Climate Dynamics

, Volume 27, Issue 1, pp 17–38 | Cite as

On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles

  • M. J. Webb
  • C. A. Senior
  • D. M. H. Sexton
  • W. J. Ingram
  • K. D. Williams
  • M. A. Ringer
  • B. J. McAvaney
  • R. Colman
  • B. J. Soden
  • R. Gudgel
  • T. Knutson
  • S. Emori
  • T. Ogura
  • Y. Tsushima
  • N. Andronova
  • B. Li
  • I. Musat
  • S. Bony
  • K. E. Taylor
Article

Abstract

Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO2 doubling climate change experiments, from the CFMIP (Cloud Feedback Model Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level cloud amount) contribute most to this figure in the CFMIP ensemble, while areas of negative cloud feedback (associated with increases in low level cloud amount and optical thickness) contribute most in QUMP. Classes associated with high-top cloud feedbacks are responsible for 33 and 20% of the cloud feedback contribution in CFMIP and QUMP, respectively, while classes where no particular cloud type stands out are responsible for 8 and 21%.

Notes

Acknowledgements

This work was funded in part by the UK Department of the Environment, Food and Rural Affairs, under contract PECD 7/12/37. Thanks are due also to IPSL for hosting of CFMIP data, to Jean-Louis Dufresne for providing the IPSL radiative forcing calculations, and to Mat Collins, James Murphy and Jonathan Gregory for helpful comments on the manuscript. We are also grateful to the anonymous reviewers whose comments led to improvements in this piece of work.

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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • M. J. Webb
    • 1
  • C. A. Senior
    • 1
  • D. M. H. Sexton
    • 1
  • W. J. Ingram
    • 1
  • K. D. Williams
    • 1
  • M. A. Ringer
    • 1
  • B. J. McAvaney
    • 2
  • R. Colman
    • 2
  • B. J. Soden
    • 3
  • R. Gudgel
    • 4
  • T. Knutson
    • 4
  • S. Emori
    • 5
  • T. Ogura
    • 5
  • Y. Tsushima
    • 6
  • N. Andronova
    • 7
  • B. Li
    • 8
  • I. Musat
    • 9
  • S. Bony
    • 9
  • K. E. Taylor
    • 10
  1. 1.Hadley Centre for Climate Prediction and Research, Met OfficeExeterUK
  2. 2.Bureau of Meteorology Research Centre (BMRC)MelbourneAustralia
  3. 3.Rosenstiel School for Marine and Atmospheric ScienceUniversity of MiamiMiamiUSA
  4. 4.Geophysical Fluid Dynamics Laboratory (GFDL)PrincetonUSA
  5. 5.National Institute for Environmental Studies (NIES)TsukubaJapan
  6. 6.Frontier Research Center for Global Change (FRCGC)Japan Agency for Marine–Earth Science and TechnologyKanagawaJapan
  7. 7.Department of Atmospheric, Oceanic and Space SciencesUniversity of MichiganAnn ArborUSA
  8. 8.Department of Atmospheric SciencesUniversity of Illinois at Urbana–Champaign (UIUC)UrbanaUSA
  9. 9.Institut Pierre Simon Laplace (IPSL)ParisFrance
  10. 10.Program for Climate Model Diagnosis and Intercomparison (PCMDI)LivermoreUSA

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