Regional probabilistic climate projection for Japan with a regression model using multi-model ensemble experiments

Abstract

We have developed a statistical downscaling method for generating probabilistic climate projections using multiple general circulation models (GCMs). A regression model was established so that the combination of coefficients of the GCMs reflects the characteristics of the variation of observations at each grid point. We adopted the elastic net penalty to estimate the regression model, considering model projection similarities. Using an observation system with a high spatial resolution, we conducted statistically downscaled probabilistic climate projections with 20-km horizontal grid spacing. Mean precipitation is generally projected to increase associated with higher temperatures and consequently increased atmospheric moisture content. Weakening of the winter monsoon may cause precipitation decreases in some areas. There is a high probability of a temperature increase in excess of 4 K in most areas of Japan by the end of the 21st century under the CMIP5 RCP8.5 scenario. The estimated probability of monthly precipitation exceeding 300 mm increases along the Pacific coast of Japan during the summer season and along the coast of the Japan Sea during the winter season. Our probabilistic climate projection using statistical methods is expected to provide useful information to stakeholders involved in impact studies and risk assessments.