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.