Climate data selection for multi-decadal wind power forecasts

Abstract

Reliable wind speed data is crucial for applications such as estimating local (future) wind power. Global climate models (GCMs) and regional climate models (RCMs) provide forecasts over multi-decadal periods. However, their outputs vary substantially, and higher-resolution models come with increased computational demands. In this study, we analyze how the spatial resolution of different GCMs and RCMs affects the reliability of simulated wind speeds and wind power, using ERA5 data as a reference. We present a systematic procedure for model evaluation for wind resource assessment as a downstream task. Our results show that while a high spatial resolution can improve the representation of wind speed characteristics, notably extremes, the model choice is more critical for capturing the full wind speed distribution and corresponding power generation. The IPSL model preserves the wind speed distribution particularly well in Europe, producing the most accurate wind power forecasts relative to ERA5 data. Therefore, selecting the right GCMs and RCMs should precede considerations of spatial resolution or GCM boundary conditions. However, higher resolution can be valuable once a suitable climate model is identified.

Keywords

• spatial resolution
• climate model
• wind speed
• wind power