Probabilistic flood risk assessments (PFRA) and flood early warning systems (FEWS) are essential tools for managing and responding to disastrous flood events, particularly in coastal deltas where flooding is often compound, resulting from the interplay of coastal and riverine water levels and local rainfall. PFRA and FEWS require assessing the compound flood hazard under a broad range of plausible or forecasted hydro-meteorological conditions. While efficient hydrodynamic models for compound flooding have been developed, such as SFINCS (Leijnse et al. 2021; van Ordmondt et al. 2024), trade-offs in the model resolution or number of simulations or stochastic variables are often required for PFRA and FEWS, at the cost of model accuracy.

The physics-guided  hybrid  LSG model (Fraehr et al. 2022, 2023) uses a Sparse Gaussian Process model trained on Empirical Orthogonal Functions (EOF) derived from simulations with a high- and low- resolution hydrodynamic model. For new events to simulate, the approach combines a simulation of the low-resolution hydrodynamic model with the trained surrogate model, to predict high-resolution water depths at low computational costs. While this model has successfully been applied for riverine flooding, it has not yet been used to predict compound flooding from multiple drivers.

This study tests the surrogate SFINCS-LSG model for compound PFRA and FEWS. We investigate the optimal choice of events to train the model and test the model for case studies in Brisbane, Australia and Charleston (NC), USA. We validate the surrogate model against the high-resolution SFINCS model for different historical compound events, hypothetical compound flood scenarios, and compound PFRA.

Based on preliminary results, we find that compared to the course-resolution SFINCS model, the surrogate model provides a significant improvement at very low computation costs, while compared to the high-resolution SFINCS model it achieves a large speedup with only a small drop in accuracy. While the results are promising for individual simulations, the surrogate model struggles to capture the transition zone based on the difference between model simulations. Nonetheless, the surrogate SFINCS-LSG models seem a promising approach to improve compound PFRA and FEWS.

References

Fraehr et al. (2022). Upskilling low-fidelity hydrodynamic models of flood inundation through Spatial analysis and Gaussian Process learning. WRR, https://doi.org/10.1029/2022WR032248

Fraehr et al (2023). Development of a fast and accurate hybrid model for floodplain inundation simulations. WRR, https://doi.org/10.1029/2022WR033836

Leijnse et al. (2021). Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes. Coastal Engineering, https://doi.org/10.1016/j.coastaleng.2020.103796

van Ormondt et al. (2024). A subgrid method for the linear inertial equations of a compound flood model, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1839

How to cite: Eilander, D., Fraehr, N., Leijnse, T., and de Goede, R.: Surrogate flood models for compound flood risk assessments and early warning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5209, https://doi.org/10.5194/egusphere-egu25-5209, 2025.

Compound Inland Flooding (CIF) arises from the interactions between multiple hydrometeorological drivers, often magnified by landfalling Atmospheric Rivers (ARs) along the Pacific Northwest coast and interior basins of North America. This study investigates the mechanisms behind two primary CIF types, Rain-on-Snow (ROS) and Saturation Excess Flooding (SEF), using the CanRCM4 large ensemble under global warming levels of +1.5°C, +2°C, and +4°C. By examining the joint occurrence of ARs with ROS and SEF across key sub-regions, including the Cascade Range, Sierra Nevada, and the Great Lakes Basin, we assess the probabilities, seasonal shifts, and hydrological impacts of CIFs in the 21st century. Results show distinct regional patterns, with ROS events projected to decrease in frequency across the Pacific Northwest and Great Lakes Basin but remain significant in high-elevation regions prone to seasonal snowmelt, such as the Canadian Rockies. Conversely, SEF events are projected to increase substantially, particularly in the eastern U.S. and southern Great Lakes, driven by intensified precipitation and persistently saturated soils. The findings indicate that under higher warming levels, the contribution of ROS to extreme runoff can decrease, while SEF-driven flood events become dominant. Signal-to-noise ratio analysis shows that internal climate variability contributes considerable uncertainty to CIF projections in transitional climate zones but is overshadowed by external climate forcing at higher warming levels, particularly in coastal regions. By capturing the compounded effects of precipitation extremes, snowmelt dynamics, and soil moisture conditions, this study underscores the necessity of integrating AR-driven compound events into regional flood risk management strategies. 

