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We used the regional climate model CCLM 4 to reconstruct the climate over the CADSES regions for the period 1961-2000. This most recent model version is currently still in a test phase. The simulation quality for this part of the entire simulation area is exemplarily analyzed by using a small but representative subset of model variables. Mean sea level pressure is chosen as an indicator for the behavior of the near-surface dynamics. Furthermore, the two regional variables 2 m temperature and precipitation are evaluated, which are of relevance for hydrological applications. For additional analyses to identify sources of uncertainty, we used soil moisture and cloudiness. Altogether, the accuracy of the simulation results can be judged as being in good agreement with the results of similar simulations by other regional climate models over Europe. Area-averaged monthly mean 2 m temperatures during winter show a cold bias of about -2 K. The simulated mean sea level pressure is with a bias of about 2 hPa slightly too high compared to the used reference data set. Although cloud cover evaluation hints at insufficiencies in the corresponding parameterization, further analyses are required to unambiguously identify the causes for this cold bias. During summer, higher 2 m temperatures linked to reduced precipitation are identified in nearly identical regions over southern and southeastern Europe. Further analyses of soil moisture evolution and cloud cover give evidence for a source of uncertainty in the surface hydrological scheme. Altogether, the detected differences do not exceed the typical ranges of uncertainty in comparable regional climate simulations over Europe. For input in hydrological applications, however, the uncertainties produced by the CCLM simulation described here have to be reduced in the medium term.
Regional climate projections have been performed for Europe with the climate version CLM (version 3.1) of the 'Lokal-Modell' (LM) of the German Meteorological Service. CLM has been forced with output of the ECHAM5/MPIOM global climate model of the Max Planck Institute for Meteorology, which contributed to the fourth climate assessment report (AR4) of the International Panel on Climate Change (IPCC). The results of the global model for the IPCC A1B and B1 scenarios as well as the reconstruction of the last four decades of the 20th century (20C3M) for the AR4 are dynamically scaled down to the CLM grid cell size of about 18×18 km2. Climatological parameters including all model variables and additional quantities characterising the climate are available from the World Data Centre for Climate database run by the German climate service group Model and Data. They can be accessed by the general public at no costs with the help of M and D. In particular, the 'Service Group Adaptation' (SGA) has been established at M and D to assist adaptation projects funded by the German Federal Ministry of Education and Research (BMBF) giving advice on the interpretation of the CLM model results.
We present two case studies that demonstrate how a common evaluation methodology can be used to assess the reliability of regional climate model simulations from different fields of research. In Case I, we focused on the agricultural yield loss risk for maize in Northeastern Brazil during a drought linked to an El-Niño event. In Case II, the present-day regional climatic conditions in Europe for a 10-year period are simulated. To comprehensively evaluate the model results for both kinds of investigations, we developed a general methodology. On its basis, we elaborated and implemented modules to assess the quality of model results using both advanced visualization techniques and statistical algorithms. Besides univariate approaches for individual near-surface parameters, we used multivariate statistics to investigate multiple near-surface parameters of interest together. For the latter case, we defined generalized quality measures to quantify the model's accuracy. Furthermore, we elaborated a diagnosis tool applicable for atmospheric variables to assess the model's accuracy in representing the physical processes above the surface under various aspects. By means of this evaluation approach, it could be demonstrated in Case Study I that the accuracy of the applied regional climate model resides at the same level as that we found for another regional model and a global model. Excessive precipitation during the rainy season in coastal regions could be identified as a major contribution leading to this result. In Case Study II, we also identified the accuracy of the investigated mean characteristics for near-surface temperature and precipitation to be comparable to another regional model. In this case, an artificial modulation of the used initial and boundary data during preprocessing could be identified as the major source of error in the simulation. Altogether, the achieved results for the presented investigations indicate the potential of our methodology to be applied as a common test bed to different fields of research in regional climate modeling.