Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-25T04:01:22.013Z Has data issue: false hasContentIssue false
  • English
  • Français

When Climate Models Agree: The Significance of Robust Model Predictions

Published online by Cambridge University Press:  01 January 2022

Wendy S. Parker
Get access Rights & Permissions [Opens in a new window]

Abstract

This article identifies conditions under which robust predictive modeling results have special epistemic significance—related to truth, confidence, and security—and considers whether those conditions hold in the context of present-day climate modeling. The findings are disappointing. When today’s climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred—via the arguments considered here anyway—that the hypothesis is likely to be true or that scientists’ confidence in the hypothesis should be significantly increased or that a claim to have evidence for the hypothesis is now more secure.

Type
Research Article
Information
Philosophy of Science , Volume 78 , Issue 4 , October 2011 , pp. 579 - 600
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

‡

Sincere thanks to Dan Steel, Reto Knutti, Kent Staley, Phil Ehrlich, Lenny Smith, Joel Katzav, Charlotte Werndl, and two anonymous referees for helpful suggestions and criticisms. Thanks also to those who provided feedback when earlier versions of this article were presented at Purdue University, University of Colorado at Boulder, University of Toronto, and University of Waterloo.

References

Abramowitz, Gab. 2010. “Model Independence in Multi-Model Ensemble Prediction.” Australian Meteorological and Oceanographic Journal 59:3–6.CrossRefGoogle Scholar
BBC (British Broadcasting Corporation). 2010. “Climate Change Experiment Results,” http://www.bbc.co.uk/sn/climateexperiment.Google Scholar
Brohan, Philip, Kennedy, J. J., Harris, I., Tett, S. F. B., and Jones, P. D.. 2006. “Uncertainty Estimates in Regional and Global Observed Temperature Changes: A New Data Set from 1850.” Journal of Geophysical Research 111:D12106, doi:10.1029/2005JD006548.CrossRefGoogle Scholar
Cartwright, Nancy. 1991. “Replicability, Reproducibility, and Robustness: Comments on Harry Collins.” History of Political Economy 23:143–55.CrossRefGoogle Scholar
Christensen, Jens Hesselbjerg, et al. 2007. “Regional Climate Projections.” In Solomon et al. 2007, 847–940.Google Scholar
Edwards, Paul N. 1999. “Global Climate Science, Uncertainty and Data: Data-Laden Models, Model-Filtered Data.” Science as Culture 8:437–72.CrossRefGoogle Scholar
Edwards, Paul N.. 2010. A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. Cambridge, MA: MIT Press.Google Scholar
Gleckler, Peter J., Taylor, Karl A., and Doutriaux, Charles. 2008. “Performance Metrics for Climate Models.” Journal of Geophysical Research 113:D06104.10.1029/2007JD008972CrossRefGoogle Scholar
Harris, Todd. 2003. “Data Models and the Acquisition and Manipulation of Data.” Philosophy of Science 70:1508–17.10.1086/377426CrossRefGoogle Scholar
Judd, Kevin, Smith, Leonard A., and Weisheimer, Antje. 2007. “How Good Is an Ensemble at Capturing Truth? Using Bounding Boxes for Forecast Evaluation.” Quarterly Journal of the Royal Meteorological Society 133:1309–25.10.1002/qj.111CrossRefGoogle Scholar
Knutti, Reto. 2008. “Why Are Climate Models Reproducing the Observed Global Surface Warming so Well?” Geophysical Research Letters 35:L18704, doi:10.1029/2008GL034932.CrossRefGoogle Scholar
Knutti, Reto, et al. 2008. “A Review of Uncertainties in Global Temperature Projections over the Twenty-first Century.” Journal of Climate 21:2651–63.CrossRefGoogle Scholar
Knutti, Reto, et al. 2010. “Challenges in Combining Projections from Multiple Climate Models.” Journal of Climate 23:2739–58.CrossRefGoogle Scholar
Ladha, Krishna K. 1992. “The Condorcet Jury Theorem, Free Speech, and Correlated Votes.” American Journal of Political Science 36 (3): 617–34.10.2307/2111584CrossRefGoogle Scholar
