Understanding and accurately projecting the changes in these hazards, their compounding nature, and how they may interact with local socioeconomics and population changes over the coming decades and centuries requires conversations across a broad range of disciplines: physical sciences, climate risk-modelling, statistics and machine learning, geography and social sciences. Recent record breaking extreme weather events highlight the urgent need to improve our scientific understanding and modelling capacities for installing climate services, early warning schemes and adaptation measures to the future risk.
This session aims to showcase recent research progress investigating natural environmental hazards, improvement in modelling, and projections over decadal to century timescales. It will foster discussion to identify outstanding research questions and form new collaborations, for instance which hazards receive less attention in the community for specific geographical regions? Or what hazard sectors should work more closely with weather and climate scientists for progress to be made?
We invite contributions on the changing risk and prediction from natural hazards, including but not limited to studies of:
- Detection and attribution of climate hazards and impacts
- Climate Hazard and Impact Modelling
- Climate change trends in hazards on decadal to centennial timescales
- Drivers and Trends in Compound Weather Extremes
- Extreme Weather Early warning Systems
- Global weather and climate teleconnections and their links to environmental hazards and impacts
Our understanding of the climate system suggests that, in response to human forcing through greenhouse gas & aerosol emissions and land use, weather and climate extremes are potentially amplified both by thermodynamic and dynamic changes. In other words, an extreme event such as a heatwave can become more intense through thermodynamic modulations, e.g., background warming and drier soils providing fuel for stronger surface sensible heating, but potentially also through atmospheric circulation changes.
Especially at regional scales, however, such anthropogenic modulations can be masked or enhanced by internal climate variability. Moreover, sea surface temperature patterns simulated by climate models systematically deviate from observations, which points to a flawed response to external forcing (e.g., Wills et al., 2022). Considering how strongly the surface ocean interacts with the atmosphere, this suggests that large-scale winds may also fail to adequately respond to anthropogenic forcing. This raises critical questions: Where do we expect notable dynamic contributions in the first place? Are observations consistent with these expected changes? And how do dynamics compare to thermodynamics driving weather and climate extremes?
To tackle these questions, we employ simulations using CESM2, a global Earth System Model, with which we disentangle the responses to anthropogenic forcings in thermodynamic state and atmospheric circulation. In doing so, we obtain the physical model truth with regards to how extreme weather and climate events are altered under additional global warming through purely thermodynamic and dynamic pathways.
References
Wills, R. C. J., Dong, Y., Proistosecu, C., Armour, K. C., & Battisti, D. S. (2022). Systematic climate model biases in the large-scale patterns of recent sea-surface temperature and sea-level pressure change. Geophysical Research Letters, 49, e2022GL100011. https://doi.org/10.1029/2022GL100011
How to cite: Schumacher, D. L., Gu, L., Hauser, M., and Seneviratne, S. I.: How do weather and climate extremes respond to anthropogenic forcing?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15684, https://doi.org/10.5194/egusphere-egu25-15684, 2025.
Convective summer rainfall in the European Alps frequently triggers hazardous events, including flash floods and debris flows, with severe implications for infrastructure and communities. Future climate warming is projected to exacerbate these risks by intensifying extreme short-duration rainfall. This study explores how such intensification might considerably increase the frequency of extreme 10-minute and hourly rainfall in the Alpine region. Using the physically-based TENAX model, which integrates temperature-dependent scaling with rainfall intensity distribution, we identified significant changes in rainfall return periods. The model combines observed temperature-rainfall relationships with a Monte Carlo approach to project future extremes under various warming scenarios, leveraging outputs from 17 regional climate models provided by the EURO-CORDEX project. Using the model, we found that the frequency of what are today’s 50-year rainfall events over 299 alpine stations is projected to double when regional temperature increases by 2°C. Additionally, the results reveal that the projected intensification is not uniform across the region, with high-altitude stations showing an even greater increase in extreme rainfall frequency compared to lower elevations. This spatial variability underscores the complexity of addressing climate impacts in mountainous terrains. These findings emphasize the urgent need for adaptive measures tailored to elevation. Our study highlights the necessity of revising infrastructure standards and enhancing risk management strategies to prepare for a future with more frequent extreme rainfall events.
How to cite: Peleg, N., Koukoula, M., and Marra, F.: Doubling the frequency of extreme short-duration summer rainfall events in the European Alps with regional warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2382, https://doi.org/10.5194/egusphere-egu25-2382, 2025.
Synthetic time series generation is an essential tool to explore different climate scenarios and their impacts. While sophisticated generation methods have been developed in the past, they often rely on physical and statistical assumptions and require extensive data for calibration and parameter estimation. We propose a straightforward method for time series generation based on constrained sampling of observations. This approach preserves the physical consistency between variables and maintains the short-term temporal structure present in the observation. We sample temperature, precipitation, surface pressure, incoming solar radiation, and wind from station observations in Switzerland. We obtain different sets of synthetic time series by constraining mean temperature and precipitation quantiles according to different future greenhouse gases emission scenarios. The sampled time series are compared with historical observations and statistically downscaled EURO-CORDEX projections. We show that, when constrained on temperature, our sampling produces more precipitation extreme events than the statistically downscaled time series. We also analyze the dependence structure between variables, including the multivariate extreme events.
