering complex topography, errors may occur in the inversion of surface geophysical exploration data, and in particular, it is necessary to use precise topographic information for the three-dimensional inversion. In this study, we would like to introduce a case study using high-resolution topographic data obtained from a drone-mounted LIDAR in the three-dimensional inversion of surface resistivity and IP data conducted for lithium exploration. The target area is the Boam Mine, located in the Middle East of Korea. Surface geophysical exploration was conducted along a road and ridge of the mountain, which are relatively easy to set up the survey line. Because existing topographic maps that are publically available did not include mining traces related to mining development and topographical changes formed by nearby roads, it is not adequate for the 3D inversion of surface resistivity and IP data. To acquire precise topographical information, aerial photography and LIDAR measurements using drones were performed. A numerical topographic model was constructed using the obtained high-precision DEM (digital elevation map). By applying this to the three-dimensional inversion, the distribution of the underground mineralization zone was estimated. The interpreted results were compared with the existing drilling results performed near the mine. Comparing the two results, drilling surveys using only surface geological information proceeded in the direction in which the mineralization zone did not develop. Drone LIDAR measurement is a costly exploration method and is difficult to use actively at all exploration sites. However, if three-dimensional inversion is required where the surface topography is very complex, as in this survey area, it could give more reliable inversion results.

How to cite: Son, J., Kim, C., and Bang, E.: Three-dimensional interpretation of DC resistivity/IP survey for Lithium exploration using high-precision topographic information from drone-mounted LIDAR., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10680, https://doi.org/10.5194/egusphere-egu23-10680, 2023.

EGU23-10689 | Posters on site | ERE4.3

Investigations of Vanadiferous Titanomagnetite Deposit using Drone Magnetic and Electrical Resistivity Surveys in Korea 

Changryol Kim, Jeongsul Son, Eunseok Bang, Gyesoon Park, and Bona Kim

Recently, the demands for energy storage minerals such as vanadium and lithium are increasing as the use of the batteries for electrical vehicles has increased. Vanadium is one of the energy storage minerals occurred in Korea. In this study, vanadium mineralized zones of the ore deposit, named as Gwanin deposit, was investigated using geophysical exploration techniques. The mineralized zone is known as vanadiferous titanomagnetite (VTM) deposit, originated from pre-cambrian igneous intrusions (850-870 m.a.), located in the northwest region of Korea. Since the vanadium has occurred along with magnetite (low electrical resistivity and high magnetic susceptibility) in the study area, geophysical exploration techniques such as magnetic and electrical resistivity surveys were employed. For magnetic exploration, the drone magnetic survey technique was used since it provides more precise and higher resolution data than any other aerial magnetic exploration techniques for relatively small and mountainous areas. In addition, electrical resistivity data were obtained from the six survey lines in the study area. 3D inversion was performed with magnetic and resistivity data. The anomaly zones of low electrical resistivities and high magnetic susceptibilities were interpreted as VTM mineralized zones from the two different inversion results. The mineralized zones were identified from the drilling investigation for overlapping locations of the anomaly zones. The results of the study have shown that magnetic and electrical resistivity techniques are very effective tools for exploring ore deposits of vanadium resource accompanied with magnetite. In the future, drone magnetic exploration technique combined with other (surface) geophysical exploration techniques would provide more effective results of precise geophysical surveys for relatively small and mountainous areas with similar ore deposit environments.

How to cite: Kim, C., Son, J., Bang, E., Park, G., and Kim, B.: Investigations of Vanadiferous Titanomagnetite Deposit using Drone Magnetic and Electrical Resistivity Surveys in Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10689, https://doi.org/10.5194/egusphere-egu23-10689, 2023.

