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.
Heat stress is projected to intensify with global warming, causing significant socioeconomic impacts and threatening human health. Wet-bulb temperature (WBT), which combines temperature and humidity effects, is a useful indicator for assessing regional and global heat stress variability and trends. However, the variations of European WBT and their underlying mechanisms remain unclear. Using observations and reanalysis datasets, we demonstrate a remarkable warming of summer WBT during the period 1958-2021 over Europe. We find that the increase in European summer WBT is driven by both near-surface warming temperatures and increasing atmospheric moisture content. We identify dominant modes of European summer WBT variability and investigate their linkage with the large-scale atmospheric circulation and sea surface temperature anomalies. The first two leading modes of the European WBT variability exhibit prominent interdecadal to long-term variations, mainly driven by a circumglobal wave train and concurrent sea surface temperature variations. The last two leading modes of European WBT variability mainly show interannual variations, indicating a direct and rapid response to large-scale atmospheric dynamics and nearby sea surface temperature variations. We also present the role of global warming and changes in mid-latitude circulations in the variations of European summer WBT. Our findings can enhance the understanding of plausible drivers of heat stress in Europe and provide valuable insights for future climate adaptation planning.
How to cite: Ma, Q., Chen, Y., and Ionita, M.: Spatiotemporal Variations and Potential Drivers of European Summer Heat Stress, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9994, https://doi.org/10.5194/egusphere-egu25-9994, 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.
Recent summers in Europe were accompanied by significant convective storm outbreaks with widespread large hail, flash floods, and severe wind phenomena. Particularly severe outbreaks have occurred upstream of heatwaves. On a continental scale, this leads to considerable compound hazards from heatwaves and thunderstorm hazards.
Utilizing reanalysis data, we investigate the link between heat anomalies and severe convective environments (SCE), which have the potential for severe convection. Our analysis reveals that SCE across Central and Western Europe are preceded by high temperatures and a slow-moving upper-level wave pattern. More strikingly, they reveal a strongly increased heatwave frequency downstream of SCE. Indeed, 75% of SCE are associated with a heatwave, usually ~500km downstream. The remaining 25% take place in much cooler, predominantly low-pressure situations, with less persistent SCE. Inversely, >80% of heatwaves are associated with upstream SCE. These heatwaves are significantly hotter by >1°C than those not associated with convection.
This strong co-occurrence of severe convective outbreaks and heatwaves implies a dynamical link. From the large scale, the upper-level wave pattern may drive both the SCE through the advection of unstable airmasses and high wind shear in the prefrontal zone, as well as the heatwave by warm air advection, radiative heating, and a strong ridge. Further feedback between heatwaves and SCE is possible via diabatic heating processes and soil moisture feedback.
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.
Posters on site: Fri, 2 May, 16:15–18:00 | Hall X5
Near-freezing precipitation (NFP) events, a type of multivariate event compounding temperature and precipitation, are associated with widespread power outages. In a society undergoing an energy transition towards electrification, outages are associated with large impacts. Thus, understanding the impacts and the future evolution of NFP events in a changing climate is increasingly important. In this study, we first establish the relation between outages and NFP events, based on reanalysis data and on a Québec-based utility outage dataset. The highest density of outages in the region is found to occur in association with mixed precipitation near the freezing point. Next, daily NFP totals in various reanalyses are evaluated against Environment Canada weather stations in power-line-dense regions of Québec, to select a gridded reference. The Canadian Surface Reanalysis (CaSR) performs best and is selected. Then, a 28 member CMIP6 ensemble, bias-adjusted using CaSR, is used to evaluate future regional changes in the frequency of near-freezing precipitation events, as a function of time and as a function of local warming. In general, it is found that warmer areas south of Québec see a decline in the frequency of events, while colder northern areas see an increase. The number of days with near-freezing precipitations over 5 mm liquid equivalent varies non monotonically with annual temperature. This number will decrease by up to 40% south of Québec in the future and increase in the north. At the latitude of Montréal, the number of days may first increase and peak before decreasing again at the end of the century, as more wet snow turns to rain. However, rare events show a more uniform pattern of increasing intensity than NFP indicator, with slight decreases mostly near the coasts in the south. For Montréal, end of century NFP increases are more preeminent in ssp245, than in the warmer scenarios, which are likely further past the maximum risk. These findings on the impact of NFP events on the grid, as well as on the future evolution of these events, can directly inform the costly grid-hardening strategies considered for future adaptation.
