Projecting Wind Energy Potential Under Climate Change with Ensemble of Climate Model Simulations

Recent years have witnessed an increasing global concern over energy sustainability and security, triggered by a number of issues, such as (though not limited to): fossil fuel depletion, energy resource geopolitics, economic efficiency versus population growth debate, environmental concerns and climate change. Wind energy is a renewable and sustainable form of energy in which wind turbines convert the kinetic energy of wind into electrical energy. Global warming and differential surface heating may significantly impact the wind velocity and hence the wind energy potential. Sustainable design of wind mills requires understanding the impacts of climate change on wind energy potential, which we evaluate here with multiple General Circulation Models (GCMs). GCMs simulate the climate variables globally considering the greenhouse emission scenarios provided as Representation Concentration path ways (RCPs). Here we use new generation climate model outputs obtained from Coupled model Intercomparison Project 5(CMIP5). We first compute the wind energy potential with reanalysis data (NCEP/ NCAR), at a spatial resolution of 2.50, where the gridded data is fitted to Weibull distribution and with the Weibull parameters, the wind energy densities are computed at different grids. The same methodology is then used, to CMIP5 outputs (resultant of U-wind and V-wind) of MRI, CMCC, BCC, CanESM, and INMCM4 for historical runs. This is performed separately for four seasons globally, MAM, JJA, SON and DJF. We observe the muti-model average of wind energy density for historic period has significant bias with respect to that of reanalysis product. Here we develop a quantile based superensemble approach where GCM quantiles corresponding to selected CDF values are regressed to reanalysis data. It is observed that this regression approach takes care of both, bias in GCMs and combination of GCMs. With superensemble, we observe that the historical wind energy density resembles quite well with reanalysis/ observed output. We apply the same for future under RCP scenarios. We observe spatially and temporally varying global change of wind energy density. The underlying assumption is that the regression relationship will also hold good for future. The results highlight the needs to change the design standards of wind mills at different locations, considering climate change and at the same time the requirement of height modifications for existing mills to produce same energy in future.