Statistically Downscaled Temperature Scenarios over China

Abstract

Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions (MLRs) of principal components (PCs). A stepwise screening procedure is used for selecting the skilful PCs as predictors used in the regression equation. The predictors include temperature at 850 hPa (T), the combination of sea-level pressure and temperature at 850 hPa (p+T) and the combination of geo—potential height and temperature at 850 hPa (H+T). The downscaling procedure is tested with the three predictors over three predictor domains. The optimum statistical model is obtained for each station and month by finding the predictor and predictor domain corresponding to the highest correlation. Finally, the optimum statistical downscaling models are applied to the Hadley Centre Coupled Model, version 3 (HadCM3) outputs under the Special Report on Emission Scenarios (SRES) A2 and B2 scenarios to construct local future temperature change scenarios for each station and month. The results show that (1) statistical downscaling produces less warming than the HadCM3 output itself; (2) the downscaled annual cycles of temperature differ from the HadCM3 output, but are similar to the observation; (3) the downscaled temperature scenarios show more warming in the north than in the south; (4) the downscaled temperature scenarios vary with emission scenarios, and the A2 scenario produces more warming than the B2, especially in the north of China.