Observational evidence that cloud feedback amplifies global warming

Ceppi, Paulo and Nowack, Peer ORCID: https://orcid.org/0000-0003-4588-7832 (2021) Observational evidence that cloud feedback amplifies global warming. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 118 (30). ISSN 1091-6490

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Abstract

Global warming drives changes in Earth’s cloud cover, which, in turn, may amplify or dampen climate change. This “cloud feedback” is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)—the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 ± 0.35 W⋅m−2⋅K−1 (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts.

Item Type: Article
Additional Information: Data Availability Code to perform the ridge-regression calculation has been deposited in GitHub (https://github.com/peernow/PNAS2021). Previously published data were used for this work. All observational, reanalysis, and GCM datasets used in this study are publicly available. CMIP data were obtained from the UK Center for Environmental Data Analysis portal (https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/). CERES data were obtained from the NASA Langley Research Center CERES ordering tool (https://ceres.larc.nasa.gov/data). Data for CFSR and MERRA2 were obtained from the Collaborative REAnalysis Technical Environment (CREATE) project (https://esgf-node.llnl.gov/search/create-ip/). JRA-55 data were downloaded from the National Center for Atmospheric Research/University Corporation for Atmospheric Research Research Data Archive (https://rda.ucar.edu/datasets/ds628.1/). ERA5 data were downloaded from the Copernicus Climate Data Store (https://doi.org/10.24381/cds.f17050d7 and https://doi.org/10.24381/cds.f17050d7). Acknowledgements: We acknowledge three anonymous reviewers for constructive comments, and thank Greg Cesana, Tim Myers, and Mark Zelinka for helpful discussions. This work used JASMIN, the UK collaborative data-analysis facility, and the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia. We acknowledge the WCRP, which, through its Working Group on Coupled Modeling, coordinated and promoted CMIP6. We thank the climate-modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access and the multiple funding agencies that support CMIP6 and ESGF. P.C. was supported by an Imperial College Research Fellowship and by Natural Environment Research Council Project NE/T006250/1. P.N. was supported by an Imperial College Research Fellowship.
Uncontrolled Keywords: clouds,global warming,climate change,machine learning,statistical learning,climate modelling,earth system analysis,climate feedbacks,satellite observations,sdg 13 - climate action ,/dk/atira/pure/sustainabledevelopmentgoals/climate_action
Faculty \ School: Faculty of Science > School of Environmental Sciences
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Depositing User: LivePure Connector
Date Deposited: 15 Jun 2021 00:08
Last Modified: 30 Oct 2022 00:48
URI: https://ueaeprints.uea.ac.uk/id/eprint/80270
DOI: 10.1073/pnas.2026290118

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