Quantifying future wind resources in complex terrain using data-driven predictions from large-scale GCM inputs

The expected lifetime energy yield of wind turbines and wind farms is to a large extent determined by the wind climate in which they operate. Importantly, the wind climate of the coming 25 years might differ significantly from that of the past 25 years as a consequence of natural climate variability and/or anthropogenically forced climate changes. Research on the uncertainty in future wind resources often relies on bias-corrected surface wind output from General Circulation Model (GCM) projection ensembles. However, for locations in complex terrain, the accuracy of modelled near-surface winds by these GCMs may be severely impacted by their coarse grid resolution and therefore also the associated wind climate change signals. Here, we assess the added value of a statistical GCM downscaling algorithm which employs GCM output from higher atmospheric levels as predictors. More specifically, we compare it to the standard, surface wind-based approach for a Chilean wind farm located in complex terrain. Furthermore, we assess the performance sensitivity to the choice of statistical model, predictor set, training data and temporal resolution. Finally, we apply both approaches to a GCM projection ensemble to illustrate the necessity of more advanced approaches for quantifying the future wind resource uncertainty for sites in complex terrain.