Title: Comparison of Two Statistical Downscaling Methods in the Taihu Basin
Abstract: Global climate models(GCMs) have been used widely for climate change impact studies;however,the spatial resolution of GCMs is too coarse to resolve regional scale effects and local impacts.Downscaling techniques can improve regional or local estimates of variables from GCM outputs.In this study,two statistical downscaling methods,an automated statistical downscaling(ASD) model and statistical downscaling(SDSM) model were used to downscale daily maximum/minimum temperature and daily precipitation and to construct future climate change scenarios for 2046-2065 and 2081-2100.Outputs from the general circulation model(BCCR) under A1B scenarios and ERA-40 reanalysis data(used to test the models over the calibration period 1961-1990,and variation period 1991-2000) were compared with observed temperature and precipitation data from eight meteorological stations in the Taihu Basin.The results underline limitations to downscaling precipitation and the strength when downscaling air temperature.When selecting predictors,the quality of SDSM results depended mostly on the skill of the user,and the best VIF and bias were obtained by trial and error;however,similar results could be obtained using ASD with ease.During the calibration and validation procedures,both ASD and SDSM methods performed well,but ASD performed better than SDSM.For future precipitation and temperature,ASD and SDSM showed similar patterns,and changes in maximum and minimum temperature were not obvious.On the whole,the simulations for annual averages of ASD rose slightly,while the values of SDSM decreased slightly.The precipitation generated by these two methods varied greatly:the deviation of ASD between future periods and the baseline period(1961-2000) was much smaller than for SDSM.The comparison of SDSM and ASD models indicates that neither could perform well for all stations and seasons.However,considering our simulation results and other studies,we conclude that ASD performs better than SDSM in the Taibu Basin.Future studies should use multi-source GCM inputs to provide better results and non-linear approaches like artificial neural networks to reduce observation noise.
Publication Year: 2012
Publication Date: 2012-01-01
Language: en
Type: article
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Cited By Count: 3
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