Original paper

Results of five regional climate studies applying a weather pattern based downscaling method to ECHAM4 climate simulation

Enke, Wolfgang; Deutschländer, Thomas; Schneider, Frank; Küchler, Wilfried

Meteorologische Zeitschrift Vol. 14 No. 2 (2005), p. 247 - 257

published: May 10, 2005

DOI: 10.1127/0941-2948/2005/0028

BibTeX file

ArtNo. ESP025011402020, Price: 29.00 €

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A weather pattern based weather generator is used to determine projections of future climate changes in Germany. A SRES B2 run of the coupled atmosphere and ocean model ECHAM4 - OPYC3 of the Max-Planck-Institute for Meteorology (Hamburg, Germany) and observations from several hundred climate stations of the German Weather Service are used as input data for the presented statistical model. The method described in this paper utilizes an objective circulation pattern classification and a specific weather generator to indicate the mean regional changes between present day climate and the future projection. A circulation pattern dependent regression is used to model future climate extremes resulting from changed intensities of atmospherical processes within the individual circulation patterns. The presented results are a composite of five regional climate studies. Generally the following statements can be inferred from our results: The predicted climate change will not progress uniformly. Instead, periods characterized by rather slight changes will be followed by others exhibiting very distinct climatologic alterations. The strongest change is identified in winter. Until the decade 2041-50 an increase of approximately 3 K and a raise of the rainfall amounts are predicted compared to the present day climate (1981-2000). This is caused by an increase of zonal circulation patterns. In summer the change is weaker but still significant. The temperature will increase approx. 2 K while rainfall will decrease especially in the south and east of Germany. The changes within the transitional seasons will be moderate compared to those within summer and winter.