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.

Clouds, circulation and climate sensitivity

Abstract

Fundamental puzzles of climate science remain unsolved because of our limited understanding of how clouds, circulation and climate interact. One example is our inability to provide robust assessments of future global and regional climate changes. However, ongoing advances in our capacity to observe, simulate and conceptualize the climate system now make it possible to fill gaps in our knowledge. We argue that progress can be accelerated by focusing research on a handful of important scientific questions that have become tractable as a result of recent advances. We propose four such questions below; they involve understanding the role of cloud feedbacks and convective organization in climate, and the factors that control the position, the strength and the variability of the tropical rain belts and the extratropical storm tracks.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: What role does convection play in cloud feedbacks?
Figure 2: What controls the position, strength and variability of storm tracks?
Figure 3: What controls the position, strength and variability of tropical rain belts?
Figure 4: What role does convective aggregation play in climate?

References

  1. Emanuel, K. in Meteorology at the Millennium (ed. Pearce, R. P.) 1–14 (Academic, 2002).

    Google Scholar 

  2. Sherwood, S. C., Bony, S. & Dufresne, J-L. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505, 37–42 (2014).

    Article  Google Scholar 

  3. IPCC Summary for Policymakers Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1–29 (Cambridge Univ. Press, 2013).

  4. Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nature Geosci. 7, 703–708 (2014).

    Article  Google Scholar 

  5. Stevens, B. & Bony, S. What are climate models missing? Science 340, 1053–1054 (2013).

    Article  Google Scholar 

  6. Bony, S. et al. in Monograph on Climate Science for Serving Society: Research, Modelling and Prediction Priorities (eds Hurrell, J. W. & Asrar, G.) 391–413 (Springer, 2013).

    Book  Google Scholar 

  7. Boucher, O. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. et al.) 571–657 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  8. Sherwood, S. C. et al. in Climate Science for Serving Society (eds Hurrell, J. W. & Asrar, G.) 73–103 (Springer, 2013).

    Book  Google Scholar 

  9. Held, I. Simplicity amid complexity. Science 343, 1206–1207 (2014).

    Article  Google Scholar 

  10. Held, I. M. & Hou, A. Y. Nonlinear axially symmetric circulations in a nearly inviscid atmosphere. J. Atmos. Sci. 37, 515–533 (1980).

    Article  Google Scholar 

  11. Emanuel, K. A. The dependence of hurricane intensity on climate. Nature 326, 483–485 (1987).

    Article  Google Scholar 

  12. Hartmann, D. L. & Larson, K. An important constraint on tropical cloud–climate feedback. Geophys. Res. Lett. 29, 1951 (2002).

    Article  Google Scholar 

  13. Cooke, R., Wielicki, B. A., Young, D. F. & Mlynczak, M. G. Value of information for climate observing systems. Environ. Syst. Decis. 34, 98–109 (2013).

    Article  Google Scholar 

  14. Stevens, B. & Bony, S. Water in the atmosphere. Phys. Today 66, 29 (June 2013).

  15. 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  Google Scholar 

  16. Zhang, M. et al. CGILS: Results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models. J. Adv. Model. Earth Syst. 5, 826–842 (2013).

    Article  Google Scholar 

  17. Zhao, M. An investigation of the connections among convection, clouds, and climate sensitivity in a global climate model. J. Clim. 27, 1845–1862 (2014).

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Butler, A. H., Thompson, D. W. J. & Heikes, R. The steady-state atmospheric circulation response to climate change-like thermal forcings in a simple general circulation model. J. Clim. 23, 3474–3496 (2010).

    Article  Google Scholar 

  20. Kang, S. M., Polvani, L. M., Fyfe, J. C. & Sigmond, M. Impact of polar ozone depletion on subtropical precipitation. Science 332, 951–954 (2011).

    Article  Google Scholar 

  21. Brayshaw, D. J., Hoskins, B. & Blackburn, M. The basic ingredients of the North Atlantic storm track. Part I: Land–sea contrast and orography. J. Atmos. Sci. 66, 2539–2558 (2009).

