气候变化领域集成服务门户(CCPortal): Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology

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DOI	10.1007/s10584-019-02393-x
	Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology
	Chen J.; Brissette F.P.; Zhang X.J.; Chen H.; Guo S.; Zhao Y.
发表日期	2019
ISSN	0165-0009
起始页码	361
结束页码	377
卷号	153 期号:3
英文摘要	Bias correction is usually applied to climate model outputs before they are used as inputs to environmental models for impact studies. Every climate model is post-processed independently of others to account for biases originating from model structure and internal variability. To better understand the role of internal variability, multi-member ensembles (multiple runs of a single climate model, with identical forcing but different initial conditions) have now become common in the modeling community. Bias correcting such ensembles requires specific considerations. Correcting all members of such an ensemble independently would force all of them to the target distribution, thus removing the signature of natural variability over the calibration period. How this undesirable effect would propagate onto subsequent time periods is unknown. This study proposes three bias correction variants of a multi-member ensemble and compares their performances against an independent correction of each individual member of the ensemble. The comparison is based on precipitation and temperature, as well as on resulting streamflows simulated by a hydrological model. Two multi-member ensembles (5-member CanESM2 and 10-member CSIRO-MK3.6) were used for a subtropical monsoon watershed in China. The results show that all bias correction methods reduce precipitation and temperature biases for all ensemble members. As expected, independent correction reduces the spread of each ensemble over the calibration period. This is, however, followed by an overestimation of the spread over the subsequent validation period. Pooling all members to calculate common bias correction factors produces the best results over the calibration period; however, the difference among three bias correction variants becomes less clear over the validation period due to internal variability, and even less so when considering streamflows, as the impact model adds its own uncertainty. © 2019, Springer Nature B.V.
语种	英语
scopus关键词	Calibration; Climate change; Hydrology; Stream flow; Bias-correction methods; Calibration periods; Climate change impact; Environmental model; Hydrological modeling; Internal variability; Natural variability; Undesirable effects; Climate models; climate modeling; environmental modeling; hydrological regime; hydrology; monsoon; sampling bias; streamflow; subtropical region; watershed; China
来源期刊	Climatic Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/147494
作者单位	State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, 299 Bayi Road, Wuchang Distinct, Wuhan, Hubei 430072, China; École de technologie supérieure, Université du Québec, 1100 Notre-Dame Street West, Montreal QC, H3C 1K3, Canada; USDA-ARS, Grazinglands Research Laboratory, 7207W. Cheyenne St., El Reno, OK 73036, United States; Huaian Hydraulic Survey and Design Institute Co., Ltd., 26 Shenzhen road, Qingjiangpu Distinct, Huaian, Jiangsu 223003, China
推荐引用方式 GB/T 7714	Chen J.,Brissette F.P.,Zhang X.J.,et al. Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology[J],2019,153(3).
APA	Chen J.,Brissette F.P.,Zhang X.J.,Chen H.,Guo S.,&Zhao Y..(2019).Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology.Climatic Change,153(3).
MLA	Chen J.,et al."Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology".Climatic Change 153.3(2019).