How to cite: Lehning, M., Rivoire, P., and Milojevic, T.: Characteristics of a Novel Sampling for Future Extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13233, https://doi.org/10.5194/egusphere-egu25-13233, 2025.
Annual wildfire occurrences are associated with a high degree of variability. As well as arising from the inherent randomness of wildfire events, this is also due to variability in the climate factors affecting wildfire risk, such as to summer precipitation. With climate change linked to the emergence of regionally catastrophic fire years, understanding the probabilistic distribution of wildfires and the extent these are linked to predictable modes of climate variability (such as El Niño Southern Oscillation, ENSO, or the Atlantic Multidecadal Oscillation, AMO) is of increasing importance. We use a large climate ensemble (KNMI-LENTIS) together with probabilistic fire occurrence model accounting for human, vegetation type, vegetation growth, and weather effects to predict 1600 simulated fire years over the contiguous US in the modern climate (2000-2009) and for +2°C global warming. There is significant spread in the distribution of fire years in the modern ensemble, with interannual variability higher in regions with a high mean rate of fire activity. Controlling for the effect of the average fire rate, the southwestern US, the Great Plains and southern Florida have proportionally highest variability. Wildfire occurrence is strongly influenced by climate modes in all three of these regions in the ensemble - with greater wildfire occurrence associated with La Niña, negative Indian Ocean Dipole (IOD), and positive Tropical North Atlantic (TNA) years. The AMO, Pacific Decadal Oscillation and Pacific/North American oscillation all exert a significant influence on US wildfire in the modern and modern +2°C climates. Climate warming results in a considerable increase in annual wildfire occurrences across the US, including in less fire-prone regions of the northern and interior US, as well as a strong effect on the likelihood of extreme fire years and long fire seasons in the southwest. There is a strengthening effect of key climate modes on annual wildfires, especially from the AMO, IOD, TNA and ENSO. This analysis, in addition to specific findings concerning US wildfire, highlights the utility of large climate ensembles in characterising the variability of the wildfire regime and projecting wildfire under future climate change.
How to cite: Keeping, T., Zhou, B., Cai, W., Shepherd, T., van der Wiel, K., Prentice, C., and Harrison, S.: Understanding Wildfire Interannual Variability using Large Ensembles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4719, https://doi.org/10.5194/egusphere-egu25-4719, 2025.
Climate change is intensifying wildfires, making them more frequent and severe. While significant research has focused on predicting burned areas using bioclimatic and anthropogenic factors, fewer studies have explored the drivers of the economic damages of wildfires. Our study addresses this gap by identifying key factors influencing global economic wildfire damages and projecting future impacts under three Shared Socioeconomic Pathways (SSPs). Using multiple linear regression analyses, we assess country-level predictors of wildfire damages and forecast future trends under SSP1-2.6, SSP2-4.5, and SSP3-7.0. We tested a wide range of predictors, covering climate, land-cover, governance, and socio-economic factors. Our findings highlight the Human Vulnerability Index (HVI), which reflects a country’s socio-economic conditions, as the strongest predictor of historical wildfire damages, followed by water vapor pressure deficit during fire seasons and population density near forested areas. These findings contrast with studies on burned areas, where climate factors dominate.
Our model projects that by 2070, global economic wildfire damages could be three times higher under SSP3-7.0 compared to SSP1-2.6. Our analyses suggest that robust socio-economic development can offset wildfire damages associated with climate hazards, though this is less certain under SSP3-7.0. The emphasis of SSP1-2.6 on equitable socio-economic progress and climate action not only reduces wildfire damages but also mitigates inequalities in their distribution across countries. For developed countries, SSP1-2.6 offers modest economic damage reductions, but the growing impact of climate hazard becomes the dominant driver of wildfire damages by century’s end if socio-economic conditions remain stable at their current high levels. For least-developed countries, which are disproportionately exposed to anthropogenic climate change, the potential gains of following a sustainable pathway by 2070 are up to nine times greater compared to developed countries. Our work complements existing research on burned areas and underscores the importance of sustainable development in addressing the economic impacts of wildfires.
How to cite: Hwong, Y.-L., Byers, E., Werning, M., and Quilcaille, Y.: Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5735, https://doi.org/10.5194/egusphere-egu25-5735, 2025.
Urban areas are commonly hotter than rural counterparts. Heat exposure leads to health risks, including excess mortality. Research suggests that marginalized groups are more exposed than the general population to environmental hazards. Inequalities of exposure to urban heat island putting higher heat stress on persons of color and people living below the poverty line have been shown for an ensemble of U.S. Cities (Hsu et al., 2021). Less is known about these inequalities of exposure in Europe. The influence of projected climate change on these inequalities is also unclear. In this study, we investigate inequalities of exposure across different types of population for 10 major French cities.
We combine surface temperature data with demographic data to answer these questions. The meteorological data (days with a high heat stress and number of heatwaves days) is extracted from the Urbclim model (De Ridder et al., 2015), at 100-meter resolution, with an emission scenario following the current policies. The demographic data consists of a census-derived ensemble of 28 variables at 200-meter resolution, including age classes, age of buildings, density, income level and types of households. We find that neighborhoods with households with lower income and a higher density of children below the age of 10 have a higher exposition to heatwaves than the rest of the population. The exposure to heatwaves grows for all groups with higher levels of global warming but the inequalities of exposure still remain.
