Due to its intrinsic exposure to the climate, agriculture is one of the economic sectors most directly affected by climate change. Although long-term average precipitation in Switzerland is sufficient to ensure crop production, summer drought is increasingly posing problems to the agricultural sector, as evidenced by the drought events of 2003, 2011, 2015, 2018, 2020, 2022 and again 2023. It is therefore not surprising that insurance companies in Switzerland and other European countries have added coverage to drought-induced crop yield losses to their product portfolio. However, defining viable insurance strategies for the future, from both an agronomic and economic perspective, depends on knowing the potential level of losses.
In this study, we assessed how climate change is likely to impact the yields of summer crops (maize and potatoes) in the four most important cropland regions in in Switzerland. Our analysis is based on the current Swiss climate scenarios (CH2018) targeting the mid-century (2050-2070) and the end of the century (2089-2099). It focuses on a representative concentration pathway (RCP) that does not envisage mitigation measures (RCP 8.5) and considers only one of the most extreme scenarios within the ensemble of available model chains. In this extreme scenario, the summer period presents a drastically negative climatic water balance (‑500 mm by the end of the century), and mean dry spell duration increasing in duration by around 50%. In the Western Plateau, these conditions entail a factor-of-two yield reduction in 60% of the years for maize and in 30% of the years for potatoes. Results further indicate that yield stability is likely to substantially decrease for both crops, as indicated by an increase in the coefficient of variation by a factor of more than two. In general, our findings stress the importance of summer crops as target of future drought-related insurance products.
How to cite: dos Reis Martins, M. and Calanca, P.: Risks from climate change for Swiss cropping systems: assessing the impacts of summer droughts on crop yields and yield stability for informing future insurance strategies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10036, https://doi.org/10.5194/egusphere-egu25-10036, 2025.
North Atlantic hurricanes rank among the costliest natural catastrophes globally, fuelled by high sea-surface temperatures (SST) in the main development region (MDR) and neutral to positive El Niño Southern Oscillation (ENSO). Record-high SSTs and a predicted shift to positive ENSO ahead of the 2024 season have raised concerns about a “hurricane season from hell”. A key issue is that catastrophe models used to estimate insured loss in practice are calibrated with observations dating far back and may not adequately reflect hurricane risk in today’s climate. Many scientific models focus long term climate change and are thus not fully fit to assess recent climate trends or are not openly accessible for commercial use. Therefore, we built a simplified, physically-based model conditioned on climate variables to quantify changes in hurricane risk from 1980 to today.
The model uses the physical proxies potential intensity (PI) and cyclone genesis index (CGI) calculated from ERA5, as well as hurricane observations. The number of tropical cyclones is modelled as Poisson process with mean equal to the CGI in the MDR. Locations of lifetime maximum intensities (LMI) are drawn from historical observations conditioned on MDR SST and ENSO. LMI is determined based on PI and historical LMI to PI ratios and translated into landfall activity using a statistical method. The model adequately reproduces observed basin and landfall activity when forced with historical climate conditions. By detrending each grid cell using Theil-Sen regression, we project the climate inputs to any specified year to assess climate driven risk changes.
Our results indicate a 17% increase in hurricane landfalls under the 2020 climate compared to historical forcing from 1980 to 2020, with major hurricanes potentially increasing by 22%. Adjusting landfall rates in a vendor catastrophe model accordingly leads to an increase of around 20% in average annual loss. This increase comes mainly from an increased frequency predicted by the CGI, in line with observations. Keeping CGI constant while incorporating PI increases results in fewer lower-category storms, but more categories 4 and 5 storms. Our approach has limitations, notably in translating basin to landfall activity, where we do not simulate the full tracks but rely on historical ratios to determine the landfall intensity. Consequently, shear and steering effects along the track are only implicitly considered, potentially yielding a conservative risk assessment.
Nevertheless, our results highlight a material increase in hurricane risk in the current climate relative to 1980-2020. Given the lag in most catastrophe models, modelled losses may not fully reflect today’s risk. Our methodology can also be used to extrapolate to 2050, to assess climate change impacts, an area of ongoing research.
How to cite: Hohermuth, B., Liu, J., Steinmann, C., and Bresch, D. N.: Quantifying the Impact of Recent Climate Trends on North Atlantic Hurricane Activity and Losses, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9927, https://doi.org/10.5194/egusphere-egu25-9927, 2025.
