A new statistical-dynamical method for downscaling global climate analyses or simulations is introduced. The method is based on the disaggregation of a multiyear time-series of large-scale data into multiday episodes of quasistationary circulation. The episodes are subsequently grouped into a defined number of classes. A regional model is used to simulate the evolution of weather during the most typical episode of each class. These simulations consider the effects of the regional topography. Finally, the regional model results are statistically weighted with the climatological frequencies of the respective circulation classes to eventually form regional climate patterns. The statistical-dynamical procedure is applied to large-scale analyses of the 12-year climate period 1981-1992. The ability of the new method is demonstrated for the winter precipitation in the Alpine region. With the help of daily precipitation analyses it was possible to validate the results and to assess the different sources of errors. It appeared that the main error originates from the regional model whereas the error of the procedure itself is relatively unimportant. The new statistical-dynamical downscaling method turned out to be an efficient alternative to the commonly used method of continuously nesting a regional model within a general circulation models (dynamical downscaling). (orig.)39 refs.Available from TIB Hannover: RR 6341(104) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman
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