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Building Trust, Removing Doubt? Robustness Analysis and Climate Modeling

Abstract

In this chapter, Odenbaugh first provides a conceptual framework for thinking about climate modeling, specifically focused on general circulation models. Second, he considers what makes models independent of one another. Third, he shows robustness analysis, which depends on models being independent of one another, can be used to remove doubts about idealizations in general climate models. Finally, he considers a dilemma for robustness analysis; namely, it leads to either an infinite regress of idealizations or a complete removal of idealizations. A response to the dilemma is given defending a form of epistemic contextualism and by drawing a distinction between relative and absolute robustness.

Keywords

  • Robustness Analysis
  • Odenbaugh
  • Epistemic Contextualism
  • Global Circulation Models (GCM)
  • Skeptical Model

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.

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Fig. 10.1
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Notes

  1. 1.

    Climate scientists refer to general circulation models as “GCM”; however, when a model includes atmospheric and oceanic components, they are referred to as “AOGCM.” For simplicity, I will refer to all such models as “GCM.”

  2. 2.

    For a useful survey of the relevant processes, see Neelin (2010), chapter 2.

  3. 3.

    It is worth noting that not every abstraction is an idealization or every idealization an abstraction. For example, a representation might not include all of the causal variables but say only true things about the ones it includes. Likewise, a representation might not omit any causally relevant variable but distort what is says about them.

  4. 4.

    On the semantic view of theories, philosophers of science assume that models are abstract objects such relational structures, phase spaces, and so on. Here I assume they are propositions (though not propositions axiomatically arranged per the received view of theories). Of course, anything I say here can be understood in one’s preferred view of theories and propositions.

  5. 5.

    For a useful discussion, see Neelin (2010), chapter 3.

  6. 6.

    I have argued that models also serve as heuristics for certain purposes (Odenbaugh 2005). That is, untested or disconfirmed models are used to explore possibilities, serve as simple baselines, and provide conceptual frameworks. Climate models of course can do this as well—for example, see the simple layer model in Archer (2012), chapter 2. However here I am concerned with model evaluation in the narrow sense, i.e. confirmation and disconfirmation.

  7. 7.

    For example, if one collects data from a system at a time and then a week later, are these different data sets? Presumably questions like this will partially depend on the questions one is asking.

  8. 8.

    The philosophical status of laws such as the conversation of mass and the ideal gas law is of course controversial. However, when modelers use the term “law,” we need not assume that they mean what philosophers do, e.g. natural necessities or relations between universals.

  9. 9.

    My approach to model robustness is largely inspired by the work of William Wimsatt (2007) and has been developed in Odenbaugh (2011) and Alexandrova and Odenbaugh (2011). Additionally, Michael Strevens (2008), Michael Weisberg (2006), Jim Woodward (2006) have provided important analyses. With regard to climate modeling and robustness analysis, I have been especially influenced by Elisabeth Lloyd (2010, 2015). For an interesting overview of model robustness in the context of climate modeling, see Wendy Parker (2011). Parker considers a variety of explications of model robustness; however, I would argue that the account of model robustness and the queries to which it is put is not found in her analysis and thus avoids her worries.

  10. 10.

    Strictly speaking, M i will be sub-types of M since they will be unspecified.

  11. 11.

    With regard to GCM, our prediction will not be a point prediction; rather, it will be that some variable takes a value in some range. Or, it will be a configuration of such variables such that say average surface temperature is increasing over some set of times.

  12. 12.

    In effect, a set of subsidiary models (or model types) becomes a single model (or model type). Note that this means that whether a given assumption or set of them are ad hoc can change through time.

  13. 13.

    Epistemological contextualism is classified as substantive or semantic where the former concerns whether one knows or is justified in believing a proposition with respect to varying standards whereas the latter concerns whether “knowing” or “justification” is context-sensitive. Here I am only concerned with substantive epistemological contextualism.

  14. 14.

    Invariantism is simply the claim that correct epistemic standards do not change with context.

  15. 15.

    Williams argues that these types of constraints are not merely practical or due to relaxed standards but are the result of the “logic of inquiry.”

  16. 16.

    If ethical or political costs of global climate change filter into model evaluation, then these norms can influence how skeptical we are (see Biddle and Winsberg 2010). For example, if we are reluctant to bear economic burdens through carbon taxes, then we may hold GCM to high standards. Alternately, if we are very worried about climate impacts on developing nations and future generations, we may want to err on the side of causation. In effect, our model skepticism becomes ethically and politically infused (see Odenbaugh 2010).

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Odenbaugh, J. (2018). Building Trust, Removing Doubt? Robustness Analysis and Climate Modeling. In: A. Lloyd, E., Winsberg, E. (eds) Climate Modelling. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-65058-6_10

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