Orals: Thu, 1 May | Room -2.32
Extreme weather events, including heatwaves, extreme precipitation, tropical cyclones, and other hazards, pose significant risks to society and ecosystems. Recent advancements in observational techniques, numerical modeling, theoretical frameworks, and AI methods have greatly improved our understanding and prediction of extreme weather events. However, despite significant progress, key challenges remain unresolved, particularly in achieving a thorough understanding of the physical drivers of extreme events, improving the transparency of AI-based prediction methods, and evaluating the vulnerability and resilience of cities to their impacts. To address these challenges, we present various approaches drawn from different fields, including dynamical systems theory, explainable AI, and NLP-based methods. Given the flexible and generalizable nature of these methods, we believe they may pave the way toward more robust solutions for addressing the challenges posed by extreme weather events.
How to cite: Mengaldo, G.: Progress and Challenges in the Study of Extreme Weather, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7685, https://doi.org/10.5194/egusphere-egu25-7685, 2025.
Many natural systems show emergent phenomena at different scales, leading to scaling regimes with signatures of chaos at large scales and an apparently random behavior at small scales. These features are usually investigated quantitatively by studying the properties of the underlying attractor. This multi-scale nature of natural systems makes it practically impossible to get a clear picture of the attracting set as it spans over a wide range of spatial scales and may even change in time due to non-stationary forcing.
Here we present a review of some recent advancements in characterizing the number of degrees of freedom and the predictability horizon of geophysical and complex systems showing non-hyperbolic chaos, randomness, state-dependent persistence and predictability. We compare classical approaches, based on Lyapunov exponents and correlation dimension, with novel approaches based on combining adaptive decomposition methods with concepts from extreme value theory. We demonstrate that the properties of the invariant set depend on the scale we are focusing on and that the proposed formalism can be generally helpful to investigate the role of multi-scale fluctuations within complex systems, allowing us to deal with the problem of characterizing the role of stochastic fluctuations across a wide range of physical systems as well as the role of different dynamical components in determining the predictability of rare events in complex systems.
How to cite: Alberti, T., Faranda, D., and Lucarini, V.: The predictable chaos of rare events in geophysical and complex systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18710, https://doi.org/10.5194/egusphere-egu25-18710, 2025.
Extreme weather events such as heatwaves, droughts, thunderstorms, and cyclones threaten human lives, ecosystems, and economic stability. Tracking and characterizing the spatiotemporal dynamics of such events is essential for understanding their cascading impacts on socioeconomic and environmental systems. When the detection and characterization of extremes are done in real-time, they can provide critical information that benefits many sectors, including agriculture, emergency management, and regulatory authorities.
To offer a tool for operational monitoring of weather-related hazards across Europe, we developed RHITA (Real-time Hazards Identification and Tracking Algorithm), an online framework designed for the rapid, automated, and objective spatiotemporal detection of hazards driven by extreme weather events. RHITA is intended for a wide range of users, including scientists, policymakers, authorities, and the general public. It leverages the ERA5 dataset for real-time detection, and the algorithm is calibrated using the EM-DAT dataset, which documents global disaster occurrences and impacts.
RHITA currently offers two main features: (1) real-time tracking and spatiotemporal characterization of extreme weather events such as heatwaves, droughts, cold spells, cyclones, and storms, focusing on associated hazards like extreme temperatures, water deficits, heavy precipitation, and strong winds; and (2) publicly available, up-to-date, transboundary historical spatiotemporal hazard catalogs for Europe.
How to cite: Cazzaniga, G., Burq, A., Vrac, M., and Faranda, D.: RHITA: a framework for real-time detection and characterization of weather extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13784, https://doi.org/10.5194/egusphere-egu25-13784, 2025.
Anthropogenic climate change (ACC) is one of the most demanding challenges facing our society. The intensification and increased frequency of many extreme events due to ACC are among its most impactful consequences, threatening human health, infrastructure, and ecosystems. In this context, raising the awareness of the general public of the relationship between ACC, extremes, and associated impacts becomes a crucial task.
This work is grounded in attribution science and focuses on quantifying and understanding the influence of internal climate variability on extreme events. Among the many tools available for attribution, we use ClimaMeter [Faranda et al. 2023], a rapid framework designed to provide context for extreme events in relation to ACC. ClimaMeter’s approach emphasizes the dynamics associated with extreme events and identifies weather conditions similar to those characterizing the event of interest, leveraging the analogues methodology for conditional attribution [Yiou, 2014]. The analysis provided by such a framework enables the evaluation of significant changes over time of the event’s dynamics and associated meteorological hazards and links them to ACC.
An essential part of ClimaMeter’s methodology is quantifying the influence of natural variability relative to ACC in explaining the changes associated with the event. Specifically, three modes of Sea Surface Temperature variability are taken into account: the El Niño-Southern Oscillation, the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation. These three modes are considered with equal weight and changes not explained by them are assumed to be due to ACC [Faranda et al., 2023]. While the methodology is rapid and easy to communicate, it also has some limitations. In this work, we investigate the implications of this approach. First, we test it on a pre-industrial simulation of the IPSL climate model to evaluate its performance under stationary climate conditions. Additionally, we explore a generalization of the current methodology, aiming to refine the quantification of natural variability by weighing the three modes based on the event region and associated hazard. This generalized approach has the potential to expand ClimaMeter’s methodology and provide new insights into the complex mechanisms linking natural variability and extremes.
