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

Qiang Zhang; Zexi Shen; Chong-Yu Xu; Peng Sun; Pan Hu; Chunyang He

doi:10.1016/j.scitotenv.2019.06.310

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

Sci Total Environ. 2019 Nov 10:690:1048-1067. doi: 10.1016/j.scitotenv.2019.06.310. Epub 2019 Jul 6.

Authors

Qiang Zhang¹, Zexi Shen², Chong-Yu Xu³, Peng Sun⁴, Pan Hu⁵, Chunyang He⁶

Affiliations

¹ Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China; Faculty of Geographical Science, Academy of Disaster Reduction and Emergency Management, Ministry of Education/Ministry of Civil Affairs, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing 100875, China. Electronic address: zhangq68@bnu.edu.cn.
² Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China; Faculty of Geographical Science, Academy of Disaster Reduction and Emergency Management, Ministry of Education/Ministry of Civil Affairs, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing 100875, China. Electronic address: shenzexi@mail.bnu.edu.cn.
³ Department of Geosciences and Hydrology, University of Oslo, P O Box 1047, Blindern, N-0316 Oslo, Norway.
⁴ College of Territorial Resource and Tourism, Anhui Normal University, Anhui 241002, China.
⁵ Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China; Faculty of Geographical Science, Academy of Disaster Reduction and Emergency Management, Ministry of Education/Ministry of Civil Affairs, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing 100875, China.
⁶ Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China.

PMID: 31470471
DOI: 10.1016/j.scitotenv.2019.06.310

Abstract

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 Non-linear 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, including 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.

Keywords: BCSD; BNRD; CMIP5; Precipitation changes; Statistical downscaling.