How to cite: Najafi, M. R., Fereshtehpour, M., Grgas-Svirac, A., and Cannon, A.: Atmospheric Rivers and Compound Inland Flooding under Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11037, https://doi.org/10.5194/egusphere-egu25-11037, 2025.

Cyclonic systems in the Eastern Mediterranean often produce compound extremes of heavy precipitation and strong winds, significantly impacting socio-economic systems. This study leverages traditional atmospheric analysis and dynamical systems theory to investigate these “wet” and “windy” extremes (Vakrat and Hochman, 2023). Using the co-recurrence ratio (α; De Luca et al., 2020) and persistence (1/θ; Faranda et al., 2017), we quantify atmospheric state dynamics and link them to extreme weather events. Results reveal that compound extremes exhibit higher co-recurrence and persistence than individual extremes, with anomalies in these metrics increasing the likelihood of extreme weather events by up to 18-fold. A case study of the mid-February 2012 Eastern Mediterranean compound event highlights the role of persistent upper-level dynamics in driving these extremes. Our findings emphasize the value of dynamical systems metrics in enhancing the predictability of compound extremes and their application to other regions and extreme weather events (Hochman et al., 2019). 

References

De Luca P, Messori G, Pons FME, Faranda D. Dynamical systems theory sheds new light on compound climate extremes in Europe and Eastern North  America. Quarterly Journal of the Royal Meteorological Society 146: 1636–1650. https://doi.org/10.1002/qj.3757

Faranda D, Messori G, Yiou P. Dynamical proxies of North Atlantic predictability and extremes. Scientific Reports 7: 41278. https://doi.org/10.1038/srep41278

Hochman A, Alpert P, Harpaz T, Saaroni H, Messori G. 2019. A new dynamical systems perspective on atmospheric predictability: eastern Mediterranean weather regimes as a case study. Science Advances 5(6): eaau0936.  https://doi.org/10.1126/sciadv.aau0936 

Vakrat, E. Hochman, A. 2023.Dynamical systems insights on cyclonic compound “wet” and “windy” extremes in the Eastern Mediterranean. Quarterly  Journal of the Royal Meteorological Society 149(757): 3593–3606. https://doi.org/10.1002/qj.4575

How to cite: Hochman, A. and Vakrat, E.: Understanding Cyclonic Compound “Wet” and “Windy” Extremes in the Eastern Mediterranean through Dynamical Systems Theory, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5532, https://doi.org/10.5194/egusphere-egu25-5532, 2025.

Global warming may trigger more frequent snow droughts (SD). SD can result from low total precipitation (dry-SD), from high temperature leading to less solid precipitation (warm-SD) or from the combination of both (dry-warm compound SD). Each of those SD type pose different ecological threats. Nevertheless, the regions dominated by SD types, transition patterns, and the future risks under climate change remain unclear. Here, we investigated the dominant SD types and clarify the transition patterns among the three SD types during the historical and the future period. The results suggest a global increase in SD frequency by about 1.5-fold and 2-fold under SSP2-4.5 and SSP5-8.5 respectively. Moreover, the shares of warm SD is increasing and may become dominant by 2050 and probability of dry-warm compound SD may reach 4–10 times that of the historical period. The global transition from dry to warm dominated SD is attributed to greenhouse gases. Those findings provide a scientific reference for addressing climate change risks on SD.

How to cite: Wang, C., Li, Z., Guyennon, N., Chen, Y., and Li, Y.: Patterns of Snow Drought Under Climate Change: From Dry to Warm Dominance, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13893, https://doi.org/10.5194/egusphere-egu25-13893, 2025.

