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Climate Dynamics

, Volume 30, Issue 2–3, pp 175–190 | Cite as

Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations

  • Benjamin M. Sanderson
  • C. Piani
  • W. J. Ingram
  • D. A. Stone
  • M. R. Allen
Article

Abstract

A linear analysis is applied to a multi-thousand member “perturbed physics" GCM ensemble to identify the dominant physical processes responsible for variation in climate sensitivity across the ensemble. Model simulations are provided by the distributed computing project, climate prediction.net . A principal component analysis of model radiative response reveals two dominant independent feedback processes, each largely controlled by a single parameter change. The leading EOF was well correlated with the value of the entrainment coefficient—a parameter in the model’s atmospheric convection scheme. Reducing this parameter increases high vertical level moisture causing an enhanced clear sky greenhouse effect both in the control simulation and in the response to greenhouse gas forcing. This effect is compensated by an increase in reflected solar radiation from low level cloud upon warming. A set of ‘secondary’ cloud formation parameters partly modulate the degree of shortwave compensation from low cloud formation. The second EOF was correlated with the scaling of ice fall speed in clouds which affects the extent of cloud cover in the control simulation. The most prominent feature in the EOF was an increase in longwave cloud forcing. The two leading EOFs account for 70% of the ensemble variance in λ—the global feedback parameter. Linear predictors of feedback strength from model climatology are applied to observational datasets to estimate real world values of the overall climate feedback parameter. The predictors are found using correlations across the ensemble. Differences between predictions are largely due to the differences in observational estimates for top of atmosphere shortwave fluxes. Our validation does not rule out all the strong tropical convective feedbacks leading to a large climate sensitivity.

