Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-27T23:34:33.221Z Has data issue: false hasContentIssue false
  • English
  • Français

Missing the Forest for the Fish: How Much Does the ‘Hawkmoth Effect’ Threaten the Viability of Climate Projections?

Published online by Cambridge University Press:  01 January 2022

William M. Goodwin  and
Eric Winsberg
Get access Rights & Permissions [Opens in a new window]

Abstract

Roman Frigg and others have developed a general epistemological argument designed to cast doubt on the capacity of a broad range of mathematical models (including many climate models) to generate “decision relevant predictions.” In this article, we lay out the structure of their argument—an argument by analogy—with an eye to identifying points at which certain epistemically significant distinctions might limit the force of the analogy. Finally, some of these epistemically significant distinctions are introduced and defended as relevant to a great many of the predictive mathematical modeling projects employed in contemporary climate science.

Type
Evidence for Climate Policy
Information
Philosophy of Science , Volume 83 , Issue 5 , December 2016 , pp. 1122 - 1132
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.)

References

Bindoff, N. L., et al. 2013. “Detection and Attribution of Climate Change: from Global to Regional.” In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, ed. Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.. Cambridge: Cambridge University Press.Google Scholar
Frigg, R., Bradley, S., Du, H., and Smith, L. A.. 2014a. “Laplace’s Demon and the Adventures of His Apprentices.” Philosophy of Science 81:31–59.CrossRefGoogle Scholar
Bradley, S., Du, H., and Smith, L. A. 2014b. “Model Error and Ensemble Forecasting: A Cautionary Tale,” In Scientific Explanation and Methodology of Science, ed. Guo, Guichun C. and Liu, Chuang, 58–66. Singapore: World Scientific.CrossRefGoogle Scholar
Frigg, R., Bradley, S., Machette, R. L., and Smith, L. A.. 2013a. “Probabilistic Forecasting: Why Model Imperfection Is a Poison Pill.” In New Challenges to Philosophy of Science. Dordrecht: Springer.Google Scholar
Frigg, R., Smith, L. A., and Stainforth, D. A.. 2013b. “The Myopia of Imperfect Climate Models.” Philosophy of Science 80:886–97.CrossRefGoogle Scholar
Lorenz, E. N. 1975. “Climate Predictability.” In The Physical Basis of Climate Modelling, 132–36. Geneva: World Meteorological Organization.Google Scholar