Extended Abstract

y the global models and COSMO-CLM compared to the GPCC data setOLR is underestimated over the ocean in all COSMO-CLM simulation.s The underestimation is evenmore in the ECHAM5-driven simulations than in the ERA-driven simulations, that is probably connected tohigher sea surface temperatures in ECHAM5 than in ERA. These sea surface temperatures are the prescribedones in the COSMO-CLM simulations. We used OLR as in indicator of wide-spread convective activity:grid boxes with OLR values below 180 W/m 2 are assumed to be convectively dominated. Thus, COSMO-CLM overestimates convective activity over the ocean and also over Central Africa with more than twice asmany convective days in the simulations as in the observations (not shown).Figure 4 compares the mean spatial patterns of OLR. It shows that COSMO-CLM increases spatialvariability of convection, which is related to the overestimation of convection. This can be seen especiallyfor ISM with the OLR index domain is largely over the ocean.Figure 4: As Fig. 3, but for outgoing longwave radiation OLRThe dynamical indices indicate that COSMO-CLM is not able to improve the representation of themonsoonal circulation. For WAM the COSMO-CLM overestimates WAMI indicating a too intensecirculation. For ISM COSMO-CLM and ECHAM5 overestimates the zonal and underestimates themeriodional wind shear suggesting that the models have difficulties in representing the monsoon Hadleycirculation (Goswami et al. 1999).Looking at temporal correlations of the analysis domain-averaged indices, we saw that the value forthe precipitation and dynamical index pair is smaller than for the precipitation and OLR pair in theobservational reference (0.6 and 0.9, respectively) for WAM. Both values are larger in the reference datathan in all the model-based data sets. COSMO-CLM overestimates and the global models underestimate thecorrelation of OLR and the dynamical index for WAM. For ISM, there is anticorrelation between the timeseries of precipitation and OLR (note the definition of the index analysis domains). And, in all models thisanticorrelation was higher than in the reference data. Thus, precipitation over India was overly influenced inthe models by convection over the Arabian Sea and the Bay of Bengal.5 ConclusionsWe found that in both simulation domains COSMO-CLM was able to represent the main features of themonsoon system. As expected the regional climate model was able to improve on the forcing data given byECHAM5 in terms of spatial distribution of precipitation. The regional model is able to better represent thecomplex topography; for instance, the orography of the Tibetan Plateau was better represented in the RCMbecause of the higher resolution. But, there was not much improvement to be seen over the ERA reanalysisdata sets in the spatial distribution of precipitation, vertical wind shear, or outgoing longwave radiation,either.COSMO-CLM showed a clear overestimation of convective activity in general, which is only partly-258-