Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Spread in model climate sensitivity traced to atmospheric convective mixing

Nature volume 505, pages 37–42 (2014)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Multimodel-mean local stratification parameter s.
Figure 2: Basis for the index S of small-scale lower-tropospheric mixing and its relationship to the warming response.
Figure 3: The structure of monthly-mean tropospheric ascent reveals large-scale lower-tropospheric mixing in observations and models.
Figure 4: Estimated water vapour source MLT, large due to large-scale lower-tropospheric mixing and its response to warming.
Figure 5: Relation of lower-tropospheric mixing indices to ECS.

Similar content being viewed by others

References

  1. Hegerl, G. C. & Zwiers, F. W. Working Group I: The Physical Science Basis of Climate Change Ch. 9, 663–745 (Cambridge Univ. Press, 2007)

    Google Scholar 

  2. Cess, R. D. et al. Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res. 95, 16601–16615 (1990)

    Article  ADS  Google Scholar 

  3. Dufresne, J. L. & Bony, S. An assessment of the primary sources of spread of global warming estimates from coupled atmosphere-ocean models. J. Clim. 21, 5135–5144 (2008)

    Article  ADS  Google Scholar 

  4. Soden, B. J. et al. Quantifying climate feedbacks using radiative kernels. J. Clim. 21, 3504–3520 (2008)

    Article  ADS  Google Scholar 

  5. Zelinka, M., Klein, S. & Hartmann, D. Computing and partitioning cloud feedbacks using cloud property histograms. Part I: cloud radiative kernels. J. Clim. 25, 3715–3735 (2012)

    Article  ADS  Google Scholar 

  6. Hartmann, D. L. & Short, D. A. On the use of earth radiation budget statistics for studies of clouds and climate. J. Atmos. Sci. 37, 1233–1250 (1980)

    Article  ADS  Google Scholar 

  7. Bony, S. & Dufresne, J. L. Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett. 32, L20806 (2005)

    Article  ADS  Google Scholar 

  8. Rieck, M., Nuijens, L. & Stevens, B. Marine boundary-layer cloud feedbacks in a constant relative humidity atmosphere. J. Atmos. Sci. 69, 2538–2550 (2012)

    Article  ADS  Google Scholar 

  9. Stevens, B. On the growth of layers of nonprecipitating cumulus convection. J. Atmos. Sci. 64, 2916–2931 (2007)

    Article  ADS  Google Scholar 

  10. Yano, J. & Emanuel, K. An improved model of the equatorial troposphere and its coupling with the stratosphere. J. Atmos. Sci. 48, 377–389 (1991)

    Article  ADS  Google Scholar 

  11. Sherwood, S. C. & Meyer, C. L. The general circulation and robust relative humidity. J. Clim. 19, 6278–6290 (2006)

    Article  ADS  Google Scholar 

  12. Allen, M. R. & Ingram, W. J. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 224–232 (2002)

    ADS  CAS  Google Scholar 

  13. Zhang, C. D., Nolan, D. S., Thorncroft, C. D. & Nguyen, H. Shallow meridional circulations in the tropical atmosphere. J. Clim. 21, 3453–3470 (2008)

    Article  ADS  Google Scholar 

  14. Nolan, D. S., Powell, S. W., Zhang, C. D. & Mapes, B. E. Idealized simulations of the Intertropical Convergence Zone and its multilevel flows. J. Atmos. Sci. 67, 4028–4053 (2010)

    Article  ADS  Google Scholar 

  15. Rio, C., Hourdin, F., Grandpeix, J. Y. & Lafore, J. P. Shifting the diurnal cycle of parameterized deep convection over land. Geophys. Res. Lett. 36, L07809 (2009)

    Article  ADS  Google Scholar 

  16. Soden, B. J. & Vecchi, G. A. The vertical distribution of cloud feedback in coupled ocean-atmosphere models. Geophys. Res. Lett. 38, L12704 (2011)

    Article  ADS  Google Scholar 

  17. Soden, B. J. & Held, I. M. An assessment of climate feedbacks in coupled ocean-atmosphere models. J. Clim. 19, 3354–3360 (2006)

    Article  ADS  Google Scholar 

  18. Webb, M. & Lock, A. Coupling between subtropical cloud feedback and the local hydrological cycle in a climate model. Clim. Dyn. 41, 1923–1939 (2013)

    Article  Google Scholar 

  19. Brient, F. & Bony, S. Interpretation of the positive low-cloud feedback predicted by a climate model under global warming. Clim. Dyn. 40, 2415–2431 (2013)

    Article  Google Scholar 

  20. Held, I. M. & Soden, B. J. Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006)

    Article  ADS  Google Scholar 

  21. Zahn, M. & Allan, R. P. Climate warming-related strengthening of the tropical hydrological cycle. J. Clim. 26, 562–574 (2013)

    Article  ADS  Google Scholar 

  22. Klein, S. A. et al. Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator. J. Geophys. Res. 118, 1329–1342 (2013)

    Google Scholar 

  23. Volodin, E. M. Relation between temperature sensitivity to doubled carbon dioxide and the distribution of clouds in current climate models. Izv. Atmos. Ocean. Phys. 44, 288–299 (2008)

    Article  Google Scholar 

  24. Clement, A. C., Burgman, R. & Norris, J. R. Observational and model evidence for positive low-level cloud feedback. Science 325, 460–464 (2009)

