An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

ABSTRACT:Hindcasts with reanalysis-driven regional climate models (RCMs) are a common tool to assess weather statistics (i.e. climate) and recent changes and trends. A remote sensing-based method to investigate the added value of surface marine RCM wind speed is introduced. The capability of the dynamical downscaling approach (with spectral nudging applied) to add value to the reanalysis wind speed forcing is assessed by the comparison with QuikSCAT Level 2B 12.5 km (L2B12) swath data in European waters for 2000-2007. Co-location criteria are within 0.1°and 0.06°in longitudinal and latitudinal distance from RCM grid points and within 10 min. In the wind speed range, QuikSCAT L2B12 is reliably reproducing (3-20 m s −1 ), dynamically downscaled wind speed does not show an added value in 'open ocean' areas. However, in coastal areas with complex topography, the regional models show an added value, especially around Iceland and the Iberian Peninsula and in the Mediterranean, Baltic and Irish Seas, validating the findings of previous in situ data-based studies on the added value. Strong interseasonal differences exist, in winter enhanced cyclonic and meso-cyclonic activity increases the potential of dynamical downscaling. In winter time, the added value is more pronounced around Iceland and Greenland, south of Iceland and within the Gulf of Lyon/Mistral region. Summarizing the presented method can be easily applied for other ocean areas, making QuikSCAT a valuable tool to identify marine regions where dynamical downscaling adds value to surface marine wind speed. A detailed comparison of 10 m winds from the National Centres of Environmental Prediction (NCEP)/National Centre for Atmospheric Research (NCAR) and the newer NCEP/DOE-II reanalyses is presented in the annex, motivating the use of the NCEP/NCAR reanalysis in the added value assessment.

Hindcasts with reanalysis-driven regional climate models (RCMs) are a common tool to assess weather statistics (i.e., climate) and recent changes and trends. The capability of different state-of-the-art RCMs (with and without spectral nudging applied) to add value for surface marine wind speed in comparison to the reanalysis wind speed forcing is assessed by the comparison with observations in the eastern North Atlantic in 1998. Added value is elaborated on instantaneous wind speeds and their frequency distribution. The observations are discriminated into groups according to their proximity to land and assimilation status, meaning whether they are assimilated into the reanalysis or not. For instantaneous wind speeds RCMs do not show added value both in ''open ocean'' areas and the German Bight. However, in the English Channel, where local topography and associated local wind regimes become important, the regional models show an added value for instantaneous wind speeds. Concerning the wind speed distribution there is a clear indication for an added value of the RCMs in coastal regions, especially for higher wind speed percentiles, while in open-ocean areas no added value is found. In comparison to the unnudged simulation, the spectrally nudged simulations better represent both instantaneous wind speeds and their frequency distribution. These results hold independently of the measurements' assimilation status. Strictly the findings of this study only hold for hindcast studies, the results may differ for other areas and years.

Consistent meteorological/oceanographic datasets derived from regional reanalyses and 1 climate change projections prove particularly useful for coastal defense and offshore industry. C oastal and offshore applications require appropriate planning and design. For most of them, statistics of extreme wind, waves, and storm surges are of central importance. To obtain such statistics long and homogeneous time series are needed.Usually such time series are hardly available. In most cases observations are either missing, cover too short periods, or are lacking homogeneity, that is, long-term changes in the time series are not entirely related to geophysical changes on the scale of interest, but are partly due to changes in instrumentation, measurement technique, or other factors, such as changes in the surrounding of the measurement site.There are in principle two approaches to address these issues (cf. WASA 1998). One is the use of proxy data that are considered to be more homogeneous and are available for longer periods. An example of this is the use of pressure data to derive indices for changes in storm activity (e.g., Schmidt and von Storch 1993). The other approach is to • Regional meteorological-marine reanalyses have been used by the Flensburger Schiffbau Gesellschaft to optimize RoRo ferry operating in the North Sea. Such data have been used for instance during the design process of the ferry Jasmine. The photo shows the vessel at the shipyard shortly before launch.

a b s t r a c tModel-based wind speed data derived from the coastDat2 data set for the North Sea were used to assess wind power potential considering both spatial and temporal variability. The atmospheric part of coastDat2 was simulated with the regional climate model COSMO-CLM 4.8. The quality of the used wind speed data is analysed by comparison with buoy and QuikSCAT data. To determine where an offshore power plant can be cost-effectively developed, the distribution of the possible production dependencies on the offshore distance is one of the more important factors. A synthetic power function was used to convert the model-derived wind speeds at a height of 100 m to wind power. The data were analyzed for the period of 1958-2012, and the results obtained for the decadal and spatial variability were mapped. The site related summaries are discussed.The inter-annual to decadal variability can reach up to 5% from the multi-decadal mean and therefore plays an important role in wind energy; wind power estimates based on short observational time series, particularly from the late 1990s, may exhibit high biases. The up-scaling from wind speeds at a height of 10 m using conventional power laws may result in similar biases. On inter-annual to decadal time scales, synergies are not expected from the different arrays in the North Sea, i.e., a decrease in the power output of an array may not be balanced by another.