Matthias Ackermann

In its Sixth Assessment Report, the Intergovernmental Panel on Climate Change (IPCC) moved from a climate model-based to a climate model-supported assessment of equilibrium climate sensitivity (ECS). Unlike all previous reports, climate model information is no longer used directly for estimates of ECS. This article offers a measurement view on the ECS assessment that allows us to evaluate this shift in practice in terms of its practical and epistemic consequences. In particular, I argue for two main conclusions involved in this move to a climate model-supported assessment. First, the change in using climate models as direct sources of evidence to a supporting role involves a kind of repurposing of climate models as epistemic tools. This change should be evaluated relative to the researchers’ aims, which include more accurately assessing ECS. Second, and more generally, I suggest that this shift in practice shares a kinship to calibration activities aimed at increasing the reliability of a measurement procedure.

Computer simulations are commonly employed when researchers work with analytically intractable or practically unsolvable mathematical modeling equations. In such cases, scientists seem to deal with two different but interrelated kinds of models: a mathematical model and a simulation model. This raises at least two philosophically interesting questions. First, does one or the other model figure centrally in the activity of generating an explanation in such situations? And second, what could an account of explanation involving both mathematical models and simulation models look like? I will argue that, in a large set of cases, the simulation model serves the central role in explanatory discovery. On this basis, I will then present a counterfactual account of simulation model-induced explanation. I claim that on this approach, we often find that the simulation model possesses an explanatory autonomy from its underlying mathematical models and conclude by relating this notion to extant views on the autonomous role of scientific models.