How to cite: Rousseau-Rizzi, R. and Roy, P.: The impacts and future changes of near-freezing precipitation events in Québec, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6508, https://doi.org/10.5194/egusphere-egu25-6508, 2025.
High storm surges, extreme sea waves, high river discharges and intense short-term rainfall are flood drivers that make densely populated coastal areas especially vulnerable to flooding. An additional increase of flood hazard and risk in coastal areas is expected due to changes in storminess, mean sea level rise, land subsidence and urbanisation. The simultaneous or consecutive occurrence of two or more flood drivers can lead to an event known as compound flooding.
In Croatia, compound flooding caused by the co-occurrence of high river discharges and storm surges is the only combination of compound flood drivers investigated to date. Consequently, other combinations of compound flood drivers remain unexplored. This study aims to address this gap by conducting further research on compound flooding in Croatia, specifically investigating the co-occurrence of extreme storm surges and rainfall along the Croatian coast. This study will provide insights into the compound flood potential due to extreme storm surges and rainfall at 42 locations along the Croatian coast. The rainfall data was obtained from rain gauge stations and the sea level data was obtained from Coastal Extremes in the Mediterranean Sea reanalysis and has been corrected by tide gauge data using machine learning.
High storm surges and heavy rainfall are flood drivers that often originate from the same weather system. Neglecting their seasonality can lead to a significant underestimation of the dependency and consequently the underestimation of the compound flood potential. With its pronounced seasonality, the Croatian coast is a great example for investigating seasonal correlation and co-occurrence of storm surges and rainfall. By disaggregating the time series into the individual seasons and analysing them separately, we gained a more detailed insight into the co-occurrence patterns of these flood drivers through maps with assigned correlation coefficients and a number of co-occurrences for each location.
As a result of this analysis, we will be able to identify the vulnerable areas with the highest probabilities of co-occurrence of high storm surges and intense rainfall more precisely. The selected locations will be eligible for a more detailed analysis of compound flood risk at the local level in future studies.
How to cite: Gržić, M. M., Petković, I., Ožanić, N., and Krvavica, N.: Joint Occurrence of Extreme Rainfall and Storm Surge along the Croatian Coast: Exploring Seasonal Variations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9949, https://doi.org/10.5194/egusphere-egu25-9949, 2025.
Denmark has experienced several significant compound flood events in recent years, and in parallel the Danish Meteorological Institute (DMI) has been developing a new flood warning system. This has led to a reassessment of how floods are conceptualized, predicted, and communicated. To support this, we here propose a framework for a flood typology tailored to both single-source and compound flood phenomena, with practical applications for public warnings and communication.
Our methodology builds upon the internationally acknowledged UNDRR/ISC hazard classes, which we filter for flood-related hazards relevant to Northern European coastal, lowland conditions. We then consider the organization of Denmark’s national agencies, local emergency response, and insurance structures. From this, we develop a flood typology for communication. As a part of the study, historical occurrences of compound flood phenomena are meticulously assessed by reviewing textual descriptions from a historical flood register (1990–2020) and conducting detailed case studies of recent events. Additionally, we examine the spatial and temporal overlap of flood-generating processes through a quantitative analysis of historical severe weather warning occurrences (2014–2024) addressing events of rainfall, storms, and sea levels.
Our findings reveal overlapping definitions within the UNDRR/ISC hazard classes, particularly regarding flood-generating processes and their geographic context. While DMI oversees severe weather warnings, observation networks are divided among four national agencies within the fields of: meteorology, oceanography, inland surface water, and groundwater. The emergency response in Denmark, as managed by 98 municipalities, is generally infrastructure-focused rather than flood-type-specific. For instance, urban water utilities often manage flood operations in cities. The insurance sector distinguishes between pluvial floods (private market) and fluvial or storm surge floods (covered by a national public disaster fund). We propose five general flood types for communication: (1) pluvial, (2) fluvial, (3) coastal, (4) groundwater, and (5) technological hazards (infrastructure failures of pumps, sluice gates, etc.). The historical flood register indicates two predominant compound flood types in Denmark: "coastal + fluvial" (driven by extratropical cyclones in winter) and "pluvial + fluvial" (caused by convective rainfall extremes in summer). It also shows that the key preconditioning variables include soil moisture, snow depth, and Baltic Sea water levels. The analysis of historical weather warnings reveals distinct regional patterns in compound flood risks. The detailed case studies provide storylines of how spatial compounding of flood types can overwhelm both national and local emergency responses.