Apatite with high REE content is common in alkaline rocks, carbonatites and products of hydrothermal processes. The REE concentrations could enter mineral structure by different substitution mechanisms (Fleet et al., 2000) and the factors controlling the composition of high-REE apatite are not completely understood. New experimental data (Stepanov et al., 2023) show that at 800 °C and 10 kbar apatite crystalizing from felsic melt with addition of NaCl contains 14 wt.% ΣREEOx and coexists with britholite (37.2 wt.% ΣREEOx). The results suggest that equilibrium has been established during the run and both apatite and britholite contained REE in [Si4+REE3+] to [Ca2+P5+] solid solution, whereas the coupled substitution [Na1+REE3+] to [2Ca2+] was insignificant despite crystallisation from an alkaline, Na-rich melt. Coupling of the new experimental data allowed to constrain the width of the miscibility gap between apatite and britholite, and suggest complete miscibility between apatite and britholite above 950 °C. The substitution [Na1+REE3+] apparently develops mainly in apatite replacement reactions. Therefore, REE content and substitution mechanisms could be useful tools for interpretation of magmatic and metasomatic/hydrothermal associations in alkaline volcanic and plutonic rocks.
References 
Fleet, M., Liu, X., Pan, Y., 2000. Rare-earth elements in chlorapatite [Ca-10(PO4)(6)Cl-2]: Uptake, site preference, and degradation of monoclinic structure. American Mineralogist 85, 1437–1446.
Stepanov, A.S., Zhukova, I.A., Jiang, S.-Y., 2023. Experimental constraints on miscibility gap and partitioning between britholite and chlorapatite in alkaline melt. American Mineralogist.

How to cite: Zhukova, I. and Stepanov, A.: Experimental data on REE in apatite in high-REE environments: distinguishing magmatic and metasomatic compositions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11255, https://doi.org/10.5194/egusphere-egu23-11255, 2023.

EGU23-11997 | Orals | ERE4.3

Hyperspectral mineral mapping for underground mining 

Moritz Kirsch, Mary Mavroudi, Sam Thiele, Sandra Lorenz, Laura Tusa, René Booysen, Erik Herrmann, Ayoub Fatihi, Robert Möckel, Thomas Dittrich, and Richard Gloaguen

Future mining will increasingly require rapid and informed decisions to optimise ore extraction and valuation. In this context, the use of hyperspectral imaging has been proven to be effective for geological mapping in surface mining operations. The potential of hyperspectral methods in underground mining environments, however, remains underexplored due to challenges associated with illumination and surface water. Our contribution addresses this gap by evaluating different lighting setups and the effect of moisture on the spectral quality of hyperspectral data in a laboratory setup. We also compared three commercially available, visible-near infrared to shortwave infrared sensors to assess their suitability for underground hyperspectral scanning. As a demonstration, we acquired hyperspectral data from three adjacent outcrops in the visitor’s mine of Zinnwald, Germany, where rocks of a Late Variscan Sn-W-Li greisen-type deposit are exposed in representative underground mining conditions. A photogrammetric 3D digital outcrop model was used to correct for illumination effects in the data. We then estimated mineral abundance and lithium content across the mine face employing an adapted workflow that combines quantitative XRD measurements with hyperspectral unmixing techniques. Laser-induced breakdown spectroscopy was used to validate the results. While there are still challenges to overcome, this study proves that hyperspectral imaging techniques can be applied underground to yield rapid and accurate geological information. This application will pave the way for the safe, digital and automated underground mine of the future.

How to cite: Kirsch, M., Mavroudi, M., Thiele, S., Lorenz, S., Tusa, L., Booysen, R., Herrmann, E., Fatihi, A., Möckel, R., Dittrich, T., and Gloaguen, R.: Hyperspectral mineral mapping for underground mining, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11997, https://doi.org/10.5194/egusphere-egu23-11997, 2023.

EGU23-12056 | ECS | Posters on site | ERE4.3

ROBOMINERS resilient reflectance/fluorescence spectrometers 

Christian Burlet, Giorgia Stasi, Simon Godon, Roza Gkliva, Laura Piho, and Asko Ristolainen

ROBOMINERS (Bio-Inspired, Modular and Reconfigurable Robot Miners, Grant Agreement No. 820971, http://www.robominers.eu) is a European project funded by the European Commission's Horizon 2020 Framework Programme. The project aims to test and demonstrate new mining and sensing technologies on a small robot-miner prototype (~1-2T) designed to target unconventional and uneconomical mineral deposits (technology readiness level 4 to 5) (Lopez and al. 2020).

As part of the ROBOMINERS sensor array development, a set of mineralogical and geophysical sensors are designed to provide the necessary data to achieve a “selective mining” ability of the miner to reduce mining waste production and increase productivity of a small mining machine. To achieve this, the robot should have the ability to react and adapt in real time to geological changes as it progresses through a mineralized body. This study focuses on a set of compact sensors designed for ultrahigh-resilience and continuous operation in high pressure/vibrations/temperature environment. They are based on reflectance/fluorescence measurements in the visible/near infrared range, using a broadband light source (tungsten-halogen lamps) in reflectance mode and 365nm UV LED in fluorescence mode. 

