Temporal clustering of severe European winter windstorms on intra-seasonal timescales and the explanatory power of large-scale modes

When severe European winter windstorms cluster in time, socioeconomic impacts and losses are magnified. Yet, the behaviour and drivers on shorter, intra-seasonal timescales have not been fully investigated. The impact-relevant footprint of the storm system is identified using the wind-based impact-oriented tracking algorithm WiTRACK (Leckebusch et al., 2008), for the core winter seasons (DJF) 1980/01-2022/23 from ERA5 reanalysis. Derived from a Poisson Process, we quantify the magnitude of clustering through the widely established dispersion statistic (Mailier et al., 2006). On fixed 45- and 30-day timescales, the spatial distribution of the dispersion statistic has been analysed. The time-development of the dispersion statistic on shorter time horizons is investigated through 21-, 15- and 11-day moving windows. Preliminary results reveal an increase in clustering in the latter half of the winter season on the fixed 45- and 30-day timescales. Shorter time horizons reveal clear peaks at the middle and the end of the season.

To analyse mechanisms that drive the defined intra-seasonal behaviour on the shorter time horizons (<30 -days), we examined the roles of several large-scale variability modes, namely the North Atlantic Oscillation (NAO), the East Atlantic pattern (EA), and the Scandinavian pattern (SCA). Results reveal a correlation between intra-seasonal variability of clustering and the occurrence of such large-scale modes, suggesting the EA as a key driver for increasing clustering. In addition, the individual contributions of large-scale modes to clustering at different times of the season can be diagnosed.