How to cite: Naldesi, C., Vrac, M., Bertrand, N., and Faranda, D.: Exploring a new methodology to quantify natural variability in conditional extreme event attribution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10822, https://doi.org/10.5194/egusphere-egu25-10822, 2025.
North Pacific blocking patterns, defined by persistent high-pressure systems that disrupt atmospheric circulation, are pivotal elements of mid-latitude weather dynamics. These blocking events play a significant role in shaping regional weather extremes, such as prolonged cold spells or heatwaves, and can redirect storm tracks across the Pacific. For instance, the 2021 Pacific Northwest heatwave demonstrated the profound impact of blocking on terrestrial temperatures, where an upstream cyclone acted as a diabatic source of wave activity, intensifying the blocking system. This led to heat-trapping stable stratification, which elevated surface temperatures to unprecedented levels (Neal et al., 2022). Similarly, marine heatwaves in the Northeast Pacific have been linked to high-latitude blocking events, which weaken westerly winds, suppress southward Ekman transport, and enhance ocean stratification, thereby increasing sea surface temperatures (Niu et al., 2023). The predictability of North Pacific blocking events is governed by the intricate interplay of large-scale atmospheric dynamics, ocean-atmosphere interactions, and internal variability (Smith et al., 2020).
This study investigates the differences in predictability between eastern and western North Pacific blocking events, using a modified version of the Davini et al. (2012) blocking index to distinguish their geographical locations. Identified blocking events were tracked using a block-tracking algorithm until they dissipated. Predictability was assessed by identifying an analogue pair for each blocking event. Specifically, after classifying blocks as eastern or western, geopotential height maps for each event were compared to all other days in the dataset. The analogue pair for an event was defined as the day with the smallest root mean square (RMS) distance. Predictability was then evaluated by averaging the error evolution of the tracks between events in each analogue pair.
Using CMIP6 model simulations and ERA5 reanalysis data, the study revealed that eastern blocks are significantly more persistent and stable than their western counterparts. Eastern blocks exhibited longer durations and greater resistance to atmospheric variability, resulting in improved forecast accuracy. In contrast, western blocks were found to be more transient and challenging to predict due to their susceptibility to dynamic instabilities.
References
Davini, P., Cagnazzo, C., Gualdi, S. and Navarra, A., 2012. Bidimensional diagnostics, variability, and trends of Northern Hemisphere blocking. Journal of Climate, 25(19), pp.6496-6509.
Neal, E., Huang, C.S. and Nakamura, N., 2022. The 2021 Pacific Northwest heat wave and associated blocking: Meteorology and the role of an upstream cyclone as a diabatic source of wave activity. Geophysical Research Letters, 49(8), p.e2021GL097699.
Niu, X., Chen, Y. and Le, C., 2023. Northeast Pacific marine heatwaves associated with high-latitude atmospheric blocking. Environmental Research Letters, 19(1), p.014025.
How to cite: K Xavier, A., Hamilton, O., Faranda, D., and Vannitsem, S.: Comparative predictability of eastern and western north pacific blocking events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12284, https://doi.org/10.5194/egusphere-egu25-12284, 2025.
The Diurnal Temperature Range (DTR) serves as a crucial meteorological indicator, reflecting the difference between daily maximum and minimum temperatures and the magnitude of diurnal extremes. The anomalous values of DTR are often linked to the occurrence of various climatic extremes such as droughts, heatwaves, and wet spells, which make it necessary to understand the evolution of DTR both historically and for the future. This study focuses on analyzing the evolution of DTR globally by employing the non-stationary Multidimensional Ensemble Empirical Mode Decomposition (MEEMD) method. To accomplish this, historical temperature data spanning 69 years (1951-2019) and CMIP6 Bias corrected data covering 150 years (1951-2100) were utilized. The non-linear trend characteristics in temperature are computed using CRU 0.50 x 0.50 gridded temperature data for historical trends and five different bias-corrected climate projection datasets of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) for the assessment of trends in future DTR by considering two SSP scenarios, i.e., SSP 245 and SSP 585, each corresponding to intermediate and high emissions scenarios. The CMIP6 models that are considered are CanESM5, GFDL CM4, MIROC6, NorESM2-MM, and MPI-ESM1-2-HR. The results from the analysis reveal the decrease in global DTR, with a faster rate of increase in minimum temperature than in maximum temperature. However, the southern regions of Australia and Africa showed an increase in DTR. The CMIP6 simulations showed that CanESM5 and MPI-ESM1-2-HR showed a decreasing trend in global DTR for both scenarios of ssp, with an increase in DTR for South America and the southern part of Africa for CanESM5, while GFDL CM4, MIROC6, and NorESM2-MM showed a decrease in global DTR. The findings underscore the importance of understanding regional climatic variations when assessing global temperature trends. The observed contrasting regional patterns in DTR highlight the influence of localized hydroclimatic factors, including land-use changes, aerosols, and atmospheric feedback mechanisms. These insights are crucial for refining climate models and improving future climate projections under different emission pathways. Overall, the study emphasizes the necessity of incorporating non-linear approaches like MEEMD to capture complex climatic trends and underscores the role of DTR as a key indicator of climate change and its impacts at both global and regional scales.