The ROBOMINERS reflectance/fluorescence spectrometer “Mk1” was developed in collaboration with Taltech University. The spectrometer is built around a monolithic spectrometer (Hamamatsu C12800MA and a wifi capable microcontroller (Arduino RP2040 Connect).. As the ROBOMINERS prototype will be operated by ROS2 (Robotic Operating System v2 - https://www.ros.org/ ), we decided to implement a Micro-ROS publisher on the microcontroller.

The first field trials of the sensor have been carried out in the entrance of abandoned mine (baryte and lead mine, Ave-et-Auffe, Belgium), with the sensor integrated directly in the propulsion mechanism of the “RM3”’ ROBOMINERS prototype. This test allowed to demonstrate the immunity of the sensors to  to shocks, water and dust with no measurable de-calibration of the spectrometer.

References.

Lopes, B. Bodo, C. Rossi, S. Henley, G. Žibret, A. Kot-Niewiadomska, V. Correia, Advances in Geosciences, Volume 54, 2020, 99–108

 

 

How to cite: Burlet, C., Stasi, G., Godon, S., Gkliva, R., Piho, L., and Ristolainen, A.: ROBOMINERS resilient reflectance/fluorescence spectrometers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12056, https://doi.org/10.5194/egusphere-egu23-12056, 2023.

EGU23-13081 | Posters on site | ERE4.3

The surface chemistry of carbonatite soils: Implications for REE resources. 

Martin Smith, Charles Beard, Isaac Watkins, Sam Broom-Fendley, Frances Wall, Xu Cheng, Yan Liu, Wei Chen, and Jindrich Kynicky

The rare earth elements (REE), and in particular neodymium and dysprosium, are essential for the development of renewable energy. At present the REE are sourced from either low concentration weathered granitoid (ion adsorption clay) deposits in southern China, or from high concentration carbonatite-related deposits [1], especially the World’s dominant REE mine at Bayan Obo, China, but also including the Mt Weld weathered carbonatite, Australia. Weathered carbonatites (e.g. Tomtor, Russia; Mount Weld, Australia) are some of the world’s highest grade REE deposits. As part of the NERC Global Partnerships Seedcorn fund project WREED, we have carried out preliminary investigations in weathering products from carbonatite hosted REE deposits. Three end member deposit styles can be identified – in situ residual deposits, where carbonate dissolution has generated primary REE mineral enrichment on palaeosurfaces or in karst; supergene enrichment from dissolution and reprecipitation of REE phosphates and fluorcarbonates forming hydrated phosphates or authigenic carbonate minerals; clay and oxide caps (either from in situ weathering or from soil transport from surrounding rocks) that may hold the REE adsorbed to mineral surfaces (c.f. the ion adsorption deposits). High grade weathered carbonatite deposits typically consist of supergene horizons, that may be phosphate-rich due to dissolution and re-precipitation of apatite and monazite during the weathering process (Mount Weld [2][3]), overlain by later sediments that may be REE enriched by accumulation of residual minerals (e.g. Tomtor [4]). The mineralogy of the ore zone is linked to, but distinct from, the unweathered carbonatite rock, and includes phosphates, crandallite-group minerals, carbonates and fluorcarbonates and oxides. We have carried out leaching studies, SEM examination and XPS characterisation of soil and weathered rock samples from a range of deposits. Residual and supergene processes can result in enrichments up to 100x times bedrock concentrations, with residual enrichments in particular hosted in monazite and bastnäsite. Supergene enrichment results in more complex mineralogy which may present processing challenges. Clay-rich soils have much lower REE concentrations. However, sequential leaching studies demonstrate that a significant proportion of REE are present at trace levels in the oxide fraction in residual and supergene deposits. In clay caps the easily leachable fraction of REE matches that of ion adsorption deposits and may represent a potentially easily extractable resource.