Winter weather events can result in costly damages and severe disruption to affected regions. While compound events research has strongly focused on heat-related events, less focus has been placed on extreme cold hydrometeorological hazards. Cold events impact a range of sectors from energy and agriculture to transport and health. The rail sector is particularly sensitive to cold weather hazards resulting in service delays and cancellations. Snowfall can lead to blocked tracks, points failures and issues with electricity supply. Loss of traction, braking issues and frozen infrastructure can arise from ice formation. The impacts of these cold events can be amplified by the compounding effect of another meteorological variable, such as whether heavy precipitation is present or not, with subsequent impacts dependent on the nature of the cold event. For example, a cold-wet event could incur heavy snowfall, whereas a cold-dry event could result in extreme low temperatures and icy conditions. In this study, we analyse the occurrence of 10,000 rail incidents in Scotland over an extended winter period of October to March for 2006-2023 to investigate the relationship between impacts and compound cold events. We use an impact dataset from Network Rail to categorise high-impact days based on two classifications: (1) days with the highest number of aggregated incidents; (2) days with the highest number of accumulated customer minutes lost. Using daily gridded observations from HadUK-Grid at a 5 km resolution we then apply a localised percentile-based methodology to determine the occurrence of cold-dry and cold-wet events on these high-impact days. Initial results show that the majority of high-impact days consisted of incidents caused by severe snow and icing. Analysis reveal that these incidents occurred under hydrometeorological conditions that can be classified as cold-dry and/or cold-wet events. These findings highlight the importance of considering co-occurring hazards rather than single hazards. The results of this study provide a useful insight into compound cold events for rail sector early warning systems, with valuable information on cold weather event hazard characterisation and their associated impacts across varying timescales.

How to cite: Mattu, K., White, C., Bloomfield, H., and Robbins, J.: Investigating the relationship between compound cold events and impacts on the Scottish rail sector. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4810, https://doi.org/10.5194/egusphere-egu25-4810, 2025.

Extreme value analysis (EVA) includes a range of methods used to study the frequency and magnitude of rare but catastrophic events, with applications in science and engineering. These methods rely on mathematical theories that assume stable input data over time. However, many long-term datasets, especially those related to natural hazards, show clear changes over time (non-stationarity). With the availability of long-term climate records, there is a need for a reliable approach to analyze non-stationary extreme events that occur together (compound events), which is crucial for hazard assessment. This study introduces a method to analyze non-stationary joint extremes by combining Transformed-Stationary Extreme Value Analysis (tsEVA) with copula theory. This approach accounts for changes in the relationship between variables over time. The method includes sampling strategies to select relevant events, applying tsEVA for non-stationary univariate distributions, and using time-varying copulas to model the evolving relationships between variables. It thus considers all possible sources of non-stationarity that may affect joint extremes. The framework also incorporates statistical tools like the Mann-Kendall test to assess the significance of trends and Monte Carlo resampling for model validation and uncertainty analysis. Using this approach, the joint distribution of extremes in various natural hazards, such as river discharge, wave height, temperature, and drought, was successfully analyzed. The results highlighted the method's effectiveness in addressing diverse sources of non-stationarity and revealed dynamic patterns in variable interrelationships. Furthermore, the methodology developed in this study offers a viable tool for future research focused on generating statistically consistent hazard scenarios to support comprehensive risk assessments.

How to cite: Bahmanpour, M. H., Mentaschi, L., Tilloy, A., Vousdoukas, M., Federico, I., Coppini, G., and Feyen, L.: A generalized method for the analysis of non-stationary joint extremes based on the transformed-stationary extreme value analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16288, https://doi.org/10.5194/egusphere-egu25-16288, 2025.