By integrating these insights, our study establishes a typology that is locally relevant for the Danish context, enhances the understanding of compound floods, and informs strategies for improved compound forecasting and communication.
How to cite: Pedersen, J. W., Su, J., Ringgaard, I. M., and Larsen, M. A. D.: Developing a flood typology for Denmark with practical applications for public warnings and communication, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10126, https://doi.org/10.5194/egusphere-egu25-10126, 2025.
Compound events leading to significant coastal flooding have become a major concern in recent years. Storm surges caused by extra-tropical cyclones and coastal precipitation are key drivers of such events. While previous research has developed various methods for storm tracking, these have not sufficiently focused on impact-relevant storm tracking that directly addresses coastal flood risks. Motivated by this research gap, we initiated our analysis by identifying storm surges from 1991 to 2021 and tracked associated low-pressure systems and the associated impact-related compound dynamics. This approach not only enables the understanding of storm impacts but also offers potential for application to downscaled regional climate-scale products.
As a case study, we use Denmark, known for its complex ocean-circulation patterns due the North-Sea/Baltic Sea interface, narrow straits and fjords, and diverse coastline orientation. We clustered 32 sea level stations in Denmark by analyzing the co-occurrence of extreme storm surge events in the period 1990-2023. We then tracked extra-tropical cyclones over Northern Europe using the CERRA mean sea level pressure dataset, by identifying minimas at each time interval and reconstructing tracks by minimizing the distance between candidate points, through the use of Mixed Integer Programming. Finally, we investigate coastal precipitation in different regions of Denmark, as defined by the clustering of sea water level stations, with precipitation estimates from the CERRA-Land dataset.
Our analysis successfully identified storm tracks associated with extreme storm surge events, which were categorized into four distinct clusters. Similarly, Danish water level stations were grouped into three clusters based on the co-occurrence of extreme surge events: (1) the West coast of Jutland, (2) Kattegat and Inner Danish water, and (3) Baltic sea coastlines. By examining the dominant storm track types and station clusters, we revealed significant differences in impacted regions associated with different storm tracks.
We conclude that storm tracks have markedly different impacts on the occurrence of storm surge events across the Danish sub-regions. Precipitation levels associated with these storm surge events, and type of storm track, can uncover the need to consider both storm track characteristics and regional vulnerabilities when assessing compound and multi-variate coastal flood risks as opposed to storm surges in isolation.
How to cite: Agertoft, N., Pedersen, J. W., Su, J., Ringgaard, I. M., and Dahl Larsen, M. A.: Understanding coastal-pluvial compound floods associated with extra-tropical cyclones in Denmark , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10158, https://doi.org/10.5194/egusphere-egu25-10158, 2025.
This study investigates the impacts of climate change on marine heatwaves and extreme precipitation events associated with atmospheric rivers. First, our findings demonstrate that high-resolution models are more adept at simulating mesoscale eddies in the ocean, thereby facilitating more accurate predictions of future changes in marine heatwaves. Under climate warming, the intensity and annual days of marine heatwaves are projected to increase significantly. Even if organisms within large coastal marine ecosystems fully adapt to long-term mean warming, the escalating intensity of marine heatwaves would nonetheless pose substantial threats to these ecosystems. Furthermore, with global warming, the intensity and annual days of subsurface marine heatwaves are also expected to rise markedly on a global scale. This increase is primarily driven by the long-term rise in subsurface temperatures and changes in their variability. After accounting for the effects of long-term warming, the magnitude of increases in the intensity and annual days of subsurface marine heatwaves is notably greater than those at the surface, further exacerbating the risks posed by global warming to marine ecosystems.
Additionally, the study explores the influence of global warming on atmospheric river events in the Northern Hemisphere. High-resolution Earth system model simulations indicate that, under approximately 4°C of global warming, elevated sea surface temperatures enhance ocean-to-atmosphere moisture flux, thereby intensifying atmospheric river events. This intensification is projected to result in a doubling of the area affected by extreme precipitation events along the western coasts of Europe and North America. By disentangling the thermodynamic and dynamic contributions to intense precipitation associated with atmospheric rivers, the study identifies differences in the direction of vertical wind velocity changes as the primary source of regional disparities in dynamic contributions.
