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DOI	10.5194/cp-16-1715-2020
	A Bayesian framework for emergent constraints: Case studies of climate sensitivity with PMIP
	Renoult M.; Douglas Annan J.; Catherine Hargreaves J.; Sagoo N.; Flynn C.; Kapsch M.-L.; Li Q.; Lohmann G.; Mikolajewicz U.; Ohgaito R.; Shi X.; Zhang Q.; Mauritsen T.
发表日期	2020
ISSN	18149324
起始页码	1715
结束页码	1735
卷号	16 期号:5
英文摘要	In this paper we introduce a Bayesian framework, which is explicit about prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on ordinary least squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7K (0.6-5.2, 5th-95th percentiles) using the PMIP2, PMIP3, and PMIP4 datasets for the LGM and 2.3K (0.5-4.4) with the PlioMIP1 and PlioMIP2 datasets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7K (0.7-5.2) using the LGM and 2.3K (0.4-4.5) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a tighter constraint of 2.5K (0.8-4.0) using the restricted ensemble. We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95% probability of climate sensitivity mostly below 5K and only exceeding 6K in a single and most uncertain case assuming a large structural uncertainty. The approach is compared with other approaches based on OLS, a Kalman filter method, and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, an artefact due to a flatter regression line in the case of lack of correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation for their potential use in future probabilistic estimations of climate sensitivity. © 2020 Author(s).
语种	英语
scopus关键词	Bayesian analysis; climate change; climate effect; ensemble forecasting; numerical model; sensitivity analysis
来源期刊	Climate of the Past
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/146672
作者单位	Department of Meteorology, Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden; Blue Skies Research Ltd, Settle, United Kingdom; Max Planck Institute for Meteorology, Hamburg, Germany; Department of Physical Geography, Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden; Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany; Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
推荐引用方式 GB/T 7714	Renoult M.,Douglas Annan J.,Catherine Hargreaves J.,et al. A Bayesian framework for emergent constraints: Case studies of climate sensitivity with PMIP[J],2020,16(5).
APA	Renoult M..,Douglas Annan J..,Catherine Hargreaves J..,Sagoo N..,Flynn C..,...&Mauritsen T..(2020).A Bayesian framework for emergent constraints: Case studies of climate sensitivity with PMIP.Climate of the Past,16(5).
MLA	Renoult M.,et al."A Bayesian framework for emergent constraints: Case studies of climate sensitivity with PMIP".Climate of the Past 16.5(2020).