EGU General Assembly 2023  

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.

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.

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.

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.

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.

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.

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.

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.

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 NO_x 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.

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.

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.

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 L_Aeq16 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 19^th to 20^th 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.

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.

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.

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.

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.

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 C_n² (which corresponds to the fluctuations of air refractive index n ) in combination with surface energy balance (SEB) and Monin-Obukhov similarity theory (MOST) . Hence, C_n² 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 C_n²(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 C_n². This research provides a complete grasp of the properties of C_n² 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.

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.

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.

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.

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 (WUE_e=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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

• 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.

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 7^th 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This study investigates winter cyclones that cause extreme 10 m winds in the central North Atlantic region (30^o to 60^o latitude, -50^o to -10^o 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 98^th 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 20^oC 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 (Z_mxg). The results illustrate that the ENSO phase space is much closer to the idealised recharge oscillator model if based on Z_mxg 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 Z_mxg 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.

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.

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.

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.

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.

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.

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 2^nd-year La Niña. We find that the negative phase of the Pacific Meridional Mode (PMM) during 1^st-year La Niña’s decaying spring, rather than the preceding strong El Niño, offers the key mechanism to produce 2^nd-year La Niña, resulting in a multi-year La Niña. It is further found that the westward extension of the 1^st-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 3^rd-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.

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.

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.

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.

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.

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.

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 21^st century; ii) models simulating ENSO events anomalously westward tend to show an eastward shift in event locations and an increased intensity in the 21^st 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 21^st 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 21^st 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.

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.

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.

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 ΔT_WP-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, ΔT_WP-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.

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.

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 5^o 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 1.5^oC colder than the re-analyses and the depth of the 20^oC 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.