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Mountain regions with complex orography are a particular challenge for regional climate simulations. High spatial resolution is required to account for the high spatial variability in meteorological conditions. This study presents a very high-resolution regional climate simulation (5 km) using the Weather Research and Forecasting Model (WRF) for the central part of Europe including the Alps. Global boundaries are dynamically downscaled for the historical period 1980–2009 (ERA-Interim and MPI-ESM), and for the near future period 2020–2049 (MPI-ESM, scenario RCP4.5). Model results are compared to gridded observation datasets and to data from a dense meteorological station network in the Berchtesgaden Alps (Germany). Averaged for the Alps, the mean bias in temperature is about −0.3 °C, whereas precipitation is overestimated by +14% to +19%. R 2 values for hourly, daily and monthly temperature range between 0.71 and 0.99. Temporal precipitation dynamics are well reproduced at daily and monthly scales (R 2 between 0.36 and 0.85), but are not well captured at hourly scale. The spatial patterns, seasonal distributions, and elevation-dependencies of the climate change signals are investigated. Mean warming in Central Europe exhibits a temperature increase between 0.44 °C and 1.59 °C and is strongest in winter and spring. An elevation-dependent warming is found for different specific regions and seasons, but is absent in others. Annual precipitation changes between −4% and +25% in Central Europe. The change signals for humidity, wind speed, and incoming short-wave radiation are small, but they show distinct spatial and elevation-dependent patterns. On large-scale spatial and temporal averages, the presented 5 km RCM setup has in general similar biases as EURO-CORDEX simulations, but it shows very good model performance at the regional and local scale for daily meteorology, and, apart from wind-speed and precipitation, even for hourly values. [ABSTRACT FROM AUTHOR]

Mountain regions are climate sensitive zones and a particular challenge for regional climate and hydrology simulations. The complex orography with extreme elevation gradients, as well as varied land cover and ecosystems at small spatial scales lead to a high variability of climatic conditions and hydrological processes. We present a case study for the Berchtesgaden Alps (Germany) using an optimized atmospheric-hydrological model chain to assess potential impacts of a changing climate on the regional hydrology.The study is based on new high-resolution (5 km) regional climate model (RCM) simulations with WRF for the time periods 1980-2009 (ERA-Interim reanalysis and MPI-ESM control run) and 2020-2049 (MPI-ESM, scenario RCP4.5). The reanalysis simulation is validated on different spatial and temporal scales, ranging from monthly climatology for Central Europe and the Alps using different gridded observation datasets to hourly values at the point scale using station measurements. The focus of the validation is on the meteorological variables that subsequently force the hydrological model (HM) WaSiM, which are temperature, relative humidity, precipitation, wind speed, and short-wave incoming radiation. To account for RCM model biases and differences in elevations between the RCM and HM grids, a specific coupling procedure is implemented using two different bias correction methods. Additionally, the hydrological model is extended to account for important mountain-specific processes (lateral snow transport and snow-canopy interaction). Validation results show that the RCM simulation is able to reproduce observed temperature from the large scale (mean bias for the Alps: -0.3 °C) to the hourly station scale (R2 between 0.71 and 0.99 with an RMSE of 1.5 °C). Precipitation is overestimated mainly in summer, whereas winter precipitation is captured very well (annual mean bias for the Alps: + 19 %). The coupled RCM-HM simulations reveal that the climate change signal until 2050 has only limited impacts on the runoff characteristics of the investigated catchment. Most remarkably, mean snow cover duration is projected to decrease with a clear seasonal shift of maximum snow melt to earlier months. These changes in snow cover dynamics are less pronounced in forested areas compared to open land. [ABSTRACT FROM AUTHOR]