Observational evidence that cloud feedback amplifies global warming

Ceppi, Paulo and Nowack, Peer (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).

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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).
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
Depositing User: LivePure Connector
Date Deposited: 15 Jun 2021 00:08
Last Modified: 06 Oct 2021 01:56
URI: https://ueaeprints.uea.ac.uk/id/eprint/80270
DOI: 10.1073/pnas.2026290118

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