Climate change and wind energy are closely intertwined, with wind energy playing a vital role in reducing greenhouse gas emissions that drive climate change. However, climate change also poses a potential threat to the wind energy industry due to possible reductions in wind resources in key regions, though it may enhance resources in other areas. This study evaluates the impact of climate change on wind energy by using climate projections from Global Climate Models (GCMs). To improve resolution and capture finer details, downscaling methods—both statistical and dynamical—are employed.
This research investigates mean wind speed variations at nine global sites using non-downscaled CMIP6 (Coupled model intercomparison project phase 6) GCMs, statistically downscaled CMIP5/6 GCMs, and dynamically downscaled CMIP5 GCMs from CORDEX, under the climate change scenarios RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. It assesses impacts on wind resource availability, annual energy yield, sensitivity factor, and capacity factor, comparing these to historical data. The study also evaluates revenue from Annual Energy Production (AEP) and compares historical projections from GCMs with reanalysis data.
Findings indicate that non-downscaled CMIP6 GCMs and statistically downscaled GCMs exhibit similar trends in predicting wind speed decline at most sites, suggesting the effectiveness of non-downscaled CMIP6 GCMs for this purpose. CORDEX models reveal a significant influence of GCMs on mean wind speed projections, highlighting the need to include multiple GCMs for reliable analysis. The study observes larger decreases in AEP and capacity factors at onshore sites compared to offshore sites, attributed to complex terrain and higher sensitivity factors at the former.
Comparisons with reanalysis data show higher percent bias, mean absolute percentage error (MAPE), and lower correlation for onshore sites, due to their complex terrain. Additionally, no significant correlation was found between the distance of GCM grid points from reanalysis sites and MAPE. These discrepancies arise from differences in dataset resolution, topographical representation, and data assimilation processes, emphasizing the need for careful consideration when comparing GCM projections with reanalysis data.