The urgency for deeper understanding compound hydro-meteorological extreme events is growing, as these events are increasingly recognized for their potential to exacerbate impacts compared to single hazards. Characterized by the concomitance of multiple natural hazards or drivers, compound events are further intensified by climate change, which influences their severity and increases their frequency of occurrence. These hydro-meteorological extremes pose a significant risk to terrestrial ecosystems and have devastating consequences for socio-territorial systems. In Italy, recent extreme events have highlighted this threat, as demonstrated by the unprecedented sequence of heavy precipitation events in 2023-2024 that led to widespread flooding in the region of Emilia Romagna in central-northern Italy. These low-probability events, which resulted in several fatalities and damages amounting to tens of billions of euros, were amplified by antecedent precipitation that saturated soils, significantly enhancing the runoff response and, consequently, flood severity and extension. Indeed, the pre-condition given by soil imbibition preceding heavy rainfall occurrences is crucial in determining the potential severity of the event, but its comprehensive understanding is still limited.

This study investigates the relationship between precipitation and soil moisture conditions in Italy, with the goal to quantitatively characterize their role in the occurrence of historical and plausible compound hydro-meteorological extremes. We employ state-of-the-art reanalysis datasets (ERA5 and ERA5-Land) to analyze a series of representative extreme precipitation events, focusing on their antecedent soil moisture conditions and estimating the typical temporal scales of the associated co-variation. The link between these hydro-meteorological quantities and riverine flood occurrences is assessed considering streamflow discharge data from EFAS hydrological reanalysis dataset. The prevailing large-scale conditions driving these events, in terms of the 500-hPa geopotential height and the integrated water vapor transport column, are explored to identify the key dynamical features responsible for the occurrence of compound flooding. Additionally, a large ensemble of seasonal numerical weather forecasts is employed to sample the phase space of precipitation-soil moisture conditions applying the so-called UNSEEN (Unprecedented Simulated Extremes using ENsembles) approach. Within this framework, the probability of compound precipitation-soil moisture extremes is assessed through a statistical event coincidence analysis to understand the dominant spatio-temporal patterns of their interaction; moreover, physical storylines of rare, yet plausible, extreme flood events will be built through ensemble pooling.

How to cite: Giordani, A., Butera, C., Ruggieri, P., and Di Sabatino, S.: Towards a storyline of compound flood events over Italy: the role of precipitation-soil moisture pre-conditioning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19481, https://doi.org/10.5194/egusphere-egu25-19481, 2025.

Heatwaves and extreme precipitations are the two prevalent types of weather-related extreme events globally. Compared with univariate extremes, impacts of compound extreme precipitations preconditioned by heatwaves (CHEPs) on the society and economy can be amplified. Previous studies demonstrated that heatwaves can trigger extreme precipitations by enhancing atmospheric instability and moisture-holding capacity. Other studies projected future changes in CHEPs under various greenhouse gas emission scenarios. However, there is a lack of studies assessing the time of emergence (ToE) of CHEP change signals, especially for record-shattering events. Since current water resource management strategies and infrastructures are based on historical data, it is crucial to understand when hydro-meteorological conditions will surpass unprecedented levels to develop effective adaptation and mitigation strategies for climate change.

Here, we present a global analysis of ToE for record-shattering CHEPs as well as their exposed GDP and population (POP). Both the frequency and magnitude of observed CHEPs have substantially increased during the past 65 years at the global scale. Using climate models from Detection and Attribution Model Intercomparison Project, we find that rarer CHEPs are increasingly attributable to anthropogenic greenhouse gas emissions, while aerosol emissions have a mitigating effect on their occurrences. To detect when historical record-shattering events will become normal, we develop a novel framework based on advanced Single Model Initial-condition Large Ensemble simulations. Our results indicate that CHEP hotspots, including East and Southeast Asia, North-central South America, and Central Africa, are likely to experience earlier ToE compared to other regions. In contrast, arid regions, such as North Africa, West Asia, and southwestern Australia, show no signs of ToE until at least 2100. GDP and POP exposure to such events reveal an alarming upward trend throughout the 21st century. By the late 21st century, 41% (29%) of sub-regions defined by the Sixth Assessment Report of the Intergovernmental Panel on Climate Change are projected to experience GDP exposure exceeding 4,000 billion USD (at 2010 purchasing power parity) to record-shattering frequency (magnitude), while 34% (27%) are expected to have POP exposure exceeding 100 million under the SSP2-4.5 scenario. Record-shattering CHEPs pose a distinct threat to the economy between 21.75°N and 53.25°N, with the most significant impact between 35.25°N and 39.75°N. Compared to the GDP exposure, the POP exposure hotspots shift toward lower latitudes, with a broader range extending from 0.75°S to 53.25°N. Additionally, we classify areas based on the Human Development Index and income levels defined by the World Bank. The unequal distribution of GDP and POP exposure reveals the poorest and least developed countries will experience more extended impacts compared to wealthier nations. This study highlights the urgent need for region-specific mitigation and adaptation strategies to combat climate change, especially for the vast high-risk and low-income regions.

