General Circulation Model Selection Technique for Downscaling: Exemplary Application to East Africa

Abstract

Downscaling is widely used in studies of local and/or regional climate as it yields a greater spatial resolution than general circulation models (GCMs) can provide. It utilizes GCM output or reanalysis data, which is transformed using mathematical relationships or used to force the lateral boundaries of a regional climate model. However, there is no set selection technique to determine which GCM realization(s) to employ. Here, a comprehensive yet easily applicable model selection technique for studies requiring GCM data as a constraint was developed. The technique evaluates, with respect to a reanalysis product and/or observational data, the ability of GCM realizations to reconstruct the mean state of the climate and the space-time climatic anomalies for the atmospheric state variables at three distinct pressure levels. It was applied to the region of East Africa, where GISS-E2-H r6i1p3 was found to perform the strongest. The top ranked realizations were found to better capture processes when evaluated for the example of the Indian Ocean Dipole. Furthermore, the surface air temperature and precipitation from three 10-year regional climate model simulations, one forced by the Modern-Era Retrospective Analysis for Research and Applications version 2 reanalysis, one forced by the top ranked GCM, and one by the lowest ranked one, were compared to gridded observations. Results show that using a top ranked GCM for the boundary conditions leads to a better dynamical downscaling simulation than a low-ranked GCM, suggesting the potential of the proposed technique for future downscaling techniques.