An ensemble of regional climate simulations is analyzed to evaluate the ability of 10 regional climate models (RCMs) and their ensemble average to simulate precipitation over Africa. All RCMs use a similar domain and spatial resolution of ~50 km and are driven by the ECMWF Interim Re-Analysis (ERA-Interim) (1989–2008). They constitute the first set of simulations in the Coordinated Regional Downscaling Experiment in Africa (CORDEX-Africa) project. Simulated precipitation is evaluated at a range of time scales, including seasonal means, and annual and diurnal cycles, against a number of detailed observational datasets. All RCMs simulate the seasonal mean and annual cycle quite accurately, although individual models can exhibit significant biases in some subregions and seasons. The multimodel average generally outperforms any individual simulation, showing biases of similar magnitude to differences across a number of observational datasets. Moreover, many of the RCMs significantly improve the precipitation climate compared to that from their boundary condition dataset, that is, ERA-Interim. A common problem in the majority of the RCMs is that precipitation is triggered too early during the diurnal cycle, although a small subset of models does have a reasonable representation of the phase of the diurnal cycle. The systematic bias in the diurnal cycle is not improved when the ensemble mean is considered. Based on this performance analysis, it is assessed that the present set of RCMs can be used to provide useful information on climate projections over Africa.

Here we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of $$\sim $$∼ 3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution ( $$\sim $$∼ 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from $$\sim \,$$∼ −40% at 12 km to $$\sim \,$$∼ −3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales.

In this paper, we present a new publicly available high‐resolution daily precipitation gridded dataset developed for peninsular Spain and the Balearic islands using 2756 quality‐controlled stations (this dataset is referred to as Spain02). The grid has a regular 0.2° (approx. 20 km) horizontal resolution and spans the period from 1950 to 2003. Different interpolation methods were tested using a cross‐validation approach to compare the resulting interpolated values against station data: kriging, angular distance weighting, and thin plane splines. Finally, the grid was produced applying the kriging method in a two‐step process. First, the occurrence was interpolated using a binary kriging and, in a second step, the amounts were interpolated by applying ordinary kriging to the occurrence outcomes. This procedure is similar to the interpolation method used to generate the E‐OBS gridded data—the state‐of‐the‐art publicly available high‐resolution daily dataset for Europe—which was used in this study for comparison purposes. Climatological statistics and extreme value indicators from the resulting grid were compared to those from the 25 km E‐OBS dataset using the observed station records as a reference. Spain02 faithfully reproduces climatological features such as annual precipitation occurrence, accumulated amounts and variability, whereas E‐OBS has some deficiencies in the southern region. When focusing on upper percentiles and other indicators of extreme precipitation regimes, Spain02 accurately reproduces the amount and spatial distribution of the observed extreme indicators, whereas E‐OBS data present serious limitations over Spain due to the sparse data used in this region. As extreme values are more sensitive to interpolation, the dense station coverage of this new data set was crucial to get an accurate reproduction of the extremes. Copyright © 2010 Royal Meteorological Society

The seasonal dependence of Weather Research and Forecasting (WRF) model surface temperature biases and sensitivity to planetary boundary layer (PBL) schemes are jointly explored. For this purpose, the year 2001 was simulated using three different PBL schemes in a domain covering all Europe. The simulations were compared with gridded observations, upper‐air data and high‐frequency station data. Seasonal and daily cycles were analysed, aimed at providing a link between long‐term biases and restricted case studies. The results show that the model mean bias significantly depends on the season, being warm in winter and cold in summer. The winter warm bias is related to misrepresented cold extremes, while a systematic cold bias dominates the whole temperature range in summer. Regarding PBL schemes, an overall underestimation of the entrainment is found, with the non‐local Yonsei University scheme producing systematically warmer temperatures. It is shown that the opposite seasonal biases and systematic behaviour of the PBL schemes during the year lead to a different best‐performing scheme in winter and summer. Moreover, the best‐performing PBL scheme in an average sense is a result of the compensation of errors. The average summer results can be partially explained by a detailed case study. It is concluded that short‐term studies should be used with caution to decide on the parametrizations to be used in long‐term simulations. Copyright © 2012 Royal Meteorological Society

In the current work we present six hindcast WRF (Weather Research and Forecasting model) simulations for the EURO-CORDEX (European Coordinated Regional Climate Downscaling Experiment) domain with different configurations in microphysics, convection and radiation for the time period 1990–2008. All regional model simulations are forced by the ERA-Interim reanalysis and have the same spatial resolution (0.44°). These simulations are evaluated for surface temperature, precipitation, short- and longwave downward radiation at the surface and total cloud cover. The analysis of the WRF ensemble indicates systematic temperature and precipitation biases, which are linked to different physical mechanisms in the summer and winter seasons. Overestimation of total cloud cover and underestimation of downward shortwave radiation at the surface, mostly linked to the Grell–Devenyi convection and CAM (Community Atmosphere Model) radiation schemes, intensifies the negative bias in summer temperatures over northern Europe (max −2.5 °C). Conversely, a strong positive bias in downward shortwave radiation in summer over central (40–60%) and southern Europe mitigates the systematic cold bias over these regions, signifying a typical case of error compensation. Maximum winter cold biases are over northeastern Europe (−2.8 °C); this location suggests that land–atmosphere rather than cloud–radiation interactions are to blame. Precipitation is overestimated in summer by all model configurations, especially the higher quantiles which are associated with summertime deep cumulus convection. The largest precipitation biases are produced by the Kain–Fritsch convection scheme over the Mediterranean. Precipitation biases in winter are lower than those for summer in all model configurations (15–30%). The results of this study indicate the importance of evaluating not only the basic climatic parameters of interest for climate change applications (temperature and precipitation), but also other components of the energy and water cycle, in order to identify the sources of systematic biases, possible compensatory or masking mechanisms and suggest pathways for model improvement.