How to cite: Liu, J., Chen, J., and Yin, J.: Time of Emergence of Record-shattering Compound Extreme Precipitations Preconditioned by Heatwaves and Their Socio-economic Exposures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2350, https://doi.org/10.5194/egusphere-egu25-2350, 2025.

Extreme weather and climate events are increasingly linked to severe socio-economic impacts, and their combination in space and/or time can further amplify these effects. This has heightened attention on compound events, which are combinations of multiple, potentially non-extreme climate events that collectively result in significant socio-economic consequences. A prominent example of a compound event in agriculture are false spring event. These occur when anomalous warm and wet conditions prevail in late winter, triggering early crop growth, followed by spring frost or severe drought. Such conditions can lead to substantial agricultural losses. To enable early warnings for such events, seasonal predictability is essential, as these phenomena typically unfold over a period of a couple of months. Seasonal predictability typically stems from slowly varying factors, such as sea surface temperatures and teleconnections, which influence the likelihood and timing of such events.

 One of the most globally influential teleconnections is the El Niño–Southern Oscillation (ENSO), with well-documented influence on climate systems worldwide. ENSO's impact on European climate, particularly during late winter, has been extensively studied, raising the question whether ENSO could play a role in triggering false spring events. Investigating these mechanisms offers valuable insights into ENSO's influence on European climate and enhances the potential for improved seasonal predictions of such events. To identify these large-scale patterns and non-linear relationships with other teleconnection patterns and modes of variability, like the North Atlantic Oscillation (NAO), we employ advanced statistical techniques, such as Kernel Regularized Generalized Canonical Correlation Analysis and Bayesian neural networks. By leveraging preimages and Accumulated Local Effect (ALE) plots, we uncover large-scale mechanisms relevant to European climate that exhibit strong interactions with the Niño3.4 region. Finally, we perform a causal analysis to trace the chain of interactions and pathways through which ENSO modulates European false spring events. 

 Our preliminary analysis focused on the first phase of the compound events, late winter. Results revealed significant interactions between the Niño3.4 region and atmospheric circulation patterns in the Euro-Atlantic region. These interactions involve a combination of well-known patterns such as the NAO, the East Atlantic/West Russia pattern, and the Scandinavian pattern. Second-order ALE plots obtained from a Bayesian neural network highlight that the interplay of these components can drive increasingly warm and wet conditions during late winter. These conditions create a favorable environment for the onset of false spring events, advancing our understanding of the mechanisms behind these impactful phenomena.

How to cite: Luther, N., Zorita, E., Luterbacher, J., Vlachopoulos, O., and Xoplaki, E.: Causal Links Between El Niño–Southern Oscillation and European Compound Events: A Focus on False Spring Events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13057, https://doi.org/10.5194/egusphere-egu25-13057, 2025.

As the climate warms, interacting weather extremes such as sequential heat events pose complex risks to societies. Regarding the global food system, laboratory experiments suggest that crop exposure to spring heat may either confer tolerance or enhance vulnerability to subsequent summer heat events. We show, under historic conditions that hot springs benefit crop yield but amplify the impacts of summer heat by 3% to 36% across crops and regions compared to average spring conditions. This increasing sensitivity results in impacts outweighing hot spring benefits when summer temperature anomalies exceed 2-4°C. Analyzing projected temperature increases, we find an eight-fold rise in the frequency of sequential heat extremes under the Shared Socioeconomic Pathway 3-7.0. Accounting for the compounding effect of sequential heat on crop yields increases projected losses by 1 to 71% depending on crop and region. This underlines the emerging nonlinear risk of sequential heat extremes to food security, which can largely be avoided when limiting warming to 1.5°C globally.

