气候变化领域集成服务门户(CCPortal): Combining emergent constraints for climate sensitivity

CCPortal

DOI	10.1175/JCLI-D-19-0911.1
	Combining emergent constraints for climate sensitivity
	Bretherton C.S.; Caldwell P.M.
发表日期	2020
ISSN	0894-8755
起始页码	7413
结束页码	7430
卷号	33 期号:17
英文摘要	A method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy Y such as equilibrium climate sensitivity (ECS). The method is based on fitting a multivariate Gaussian PDF for Y and the emergent constraints using an ensemble of global climate models (GCMs); it can be viewed as a form of multiple linear regression of Y on the constraints. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Its general form (Method C) accounts for correlations between the constraints. Method C becomes less robust when some constraints are too strongly related to each other; this can be mitigated using regularization approaches such as ridge regression. An illuminating special case, Method U, neglects any correlations between constraints except through their mutual relationship to the climate proxy; it is more robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and the climate feedback parameter using a previously published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). The 62s posterior range of ECS for Method C with no overconfidence adjustment is 4.3 6 0.7 K. For Method U with a large overconfidence adjustment, it is 4.0 6 1.3 K. This study adds confidence to past findings that most constraints predict higher climate sensitivity than the CMIP mean. © 2020 American Meteorological Society.
英文关键词	Linear regression; Climate feedbacks; Climate sensitivity; Coupled Model Intercomparison Project; Global climate model; Multiple linear regressions; Multivariate Gaussian pdf; Probabilistic estimates; Regularization approach; Climate models; climate modeling; climate prediction; correlation; ensemble forecasting; Gaussian method; global climate; multiple regression; multivariate analysis; probability; uncertainty analysis
语种	英语
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/171165
作者单位	University of Washington, Seattle, WA, United States; Lawrence Livermore National Laboratory, Livermore, CA, United States
推荐引用方式 GB/T 7714	Bretherton C.S.,Caldwell P.M.. Combining emergent constraints for climate sensitivity[J],2020,33(17).
APA	Bretherton C.S.,&Caldwell P.M..(2020).Combining emergent constraints for climate sensitivity.Journal of Climate,33(17).
MLA	Bretherton C.S.,et al."Combining emergent constraints for climate sensitivity".Journal of Climate 33.17(2020).