Reanalysis products have become a tool for wind energy users requiring information about the wind speed long-term variability. These users are sensitive to many aspects of the observational references they employ to estimate the wind resource, such as the mean wind, its seasonality and long-term trends. However, the assessment of the ability of atmospheric reanalyses to reproduce wind speed trends has not been undertaken yet. The wind speed trends have been estimated using the ERA-Interim reanalysis (ERA-I), the second version of the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) and the Japanese 55-year Reanalysis (JRA-55) for the period 1980–2015. These trends show a strong spatial and seasonal variability with an overall increase of the wind speed over the ocean and a tendency to a decline over land, although important disagreements between the different reanalyses have been found. In particular, the JRA-55 reanalysis produces more intense trends over land than ERA-I and MERRA-2. This can be linked to the negative bias affecting the JRA-55 near-surface wind speeds over land. In all the reanalyses high wind speeds tend to change faster than both low and average wind speeds. The agreement of the wind speed trends at 850 hPa with those found close to the surface suggests that the main driver of the wind speed trends are the changes in large-scale circulation.

The authors acknowledge funding support from the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2), the New European Wind Atlas (NEWA) project funded by ERA-NET Plus, Topic FP7 ENERGY.2013.10.1.2, the RESILIENCE (CGL2013–41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO), and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Thanks to Daniel Cabezón and Sergio Lozano for their valuable feedback. We acknowledge the s2dverification R-based package (http://cran.rproject. org/web/packages/s2dverification) developers. Finally, we would like to thank Pierre-Antoine Bretonniere, Júlia Giner, Nicolau Manubens and Javier Vegas for their technical support at different stages of this project.

Peer Reviewed