Application of GCM Bias Correction to RCM Simulations of East Asian Winter Climate - Chang-Mook Lim; Yoo-Bin Yhang; Suryun Ham (Atmosphere, 2019)
Both the global circulation model (GCM) and regional climate model (RCM) simulations suffer from model biases that eventually result in significant errors in regional forecasts. This model bias issue is addressed using the bias correction approach. This study examines the influence of bias correction on the performance of downscaling simulations of the East Asian winter climate using the Global/Regional Integrated Model system (GRIMs). To assess the bias correction approach, we conducted three sets of simulations for 25 winters (December to February) from 1982 to 2006 over East Asia. The GRIMs were forced by the (1) National Centers for Environmental Prediction (NCEP) Department of Energy (DOE) reanalysis data, (2) original NCEP Climate Forecast System (CFS) data, and (3) bias-corrected CFS data. The GCM climatological means were adjusted based on the NCEP−DOE reanalysis data. The bias correction method was applied to zonal and meridional wind, temperature, geopotential height, specific humidity, and sea surface temperature of the CFS data. The GCM-driven experiments with/without bias correction were compared with the reanalysis-driven simulation. The results of this comparison suggest that the application of bias correction improves the downscaled climate in terms of the climatological mean, inter-annual variability, and extreme events owing to the elimination of errors in large-scale circulations. The effect of bias correction on the simulated extreme event is not as significant as those on the climatological mean and inter-annual variability, but the increased skill appears to be a clue for potential use for predicting extreme events.