Using the regional climate model ALARO-0, the Royal Meteorological Institute of Belgium and Ghent University have performed two simulations of the past observed climate within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The ERA-Interim reanalysis was used to drive the model for the period 1979-2010 on the EURO-CORDEX domain with two horizontal resolutions, 0.11 and 0.44 •. ALARO-0 is char-acterised by the new microphysics scheme 3MT, which allows for a better representation of convective precipitation. In Kotlarski et al. (2014) several metrics assessing the performance in representing seasonal mean near-surface air temperature and precipitation are defined and the corresponding scores are calculated for an ensemble of models for different regions and seasons for the period 1989-2008. Of special interest within this ensemble is the ARPEGE model by the Centre National de Recherches Météorologiques (CNRM), which shares a large amount of core code with ALARO-0. Results show that ALARO-0 is capable of representing the European climate in an acceptable way as most of the ALARO-0 scores lie within the existing ensemble. However, for near-surface air temperature, some large biases, which are often also found in the ARPEGE results, persist. For precipitation , on the other hand, the ALARO-0 model produces some of the best scores within the ensemble and no clear resemblance to ARPEGE is found, which is attributed to the inclusion of 3MT. Additionally, a jackknife procedure is applied to the ALARO-0 results in order to test whether the scores are robust , meaning independent of the period used to calculate them. Periods of 20 years are sampled from the 32-year simulation and used to construct the 95 % confidence interval for each score. For most scores, these intervals are very small compared to the total ensemble spread, implying that model differences in the scores are significant.

Orography is known to affect local meteorological conditions by inducing orographic rainfall and a rain shadow i.e. reduced rainfall on the mountain's leeside with respect to the windward side. Therefore it has a strong effect on the local population and agriculture. Recent work highlights the ambiguities in the definition and difficulties in quantification of the rain shadow effect using observational data. A statistical approach is presented that allows its investigation based on climatological model data in geographically complex regions. This approach requires gridded rainfall and wind along with the model topography. The statistical aspects that contribute to the rainfall enhancement at the windward side are disentangled. These include, for windward and leeward events separately: frequency of occurrence, rainfall-event frequency, rainfall depth per event. By spatial aggregation the regional dependence of these statistics are calculated and visualized. The approach is used to characterize summer rain over the Ethiopian Highlands based a 21year long simulation with the regional climate model ALARO-0 at 4 km resolution. There is an overall increased rainfall of 40% for windward events as compared to leeward events, but regionally this can exceed 150%. This increase can be attributed to the positive differences between windward and leeward events in their frequency of occurrence (on average 20%), and, in the rainfall per rainfall event (on average 16%). Mapped rain shadows correspond well to earlier qualitative observations and the small differences in probability of precipitation confirm that the mechanisms underlying the shadow effect are more complex than the textbook explanation.

Precipitation amounts of 11 models of the Coordinated Regional climate Downscaling Experiment (CORDEX) are evaluated for northwest Ethiopia against eight sets of gridded observational data sets. The model runs are forced with ERA‐Interim reanalysis and have a resolution of 0.44° while the resolution of the observational data sets varies between 0.0375 and 0.75°. Although the CORDEX ensemble mean overestimates the monthly precipitation compared to observational data sets, observations generally lie within the range of the model ensemble. Models generally overestimate the elevational sensitivity, that is, the simulated rainfall is too large for high elevations and too small for low elevations. The models that use the most smoothened representation of the orography perform the best. A group of six models, responsible for the largest biases, also include poor model orography. Given their strong elevation–precipitation correlation of about 60% even larger biases could be expected in case these models incorporate a correct orography. Also, the sensitivity to the choice of the model domain is investigated. While the eight CORDEX members on the African domain overestimate rainfall, the two members on the South Asia domain underestimate summer precipitation.