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.2021 Jul 27;118(30):e2026290118.
doi: 10.1073/pnas.2026290118.

Observational evidence that cloud feedback amplifies global warming

Affiliations

Observational evidence that cloud feedback amplifies global warming

Paulo Ceppi et al. Proc Natl Acad Sci U S A..

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.

Keywords: climate change; climate feedbacks; climate modeling; climate sensitivity; clouds.

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Conflict of interest statement

The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
CMIP mean SW cloud-radiative sensitivities to surface temperature,ΘTsfc, and EIS,ΘEIS (Eq.1), for a sample 5°× 5° target grid box in the Southeast Pacific (82.5° W, 17.5° S; black box). Radiative anomalies are normalized for a one-SD (σ) anomaly in the controlling factors, based on monthly variability. SeeSI Appendix, Fig. S1 for the remaining three controlling factors.
Fig. 2.
Fig. 2.
(A) Actual vs. predicted global-mean cloud feedback for 52 CMIP models (circles) and the multimodel mean (square). The one-to-one line is shown in solid black. Dashed lines represent the least-squares fit (black) and the 5 to 95% prediction intervals (blue). Blue curves represent probability distributions for the observational estimates (amplitudes scaled arbitrarily). (B) Ranges of cloud-feedback values for the IPCC AR5, the WCRP assessment, the CMIP models, and the observational constraint (Obs). Thin and thick bars denote 90% and 66% CIs, respectively. Black horizontal bars indicate the medians for the IPCC, WCRP, and observational estimates and the mean for the CMIP models. No 66% interval was provided for the IPCC cloud-feedback estimate.
Fig. 3.
Fig. 3.
(A) Predicted cloud feedback based on observed cloud responses to controlling factors (Eq.2), calculated by averaging the sensitivities across the four reanalyses (SI Appendix, Figs. S8 and S9) and multiplying by the CMIP mean changes in controlling factors (SI Appendix, Fig. S2A andB). (B) CMIP mean predicted cloud feedback. (C) CMIP mean actual cloud feedback. InA, hatching denotes regions where the sign of the prediction is consistent for any choice of the set of sensitivities (based on one of four reanalyses) and controlling factor responses (based on one of 52 CMIP models). InB andC, hatching denotes regions where 90% of the models agree on the sign of the feedback.
Fig. 4.
Fig. 4.
(A) Negative inverse of equilibrium climate sensitivity (−1/ECS) vs. predicted cloud feedback for 52 CMIP models (circles) and the multimodel mean (square). Dashed lines represent the least-squares fit (black) and the 5 to 95% prediction intervals (blue). Blue curves represent probability distributions for the observational estimates (amplitudes scaled arbitrarily). Note that they axis on the right-hand side is in units of ECS. (B) Ranges of ECS values based on the IPCC AR5, the WCRP assessment, the CMIP models, and the observational constraint (Obs). The thick blue and red bars denote 66% CIs. Black horizontal bars indicate the CMIP mean and the median (50% quantile) of the observational constraint. No central ECS estimate was provided in the IPCC AR5 report.
See this image and copyright information in PMC

References

    1. Boucher O., et al. , “Clouds and aerosols” in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, Cambridge, UK, 2013), pp. 571–657.
    1. Zelinka M. D., Randall D. A., Webb M. J., Klein S. A., Clearing clouds of uncertainty. Nat. Clim. Change 7, 674–678 (2017).
    1. Sherwood S., et al. , An assessment of Earth’s climate sensitivity using multiple lines of evidence. Rev. Geophys. 58, 1–92 (2020). - PMC - PubMed
    1. Ceppi P., Brient F., Zelinka M. D., Hartmann D. L., Cloud feedback mechanisms and their representation in global climate models. Wiley Interdisc. Rev. : Clim. Change 8, e465 (7 2017).
    1. Zelinka M. D., et al. , Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 47, e2019GL085782 (2020).

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