Matthias Ackermann

Recent discussion suggests that uncertainty in climate models’ representation of the interaction between external radiative forcing and internal variability prevents scientists from reliably providing local (or regional) climate change information anytime soon. In this paper, I articulate ‘predictable uncertainty’ as an epistemic criterion of reliable climate change information: the ability of climate scientists to systematically characterize and constrain the uncertainty envelope associated with their epistemic situation. There are two related implications. First, internal variability can be an informative signal for future climate change, and controlling for it can help identify the scales at which climate model-based or climate model-supported information may be reliable. Second, the reliability of local climate change information should be evaluated based not primarily on the amount of uncertainty in the interaction between the forced response and internal variability, but rather on climate scientists’ ability to characterize and constrain that uncertainty.

The dissemination of economic models and predictions can affect social outcomes; this is roughly what has been called the "performativity of economics." For example, consider an economist's prediction of a bank failure, which may turn into a self-fulfilling prophecy contributing to a bank run. Recent contributions have argued that economists have responsibility towards the performative effects they cause. We argue that a more complex picture emerges once we make explicit widely acknowledged conditions for moral responsibility that are currently only implicitly alluded to in the literature. Towards this aim, we highlight four factors relevant for assessing the responsibility of economists, and systematically explore the impact of their different conceptualizations and specifications for assigning moral responsibility for performative effects: 1. the foreseeability of the performative effect in question, 2. its causal strength, 3. the degree to which causal control is distributed, and 4. how we conceptualize the social role of economists.

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.