Abstract
Whilst the majority of the climate research community is now set upon the objective of generating probabilistic predictions of climate change, disconcerting reservations persist. Attempts to construct probability distributions over socio-economic scenarios are doggedly resisted. Variation between published probability distributions of climate sensitivity attests to incomplete knowledge of the prior distributions of critical parameters and structural uncertainties in climate models. In this paper we address these concerns by adopting an imprecise probability approach. We think of socio-economic scenarios as fuzzy linguistic constructs. Any precise emissions trajectory (which is required for climate modelling) can be thought of as having a degree of membership in a fuzzy scenario. Next, it is demonstrated how fuzzy scenarios can be propagated through a low-dimensional climate model, MAGICC. Fuzzy scenario uncertainties and imprecise probabilistic representation of climate model uncertainties are combined using random set theory to generate lower and upper cumulative probability distributions for Global Mean Temperature anomaly. Finally we illustrate how non-additive measures provide a flexible framework for aggregation of scenarios, which can represent some of the semantics of socio-economic scenarios that defy conventional probabilistic representation.
Similar content being viewed by others
References
Allen M, Raper S, Mitchell J (2001) Climate change—uncertainty in the IPCC’s third assessment report. Science 293(5529):430–433
Anderson D, Winne S (2004) Modelling innovation and threshold effects in climate change mitigation. Tyndall Centre working paper 59
Andronova NG, Schlesinger ME (2001) Objective estimation of the probability density function for climate sensitivity. J Geophys Res Atmospheres 106(D19):22605–22611
Ben-Haim Y (2001) Information-gap decision theory: decisions under severe uncertainty. Academic, San Diego
Curley SP, Golden JI (1994) Using belief functions to represent degrees of belief. Org Behav Hum Decis Process 58:271–303
Dessai S, Hulme M (2003) Does climate policy need probabilities? Tyndall Centre working paper 34
Dubois D, Prade H (1990) Measuring properties of fuzzy sets: a general technique and its use in fuzzy query evaluation. Fuzzy Sets Syst 38:137–152
Ferson S, Tucker WT (2005) Probability bounds analysis is a global sensitivity analysis. Reliab Eng Syst Saf 91(10,11):1435–1442
Fetz T, Oberguggenberger M (2004) Propagation of uncertainty through multivariate functions in the framework of sets of probability measures. Reliab Eng Syst Saf 85(1–3):73–87
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
Giorgi F, Francisco R (2000) Evaluating uncertainties in the prediction of regional climate change. Geophys Res Lett 27(9):1295–1298
Grabisch M (1995) Fuzzy integral in multicriteria decision making. Fuzzy Sets Syst 69(3):279–298
Grabisch M (1996) The application of fuzzy integrals in multicriteria decision making. Eur J Oper Res 89:445–456
Gritsevskyi A, Nakicenovic N (2000) Modeling uncertainty of induced technological change. Energy Policy 28(13):907–921
Grubler A, Nakicenovic N (2001) Identifying dangers in an uncertain climate. Nature 412(6842):15
Hayes B (2003) A lucid interval. Am Sci 91(6):484–488
Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (2001) Climate change 2001: the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press
IPCC (2000) Special report on emissions scenario. IPCC, Geneva
Knutti R, Stocker TF, Joos F, Plattner GK (2002) Constraints on radiative forcing and future climate change from observations and climate model ensembles. Nature 416(6882):719–723
Kriegler E (2005) Imprecise probability analysis for integrated assessment of climate change. PhD Thesis, University of Postdam
Kriegler E, Held H (2005) Utilizing belief functions for the estimation of future climate change. Int J Approx Reason 39(2–3):185–209
Kriegler E, Held H, Bruckner T (2006) Climate protection strategies under ambiguity about catastrophic consequences. In: Kropp J, Scheffran J (eds) Decision making and risk management in sustainability science. Nova Science Publishers, New York
Lempert RJ, Schlesinger ME (2000) Robust strategies for abating climate change. Clim Change 45(3–4):387–401
Lempert RJ, Schlesinger ME (2001) Climate-change strategy needs to be robust. Nature 412:375
Lempert RJ, Popper SW, Bankes SC (2003) Shaping the next one hundred years: new methods for quantitative, long-term policy analysis. RAND, Santa Monica, CA
Levi I (1974) On indeterminate probabilities. J Philos 71:391–418
Lindley DV (1982) Scoring rules and the inevitability of probability. Int Stat Rev 50:1–26
Lindley DV (1990) Making decisions. Wiley, London
Lou WB, Caselton W (1997) Using Dempster–Shafer theory to represent climate change uncertainties. J Environ Manag 49:73–93
Manne AS, Richels RG (1994) The costs of stabilizing global Co2 emissions: a probabilistic model based on expert judgments. Energy J 15(1):31
Marichal JL (2000) An axiomatic approach of the discrete choquet integral as a tool to aggregate interacting criteria. IEEE Trans Fuzzy Syst 8(6):800–807
Merkhoffer MW (1987) Quantifying judgmental uncertainty: methodology, experiences and insights. IEEE Trans Syst Man Cybern 17:741–752