 

References

[1] Wall and Chakhmouradian, 2012, Elements 8, 333-340;

[2] Duncan and Willett, 1990, Geology of Mineral Deposits of Australia pp. 591-597;

[3] Lottermoser, 1990, Lithos 24, 151-167;

[4] Kravchenko and Pokrovsky, 1995, Econ. Geol. 90, 676-689;

How to cite: Smith, M., Beard, C., Watkins, I., Broom-Fendley, S., Wall, F., Cheng, X., Liu, Y., Chen, W., and Kynicky, J.: The surface chemistry of carbonatite soils: Implications for REE resources., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13081, https://doi.org/10.5194/egusphere-egu23-13081, 2023.

EGU23-13899 | ECS | Posters on site | ERE4.3

Robot-aided autonomous hyperspectral mapping in mining environments 

Sandra Lorenz, Moritz Kirsch, Margret Fuchs, Sam Thiele, and Richard Gloaguen

Geological face mapping is a frequently recurring task in mining operations, the results of which have an immediate influence on the mines’ profitability, safety, and environmental impact. Hyperspectral imaging is an increasingly applied technology to improve the efficiency and accuracy of mapping tasks. The rapid and non-destructive acquisition of spectral material properties allows meaningful material information such as mineralogical surface composition to be obtained in a safe and efficient manner. The fusion product of backprojected hyperspectral data with 3D surface information (so-called “hyperclouds”) further enhances the data value by enabling easier data correction, integration, and implementation into digital archives and models. Mining environments, however, remain a challenge for operational hyperspectral mapping, particularly underground where inadequate lighting, access, and safety of operation make data collection difficult. Data processing and interpretation require expert knowledge and are typically performed semi-manually and offline. To be economically viable in such mining environments, the hypercloud technology has to mature toward autonomy and real-time delivery of results. In recent years, terrestrial autonomous platforms have entered the market that are suited to the challenging conditions of underground mining and can maneuver and navigate even in confined, uneven, and poorly lit environments. They provide optimal carriers for hyperspectral sensors, which have simultaneously evolved into lighter, faster, and more robust devices. However, implementing hyperspectral sensors as payload for terrestrial autonomous robots remains challenging, especially in terms of  technical compatibility, ensuring data quality under complex conditions,  and processing large amounts of data quickly and autonomously. In our contribution, we demonstrate the potential of autonomous terrestrial robots combined with hyperspectral technology and advanced data processing for the automation of geological mapping. We present results of hyperspectral data acquisition using an autonomous robotic platform in a confined underground mining environment and discuss strategies for adapted sensor design, autonomous validation, real-time hypercloud processing, and enhanced autonomous navigation supported by hyperspectral information. 

How to cite: Lorenz, S., Kirsch, M., Fuchs, M., Thiele, S., and Gloaguen, R.: Robot-aided autonomous hyperspectral mapping in mining environments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13899, https://doi.org/10.5194/egusphere-egu23-13899, 2023.

EGU23-15053 | Orals | ERE4.3

TIMREX – a European joint master programme to implement innovative mineral exploration achievements in geoscience education 

Ferenc Madai, Sibila Borojević Šoštarić, Gabriela Paszkowska, and Nils Jansson

Mineral resource exploration techniques and methodologies have undergone a very strong development in the last decade: e.g. portable and higher sensitive equipment, robotized exploration equipment, and tools for processing and interpreting of large, multidimensional datasets. In order to meeti the raw materials policy goals of the EU, these technologies should also be incorporated in higher education (Mádai, 2022).

 

TIMREX is a new EIT-Labelled joint master's program to train geoscience students focusing on innovative raw materials prospecting and exploration methods. The consortium consists of four academic partners – University of Miskolc, Hungary, University of Zagreb, Croatia, Wroclaw University of Science and Technology, Poland and Luleå University of Technology, Sweden. All four academic partners run their mineral exploration-focussed, geoscience engineering-type master programmes which comprise the ground for the joint master programme. Participating Universities are located within Fennoscandian, Fore-Sudetic and Tethyan/Carpathian-Balkan metallogenic belts hosting numerous primary, secondary and critical mineral resources essential for green transition of Europe. Scandinavian and West Balkan countries holds first and second place according to total mineral resources investments in Europe (data from 2019).

 

The TIMREX consortium incorporates eight non-academic partners who are at the frontier of mineral resource prospecting and exploration equipment and methodology development in the EU. They represent leading European mining companies such as Boliden Mineral and KGHM, but also SMEs and start-ups such as the Unexmin Georobotics (UGR) and the Geogold Kárpátia Ltd., as well as research institutes such as the Portuguese INESC TEC and the Slovenian Geological Survey (GeoZS).

