气候变化领域集成服务门户(CCPortal): Confidence intervals in optimal fingerprinting

CCPortal

DOI	10.1007/s00382-018-4356-3
	Confidence intervals in optimal fingerprinting
	DelSole T.; Trenary L.; Yan X.; Tippett M.K.
发表日期	2019
ISSN	0930-7575
起始页码	4111
结束页码	4126
卷号	52 期号:2020-07-08
英文摘要	Optimal fingerprinting is a standard method for detecting climate changes. Among the uncertainties taken into account by this method, one is the fact that the response to climate forcing is not known exactly, but in practice is estimated from ensemble averages of model simulations. This uncertainty can be taken into account using an Error-in-Variables model (or equivalently, the Total Least Squares method), and can be expressed through confidence intervals. Unfortunately, the predominant paradigm (likelihood ratio theory) for deriving confidence intervals is not guaranteed to work because the number of parameters that are estimated in the Error-in-Variables model grows with the number of observations. This paper discusses various methods for deriving confidence intervals and shows that the widely-used intervals proposed in the seminal paper by Allen and Stott are effectively equivalent to bias-corrected intervals from likelihood ratio theory. A new, computationally simpler, method for computing these intervals is derived. Nevertheless, these confidence intervals are incorrect in the “weak-signal regime”. This conclusion does not necessarily invalidate previous detection and attribution studies because many such studies lie in the strong-signal regime, for which standard methods give correct confidence intervals. A new diagnostic is introduced to check whether or not a data set lies in the weak-signal regime. Finally, and most importantly, a bootstrap method is shown to give correct confidence intervals in both strong- and weak-signal regimes, and always produces finite confidence intervals, in contrast to the likelihood ratio method which can give unbounded intervals that do not match the actual uncertainty. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Detection and attribution; Optimal fingerprinting; Total least squares
语种	英语
scopus关键词	bootstrapping; climate change; climate forcing; detection method; least squares method; uncertainty analysis
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/146427
作者单位	Department of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, United States; Program in Atmospheric, Oceanic Sciences, Princeton University, Princeton, United States; Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, United States; Department of Meteorology, Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah, Saudi Arabia
推荐引用方式 GB/T 7714	DelSole T.,Trenary L.,Yan X.,et al. Confidence intervals in optimal fingerprinting[J],2019,52(2020-07-08).
APA	DelSole T.,Trenary L.,Yan X.,&Tippett M.K..(2019).Confidence intervals in optimal fingerprinting.Climate Dynamics,52(2020-07-08).
MLA	DelSole T.,et al."Confidence intervals in optimal fingerprinting".Climate Dynamics 52.2020-07-08(2019).