How to cite: Guo, X., Gao, Y., Zhang, S., Cai, W., Leung, R., Zscheischler, J., Thompson, L., Chen, D., Guo, C., Gao, H., and Wu, L.: Impacts of Climate Change on Precipitation and Marine Heatwaves: Insights from High-Resolution Earth System Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11126, https://doi.org/10.5194/egusphere-egu25-11126, 2025.
Compound events (CE), characterized by the combination of climate phenomena that are not necessarily extreme individually, can result in severe impacts when they occur concurrently or sequentially. Understanding past and potential future changes in their occurrence is thus crucial. The present study investigates historical changes in the probability of hot and dry compound events over Europe and North Africa, using ERA5 reanalyses spanning the 1950-2023 period. Two key questions are addressed: (1) Where and when did the probability of these events emerge from natural variability, and what is the spatial extent of this emergence? This is explored through the analysis of “time” and “periods” of emergence, noted ToE and PoE, defined as the year from which and the moments during which changes in compound event probabilities exceed natural variability. The new concept of PoE allows for more in-depth signal analysis. (2) What drives the emergence? More specifically, what are the relative contributions of changes in marginal distributions versus in the dependence structure to the change of compound events probability? The signal is modelled with bivariate copula, allowing for the decomposition of these contributions. A focus on the dependence component is explored to quantify its effect on the signal’s emergence.
The results reveal clear spatial patterns in terms of emergence and contributions. Five areas are studied in greater depth, selected for their similar signal behaviors. For example, the frequency of hot and dry events sharply increased in Maghreb and in the Iberian peninsula (ToE around 1980) and this rise is mainly due to a change in the marginals. Conversely, in eastern Europe the signal experienced a long PoE lower the natural variability, and this decline of CE probability is mainly driven by a change in the drought index. Although the dependence component is rarely the main contributor to PoE, it remains necessary to detect signal’s emergence. The date of ToE and the duration of PoE can be overestimated as well as underestimated (even more than 20 years) without considering this component. These findings provide new insights into the drivers of CE probability changes and open avenues for advancing attribution studies, ultimately improving assessments of risks associated with past and future climate change.
How to cite: Schmutz, J., Vrac, M., and François, B.: Spatial structures of emerging hot and dry compound events over Europe from 1950 to 2023, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13915, https://doi.org/10.5194/egusphere-egu25-13915, 2025.
Drought episodes combined with hot events usually trigger dramatic impacts on ecosystems and agricultural production. However, most existing studies on climate stress focus primarily on individual events, leading to a neglect of compound information. Based on various combinations of climate conditions, we investigate the impact of 6 modes of events, namely, compound dry and cold events, compound wet and hot events, compound dry and hot events (CDHEs), compound wet and cold events, droughts, and hot events, on maize yield in China. Evidence from both country–level and province–level yield data indicates that CDHEs have emerged as a major threat to maize yield, with higher yield reduction than the other 5 modes of climate events. Negative maize yield anomalies caused by CDHEs have increased over the past decades, partly due to the rising frequency, spatial extent, and severity of compound events. Moreover, the El Niño–Southern Oscillation (ENSO) has recently intensified yield losses associated with CDHEs. Findings from this investigation underscore the urgent need for adaptation strategies to prevent the occurrence of CDHEs, and to mitigate their impacts.
How to cite: Wu, X.: Increasing impact of compound dry and hot events on maize yield in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15475, https://doi.org/10.5194/egusphere-egu25-15475, 2025.
With the expected rapid growth of renewables in the French power system, periods of prolonged low renewable energy generation are expected to have a greater impact on the power system, especially if compounded with high electricity demand. In particular, compound winter low wind and cold events are identified by the French electricity transmission system operator as events that can drive major risks to the future French power system. Using CMIP6 climate simulations, the scope of this study is to characterize the future changes of these climate compound events in the mid- and long-term and assess the associated uncertainties.