Bibliography:
De Ridder, Koen, Dirk Lauwaet, and Bino Maiheu. "UrbClim–A fast urban boundary layer climate model." Urban Climate 12 (2015): 21-48.
Hsu, A., Sheriff, G., Chakraborty, T. et al. Disproportionate exposure to urban heat island intensity across major US cities. Nat Commun 12, 2721 (2021). https://doi.org/10.1038/s41467-021-22799-5
How to cite: Jezequel, A., Rufat, S., De Brito, M., Liu, C., Canchon, G., Ermis, S., and Carvalho, T.: Unequal exposure to heatwaves in French cities in a changing climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6988, https://doi.org/10.5194/egusphere-egu25-6988, 2025.
Climate-related extreme events impose a heavy toll on humankind, and many will likely become more frequent in the future. The compound (joint) occurrence of different climate-related hazards and impacts can further exacerbate the detrimental consequences for society. By analysing postprocessed data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we provide a global mapping of future changes in the compound occurrence of six categories of hazards or impacts related to climate extremes. These are: river floods, droughts, heatwaves, wildfires, tropical cyclone-induced winds and crop failures. The use of impact model data provides a unique perspective on the compound occurrence of these hazards and impacts, beyond what can be obtained from Global Climate Model output.
In line with the existing literature, we find sharp increases in the occurrence of many individual hazards and impacts, notably heatwaves and wildfires. Under a medium-high emission scenario, many regions worldwide transition from chiefly experiencing a given category of hazard or impact in isolation to routinely experiencing compound hazard or impact occurrences. A similarly striking change is projected for the future recurrence of compound hazards or impacts, with many locations experiencing specific compound occurrences at least once a year for several years, or even decades, in a row. Moreover, we show a nonlinearity in compound occurrences for different global warming levels, with higher warming giving a faster-than-linear increase in compound occurrences. In the absence of effective global climate mitigation actions, we may thus witness a qualitative regime shift from a world dominated by individual climate-related hazards and impacts to one where compound occurrences become the norm.
How to cite: Messori, G., Muheki, D., Batibeniz, F., Bevacqua, E., Suarez-Gutierrez, L., and Thiery, W.: Global mapping of concurrent hazards and impacts associated with climate extremes under climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4486, https://doi.org/10.5194/egusphere-egu25-4486, 2025.
As climate change intensifies, coastal communities face growing threats from tropical cyclones and rising seas. These communities need practical ways to plan their adaptation strategies. Our study presents a new approach that integrates storyline analysis with local adaptation planning. For that, we combine different climate and adaptation scenarios with a modelling framework that allows cascading hydrometeorological conditions to flood hazards and to socio-economic impacts (including exposure and vulnerability information). We adopt as case study cyclone Idai's (2019) flood impacts on the coastal city of Beira, Mozambique.
The storylines of Idai are based on different scenarios of climate change and tidal cycles, but also on testing potential different adaptation responses to coastal protection. Our findings show that when climate change combines with high tides, the flood impacts grow considerably, affecting more people and causing more economical damage. Among the different adaptation strategies considered, building only seawalls offer limited protection against very extreme events, while strategies that mix different adaptation measures significantly reduce potential damage across all scenarios.
By offering localized, detailed information on compound climate hazards and adaptation, storylines can be used to measure the effectiveness of adaptation strategies against extreme events. This approach allows us to evaluate the robustness of different strategies across scenarios and quantify residual impacts, complementing traditional climate risk assessments. Our framework helps bridge the gap between climate projections and practical adaptation planning, supporting more informed decision-making at the local level.
How to cite: Moreno Dumont Goulart, H., Athanasiou, P., van Ginkel, K., van der Wiel, K., Winter, G., Pinto, I., and van den Hurk, B.: Storylines for compound flood impacts and adaptation: a case study of cyclone Idai in Beira, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6034, https://doi.org/10.5194/egusphere-egu25-6034, 2025.
Compound climate hazards, such as co-occurring temperature and precipitation extremes, substantially impact people and ecosystems. Internal climate variability combines with the forced global warming response to determine both the magnitude and spatial distribution of these events, and their consequences can propagate from one country to another via many pathways. We examine how exposure to compound climate hazards in one country is transmitted internationally via agricultural trade networks by analyzing a large ensemble of climate model simulations and comprehensive trade data of four crops (i.e. wheat, maize, rice and soya). Combinations of variability-driven climate patterns and existing global agricultural trade give rise to a wide range of possible outcomes in the current climate. In the most extreme simulated year, 20% or more of the caloric supply in nearly one third of the world’s countries are exposed to compound heat and precipitation hazards. Countries with low levels of diversification, both in the number of suppliers and the regional climates of those suppliers, are more likely to import higher fractions of calories (up to 93%) that are exposed to these compound hazards. Understanding how calories exposed to climate hazards are transmitted through agricultural trade networks in the current climate can contribute to improved anticipatory capacity for national governments, international trade policy, and agricultural-sector resilience. We recommend concerted effort be made toward merging cutting-edge seasonal-to-decadal climate prediction with international trade analysis in support of a new era of anticipatory Anthropocene risk management.
