4 results
Search Results
Now showing 1 - 4 of 4
- ItemProjections of global warming-induced impacts on winter storm losses in the German private household sector(Dordrecht [u.a.] : Springer, 2013) Held, H.; Gerstengarbe, F.-W.; Pardowitz, T.; Pinto, J.G.; Ulbrich, U.; Born, K.; Donat, M.G.; Karremann, M.K.; Leckebusch, G.C.; Ludwig, P.; Nissen, K.M.; Österle, H.; Prahl, B.F.; Werner, P.C.; Befort, D.J.; Burghoff, O.We present projections of winter storm-induced insured losses in the German residential building sector for the 21st century. With this aim, two structurally most independent downscaling methods and one hybrid downscaling method are applied to a 3-member ensemble of ECHAM5/MPI-OM1 A1B scenario simulations. One method uses dynamical downscaling of intense winter storm events in the global model, and a transfer function to relate regional wind speeds to losses. The second method is based on a reshuffling of present day weather situations and sequences taking into account the change of their frequencies according to the linear temperature trends of the global runs. The third method uses statistical-dynamical downscaling, considering frequency changes of the occurrence of storm-prone weather patterns, and translation into loss by using empirical statistical distributions. The A1B scenario ensemble was downscaled by all three methods until 2070, and by the (statistical-) dynamical methods until 2100. Furthermore, all methods assume a constant statistical relationship between meteorology and insured losses and no developments other than climate change, such as in constructions or claims management. The study utilizes data provided by the German Insurance Association encompassing 24 years and with district-scale resolution. Compared to 1971-2000, the downscaling methods indicate an increase of 10-year return values (i.e. loss ratios per return period) of 6-35 % for 2011-2040, of 20-30 % for 2041-2070, and of 40-55 % for 2071-2100, respectively. Convolving various sources of uncertainty in one confidence statement (data-, loss model-, storm realization-, and Pareto fit-uncertainty), the return-level confidence interval for a return period of 15 years expands by more than a factor of two. Finally, we suggest how practitioners can deal with alternative scenarios or possible natural excursions of observed losses.
- ItemReliability of regional climate model simulations of extremes and of long-term climate(Göttingen : Copernicus GmbH, 2004) Böhm, U.; Kücken, M.; Hauffe, D.; Gerstengarbe, E.-W.; Werner, P.C.; Flechsig, M.; Keuler, K.; Block, A.; Ahrens, W.; Nocke, T.We present two case studies that demonstrate how a common evaluation methodology can be used to assess the reliability of regional climate model simulations from different fields of research. In Case I, we focused on the agricultural yield loss risk for maize in Northeastern Brazil during a drought linked to an El-Niño event. In Case II, the present-day regional climatic conditions in Europe for a 10-year period are simulated. To comprehensively evaluate the model results for both kinds of investigations, we developed a general methodology. On its basis, we elaborated and implemented modules to assess the quality of model results using both advanced visualization techniques and statistical algorithms. Besides univariate approaches for individual near-surface parameters, we used multivariate statistics to investigate multiple near-surface parameters of interest together. For the latter case, we defined generalized quality measures to quantify the model's accuracy. Furthermore, we elaborated a diagnosis tool applicable for atmospheric variables to assess the model's accuracy in representing the physical processes above the surface under various aspects. By means of this evaluation approach, it could be demonstrated in Case Study I that the accuracy of the applied regional climate model resides at the same level as that we found for another regional model and a global model. Excessive precipitation during the rainy season in coastal regions could be identified as a major contribution leading to this result. In Case Study II, we also identified the accuracy of the investigated mean characteristics for near-surface temperature and precipitation to be comparable to another regional model. In this case, an artificial modulation of the used initial and boundary data during preprocessing could be identified as the major source of error in the simulation. Altogether, the achieved results for the presented investigations indicate the potential of our methodology to be applied as a common test bed to different fields of research in regional climate modeling.
- ItemEnsemble simulations for the RCP8.5-Scenario(Stuttgart : Gebrueder Borntraeger Verlagsbuchhandlung, 2015) Gerstengarbe, F.-W.; Hoffmann, P.; Österle, H.; Werner, P.C.
- ItemA new validation scheme for the evaluation of multiparameter fields(Abingdon : Taylor and Francis Ltd., 2005) Gerstengarbe, F.-W.; Kücken, M.; Werner, P.C.On the basis of an extended cluster analysis algorithm, we present a new validation method for the evaluation of simulation experiments characterized by more than one parameter. This method allows the assessment of any parameter combination in space and time. As an example for the effectiveness of the algorithm, the results of two regional climate model runs and observational data have been tested and interpreted.