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Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity

Nature Climate Change volume 11, pages 501–507 (2021)Cite this article

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Abstract

Marine low clouds strongly cool the planet. How this cooling effect will respond to climate change is a leading source of uncertainty in climate sensitivity, the planetary warming resulting from CO2 doubling. Here, we observationally constrain this low cloud feedback at a near-global scale. Satellite observations are used to estimate the sensitivity of low clouds to interannual meteorological perturbations. Combined with model predictions of meteorological changes under greenhouse warming, this permits quantification of spatially resolved cloud feedbacks. We predict positive feedbacks from midlatitude low clouds and eastern ocean stratocumulus, nearly unchanged trade cumulus and a near-global marine low cloud feedback of 0.19 ± 0.12 W m−2 K−1 (90% confidence). These constraints imply a moderate climate sensitivity (~3 K). Despite improved midlatitude cloud feedback simulation by several current-generation climate models, their erroneously positive trade cumulus feedbacks produce unrealistically high climate sensitivities. Conversely, models simulating erroneously weak low cloud feedbacks produce unrealistically low climate sensitivities.

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Fig. 1: Observed and predicted low cloud response during a marine heatwave.
Fig. 2: Marine low cloud feedback constrained by observations and simulated by GCMs.
Fig. 3: Regime-partitioned marine low cloud feedbacks.
Fig. 4: Constraints on low cloud feedback and climate sensitivity.
Fig. 5: Impact on climate sensitivity range derived using multiple lines of evidence.

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Data availability

The meteorological cloud radiative kernels applied in this study are freely available for download at https://github.com/tamyers87/meteorological_cloud_radiative_kernels. All data used in this work are freely available for download at https://modis-atmos.gsfc.nasa.gov/MOD06_L2/index.html (MODIS), https://ceres.larc.nasa.gov/data/ (CERES-FBCT61), https://www.ncdc.noaa.gov/isccp/isccp-data-access (ISCCP62), http://climserv.ipsl.polytechnique.fr/gewexca/instruments/PATMOSX.html (PATMOS-x), https://github.com/mzelinka/cloud-radiative-kernels (cloud radiative kernels), https://www.esrl.noaa.gov/psd/data/gridded/ (NOAA OI SST), https://cds.climate.copernicus.eu/cdsapp#!/home (ERA5, ref. 63,64), https://disc.gsfc.nasa.gov/ (MERRA-2, refs. 65,66,67), https://esgf-node.llnl.gov (CMIP5 and CMIP6) and https://data.giss.nasa.gov/clouds/casccad/ (CASCCAD).

Code availability

The MATLAB and Python code used to process and analyse data can be obtained by contacting the corresponding author. The original code from ref. 46 used to produce Fig. 5 is available at https://doi.org/10.5281/zenodo.3945276 (ref. 60).

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Acknowledgements

T.A.M., M.D.Z., S.A.K. and P.M.C. worked under the auspices of the United States Department of Energy (DOE), Lawrence Livermore National Laboratory under contract no. DE-AC52-07NA27344 and were supported by the Regional and Global Model Analysis Program of the Office of Science at the DOE. This material is based on work done by R.C.S. and J.R.N. that was supported by the National Aeronautics and Space Administration under grant no. 80NSSC18K1020. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. We also thank the Earth System Grid Federation (ESGF) for archiving the model output and providing access, and we thank the multiple funding agencies who support CMIP and ESGF.

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Authors and Affiliations

  1. Lawrence Livermore National Laboratory, Livermore, CA, USA

    Timothy A. Myers, Mark D. Zelinka, Stephen A. Klein & Peter M. Caldwell

  2. Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA

    Ryan C. Scott & Joel R. Norris

  3. Science Systems and Applications, Inc., Hampton, VA, USA

    Ryan C. Scott

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Contributions

T.A.M. performed the primary analysis and wrote the paper. R.C.S. and M.D.Z. helped process observational data. M.D.Z. helped process CMIP output. M.D.Z. and S.A.K. computed the updated baseline PDF of climate sensitivity. T.A.M., R.C.S., M.D.Z., S.A.K. and J.R.N provided key ideas that shaped the study. P.M.C. computed the posterior PDF of ECS. All authors helped revise the original draft manuscript.

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Correspondence to Timothy A. Myers.

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Supplementary Figs. 1–19, Tables 1 and 2, Methods and References.

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Myers, T.A., Scott, R.C., Zelinka, M.D. et al. Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity. Nat. Clim. Chang. 11, 501–507 (2021). https://doi.org/10.1038/s41558-021-01039-0

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