How to cite: Keys, P., Barnes, E., Diffenbaugh, N., Hertel, T., Baldos, U., and Hedlund, J.: Exposure to compound climate hazards transmitted via global agricultural trade networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7801, https://doi.org/10.5194/egusphere-egu25-7801, 2025.
A warming climate is expected to alter the magnitude, frequency and spatial pattern of floods. The widespread flooding observed in the UK and Western Europe over the winter half-year 2023/24 followed on from a number of other notable floods in 2013/14, 2015/16 and 2019/20. However, detecting a climate-driven trend in river flows is complicated by the influence of internal variability and relatively short observational records. End-to-end probabilistic attribution that includes river flows also remains challenging as hydrological responses do not scale linearly with changes in rainfall. Recent studies have encouraged the routine creation of event storylines in a forensic manner to explore a full range of plausible outcomes and enhance risk awareness of UNSEEN outcomes. Few studies to date have attempted to harmonise the different approaches when conducting retrospective analysis of hydrological extremes.
A consistent framework for post-event analyses of hydrological extremes is demonstrated here using the winter half-year 2023/24 UK floods as a case study. We aim to place the winter half-year 2023/24 in context of past climate change, consider the possibility of UNSEEN outcomes beyond historical observations in a present-day climate and appraise trend detectability over the 21st century. The ‘ClimaMeter’ circulation analogue-based attribution approach suggests that a 6-month period with similar atmospheric circulation patterns to the observed winter half-year 2023/24 has become warmer and wetter (by an average 8.8%) in the recent past (1945-2021) compared to the more distant past (1850-1925). Monthly river flow reconstructions extended back to 1850 show river flows during analogue events have increased by 13.5%. Pooling seasonal hindcasts following the UNSEEN approach show the potential for river flows to be 46% higher than the observed given a worst-case storyline. A maximised rainfall storyline further explores consequences of slight changes to the tracks of two major winter storms which could have resulted in much larger rainfall accumulations. Finally, river flow simulations driven by a single-model-initial-condition large ensemble place observed trends in context of internal variability, suggesting early emergence of climate signals for winter half year river flows for some areas but a signal may not emerge for some regions until mid-21st century. Our research provides a proof of concept in extending storyline attribution approaches to river flows and highlights the changing risk of winter flooding in the UK. The same framework for post-event analyses can be applied to future events and elsewhere globally to assist long-term planning for climate change adaptation.
How to cite: Chan, W., Barker, L., Faranda, D., and Hannaford, J.: River flow amplification under climate change: attribution and climate-driven storylines of the winter 2023/24 UK floods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8587, https://doi.org/10.5194/egusphere-egu25-8587, 2025.
Weather and climate extremes such as heatwaves are crucial climate hazards to people and ecosystems worldwide. In any region, climate change may alter their characteristics in complex ways so that a rigorous and holistic quantification of the extremity of such events remains a challenge, impeding also uses by climate change impact, attribution, litigation and many other communities.
Here we introduce a new class of threshold-exceedance-amount metrics that consistently track changes in event frequency, duration, magnitude, area, and timing aspects like daily exposure and seasonal shift—as separate metrics, partially compound like as average event severity in a region, and up to compound total events extremity (TEX). Building on state-of-the-art daily and hourly temperature datasets over 1961 to 2024, we applied the new metrics to extreme heat events at local- to country-scale (example Austria, SPARTACUS 1-km-scale data) as well as across European land regions (whole of Europe, ERA5 10-km-scale data), demonstrating their utility through this example application. Comparing the recent period 2010-2024 to the reference climate period 1961-1990, we revealed about five- to twenty-five-fold amplifications of the TEX of extreme heat over Austrian and southern & mid-latitude European regions, finding these amplification signals strongly emerged from natural variability and an unequivocal evidence of anthropogenic climate change.
Given their fundamental capacity to reliably track any threshold-defined hazard at any location, the new metrics enable a myriad of uses beyond this example application. We hence close with summarizing such possible applications by scientific users but also practice users in the weather and climate services and action domains (e.g., hydro-met services, environmental agencies, insurance companies, law firms, public administrations, policymakers). These range from climate risk and impact analyses related to key extremes such as heatwaves, heavy precipitation, droughts, wildfires, flooding, and storminess to extreme events attribution, which quantifies the share of a hazard extremity, and optionally of its damage to properties and harm to people, that is estimated as attributable to anthropogenic climate change.
How to cite: Kirchengast, G., Haas, S., and Fuchsberger, J.: A new class of climate hazard metrics and demonstration based on tracking extreme heat amplification over Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10409, https://doi.org/10.5194/egusphere-egu25-10409, 2025.