Non-academic partners are actively involved in the TIMREX joint programme as trainers in field programs, internship mentors or thesis topic providers. Students of the programme can join research and development work at the partners. Examples are development of underwater robotized exploration methodologies (INESC TEC, UGR), drone-based multispectral surveys and complex dataset evaluation (Boliden, KGHM Cuprum, GeoZS, Geogold). The European Federation of Geologists provides a wider network of European prospectors and explorers to the joint programme and contributes to teaching of entrepreneurial skills. Therefore, TIMREX directly address major gaps of the Raw Materials sector: limited availability of qualified technical, scientific and managerial personnel involved in the whole mineral cycle (Borojević Šoštarić et al., 2022) as well as lack of generic skills crucial for increasing the innovation capacity of universities and their graduates (Grgasović and Borojević Šoštarić, 2021).

 

 

Borojević Šoštarić, S., Giannakopoulou, S., Adam, K. i Mileusnić, M. (2022). The future of mining in the Adria region: current status, SWOT and Gap analysis of the mineral sector. Geologia Croatica, 75 (Special issue), 317-334. https://doi.org/10.4154/gc.2022.26

Grgasović, P.; Šoštarić, S.B. (2021) Systematic Development of Generic Skills to Enhance Innovation Capacity of Eastern and Southeastern European Universities. Mater. Proc.

5, 99, 1-7. https://doi.org/10.3390/ materproc2021005099

Mádai F. (2022) Competence requirements of innovation and entrepreneurship oriented training programmes for the mineral exploration sector. In: Veresné Somosi M.; Lipták K.; Harangozó  Zs.(eds) "Mérleg és Kihívások - Fenntarthatóság" Miskolci Egyetem Gazdaságtudományi Kar (2022) pp. 537-547

How to cite: Madai, F., Borojević Šoštarić, S., Paszkowska, G., and Jansson, N.: TIMREX – a European joint master programme to implement innovative mineral exploration achievements in geoscience education, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15053, https://doi.org/10.5194/egusphere-egu23-15053, 2023.

EGU23-15445 | ECS | Posters virtual | ERE4.3

Re-evaluating Caledonian magmatism and associated base metal mineralisation: a case study of the Black Stockarton Moor porphyry copper system 

Chloe Gemmell, David Currie, Iain Neill, Josh Einsle, and Careen MacRae

Following the British Geological Survey’s (BGS) 1970s – 1990s Mineral Reconnaissance Programme (MRP), there has been limited characterisation and quantification of base and precious metal mineralisation in the UK, with the notable exception of Au. Data gaps still exist regarding mineral paragenesis, geochronology, deportment of critical raw materials (CRM), and ore forming processes. With increased focus on CRM, NetZero, and supply risk we must improve our knowledge of deportment in base metal systems. The BGS Critical Minerals Intelligence Centre (CMIC) was recently established to aid the UK in meeting projected future CRM demand and will act as a nexus for industry and academia. Here, we establish a workflow and document a case study where academia and the CMIC have partnered to re-evaluate a potential mineral resource, a starting point for renewed studies elsewhere in the UK. 

The Black Stockarton Moor (BSM) post-subduction porphyry Cu system is thought to have formed by interaction of Devonian plutonic to sub-volcanic complexes with Silurian turbidites in the Southern Uplands of Scotland. No study of the BSM has been undertaken since the 1979 MRP report, thus whether it is of any modern value remains unproven. Field sampling and utilising the National Geological Repository at BGS will allow for optical and scanning electron microscopy (SEM) to quantitatively establish paragenesis and primary mineralogy. Sites will then be identified for chemical mapping to quantify CRM deportment in base metals using SEM-energy dispersive X-ray analysis (EDX), with areas of particular interest further quantified by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Focused ion beam (FIB) nano-tomography will be used to identify the cm to nano-scale distribution of CRM. Finally, magmatism and mineralisation will be fully temporally constrained using U-Pb analysis of zircon, titanite, calcite and epidote and/or Re-Os analysis of sulphides as appropriate. On a large scale, this study will address one set of data gaps by re-invigorating our knowledge of the geology and geodynamic associations of mineralisation. However, by also identifying the quantities and associations of metals at the cm to micron scale, it addresses another, by constraining the extent and nature of processes responsible for the distribution of metals in such deposits. This workflow is to be refined for application to mineralisation elsewhere in the UK including work underway on the Strontian Caledonian granite and associated Pb-Zn mineralisation in the Northern Scottish Highlands.

