Abstract
In the philosophy of climate science, debate surrounding the issue of variety of evidence has mostly taken the form of attempting to connect these issues in climate science and climate modeling with philosophical accounts of what has come to be known as “robustness analysis.” I argue that an “explanatory” conception of robustness is the best candidate for understanding variety of evidence in climate science. I apply the analysis to both examples of model agreement, as well at to the convergence of evidence from both model and non-model sources.
Similar content being viewed by others
Notes
Mastrandrea et al. (2010).
Levins (1966), p. 20.
ibid.
In addition to weakening the demand that M be true, to the claim that it be capable of H, we are also weakening the (probably unreasonable) demand that RA be able to demonstrate that M has this property to the demand that it be likely that M has this property. (Perhaps to the demand that it raise that likelihood above some critical threshold for acceptance—one perhaps dictated by context.)
2015 paper.
Kirtman et al. (2013).
A second major source of uncertainty regarding what a particular emissions scenario will do to the climate is the “climate-carbon cycle feedback”. As the planet warms, it likely becomes less efficient at sequestering carbon. But this effect is often no included in the standard definition of ECS, because ECS is defined in terms of the response to a particular level of carbon. (And of course, that means that we might very well cause a doubling of the carbon level without having added that much carbon ourselves.)
“Emergent” here doesn’t mean anything spooky: it just means that a systematic relationship arises out of the dynamics, rather than being one that can be seen, by mere inspection, to be built into the governing equations”.
This last qualification is important: I am concerned in this section specifically with the question of what we can learn from model agreement. Other kinds of RA are applicable when we consider other lines of evidence.
Klein and Hall (2015).
Assuming they are linearly additive to a reasonable degree of approximation, which they probably are.
Most of the scientific claims here follow (Knutti and Hegerl 2008).
The evidence has a small degree of robustness because if the hypothesis that ECS is greater than 6 were true, it would be a little bit of a coincidence that the correct value was falling at very high end of both our simulation models and our long-span paleo-data. But it would only be a minor coincidence. So this should not make us confident in our ability to rule such a hypothesis out.
References
Calcott, B. (2011). Wimsatt and the robustness family: Review of Wimsatt’s re-engineering philosophy for limited beings. Biology and Philosophy, 26, 281–293.
Kirtman, B., Power, S. B., Adedoyin, J. A., Boer, G. J., Bojariu, R., Camilloni, I., et al. (2013). Near-term climate change: Projections and predictability. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press.
Klein, S. A., & Hall, A. (2015). Emergent constraints for cloud feedbacks. Current Climate Change Reports, 1, 276–287.
Knutti, R., & Hegerl, G. C. (2008). The equilibrium sensitivity of the Earth’s temperature to radiation changes. Nature Geosciences., 1, 735–743.
Levins, R. (1966). The strategy of model building in population biology. In E. Sober (Ed.), Conceptual issues in evolutionary biology (1st ed., pp. 18–27). Cambridge: MIT Press.
Lloyd, E. A. (2009). Varieties of support and confirmation of climate models. In Proceedings of the Aristotelian Society (Supplementary Vol. LXXXIII, pp. 217–236).
Lloyd, E. A. (2010). Confirmation and robustness of climate models. Philosophy of Science, 77, 971–984.
Lloyd, E. A. (2015). Model robustness as a confirmatory virtue: The case of climate science. Studies in History and Philosophy of Science Part A, 49, 58–68.
Mastrandrea, M. D., Field, C. B., Stocker, T. F., Edenhofer, O., Ebi, K. L., Frame, D. J., et al. (2010). Guidance note for lead authors of the IPCC fifth assessment report on consistent treatment of uncertainties. Intergovernmental Panel on Climate Change (IPCC). Available at <http://www.ipcc.ch>.
Orzack, S. H., & Sober, E. (1993). A critical assessment of Levins’s ‘The strategy of model building in population biology’ (1966). Quarterly Review of Biology, 68(4), 533–546.
Parker, W. S. (2011). When climate models agree: The significance of robust model predictions. Philosophy of Science, 78, 579–600.
Pirtle, Z., Meyer, R., & Hamilton, A. (2010). What does it mean when climate models agree? A case for assessing independence among general circulation models. Environmental Science & Policy, 13(5), 351–361.
Schupbach, J. N. (2016). Robustness analysis as explanatory reasoning. British Journal for the Philosophy of Science. https://doi.org/10.1093/bjps/axw008.
Weisberg, M. (2006). Robustness analysis. Philosophy of Science, 73, 730–742.
Wimsatt, W. C. (1994). The ontology of complex systems: Levels of organization, perspectives, and causal thickets. In W. C. Wimsatt (Ed.), Re-engineering philosophy for limited beings (pp. 193–240). Cambridge: Harvard University Press.
Wimsatt, W. C. (2011). Robust re-engineering: A philosophical account? Biology and Philosophy, 26, 295–303.
Woodward, J. (2006). Some varieties of robustness. Journal of Economic Methodology, 13, 219–240.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Winsberg, E. What does robustness teach us in climate science: a re-appraisal. Synthese 198 (Suppl 21), 5099–5122 (2021). https://doi.org/10.1007/s11229-018-01997-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11229-018-01997-7