The year 2024 witnessed unprecedented heatwaves across the globe, with extreme temperatures affecting South America, Africa, Europe, and many other regions. These intense heat events have become more frequent and severe due to human-induced climate change; therefore, it is critical to assess their current state and future projections. In this study, the global 2024 Mega-heatwave was analyzed. We used the ECMWF Reanalysis v5 (ERA5) data to calculate heatwave indices (number, frequency, and magnitude) and assessed the normality of the event. We further developed a novel heatwave normalized index (HWNI) that combines the three conventional indices. Additionally, we calculated HWNIs for future projections under four Shared Socioeconomic Pathway (SSP) scenarios (SSP126, SSP245, SSP370, and SSP585) using 14 Global Circulation Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the expected frequency of global heatwaves as strong as the 2024 Mega-Heatwave over time. Our results confirmed that significant increases in the number of heatwaves and total heatwave days in 2024, and also found regional differences in the major characteristics of the 2024 Mega-heatwave across the globe. This study underscores the critical importance of continued monitoring and analysis of extreme heat events to inform climate policy and adaptation strategies in the face of rapidly changing global temperatures.
This study is supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (2022R1C1C2009543, RS-2022-KE002030) and the Korea Environment Industry & Technology Institute (KEITI) funded by Korea Ministry of Environment (2022003640002).
How to cite: Kim, J., Sung, K., and Kim, Y.: 2024 Mega-Heatwaves, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3396, https://doi.org/10.5194/egusphere-egu25-3396, 2025.
When weather and climate events occur concurrently in various locations, their combined impacts pose significant threats to connected socio-economic systems. Compound dry and hot events have become major natural disasters that affect production and daily life under global warming. However, the patterns of synchronized compound dry and hot events remain unclear. This study uses temperature data and drought indices to identify compound dry and hot events and adopts the climate network approach to explore their spatial synchronization patterns and temporal change. The findings indicate a significant increase in global compound dry and hot events, with a notable expansion in the extent of their spatial synchronization. However, there is no significant trend in the average distance of synchronization. Spatial synchronization of compound dry and hot events exhibits heterogeneity, with hotspots in Central and Southern Europe, the Middle East, and Central South America. Additionally, some regions exhibit teleconnections of compound hot and dry events, such as the Western United States and Southern Europe. These insights could support adaptation and risk management for compound dry and hot events under climate change.
How to cite: Zhang, X., Gao, P., and Song, C.: Teleconnections of global compound hot and dry events: a climate network perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-956, https://doi.org/10.5194/egusphere-egu25-956, 2025.
Drought is a dry period characterized by an abnormal water deficit relative to local climatology, propagating through the land surface hydrological cycle. Soil drought, in particular, refers to a deficit of accessible water for vegetation, affecting ecosystems and societies through activities such as agriculture and infrastructure stability. Soil droughts are expected to evolve under climate change since their two meteorological drivers, precipitation and reference evapotranspiration (which represent the atmospheric water demand), are evolving, too. Under climate change, reference evapotranspiration is projected to increase, and precipitation patterns are expected to shift. However, the evolution of droughts in France remains uncertain, and understanding these changes brings information for adaptation strategies. Since drought events unfold over both space and time and their impacts depend on these spatiotemporal characteristics, this study analyzes them as contiguous spatiotemporal phenomena.
This study proposes a methodological framework to (1) identify spatiotemporally contiguous soil drought events, (2) analyze changes in their characteristics under climate change, and (3) attribute these changes to meteorological drivers. The detection method, adapted from existing algorithms for identifying large-scale spatiotemporal extreme events, is here tailored to study soil droughts at a regional scale using high-resolution data. This method connects contiguous points where standardized water deficits exceed a predefined threshold in space and time. Additionally, the framework integrates an attribution approach adapted from Zscheischler et al. (2013) that links detected changes in drought characteristics to meteorological drivers, here precipitation and evapotranspiration, offering a detailed perspective on the mechanisms underlying these changes.
The framework is applied to France using high-resolution monthly data (8 km × 8 km) from the SAFRAN atmospheric reanalysis (1958-2020) and 12 climate simulations under greenhouse gases emission scenario RCP 8.5 (1950–2100) from the EXPLORE2 project, which drive a Land Surface Model offline. Precipitation, reference evapotranspiration, and soil wetness are standardized relative to the 1960–2020 baseline using the Standardized Precipitation Index method. Uncertainty is assessed by evaluating the spread across the ensemble of 17 climate simulations and comparing simulated historical events against reanalysis data. Results show that simulations reproduce past drought characteristics with sufficient accuracy to analyze future trends. Projections indicate an increase in drought intensity by the end of the 21st century, primarily driven by rising reference evapotranspiration.
Zscheischler, Jakob, Miguel D. Mahecha, Stefan Harmeling, and Markus Reichstein. 2013. “Detection and Attribution of Large Spatiotemporal Extreme Events in Earth Observation Data.” _Ecological Informatics_ 15 (May):66–73. [https://doi.org/10.1016/j.ecoinf.2013.03.004](https://doi.org/10.1016/j.ecoinf.2013.03.004).
How to cite: Belin, M., Jézéquel, A., and Ducharne, A.: Spatiotemporal Analysis of Soil Drought Evolution in France: Attribution to Atmospheric Drivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3681, https://doi.org/10.5194/egusphere-egu25-3681, 2025.