The rapid intensification (RI) of tropical cyclones (whereby the maximum sustained wind increases by 30 kt (15.4 m s−1) or over in a 24-period) has garnered particular attention in recent years, with insurers and risk managers increasingly concerned that warmer ocean basins are fuelling increasingly intense landfalling hurricanes (Kaplan et al 2010).
RI was a notable characteristic of both Hurricanes Helene and Milton during the 2024 North Atlantic Hurricane Season. These two storms caused 78bn and 35bn in economic losses respectively (Gallagher Re), with Helene undergoing explosive RI of 55mph in the 24-hours ahead of landfall, increasing its windspeed upon impacting the Florida coast to 140mph, classifying it as a category 4 storm (Saffir-Simpson scale).
In this study, key trends have been analysed in the pattern of RI of Tropical Cyclones globally over the period 1990-2023, including the response of different ocean basins as well as the critical impact of teleconnection patterns such as the El Nino Southern Oscillation (ENSO) in modulating the geographic dispersion of intensifying cyclones. The study shows that while most Tropical Cyclones (>90%) in recent decades have exhibited some form of RI in their development prior to landfall, there is a clear upward trend in recent years in some ocean basins towards a pattern of so-called ‘Explosive’ Rapid Intensification (whereby a storm intensifies at a rate >50 kt in 24 hours).
With the most extreme Tropical Cyclones undergoing explosive RI and potentially landfalling with greater intensity than in previous decades, this research studies the potential economic and (re)insured loss implications for global risk management. Particular focus is given to the North Atlantic as well as the strong signal of RI occurrence changes under ENSO and over the study period in the North-West and Eastern Pacific basins.
Kaplan, J., DeMaria, M., & Knaff, J. A. (2010). A revised tropical cyclone rapid intensification index for the Atlantic and eastern North Pacific basins. Weather and forecasting, 25(1), 220-241.
How to cite: Robson, A. and Willis, I.: Tropical Cyclone Rapid Intensification & it’s Impact for (Re)insurers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16606, https://doi.org/10.5194/egusphere-egu25-16606, 2025.
Catastrophe (cat) models are widely used to combine information on the probability distribution of hazard intensity, exposure location, and exposure vulnerability to quantify risk, usually expressed in terms of financial loss. While substantial attention has been paid to improving hazard and vulnerability components (including incorporating climate change), exposure data often lags in terms of quality and detail and may vary widely in granularity and reliability. For instance, reinsurers frequently receive aggregated portfolios from insurers, which may lead to loss of critical information about location-specific risks. This lack of detail undermines the precision of loss estimates, even if hazard and vulnerability components are highly refined. This raises an important question: how influential is the level of detail exposure information on risk estimates with respect to uncertainties in vulnerability and climate change model?
In this presentation we will answer this question via a global sensitivity analysis (GSA) of the JBA flood cat model. GSA is a methodology to systematically investigate the propagation of input uncertainties through mathematical models and quantify the relative importance of those uncertainties on the variability of model outputs. Differently from local sensitivity analyses, in GSA all input uncertainties are varied simultaneously within their plausible variability ranges, instead of being varied one at the time from a baseline. This enables us to capture interaction effects between uncertain inputs and ensure that sensitivity results are not conditional on the chosen baseline. In our application, the three input uncertainties are hazard (including climate change), vulnerability, and exposure data and we quantify their relative influence on financial loss estimates.
Overall, the analysis and the results will highlight how hazard, vulnerability and exposure data quality impact loss estimates guiding cat model developers to prioritize their efforts on model improvement and reinsurers to leverage better quality exposure data.
How to cite: Sarailidis, G., Pianosi, F., and Styles, K.: Importance of exposure data quality versus uncertainty in vulnerability and hazard for catastrophe modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6169, https://doi.org/10.5194/egusphere-egu25-6169, 2025.
