Future changes in European winter storm losses and extreme wind speeds inferred from GCM and RCM multi-model simulations

Abstract

Abstract. Extreme wind speeds and related storm loss potential in Europe have been investigated using multi-model simulations from global (GCM) and regional (RCM) climate models. Potential future changes due to anthropogenic climate change have been analysed from these simulations following the IPCC SRES A1B scenario. The large number of available simulations allows an estimation of the robustness of detected future changes. All the climate models reproduced the observed spatial patterns of wind speeds, although some models displayed systematic biases. A storm loss model was applied to the GCM and RCM simulated wind speeds, resulting in realistic mean loss amounts calculated from 20th century climate simulations, although the inter-annual variability of losses is generally underestimated. In future climate simulations, enhanced extreme wind speeds were found over northern parts of Central and Western Europe in most simulations and in the ensemble mean (up to 5%). As a consequence, the loss potential is also higher in these regions, particularly in Central Europe. Conversely, a decrease in extreme wind speeds was found in Southern Europe, as was an associated reduction in loss potential. There was considerable spread in the projected changes of individual ensemble members, with some indicating an opposite signature to the ensemble mean. Downscaling of the large-scale simulations with RCMs has been shown to be an important source of uncertainty. Even RCMs with identical boundary forcings can show a wide range of potential changes. The robustness of the projected changes was estimated using two different measures. First, the inter-model standard deviation was calculated; however, it is sensitive to outliers and thus displayed large uncertainty ranges. Second, a multimodel combinatorics approach considered all possible subensembles from GCMs and RCMs, hence taking into account Correspondence to: M. G. Donat (m.donat@unsw.edu.au) the arbitrariness of model selection for multi-model studies. Based on all available GCM and RCM simulations, for example, a 25% mean increase in risk of loss for Germany has been estimated for the end of the 21st century, with a 90% confidence range of +15 to +35%.

Comparison of storm damage functions and their performance

“…Donat et al, 2011a). In contrast, the finely resolved loss data for our study allowed a local parametrisation and the simple summation of loss to the national level.…”

“…On the one hand, Heneka and Hofherr (2011) applied their damage function to Germany by employing a static parametrisation originally obtained for the federal state of Baden-Württemberg. Donat et al (2011a), on the other hand, assume the same vulnerability for nation-wide building stock. In both cases, spatial extension of the model comes at cost of blurring regional vulnerability.…”

“…Finally, Donat et al (2011a) perform the regression against annual loss aggregates, while Donat et al (2011b) demonstrate calibration against a selected sample of the 34 most loss-intensive storm passages. We find that the former calibration method produces better results.…”

The 21st century decline in damaging European windstorms

“…Klawa and Ulbrich, 2003;Haylock, 2011;Bonazzi et al, 2012;Cusack, 2013;Roberts et al, 2014) or under future climate change conditions (e.g. Pinto et al, 2007Pinto et al, , 2012Leckebusch et al, 2008;Donat et al, 2011). In each study the severity of an event is measured using a storm severity index (SSI), i.e.…”

“…These studies used various measures of storm severity, for exam- ple, surface pressure, surge height and wind speed, and give conflicting trends in storm characteristics due to differences in the data, study region and length of historical period used. For example, Donat et al (2011) used atmospheric modelgenerated surface pressure fields for the period 1871 to 2008 and identified a significant increasing trend in northern, central and western Europe, while Smits et al (2005) used observed wind speed data in the Netherlands and found a decline in the frequency of wind speed threshold exceedance between 1962and 2002. Cusack (2013 used a loss function, based on the exceedance of observed wind gust speeds above a damage threshold at five observation locations, to explore the change in windstorm losses in the Netherlands from 1910 to 2011.…”