How to cite: Reddy, M. S. B., Rajendran, V., and Tewari, M.: Analyzing the Historical and Projected Evolution of the Global Diurnal Temperature Range (DTR), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18740, https://doi.org/10.5194/egusphere-egu25-18740, 2025.
Extreme rainfall events during the Indian monsoon season pose significant challenges due to their socioeconomic and environmental impacts. Understanding the spatial and temporal dynamics of these events requires robust analytical and statistical methods capable of capturing complex relationships within rainfall generating systems. Complex network approaches have emerged as powerful tools for analyzing spatiotemporal patterns in climate data, offering new insights into extreme weather phenomena.
This study compares two methodologies for constructing and analyzing climate networks to study the spatiotemporal structure and dynamics of heavy precipitation events in India during the monsoon season across multiple time scales. Specifically, we introduce a novel combination of Discrete Wavelet Decomposition with Event Coincidence Analysis (ECA), referred to as Multi-Scale Event Coincidence Analysis (MSECA) and compare the results with the existing Multi-Scale Event Synchronisation (MSES). From a conceptual perspective, MSECA appears to be a more reasonable method compared to MSES, as it mitigates certain undesired effects of temporal clustering of rainfall extremes across various timescales.
Our results reveal distinct differences in network properties depending on the methodology used, highlighting the sensitivity of network-based analyses to the choice of construction technique. These differences affect the identification of dominant heavy rainfall patterns and their underlying drivers, such as large-scale atmospheric circulation and/or local feedback mechanisms at daily to monthly temporal scales.
Our work underscores the importance of methodological rigor and the potential of complex network approaches in advancing the understanding of extreme rainfall events in monsoon-dominated regions. This comparison provides a foundation for developing standardized practices for network-based climate studies, enabling more robust assessments of extreme weather phenomena.
How to cite: Bishnoi, G., Dhanya, C. T., and Donner, R. V.: A Comparison of Methodologies for Studying Heavy Precipitation Events during the Summer Monsoon Season in India Using Complex Network Approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21222, https://doi.org/10.5194/egusphere-egu25-21222, 2025.
Since Lorenz's pioneering work, dynamical systems theory has provided a powerful framework for studying complex systems. Among these, the study of their instantaneous properties is particularly significant for understanding short-lived yet impactful extreme events. Here, we propose an analogues-based index to measure the instantaneous predictability of dynamical systems over different forecasting horizons. We demonstrate its application in both classical dynamical systems and the Euro-Atlantic sector atmospheric circulation. Furthermore, recognizing that the onset of extreme events often involves processes operating across different scales, we introduce a novel framework that enables the exploration of scale-dependent dynamical properties. Given the flexible and generalizable nature of these methods, we believe they open new research avenues for studying extreme events from a dynamical systems perspective and will serve as valuable tools for deepening our understanding of extreme events.
How to cite: Dong, C., Gualandi, A., Lucarini, V., and Mengaldo, G.: Advancing the understanding of extreme events through the lens of dynamical system theory, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7645, https://doi.org/10.5194/egusphere-egu25-7645, 2025.
The physical mechanisms underlying climate-induced extreme events are inherently complex, arising from the compounding nature of multiple drivers and/or hazards. Leveraging the chaotic nature of the atmosphere, a novel approach, based on results from dynamical system theory, has recently been adopted to reveal the drivers of both individual and compound extremes. Central to this approach is the co-recurrence ratio, which quantifies the instantaneous dynamical coupling between multiple variables in terms of joint recurrences of atmospheric configurations to similar ones in the past.
While the co-recurrence ratio has demonstrated potential in revealing the atmospheric drivers of certain extremes, its performance may depend heavily on factors such as the choice of geographical domain(s), selection of variables, and the thresholds used to define extremes. These sensitivities remain underexplored, limiting the broader applicability of this approach.
In this study, we aim to address these gaps by assessing the sensitivity of the co-recurrence ratio in a European setting, focusing on daily winter extremes in temperature, wind, and precipitation. For this analysis, we adopt a bivariate focus, diagnosing the coupling between large-scale circulation patterns and single hazard variables.
By exploring these sensitivities, this work seeks to enhance the understanding of the robustness of the co-recurrence ratio and its effectiveness in diagnosing the atmospheric drivers of various types of extremes.
How to cite: Reiter, A. C., Drews, M., Messori, G., Faranda, D., and Dahl Larsen, M. A.: Sensitivity of Dynamical Coupling to Large-Scale Circulation in European Winter Extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13213, https://doi.org/10.5194/egusphere-egu25-13213, 2025.
