Abstract
We characterise the long-term variability of European near-surface wind speeds using 142 years of data from the Twentieth Century Reanalysis (20CR), and consider the potential of such long-baseline climate data sets for wind energy applications. The low resolution of the 20CR would severely restrict its use on its own for wind farm site-screening. We therefore perform a simple statistical calibration to link it to the higher-resolution ERA-Interim data set (ERAI), such that the adjusted 20CR data has the same wind speed distribution at each location as ERAI during their common period. Using this corrected 20CR data set, wind speeds and variability are characterised in terms of the long-term mean, standard deviation and corresponding trends. Many regions of interest show extremely weak trends on century timescales, but contain large multidecadal variability. Since reanalyses such as ERAI are often used to provide the background climatology for wind farm site assessments, but contain only a few decades of data, our results can be used as a way of incorporating decadal-scale wind climate variability into such studies, allowing investment risks for wind farms to be reduced.
Highlights
Wind is a highly variable phenomenon over all time scales, from gusts lasting seconds, to long-period variations spanning decades (e.g. Watson, 2014)
The calibrated data retains the interannual variability of the original 20CR wind speeds, but with a climatology matching that of ERA-Interim over 1979–2012
An exception is a residual positive bias east of Gibraltar: we expect this area to be heavily affected by differences in how well the complex orography here is resolved between ERA-Interim data set (ERAI) and 20CR
Summary
Wind is a highly variable phenomenon over all time scales, from gusts lasting seconds, to long-period variations spanning decades (e.g. Watson, 2014). Wind is a highly variable phenomenon over all time scales, from gusts lasting seconds, to long-period variations spanning decades The impact of long-term, decadal-scale variations in the wind climate is less well understood. This is partly due to a historical lack of data. Standard NAO indices correlate well with winter wind speeds in northern/western parts of Europe. This is not true more generally, such as at other times of the year or in other locations (Hurrell et al 2003), and alternative indices must be used in these cases There is scope for improvement over all these techniques
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