Keywords

Ensemble Member Climate Sensitivity Outgoing Longwave Radiation Cloud Radiative Force Shortwave Flux 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Allan R, Pamment A, Ringer M, Slingo A (2001) First annual report on WP4200, Tech. rep., UK Met OfficeGoogle Scholar
  2. Allen MR, Stainforth DA (2002) Towards objective probabalistic climate forecasting. Nature 419:228CrossRefGoogle Scholar
  3. Allen MR, Stott PA, Mitchell JFB, Schnur R, Delworth TL (2000) Quantifying the uncertainty in forecasts of anthropogenic climate change. Nature 407:617–620CrossRefGoogle Scholar
  4. Bajuk LJ, Leovy CB (1998) Seasonal and interannual variations in stratiform and convective clouds over the Tropical Pacific and Indian Oceans from ship observations. J Clim 11:2922–2941CrossRefGoogle Scholar
  5. Bony S, Dufresne JL (2005) Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys Res Lett 32:20806–20806CrossRefGoogle Scholar
  6. Dai A, Genio ADD, Fung IY (1997) Clouds, precipitation and temperature range. Nature 386:665–666CrossRefGoogle Scholar
  7. Frame DJ, Booth BBB, Kettleborough JA, Stainforth DA, Gregory JM, Collins M, Allen MR (2005) Constraining climate forecasts: the role of prior assumptions. Geophys Res Lett 32:9702–9702CrossRefGoogle Scholar
  8. Giorgi F, Francisco R (2000) Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HadCM2 coupled AOGCM. Clim Dyn 16:169–182CrossRefGoogle Scholar
  9. Grabowski WW (2000) Cloud microphysics and the tropical climate: cloud-resolving model perspective. J Clim 13:2306–2322CrossRefGoogle Scholar
  10. Gregory D, Morris D (1996) The sensitivity of climate simulations to the specification of mixed phase clouds. Clim Dyn 12:641–651Google Scholar
  11. Gregory D, Rowntree PR (1990) A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon Weather Rev 118:1483–1506CrossRefGoogle Scholar
  12. Hahn CJ, Warren SG, London J (1996) Edited synoptic cloud reports from ships and land stations over the globe, 1982-1991Google Scholar
  13. Hall A, Qu X (2006) Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys Res Lett 33Google Scholar
  14. Ingram W (1990) Unified Model Documentation Paper no. 23: Radiation, Tech. rep., UK Met OfficeGoogle Scholar
  15. Knutti R, Meehl GA, Allen MR, Stainforth DA (2005) Constraining climate sensitivity from the seasonal cycle in surface temperature. J Clim 19:4224–4233CrossRefGoogle Scholar
  16. Lau KM, Wu HT (2003) Warm rain processes over tropical oceans and climate implications. Geophys Res Lett 30Google Scholar
  17. Lindzen RS, Kirtman B, Kirk Davidoff D, Schneider EK (1995) Seasonal surrogate for climate. J Clim 8Google Scholar
  18. Manabe S, Wetherald RT (1975) The effects of doubling the CO2 concentration on the climate of a general circulation model. J Atmos Sci 32:3–15CrossRefGoogle Scholar
  19. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772CrossRefGoogle Scholar
  20. North GR, Bell TL, Cahalan RF, Moeng FJ (1982) Sampling errors in the estimation of empirical orthogonal functions. Mon Weather Rev 110:699–706CrossRefGoogle Scholar
  21. Piani C, Frame DJ, Stainforth DA, Allen MR (2005) Constraints on climate change from a multi-thousand member ensemble of simulations. Geophys Res Lett 32:23825–23825CrossRefGoogle Scholar
  22. Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000) The impact of new physical parametrizations in the Hadley centre climate model: HadAM3. Clim Dyn 16:123–146CrossRefGoogle Scholar
  23. Reilly J, Stone PH, Forest CE, Webster MD, Jacoby HD, and Prinn RG (2001) Climate change: uncertainty and climate change assessments. Science 293: 430CrossRefGoogle Scholar
  24. Rodwell MJ, Palmer TN (2007) Assessing model physics with initial forecast tendencies: Application to climate change uncertainty. Q J R Meteorol Soc (submitted)Google Scholar
  25. Slingo A (1989) A GCM parameterisation for the shortwave radiative properties of water clouds. J Atmos Sci 46:1419–1427CrossRefGoogle Scholar
  26. Smith RNB (1990) A scheme for predicting layer clouds and their water content in a general circulation model. Q J R Meteorol Soc 116:435–460CrossRefGoogle Scholar
  27. Smith R, Gregory D, Wilson C, and Bushell A (1997) Calculation of saturates specific humidity and large-scale cloud, Tech. rep., UK Met OfficeGoogle Scholar
  28. Smith R, Gregory D, Mitchell J, Bushell A, Wilson D (1998) UM Documentation No. 26: Large Scale Precipitation, Tech. rep., UK Met OfficeGoogle Scholar
  29. Stainforth DA, Kettleborough JA, Allen MR, Collins M, Heaps A, Murphy JM (2002) Distributed computing for public-interest climate modeling research. Comput Sci Eng 4:82–89CrossRefGoogle Scholar
  30. Stainforth DA, Aina T, Christensen C, Collins M, Faull N, Frame DJ, Kettleborough JA, Knight S, Martin A, Murphy JM et al (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433:403–406CrossRefGoogle Scholar
  31. Tselioudis G, DelGenio AD, Kovari Jr W, and Yao MS (1998) Temperature dependence of low cloud optical thickness in the GISS GCM: contributing mechanisms and climate implications. J Clim 11:3268–3281CrossRefGoogle Scholar
  32. Weare BC (1997) Comparison of NCEP–NCAR cloud radiative forcing reanalyses with observations. J Clim 10:2200–2209CrossRefGoogle Scholar
  33. Webb MJ, Senior CA, Sexton DMH, Ingram WJ, Williams KD, Ringer MA, McAvaney BJ, Colman R, Soden BJ, Gudgel R et al (2006) On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim Dyn 27:17–38CrossRefGoogle Scholar
  34. Wu X (2001) Effects of ice microphysics on tropical radiative–convective–oceanic Quasi-equilibrium states. J Atmos Sci 59:1885–1897CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Benjamin M. Sanderson
    • 1
  • C. Piani
    • 2
  • W. J. Ingram
    • 1
    • 3
  • D. A. Stone
    • 1
    • 4
  • M. R. Allen
    • 1
  1. 1.AOPP, Department of PhysicsUniversity of Oxford, Clarendon LaboratoryOxfordUK
  2. 2.International Center for Theoretical PhysicsTriesteItaly
  3. 3.Meteorological OfficeExeterUK
  4. 4.Tyndall Centre for Climate Change ResearchOxfordUK

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