Abstract
In this chapter, we argue for and describe the gap that exists between current practice in mainstream academic climate science, and the practical needs of policymakers charged with exploring possible interventions in the context of climate change. By ‘mainstream academic climate science’ we mean the type of climate science that dominates in universities and research centres. We argue that academic climate science does not equip climate scientists to be as helpful as they might be, when involved in climate policy assessment. We attribute this partly to an over-investment in high-resolution climate simulators, and partly to a culture that is uncomfortable with the inherently subjective or personalistic nature of the probabilities in climate science.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andrieu, C., A. Doucet, and R. Holenstein. 2010. Particle Markov Chain Monte Carlo Methods. Journal of the Royal Statistical Society, Series B 72 (3): 269–302. With Discussion, 302–342.
Aspinall, W.P. 2010. A Route to More Tractable Expert Advice. Nature 463: 294–295.
Aspinall, W.P., and R.M. Cooke. 2013. Quantifying Scientific Uncertainty from Expert Judgment Elicitation. In Rougier et al. (2013), Chapter 4.
Barnston, A.G., M.K. Tippett, M.L. L’Heureux, S. Li, and D.G. DeWitt. 2012. Skill of Real-Time Seasonal ENSO Model Predictions During 2002–11: Is Our Capability Increasing? Bulletin of the American Meteorological Society 93 (5): 631–651.
Cooke, R.M., and L.H.J. Goossens. 2000. Procedures Guide for Structured Expert Judgement in Accident Consequence Modelling. Radiation Protection Dosimetry 90 (3): 303–309.
Crucifix, M. 2012. Oscillators and Relaxation Phenomena in Pleistocene Climate Theory. Philosophical Transactions of the Royal Society, Series A, Reprint Available at arXiv:1103.3393v1.
Curry, J.A., and P.J. Webster. 2011. Climate Science and the Uncertainty Monster. Bulletin of the American Meteorological Society 92 (12): 1667–1682.
de Finetti, B. 1964. Foresight: Its Logical Laws, Its Subjective Sources. In Studies in Subjective Probability, ed. H. Kyburg and H. Smokler, 93–158. New York: Wiley. (2nd ed., New York: Krieger, 1980).
Gigerenzer, G. 2003. Reckoning with Risk: Learning to Live with Uncertainty. London: Penguin.
Goldstein, M. 1997. Prior Inferences for Posterior Judgements. In Structures and Norms in Science. Volume Two of the Tenth International Congress of Logic, Methodology and Philosophy of Science, Florence, August 1995, ed. M.L.D. Chiara, K. Doets, D. Mundici, and J. van Benthem, 55–71. Dordrecht: Kluwer.
Goldstein, M., and D.A. Wooff. 2007. Bayes Linear Statistics: Theory & Methods. Chichester: Wiley.
Goodman, S. 1999. Toward Evidence-Based Medical Statistics. 1: The p-value Fallacy. Annals of Internal Medicine 130: 995–1004.
Goodman, S., and S. Greenland. 2007. Why Most Published Research Findings Are False: Problems in the Analysis. PLoS Medicine 4(4): e168. A Longer Version of the Paper Is Available at http://www.bepress.com/jhubiostat/paper135
Guilyardi, E., A. Wittenberg, A. Fedorov, M. Collins, C. Wang, A. Capotondi, G.J. van Oldenborgh, and T. Stockdale. 2009. Understanding El Niño in Ocean—Atmosphere General Circulation Models: Progress and Challenges. Bulletin of the American Meteorological Society 90 (3): 325–340.
Hájek, A. 2012. Interpretations of Probability. In ed. E.N. Zalta, The Stanford Encyclopedia of Philosophy (Summer Edition). Forthcoming URL http://plato.stanford.edu/archives/sum2012/entries/probability-interpret/
Howson, C., and P. Urbach. 2006. Scientific Reasoning: The Bayesian Approach. 3rd ed. Chicago: Open Court Publishing Co.
Ioannidis, J.P.A. 2005. Why Most Published Research Findings Are False. PLoS Medicine 2 (8): e124. See also Goodman and Greenland (2007) and Ioannidis (2007).
