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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]

Satellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of the user community and are now available with 0.25° × 0.25° (or less) subdaily resolutions. This paper evaluates a number of commonly available satellite-derived HRPPs covering northwest Europe over a 6-yr period. Precipitation products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing (CMORPH) technique, the CPC merged microwave technique, the Naval Research Laboratory (NRL) blended technique, and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) technique. In addition, the Geosynchronous Operational Environmental Satellite (GOES) precipitation index (GPI) and the European Centre for Medium-Range Weather Forecasting (ECMWF) operational forecast model products are included for comparison. Surface reference data from the European radar network is used as ground truth, supported by the Global Precipitation Climatology Centre (GPCC) precipitation gauge analysis and gauge data over the United Kingdom. Measures of correlation, bias ratio, probability of detection, and false alarm ratio are used to evaluate the products. Results show that satellite products generally exhibit a seasonal cycle in correlation, bias ratio, probability of detection, and false alarm ratio, with poorer statistics during the winter. The ECMWF model also shows a seasonal cycle in the correlation, although the results are poorer during the summer, while the bias ratio, probability of detection, and false alarm ratio are consistent through all seasons. Importantly, all the satellite HRPPs underestimate precipitation over northwest Europe in all seasons. [ABSTRACT FROM AUTHOR]

Abstract: Understanding climate patterns, with their decisive influence on plant distribution and development, is crucial to understanding the history of vegetation patterns in Europe during the Miocene. This paper presents the detailed analyses of several precipitation parameters, including monthly precipitation of the wettest, driest and warmest months, for five Miocene stages. In conjunction with seasonality of temperature, those parameters provide a meaningful measure of continentality and can help to document Miocene climate changes and patterns and their possible influence on vegetation. Climate reconstructions provided here are entirely based on palaeobotanical material. In total, 169 Miocene floras were selected, including 14 Burdigalian, 41 Langhian, 40 Serravallian, 36 Tortonian, and 38 Messinian localities. All floras were analysed using the Coexistence Approach. The analysis of several precipitation parameters, the statistical inter-correlation of results, and the comparison with modern patterns provides a comprehensive account on Miocene precipitation. Miocene climatic changes after the Mid Miocene Climatic Optimum (MMCO) are evidenced in our data set by three major factors, i.e. (1) increasing seasonality of temperature, (2) changes in the annual distribution of precipitation towards a precipitation peak in summer, and (3) a late increase of longitudinal gradients of precipitation parameters. Evidence of continental climate in Eastern Europe first appears during the Messinian. In addition to changes in temperature, shifts in the annual distribution of precipitation may have played a major role in post-Langhian climate changes. However, the most significant climatic transformations occurred later, from the end of Miocene through to the present. Several authors have described patterns of vegetation development in Europe that are in good agreement with our finding of the first evidence for continental climate in Eastern Europe during the Messinian. Our data do not support an onset of opening of vegetation during the Tortonian or even earlier, as has been described for some parts of Eastern and Southern Europe. Possibly either non climatic parameters influenced such an early development, or our data lack the required resolution and/or spatial coverage to fully decipher the influence of continentality on vegetation and to correlate climate and vegetation statistically. Nevertheless, climatic data that quantify continentality can provide a sound basis for explaining the expansion of grassland in Eurasia. [Copyright &y& Elsevier]