Open Access

The aim of this study is a better understanding of radiation processes in regional climate models (RCMs) in order to quantify their impact and to reduce possible errors. A first important task in finding an answer to this question was to examine the accuracy of the components of the radiation budget in regional climate simulations. To this end, the simulated radiation budgets of two regional climate simulations for Europe were compared with a satellite-based reference. In the simulations with the RCM COSMO-CLM there were some serious under- and overestimations of short- and long-wave net radiation in Europe. However, taking into account the differences in the reference datasets, the results of the COSMO-CLM were quite satisfactory. Using statistical methods, the influence of potential sources of uncertainties was estimated. Uncertainties in the cloud cover and surface albedo had a significant impact on uncertainties in short-wave net radiation, the explained variance of uncertainties in cloud cover was two to three times higher than that of uncertainties in surface albedo. Uncertainties in the cloud cover resulted in significant errors in the net long-wave radiation. However, the influence of uncertainties in soil temperature on errors in the long-wave radiation budget was low or even negligible. These results were confirmed in a comparison with simulations of the REMO and ALADIN regional climate models. It is reasonable to expect that a better parameterization of relatively simple parameters such as cloud cover and surface albedo is a means of significantly improving the simulation of radiation budget components in the COSMO-CLM. An important question for the application of RCMs is to examine whether the results of radiation uncertainties and their impact factors are comparable if the model is applied in a region that is not the one for which it was originally created. Comparisons of the simulated radiation budgets of different RCMs for West Africa showed that problems in the simulation of short- and long-wave radiation fluxes were a widespread problem. Most of the tested models showed some considerable under- or overestimation of the short- and long-wave radiation fluxes. Similar to Europe uncertainties in cloud cover were also in the simulations for Africa a significant factor affecting uncertainties in the simulated radiation fluxes. However, for the African simulations uncertainties in the parameterization of surface albedo were much more important than in Europe. On average, overland uncertainties in the cloud cover and surface albedo were of similar importance. Uncertainties in soil temperature simulations were of higher importance in Africa, and reached overland similar values of the mean explained variance (R2 ≈ 0.2) such as uncertainties in the cloud cover. This indicates a geographical dependence of the model error. This study confirmed the assumption that an improved parameterization of relatively simple parameters such as the surface albedo in RCMs leads to a significant improvement in the modeled radiation budget, particularly in Africa. The influence of errors in the simulated radiation budget components on the simulation of climate processes, such as the West-African monsoon (WAM), was investigated in a next step. The evaluation of ERA-Interim and ECHAM5 driven COSMO-CLM simulations for Africa showed that the main features of the WAM were well reproduced by the model, but there were only slight improvements compared to the driving data. The index of convective activity in the model simulations was much too high and precipitation was underestimated in large parts of tropical Africa. The partly considerable differences between the ERA-Interim and ECHAM5 driven simulations demonstrated the sensitivity of the RCM to the boundary conditions and in particular to the sea surface temperature. An excessive northwards shift of the monsoon in the model was influenced by the land-sea temperature gradient and the strength of the Saharan heat low. Consequently, a part of the error was due to the driving data and the model itself produced another part. By modifying the parameterization of the bare soil albedo the errors in the radiation budget and 2 m temperature in the Sahara region were significantly reduced. Similarly, the overesti-mation of precipitation and convection has been reduced in the Sahel. The effect of this modifi-cation on the examined WAM area was low. This confirmed that especially in desert regions, errors in the surface albedo were a driving factor for errors in the radiation budget. However, there are other important factors not yet sufficiently understood that have a strong influence on the quality of the simulation of the WAM. The analysis of the actual state, the quantification of error sources and the highlighting of connections made it possible to find means to reduce uncertainties in the simulated radiation in RCMs and to have a better understanding of radiation processes. However, the magnitude of the errors found, the number of possible influencing factors, and the complexity of interactions, indicate that there is still a need for further research in this area.

The prediction of climate on time scales of years to decades is attracting the interest of both climate researchers and stakeholders. The German Ministry for Education and Research (BMBF) has launched a major research programme on decadal climate prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal Climate Prediction) in order to investigate the prediction potential of global and regional climate models (RCMs). In this paper we describe a regional predictive hindcast ensemble, its validation, and the added value of regional downscaling. Global predictions are obtained from an ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs), which were downscaled for Europe using the COSMO-CLM regional model. Decadal hindcasts were produced for the 5 decades starting in 1961 until 2001. Observations were taken from the E-OBS data set. To identify decadal variability and predictability, we removed the long-term mean, as well as the long-term linear trend from the data. We split the resulting anomaly time series into two parts, the first including lead times of 1–5 years, reflecting the skill which originates mainly from the initialisation, and the second including lead times from 6–10 years, which are more related to the representation of low frequency climate variability and the effects of external forcing. We investigated temperature averages and precipitation sums for the summer and winter half-year. Skill assessment was based on correlation coefficient and reliability. We found that regional downscaling preserves, but mostly does not improve the skill and the reliability of the global predictions for summer half-year temperature anomalies. In contrast, regionalisation improves global decadal predictions of half-year precipitation sums in most parts of Europe. The added value results from an increased predictive skill on grid-point basis together with an improvement of the ensemble spread, i.e. the reliability.