Methodology of the Constraint Condition in Dynamical Downscaling for Regional Climate Evaluation: A Review

Abstract

The dynamical downscaling method with a regional climate model (RCM) is widely used to assess the spatially detailed information about regional climate. However, the RCM result is considerably influenced by the systematic errors inherent to a general circulation model (GCM), which provides the initial and boundary conditions to the RCM. Such systematic errors sometimes lead to meaningless downscaled results. Many modified boundary dynamical downscaling (MBDDS) methods have been proposed to reduce the influences of the systematic errors of a GCM and extract meaningful signals for regional climate change. This study comprehensively reviews the MBDDS methods. The MBDDS methods partially modify the climate information projected by a GCM and use them as the boundary conditions of an RCM. The objectives of the methods are organized into two main objectives, that is, to obtain more reliable projections by correcting the biases in boundary conditions and to better understand the regional climate change mechanisms. To ensure comprehensive understanding of the MBDDS methods, this study attempts to interpret the errors included in the downscaled results using mathematical expressions, separating the GCM-originated bias and RCM's own bias. Using this analysis, the MBDDS methods are classified based on the following questions: What effect is expected from the bias correction? Which of the climate change components projected by a GCM is considered when assessing the future climate change? The direction and issues that need to be addressed in the future for better understanding the regional climate change are also discussed.