气候变化领域集成服务门户(CCPortal): Remaining error sources in bias-corrected climate model outputs

CCPortal

DOI	10.1007/s10584-020-02744-z
	Remaining error sources in bias-corrected climate model outputs
	Chen J.; Brissette F.P.; Caya D.
发表日期	2020
ISSN	0165-0009
起始页码	563
结束页码	582
卷号	162 期号:2
英文摘要	Bias correction methods have now emerged as the most commonly used approach when applying climate model outputs to impact studies. However, comparatively much fewer studies have looked at the limitations of bias correction caused by the very nature of the climate system. Two main sources of errors can affect the efficiency of bias correction over a future period: climate sensitivity and internal variability of the climate system. The former is related to differences in the forcing response between a climate model and the real climate system, whereas the latter results from the chaotic nature of the climate system. Using a “pseudo-reality” approach, this study investigates the contribution of these two sources of error to remaining biases of climate model after bias correction for future periods. The pseudo-reality approach uses one climate model as a reference dataset to correct other climate models. Results indicate that bias correction is beneficial over the reference period and in near future periods. However, large biases remain in future periods. The difference in climate sensitivities is the main contributor to the remaining biases in corrected data. Internal variability affects the near and far future similarly and may dominate in the near future, especially for precipitation. The impact of differences in climate sensitivity between the reference dataset and climate model data cannot be eliminated, while the impact of internal variability can be lessened by using a reference period for as long as possible to filter out low-frequency modes of variability. © 2020, Springer Nature B.V.
英文关键词	Bias correction; Climate change; Climate models; Climate sensitivity; Impact studies; Internal climate variability; Pseudo-reality
语种	英语
scopus关键词	Errors; Bias correction; Bias-correction methods; Chaotic nature; Climate sensitivity; Climate system; Error sources; Internal variability; Low-frequency modes; Climate models; climate change; climate modeling; correction; data set; detection method; error correction
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/147110
作者单位	State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, Hubei 430072, China; Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan, China; École de technologie supérieure, Université du Québec, 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada; Centre ESCER, Université du Québec à Montréal, Case postale 8888, succursale Centre-Ville,, Montreal, QC H3C 3P8, Canada
推荐引用方式 GB/T 7714	Chen J.,Brissette F.P.,Caya D.. Remaining error sources in bias-corrected climate model outputs[J],2020,162(2).
APA	Chen J.,Brissette F.P.,&Caya D..(2020).Remaining error sources in bias-corrected climate model outputs.Climatic Change,162(2).
MLA	Chen J.,et al."Remaining error sources in bias-corrected climate model outputs".Climatic Change 162.2(2020).