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The strategy of model building in climate science

  • Computational Modelling in Philosophy
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Abstract

In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the “brute force” strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited by computational tractability, and they are incomprehensible to the human modeller. Along the lines Levins described, this uncertainty generates a trade-off between realistic, precise models with predictive power and simple, highly idealised models that facilitate understanding. In addition to building ensembles of highly complex dynamical models, climate modellers can address model uncertainty by comparing models of different types, such as dynamical and data-driven models, and by systematically comparing models at different levels of what climate modellers call the model hierarchy. Despite its age, Levins’ paper remains incredibly insightful and should be considered an important entry into the philosophy of computational modelling.

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Notes

  1. Following Michael Weisberg (2006a), I take a more realistic model to be one that explicitly represents more of its target’s causal structure, a more precise model to be one with more finely specified parameters, and a more general model to be one that applies to more actual or possible targets. I describe these trade-offs further in Sect. 3.

  2. In some conditions, models do not rely on specific initial conditions. ESMs and GCMs, in fact, are allowed to “spin-up” for some simulation time, falling into their own natural equilibria before forcing scenarios, such as different carbon emission schemes, are imposed and the model system moves away from its equilibrium.

  3. This claim is based on what can be found in the Intergovernmental Panel on Climate Change’s Summary for Policymakers of The Physical Science Basis (Stocker, 2014), a report which focuses specifically on the phenomena of Twentieth and Twentifirst Century climate change. Of course, many climate modellers are interested in different time periods. Paleoclimatologists, most obviously, investigate phenomena in the distant past, far outside of the roughly 350-year window that is most relevant to anthropogenic climate change. For the purposes of this paper, however, I am focusing only on one (very prominent!) branch of climate science.

  4. Levins (1993, p. 554) acknowledged that understandability was another important modelling desiderata beyond the three discussed in his (1966).

  5. In Bony et al.’s two-dimensional model space, there are three regions that remain unoccupied. First, there are few if any complex models of simple systems because they are of limited scientific value. Second, in a region Bony et al. label a “conceptual abyss,” there are few simple models of highly complex systems due to a lack of understanding. Finally, in a region they label a “computational abyss,” there are few very complex models (relative to the target system) of very complex target systems due to computational limitations. Bony et al. place a “descriptive horizon” between the second and third unoccupied regions and the rest of the model space.

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Acknowledgements

I would like to John Matthewson and Kim Sterelny for their many helpful comments on this material as well as two anonymous reviewers whose advice led to a much-improved paper. I would also like to thank audiences at the AAP/NZAP2018 in Wellington and PSA2018 in Seattle, and particularly Joel Katzav and Michael Weisberg, for their feedback on earlier versions of this paper presented at those events. Funding was provided by Australian Research Council (Grant No. ARC FL13).

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    Lachlan Douglas Walmsley

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Walmsley, L.D. The strategy of model building in climate science. Synthese 199, 745–765 (2021). https://doi.org/10.1007/s11229-020-02707-y

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