The El Niño-Southern Oscillation (ENSO) represents one of the most significant drivers of global climate variability. This study investigates the chaotic parameter regimes of the Jin-Timmermann model, particularly focusing on the dynamics identified by Guckenheimer et al. where chaotic attractors emerge. We analyze the reduced three-dimensional system with specific attention to the critical parameters δ = 0.225423, ρ = 0.3224, which govern the time-scale interactions between oceanic and atmospheric processes. Using topological data analysis (TDA), we characterize the structural transitions between periodic and chaotic behaviors in the model's parameter space. Our methodology combines persistent homology with dynamical systems theory to identify distinct topological signatures associated with strong El Niño events. We validate these theoretical findings against observational data from the ERA5 reanalysis and NOAA/ERSSTv5 Niño 3.4 index, focusing particularly on the relationship between topological features and prolonged dry conditions in Southeast Asia. This approach provides new insights into the non-systematic relationship between strong El Niño events and regional climate impacts, while establishing a novel framework for comparing theoretical models with observational data. Our results demonstrate the utility of topological methods in understanding complex climate phenomena and suggest new possibilities for improving ENSO prediction capabilities.
How to cite: Sanchez Muniz, M., Brown, M., and Paranamana, P.: Characterizing ENSO Through Topological Analysis of Jin-Timmermann Model's Chaotic Regimes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20545, https://doi.org/10.5194/egusphere-egu25-20545, 2025.
Turbulence remains a pressing challenge for aviation safety and efficiency, as highlighted by recent incidents involving Singapore Airlines, Qatar Airways, and Scandinavian Airlines. Among the various types, Clear Air Turbulence (CAT) poses the greatest hazard due to its occurrence in clear skies, rendering it difficult to detect and predict. Furthermore, the unprecedented changes in Earth's climate are reshaping atmospheric dynamics on a global scale, with profound implications on aviation. As a companion of ClimaMeter, a platform designed to assess and contextualize extreme weather phenomena in relation to climate change, we introduce here TurboMeter. It is designed to use ERA5 reanalysis data to investigate the meteorological drivers of turbulence events by comparing them with historical analogues under similar atmospheric conditions. Turbulence diagnostics, including Ellrod’s indices, are used to evaluate the roles of jet streams, wind shear, and convective activity at typical cruising altitudes.
To illustrate TurboMeter, we present some recent aviation turbulence events occurred during 2024. Our findings reveal that they are closely linked to intensified jet streams and enhanced convective activity, influenced by the growing impacts of anthropogenic climate change. These results highlight a concerning trend: changing climatic patterns are altering the atmospheric drivers of turbulence, particularly CAT, with significant implications for flight safety and operational planning. Our study evidences the urgent need for improved weather forecasting and turbulence prediction models to mitigate aviation risks in a rapidly warming climate.
How to cite: Alberti, T., Rapella, L., Coppola, E., and Faranda, D.: TurboMeter: attributing aviation turbulence events to climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5631, https://doi.org/10.5194/egusphere-egu25-5631, 2025.
People are increasingly turning to courts to combat climate crisis. In the early 2000s, fewer than 10 climate change litigation cases had been filed globally. By 2024, this number has grown to over 2,500, with more than half originating in the United States. Some of these cases rely on extreme weather attribution science to link damages to anthropogenic climate change. Developing rigorous, legally useful assessments of damage attributable to climate change is an increasingly pressing need.
We present a framework for forecast-based impact attribution which can link physically consistent hazards to impacts, providing evidence for legal cases and climate cost recovery laws. As a case study, we analyze the severe impacts of Storm Irene in August 2011 when it was undergoing extratropical transition in the north-eastern USA. In the state of Vermont, Irene caused rainfall of up to 180 mm within a few hours, leading to fluvial and pluvial flooding with catastrophic consequences that caused $850 million in economic damages. By integrating an operational weather forecast model (ECMWF’s IFS) and hydrological models with economic impact assessments, we assess the extent to which these damages can be attributed to anthropogenic climate change.
This research underscores the potential of interdisciplinary attribution methodologies to enhance the scientific basis for judicial adjudication on climate change and climate law-making.
How to cite: Ginesta, M., Ermis, S., Stuart-Smith, R., and Franta, B.: Impact Attribution for Climate Law: The Case of Storm Irene, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17937, https://doi.org/10.5194/egusphere-egu25-17937, 2025.
Starting January 7, 2025, devastating wildfires have swept through the Los Angeles metropolitan area and nearby regions. By January 10, the fires had caused ten deaths, destroyed thousands of structures, displaced nearly 180,000 residents, and scorched approximately 30,000 acres. This study employs the extended ClimaMeter (climameter.org <http://climameter.org/>) protocol to explore the potential role of climate change in exacerbating the severity of this event. Specifically, we examine whether climate change has modified the atmospheric conditions, represented by the mean sea level pressure, that contribute to wildfire occurrence, represented by the fire weather index, by analyzing historical and current weather patterns similar to those observed during the fires. Our methodology integrates both reanalysis datasets and high-resolution regional climate models to assess observed changes and project future fire risk scenarios. The results indicate a significant increase in the fire weather index across much of California and surrounding regions, which suggests that this event can be ascribed to human-driven climate change. The models show a similar signal in the present climate and project increases in fire weather hazard in the future.
How to cite: Nogherotto, R., Lu, C., Cazzaniga, G., Erika, C., and Faranda, D.: January 2025 Wildfires in Southern California are attributable to Anthropogenic Global Warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19698, https://doi.org/10.5194/egusphere-egu25-19698, 2025.