———. 2007. Why Most Published Research Findings Are False: Author’s Reply to Goodman and Greenland. PLoS Medicine 4 (6): e215.
Jaynes, E.T. 2003. Probability Theory: The Logic of Science. Cambridge: Cambridge University Press.
Jeffrey, R.C. 2004. Subjective Probability: The Real Thing. Cambridge: Cambridge University Press. Unfortunately This First Printing Contains Quite a Large Number of Typos.
Kalnay, E. 2002. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge: Cambridge University Press.
Lad, F. 1996. Operational Subjective Statistical Methods. New York: Wiley.
Lorenz, A., M.G.W. Schmidt, E. Kriegler, and H. Held. 2012. Anticipating Climate Threshold Damages. Environmental Modeling and Assessment 17: 163–175.
Mastrandrea, M.D., C.B. Field, T.F. Stocker, O. Edenhofer, K.L. Ebi, D.J. Frame, H. Held, E. Kriegler, P.R. Matschoss K.J. Mach, G.-K. Plattner, G.W. Yohe, and F.W. Zwiers. 2010. Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. Technical report, Intergovernmental Panel on Climate Change (IPCC).
Murphy, J.M., D.M.H. Sexton, D.N. Barnett, G.S. Jones, M.J. Webb, M. Collins, and D.A. Stainforth. 2004. Quantification of Modelling Uncertainties in a Large Ensemble of Climate Change Simulations. Nature 430: 768–772.
Murphy, J., R. Clark, M. Collins, C. Jackson, M. Rodwell, J.C. Rougier, B. Sanderson, D. Sexton, and T. Yokohata. 2011. Perturbed Parameter Ensembles as a Tool for Sampling Model Uncertainties and Making Climate Projections. In Proceedings of ECMWF Workshop on Model Uncertainty, 20–24 June 2011, pp. 183–208. Available Online, http://www.ecmwf.int/publications/library/ecpublications/_pdf/workshop/2011/Model_uncertainty/Murphy.pdf
Newman, T.J. 2011. Life and Death in Biophysics. Physical Biology 8: 1–6.
Paris, J.B. 1994. The Uncertain Reasoner’s Companion: A Mathematical Perspective. Cambridge: Cambridge University Press.
Parker, W.S. 2010. Predicting Weather and Climate: Uncertainty, Ensembles and Probability. Studies in History and Philosophy of Modern Physics 41: 263–272.
Ramsey, F.P. 1931. Truth and Probability. In Foundations of Mathematics and Other Essays, ed. R.B. Braithwaite, 156–198. London: Kegan, Paul, Trench, Trubner, & Co.
Rougier, J.C. 2007. Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations. Climatic Change 81: 247–264.
Rougier, J.C., R.S.J. Sparks, and L.J. Hill, eds. 2013. Risk and Uncertainty Assessment for Natural Hazards. Cambridge: Cambridge University Press.
Santner, T.J., B.J. Williams, and W.I. Notz. 2003. The Design and Analysis of Computer Experiments. New York: Springer.
Savage, L.J. 1954. The Foundations of Statistics. New York: Dover. Revised 1972 Edition.
Savage, L.J., et al. 1962. The Foundations of Statistical Inference. London: Methuen.
Smith, J.Q. 2010. Bayesian Decision Analysis: Principle and Practice. Cambridge: Cambridge University Press.
Tetlock, P.E. 2005. Expert Political Judgment: How Good Is It? How Can We Know? Princeton/Oxford: Princeton University Press.
Walley, P. 1991. Statistical Reasoning with Imprecise Probabilities. London: Chapman & Hall.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 The Author(s)
About this chapter
Cite this chapter
Rougier, J., Crucifix, M. (2018). Uncertainty in Climate Science and Climate Policy. In: A. Lloyd, E., Winsberg, E. (eds) Climate Modelling. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-65058-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-65058-6_12
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-319-65057-9
Online ISBN: 978-3-319-65058-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)
Keywords
- Climate Science
- Climate Policy Assessment
- High-resolution Climate Simulations
- Mind Projection Fallacy
- Tetlock
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.