- While climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterization schemes, but optimal configurations may vary e.g., from region to region. Besides land-surface processes, the most crucial processes to be parameterized in RCMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterization schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-InterimWhile climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterization schemes, but optimal configurations may vary e.g., from region to region. Besides land-surface processes, the most crucial processes to be parameterized in RCMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterization schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-Interim as lateral boundary conditions, we derived an ensemble of 16 physics parameterization runs for a larger domain in Northern sub-Saharan Africa (NSSA), northwards of the equator, using two different CU-, MP-, PBL-, and RA schemes, respectively, using the Weather Research and Forecasting (WRF) model for the period 2006–2010 in a horizontal resolution of approximately 9 km. Based on different evaluation strategies including traditional (Taylor diagram, probability densities) and more innovative validation metrics (ensemble structure-amplitude-location (eSAL) analysis, Copula functions) and by means of different observation data for precipitation (P) and temperature (T), the impact of different physics combinations on the representation skill of P and T has been analyzed and discussed in the context of subsequent impact modeling. With the specific experimental setup, we found that the selection of the CU scheme has resulted in the highest impact with respect to the representation of P and T, followed by the RA parameterization scheme. Both, PBL and MP schemes showed much less impact. We conclude that a multi-facet evaluation can finally lead to better choices about good physics scheme combinations.…
