Preprints
https://doi.org/10.5194/egusphere-2024-686
https://doi.org/10.5194/egusphere-2024-686
14 May 2024
 | 14 May 2024
Status: this preprint is open for discussion.

Using seasonal forecasts to enhance our understanding of extreme European windstorm impacts

Jacob William Maddison, Jennifer Louise Catto, Sandra Hansen, Ching Ho Justin Ng, and Stefan Siegert

Abstract. Considerable effort is spent at insurance and reinsurance companies to estimate the risk posed by windstorms. Among these risks, strong near surface wind speeds can be particularly damaging, threatening infrastructure, human life, and billions of pounds in insured losses. Here, we use nearly 700 years worth of extended wintertime seasonal forecast output to estimate the impact of extreme European windstorms, with insured losses estimated using a storm severity index (SSI). Using the full integration period of the seasonal forecast model, we follow the UNprecedented Simulated Extreme ENsemble (UNSEEN) method, here applied to windstorms for the first time. After demonstrating that the seasonal forecast model of the UK Met Office represents windstorms with good accuracy, and developing a new method to convert from wind speed to wind gust derived SSIs, the likelihood of occurrence of unprecedented windstorms is quantified for several countries within Europe. The probability that a windstorm that impacts a country will be more extreme than any observed (i.e. an unprecedented or unseen windstorm) is generally between 0.5 % and 1.6 %. The North Atlantic Oscillation (NAO) is shown to influence European windstorms: strongly positive and negative NAO values strongly increase and decrease the likelihood of an unprecedented storm, respectively.. Serial clustering of windstorms within an extended winter is also found to increase the aggregated seasonal impact of windstorms for the countries analysed herein. These results may aid in the prediction of seasonal loss totals, as the NAO, for example, is predictable several months in advance. The analyses presented could be extended to other datasets, thus increasing the sample size of windstorms and allowing for the estimation of very high return period storms and the potential losses insurance companies will be liable to cover.

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Jacob William Maddison, Jennifer Louise Catto, Sandra Hansen, Ching Ho Justin Ng, and Stefan Siegert

Status: open (until 25 Jun 2024)

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Jacob William Maddison, Jennifer Louise Catto, Sandra Hansen, Ching Ho Justin Ng, and Stefan Siegert
Jacob William Maddison, Jennifer Louise Catto, Sandra Hansen, Ching Ho Justin Ng, and Stefan Siegert

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Short summary
In this work we estimate the impact of the most extreme European windstorms that could occur in the current climate. Using a large dataset of windstorm footprints created seasonal forecast model output, we find windstorms that are more extreme than any previously observed for most of the countries considered. Impacts from these extreme windstorms are expected to be around 1.5 times stronger than the most extreme storm on record. This information is highly valuable in the insurance industry.