Climate change poses significant challenges to agricultural systems worldwide, including increased agroclimatic risks that threaten crop productivity and sustainability. This study investigates how climate change will influence the agroclimatic risk of high temperatures on tomato cultivation in Malta, a region already experiencing Mediterranean climatic pressures. Using climate projections under different greenhouse gas emission scenarios, we analyzed temperature trends, heat stress events, and their potential impacts on key growth stages of tomatoes, including flowering and fruit development. The results indicate a marked increase in the frequency and intensity of high-temperature events, particularly during critical phenological phases, which could significantly reduce yields and quality. Our findings also reveal spatial variability in risk levels across Malta, emphasizing the need for localized adaptation strategies. To mitigate these risks, we propose targeted interventions such as selecting heat-tolerant tomato varieties, optimizing irrigation schedules, and implementing shading techniques. This research underscores the urgency of integrating climate-resilient practices into tomato production systems to ensure sustainable agricultural productivity in Malta amidst a changing climate.
How to cite: Tarquis, A. M., Rodriguez, A., Hernández-Montes, E., Sanz, E., Almeida-Ñauñay, A. F., and Garrido, A.: Projected Impacts of Climate Change on High Temperatures for Tomato Cultivation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20119, https://doi.org/10.5194/egusphere-egu25-20119, 2025.
Cooling Degree Days (CDD) are commonly used to quantify energy demand for cooling and recent works highlighted the importance of population weighting to better represent energy load distribution. This study builds on the work of Scoccimarro et al. (2023), who assessed country-level cooling demand from 2000 to 2020 using both standard dry CDDs and humid CDDs (CDDhum), corrected with population weighting (CDD values are averaged at the national level, weighted by population). The humidity correction uses perceived temperature, which combines both temperature and humidity effects, rather than relying on temperature only. This adjustment offers a more accurate representation of cooling needs, as humidity plays a significant role in human stress and the demand for cooling.
This study aims to assess future cooling demand by utilising a selection of CMIP6 global climate models (GCMs), combined with country-level population projections from the United Nations World Population Prospects 2024. We analyse future trends (2015–2100) for the two mentioned metrics—standard cooling degree days (CDD) and humidity-adjusted cooling degree days (CDDhum) — both weighted by country-level population projections. Temporal evolution of these two metrics is assessed according SSP1-2.6 and SSP5-8.5 societal/emission scenarios, applying a consistent population weighting for both. GCM biases affecting population-weighted CDD and CDDhum are also assessed by considering ERA5 as reference product.
Preliminary results —calculated over Europe during the reference period 1971-2000 and without the application of humidity correction or population weighting — show that, despite some biases in the trend magnitude, the CMIP6 GCMs generally capture the spatial pattern of ERA5 CDD showing a general increasing trend in the energy required for cooling buildings during summer season. In particular, the Mediterranean Basin is projected to experience the most significant increase in CDDs, with considerable inter-model variability. In contrast, some northern European regions, such as the Scandinavian Peninsula and Iceland, show no trend in CDDs.
This work is based on ERA5 and CMIP6 data, collected and tailored as part of the H2020 BlueAdapt project (Grant agreement action Number 101057764), and on analysis codes developed under the Copernicus-funded contract (C3S2_520).
How to cite: Martinez-Boti, A., Sangelantoni, L., Peano, D., Gualdi, S., Tibaldi, S., and Scoccimarro, E.: Country-level energy demand for cooling using CMIP6 and world population projections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19732, https://doi.org/10.5194/egusphere-egu25-19732, 2025.
We present a novel method to construct a 10,000-year event set for European weather using expired ensemble forecasts from ECMWF [1]—requiring no additional computational effort. Derived from the same numerical model underlying ERA5, this approach naturally extends it more than two orders of magnitude, whilst inherently overrepresenting the climates of the 2010s and 2020s. Hence, it provides a valuable resource for quantifying risks in today’s already-warmed climate
Our evaluation focuses on extreme wind speeds from extra-tropical cyclones impacting major European cities. With a rigorous order statistics framework, we confirm that this dataset replicates the statistical tails of ERA5 for return periods up to RP40 and extends exceedance probability (EP) curves up to RP10,000. Crucially, its physical consistency enables robust analysis of joint distributions across space and time, offering precise insights into compound and correlated risks. Using empirical copulas, we quantify critical conditional probabilities, such as P(Paris = RP100 London = RP50), a task infeasible with only the weather record beyond RP5.
