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We may anticipate that climate change will bring changes to the intensity and variability of near surface winds, either through local effects or by altering the large-scale flow. The impact of climate change on European wind resources has been assessed using a single-modelensemble of the latest regional climate model from the Rossby Centre, RCA4. These simulations used data from five of the global climate models in the contemporary Climate Model Intercomparison Project (CMIP5) as boundary conditions, and the results are publicly available under the COordinated Regional climate Downscaling EXperiment (CORDEX) project. Overall we find a consistent pattern of a decrease in the wind resources over the European domain under both the RCP 4.5 and RCP 8.5 scenarios, although there are some regions, principally North Africa and the Barents Sea, with projected increases in wind resources. The pattern of change is both robust across the choice of scenario, and persistent: there is a very similar pattern of change found in the latter part of the 21 st century as in the earlier. A case study was chosen to assess the potential for offshore wind-farms in the Black Sea region. We developed a realistic methodology for extrapolating near-surface wind speeds up to hub-height using a time-varying roughness length, and determined the extractable wind power at hub-height using a realistic model of contemporary wind-turbine energy production. We demonstrate that, unlike much of the Mediterranean basin, there is no robust pattern of a negative climate change impact on wind resources in the studied regions of the Black Sea. Furthermore, the seasonality of wind resources, with a strong peak in the winter, matches well to the seasonality of energy-demand in the region, making offshore wind-farms in the Black Sea region a viable source of energy for neighbouring countries.

The effect of the meridional atmospheric heat and moisture transport on the Arctic warming is estimated using the ERA‐Interim reanalysis over 1979–2015. Major influx of sensible and latent heat into the Arctic occurs through the Atlantic sector 0°–80°E between the surface and the 750 hPa level. This influx explains more than 50% of the average temperature variability in the area 70°–90°N in winter with almost equal contribution of both fluxes. Calculations using MPI‐ESM‐MR Earth System model from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble showed the similar effect of the meridional atmospheric heat and moisture transport and its increase by the end of the century. Mean summer transport in the low troposphere is directed from the Arctic and transfers out the moisture produced by summer melting of sea ice. The major drivers of summer warming are the radiation processes especially downwards longwave radiation.

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.

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