The framework of weather analogues is a powerful methodology for the detection of the climate change fingerprint on weather extremes, that has been widely used in several contexts. The procedure has several advantages compared to standard model-based attribution exercises, being fast and not computationally expensive. Here we address whether the detection of analogs based on impacts (e.g., environmental, socio-economic) of a severe weather event can provide added value on the attribution of the event intensity or likelihood to climate change.
As a case study, we analyse the twin Emilia-Romagna flood event of May 2023. It caused a sizable amount of casualties, widespread destruction and substantial economic damage. We detect analogues of the river runoff as an impact-based observable of interest, addressing it in an univariate context, but also jointly with other observables (i.e., in a multivariate framework), such as mean sea-level pressure, total precipitation, and 850 hPa vorticity. We therefore detect the optimal set of variables for performing multivariate analysis and the appropriate analysis domain. We suggest that by combining river runoff with other observables by carefully selecting the spatial domain, we obtain a clearer view of the role played by anthropogenic climate change for this event, also including the additional vulnerability linked to the environmental impact of human activities, such as land-use change and freshwater diversion.
How to cite: Lembo, V., Ginesta, M., Alberti, T., D'Agostino, R., and Faranda, D.: Towards an impact-based approach to the detection of analogues: the case study of Emilia-Romagna floods in May 2023, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17947, https://doi.org/10.5194/egusphere-egu25-17947, 2025.
Heatwaves (HWs) are extreme climate events increasingly magnified under climate change, posing significant risks to both human and environmental systems. The Mediterranean region, recognized as a climate change hotspot, is experiencing a worrying amplification of both atmospheric and marine heatwaves. In this presentation we will discuss the evolution and interplay of these phenomena emphasizing their compounding effects when occurring simultaneously.
Our findings reveal a clear increase in HW frequency, intensity, and duration, with the concurrence of atmospheric and marine heatwaves resulting in a significant local amplification of marine heatwave intensity. While atmospheric heatwaves remain largely unaffected by this interaction. This interaction has become more prominent in recent years, highlighting the increasing complexity of extreme climate phenomena in this region.
The results underscore the urgent need for regionally tailored strategies to mitigate the cascading impacts of compounding heatwaves, as their intensification under climate change exacerbates threats to Mediterranean ecosystems and communities.
How to cite: Khodayar Pardo, S., Pastor, P., and Paredes-Fortuny, L.: Unraveling the Rising Threat of Atmospheric and Marine Heatwaves in the Mediterranean Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19577, https://doi.org/10.5194/egusphere-egu25-19577, 2025.
Climate change is an ongoing process that is modifying weather patterns and influencing weather phenomena and extreme events such as heatwaves, droughts, and floods. In this study, we investigate whether climate change can also play a role in enhancing wildfires by focusing on a set of three recent wildfires in Europe (i.e., events occurred in Central Sweden in July 2018, France in July 2022, and in Sicily and Greece in July 2023). We employ the concept of analogues to assess the influence of climate change on the atmospheric conditions underlying wildfire development monitored through the fire weather index, by comparing past and present atmospheric patterns similar to those that occurred during the wildfire. Our analysis focuses on both reanalysis data and high-resolution regional climate models to attribute the observed changes and provide future projections. Our findings show that climate change has altered critical factors supporting wildfire development, such as temperature, humidity, and wind patterns. The results from our sample of three events point out that climate change has increased wildfire hazards in Europe, which is projected to further increase for similar fire weather conditions in the future.
How to cite: Lu, C., Nogherotto, R., Alberti, T., Messori, G., Coppola, E., and Faranda, D.: Assessing the impact of climate change on wildfire development: insights from analogues and regional climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10235, https://doi.org/10.5194/egusphere-egu25-10235, 2025.
Compound climate and weather extremes have received significant attention in recent years due to the increased risks that they pose to the environment, human societies, and the economy. While prior studies have identified associations between various hazards in disaster databases, investigations focussing on droughts and floods remain rare. In this study, we analyze the impacts of concurrent or sequential drought-flood extremes from two widely used disaster databases: the Emergency Events Database (EM-DAT) and its geocoded version (GDIS), as well as the DesInventar database. The analysis focuses on the period from 1960 to 2018, aligning with GDIS temporal coverage. We define concurrent or sequential hazards as instances where a flood occurs during a drought period or within four months following a drought.
Our findings for the global extratropics reveal that the economic losses and the number of affected people resulting from the identified drought-flood events are two to eight times higher than those ascribed to isolated droughts or floods, with a confidence interval ranging from two to twelve. Specifically, in DesInventar, the impact ratio (the mean impact of concurrent or sequential events divided by the mean impact of isolated events) for indirectly affected individuals and financial losses is approximately three. In EM-DAT, the impact ratio reaches three for economic damages and eight for affected individuals. Furthermore, the impact ratios are notably higher in the last 30 years of the study period compared to earlier decades, emphasizing the increasing severity of the drought-flood compound events.
These results highlight the amplified negative impacts when droughts and floods occur concomitantly or sequentially, highlighting the need for more robust policies to address their socio-economic risks, particularly under changing climatic conditions.
How to cite: Worou, K. and Messori, G.: Amplified Socio-Economic Impacts of Concurrent or Sequential Drought-Flood Events: Insights from Disaster Databases (1960–2018), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8719, https://doi.org/10.5194/egusphere-egu25-8719, 2025.
Regional climate emulators provide computationally efficient tools for generating high-resolution climate projections, bridging the gap between coarse-scale models and the detailed resolution required for local-scale hazard assessments. Climate hazards from extreme precipitation events are projected to increase in frequency and intensity under global warming, emphasizing the need for accurate modeling of convective processes. However, traditional numerical methods are constrained by low resolution or the high computational costs of kilometer-scale simulations.
To overcome these limitations, we introduce GNN4CD, a novel deep learning emulator that estimates kilometer-scale (3 km) hourly precipitation from coarse atmospheric data (~25 km). The model leverages graph neural networks and a hybrid imperfect approach (HIA) for downscaling, initially trained on ERA5 reanalysis and observational data, and applied using regional climate model (RegCM) data for present-day and future projections.
GNN4CD demonstrates exceptional performance in reproducing precipitation distributions, seasonal diurnal cycles, and extreme percentiles across Italy, even when trained on northern Italy alone. The model captures shifts in precipitation distributions, particularly for extremes, across historical, mid-century, and end-of-century scenarios. Additionally, evaluations using an ensemble of convection-permitting regional models confirm GNN4CD's ability to replicate ensemble spreads for both present-day and future projections essential for estimating the uncertainty in the future climate change signal..
How to cite: Coppola, E., Blasone, V., Di Gioia, S., Sanguinetti, G., Arora, V., and Bortolussi, L.: Graph neural networks based climate emulator for kilometer scale hourly precipitation : a novel hybrid imperfect approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17645, https://doi.org/10.5194/egusphere-egu25-17645, 2025.
Europe has been identified as a heatwave hotspot, where heatwave intensities have outpaced other mid-latitude regions in the Northern Hemisphere (Rousi et al. Nat. Comms. 2022). Accelerated European heatwave trends have been found to be associated with the increased persistence of Eurasian double jets, a specific set-up of the large-scale circulation in which the Northern hemisphere polar and subtropical jets occur as two clearly separated branches. However, if observed trends are projected to continue with anthropogenic warming and to what degree the present generation of climate models constitute useful tools to assess changes in the atmospheric circulation has not yet been ascertained.
In this study, we benchmark 11 CMIP6 climate models to evaluate their ability to reproduce the main characteristics of double jets and their relationship to heat extremes, aiming to identify the best-performing models for future projections. Our findings show that, on average, the models tend to underestimate the frequency of double jets by 80%. Moreover, half of the climate models underestimate the intensity of double-jet-associated heatwaves over Western Europe, with the remaining models even showing a negative anomaly in heatwave intensity during double jet events in the region. Furthermore, climate models fail to capture the growth rate of double jet persistence, with the model mean trend at -0.4 days per decade, while the observed rate is approximately 1.5 days per decade. The bias in the persistence trend of double jet in models is strongly correlated with the underestimation of the western European heat extreme trend, with an R2 value of 0.42.
Despite this, some models show reasonable agreement with the observations, and these models are further analyzed to project circulation-driven changes in extreme heat. Using EC-Earth3-Veg-LR, we observe an increase in double jet frequency from 2020 to 2060, at a rate of 0.2 days per decade. Our work highlights the need for better representation of double jet characteristics and their relationship with heat extremes in climate models to enhance preparedness for future heat risks.
How to cite: Liu, S., Tian, Y., and Kornhuber, K.: Large-scale atmospheric circulation as a source of uncertainty in western European heat extreme projections , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14873, https://doi.org/10.5194/egusphere-egu25-14873, 2025.
Posters on site: Wed, 30 Apr, 10:45–12:30 | Hall X3
How to cite: Faranda, D. and the The ClimaMeter team: ClimaMeter: a rapid attribution framework for weather extreme events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5780, https://doi.org/10.5194/egusphere-egu25-5780, 2025.
The co-occurrence of wintertime cold spells in North America and wet, windy extremes in Europe, known as the Pan-Atlantic compound extremes, is linked to distinct dynamical pathways. One of those dynamical pathways involves the presence of a persistent high-pressure system west of Greenland. This high-pressure anomaly tends to simultaneously induce a southward displacement of a trough over the eastern United States and sustain an upper-level trough over southwestern Europe, creating conditions that induce both cold spells in North America and extreme precipitation in Europe. The co-occurrence of the Pan-Atlantic compound extremes has been investigated in previous studies. However, the causal association between extremes on both sides of the Atlantic has yet to be verified. In this study, we aim to assess the relationship between these compound extremes and to uncover the causal mechanisms driving their co-occurrence. Preliminary findings indicate that high-pressure anomalies over Greenland are a main driver of both phenomena, providing a coherent dynamical link that bridges these geographically distinct extreme events. The study further seeks to clarify the underlying dynamics and improve predictability for such interconnected extreme weather events, which can help to better manage and mitigate their impacts.
How to cite: Krouma, M. and Messori, G.: Causality and predictability of the Pan Atlantic compound extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13374, https://doi.org/10.5194/egusphere-egu25-13374, 2025.