This method leverages years of historical computational investments by ECMWF, that created a vast global low-bias source of simulated weather data, fully interchangeable with ERA5 for seamless integration into existing pipelines. Following two years of archive extraction efforts, we compiled a subset of surface variables (t2m, 10m/100m wind, runoff,...) and make it widely available to the community [2].
[1] European Centre for Medium-Range Weather Forecasts (ECMWF) __Atmospheric Model Ensemble extended forecast__ https://www.ecmwf.int/en/forecasts/datasets/set-vi
[2] Dolezal P., Expired ECMWF ENSemble Extended forecasts and Reforcasts for Renewable power in Europe. NERC EDS Centre for Environmental Data Analysis,
https://catalogue.ceda.ac.uk/uuid/7783f79c7080456088d98a34ca238bfa
How to cite: Dolezal, P. and Shuckburgh, E.: Spatial Coincidence of Extreme Wind Across European Cities: Evidence from 10,000 Years of Expired ECMWF Forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19805, https://doi.org/10.5194/egusphere-egu25-19805, 2025.
Catastrophe (cat) models are tools, typically used in the (re)insurance industry, that evaluate the risks to a given portfolio by modelling the impact of thousands of years of synthetic hazard events. Of particular interest to users is an evaluation of the low probability (tail) risks. This includes asking questions such as, “what is the worst loss event that will be exceeded, on average, every 200 years?”
An assessment of tail risks is inherently uncertain. This is compounded by a large number of uncertain or free parameters throughout the modelling chain which may be set via expert (subjective) judgement or via a process of calibration. The calibration process would take a given portfolio with known historical losses and adjust some of the free parameters to match the historical losses. This process may be reframed as creating a structured ensemble of catastrophe models with a range of each of the free or uncertain parameters. The process would then compare the modelled losses from each of the ensemble members to the known historical record and select the model that best represents the historical losses.
A major limitation of the ensemble approach to catastrophe model calibration is the short historical record from which to select the most representative model. This work uses a flood catastrophe model ensemble to explore the calibration process by creating a short synthetic loss record from a single ensemble member and examining the downstream effects of using this loss record for model selection.
How to cite: Lamb, C., Haylock, M., Wing, O., and Sloan, O.: Using an ensemble of flood catastrophe models to explore the interplay of loss variability and the catastrophe model calibration process, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6304, https://doi.org/10.5194/egusphere-egu25-6304, 2025.
The HuT (The Human-Tech Nexus) project aims at finding effective strategies to manage the risks associated with extreme climate events by means of specific demonstrators over the European territory in which different Disaster Risk Reduction strategies are prototyped and tested. In this context, we show here two distinct innovative insurance prototypes to cope with risks associated with wildfires and landslides over two peculiar areas in Sardinia and Campania regions (Italy). While the hazard posed by the two perils show distinct characteristics and origins, in both cases an insurance product can play a crucial role in the aftermath of the events for communities and private stakeholders. Since the risk assessment is crucial both in terms of financial structure and pricing strategies of a natural hazard insurance product, prototypes are developed through a Nat Cat modeling-based hazard assessment, while the vulnerability and finance considerations are related to the specific characteristics of the area of interest. Eventually, two prototypes are fully developed: “Landslide First Rescue”, a semi-parametric product designed to cope with the immediate economic needs after a landslide events; and “Fire Safe Community”, proposed as a community-based efficient tools to restore the economic losses related to wildfires. The prototypes present specific discounts if the policy holders are willing to implement risk reduction solutions to cope with the specific natural hazard. Results prove that the final premium associated with the products would be affordable and several consultations with interested stakeholders have shown how these kinds of products could also play a role in the development of nature-based solutions over broader regions.
How to cite: Lo Conti, F., Gallotti, G., Tirri, A., Santoro, A., Rianna, G., Bacciu, V., and Calvello, M.: Disaster Risk Reduction through innovative insurance solutions , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7030, https://doi.org/10.5194/egusphere-egu25-7030, 2025.
To mitigate the growing intensity, duration, and frequency of wildfires in recent years, leveraging the latest forecasting tools and maximizing their capabilities is essential. The FireHUB platform, provided by Beyond Operational Unit of the National Observatory of Athens, has been a reliable decision-support system utilized by numerous decision-makers and public bodies. It is also a continuously evolving platform. The most recent enhancement, implemented under the framework of the MedEWSa project, involves the deployment of a brand-new fire-spread model, offering several comparative advantages that are presented in this study.