In July 2022, regions with elevations exceeding 5000 meters on the inner Tibetan Plateau (TP) witnessed a record-breaking heatwave. But how the atmospheric circulation and soil moisture play roles in the occurrence and maintenance of the heatwave in such high elevation climate sensitive region remains unknown. Here, by using the flow analogue method, we find that negative soil moisture anomalies explain more than half of the extreme high temperature during the heatwave, while atmospheric circulation explains less than half. The high soil moisture-temperature coupling metric and the increased correlation between latent heat flux and soil moisture during heatwave revealed strong land-atmosphere feedback in the Qiangtang Plateau which has amplified the heatwave. Analyses of numerical experiments confirm that the presence of interaction between soil moisture and the atmosphere has increased the intensity of hot extreme event under the same atmospheric circulation conditions. Under the warming background, land-atmosphere coupling leads to a faster increase in extreme high temperatures compared to the global mean warming rate, and it is twice as fast as the increase in extreme high temperatures without coupling. We highlight the increased probability of extreme high temperature over the TP in the future due to soil moisture feedback and the results are hoped to inform policymakers in making decisions for climate adaptation activities.
How to cite: Gui, K., Zhou, T., Zhang, W., and Zhang, X.: Land-atmosphere coupling amplified the record-breaking heatwave at altitudes above 5000 meters on the Tibetan Plateau in July 2022, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7743, https://doi.org/10.5194/egusphere-egu25-7743, 2025.
Climate change has markedly increased the intensity and frequency of wildfires, emphasizing the need for predictive tools to inform adaptive management and mitigation strategies. This study presents a dynamic framework for assessing wildfire susceptibility, focusing on Southeastern Europe, a region particularly vulnerable due to diverse topographical and climatic conditions. By integrating machine learning (ML) with historical wildfire records and climate projections, the framework provides high-resolution susceptibility and fuel maps essential for informed decision-making.
The methodology incorporates data from the European Forest Fire Information System (EFFIS), CORINE land cover, and the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) climate projections. Climatic variables such as precipitation, wind speed, maximum and average daily temperatures, and consecutive dry/wet days were included in the predisposing factors of wildfire occurrence. These were combined with topographical and land cover information to train a supranational machine learning model capable of mapping annual wildfire susceptibility at a 100-meter resolution. The use of ISIMIP dataset (2008-2019) ensures using coherent datasets for historical and future time periods, allowing for dynamic projections under multiple climate scenarios (SSP126, SSP245, SSP585).
The susceptibility maps highlight regions where climatic and environmental conditions have historically facilitated wildfire occurrences. Susceptibility data were integrated with vegetation classifications, producing detailed wildfire hazard maps (or fuel maps). These maps categorize terrain into 12 classes based on a contingency matrix of susceptibility levels and fuel types, combining potential fire behavior in a worst-case scenario and its likelihood. As a sample case, areas classified as high susceptibility combined with coniferous forest cover represent hotspots where mitigation efforts should be concentrated. The possibility to generate future projected fuel maps leads to estimate the areas where wildfire hazard increases the most.
This study provides actionable insights for stakeholders by identifying critical zones for fuel management, ignition prevention, and adaptive planning. The dynamic nature of the model also allows for periodic updates as new data become available, ensuring its relevance under evolving climatic conditions. It establishes a foundation for risk assessment methodologies and potentially enables the estimation of annual losses and their temporal evolution in the next decades. This framework not only advances the scientific understanding of wildfire susceptibility but also supports practical applications in disaster risk reduction and land-use planning.
Keywords: Wildfire susceptibility, hazard mapping, machine learning, climate change, fuel type dynamics
How to cite: Meschi, G., Ghasemiazma, F., Trucchia, A., Perello, N., Degli Esposti, S., and Fiorucci, P.: Climate driven dynamic fuel maps in wildfire management under climate change: an AI approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9074, https://doi.org/10.5194/egusphere-egu25-9074, 2025.
With the large forest fire in Sala 2014 and the forest fires during summer 2018 in mind, evaluating the tendency of high-risk fire season (HRS) under a changing climate shows its importance for risk management.
This study focuses on exploring the behaviours of several user-defined fire-risk indicators concerning start, end, length, HRS and frequency of HRS, and impact of preconditions, e.g., snow cover and overwintering conditions. Here, we carry out the study by driving a Canadian forest fire model, the Fire Weather Index (FWI, Van Wagner,1987), using meteorological forcing from an ensemble of regional climate projections compiled from the Coupled Model Intercomparison Project Phase 5 (CMIP5, Taylor et al., 2012). The used CMIP5 data covers historical and representative concentration pathway projections (RCPs) from 1971 to 2100. The bias in the climate model projections is adjusted using the MultI-scale bias AdjuStment (MIdAS, Berg et al., 2022) with Copernicus regional reanalysis for Europe (CERRA, Schimankes et al., 2021) as a reference. The impact of climate change on the fire risk for three future periods (i.e., 2011–2040, 2041–2070 and 2071–2100) is explored under three RCPs (RCP2.6, 4.5 and 8.5). The ensemble agreement is used to evaluate the robustness of the fire risk indicators.
The results show that all robust changes are toward increasing risk. More specifically, the length of HRS increases in southern and eastern Sweden. The start of HRS shifts to earlier in the eastern coastal and northern regions of Sweden in RCP4.5 and 8.5. In all RCPs the end of HRS is delayed by a couple of weeks in the southern regions in the period after 2041. The HRS is likely to become more frequent in the regions along the east coast and in southern Sweden.