    Article  Google Scholar 

  22. Simpson, I. R., Shaw, T. A. & Seager, R. A Diagnosis of the seasonally and longitudinally varying midlatitude circulation response to global warming. J. Atmos. Sci. 71, 2489–2515 (2014).

    Article  Google Scholar 

  23. Woollings, T. Dynamical influences on European climate: an uncertain future. Phil. Trans. R. Soc. A 368, 3733–3756 (2010).

    Article  Google Scholar 

  24. Grise, K. M. & Polvani, L. M. Southern hemisphere cloud–dynamics biases in CMIP5 models and their implications for climate projections. J. Clim. 27, 6074–6092 (2014).

    Article  Google Scholar 

  25. Ceppi, P., Zelinka, M. D. & Hartmann, D. L. The response of the southern hemispheric eddy-driven jet to future changes in shortwave radiation in CMIP5. Geophys. Res. Lett. 41, 3244–3250 (2014).

    Article  Google Scholar 

  26. Miyamoto, Y. et al. Deep moist atmospheric convection in a subkilometer global simulation. Geophys. Res. Lett. 40, 4922–4926 (2013).

    Article  Google Scholar 

  27. Rivière, G., Laîné, A., Lapeyre, G., Salas-Mélia, D. & Kageyama, M. Links between Rossby wave breaking and the North Atlantic Oscillation–Arctic Oscillation in present-day and last glacial maximum climate simulations. J. Clim. 23, 2987–3008 (2010).

    Article  Google Scholar 

  28. Kohfeld, K. E. & Harrison, S. C. How well can we simulate past climates? Evaluating the models using global palaeoenvironmental datasets. Quat. Sci. Rev. 19, 321–346 (2000).

    Article  Google Scholar 

  29. Braconnot, P. et al. Evaluation of climate models using palaeoclimatic data. Nature Clim. Change 2, 417–424 (2012).

    Article  Google Scholar 

  30. Marsham, J. H. et al. The role of moist convection in the West African monsoon system: Insights from continental-scale convection-permitting simulations. Geophys. Res. Lett. 40, 1843–1849 (2013).

    Article  Google Scholar 

  31. Biasutti, M. & Giannini, A. Robust Sahel drying in response to late 20th century forcings. Geophys. Res. Lett. 33, L11706 (2006).

    Article  Google Scholar 

  32. Kang, S. M., Held, I. M., Frierson, D. M. W. & Zhao, M. The response of the ITCZ to extratropical thermal forcing: Idealized slab-ocean experiments with a GCM. J. Clim. 21, 3521–3532 (2008).

    Article  Google Scholar 

  33. Hwang, Y. T. & Frierson, D. Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean. Proc. Natl Acad. Sci. USA 110, 4935–4940 (2013).

    Article  Google Scholar 

  34. Held, I. M., Delworth, T. L., Lu, J., Findell, K. L. & Knutson, T. R. Simulation of Sahel drought in the 20th and 21st centuries. Proc. Natl Acad. Sci. USA 102, 17891–17896 (2005).

    Article  Google Scholar 

  35. Perez-Sanz, A., Li, G., González-Sampériz, P. & Harrison, S. P. Evaluation of modern and mid-Holocene seasonal precipitation of the Mediterranean and northern Africa in the CMIP5 simulations. Clim. Past 10, 551–568 (2014).

    Article  Google Scholar 

  36. Donohoe, A., Marshall, J., Ferreira, D. & McGee, D. The relationship between ITCZ location and cross-equatorial atmospheric heat transport: From the seasonal cycle to the last glacial maximum. J. Clim. 26, 3597–3618 (2013).

    Article  Google Scholar 

  37. Houze, R. A. Jr. Cloud clusters and large-scale vertical motions in the tropics. J. Meteorol. Soc. Japan 60, 396–408 (1982).

    Article  Google Scholar 

  38. Bretherton, C. S., Blossey, P. N. & Khairoutdinov, M. An energy-balance analysis of deep convective self-aggregation above uniform SST. J. Atmos. Sci. 62, 4273–4292 (2005).

    Article  Google Scholar 

  39. Tobin, I., Bony, S. & Roca, R. Observational evidence for relationships between the degree of aggregation of deep convection, water vapor, surface fluxes, and radiation. J. Clim. 25, 6885–6904 (2012).

