气候变化领域集成服务门户(CCPortal): Bias-corrections on aridity index simulations of climate models by observational constraints

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DOI	10.1002/joc.7279
	Bias-corrections on aridity index simulations of climate models by observational constraints
	Yu, Haipeng; Zhang, Qiang; Wei, Yun; Liu, Chenxi; Ren, Yu; Yue, Ping; Zhou, Jie
通讯作者	Zhang, Q (通讯作者)，China Meteorol Adm, Inst Arid Meteorol, Key Lab Arid Climate Change & Reducing Disaster G, Key Open Lab Arid Climate Change & Disaster Reduc, Lanzhou, Peoples R China.
发表日期	2022
ISSN	0899-8418
EISSN	1097-0088
起始页码	889
结束页码	907
卷号	42 期号:2
英文摘要	Aridity Index (AI) indicates the balance between water supply and water demand on the atmosphere-land interface. Despite continuous improvements, coupled climate models still have significant systematic errors in simulating AI in terms of temporal and spatial variabilities. One of the approaches to bias-correct simulations is extracting the linear relationship between historical observations and model outputs by utilizing the empirical orthogonal function (EOF). In this study, the methodology of ensemble EOF-based bias-correction by observational constraints is developed based on previous bias-correction approach, with the improvement on seeking the optimal combinations of the leading modes with sensitivity test and replacing the certain correction with the ensemble means of optimal members. In verification, the ensemble mean of Coupled Model Intercomparison Project phase 5 (CMIP5-EM) is bias-corrected towards the CPC/GLDAS observations, and the extracted leading modes present high correlations with internal climate variability. By cross-validation and posteriori independent validation of hindcasts over the historical period (1948-2005), the ensemble EOF-based bias-correction could better present spatial patterns compared to the CMIP5-EM after systematic bias-correction, as indicated by the anomaly correlation and the root mean square error. The verifications also indicate that the temporal variability in aridity over different dryland regions is much closer to that in the observations and that the dryland subtype changes are improved significantly by bias-corrections. Besides, another observational dataset of UDel/CRU is applied to assess the uncertainty on different datasets and the improvement on skill scores is robust. The above results verify that the ensemble EOF-based bias-corrections provide better reference for assessing and projecting global aridity changes by climate models.
关键词	POTENTIAL EVAPOTRANSPIRATION SPATIAL VARIABILITY DOWNSCALING MODEL SYSTEMATIC-ERROR PRECIPITATION SURFACE PREDICTION CHINA EXPANSION FORECASTS
英文关键词	aridity; bias-correction; CMIP5; EOF; observational constraints
语种	英语
WOS研究方向	Meteorology & Atmospheric Sciences
WOS类目	Meteorology & Atmospheric Sciences
WOS记录号	WOS:000673992400001
来源期刊	INTERNATIONAL JOURNAL OF CLIMATOLOGY
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/255053
作者单位	[Yu, Haipeng] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Land Surface Proc & Climate Change Cold &, Lanzhou, Peoples R China; [Yu, Haipeng; Zhang, Qiang; Yue, Ping] China Meteorol Adm, Inst Arid Meteorol, Key Lab Arid Climate Change & Reducing Disaster G, Key Open Lab Arid Climate Change & Disaster Reduc, Lanzhou, Peoples R China; [Wei, Yun; Liu, Chenxi; Ren, Yu; Zhou, Jie] Lanzhou Univ, Coll Atmospher Sci, Minist Educ, Key Lab Semiarid Climate Change, Lanzhou, Peoples R China
推荐引用方式 GB/T 7714	Yu, Haipeng,Zhang, Qiang,Wei, Yun,et al. Bias-corrections on aridity index simulations of climate models by observational constraints[J]. 中国科学院西北生态环境资源研究院,2022,42(2).
APA	Yu, Haipeng.,Zhang, Qiang.,Wei, Yun.,Liu, Chenxi.,Ren, Yu.,...&Zhou, Jie.(2022).Bias-corrections on aridity index simulations of climate models by observational constraints.INTERNATIONAL JOURNAL OF CLIMATOLOGY,42(2).
MLA	Yu, Haipeng,et al."Bias-corrections on aridity index simulations of climate models by observational constraints".INTERNATIONAL JOURNAL OF CLIMATOLOGY 42.2(2022).