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The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971-2000) and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly. [ABSTRACT FROM AUTHOR]

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]

Understanding, modeling, and predicting hydrometeorological extreme events are major topics in geoscience. But since, in particular, extreme events depend on various factors and hydrometeorological parameters as well as their interactions and feedbacks, it is crucial to analyze and understand their spatial and temporal dependencies. However, simple metrics like spatial correlation coefficients only consider the linear relationship between variables and therefore neglect significant non-linear dependencies. Therefore, we propose Copula-based dependency-metrics, with which we can analyze the full dependency structure of variables, including the statistical relationship of extreme events. In this study, we use the proposed approach for analyzing the spatial dependency structures of atmospheric wet refractivity, which is a key parameter in GNSS-tomography and hence the reconstruction of a local tropospheric model. Therefore, the Weather Research and Forecast (WRF) model has been applied, with which high-resolution fields of atmospheric pressure, temperature and water vapor were simulated. We used the model results for deriving pixel-based wet refractivities on multiple pressure levels in high spatial and temporal resolution. The model was run with a spatial resolution of 3km for a domain in Central Europe during the period April to October 2016. The dynamic patterns and spatial correlations of pixel-based hourly wet refractivity on different pressure levels were then evaluated using several Copula-based dependency measures. While we currently focus on the analysis of wet refractivity, the dependency measures can be easily applied to other hydrometeorological quantities and therefore provide an innovative approach for analyzing spatial correlations including the spatial dependencies of extreme events. In our presentation, we show the different steps for deriving the dependency measures as well as first results which show the advantages compared to classical correlation-based analyses. [ABSTRACT FROM AUTHOR]