Moore RE (1966) Interval analysis. Prentice Hall, Englewood Cliffs, NJ
Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430(7001):768–772
New M, Hulme M (2000) Representing uncertainty in climate change scenarios: a Monte Carlo approach. Integrated Assessment 1:203–213
Pittock AB, Jones RN, Mitchell CD (2001) Probabilities will help us plan for climate change—without estimates, engineers and planners will have to delay decisions or take a gamble. Nature 413(6853):249
Prinn R, Jacoby H, Sokolov A, Wang C, Xiao X, Yang Z, Eckhaus R, Stone P, Ellerman D, Melillo J, Fitzmaurice J, Kicklighter D, Holian G, Liu Y (1999) Integrated global system model for climate policy assessment: feedbacks and sensitivity studies. Clim Change 41(3–4):469–546
Reilly J, Stone PH, Forest CE, Webster MD, Jacoby HD, Prinn RG (2001) Climate change—uncertainty and climate change assessments. Science 293(5529):430–433
Rotmans J, Van Asselt MBA (2001) Uncertainty in integrated assessment modelling: a labyrinthic path. Integrated Assessment 2:43–55
Savage LJ (1954) The foundations of statistics. Wiley, New York
Scherm H (2000) Simulating uncertainty in climate–pest models with fuzzy numbers. Environ Pollut 108(3):373–379
Schmeidler D (1989) Subjective probability and expected utility without additivity. Econometrica 57:571–587
Schneider SH (2001) What is ‘dangerous’ climate change? Nature 411(6833):17–19
Schneider SH (2002) Can we estimate the likelihood of climatic changes at 2100? Clim Change 52(4):441–451
Shackle GLS (1961) Decision, order and time in human affairs. Cambridge University Press, Cambridge
Shackley S, Young P, Parkinson S, Wynne B (1998) Uncertainty, complexity and concepts of good science in climate change modelling: are GCMS the best tools? Clim Change 38(2):159–205
Simon H (1982) Models of bounded rationality. Behavioral economics and business organization. MIT Press, Cambridge
Stainforth DA, Aina T, Christensen C, Collins M, Faull N, Frame DJ, Kettleborough JA, Knight S, Martin A, Murphy JM, Piani C, Sexton D, Smith LA, Spicer RA, Thorpe AJ, Allen MR (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433(7024):403–406
Stocker TF, Schmittner A (1997) Influence of Co2 emission rates on the stability of the thermohaline circulation. Nature 388(6645):862–865
Stott PA, Kettleborough JA (2002) Origins and estimates of uncertainty in predictions of twenty-first century temperature rise. Nature 416(6882):723–726
Titus JG, Narayanan V (1996) The risk of sea level rise. Clim Change 33(2):151–212
Tol RSJ, De Vos AF (1998) A bayesian statistical analysis of the enhanced greenhouse effect. Clim Change 38(1):87–112
Tonon F (2004) On the use of random set theory to bracket the results of Monte Carlo simulations. Reliab Comput 10(2):107–137
Tschang FT, Dowlatabadi H (1995) A bayesian technique for refining the uncertainty in global energy model forecasts. Int J Forecast 11(1):43–61
United Nations (1998) World Population Projections to 2150. United Nations Department of Economic and Social Affairs Population Division, New York
von Neumann J, Morgenstern O (1947) Theory of games and economic behaviour. Princeton University Press, Princeton NJ
Walley P (1991) Statistical reasoning with imprecise probabilities. Chapman and Hall, London
Walley P (2000) Towards a unified theory of imprecise probabilities. Int J Approx Reason 24(2–3):125–148
Webster MD, Sokolov AP (2000) A methodology for quantifying uncertainty in climate projections. Clim Change 46(4):417–446
Webster MD, Babiker M, Mayer M, Reilly JM, Harnisch J, Hyman R, Sarofim MC, Wang C (2002) Uncertainty in emissions projections for climate models. Atmos Environ 36(22):3659–3670
Wigley TML (2003) Magicc/Scengen 4.1: technical manual
Wigley TML, Raper SCB (2001) Interpretation of high projections for global-mean warming. Science 293(5529):451–454
Wigley TML, Raper SCB (2002) Reasons for larger warming projections in the IPCC third assessment report. J Climate 15(20):2945–2952
Williamson T (1994) Vaugeness. Routledge, London
Williamson RC, Downs T (1990) Probabilistic arithmetic I: Numerical methods for calculating convolutions and dependency bounds. Int J Approx Reason 4(2):89–158
World Bank (1991) World development report 1991: the challenge of development. Oxford University Press, Oxford
Wright G, Ayton P (1994) Subjective probability. Wiley, Chichester
Yager RR (1986) Arithmetic and other operations on dempster–shafer structures. Int J Man–Mach Stud 25(4):357–366
Young P, Parkinson S, Lees M (1996) Simplicity out of complexity in environmental modelling: Occam’s razor revisited. J Appl Stat 23(2–3):165–210
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning. Inf Sci 8:199–249
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Zapert R, Gaertner PS, Filar JA (1998) Uncertainty propagation within an integrated model of climate change. Energy Econ 20(5–6):571–598
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hall, J., Fu, G. & Lawry, J. Imprecise probabilities of climate change: aggregation of fuzzy scenarios and model uncertainties. Climatic Change 81, 265–281 (2007). https://doi.org/10.1007/s10584-006-9175-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10584-006-9175-6