Regional climate simulation for Korea using dynamic downscaling and statistical adjustment

Abstract

Recently the regional impact assessment due to global warming is one of the urgent tasks to every country in the world, under the circumstances of increasing carbon dioxide in the atmosphere. This assessment must include not only meteorological factors, such as surface air temperature and precipitation, etc., but also the response of the local ecosystem. Based on a previous study, for example, it has been known that Phyllostachys' habitation, which is one of the bamboo species popular in Korea, is quite sensitive to temperature change, in particular during the winter season. Thus, adequate climate information is essential to derive a solid conclusion on the regional impact assessment for future climate change. In this study, we adopted a dynamical downscaling technique to get regional future climate information, with the regional climate model (MM5, Pennsylvania State University/National Center for Atmospheric Research mesoscale model) from the Max-Planck Institute for Meteorology Models and Data Group's Atmosphere-Ocean General Circulation Model (AOGCM) ECAHM4, and HOPE-G (ECHO-G) simulation for future climate, based on future greenhouse gas (GHG) emission scenario of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) A2. Through this nesting process we got reasonable regional climate change information. However, we found a couple of systematic differences, such as a cold bias in the surface air temperature, simulated by MM5 compared to that by the AOGCM ECHO-G. This cold bias may cause to loose credibility on the future climate scenario to the impact assessment studies. Accordingly, we introduced a transfer function to correct the systematic bias of the dynamic model in the regional-scale, and to predict the regional climate from large-scale predictors. These transfer functions are obtained from the daily mean temperature of 17 surface observation stations in Korea for 10 years from 1992 to 2001, and 10-year simulation data obtained from regional climate model (RCM) for each mode of EOFA to correct the systematic bias of RCM data. With these transfer functions, we can correct the RMS error of the daily mean temperature in RCM as much as 47.6% in winter and 86.5% in summer. After dynamical downscaling and statistical adjustment, we may provide adequate climate change information for regional assessment studies.