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Uncertainly in climate science and climate policy

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Rougier, J.C. Crucifix, Michel [UCL]

(eng) 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.

Document type Contribution à ouvrage collectif (Book Chapter) – Chapitre
Access type Accès libre
Publication date 2018
Language Anglais
Host document Lloyd, L. (ed.) ; Winsberg , E (ed.) ; "Climate Modeling" (ISBN : 978-3-319-65057-9)
Edition statement University of Chicago Press
Peer reviewed yes
Publisher L. Lloyd, E. Winsberg
Publication status Publié
Affiliation UCL - SST/ELI/ELIC - Earth & Climate
Keywords 1443
Links
  1. Andrieu Christophe, Doucet Arnaud, Holenstein Roman, Particle Markov chain Monte Carlo methods : Particle Markov Chain Monte Carlo Methods, 10.1111/j.1467-9868.2009.00736.x
  2. Aspinall Willy, A route to more tractable expert advice, 10.1038/463294a
  3. Aspinall, W.P., and R.M. Cooke. 2013. Quantifying Scientific Uncertainty from Expert Judgment Elicitation. In Rougier et al. (2013), Chapter 4.
  4. Barnston Anthony G., Tippett Michael K., L'Heureux Michelle L., Li Shuhua, DeWitt David G., Skill of Real-Time Seasonal ENSO Model Predictions during 2002–11: Is Our Capability Increasing?, 10.1175/bams-d-11-00111.1
  5. Cooke R.M., Goossens L.H.J., Procedures Guide for Structural Expert Judgement in Accident Consequence Modelling, 10.1093/oxfordjournals.rpd.a033152
  6. 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.
  7. Curry J. A., Webster P. J., Climate Science and the Uncertainty Monster, 10.1175/2011bams3139.1
  8. 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).
  9. Gigerenzer, G. 2003. Reckoning with Risk: Learning to Live with Uncertainty. London: Penguin.
  10. 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.
  11. Goldstein Michael, Wooff David, Bayes Linear Statistics, ISBN:9780470065662, 10.1002/9780470065662
  12. Goodman Steven N., Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy, 10.7326/0003-4819-130-12-199906150-00008
  13. 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
  14. Guilyardi Eric, Wittenberg Andrew, Fedorov Alexey, Collins Mat, Wang Chunzai, Capotondi Antonietta, van Oldenborgh Geert Jan, Stockdale Tim, Understanding El Niño in Ocean–Atmosphere General Circulation Models: Progress and Challenges, 10.1175/2008bams2387.1
  15. 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/
  16. Howson, C., and P. Urbach. 2006. Scientific Reasoning: The Bayesian Approach. 3rd ed. Chicago: Open Court Publishing Co.
  17. 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).
  18. ———. 2007. Why Most Published Research Findings Are False: Author’s Reply to Goodman and Greenland. PLoS Medicine 4 (6): e215.
  19. Jaynes E. T., Probability Theory : The Logic of Science, ISBN:9780511790423, 10.1017/cbo9780511790423
  20. Jeffrey Richard, Subjective Probability : The Real Thing, ISBN:9780511816161, 10.1017/cbo9780511816161
  21. Kalnay Eugenia, Atmospheric modeling, data assimilation and predictability, ISBN:9780511802270, 10.1017/cbo9780511802270
  22. Lad, F. 1996. Operational Subjective Statistical Methods. New York: Wiley.
  23. Lorenz Alexander, Schmidt Matthias G. W., Kriegler Elmar, Held Hermann, Anticipating Climate Threshold Damages, 10.1007/s10666-011-9282-2
  24. 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).
  25. Murphy James M., Sexton David M. H., Barnett David N., Jones Gareth S., Webb Mark J., Collins Matthew, Stainforth David A., Quantification of modelling uncertainties in a large ensemble of climate change simulations, 10.1038/nature02771
  26. 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
  27. Newman, T.J. 2011. Life and Death in Biophysics. Physical Biology 8: 1–6.
  28. Paris, J.B. 1994. The Uncertain Reasoner’s Companion: A Mathematical Perspective. Cambridge: Cambridge University Press.
  29. Parker Wendy S., Predicting weather and climate: Uncertainty, ensembles and probability, 10.1016/j.shpsb.2010.07.006
  30. 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.
  31. Rougier Jonathan, Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations, 10.1007/s10584-006-9156-9
  32. Rougier, J.C., R.S.J. Sparks, and L.J. Hill, eds. 2013. Risk and Uncertainty Assessment for Natural Hazards. Cambridge: Cambridge University Press.
  33. Santner Thomas J., Williams Brian J., Notz William I., The Design and Analysis of Computer Experiments, ISBN:9781441929921, 10.1007/978-1-4757-3799-8
  34. Savage, L.J. 1954. The Foundations of Statistics. New York: Dover. Revised 1972 Edition.
  35. Savage, L.J., et al. 1962. The Foundations of Statistical Inference. London: Methuen.
  36. Smith Jim Q., Bayesian Decision Analysis : Principles and Practice, ISBN:9780511779237, 10.1017/cbo9780511779237
  37. Tetlock, P.E. 2005. Expert Political Judgment: How Good Is It? How Can We Know? Princeton/Oxford: Princeton University Press.
  38. Walley Peter, Statistical Reasoning with Imprecise Probabilities, ISBN:9780412286605, 10.1007/978-1-4899-3472-7
Bibliographic reference Rougier, J.C. ; Crucifix, Michel. Uncertainly in climate science and climate policy. In: Lloyd, L. (ed.) ; Winsberg , E (ed.), Climate Modeling, L. Lloyd, E. Winsberg  2018
Permanent URL http://hdl.handle.net/2078.1/154834