How to cite: Yang, W., Berg, P., Bozhinova, D., Böhlin, J., Gustafsson, D., Jansson, A., Klehmet, K., Landelius, T., and Schützer, S.: Risk of forest fire in Sweden under historical and future climate projections from 1971 to 2100t fire in Sweden under historical and future climate projections from 1971 to 2100, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13372, https://doi.org/10.5194/egusphere-egu25-13372, 2025.
Climate change is driving increased fire weather across the world: hot, dry and windy conditions lead to higher risk of fire ignition and spread and make fire suppression more difficult. With further warming, fire weather is projected to increase in many parts of the world, meaning today’s children and young people will be exposed to an ever-greater number of high-risk fire weather days during their lifetime. In this study, we analyze historical fire weather index (FWI) conditions over Portugal from ERA5 reanalysis to assess the representativity of the index to explain historical monthly burned area records from European Forest Fire Information System (EFFIS), for the period 1980-2020. Turning to EURO-CORDEX high resolution projections for the future, we then analyze exceedances of FWI values representing high (FWI>30) and very high (FWI>45) fire risk. Combining this with spatially explicit demographic projections from the Wittgenstein Capital Data Explorer (WCDE), we then apply a lifetime exposure framework to estimate the number of high and very high fire risk days that people of different generations in Portugal are projected to be exposed to during their lifetimes and under different SSP-RCP warming scenarios. We find young people in Portugal will be disproportionately exposed to high fire weather risk days compared to older generations during their lifetimes, and that they have the most to gain from ambitious mitigation. Our research highlights the intergenerational inequity inherent in anthropogenic climate change and underlines the urgency of ambitious mitigation and adaptation action to safeguard the rights of present and future generations.
How to cite: Pietroiusti, R., Prudencio Montano, S., and Thiery, W.: Young people disproportionately exposed to lifetime fire risk: a Portuguese case study , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16081, https://doi.org/10.5194/egusphere-egu25-16081, 2025.
A refined geometric variability model is employed to examine the environmental relationship to supertyphoon climatology, which is one of the major concerns about climate change and disasters. It is noted that the recent ten years have led to a remarkable weakening of the environmental explanatory power on supertyphoon climatology compared to the past.
Different from simple correlation analysis, this study shows how a deformation of the former climatic connection among variables, in a changing climate, is printed on annual covariance elements. Looking into the annual covariance elements, we find that recent observations showing a group of outlying events with a particular drift are more than unfamiliar compared to the former stable relationship from 1985 through 2012. Greater uncertainty thereby amplifies concerns about the looming climate crisis.
The drifting climate connection in recent observations is also clear in the eastern North Pacific and the North Atlantic, which observe a sufficient number of super typhoons for reliable statistical analysis. Global analysis is done by applying twelve-month (Jan. to Dec.) observations. While the last few years may look as if the climate connection came back to the former relationship, the drifting of the climate connection is seen to have a certain trend. Interruptions also indicate that the climate system is suffering from unfamiliar conditions on a global scale.
Annual monitoring of the climatic connection may show that the relationship might have returned to its past normal, but it seems that more time is needed to confirm the cessation of the drift.
How to cite: Kang, N. and Jang, C. J.: Monitoring the environmental connection to super typhoon climatology , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1972, https://doi.org/10.5194/egusphere-egu25-1972, 2025.
Landfalling tropical cyclones (TCs) often lead to widespread societal impacts due to their associated wind and flood hazards. Among these, pluvial and fluvial flooding depend primarily on the intensity and total rainfall released during TC events. As global warming increases atmospheric humidity according to the Clausius-Clapeyron relationship, TC rainfall is expected to intensify, exacerbating flood risks. However, additional climatic drivers may also contribute to long-term changes in TC-induced rainfall. To understand and disentangle these drivers, robust modeling efforts and reliable observational datasets are essential.
In this study, we utilize a dataset from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which includes rainfall estimates for historical TCs from 1950 to 2023. These estimates are derived using IBTrACS best-track data, two parametric wind models, and a physics-based Tropical Cyclone Rainfall (TCR) model. We validate the TCR model simulations by comparing them with TC rainfall estimates from ERA5 reanalysis data and the Integrated Multi-Satellite Retrievals for GPM (IMERG). This validation includes comparisons of lifetime accumulated rainfall for individual events and associated temporal trends across all events. Additionally, we use the TCR model to assess the role of climate change in driving long-term trends in TC rainfall. By generating counterfactual rainfall estimates, where the influence of increasing global mean temperature is removed through detrending of the temperature input data, we isolate a thermodynamic contribution of climate change to observed trends.
We find that the TCR model produces higher maxima and more extreme rainfall events compared to ERA5, consistent with the tendency of reanalysis data to underestimate extremes. However, the relative intensity distribution of TC rainfall is captured in ERA5 and aligns with the patterns produced by the TCR model. The relative temporal trends between the datasets also align. Therefore, the TCR model might be a valuable tool for overcoming the underrepresentation of extreme TC rainfall in reanalysis data. Furthermore, our counterfactual estimates reveal that while the Clausius-Clapeyron relationship explains a significant portion of the observed increases in lifetime accumulated rainfall, residual trends persist, suggesting the influence of additional climatic drivers. This research highlights the importance of robust modeling frameworks, such as TCR, for understanding and attributing changes in TC rainfall, providing critical insights into the evolving hazards posed by tropical cyclones in a warming world.
