Abstract
Winter windstorms are known to be among the most dangerous and loss intensive natural hazards in Europe. In order to gain a better understanding of their variability and driving mechanisms, this study analyses the temporal variability which is often referred to as serial or seasonal clustering. This is realized by developing a statistical model relating the winter storm counts to known teleconnection patterns affecting European weather and climate conditions (e.g., North Atlantic Oscillation [NAO], Scandinavian pattern [SCA], etc.). The statistical model is developed via a stepwise Poisson regression approach that is applied to windstorm counts and large‐scale indices retrieved from the ERA‐20C reanalysis. Significant large‐scale drivers accountable for the inter‐annual variability of storms for several European regions are identified and compared. In addition to the SCA and the NAO which are found to be the essential drivers for most areas within the European domain, other teleconnections (e.g., East Atlantic pattern) are found to be more significant for the inter‐annual variability in certain regions.Furthermore, the statistical model allows an estimation of the expected number of storms per winter season and also whether a season has the characteristic of being what we define an active or inactive season. The statistical model reveals high skill particularly over British Isles and central Europe; however, even for regions with less frequent storm events (e.g., southern and eastern Europe) the model shows adequate positive skill. This feature could be of specific interest for the actuarial sector.
Highlights
Winter windstorms embody a prominent feature of the European climate
In order to examine the physical perspective of the influence of large-scale drivers on the inter-annual variability of windstorms on grid cell level, we implemented an independent Poisson generalized linear model (GLM) approach for every grid cell using five predominant drivers which are partly taken from literature (e.g., Mailier et al, 2006; Vitolo et al, 2009) and partly from results of the impact-based statistical model
To further investigate the spatial distribution of some of the large-scale drivers, we refer the reader to section 4.2 where we present the result for the grid-cell-based analysis for some selected large-scale drivers (“map of drivers”)
Summary
Winter windstorms embody a prominent feature of the European climate. They are often accompanied by severe surface winds that can result in extensive socio-economic losses. Seierstad, Stephenson, and Kvamstø (2007) related extratropical storminess, defined as monthly mean variance of high-pass-filtered sea level pressure, to large-scale patterns by using a Gamma regression They showed that five teleconnection patterns are significant at the 5% level with regard to explaining the inter-annual variability: NAO, SCA, EA pattern, EA/WR and the Polar pattern (POL). Seasonal clustering has been investigated statistically by Mailier et al (2006), Vitolo, Stephenson, Cook, and Mitchell-Wallace (2009) and Pinto, Bellenbaum, Karremann, and Della-Marta (2013) Their studies reveal the overall pattern of cyclone clustering (overdispersion) on both sides and downstream of the North Atlantic storm track, while under-dispersion is found around the entrance of the storm track, close to Newfoundland. To examine these questions this article is aiming at answering two central questions: 1. Looking at different predefined regions in Europe: What are the main drivers responsible for serial clustering in a particular region?
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