How to cite: Hamed, R., Steinmann, C. B., Ma, Q., Balanzategui, D., Broadman, E., Lesk, C., and Kornhuber, K.: Amplified agricultural impacts from increasingly sequential heat extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19545, https://doi.org/10.5194/egusphere-egu25-19545, 2025.

Consecutive heatwave and heavy rainfall (HW-HR) events are occurring with increasing frequency in a warming climate. The time interval affects both environmental conditions and the regional recovery between two consecutive extreme events. However, the dynamics of the transition between consecutive HW-HR events remain poorly understood. In this study, we examine the changes in the time interval of consecutive HW-HR events in China from 1990 to 2019, using meteorological data from over 2,000 stations across mainland China. Our results reveal that the time interval has significantly shortened at 28.2% of the stations. The increased proportion of short-time events (STEs), defined by consecutive events with time intervals of 1 to 2 days, is the primary driver of this trend. From 1990 to 2019, the proportion of STEs increased significantly, at a rate of 2.2% per decade. We also find that climate change-induced anomalies in atmospheric variables during the consecutive HW-HR events may contribute to this rise in the proportion of STEs. Additionally, we assess changes in population exposure to STEs over the past two decades. Exposure has increased at more than three-quarters of the stations, with the increased STEs contributing to over 80% of the rise in exposure. Our findings highlight the need for policymakers to prioritize disaster response during consecutive HW-HR events and implement effective risk management strategies to mitigate population exposure to extreme events.

How to cite: Li, J., Wang, S., Zhu, J., Wang, D., and Zhao, T.: Accelerated Shifts from Heatwaves to Heavy Rainfall in a Changing Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2385, https://doi.org/10.5194/egusphere-egu25-2385, 2025.

How to cite: Feldmann, M., Domeisen, D. I. V., and Martius, O.: Severe convective outbreaks and heatwaves – a continental-scale compound event, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6437, https://doi.org/10.5194/egusphere-egu25-6437, 2025.

Drought-flood abrupt alternation (DFAA), characterized by a period of persistent drought followed by sudden heavy precipitation at a certain level, has significant impacts on ecosystems and socioeconomic environment. As previous studies mainly focused on the monthly scale and regional scale, our study proposed a multi-indicator daily-scale method for identifying the DFAA occurrence. Then we applied the method on exploring the DFAA events over China from 1961 to 2018. The results show that: i) The DFAA events mainly occurred in the center and southeast of China. ii) The spatial coverage has a statistically significant (p < 0.05) increasing trend over China, of 0.355 %/decade. iii) The occurrence and spatial coverage of DFAA events increased by decades, and were mainly concentrated in summer (around 85%). Meanwhile, we conducted field experiments in typical area. The measurements revealed that DFAA events increased the soil nitrogen and phosphorus pollution in surface water.

How to cite: Bi, W., Weng, B., Zhang, D., Wang, F., Wang, W., Lin, W., Qi, X., and Lu, M.: Drought-flood abrupt alternation events and their impacts in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14385, https://doi.org/10.5194/egusphere-egu25-14385, 2025.

In early September 2023, Europe experienced a pronounced atmospheric omega-blocking event, which led to spatially compounding precipitation and heat extremes across Europe. Omega-blocking is characterized by a persistent anticyclone at its core, flanked by two low-pressure systems to the southwest and southeast. During the September 2023 event, the center of the omega block was positioned over Central Europe and Southern Scandinavia, which experienced a significant heatwave during the first week of September 2023. Conversely, regions on the southwestern flanks (Spain) and southeastern flanks (Greece, Bulgaria, and subsequently Libya) were affected by extreme precipitation events, leading to severe flooding.