A variety of atmospheric and soil parameters (e.g., wind, air/soil temperature and humidity, fuel density) are necessary to accurately predict fire spread information. Many of these factors are influenced by local topographical features, making high-resolution forecasts crucial. Additionally, the ability of a fire-spread model to ingest and process spatiotemporally variable fields is critical. Deploying the ForeFIRE code in combination with finer grid scales from our atmospheric operational forecasts (2-km resolution) demonstrated significant strengths over the existing system. In a series of simulated fire episodes, predictions from the old model and the new model are compared against satellite-derived burnt scar maps to evaluate their performance. The new system is expected to operate in a pseudo-operational mode alongside the existing service during the 2025 fire season and to fully replace the operational fire-spread model by 2026.
How to cite: Bartsotas, N. S., Herekakis, T., Girtsou, S., and Kontoes, C.: First results from the implementation of a new fire-spread model in FireHUB platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9693, https://doi.org/10.5194/egusphere-egu25-9693, 2025.
Freeze hazard represents the costliest peril associated with winter weather in the United States (US). This study focuses on the development and validation of a Freeze Index (FI) to model the impact of freeze effectively. The FI integrates both the intensity and duration of freeze events, offering a more accurate modelling of freeze hazards. The updated FI is used to select US-wide events targeting mainly the spatial scale of cold air outbreaks (CAOs). Validation of the hazard footprints is performed against historical data, including the December 2022 CAO and the Texas freeze of 2021. The findings underscore the importance of considering both temperature and duration in freeze hazards to model the damages accurately.
The freeze events obtained above are used to investigate trends in duration and FI, using 2-metre temperature (T2M) from the reanalysis data (1950 – 2024) and compared with the events from the detrended T2M. In the detrended set, no significant trend is observed in the duration of events from 1950 onwards. The average FI obtained from the footprints of each event also did not show a significant trend. The freeze events obtained from the non-detrended T2M also do not show a significant trend in duration and average FI for the events. However, there is a clear decrease in the occurrence of long-duration events with only four events greater than 10 days from 1990 onwards compared to thirteen events in the 1950 – 1985 period.
How to cite: Ali, M., Ait-Chaalal, F., Dobbin, A., and Grieser, J.: Modelling Freeze Hazard for the North American Winters , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9897, https://doi.org/10.5194/egusphere-egu25-9897, 2025.
Historical cyclone data indicate significant variations in cyclone activity during different phases of the El Niño-Southern Oscillation (ENSO). However, the impact of these variations on cyclone risk and damage has not been thoroughly investigated due to limited historical loss record. Understanding these variations could be crucial for effective risk management.
This study examines the variation in cyclone risk associated with ENSO phases, utilizing the cyclone risk assessment model by Impact Forecasting for Australia. The model employs a stochastic event set of cyclones, representing about forty-two thousand years of basin-wide activity, developed using environmental data from reanalysis and machine learning techniques. Our analysis demonstrate that the stochastic event set accurately reflects the seasonal variation in cyclone activity due to ENSO phases, making it a reliable tool for risk assessment.
To evaluate risk by ENSO phases, we segregated the stochastic event set using the Oceanic Nino Index and estimated wind-driven losses for each phase. The model results shows a significant variation in cyclone risk in Australia during El Niño and La Niña. However, the risk during the Neutral phase is found to be comparable with the long-term average. Annual average losses (AAL) during La Niña increases by 40%, while El Niño phases show a 37% reduction compared to the long-term average. Additionally, a one-in-hundred-year event during La Niña can result in 21% higher losses, whereas losses are 28% lower during El Niño compared to the long-term average.
The modeled loss variations across ENSO phases are consistent with observed changes in cyclone activity in Australia and are supported by the historical loss records.
How to cite: Bongirwar, V., Abraham Joseph, L., Ranjan Tripathy, R., Martin Kalbermatter, D., Roy, T., and Yang, P.: Modeling cyclone risk variations in Australia by ENSO phases., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10365, https://doi.org/10.5194/egusphere-egu25-10365, 2025.
