Extended Abstract

4 ResultsFigure 2: Monsoon mean precipitation difference of COSMO-CLM simulations driven by re-analysis dataand the GPCC datasetFigure 2 shows the mean monsoonal difference patterns of the COSMO-CLM simulations forced with ERAreanalysis with the observational data set. There are substantial differences. For example, COSMO-CLMoverestimates precipitation in the Sahel and underestimates precipitation by up to 50% along the West coastand Central Africa. In both simulation, ERA and ECHAM5 driven, COSMO-CLM extends the mainprecipitation region too far north. In the South-Asian domain both simulations overestimate precipitationalong the west coasts. COSMO-CLM with ERA forcing even overestimates precipitation by more than 100%at the western coast of India windward of the coastal mountain ridge. At the same time the modelunderestimates precipitation in the northern part of India, in the Himalayan foothills, with mean values of402 mm and 340 mm for the ERA and ECHAM5 driven simulations, respectively, compared to 900 mmestimated by the observational data set.Figure 3 compares the mean spatial precipitation fields of the different model based data sets with theGPCC data set using simple statistics. Despite the spatial patterns of differences in COSMO-CLMsimulations discussed above, the regional climate model is able to add value on the simulations by the globalclimate model ECHAM5. COSMO-CLM is not able to improve on the re-analyses. The regional modelseems to overestimate variability for ISM because of the systematic and substantial over- andunderestimation in discussed regions. Thus, this is implied by large-scale differences and by errors at thesub-global data scale.Figure 3: Taylor-diagram of the mean spatial precipitation fields for WAM (left) and ISM (right) simulated-257-