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Spread in model climate sensitivity traced to atmospheric convective mixing

Author

Listed:
  • Steven C. Sherwood

    (Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney 2052, Australia)

  • Sandrine Bony

    (Laboratoire de Météorologie Dynamique and Institut Pierre Simon Laplace (LMD/IPSL), CNRS, Université Pierre et Marie Curie, Paris 75252, France)

  • Jean-Louis Dufresne

    (Laboratoire de Météorologie Dynamique and Institut Pierre Simon Laplace (LMD/IPSL), CNRS, Université Pierre et Marie Curie, Paris 75252, France)

Abstract

Equilibrium climate sensitivity refers to the ultimate change in global mean temperature in response to a change in external forcing. Despite decades of research attempting to narrow uncertainties, equilibrium climate sensitivity estimates from climate models still span roughly 1.5 to 5 degrees Celsius for a doubling of atmospheric carbon dioxide concentration, precluding accurate projections of future climate. The spread arises largely from differences in the feedback from low clouds, for reasons not yet understood. Here we show that differences in the simulated strength of convective mixing between the lower and middle tropical troposphere explain about half of the variance in climate sensitivity estimated by 43 climate models. The apparent mechanism is that such mixing dehydrates the low-cloud layer at a rate that increases as the climate warms, and this rate of increase depends on the initial mixing strength, linking the mixing to cloud feedback. The mixing inferred from observations appears to be sufficiently strong to imply a climate sensitivity of more than 3 degrees for a doubling of carbon dioxide. This is significantly higher than the currently accepted lower bound of 1.5 degrees, thereby constraining model projections towards relatively severe future warming.

Suggested Citation

  • Steven C. Sherwood & Sandrine Bony & Jean-Louis Dufresne, 2014. "Spread in model climate sensitivity traced to atmospheric convective mixing," Nature, Nature, vol. 505(7481), pages 37-42, January.
    • Handle: RePEc:nat:nature:v:505:y:2014:i:7481:d:10.1038_nature12829
    DOI: 10.1038/nature12829
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    Citations

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    Cited by:

    1. Kathleen A. Schiro & Hui Su & Fiaz Ahmed & Ni Dai & Clare E. Singer & Pierre Gentine & Gregory S. Elsaesser & Jonathan H. Jiang & Yong-Sang Choi & J. David Neelin, 2022. "Model spread in tropical low cloud feedback tied to overturning circulation response to warming," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Wenyu Zhou & L. Ruby Leung & Nicholas Siler & Jian Lu, 2023. "Future precipitation increase constrained by climatological pattern of cloud effect," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    3. Young, Peter C., 2018. "Data-based mechanistic modelling and forecasting globally averaged surface temperature," International Journal of Forecasting, Elsevier, vol. 34(2), pages 314-335.
    4. Pezzey, John C.V. & Burke, Paul J., 2014. "Towards a more inclusive and precautionary indicator of global sustainability," Ecological Economics, Elsevier, vol. 106(C), pages 141-154.
    5. Gregory Duveiller & Federico Filipponi & Andrej Ceglar & Jędrzej Bojanowski & Ramdane Alkama & Alessandro Cescatti, 2021. "Revealing the widespread potential of forests to increase low level cloud cover," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    6. Alexandra Jonko & Nathan M. Urban & Balu Nadiga, 2018. "Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data," Climatic Change, Springer, vol. 149(2), pages 247-260, July.
    7. Junichi Tsutsui, 2017. "Quantification of temperature response to CO2 forcing in atmosphere–ocean general circulation models," Climatic Change, Springer, vol. 140(2), pages 287-305, January.
    8. Randall Parkinson & Peter Harlem & John Meeder, 2015. "Managing the Anthropocene marine transgression to the year 2100 and beyond in the State of Florida U.S.A," Climatic Change, Springer, vol. 128(1), pages 85-98, January.
    9. Enrico Biffis & Erik Chavez, 2017. "Satellite Data and Machine Learning for Weather Risk Management and Food Security," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1508-1521, August.

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