Extreme weather and climate events, marked by unexpected and severe conditions at the edges of historical distributions, significantly impact human health, society, and ecosystems. With global warming driving an increase in the frequency and intensity of these extremes, there is an urgent need to enhance weather prediction beyond the typical 7–10-day range. Among the atmospheric and oceanic components studied for improving predictability, the stratosphere stands out due to its slower and more predictable changes, which can have persistent impacts on surface weather patterns.
Research has highlighted the stratosphere's role in driving weather and climate extremes, particularly in the extratropical Northern Hemisphere. Events involving a weak or strong stratospheric polar vortex can precede the occurrence of surface extremes, making the polar vortex a key link between stratospheric variability and surface climate predictability. While various studies have previously identified this teleconnection, the processes connecting anomalous vortex states to extreme surface events are not yet fully understood.
In VORTEX project we employ a methodology based on advancements in dynamical systems theory to explore the relationship between anomalous polar vortex states and extreme precipitation and temperature events. This approach characterizes each vortex-extreme event's recurrence, persistence, and predictability, providing dynamic insights that traditional methods cannot. By identifying the intrinsic predictability of stratospheric patterns tied to extremes, this methodology offers a pathway to improve sub-seasonal to seasonal climate models, focusing future efforts on better representing critical patterns that influence extreme weather.
How to cite: Alvarez-Castro, C., Peña-Ortiz, C., Gallego, D., and Faranda, D.: VORTEX project: The role of the polar vortex on the predictabIlity of extreme events in the Northern Hemisphere, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10570, https://doi.org/10.5194/egusphere-egu25-10570, 2025.
Even though tropical cyclones (TCs) are well documented from the moment they reach a certain intensity to the moment they start to evanesce, many physical and statistical properties governing them are not well captured by gridded reanalysis or simulated by earth system models. Thus, the tracking of TCs remain a matter of interest for the investigation of observed and simulated tropical. Many cyclone tracking schemes are available. On the one hand, there are trackers that rely on physical and dynamical properties of the TCs and users prescribed thresholds, which make them rigid, and need numerous variables that are not always available in the models. On the other hand, there are trackers leaning on deep learning which, by nature, need large amounts of data and computing power. Besides, given the number of physical variables needed for the tracking, they can be prone to overfitting, which hinders their transferability to climate models. In this study, the ability of a Random Forest (RF) approach to track TCs with a limited number of aggregated variables is explored. Hence, it becomes a binary supervised classification problem of TC-free (zero) and TC (one) situations. Our analysis focuses on the Eastern North Pacific and North Atlantic basins, for which respectively 514 and 431 observed tropical cyclones tracks record are available from the IBTrACS database over the 1980-2021 period. For each 6-hourly time step, RF associates TC occurrence or absence (1 or 0) to atmospheric situations described by predictors extracted from the ERA5 reanalysis. Then situations with TC occurrences are joined for reconstructing TC trajectories. Results show good ability of the method for tracking of tropical cyclones over both basins and good ability for spatial and temporal generalization as well. It also shows similar TC detection rate as trackers based on TCs' properties and significantly lower false alarm rate. RF allows us to detect TC situations for a range of predictor combinations, which brings more flexibility than threshold based trackers. Last but not least, this study shed light on the most relevant variables allowing to detect tropical cyclone.
How to cite: Vaittinada Ayar, P., Bourdin, S., Faranda, D., and Vrac, M.: Ensemble Random Forest for Tropical Cyclone Tracking, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13891, https://doi.org/10.5194/egusphere-egu25-13891, 2025.
Hemispheric symmetries, including those in zonal-mean eddy kinetic energy and in hemispheric-mean planetary albedo, are a characteristic feature of Earth’s climate. Whether such a symmetry also holds for extreme surface windspeeds driven by midlatitude cyclones is currently unclear. We address this question by focusing on the regions with the peak of storm tracks over the North Atlantic, North Pacific and Southern Ocean. We analyse reanalysis and satellite datasets and employ objectively calculated storm tracks to associate cyclones with surface winds they produce. Additionally, we check for existence of trends in extreme windspeeds of each basin. Results show a statistically distinguishable hemispheric asymmetry in extreme surface windspeeds, with the North Hemisphere having stronger extremes, driven primarily by extreme windspeeds occurring during winter and in proximity to cyclones. This implies that cyclones in the North Hemisphere drive stronger surface windspeed extremes than in the South Hemisphere. The North Hemisphere also has higher extreme windspeeds above the boundary layer (700 hPa), pointing to the role of large-scale processes in driving these differences. Lastly, trends in the extreme surface windspeeds across all basins are positive in the reanalysis dataset, and statistically significant in the North Pacific and Southern Ocean.
How to cite: Stanković, A. and Caballero, R.: Winter cyclones drive stronger surface wind extremes in the North Atlantic than in the Southern Ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10966, https://doi.org/10.5194/egusphere-egu25-10966, 2025.
The North Atlantic Oscillation (NAO) is the dominant pattern of atmospheric variability over the North Atlantic region, having its greatest influence on Europe during the winter months. In winter, positive NAO index values are linked to warmer temperatures and increased precipitation in western and northern Europe, whereas southern Europe tends to experience colder and drier conditions. These drier conditions can pose significant challenges for agriculture and livelihoods. An overall positive trend in the NAO index has been observed in winter in recent decades. However, how precipitation dynamics in the Mediterranean region respond to the shift towards a higher NAO index are largely unknown, partly due to the poor capture of NAO’s upward shift in climate models.
