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Abstract:
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We present an approach to obtain probabilistic information on climate change signals from ensembles of global and regional climate models. Since single-integration climate models only provide one possible realization of climate variability, ensembles are a promising way to estimate the uncertainty in climate modeling. Recently, several methods, mostly from the field of numerical weather prediction, have been developed (SKD, AKD, NGR, BMA). The approach presented here is related to kernel dressing. In opposition to BMA, the ensemble members are assumed to be indistinguishable. Hence, we attribute equal mixture weights while the dressing variance is extended to a spatio-temporal covariance matrix. The method has been applied to ensembles of the coupled general circulation model ECHO-G. Current applications include temperature and precipitation as well as seasonal variations over Europe.
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