To identify compound low wind and cold events, a wind power capacity factor and an electricity demand indices are derived using near-surface wind speed and temperature data from CMIP6 models, including several Single Model Initial-condition Large Ensemble (SMILE), for the 1950-2099 period. Due to large differences between observed and modeled indices, bias adjustment is first applied to raw temperature and near-surface wind speed data. The benefit of multivariate bias adjustment over univariate methods is assessed.
First, we characterize the future changes of compound low wind and cold events frequency in the mid- and the long-term, and which of the marginal characteristics (i.e., cold or low wind events) primarily drive these changes. Then, we assess the associated uncertainties, including uncertainties from internal variability, climate models, emission scenarios, and bias correction methods. Finally, we identify the role of climate drivers, including the global warming level, and exposure drivers, including the installed wind power capacity and the electricity demand parameters. This work demonstrates the relevance of CMIP6 large ensemble of simulations and methodologies currently used in the compound weather and climate events community to assess future risks for the power system.
How to cite: Collet, F., Boé, J., Bador, M., Dubus, L., and Joudier, B.: Future evolution of compound low wind and cold events in winter impacting the French electricity system in CMIP6 climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15803, https://doi.org/10.5194/egusphere-egu25-15803, 2025.
Compound extreme events are characterized by the combination of two or more events (not necessarily extreme) that can increase their respective individual impact. These phenomena can be of temporal nature (events that occur at the same time or in close succession), spatial nature (what happens in a place affects another) and/or multivariable nature (combination of several variables). This work focuses on the analysis of hot-dry compound extreme events —characterized by the simultaneous occurrence of high daily maximum temperature and low precipitation— and assesses their frequency, duration and severity.
For this purpose, both observational data (for a recent historical period) and climate model simulations provided by the CORDEX initiative, which gathers international efforts devoted to regional climate modeling, are considered. In particular, we use the CORDEX-CORE (CORDEX Coordinated Output for Regional Evaluations) ensemble, which comprises two Regional Climate Models (RCMs) driven by three Global Climate Models (GCMs) under two distinct emission scenarios, covering most continental CORDEX domains at 0.22º spatial resolution (approx. 25km). Systematic biases, typically present in these simulations, have been alleviated with the application of bias adjustment, using a semi-parametric, trend-preserving, quantile mapping method (ISIMIP).
Our overall results show that hot-dry compound extreme events are enhanced over the next decades, with a general but region-dependent increase in frequency, duration and severity for different levels of global warming (+1.5, +2, +3 and +4 ºC, with respect to pre-industrial conditions), which can have important impacts across various sectors such as health, economy, tourism and agriculture, among others.
This work is part of Project COMPOUND (TED2021-131334A-I00) funded by MCIU/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. A. C. and R. M. acknowledge support from PID2023-149997OA-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.
Keywords: compound events, regional climate models, climate change, extreme climate.
How to cite: Fuente-Gonzalez, M., Manzanas, R., Diez-Sierra, J., Chantreux, A., and Casanueva, A.: Evaluation and projection of hot-dry compound extreme events in a warmer climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16140, https://doi.org/10.5194/egusphere-egu25-16140, 2025.
Abstract. Droughts significantly affect socioeconomic conditions globally. As a multifaceted phenomenon, droughts are assessed through various indices distinguished in meteorological, agricultural, and hydrological typologies, each designed to capture distinct aspects. So, there is a strong demand for comprehensive drought monitoring tools that integrate multiple aspects to offer a holistic view of drought conditions. Typically, when introducing a new composite drought index, it is evaluated in comparison to existing indices, however this approach cannot allow to evaluate its accuracy in actual conditions. Therefore, shifting the paradigm from model-by-model evaluations to impact-oriented analysis is crucial. This work introduces a drought index based on deep learning where economic losses induced by drought are used as a key metric in assessing the index performance. The introduced index is calculated using cutting-edge deep learning algorithms based on various drought-related variables. Different types of self-supervised learning models, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Variational Autoencoders, are employed to enhance the model's accuracy and robustness. We use reanalysis data (ERA5) spanning from 1980 to 2022 for Italy, coupled with the EM-DAT database, to conduct impact analysis. The performance of each model is outlined based on their accuracy in estimating economic losses induced by droughts.
Keywords: Drought Index, Deep learning, Autoencoder, impact-oriented analysis.
How to cite: Khosh Chehreh, M. and De Michele, C.: Development of a Composite Drought Index using deep learning: A Unified Framework for Multi-Dimensional Drought Characterization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16322, https://doi.org/10.5194/egusphere-egu25-16322, 2025.
As global warming intensifies, high-latitude and mid-to-high-elevation watersheds are increasingly experiencing compound low-snow high-temperature events, posing serious challenges to water resources and ecosystem stability. However, the spatiotemporal characteristics of these events and their impacts on vegetation productivity and physiological processes remain insufficiently understood. In this study, drawing on multiple reanalysis datasets and hydrological models, we systematically evaluated the historical and future trajectories of low-snow high-temperature events across the Northern Hemisphere, including their potential lagged effects on ecosystems. By integrating diverse Gross Primary Productivity (GPP) datasets derived from observations, satellite products, and models—and employing an explainable causal machine learning framework—we identified key climatic and plant physiological drivers influencing GPP under these compound conditions. The findings highlight an increasingly frequent and persistent occurrence of low-snow high-temperature events, along with significant effects on vegetation functions, such as water-use efficiency, carbon uptake, and community structural adaptations. Overall, this research not only traces the upward trend of these compound events but also underscores their profound ecological implications, offering valuable insights for advancing global carbon cycle assessments and informing future climate adaptation strategies.
How to cite: Yang, Y.: Rising Compound Low-Snow High-Temperature Events: Drivers, Ecosystem Responses, and Future Outlook, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17066, https://doi.org/10.5194/egusphere-egu25-17066, 2025.
The Amazon rainforest, a critical global ecosystem, is increasingly threatened by climate change and extreme weather events. Over recent decades, the region has experienced record-high temperatures and unprecedented droughts. Compound drought and heatwave events (CDHWs), characterized by simultaneous dry and hot conditions, along with soil moisture (SM) deficits and high vapor pressure deficits (VPD), exacerbate ecosystem stress and intensify drought severity. This study investigates the climatology of CDHWs and compound low-SM/high-VPD events in the Amazon from 1981 to 2024 using the ERA5 dataset. Most compound events occurred during well-known drought years, including 1983, 1997/1998, 2010, 2015/2016, and 2023/2024. While compound events rarely impacted more than 20% of the region before 2010, subsequent years saw widespread effects, with the 2023/2024 drought ranking as the most extreme on record. During the austral summer of 2023/2024, CDHWs affected 70% of the Amazon's area, compared to 40% in 2015/2016. Similarly, low-SM/high-VPD conditions impacted 30% of the region in 2015/2016 and an unprecedented 60% in 2023/2024. Our results suggest an increase in the frequency, extent, and duration of compound extremes in the Amazon region, particularly over the last two decades, which could have critical implications for ecosystem resilience and climate adaptation strategies. The previous record compound event of 2015/2016 was particularly significant due to its ecological impacts, including tree mortality, biomass growth decline, and reductions in net primary productivity (NPP), gross primary productivity (GPP), and carbon uptake. Therefore, the ongoing record-breaking CDHW and low-SM/VPD conditions in 2023/2024 are expected to have even more severe impacts on the Amazon rainforest.
How to cite: Ferreira, V., Buras, A., Zscheischler, J., Machecha, M., and Ramming, A.: Increasing frequency and intensity of compound droughts in the Amazon region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18021, https://doi.org/10.5194/egusphere-egu25-18021, 2025.
Spatially compound extreme precipitation events can result in more severe impacts than individual extremes, posing significant challenges to both human and natural systems. Understanding their spatial distribution and trends is crucial for developing effective mitigation and adaptation strategies. In this study, we analyze multiple datasets, including reanalysis datasets (ERA-5, MERRA-2) and gridded networks derived from meteorological station data, to investigate long-term trends in precipitation over land and oceans at global, regional, and gridded scales. Using fixed thresholds, we assess the joint occurrence of extreme precipitation events and examine how these events change relative to temperature in different regions.
Our findings show that the proportion of areas affected by spatially compound extreme precipitation events has increased significantly, particularly in tropical and coastal regions. Moreover, the growth trend in areas experiencing co-occurring extreme precipitation exceeds the trend observed at individual pixel scales, highlighting that focusing solely on pixel-scale changes underestimates the full extent of natural disasters caused by extreme precipitation. This synthesis underscores the escalating risks of compound climate extremes under global warming, driven by the complex interplay of joint precipitation occurrences.