Here we examine the impact of the shift towards a higher NAO index on precipitation dynamics in the Mediterranean region in winter. We employ a novel statistical model to analyse next-day precipitation conditional on past observations. The analysis focuses on conditioning drought persistence on different NAO states to assess their influence on the distributional characteristics of drought durations across the Mediterranean region. We present preliminary analyses that contribute to the growing body of evidence that long-term positive trends in the NAO index have an impact on rainfall patterns and drought occurrence in Europe. Understanding the role of teleconnections in regional climate variability and long-term trends is essential for robust regional climate projections for improved risk assessment and policy planning.
How to cite: Schultz, E., Spanjers, B., and Coumou, D.: The impact of the upward trend in the NAO index on precipitation dynamics in the Mediterranean region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17852, https://doi.org/10.5194/egusphere-egu25-17852, 2025.
Extreme precipitation events (EPEs) are meteorological phenomena that are likely to intensify as a result of climate change. They are a major concern for our society, especially in densely populated areas, as they can have significant economic and environmental impacts. Therefore, identifying large-scale atmospheric circulation that lead to EPEs is crucial for detecting geographical areas at risk and mitigating their adverse impacts.
To achieve this objective, we study the circulation patterns associated with EPEs in Italy. Initially, we focus on North-Central Italy and we identify the precipitation extremes in three datasets: ARCIS 3.0, MSWEP, and CERRA LAND. Circulation types associated with the EPEs are obtained by applying Self Organizing Maps (SOMs), an unsupervised artificial neural network widely used in synoptic climatology, to anomalies of geopotential height at 500 hPa and mean sea level pressure. Since ArCIS, the reference dataset, is limited to North-Central Italy, we extend the analysis to the whole of Italy using CERRA-Land. Such choice is based on the fact that it produced the most similar results to ArCIS in North-Central Italy compared to MSWEP.
We then generate composites of various variables (all retrieved from ERA5) for each SOM pattern to better understand the circulation patterns and characterize the atmospheric dynamics associated with extreme events. Additionally, we analyze the probability of exceeding the 99th percentile of wet-days to identify the areas impacted by each pattern. Composites for the different circulation types show variations in the synoptic pattern's position within the Mediterranean basin, as well as differences in the direction and intensity of moisture flux. These patterns influence distinct regions and display varying frequencies across seasons.
In future works the classification obtained by this study will be applied to climate model simulations, aiming to investigate the role of anthropogenic climate change in the dynamics leading to EPEs in Italy.
How to cite: Iacomino, C., Pascale, S., Zappa, G., Iotti, M., Grazzini, F., Portal, A., and Ghinassi, P.: High-risk atmospheric circulation patterns for Italian precipitation extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15668, https://doi.org/10.5194/egusphere-egu25-15668, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot 4
EGU25-12947 | ECS | Posters virtual | VPS20
Topological fingerprinting of dynamical systemsFri, 02 May, 14:00–15:45 (CEST) | vP4.13
Poincaré established a framework for understanding the dependence of a dynamical system's properties on its topology. Topological properties offer detailed insights into the fundamental mechanisms — stretching, squeezing, tearing, folding, and twisting — that govern the shaping of a dynamical system's flow in state space. These mechanisms serve as a conduit between the system's dynamics and its topology [Ghil & Sciamarella, NPG, 2023]. A topological analysis based on the templex approach [Charó et al., Chaos, 2022] involves finding a topological representation of the underlying structure of the flow by the construction of a cell complex that approximates its branched manifold and a directed graph on this complex. A pivotal feature of the cell complex that facilitates the characterization of the flow dynamics is the joining locus, upon which all the fundamental mechanisms that sculpt the flow leave a pronounced signature.
The local dimension d(x) and the inverse persistence θ(x) of the state x of a dynamical system [Lucarini et al., 2016; Faranda et al., Sci. Rep., 2017] provide information on the rarity and predictability of specific states, respectively. We demonstrate herein that these two measures, d and θ also provide information about the localization of the joining locus.
The present work proposes a new topological method for fingerprinting a system’s nonlinear behavior using the concept of persistent generatexes. This novel approach integrates the strengths of two topological data analysis methods: the templex and persistent homologies. Rather than employing a single cell complex and a digraph to characterize the flow of the system, our approach emphasizes the localization of the joining locus through the calculation of local dimension and the inverse persistence, leading to the construction of a family of nested digraphs. The dynamical paths, namely the nonequivalent ways of travelling through the flow, are found to be the most persistent cycles; here the concept of persistence is used in the sense of the persistent homology approach [Edelsbrunner & Harer, Contemporary mathematics, 2008]. The dynamical paths give us the ‘topological fingerprinting’ of a system’s dynamics.
How to cite: Charó, G. D., Faranda, D., Ghil, M., and Sciamarella, D.: Topological fingerprinting of dynamical systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12947, https://doi.org/10.5194/egusphere-egu25-12947, 2025.
