A new statistical downscaling approach for global evaluation of the CMIP5 precipitation outputs: Model development and application

被引:43
作者
Zhang, Qiang [1 ,2 ,3 ]
Shen, Zexi [1 ,2 ,3 ]
Xu, Chong-Yu [4 ]
Sun, Peng [5 ]
Hu, Pan [1 ,2 ,3 ]
He, Chunyang [6 ]
机构
[1] Beijing Normal Univ, Minist Educ, Key Lab Environm Change & Nat Disaster, Beijing 100875, Peoples R China
[2] Beijing Normal Univ, Fac Geog Sci, Acad Disaster Reduct & Emergency Management, Minist Educ,Minist Civil Affairs, Beijing 100875, Peoples R China
[3] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resources Ecol, Beijing 100875, Peoples R China
[4] Univ Oslo, Dept Geosci & Hydrol, POB 1047, N-0316 Oslo, Norway
[5] Anhui Normal Univ, Coll Terr Resource & Tourism, Wuhu 241002, Anhui, Peoples R China
[6] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, CHESS, Beijing 100875, Peoples R China
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
Statistical downscaling; BCSD; BNRD; CMIP5; Precipitation changes; FUTURE CHANGES; EXTREME PRECIPITATION; CLIMATE MODELS; TEMPERATURE; SIMULATIONS; PROJECTIONS; CHINA; RAINFALL; DRY;
D O I
10.1016/j.scitotenv.2019.06.310
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models have been widely used in studies of climate changes related to scenarios at global and regional scales. However, CMIP5 outputs cannot be used directly in analysis of climate changes due to coarse spatial resolution. Here, we proposed a new statistical downscaling method for the downscaling practice of the CMIP5 outputs, i.e. Bias-corrected and station-based Nonlinear Regression Downscaling method based on Randomly-Moving Points (BNRD). And up to now, there are only two global downscaled CMIP5 precipitation datasets, i.e. NASA daily downscaled CMIP5 precipitation product and BCSD-based (Bias Correction Spatial Disaggregation) monthly downscaled CMIP5 precipitation product available online, which are both based on BCSD downscaling method. Hence, we evaluated downscaling performance of BNRD by comparing it with the downscaled CMIP5 outputs using the BCSD method in this current study. The results indicate that: (1) during the period for development of the model (1964-2005), the error between downscaled CMIP5 precipitation and GPCC ranges between -50 mm-50 mm at monthly scale. When compared to BCSD-downscaled CMIP5 precipitation, BNRD-downscaled CMIP5 precipitation well reduces errors and avoids underestimation and overestimation of GPCC by BCSD-downscaled CMIP5 precipitation: (2) during period for verification of the downscaling models (2006-2013), the maximum (182 mm), minimum (15 mm) and average (68 mm) RMSEs between BNRD-downscaled CMIP5 precipitation and GPCC are all lower than those between BCSD-downscaled CMIP5 precipitation and GPCC at continental scales. Besides, from the average precipitation viewpoint, BNRD-downscaled CMIP5 precipitation is in higher correlation (around 0.75) with GPCC than BCSD-downscaled CMIP5 precipitation under RCP4.5 and RCP8.5 scenarios at continental scales; (3) BNRD resolved the negative relation to GPCC in the areas near equator, induding north part of the South America, southern Africa, northern Australia. In all, BNRD downscaling method developed in this study performs better in describing GPCC changes in both space and time when compared to BCSD and can be used for downscaling practice of CMIP5 and even potentially CMIP6 precipitation outputs over the globe. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:1048 / 1067
页数:20
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