Assessing Climate Change Impact on Green (Wind) Energy Resources in the Northeast U.S. using a Statistical Downscaling Approach

Wind is one of the fastest-growing renewable energy sources for the North Atlantic region marked in recent years with increased investments in offshore wind farms. The main purpose of this study is to improve wind data representation and enhance the reliability of GCM (CMIP6) downscaled output from coarser resolution to a locally relevant scale by identifying the optimal combination of variables. This is a continuation of the research work presented at the last AMS annual meeting. We employ a statistical technique called Generalized Analog Regression Downscaling (GARD) to downscale wind speed from Global Climate Models (GCM). The latest update includes testing multiple variable combinations to find the most efficient number and set of variables to downscale wind speed. We use ERA5 atmospheric reanalysis products (~30-km) and high resolution(~4-km) WRF simulations to validate the methodology. The validated model is applied to downscale CMIP6 model outputs for both Historical and Future (SSP2-4.5, and SSP5-8.5) scenarios and statistically assess future changes of wind speed in the NE US.