Comparison of Metrics for Probabilistic Climate Change Projections of Mediterranean Precipitation (COMEPRO)

• Applicants: Professor Dr. Jucundus Jacobeit; Professor Dr. Heiko Paeth
• Subject Area: Physical Geography
• Term: from 2014 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 256844028

Climate mitigation and adaptation strategies require reliable estimates of future climate change for which coupled atmosphere-ocean general circulation models (AOGCMs) still represent the state-of-the-art tool. However, climate projections from different climate models vary considerably in some regions of the Earth and in terms of some climatic variables like for instance precipitation, implying important uncertainties on the side of climate impact research. The differences between climate model simulations arise from the unknown initial conditions, different resolutions and forcing agents, different physical parameterizations and the assumed emission scenarios. As it cannot be anticipated which model projection is most realistic, the most comprehensive climate change information is composed of all available climate change simulations. By transforming this multi-model information into a probability density function (PDF) the probability above or below a given level of climate change can directly be assessed. Such probabilistic predictions are indeed relevant for planning processes because they provide a multi-model mean estimate of climate change which represents expectation, and a quantitative measure of model uncertainty. The proposed project aims at evaluating such probabilistic predictions of future precipitation changes from the CMIP3, CMIP5, EMSEMBLES and CORDEX multi-model ensembles and from statistical downscaling approaches. The focus is on the Mediterranean region as a so-called hot spot of climate change and on the comparison of PDFs between seasonal means and extremes. The major novel aspect is to investigate to what extent the width of the multi-model PDFs can be reduced by appropriate weighting factors of the participating climate change projections. This implies that some amount of model uncertainty has to be accepted because it is an intrinsic problem arising from the unknown initial conditions or from uncertain empirical parameters. The weighting factors are assessed by various comparative metrics like Bayesian statistics, regression analysis, spatio-temporal filtering, optimal fingerprinting, and model performance concerning the representation of dynamical modes, circulation types and important predictors for statistical downscaling techniques. In addition, the PDFs over the multi-model ensembles are also assessed for various phenomena in the process chain towards the generation of precipitation, including radiation, evaporation, advection and cloudiness. This will help to identify the level at which the dispersion between different climate model projections begins.

• DFG Programme: Research Grants