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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andronova NG, Schlesinger ME (2001) Objective estimation of the probability density function for climate sensitivity. J Geophys Res 108(D8):22605–22611
Annan JD, Hargreaves JC (2006) Using multiple observationally-based constraints to estimate climate sensitivity. Geophys Res Lett 33(L06704)
Annan JD, Hargreaves JC, Ohgaito R, Abe-Ouchi A, Emori S (2005) Efficiently constraining climate sensitivity with paleoclimate simulations. SOLA 1:181–184
Arrhenius S (1896) On the influence of carbonic acid in the air upon the temperature of the ground. Philos Mag 41:237–276
Bernardo JM, Smith AFM (1994) Bayesian theory. Wiley, Chichester
Betz G (2007) Probabilities in climate policy advice: a critical comment. Clim Change 85(1):1–9
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):113–117
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
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)
Gregory JM, Stouffer RJ, Raper SCB, Stott PA, Rayner NA (2002) An observationally based estimate of the climate sensitivity. J Climate 15(22):3117–3121
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 3:609–662
Hansen J, Russel G, Lacis A, Fung I, Rind D (1985) Climate response times: dependence on climate sensitivity and ocean mixing. Science 229:857–859
Harvey LDD (2007) Dangerous anthropogenic interference, dangerous climatic change, and harmful climatic change: non-trivial distinctions with significant policy implications. Clim Change 82(1):1–25
Hegerl GC, Crowley TJ, Hyde WT, Frame DJ (2006) Climate sensitivity constrained by temperature reconstructions over the past seven centuries. Nature 440:1029–1032
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, Cambridge
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, Cambridge
Kriegler E (2005) Imprecise probability analysis for integrated assessment of climate change. PhD thesis, Potsdam University
Lee TCK, Zwiers FW, Hegerl GC, Zhang X, Tsao M (2005) A Bayesian climate change detection and attribution assessment. J Climate 18(13):2429–2440
Leroy SS (1998) Detecting climate signals: some Bayesian aspects. J Climate 11(4):640–651
Manabe S, Wetherald RT (1967) Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J Atmos Sci 24(3):241–259
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, Cambridge
Morgan MG, Keith D (1995) Subjective judgments by climate experts. Env Sci Tech 29:468–476
Morgan MG, Henrion M (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, Cambridge
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 33–51
Nordhaus WD (2008) A question of balance: weighing the options on global warming policies. Yale Univ Pr
National Research Council NRC (1979) Carbon dioxide and climate: a scientific assessment. National Academy Press, Washington, DC
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, Cambridge
Risbey JS (2007) Subjective elements in climate policy advice. Clim Change 85(1):11–17
Risbey JS, Kandlikar M (2007) Expressions of likelihood and confidence in the IPCC uncertainty assessment process. Clim Change 85(1):19–31
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):149–163
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 change
Stern N (2007) The economics of climate change: the stern review. Cambridge University Press, Cambridge
Walley P (1991) Statistical reasoning with imprecise probabilities. Chapman and Hall, London
Webster MD, Sokolov AP (2000) A methodology for quantifying uncertainty in climate projections. Clim Change 46(4):417–446
Wigley TML, Amman CM, Santer BD, Raper SCB (2005) Effect of climate sensitivity on the response to volcanic forcing. J Geophys Res 110(D09107)
Wynn HP (2008) Bayesian information-based learning and majorization. Biometrika (submitted)
Yohe G, Andronova N, Schlesinger M (2004) To hedge or not against an uncertain climate future? Science 306:416–417
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)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Annan, J.D., Hargreaves, J.C. On the generation and interpretation of probabilistic estimates of climate sensitivity. Climatic Change 104, 423–436 (2011). https://doi.org/10.1007/s10584-009-9715-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10584-009-9715-y