How to cite: Gemmell, C., Currie, D., Neill, I., Einsle, J., and MacRae, C.: Re-evaluating Caledonian magmatism and associated base metal mineralisation: a case study of the Black Stockarton Moor porphyry copper system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15445, https://doi.org/10.5194/egusphere-egu23-15445, 2023.

EGU23-16567 | ECS | Posters on site | ERE4.3

Deep Electrical Resistivity Tomography as a mineral exploration tool: the Calamita distal Fe-skarn, Elba Island (Italy) 

Damian Braize, Julien Sfalcin, Matteo Lupi, Kalin Kouzmanov, Andrea Dini, and Gianfranco Morelli

To face the growing demand for raw materials, the discovery of new mineral deposits is essential for the future. Geophysical methods, and in particular electrical and electromagnetic tools, have an important role in mineral exploration. Recently, new technological developments made possible targetting deeper ore bodies and large areas with logistical challenges. We use the Deep Electrical Resistivity Tomography (DERT) method to investigate its application in mineral exploration. In particular, we use the Fullwaver technology developed by IRIS Instruments to study the full 3D resistive structure of the Calamita distal Fe-skarn deposit, Elba Island, Italy. This innovative hardware allows a full 3D deployment of autonomous and cable-less receivers and contrasts with traditional resistivity methods by its easy set-up and applicability in difficult contexts.

In November 2022, a 3D DERT survey has been carried out to investigate the Calamita deposit, consisting of massive magnetite-hematite ore bodies hosted in marbles overlaying micaschists of Tuscan Units. Skarn mineralogy/geochemistry and fluid inclusion characteristics suggest a magmatic source for the mineralizing fluids. 148 current injections have been performed on 48 receivers over an area of 2km² with the aim to reach exploration depths ranging from 600 m to 700 m. Geophysical data were combined with a high-resolution 3D Digital Elevation Model acquired by standard and thermal drone imagery.

The 3D inverted resistivity and induced polarization models match with the surface geology and shallow exploration drill hole data and highlight the architecture of Calamita deposit. Strong resistivity contrasts reveal the presence of sub-vertical conductive and chargeable pipes connecting the different skarn bodies at depth, interpreted to represent the paleo-hydrothermal upflow zones. The pipes point towards the inferred cupola of a magmatic intrusion that potentially triggered the formation of the ore deposit. High chargeability anomalies suggest the presence of hidden massive ore bodies and disseminated mineralisation on the flanks of the system.

DERT has the potential to investigate and explore mineral deposits in full 3D, with high sensitivity, and in logistically complex settings.

How to cite: Braize, D., Sfalcin, J., Lupi, M., Kouzmanov, K., Dini, A., and Morelli, G.: Deep Electrical Resistivity Tomography as a mineral exploration tool: the Calamita distal Fe-skarn, Elba Island (Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16567, https://doi.org/10.5194/egusphere-egu23-16567, 2023.

EGU23-17258 | Orals | ERE4.3

Dig_IT – A human-centred Internet of Things platform for the sustainable digital mine of the future 

Diego Grimani, Lorenzo Bortoloni, Damiano Vallocchia, Maria Garcia Camprubi, and David de Paz

Dig_IT project aims to develop a human-centred IIoT platform connecting the mining ecosystem of assets, environment, and humans to increase mining efficiency: saving costs using optimised scheduling, increasing uptime using predictive operation and maintenance, identifying new revenue opportunities using advanced geological interpretation on exploration mining phase. To address industry needs of minimising accidents, optimising production processes and reducing costs, intelligent systems will provide real-time insights for the enterprise at all operational levels.

Dig_IT follows a market need & technology offer approach aiming at covering all aspects of technical, industrial and business requirements towards a sustainable future in mining. The project’s value chain and concept has been built with the utmost objective to provide new solutions addressing the needs for safety, efficiency and sustainability, bringing innovative and competitive solutions to the mining business, face future challenges regarding standards and legislation, and spread the knowledge to as many sectors of the European extractive industry as possible.

