Using seasonal forecasts to enhance our understanding of extreme European windstorm impacts
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 yea...
| Main Authors: | , , , , |
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| Format: | Text |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/egusphere-2024-686 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-686/ |
| Summary: | 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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