We employ the method of ensemble boosting to explicitly simulate omega-blocking situations with spatially compounding extremes (heatwave and extreme precipitation) with the Community Earth System Model 2 (CESM2). We therefore select analogs to the September 2023 event in a 30-member initial condition large ensemble of the CESM2 and use the model re-initialization approach of ensemble boosting to introduce slight perturbations to initial conditions 10 to 25 days prior to the event. This enables the generation of hundreds of coherent physical event trajectories, supporting the investigation of two key research questions: the first focuses on assessing the capability of the climate model to reproduce the 2023 event in its severity, while the second focuses on identifying the key characteristics of the omega block and its emergence that contribute to the most severe impacts on the ground.

In our talk, we introduce the research concept and address the following research questions: Is the CESM2 model capable of reproducing an omega blocking event with spatially compounding heat and precipitation extremes in the magnitude of the September 2023 event? Could the September 2023 event have been even more devastating by chance? What characteristics of the omega block and its emergence precondition the occurrence of the most extreme spatially compounding impacts in terms of heatwaves and extreme precipitation within the boosted ensemble?

How to cite: Mittermeier, M., Guo, Y., Suarez-Gutierrez, L., Bevacqua, E., and Fischer, E.: Spatially compounding heat and precipitation extremes under omega blocking in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17021, https://doi.org/10.5194/egusphere-egu25-17021, 2025.

Extreme compound events, defined as the “combination of multiple drivers and/or hazards that contributes to societal or environmental risk”, present a
growing concern for the scientists and the civil society (Zscheischler et al. 2020). Climate models provide physical simulations of the climate until 2100, which permits to better understand the evolution of the extreme events under climate change. This study proposes a novel modeling of bivariate extreme events using bivariate Generalized Pareto Distributions (biGPD), with Extended GPD for the univariate part (EGPD). This novel semi-parametric modeling is applied to an extreme event: the flooding of the Seine and the Loire watersheds in June 2016. This event is a spatially compound event between the accumulated precipitations over the two watersheds. The accumulation of rain over several days is approximated by the Antecedent Precipitation Index (API), and high values of API are considered to lead to flooding. This approach is compared to a more classic copula modeling over simulations in Jacquemin et al. (2025, submitted). As climate simulations often have statistical biases, they must be corrected using bias correction algorithms. This is also the case for their simulations of compound events. This study compares several multivariate bias correction algorithms (CDF-t, dOTC and R2D2) on this event. dOTC seems to perform better than R2D2 for extreme values. The proposed methodology illustrates how compound events can be analyzed, and their evolution in frequency projected. As a perspective, this method can be applied to more diverse compound events, and it could be generalized to events in higher dimensions.

Bibliography:
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M.
D., et al.: A typology of compound weather and climate events, Nature reviews earth & environment, 1, 333–347, 2020.
Jacquemin, G., Vrac, M., Allard, D., and Freulon, X.: Estimating the return period of climate compound events using a non parametric bivariate Generalized Pareto representation. Submitted

How to cite: Jacquemin, G., Vrac, M., Allard, D., and Freulon, X.: Projecting frequencies of extreme rainfall compound events under climate change using bivariate extreme value modeling and multivariate bias corrections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19231, https://doi.org/10.5194/egusphere-egu25-19231, 2025.

The complexity of climate risk can lead to cascading impacts across the coupled climate, ecological, agricultural, and socioeconomic systems, which may involve potentially unprecedented outcomes and feedbacks, nonlinear behaviors or tipping points. While advances have been made in understanding such interconnected risks, particularly within specific disciplines, significant gaps remain in our understanding and modelling of such risks, and especially of how they cascade across systems. 