The project aims to achieve several objectives: design and validate a smart Industrial Internet of Things platform to improving efficiency and sustainability of mining operations, achieving on-line measurements of asset-bound mining operations and online distributed measurements for broad area sustainability and occupational work environment, and Big Data optimisation through improving data quality. Furthermore, the project aims to develop Digital Twins of the physical mine entities, systems and processes, a Smart Garment and an Intelligent Toolbox for mining personnel sensing OHSE parameters, a Decision Support System and a Predictive Operation System.

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How to cite: Grimani, D., Bortoloni, L., Vallocchia, D., Garcia Camprubi, M., and de Paz, D.: Dig_IT – A human-centred Internet of Things platform for the sustainable digital mine of the future, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17258, https://doi.org/10.5194/egusphere-egu23-17258, 2023.

EGU23-17279 | Orals | ERE4.3

Underwater measurements with UX robots; a new and available tool developed by UNEXUP 

Norbert Zajzon, Boglárka Anna Topa, Richárd Zolzán Papp, Jussi Aaltonen, José Almeida, Balazs Bodo, Stephen Henley, Marcio Pinto, and Gorazd Zibret

The UX-2 robot of the UNEXMIN technology represents the newest generation of underwater explorers capable of operating in flooded mines and other closed underwater environments meanwhile providing geoscientific information. The technology was developed by an international team of scientists during the UNEXMIN (https://www.unexmin.eu/) Horizon 2020 project (2016–2019) and the UNEXUP (https://unexup.eu/) EIT RawMaterials project (2020–2022). The concept was proven in various environments and the first generation of robots was built in the UNEXMIN project. Besides technological upgrades, the UNEXUP project was focusing also on marketing and commercialization thru UNEXMIN Georobotics Ltd. (https://unexmin-georobotics.com/), the spin-off of the consortium.

The technology proved its capabilities at numerous flooded sites in various harsh environments during the last years including, abandoned mines, caves, historical sites and even drinking water facilities.

Although very bad visibility was observed in the South Crofty mine, Camborne (UK), the robot could manoeuvre down to -300 m and investigate a narrow shaft relying mainly on sonar-based navigation.

The Csór water well, the main drinking source of Székesfehérvár (Hungary) was another location where the UX technology proved its usefulness and 3D-mapped the well with centimetre accuracy for reconstruction purposes.

In August of 2022, the UX robot created a 3D topography map and continuous water parameter measurements further exploring the flooded karstic cave Hranice Abyss (Czech Republic) down to -450 m – setting up the current word depth record.

Even remote-control and full autonomy were demonstrated in Kőbánya-mine, Budapest, Hungary. During the remote-control test, the Budapest team launched the robot, but the underwater robot operation was done from INESCTEC, Portugal.

Ecton copper mine (UK) used to be the deepest mine of its age in the 18th century, closed and partially flooded for more than 160 years. Now it is a listed National Monument in the UK and is under strict protection within a site of special scientific interest. Here the UX robots proved their value in discovering new workings, connections, and technological solutions helping the archaeologists which could not be recovered by other methods as well as elucidating the geological structure.

The salt mine of Solotvyno, Ukraine was a demanding challenge as the UX robot had to be capable of operating and measuring in freshwater as well as in fully saturated (ca. 330g/l) brine with 1.25 g/cm3 density, which was located below a freshwater layer.

The abandoned fluorspar mine of Würmtal, Pforzheim, Germany was the last site visited within the frame of the UNEXUP project where the UX robot revealed its unique capabilities by exploring a large part of the flooded workings. More than 3 km was covered laterally in a single dive down to the fluorspar vein, and colour- and UV-images of the ore were delivered successfully. UX robot also brought back data, helping to assess the stability of the walls.

The UNEXMIN project was funded by the European Union thru the Horizon 2020 research and innovation programme under the no. 690008 grant agreement.

The UNEXUP project was funded partially by the European Union thru EIT RawMaterials no. 19160.

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How to cite: Zajzon, N., Topa, B. A., Papp, R. Z., Aaltonen, J., Almeida, J., Bodo, B., Henley, S., Pinto, M., and Zibret, G.: Underwater measurements with UX robots; a new and available tool developed by UNEXUP, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17279, https://doi.org/10.5194/egusphere-egu23-17279, 2023.

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