Ladha, Krishna K.. 1995. “Information Pooling through Majority-Rule Voting: Condorcet’s Jury Theorem.” Journal of Economic Behavior and Organization 26:353–72.10.1016/0167-2681(94)00068-PCrossRefGoogle Scholar
Levins, Richard. 1966. “The Strategy of Model Building in Population Biology.” American Scientist 54:421–31.Google Scholar
Meehl, Gerald A., et al. 2007. “Global Climate Projections.” In Solomon et al. 2007, 747–846.Google Scholar
Muldoon, Ryan. 2007. “Robust Simulations.” Philosophy of Science 74:873–83.CrossRefGoogle Scholar
Murphy, James M., et al. 2007. “A Methodology for Probabilistic Predictions of Regional Climate Change from Perturbed Physics Ensembles.” Philosophical Transactions of the Royal Society A 365:1993–2028.Google ScholarPubMed
Odenbaugh, Jay. Forthcoming. “Consensus, Climate, and Contrarians.” In Topics in Contemporary Philosophy: Environment. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Orzack, Steven H., and Sober, Elliott. 1993. “A Critical Assessment of Levins’s The Strategy of Model Building in Population Biology (1966).” Quarterly Review of Biology 68:533–46.CrossRefGoogle Scholar
Owen, Guillermo, Grofman, Bernard, and Feld, Scott L.. 1989. “Proving a Distribution-Free Generalization of the Condorcet Jury Theorem.” Mathematical Social Sciences 17:1–16.10.1016/0165-4896(89)90012-7CrossRefGoogle Scholar
Parker, Wendy S. 2006. “Understanding Pluralism in Climate Modeling.” Foundations of Science 11:349–68.CrossRefGoogle Scholar
Parker, Wendy S.. 2009. “Confirmation and Adequacy-for-Purpose in Climate Modelling.” Proceedings of the Aristotelian Society 83 (Suppl.): 233–49.Google Scholar
Parker, Wendy S.. 2010. “Whose Probabilities? Predicting Climate Change with Ensembles of Models.” Philosophy of Science 77 (5): 985–97.CrossRefGoogle Scholar
Pennell, Christopher, and Reichler, Thomas. 2011. “On the Effective Number of Climate Models.” Journal of Climate 24:2358–67.10.1175/2010JCLI3814.1CrossRefGoogle Scholar
Pirtle, Zachary, Meyer, Ryan, and Hamilton, Andrew. 2010. “What Does It Mean When Climate Models Agree? A Case for Assessing Independence among General Circulation Models.” Environmental Science and Policy 13:351–61.CrossRefGoogle Scholar
Randall, David A., et al. 2007. “Climate Models and Their Evaluation.” In Solomon et al. 2007, 589–662.Google Scholar
Solomon, Susan, et al., eds. 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press.Google Scholar
Stainforth, David A., et al. 2005. “Uncertainty in Predictions of the Climate Response to Rising Levels of Greenhouse Gases.” Nature 433:403–6.CrossRefGoogle ScholarPubMed
Staley, Kent. 2004. “Robust Evidence and Secure Evidence Claims.” Philosophy of Science 71:467–88.CrossRefGoogle Scholar
Suppes, Patrick. 1962. “Models of Data.” In Logic, Methodology and Philosophy of Science: Proceedings of the 1960 International Congress, ed. Nagel, Ernest, Suppes, Patrick, and Tarski, Alfred, 252–61. Stanford, CA: Stanford University Press.Google Scholar
Tebaldi, Claudia, and Knutti, Reto. 2007. “The Use of the Multi-Model Ensemble in Probabilistic Climate Projections.” Philosophical Transactions of the Royal Society A 365:2053–75.Google ScholarPubMed
Tebaldi, Claudia, Smith, Richard L., Nychka, Doug, and Mearns, Linda O.. 2005. “Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach.” Journal of Climate 18:1524–40.CrossRefGoogle Scholar
Weisberg, Michael. 2006. “Robustness Analysis.” Philosophy of Science 73:730–42.10.1086/518628CrossRefGoogle Scholar
Wimsatt, William C. 1981/2007. “Robustness, Reliability, and Overdetermination.” In Re-engineering Philosophy for Limited Beings. Repr., Cambridge, MA: Harvard University Press.Google Scholar
Woodward, James. 2006. “Some Varieties of Robustness.” Journal of Economic Methodology 13:219–40.CrossRefGoogle Scholar