© Author(s) 2011. This work is distributed
under the Creative Commons Attribution 3.0 License.
Future changes in European winter storm losses and extreme wind speeds inferred from GCM and RCM multi-model simulations
1Institute of Meteorology, Freie Universität Berlin, Germany
2Climate Change Research Centre, University of New South Wales, Sydney, Australia
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 multi-model combinatorics approach considered all possible sub-ensembles from GCMs and RCMs, hence taking into account 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%.