    Article  ADS  CAS  Google Scholar 

  25. Fasullo, J. T. & Trenberth, K. E. A less cloudy future: the role of subtropical subsidence in climate sensitivity. Science 338, 792–794 (2012)

    Article  ADS  CAS  Google Scholar 

  26. Webb, M. J., Lambert, F. H. & Gregory, J. M. Origins of differences in climate sensitivity, forcing and feedback in climate models. Clim. Dyn. 40, 677–707 (2013)

    Article  Google Scholar 

  27. Vial, J., Dufresne, J.-L. & Bony, S. On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Clim. Dyn. 41, 3339–3362 (2013)

    Article  Google Scholar 

  28. Randall, D. A. & Wood, R. A. Working Group I: The Physical Science Basis of Climate Change Ch. 8, 589–662 (Cambridge University Press, 2007)

    Google Scholar 

  29. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012)

    Article  ADS  Google Scholar 

  30. Gregory, J. M. et al. A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett. 31, L03205 (2004)

    ADS  Google Scholar 

  31. Forster, P. M. et al. Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models. J. Geophys. Res. 118, 1139–1150 (2013)

    Google Scholar 

  32. Uppala, S. M. et al. The ERA-40 re-analysis. Q. J. R. Meteorol. Soc. 131, 2961–3012 (2005)

    Article  ADS  Google Scholar 

  33. Rienecker, M. M. et al. MERRA: NASA's modern-era retrospective analysis for research and applications. J. Clim. 24, 3624–3648 (2011)

    Article  ADS  Google Scholar 

  34. Sherwood, S. C., Meyer, M. L., Allen, R. J. & Titchner, H. A. Robust tropospheric warming revealed by iteratively homogenized radiosonde data. J. Clim. 21, 5336–5352 (2008)

    Article  ADS  Google Scholar 

  35. Sherwood, S. C., Roca, R., Weckwerth, T. M. & Andronova, N. G. Tropospheric water vapor, convection and climate. Rev. Geophys. 48, RG2001 (2010)

    Article  ADS  Google Scholar 

Download references

Acknowledgements

This work was supported by the FP7-ENV-2009-1 European project EUCLIPSE (number 244067). 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, especially the participants contributing additional CFMIP2 experiments and diagnostics crucial to our study. The US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support for CMIP and led the development of software infrastructure in partnership with the Global Organisation for Earth System Science Portals. We also thank the National Center for Atmospheric Research and the Earth System Grid Federation for providing access to PCM output, the Australian National Computational Infrastructure, and the IPSL Prodiguer-Ciclad facility for providing a convenient archive of CMIP data. Finally, we thank B. Stevens, C. Bretherton and G. Schmidt for comments on early versions of the manuscript.

Author information

Authors and Affiliations

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

    Steven C. Sherwood

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

    Sandrine Bony & Jean-Louis Dufresne

Authors
  1. Steven C. Sherwood
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandrine Bony
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Louis Dufresne
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.C.S. led the study and the writing of the paper, and did the calculations of LTMI and related diagnostics. S.B. computed cloud radiative effect and assisted in interpreting results and writing the paper. J.-L.D. computed ECS and assisted in interpreting results and writing the paper.

Corresponding author

Correspondence to Steven C. Sherwood.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Illustration of atmospheric overturning circulations.

Deep overturning strongly coupled to the hydrological cycle and atmospheric energy budget is shown by solid lines; lower-tropospheric mixing is shown by dashed lines. The MILC feedback results from the increasing relative role of lower-tropospheric mixing in exporting humidity from the boundary layer as the climate warms, thus depleting the layer of water vapour needed to sustain low cloud cover.

Extended Data Figure 2 Small-scale moisture source Msmall.

Vertical profile averaged over all tropical oceans, for two selected climate models (see legend) with very different warming responses, in present-day (solid) and +4 K (dashed) climates.

Extended Data Figure 3 Response of cloud fraction to warming.

Profile of average change in model cloud fractional cover at +4 K in the four atmosphere models with largest (magenta) and smallest (blue) estimated +4 K increases in planetary-boundary-layer drying, averaged from 30° S to 30° N (dashed) or 60° S to 60° N (solid). The drying estimate is obtained by adding the explicitly computed change in MLT, large to the change in Msm estimated from S via the relationship shown in Fig. 2a. The typical mean cloud fraction below 850 hPa is about 10% to 20%, and the changes shown are absolute changes in this fraction, so are of the order of 10% of the initial cloud cover.

Extended Data Figure 4 Response of large-scale lower-tropospheric mixing to warming.

Profiles of mean vertical velocity in regions of shallow ascent, in control and +4 K climates. The similarity of dashed and solid lines indicates that mass overturning associated with these regions is roughly the same in the warmer simulations, on average.

Extended Data Figure 5 Response of small-scale, low-level drying to warming.

Change in convective moisture source Msmall below 850 hPa upon a +4 K warming in eight atmosphere models and one CMIP3 coupled model; units are W m−2, with negative values indicating stronger drying near the surface. Zero contours are shown in white (a few off-scale regions also appear white). The models used for calculating Mlarge are the eight shown here plus two for which Msmall data were unavailable: CNRM-CM5 and FGOALS-g2.

Extended Data Table 1 List of CMIP5 coupled models used
Full size table
Extended Data Table 2 List of CMIP3 coupled models used
Full size table

PowerPoint slides

Source data

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sherwood, S., Bony, S. & Dufresne, JL. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505, 37–42 (2014). https://doi.org/10.1038/nature12829

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature12829

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing