Return periods and clustering of potential losses associated with European windstorms in a changing climate

Windstorms are one of the most damaging natural hazards in Western and Central Europe. A recent example was the windstorm series in winter 2013/2014, which affected primarily Great Britain. This indicates the importance of the estimation of potential losses linked to extreme windstorms as well as their return periods for present and future climate conditions. In particular, the occurrence of groups of windstorms (clustering) is of high interest, as they cause the top year losses. The present thesis consists of three studies. The first study quantifies the intensity of individual storms by potential losses estimated with empirical models. One model considers only impacts due to wind speeds (MI), while another also includes population density information as proxy for insured values within an area (LI). The models are applied to reanalysis data and general circulation model (GCM) data for recent (20C: 1960-2000) and future climate conditions for three Intergovernmental Panel on Climate Change climate scenarios (B1, A1B, A2: 2060-2100). Focus of the investigation is given on Europe. The projected tendencies for LI and MI are generally in accordance, with a correlation of about 99%, e.g. for Germany. However, the relationship between MI and LI is reduced when the evaluated area increases. Based on the identified event set, changes of intensity and return periods of single storm events are quantified. Return periods are estimated using the extreme value distribution with the peak over threshold method. Independent from the future climate scenario, results show shorter return periods and higher intensities for most countries. Nevertheless, changes are not always statistically significant. In the second study, a reliable method to quantify clustering of losses associated with historical storm series and return periods of clustered events are quantified for Germany. With this aim, the empirical storm loss model used in study 1 is further developed and applied to clearly separate potential losses associated with individual storms. Using reanalysis datasets and observations from German weather stations for 30 winters, event sets exceeding selected return levels (1-, 2- and 5-year) are analysed. The distribution of the chosen events over the winters is used as basis for the Poisson and the negative Binomial distribution. Additionally, about 4000 years of GCM simulations with current climate conditions (20C) are investigated. Results of reanalysis data differ between the methods: in particular, for less frequent series the Poisson distribution based assessments clearly deviate from empirical data. The negative Binomial distribution provides better estimates, even though a dependency on return levels of single storms and the dataset is identified. The consideration of about 4000 years GCM data provides similar estimates and a strong reduction of uncertainties. In the third study, the methods of study 2 are applied to quantify possible changes of clustering and return periods of high potential losses associated with multiple extreme storms in Europe. 21 countries and regions are investigated. Additionally,a second critical value for the event identification is used (fixed LI 20C for 1-, 2-, 5-year return level), and thus possible changes in intensity of events can be regarded. Reanalysis data for 40 years as well as simulations for 800 years each of the present (20C) and the future (A1B) scenario are investigated. As for Germany,for present day climate conditions results for other regions obtained with the negative Binomial distribution show better agreements with empirical data than the Poisson distribution. Future changes in return periods and clustering estimated with both empirical and with the negative Binomial distribution depend on the region, the return level, the method and number of events per winter. For fixed return levels (e.g. 1-year), only small changes in return periods of storm series are identified. Shorter return periods of storm clusters under future climate conditions are identified for Europe, except for the Mediterranean area considering a fixed LI of 20C. However, evidence is found that the projected changes may be within the range of natural climate variability. The detected change in clustering of extreme losses is of high interest for re-insurance companies, especially for the risk assessment. The three studies are an essential extend of the current state of research about future changes of clustering of losses associated with windstorms and may help to develop protection and mitigation measures for the infrastructure.