[1] Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of spacetime variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

We present a 1-km 2 gridded German dataset of hourly surface climate variables covering the period 1995 to 2012. The dataset comprises 12 variables including temperature, dew point, cloud cover, wind speed and direction, global and direct shortwave radiation, down-and up-welling longwave radiation, sea level pressure, relative humidity and vapour pressure. This dataset was constructed statistically from station data, satellite observations and model data. It is outstanding in terms of spatial and temporal resolution and in the number of climate variables. For each variable, we employed the most suitable gridding method and combined the best of several information sources, including station records, satellite-derived data and data from a regional climate model. A module to estimate urban heat island intensity was integrated for air and dew point temperature. Owing to the low density of available synop stations, the gridded dataset does not capture all variations that may occur at a resolution of 1 km 2 . This applies to areas of complex terrain (all the variables), and in particular to wind speed and the radiation parameters. To achieve maximum precision, we used all observational information when it was available. This, however, leads to inhomogeneities in station network density and affects the long-term consistency of the dataset. A first climate analysis for Germany was conducted.

Climate parameters, especially temperature, sunlight, and precipitation, play a decisive role in growing and maturing processes. The aim of this study is to investigate the relationship between climate variability and variations in phenological events in viticulture. Long time series of daily meteorological observations are used to quantify these relations. The primary aim is to predict the date of phenological events by relationships between plant morphology and environmental conditions. Causal relationships between environment and internal activities of the vine (phytochemistry, cellular interactions, molecular and cell biology) are not our focus. The dates of the phenological events are important for planning treatments in the vineyards like pest management, for predicting the duration of the ripening phase and estimating the quality of the grapes and the vintage. The focus is layed on the region of the Upper Moselle, especially the Luxembourgian viticulture. First the regional climate and the phenological states of different vine varieties during the time period 1951-2005 are analysed. Significant trends are detected in annual, spring and summer temperatures. Vine phenology is also found to have changed significantly; budburst date and flowering events occur earlier by about two weeks. In a second step, relationships between phenological events and climate parameters are used to develop a prediction model. The parameterisation used in this study is based on a linear multiple regression method with forward and backward steps. The predictors tested are mainly temperature means for different time periods or temperature derived indices. In addition precipitation and sunshine duration for different time periods are evaluated, but only the temperature based predictors showed sufficient skill. For the budburst event, the significant predictors are the accumulated degree

As the nonhydrostatic regional model of the Consortium for Small-Scale Modelling in Climate Mode (COSMO-CLM) is increasingly employed for studying the effects of urbanization on the environment, the authors extend its surface-layer parameterization by the Town Energy Budget (TEB) parameterization using the “tile approach” for a single urban class. The new implementation COSMO-CLM+TEB is used for a 1-yr reanalysis-driven simulation over Europe at a spatial resolution of 0.11° (~12 km) and over the area of Berlin at a spatial resolution of 0.025° (~2.8 km) for evaluating the new coupled model. The results on the coarse spatial resolution of 0.11° show that the standard and the new models provide 2-m temperature and daily precipitation fields that differ only slightly by from −0.1 to +0.2 K per season and ±0.1 mm day−1, respectively, with very similar statistical distributions. This indicates only a negligibly small effect of the urban parameterization on the model's climatology. Therefore, it is suggested that an urban parameterization may be omitted in model simulations on this scale. On the spatial resolution of 0.025° the model COSMO-CLM+TEB is able to better represent the magnitude of the urban heat island in Berlin than the standard model COSMO-CLM. This finding shows the importance of using the parameterization for urban land in the model simulations on fine spatial scales. It is also suggested that models could benefit from resolving multiple urban land use classes to better simulate the spatial variability of urban temperatures for large metropolitan areas on spatial scales below ~3 km.

ABSTRACT:The data base of daily precipitation over Germany has been recently extended by digitizing additional historical hand-written observations. The extension from 65 to 118 stations has increased the density of the available station network to a degree which allows both meaningful regional analyses and less error-prone trend and return level estimates. In this article first results of the examination of the precipitation behaviour in the winter and summer season throughout the entire 20th century are presented. To assess the spatial scale of similarities or spatial coherence of several precipitation indices from the 118 stations, Principal Component Analysis is used. The extracted leading patterns (six in winter, nine in summer) resemble nicely regions of different geographical characteristics and prevailing wind directions which puts some credibility on the quality of the newly digitized observations. The long-term linear trends of the regionally averaged time series differ substantially between precipitation indices, regions, seasons, and sub-periods. For the whole century, significant increases are found for most of the intensity-related indices in the Southern part of the country in winter and in some of those indices in the Rhineland/Sauerland and Alpine regions in summer. The two halves of the 20th century, are, however, characterized by partly opposite trends in the precipitation indices, and different regions are affected by these changes. An analysis of trend robustness by means of 30-year moving trends indicates a low stability of the trends during the 20th century, which is partly caused by interdecadal variability of the precipitation characteristics. For 100-year return levels, changes between the estimates obtained from the entire century and from the first and second 50 years differ in particular in the extent of their confidence intervals. In consequence, the availability of long precipitation records is very important for practical applications, and further extension of data records is recommended.