Spatiotemporal Reliability Ensemble Averaging of Multimodel Simulations

Abstract

AbstractMultimodel combining approaches can extract reliable climate information from a large number of climate projections by exploiting the strengths and discounting the weaknesses of each climate simulator; however, most of them (e.g., reliability ensemble averaging [REA]) assign weights to climate simulators without accounting for spatial and temporal variabilities in climate model skills. Here we tested several REAs and proposed a full version that reflects the spatiotemporal (ST) variability of climate model skills. Interperformance evaluations between REA versions showed that, on average, ST‐REA reduced the bias by 33.78% and the root mean square error by 11.61%. Therefore, spatial and temporal variabilities in climate model skills can enhance the overall reliability of precipitation projections. ST‐REA was applied to project future precipitations over South Korea by combining seven climate models: The spatially averaged projected changes were 2.77%, 8.15%, and 7.58% for the 2020–2039, 2040–2069, and 2070–2099 periods, respectively.