Reducing Uncertainties in Climate Projections with Emergent Constraints:Concepts, Examples and Prospects

作     者：Florent BRIENT Florent BRIENT

作者机构：CNRM Université de Toulouse Météo-France CNRS 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2020年第37卷第1期

页      面：1-15页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：funding from the Agence Nationale de la Recherche (ANR) [grant HIGH-TUNE ANR-16-CE01-0010] 

主　　题：climate modeling emergent constraint climate change climate sensitivity 

摘      要：Models disagree on a significant number of responses to climate change,such as climate feedback,regional changes,or the strength of equilibrium climate *** constraints aim to reduce these uncertainties by finding links between the inter-model spread in an observable predictor and climate *** this paper,the concepts underlying this framework are recalled with an emphasis on the statistical inference used for narrowing uncertainties,and a review of emergent constraints found in the last two *** links between highlighted predictors are explored,especially those targeting uncertainty reductions in climate sensitivity,cloud feedback,and changes of the hydrological *** the disagreement across emergent constraints suggests that the spread in climate sensitivity can not be significantly *** calls for weighting the realism of emergent constraints by quantifying the level of physical understanding explaining the *** would also permit more efficient model evaluation and better targeted model *** the context of the upcoming CMIP6 model intercomparison a growing number of new predictors and uncertainty reductions is expected,which call for robust statistical inferences that allow cross-validation of more likely estimates.