Climate change has modified climatic hazards features and requires to reconsider agro-climatic risks. Among these, drought is one of the risks with the strongest impact on both crop production and crop weather insurance performance (Brisson et al., 2010). Understanding the effects of climate change on agro-climatic risks at regional to local scale is therefore a major challenge for the agricultural sector, specifically for insurers offering crop weather insurance policies. This work, resulting from a collaboration between an insurer and a research laboratory, focuses on the development of a drought index that well explain the evolution of crop weather insurance loss ratio. As maize is a major crop in the company's portfolio, the study focuses on this crop in particular. The aim of this work is to find the optimal set of parameters that maximizes the correlation between the drought index and the drought-related losses on crop weather insurance.
The Safran-Isba-Modcou reanalysis produced by Météo France provides spatially and temporally continuous climate data over metropolitan France of relevant interest to address this topic (Le Moigne et al., 2020; Soubeyroux et al., 2008). At the regional scale, these data allow us to quantify the evolution of climate hazards related to the water cycle from 1960 to present day. Taking into account the vulnerability of the crop of interest through the use of a simplified two reservoirs water balance model provides an opportunity to assess changes in maize water stress (Jacquart and Choisnel, 1995). The definition of a water stress threshold leads to the development of an annual drought index (Laurent et al., under review). The correlation with the crop weather insurance loss ratio due to drought is tested at various spatial scales (municipality, production basin), for different varieties, different sowing dates and different stress thresholds.
Our results indicate that climate change has affected the frequency and intensity of drought risk on maize crops in France, depending on the French production area studied. The significance of the correlation depends on maize variety, sowing date and hydric stress threshold. It seems that using drought index computed with low stress thresholds and analyzing correlations at large spatial scales gives the best results.
For non-irrigated maize area at production basin scale, our drought index can explain a significant part of drought-related losses in crop weather insurance. The results suggest that such an index may be relevant to improve the actuarial loss model of the insurer. However, further analysis is required in areas where correlations are weaker, particularly in production basins with high irrigation levels.
References:
Brisson et al., 2010. Field Crops Res. 119, 201–212. https://doi.org/10.1016/j.fcr.2010.07.012
Jacquart, Choisnel, 1995. La Météorologie 8ème série, 29–44. https://doi.org/10.4267/2042/51939
Laurent et al., under review. J. Agric. For. Meteorol.
Le Moigne et al., 2020. Geosci. Model Dev. 13, 3925–3946. https://doi.org/10.5194/gmd-13-3925-2020
Soubeyroux et al., 2008. La Météorologie 8, 40. https://doi.org/10.4267/2042/21890
How to cite: Laurent, L., Ullmann, A., and Castel, T.: How drought risk evolution impacts crop weather insurance loss ratio in France?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10429, https://doi.org/10.5194/egusphere-egu25-10429, 2025.
Clusters of storms are defined as sequences of multiple storms occurring within a short time frame and a limited spatial extent. In this study, storm clusters are identified using a Lagrangian approach combined with an absolute frequency metric within a 96-hour time window, reflecting reinsurance contract specifications for an insurance company. Compound storms are further constrained to affect a common area, determined by the intersection of their footprints. Those footprints can be delineated using various radii of different sizes, depending on the desired granularity for compounding analysis.
The motivation for this definition stems from the potentially severe impacts of such events on the insurance sector. Storms are known to be among the costliest events for Insurance in Europe, with an average annual insured loss of €217 billion [Copernicus, 2023]. The repetition of such intense wind and strong precipitation events is no exception. The successive storms Lothar and Martin in December 1999 remain the costliest events observed in France with an estimated loss of €17 billion [EEA, 2023]. Despite the substantial risks associated with these compound events, few studies have investigated their role in amplifying both the hazard and the vulnerability.
We apply this approach to Generali, an Italian insurance company with approximately 5% market share in France. Using Generali’s historical claims data from 1998 to 2024, we propose a novel methodology linking high-resolution claims to individual storm events. This approach represents a significant advance in understanding loss drivers. Applied to storm clusters, the methodology distinguishes the relative contribution of each storm in a cluster to the total observed loss. By comparing the findings with Generali’s portfolio from 2018 to 2024, we identify key factors contributing to the additional damages caused by storm clusters. These insights are crucial for enhancing risk prevention and adapting current insurance strategies to better address compound storm events.
