Abstract
AbstractAn important epistemic issue in climate modelling concerns structural uncertainty: uncertainty about whether the mathematical structure of a model accurately represents its target. How does structural uncertainty affect our knowledge and predictions about the climate? How can we identify sources of structural uncertainty? Can we manage the effect of structural uncertainty on our knowledge claims? These are some of the questions that an epistemology of structural uncertainty faces, and these questions are also important for climate scientists and policymakers. I develop three desiderata for an epistemological account of structural uncertainty. In my view, an account of structural uncertainty should (1) identify sources of structural uncertainty, (2) explain how these sources limit the applicability of a model, and (3) show how the severity of structural uncertainty depends on the questions that can be asked of a model. I argue that analyzing structural uncertainty by paying attention to the details of model building can satisfy these desiderata. I focus on parametrizations, which are representations of important processes occurring at scales that are not resolved by climate models. Parametrizations are often thought to be ad-hoc, but I show that some important parametrizations are theoretically justified by explicit or implicit scale separation assumptions. These assumptions can also be supported empirically. Analyzing these theoretical and empirical justificatory roles of the scale separation assumptions can provide insights into how parametrizations contribute to structural uncertainty. I conclude by sketching how my approach can satisfy the desiderata I set out at the beginning, highlighting its importance for policy-relevant scientific statements about the climate.
Publisher
Springer Science and Business Media LLC
Subject
General Social Sciences,Philosophy
Reference61 articles.
1. Adams, D. K., & Rennó, N. O. (2003). Remarks on quasi-equilibrium theory. Journal of the Atmospheric Sciences, 60, 178–181.
2. Allen, M. R., Kettleborough, J. A., & Stainforth, D. A. (2006). Model error in weather and climate forecasting. In T. Palmer & R. Hagedorn (Eds.), Predictability of weather and climate (pp. 391–427). Cambridge: Cambridge University Press.
3. Arakawa, A. (2004). The cumulus parameterization problem: Past, present, and future. Journal of Climate, 17, 2493–2525.
4. Arakawa, A., & Chen, J. (1987). Cumulus assumptions in the cloud parameterization problem. In T. Matsuno (Ed.), Short- and medium-range numerical weather prediction: Collection of papers presented at the WMO/IUGG NWP symposium, Tokyo, 4–8 August 1986 (pp. 107–131). Tokyo: Meteorological Society of Japan.
5. Arakawa, A., & Schubert, W. H. (1974). Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. Journal of the Atmospheric Sciences, 31, 674–701.
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献