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Winter storm-track activity over the Northern Hemisphere and its changes in a greenhouse gas scenario (the Special Report on Emission Scenarios A1B forcing) are computed from an ensemble of 23 single runs from 16 coupled global climate models (CGCMs). All models reproduce the general structures of the observed climatological storm-track pattern under present-day forcing conditions. Ensemble mean changes resulting from anthropogenic forcing include an increase of baroclinic wave activity over the eastern North Atlantic, amounting to 5%–8% by the end of the twenty-first century. Enhanced activity is also found over the Asian continent and over the North Pacific near the Aleutian Islands. At high latitudes and over parts of the subtropics, activity is reduced. Variations of the individual models around the ensemble average signal are not small, with a median of the pattern correlation near r = 0.5. There is, however, no evidence for a link between deviations in present-day climatology and deviations with respect to climate change.

together with an initialization procedure and a model evaluation system. This paper 31 summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern 32 strategies and structures of research that targets future operational use. 33Three prediction-system generations have been constructed, characterized by 34 alternative initialization strategies; the later generations show a marked improvement in 35 hindcast skill for surface temperature. Hindcast skill is also identified for multi-year-mean 36European summer surface temperatures, extra-tropical cyclone tracks, the Quasi-Biennial 37Oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly 38 enhances the skill in European surface temperature inherited from the global model and also 39 displays hindcast skill for wind-energy output. A new volcano code package permits rapid 40 modification of the predictions in response to a future eruption. 41MiKlip has demonstrated the efficacy of subjecting a single global prediction system 42 to a major research effort. The benefits of this strategy include the rapid cycling through the 43 prediction-system generations, the development of a sophisticated evaluation package usable 44 by all MiKlip researchers, and regional applications of the global predictions. Open research 45 questions include the optimal balance between model resolution and ensemble size, the 46 appropriate method for constructing a prediction ensemble, and the decision between full-47 field and anomaly initialization. 48

Renewable energy production is strongly influenced by weather and climate. Regional climate projections can be useful to quantify climate change impacts on renewable energies. With this aim, we analyze future changes of wind speed and wind energy potentials using a multimodel ensemble of EURO-CORDEX simulations at 12 km and three-hourly resolution, considering nine different global and regional climate model chains. A comparison between modeled historical 10 m wind speeds and ERA-Interim-driven evaluation runs for the same regional climate models uncovers some substantial model biases. The bias-corrected 10 m wind speeds are extrapolated to the hub height of a wind turbine to derive gridded wind energy output (Eout). The ensemble mean responses project only small changes of mean annual and winter Eout for large parts of Europe in future decades, but a considerable decrease for summer Eout. In terms of variability, increasing intraannual and interdaily variabilities are projected for large parts of northern, central, and eastern Europe. While the ensemble spread is quite large for interdaily variability, results are more robust for intraannual variability. With respect to wind speed characteristics relevant for wind energy production, a robust increase in the occurrence of low wind speeds (<3 m/s) is detected. Due to a combination of higher annual mean Eout and lower intraannual variability, climate change could be beneficial for regions like Baltic and Aegean Sea. For large parts of Germany, France, and Iberia, a lower mean Eout and increased intraannual variability may imply larger temporal/spatial fluctuations in future wind energy production and therefore a more challenging wind energy management.