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.
A statistical-dynamical downscaling (SDD) approach for the regionalization of wind energy output (E out ) over Europe with special focus on Germany is proposed. SDD uses an extended circulation weather type (CWT) analysis on global daily mean sea level pressure fields with the central point being located over Germany. Seventy-seven weather classes based on the associated CWT and the intensity of the geostrophic flow are identified. Representatives of these classes are dynamically downscaled with the regional climate model COSMO-CLM. By using weather class frequencies of different data sets, the simulated representatives are recombined to probability density functions (PDFs) of near-surface wind speed and finally to E out of a sample wind turbine for present and future climate. This is performed for reanalysis, decadal hindcasts and long-term future projections. For evaluation purposes, results of SDD are compared to wind observations and to simulated E out of purely dynamical downscaling (DD) methods.For the present climate, SDD is able to simulate realistic PDFs of 10-m wind speed for most stations in Germany. The resulting spatial E out patterns are similar to DD-simulated E out . In terms of decadal hindcasts, results of SDD are similar to DD-simulated E out over Germany, Poland, Czech Republic, and Benelux, for which high correlations between annual E out time series of SDD and DD are detected for selected hindcasts. Lower correlation is found for other European countries. It is demonstrated that SDD can be used to downscale the full ensemble of the Earth System Model of the Max Planck Institute (MPI-ESM) decadal prediction system.Long-term climate change projections in Special Report on Emission Scenarios of ECHAM5/MPI-OM as obtained by SDD agree well to the results of other studies using DD methods, with increasing E out over northern Europe and a negative trend over southern Europe. Despite some biases, it is concluded that SDD is an adequate tool to assess regional wind energy changes in large model ensembles.
A statistical-dynamical downscaling method is used to estimate future changes of wind energy output (Eout) of a benchmark wind turbine across Europe at the regional scale. With this aim, 22 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble are considered. The downscaling method uses circulation weather types and regional climate modelling with the COSMO-CLM model. Future projections are computed for two time periods (2021-2060 and 2061-2100) following two scenarios (RCP4.5 and RCP8.5). The CMIP5 ensemble mean response reveals a more likely than not increase of mean annual Eout over Northern and Central Europe and a likely decrease over Southern Europe. There is some uncertainty with respect to the magnitude and the sign of the changes. Higher robustness in future changes is observed for specific seasons. Except from the Mediterranean area, an ensemble mean increase of Eout is simulated for winter and a decreasing for the summer season, resulting in a strong increase of the intra-annual variability for most of Europe. The latter is, in particular, probable during the second half of the 21st century under the RCP8.5 scenario. In general, signals are stronger for 2061-2100 compared to 2021-2060 and for RCP8.5 compared to RCP4.5. Regarding changes of the inter-annual variability of Eout for Central Europe, the future projections strongly vary between individual models and also between future periods and scenarios within single models. This study showed for an ensemble of 22 CMIP5 models that changes in the wind energy potentials over Europe may take place in future decades. However, due to the uncertainties detected in this research, further investigations with multi-model ensembles are needed to provide a better quantification and understanding of the future changes.
Interaction effects of different stressors, such as extreme drought and plant invasion, can have detrimental effects on ecosystem functioning and recovery after drought. With ongoing climate change and increasing plant invasion, there is an urgent need to predict the short-and long-term interaction impacts of these stressors on ecosystems. We established a combined precipitation exclusion and shrub invasion (Cistus ladanifer) experiment in a Mediterranean cork oak (Quercus suber) ecosystem with four treatments: 1) Q. suber control, 2) Q. suber with rain exclusion, 3) Q. suber invaded by shrubs, 4) Q. suber with rain exclusion and shrub invasion. As key parameter, we continuously measured ecosystem water fluxes. In an average precipitation year, the interaction effects of both stressors were neutral.However, the combination of imposed drought and shrub invasion led to amplifying interaction effects during an extreme drought by strongly reducing tree transpiration.Contrarily, the imposed drought reduced the competitiveness of the shrubs in the following recovery period, which buffered the negative effects of shrub invasion on Q. suber. Our results demonstrate the highly dynamic and non-linear effects of interacting stressors on ecosystems and urges for further investigations on biotic interactions in a context of climate change pressures.
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