Climatic Change

, Volume 104, Issue 3–4, pp 423–436

On the generation and interpretation of probabilistic estimates of climate sensitivity

Article

Abstract

The equilibrium climate response to anthropogenic forcing has long been one of the dominant, and therefore most intensively studied, uncertainties in predicting future climate change. As a result, many probabilistic estimates of the climate sensitivity (S) have been presented. In recent years, most of them have assigned significant probability to extremely high sensitivity, such as P(S > 6C) > 5%. In this paper, we investigate some of the assumptions underlying these estimates. We show that the popular choice of a uniform prior has unacceptable properties and cannot be reasonably considered to generate meaningful and usable results. When instead reasonable assumptions are made, much greater confidence in a moderate value for S is easily justified, with an upper 95% probability limit for S easily shown to lie close to 4°C, and certainly well below 6°C. These results also impact strongly on projected economic losses due to climate change.

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References

  1. Andronova NG, Schlesinger ME (2001) Objective estimation of the probability density function for climate sensitivity. J Geophys Res 108(D8):22605–22611CrossRefGoogle Scholar
  2. Annan JD, Hargreaves JC (2006) Using multiple observationally-based constraints to estimate climate sensitivity. Geophys Res Lett 33(L06704)Google Scholar
  3. Annan JD, Hargreaves JC, Ohgaito R, Abe-Ouchi A, Emori S (2005) Efficiently constraining climate sensitivity with paleoclimate simulations. SOLA 1:181–184CrossRefGoogle Scholar
  4. Arrhenius S (1896) On the influence of carbonic acid in the air upon the temperature of the ground. Philos Mag 41:237–276Google Scholar
  5. Bernardo JM, Smith AFM (1994) Bayesian theory. Wiley, ChichesterCrossRefGoogle Scholar
  6. Betz G (2007) Probabilities in climate policy advice: a critical comment. Clim Change 85(1):1–9CrossRefGoogle Scholar
  7. Forest CE, Stone PH, Sokolov AP, Allen MR, Webster MD (2002) Quantifying uncertainties in climate system properties with the use of recent climate observations. Science 295(5552):113–117CrossRefGoogle Scholar
  8. Forster PM, Gregory JM (2006) The climate sensitivity and its components diagnosed from earth radiation budget data. J Climate 19(1):39–52. doi:10.1175/JCLI3611.1 CrossRefGoogle Scholar
  9. 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(L09702)Google Scholar
  10. Gregory JM, Stouffer RJ, Raper SCB, Stott PA, Rayner NA (2002) An observationally based estimate of the climate sensitivity. J Climate 15(22):3117–3121CrossRefGoogle Scholar
  11. Hansen JE, Russell G, Rind D, Stone P, Lacis A, Lebedeff S, Ruedy R, Travis L (1983) Efficient three dimensional global models for climate studies; models I and II. Mon Weather Rev 3:609–662CrossRefGoogle Scholar
  12. Hansen J, Russel G, Lacis A, Fung I, Rind D (1985) Climate response times: dependence on climate sensitivity and ocean mixing. Science 229:857–859CrossRefGoogle Scholar
  13. Harvey LDD (2007) Dangerous anthropogenic interference, dangerous climatic change, and harmful climatic change: non-trivial distinctions with significant policy implications. Clim Change 82(1):1–25CrossRefGoogle Scholar
  14. Hegerl GC, Crowley TJ, Hyde WT, Frame DJ (2006) Climate sensitivity constrained by temperature reconstructions over the past seven centuries. Nature 440:1029–1032CrossRefGoogle Scholar
  15. Houghton JT, Meiro Filho LG, Callander BA, Harris N, Kattenberg A, Maskell K (1996) Climate Change 1995: the science of climate change. Contribution of working group I to the second assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  16. Houghton JT, Ding Y, Griggs DJ, Noguer M, Van Der Linden PJ, Dai X, Maskell K, Johnson CA (2001) Climate change 2001: contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  17. Kriegler E (2005) Imprecise probability analysis for integrated assessment of climate change. PhD thesis, Potsdam UniversityGoogle Scholar