Several of such examples of cascading impacts can be found across the world, just in the last few years. The Australian bushfires of 2019-2020, fueled by extreme heat and prolonged drought, caused massive biodiversity loss, widespread air pollution, and significant economic damages. The 2021 Himalayan glacier collapse led to catastrophic flooding, infrastructure damage, and disruptions to local livelihoods, highlighting the fragility of mountain ecosystems in a warming climate. The global food and energy crisis of 2022, driven by geopolitical conflict and the disruption of supply chains compounded by low crop yields revealed the vulnerability of interconnected supply chains, with far-reaching implications for global stability. The 2024 DANA flooding in Spain, caused by a record-breaking atmospheric instability event and delayed emergency response, resulted in devastating loss of human lives and damage to infrastructure, agriculture, and urban areas, which eventually led to civil unrest in the region. All these examples underscore the need for comprehensive risk assessment, modelling and projection that better captures how shocks may compound and cascade across systems leading to high-impact outcomes larger than the sum of their parts. 

Existing frameworks and methodologies frequently fail to account for nonlinearities and worst-case outcomes or compartmentalize risks, in part to make an extremely complex problem simpler. This limits our ability to capture effects and impacts cascading to and from other sectors and systems, resulting in an incomplete understanding of the systemic nature of risk. Here, we assess to which extent cascading impacts have been included in impact assessments across sectors given our current methodologies and frameworks, to which extent our current methodologies and frameworks are insufficient for the task, and the cases where, even though current technology may allow it, cascading risks may have been overlooked. We reflect on recent examples of cascading impacts and their drivers, and outline critical directions for improving their integration into future risk assessments.

How to cite: Suarez-Gutierrez, L., Bastos, A., and Hegerl, G. C.: Cascading impacts across the coupled climate, ecological, agricultural and socioeconomic systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17367, https://doi.org/10.5194/egusphere-egu25-17367, 2025.

In recent years, compound events, i.e., events resulting from the interaction between multiple physical drivers, have gained great attention in the scientific community especially as they can lead to greater impacts than events controlled by a main single physical driver. The majority of the studies relied on the use of statistical measures of dependence and multivariate analyses to show potential for compound events across diverse climatic and geographical regions. However, these approaches provide limited insights into the system being investigated and its behavior.

Here, we propose an approach to characterize the 'compoundness' of a system in terms of two components: structural compoundness, which refers to the overall tendency of physical drivers to jointly occur and interact, and transient compoundness, which refers to the specific occurrence or manifestation of interacting physical drivers. We provide example applications of the proposed characterization and discuss their implications for developing climate-resilient adaptation strategies.

How to cite: Ragno, E. and De Michele, C.: Structural and Transient Compoundness in Natural Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14749, https://doi.org/10.5194/egusphere-egu25-14749, 2025.

Electricity networks are an important component of critical national infrastructure. Their failure, leading to power outages, can cascade through other infrastructure networks and compromise the function of other critical services. Electricity networks are facing a massive transformation to handle the increased demands placed on them by net zero commitments. Alongside this, future increases in the frequency and intensity of extreme weather will test electricity infrastructure that is already perceived to have insufficient resilience. Transforming networks as part of the net zero transition presents an opportunity to increase their resilience: this requires an understanding of the causes of network failures, the challenges that utility operators face in managing infrastructure risks, and the quantification of weather driven risks.

In this presentation, we present results from two projects. The first project used interviews and round-table discussions with energy industry experts in the UK to understand their needs from climate science as well as to uncover what they consider to be the largest weather and climate risks, the operational difficulties these present to electricity networks and what they believe to be low-regret options for enhancing climate resilience. The second project used statistical analysis to predict damage to electricity infrastructure from key weather hazards (windstorms, heat waves) and assessed how electricity infrastructure risks may change in the future using high-resolution 2.2 km climate simulations.

We discuss the main outcomes, strengths and limitations of both approaches and conclude that 1) expert elicitation provides a detailed and nuanced understanding of the range and severity of societal consequences produced by extreme weather and various compounding factors, and 2) probabilistic impact models that do not include multi-hazard and compounding effects underestimate the potential damages of extreme weather to electricity infrastructure – specifically the effects of wind direction, soil moisture and leaf cover during windstorms.

How to cite: Manning, C., Wilkinson, S., and Fowler, H.: Understanding compound weather and climate risks facing electricity networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17762, https://doi.org/10.5194/egusphere-egu25-17762, 2025.