How to cite: Hamester, L., Mengel, M., Sauer, I., and Frieler, K.: Landfalling Tropical Cyclones: Investigating Rainfall Trends under Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12501, https://doi.org/10.5194/egusphere-egu25-12501, 2025.
The rising severity of tropical cyclones in the Bay of Bengal presents significant challenges for the densely populated coastal communities of India, Bangladesh, and Myanmar. Over the 2000–2024 period, the region has experienced a 40% increase in very severe cyclonic storms (VSCS), as defined by wind speeds exceeding 150 km/h. Events such as Cyclone Amphan (2020), which caused economic losses exceeding $13 billion and displaced millions, underscore the devastating socioeconomic impacts of these intensified cyclonic systems. High-density cyclone path zones, concentrated between 10°N–20°N latitude and 80°E–100°E longitude, mark the geographic hotspots of vulnerability, where unique oceanographic and meteorological conditions facilitate cyclogenesis. This study provides a deeper understanding of the dynamic interactions between ocean-atmosphere parameters that drive cyclone behavior in the region. Elevated sea surface temperatures (SSTs), consistently surpassing 28°C, serve as the primary energy source for cyclone intensification. Combined with weaker vertical wind shear and enhanced low-level vorticity during the pre- and post-monsoon seasons, these conditions promote rapid intensification (RI) events. The findings also highlight the impact of evolving large-scale atmospheric phenomena, such as shifts in the Indian Ocean Dipole (IOD) and Madden-Julian Oscillation (MJO), which influence cyclone trajectories and the likelihood of landfall. The study further identifies that cyclones with shorter landfall distances (<200 km from their origin) are particularly destructive, often associated with prolonged rainfall, storm surges, and flooding. These cyclones exacerbate risks to critical infrastructure, agriculture, and coastal ecosystems, particularly in low-lying deltas like the Ganges-Brahmaputra-Meghna basin. Additionally, the degradation of natural buffers such as mangroves in the Sundarbans has heightened susceptibility to storm surges and coastal erosion, amplifying the scale of human and economic losses. Scientific advancements presented in this work emphasize the need for enhanced predictive models that integrate real-time atmospheric and oceanographic data to improve cyclone tracking and landfall projections. These models can support the development of robust early warning systems, reducing the lead time required for effective evacuation and disaster response. Furthermore, the research underscores the importance of climate-resilient infrastructure—such as cyclone-resistant housing, flood barriers, and storm surge protection systems—tailored to the unique vulnerabilities of the region. Ecosystem restoration, including mangrove reforestation in the Sundarbans, emerges as a critical strategy for mitigating storm surge impacts and enhancing long-term coastal resilience. In conclusion, this study calls for a multidisciplinary approach to address the growing risks posed by intensifying cyclones. By combining advancements in meteorology, oceanography, and socio-economic planning, policymakers and researchers can work toward developing comprehensive disaster preparedness and resilience strategies. These efforts are essential to safeguarding vulnerable coastal populations and ecosystems in the Bay of Bengal against the escalating impacts of climate change-driven cyclonic activity.
How to cite: Saini, A. K., Singh, A., and Jothiprakash, V.: Socioeconomic Impacts and Preparedness for Intensifying Cyclones in the Bay of Bengal, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16644, https://doi.org/10.5194/egusphere-egu25-16644, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 3
EGU25-7204 | Posters virtual | VPS13
Pseudo–Global Warming climate projections at convection-permitting resolution in the Macaronesia regionWed, 30 Apr, 14:00–15:45 (CEST) vPoster spot 3 | vP3.25
In territories with complex orography such as the Macaronesian archipelagos of Madeira, Azores, Canary Islands, and Cape Verde, the spatial resolution of the Coupled Model Intercomparison Project Phase 6 (CMIP6) is not sufficient to account for all the atmospheric phenomena that occur in these archipelagos with such a complex microclimatic structure. This research presents a climatic dataset at a spatial resolution of 3x3 km2 in all the Macaronesian archipelagos derived from high-resolution regional climate simulations performed with Weather Research and Forecasting (WRF) model, applying the pseudo-global warming (PGW) method. The dataset is focused on the following parameters: temperature, precipitation, solar radiation, wind, and cloud coverage. Meteorological stations (ECAD and METAR) and reanalysis ERA5 data were used for the validation of the model results in the recent past period (1982–2019). We worked with two periods for future projections (2030–2059 and 2070–2099) under two representative scenarios (SSP2.6 and SSP8.5). These indicators include annual and seasonal statistics and variability for each parameter. The dataset aims to support regional climate adaptation strategies, contributing to the broader scientific understanding of climate in insular environments.
How to cite: Barrancos, J., Carrillo, J., Tondreau, P. S., Expósito, F. J., Pérez, J. C., González, A., and Díaz, J. P.: Pseudo–Global Warming climate projections at convection-permitting resolution in the Macaronesia region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7204, https://doi.org/10.5194/egusphere-egu25-7204, 2025.