    Article  Google Scholar 

  40. Wing, A. A. & Emanuel, K. A. Physical mechanisms controlling self-aggregation of convection in idealized numerical modeling simulations. J. Adv. Model. Earth Syst. 6, 59–74 (2014).

    Article  Google Scholar 

  41. Seifert, A. & Heus, T. Large-eddy simulation of organized precipitating trade wind cumulus clouds. Atmos. Chem. Phys. 13, 5631–5645 (2013).

    Article  Google Scholar 

  42. Muller, C. J. & Held, I. M. Detailed investigation of the self-aggregation of convection in cloud-resolving simulations. J. Atmos. Sci. 69, 2551–2565 (2012).

    Article  Google Scholar 

  43. Neggers, R. A. J., Neelin, J. D. & Stevens, B. Impact mechanisms of shallow cumulus convection on tropical climate dynamics. J. Clim. 20, 2623–2642 (2007).

    Article  Google Scholar 

  44. Jakob, C. Accelerating progress in global atmospheric model development through improved parameterization. Bull. Am. Meteorol. Soc. 91, 869–875 (2010).

    Article  Google Scholar 

  45. Lorenz, E. N. in The General Circulation of the Atmosphere (ed. Corby, G. A.) 3–23 (Royal Meteorological Society, 1969); http://go.nature.com/Y3b8bO

    Google Scholar 

  46. Slingo, A. & Slingo, J. The response of a general circulation model to cloud longwave radiative forcing. I: Introduction and initial experiments. Q. J. R. Meteorol. Soc. 114, 1027–1062 (1988).

    Article  Google Scholar 

  47. Bony, S. & Emanuel, K. A. On the role of moist processes in tropical intraseasonal variability: Cloud-radiation and moisture-convection feedbacks. J. Atmos. Sci. 62, 2770–2789 (2005).

    Article  Google Scholar 

  48. Chagnon, S., Gray, S. L. & Methven, J. Diabatic processes modifying potential vorticity in a North Atlantic cyclone. Q. J. R. Meteorol. Soc. 139, 1270–1282 (2013).

    Article  Google Scholar 

  49. Joos, H. & Wernli, H. Influence of microphysical processes on the potential vorticity development in a warm conveyor belt: a case study with the limited area model COSMO. Q. J. R. Meteorol. Soc. 138, 407–418 (2012).

    Article  Google Scholar 

  50. Martin, G. M. et al. Analysis and reduction of systematic errors through a seamless approach to modeling weather and climate. J. Clim. 23, 5933–5957 (2010).

    Article  Google Scholar 

Download references

Acknowledgements

This paper was developed as part of the Grand Challenge on Clouds, Circulation and Climate Sensitivity of the World Climate Research Programme. The process of identifying a handful of key scientific questions culminated in a workshop whose participants are gratefully acknowledged: D. Abbot, P. Bauer, M. Biasutti, H. Douville, J-L. Dufresne, A. Del Genio, K. Emanuel, Q. Fu, J. Hargreaves, S. Harrison, I. Held, C. Hohenegger, B. Hoskins, S. Kang, H. Kawai, S. A. Klein, N. Loeb, T. Mauritsen, B. Mapes, M. Miller, C. Muller, C. Prentice, C. Risi, M. Satoh, C. Schumacher, B. Wielicki, M. Yoshimori and P. Zuidema. We thank the German Weather Service, PMIP, EUMETSAT and NASA for data. M. Doutriaux-Boucher (EUMETSAT) provided the satellite products used in Fig. 2a and b. S.B. and B.S. acknowledge support from the LABEX L-IPSL and the Max Planck Society for the Advancement of Science. M.J.W. was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101).

Author information

Authors and Affiliations

Authors

Contributions

S.B. and B.S. led the writing of the paper. All authors contributed to the development and writing of the manuscript.

Corresponding author

Correspondence to Sandrine Bony.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bony, S., Stevens, B., Frierson, D. et al. Clouds, circulation and climate sensitivity. Nature Geosci 8, 261–268 (2015). https://doi.org/10.1038/ngeo2398

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ngeo2398

Further reading

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