How to cite: Xing, T. and De Michele, C.: Spatially compound and local extreme precipitation events: behaviors and trends , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18415, https://doi.org/10.5194/egusphere-egu25-18415, 2025.
The increasing frequency and intensity of extreme environmental and climatic stressors, such as heatwaves, droughts, wildfires, and air pollution episodes, highlight the urgency of understanding their interconnected nature. Traditionally studied in isolation, these stressors often interact in complex ways, amplifying their individual and cumulative impacts on ecosystems, economies, and public health. This study explores the global occurrence of compound events involving heatwaves, droughts, wildfires, and poor air quality, identifying their key drivers, spatial distribution, and associated consequences.
ERA5 reanalysis were used to identify drought periods using the Standardized Precipitation-Evapotranspiration Index (SPEI) and detected heatwaves based on temperature anomalies. Fire activity was assessed using Fire Radiative Power (FRP) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites, while air pollution levels, specifically particulate matter (PM2.5), were derived from the Copernicus Atmosphere Monitoring Service (CAMS) global reanalysis (EAC4). The co-occurrence of these phenomena was analyzed to pinpoint regions experiencing compound hot, dry, fire, and pollution events.
Our findings reveal distinct global hotspots where multiple stressors interact. Heatwaves and air pollution events were predominantly observed in regions such as India, the Arabian Peninsula, and eastern China. Meanwhile, the Brazilian Cerrado, northern Australia, and South African savannas frequently experienced simultaneous heatwave and wildfire occurrences. The Mediterranean region, particularly Greece, Portugal, and Italy, exhibited a high prevalence of concurrent heat, drought, wildfire, and air pollution episodes. Notably, in North America and Asia, PM2.5 concentrations reached significantly higher levels during simultaneous extreme events compared to isolated pollution occurrences.
The interplay of compound hot and dry conditions with wildfires, and ultimately with pollution events, presents critical challenges for public health and environmental management. The cascading effects of these interactions underscore the need for integrated approaches that encompass climate adaptation strategies, wildfire risk mitigation, and stringent air quality regulations. Understanding these linkages is essential for formulating policies that enhance climate resilience and safeguard communities against the escalating threats posed by climate-driven extreme events.
This research was funded by the Portuguese Fundação para a Ciência e a Tecnologia, FCT, I.P./MCTES through national funds (PIDDAC): UID/50019/2025 and LA/P/0068/2020 https://doi.org/10.54499/LA/P/0068/2020. This study was conducted within the scope of project https://doi.org/10.54499/2022.09185.PTDC (DHEFEUS) and supported by national funds through FCT. DL and AR acknowledge FCT I.P./MCTES for grants https://doi.org/10.54499/2022.03183.CEECIND/CP1715/CT0004 and https://doi.org/10.54499/2022.01167.CEECIND/CP1722/CT0006, respectively.
How to cite: Russo, A., Bento, V., Lima, D., and Careto, J.: Global Compound Climate Events: Intensified Air Pollution During Simultaneous Extreme Events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18746, https://doi.org/10.5194/egusphere-egu25-18746, 2025.
Renewable energy production plays a major role in Norway’s energy sector accounting for approximately 98% of the national electricity production. Unusually little precipitation in southern Norway in year 2021 resulted in reduced filling of the hydropower reservoirs. Accompanied with calm wind conditions over major wind-energy producing areas of Europe this led to exceptionally high electricity prices in Norway during winter 2021/2022.
As most renewable energy sources depend inherently on weather and climate condition, they are sensitive to large-scale weather regimes, natural climate variability, climate change and extreme weather events that can threaten the renewable energy system’s stability and reliability. By transitioning to more renewable sources such as hydro, wind and solar power, societies may expose themselves to an increased risk of potential instabilities and unreliability in the power supply. Within the SusRenew project we expand on the concept of compound events by looking at climate hazards that are specifically relevant for the energy system in Norway, and by looking at the possible joint occurrence of hazard pairs in different regions that are linked in the northern European energy system.
How to cite: Mayer, S., Ridjan Skov, I., Mati, A., Botnen Holm, T., Aall, C., and Deciron, C.: Identifying climate related hazards relevant for the Norwegian Energy System , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18782, https://doi.org/10.5194/egusphere-egu25-18782, 2025.