How to cite: Hasbini, L., Yiou, P., and Boissier, L.: Analysis of the insurance impacts of storm clusters: a case study with Generali France, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11503, https://doi.org/10.5194/egusphere-egu25-11503, 2025.
Storm surge, a coastal flooding phenomenon driven by high-speed winds pushing water onshore poses a significant natural hazard across the globe. In recent decades, Europe has experienced several destructive extratropical cyclones that have severely impacted coastal communities and economies, such as Eunice (2022), David (2018), or Xaver (2013). Storm Xynthia in 2010 was especially notable, with substantial fatalities and material losses in France, highlighting the need for accurate storm surge risk assessment for societies and the (re)insurance industry involved. Yet, current modelling solutions are limited. Main commercial models only provide partial coverage of the risk in Europe, with a primary focus on the United Kingdom. To address this gap, AXA proposes a scenario-based approach to assess storm surge risk across North-Western Europe. Using the SCHISM 2D hydrodynamic model, we reproduced 10 significant historical events notably affecting France, Germany, and the United Kingdom, then perturbed them along three parameters: wind speeds, storm sizes and tide timings, generating 480 scenarios. The study presents the challenges of scenario selection and variability representation. It further provides findings on the modelling results by parameter and country, and on the estimation of the loss potential using a representative North-Western Europe insured market portfolio. Finally, key limitations are discussed, related to unmodelled defences and Digital Elevation Model accuracy. The approach provides valuable insights for AXA’s risk assessment and is a crucial step towards building a robust understanding of our risk.
How to cite: Diouf, A., Pranantyo, I. R., Joffrain, M., and Bruneau, N.: Designing representative European storm surge scenarios for insurance risk assessment: challenges, results, and limitations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17961, https://doi.org/10.5194/egusphere-egu25-17961, 2025.
Due to intense destructive winds and heavy rainfall associated with storm surges, large waves and flooding, tropical cyclones are one of the most damaging natural catastrophes. They are a major threat to human lives and properties across the globe. When travelling over the ocean and approaching shallow water regions, tropical cyclones generate storm surge and waves that can devastate coastal communities and local economies.
In the recent years, Typhoons Hato (2017) and Mangkhut (2018) produced material surge damages to insurers in the Northwest Pacific basin, and therefore raised the need for accurate natural catastrophe models. Cat models consist of very large catalogues of synthetic but realistic events also called “event sets”. These event sets are consistent with experienced historical data but allow extrapolation beyond what was observed.
In this study, we focus in winds and surges on the Philippines and Hong Kong regions. Driven by an existing tropical cyclone wind event set, over 10k full-physic simulations of storm surge and waves are computed for each region to estimate the complete distribution of coupled wind and surge losses over an exposure dataset. Due to computationally expensive dynamical simulations of storm surges and waves, we first rank and select a subset of events (10k) based on an IKE (Integrated Kinetic energy) index. For each of these 10k event, the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM; Zhang & Baptista, 2008, Zhang et al., 2016) is forced by atmospheric winds and pressure fields to derive wave and surge footprints.
Second, we use adjusted Hazus (FEMA) damage functions to convert the water heights and windspeeds from the simulated events into damage factors. These factors are then multiplied to the considered exposure to derive losses. Third, we study the relationship between the wind and the surge modeled losses based on two criteria, (i) the event level correlation between IKE and surge losses, to ensure this index stands as a robust risk representation, and (ii) the event level proportion of surge losses out of the wind losses, which provides a set of reusable inter perils correlation factors.
How to cite: Joffrain, M., Pranantyo, I. R., and Bruneau, N.: Evaluating the relationship between wind and storm surge risk in the Philippines and Hong-Kong, an insurance industry perspective., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18013, https://doi.org/10.5194/egusphere-egu25-18013, 2025.
Recent years have seen wildfires causing widespread environmental and economic damage as well as numerous fatalities globally. With record breaking yearly burnt areas, longer fire seasons, and more extreme events, wildfire is emerging as a growing concern for populations, governments and the private sector alike. In Europe, destruction and disruption have been historically more prominent in southern countries where key sectors of the economy like tourism, forestry, and agriculture can remain severely affected for years in the aftermath of catastrophic events.
Over the last 30 years, catastrophe modelling solutions have been crucial in aiding the understanding of the economic impacts of natural risks like wildfire, making them essential tools for the (re)insurance industry for managing their exposure and quantifying potential losses. Such solutions typically involve the development of large scale and physically-based probabilistic models.
We present here a climate-driven stochastic event catalogue for wildfire in Europe. The model allows us to expand on the limited historical records by generating millions of synthetic event footprints. For this, we first consider how climate conditions drive spatio-temporal patterns of wildfire activity in terms of yearly burnt area (fire activity module). In a second step, events are sampled via an ignition module that leverages machine learning algorithms and draws correlations between anthropogenic and bio-climate factors, and historical events. Finally, a propagation module generates event footprints given the local topography, fuel data, and meteorological conditions. The stochastic catalogue consists of 50K synthetic years and about 25M unique footprints at 100m resolution. This allows us to estimate hazard metrics like event frequency, event size, and tail risk over the whole continent as well as performing impact analyses. Lastly, we present an evaluation of structures at risk in France by intersecting our catalogue with a representative dataset of buildings.
How to cite: Azemar, F., Shaylor, M., Bruneau, N., Loridan, T., Swain, D., and Joffrain, M.: Development of a climate-driven stochastic event catalogue for Wildfire in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18168, https://doi.org/10.5194/egusphere-egu25-18168, 2025.
With global temperatures continuing to rise year on year, drought conditions are becoming increasingly frequent and severe, across all continents. More and more, the negative effects of these worsening drought conditions are being experienced by people across the world both directly, through damage to agricultural systems, water scarcity or damage to homes from subsidence, as well as indirectly, through cascading effects on other perils such as heatwaves and wildfires, which in turn may devastate communities and drive great economic losses. For these reasons, drought is of growing concern to the (re)insurance industry, as an emerging peril. It is therefore essential that reinsurers have access to tools which can aid in their understanding of drought hazard and risk in a changing climate. One such tool we present here – a climate driven, globally connected stochastic drought hazard model, which responds dynamically to the climate of any given year, enabling this understanding of how drought conditions change with the climate.
In this presentation, we describe the novel methodology applied to generate this globally connected and climate-driven stochastic drought model. The model is generated in two stages, the first addressing global variability in drought trends and teleconnections, and the second looking at continental scale patterns. In the first instance, we apply a dimensionality reduction to a selection of historical drought indexes over different time scales, allowing extraction of the key modes of variability of drought at the global scale. We then condition the top key modes of variability to the climate state using reanalysis (ERA5) data, allowing us to drive our stochastic set at the global scale, based on the global climate state.
Once these global patterns have been determined, we use the residual drought signal to condition a regional (continental) model using similar reduction and conditioning techniques. This regional layer is then effectively layered onto the global model, allowing us to recreate globally and regionally consistent drought variability in the stochastic set. A Bayesian framework is used to sample a range of realistic drought conditions, aligned with the climate of any given year. Global and regional drought conditions are then combined in order to generate >100K stochastic years of global drought severity as well as duration of drought for three severity levels (moderate, severe, extreme). This framework can also be applied to any other climate model data (for example, CESM LENS2) to generate a stochastic event set up to the year 2100. Here we present initial results from this stochastic catalogue, showcasing the spatial and temporal variation in drought hazard from 1950 – 2100, return periods, and comparisons to historical records. This work also builds upon a previous, continental only version of the drought model.
How to cite: Shaylor, M., Bruneau, N., Azemar, F., and Loridan, T.: Development of a Globally Connected, Climate-Driven, Stochastic Drought Model for Hazard Assessment using Machine Learning Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18838, https://doi.org/10.5194/egusphere-egu25-18838, 2025.
Diagnosing the drivers of changing insured losses year on year is an important component of developing a sustainable insurance portfolio. The common assumption is that losses for most perils are increasing year on year. However, there are many factors that could drive the change in losses: economic versus insured losses, impacts of inflation, changes in societal wealth over time, movement toward riskier property locations as well as potential changes in the frequency and severity of European wind and flood events. This presentation will quantify each of the above factors to determine the drivers of changes in insured losses over time.
How to cite: Milner, C. and Mulder, K.: Insured Losses from European Natural Catastrophes: Is there a trend over time?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7007, https://doi.org/10.5194/egusphere-egu25-7007, 2025.