  18. Lee TCK, Zwiers FW, Hegerl GC, Zhang X, Tsao M (2005) A Bayesian climate change detection and attribution assessment. J Climate 18(13):2429–2440CrossRefGoogle Scholar
  19. Leroy SS (1998) Detecting climate signals: some Bayesian aspects. J Climate 11(4):640–651CrossRefGoogle Scholar
  20. Manabe S, Wetherald RT (1967) Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J Atmos Sci 24(3):241–259CrossRefGoogle Scholar
  21. Meinshausen M (2006) What does a 2C target mean for greenhouse gas concentrations? a brief analysis based on multi-gas emission pathways and several climate sensitivity uncertainty estimates. In: Schellnhuber HJ (ed) Avoiding dangerous climate change, chapter 28. Cambridge University Press, CambridgeGoogle Scholar
  22. Morgan MG, Keith D (1995) Subjective judgments by climate experts. Env Sci Tech 29:468–476CrossRefGoogle Scholar
  23. Morgan MG, Henrion M (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, CambridgeGoogle Scholar
  24. Moss RH, Schneider SH (2000) Uncertainties in the IPCC TAR: recommendations to lead authors for more consistent assessment and reporting. Guidance papers on the cross cutting issues of the third assessment report of the IPCC, pp 33–51Google Scholar
  25. Nordhaus WD (2008) A question of balance: weighing the options on global warming policies. Yale Univ PrGoogle Scholar
  26. National Research Council NRC (1979) Carbon dioxide and climate: a scientific assessment. National Academy Press, Washington, DCGoogle Scholar
  27. Parry M, Canzaiani O, Palutikof J, Van der Linden P, Hanson, C (2007) Climate change 2007: impacts, adaptation and vulnerability; Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  28. Risbey JS (2007) Subjective elements in climate policy advice. Clim Change 85(1):11–17CrossRefGoogle Scholar
  29. Risbey JS, Kandlikar M (2007) Expressions of likelihood and confidence in the IPCC uncertainty assessment process. Clim Change 85(1):19–31CrossRefGoogle Scholar
  30. Schneider von Deimling T, Held H, Ganopolski A, Rahmstorf S (2006) Climate sensitivity estimated from ensemble simulations of glacial climate. Clim Dyn 27(2–3):149–163CrossRefGoogle Scholar
  31. Solomon S, Qin D, Manning M, Chen Z, et al. (2007) Climate change 2007: the physical science basis. Contribution of the working group I to the fourth assessment report of the intergovernmental panel on climate changeGoogle Scholar
  32. Stern N (2007) The economics of climate change: the stern review. Cambridge University Press, CambridgeGoogle Scholar
  33. Walley P (1991) Statistical reasoning with imprecise probabilities. Chapman and Hall, LondonGoogle Scholar
  34. Webster MD, Sokolov AP (2000) A methodology for quantifying uncertainty in climate projections. Clim Change 46(4):417–446CrossRefGoogle Scholar
  35. Wigley TML, Amman CM, Santer BD, Raper SCB (2005) Effect of climate sensitivity on the response to volcanic forcing. J Geophys Res 110(D09107)Google Scholar
  36. Wynn HP (2008) Bayesian information-based learning and majorization. Biometrika (submitted)Google Scholar
  37. Yohe G, Andronova N, Schlesinger M (2004) To hedge or not against an uncertain climate future? Science 306:416–417CrossRefGoogle Scholar
  38. Yokohata T, Emori S, Nozawa T, Tsushima Y, Ogura T, Kimoto M (2005) Climate response to volcanic forcing: validation of climate sensitivity of a coupled atmosphere-ocean general circulation model. Geophys Res Lett 32(L21710)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Research Institute for Global ChangeJapan Agency for Marine-Earth Science and TechnologyYokohamaJapan

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