Methods for simulating climate scenarios with improved spatiotemporal specificity and less uncertainty

doi:10.1016/j.gloplacha.2019.05.013

	Methods for simulating climate scenarios with improved spatiotemporal specificity and less uncertainty
	Yue, T. -X.2,3; Zhao, N.2,3; Fan, Z. -M.2,3; Li, J.2,3; Chen, C. -F.4; Lu, Y. -M.5; Wang, C. -L.2; Gao, J.6; Xu, B.7; Jian, Y. -M.1
刊名	GLOBAL AND PLANETARY CHANGE
	2019-10-01
卷号	181 页码:18
关键词	Climate change CMIP5 scenarios Downscaling Uncertainty
ISSN号	0921-8181
DOI	10.1016/j.gloplacha.2019.05.013
通讯作者	Wilson, J. P.(jpwilson@usc.edu)
英文摘要	The General Circulation Models and Coupled Model Intercomparison Project Phase 5 (CMIP5) datasets used for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) painted future opportunities and challenges afforded by climate change with broad strokes. The model outputs incorporate substantial uncertainty due to the relatively coarse spatial scales and the complexity of the processes incorporated in these models and as a consequence, it is difficult to predict future trends and infrastructure needs in large countries like China at local and regional scales over the next century. The work reported in this article describes several statistical transfer functions that were used to downscale CMIP5 climate predictions in China. The original and new downscaled CMIP5 predictions are compared with observations from 735 meteorological stations scattered across China for the period 2006-2015 to show the various improvements achieved with downscaling. Comparing the three RCP scenarios (2.6, 4.5 and 8.5) during the period 2006-2015 with observations from 735 meteorological stations indicates that MAEs of mean annual temperature were 1.9 degrees C for China on average and that the actual temperature was under-estimated at 87% of the meteorological stations under all three scenarios. After the downscaling process using a High Accuracy Surface Modeling (HASM)-based method, the MAEs for mean annual temperature under the three scenarios were reduced to 0.62 degrees C for China on average. The MAEs of annual mean precipitation were 317.29, 315.24 and 315.49 mm under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively for China on average and the actual precipitation was over-estimated by all three scenarios at approximately 75% of the meteorological stations. The HASM-based downscaling process meant that the MAEs for the three scenarios were reduced to 80-85 mm for China on average. The downscaled predictions are used to show how temperature and precipitation are likely to vary by region in China from 2011 to 2100. The downscaled results suggest that most of China will become warmer and wetter on average under all three scenarios over the next 30 years and provide improved information to guide the investments and actions that will be needed to improve climate change resilience across China's varied landscapes in the 21st century.
资助项目	National Natural Science Foundation of China[41421001] ; National Natural Science Foundation of China[41590844] ; Innovation Project of LREIS[088RA600YA]
WOS关键词	SURFACE-TEMPERATURE ; CMIP5 ; PRECIPITATION ; MULTIMODEL ; CHINA
WOS研究方向	Physical Geography ; Geology
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000488666200003
资助机构	National Natural Science Foundation of China ; Innovation Project of LREIS
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/129725]
专题	中国科学院地理科学与资源研究所
通讯作者	Wilson, J. P.
作者单位	1.China Univ Geosci Beijing, Sch Earth Sci & Resources, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, 11A Datun Rd, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 101407, Peoples R China 4.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266510, Shandong, Peoples R China 5.Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, 523 Gongye Rd, Fuzhou 350002, Fujian, Peoples R China 6.Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, 1A Fuxing Rd, Beijing 100038, Peoples R China 7.Tsinghua Univ, Ctr Earth Syst Sci, Beijing 100084, Peoples R China 8.Univ Southern Calif, Spatial Sci Inst, Dornsife Coll Letters Arts & Sci, Los Angeles, CA 90089 USA
推荐引用方式 GB/T 7714	Yue, T. -X.,Zhao, N.,Fan, Z. -M.,et al. Methods for simulating climate scenarios with improved spatiotemporal specificity and less uncertainty[J]. GLOBAL AND PLANETARY CHANGE,2019,181:18.
APA	Yue, T. -X..,Zhao, N..,Fan, Z. -M..,Li, J..,Chen, C. -F..,...&Wilson, J. P..(2019).Methods for simulating climate scenarios with improved spatiotemporal specificity and less uncertainty.GLOBAL AND PLANETARY CHANGE,181,18.
MLA	Yue, T. -X.,et al."Methods for simulating climate scenarios with improved spatiotemporal specificity and less uncertainty".GLOBAL AND PLANETARY CHANGE 181(2019):18.