A long record of European windstorm losses and its comparison to standard climate indices

Abstract. Traditional insurance has both a great exposure to decadal variations in European storm activity and the ability to adjust its business strategy over these timescales. Hence, the recent development of skilful predictions of multiannual mean European winter climate seems a very welcome addition to the long list of ways that researchers have improved management of windstorm risk. Yet companies do not use these forecasts of mean winter climate to adjust their view of risk. The main reason is the lack of a long, reliable record of losses to understand how forecasted time-mean circulation anomalies relate to the damage from a few, intense storms. This study fills that gap with a European windstorm loss record from 1950 to 2022, based on ERA5 peak near-surface winds per event which were converted to losses using an established damage function. The resulting dataset successfully identifies major storms over the past 70 years and simulates the multidecadal variations from low values in the 1960s up to high levels in the 1980s and 1990s then down to the 2010s. However, it underestimated the steepness of the observed loss decline from the stormy end of the 20th century to the lull over the past 20 years. This was caused by a quite flat trend in ERA5 extreme winds over the period, in contrast to the significant decline in observed peak gusts. Imposing these gust trends on ERA5 peak winds reconciled modelled losses with industry experience over the past few decades. Indices of European winter climate used in long-range forecasting were compared to the new modelled loss dataset. They had correlations of around 0.4 at interannual timescales, rising to about 0.7 for decadal and longer variations. Notably, the climate indices have a similar multidecadal trend as ERA5 extreme winds in modern times, including a less steep decline than found in observed gusts and losses. Further investigation of the modern-day divergence between climate indices and losses may help connect decadal forecasting to insurance.