When is an ensemble like a sample? “Model-based” inferences in climate modeling

Abstract

Climate scientists often apply statistical tools to a set of different estimates generated by an “ensemble” of models. In this paper, I argue that the resulting inferences are justified in the same way as any other statistical inference: what must be demonstrated is that the statistical model that licenses the inferences accurately represents the probabilistic relationship between data and target. This view of statistical practice is appropriately termed “model-based,” and I examine the use of statistics in climate fingerprinting to show how the difficulties that climate scientists encounter in applying statistics to ensemble-generated data are the practical difficulties of normal statistical practice. The upshot is that whether the application of statistics to ensemble-generated data yields trustworthy results should be expected to vary from case to case.

Against “Possibilist” Interpretations of Climate Models

“…To begin, it’s worth reiterating the main point from the last section: that a probability distribution involves some sort of misrepresentation is not sufficient to motivate abandoning it. As I stress in Dethier (2022b), we accept misrepresentations in the form of idealizations and abstractions throughout the sciences, and there’s no obvious reason why probability distributions should be exceptions to the general rule. Even if we accept that extant ensembles misrepresent, they may nevertheless be our best option for representing the “true” probabilities (Katzav and Parker 2015).…”

Robustness of Climate Models

“…Since ensembles in climate science tend to be "ensembles of opportunity," the models in E are likely tightly bunched together (figure 2a) and thus do not span much of the space of B (Sanderson andKnutti 2012, Tebaldi andKnutti 2007). 6 Furthermore, since there is no way to define the boundaries nor extent of B (figure 2b), there is no way to know whether E in fact spans B (Dethier 2022). Parker is interested in the epistemic status of the predictions.…”

Diagnosing errors in climate model intercomparisons

“…Climate model error diagnosis is either misunderstood or has been given little attention in philosophy of climate science. Many scholars have discussed the significance of model agreement (e.g., Parker 2011Parker , 2018aLloyd 2015a;Winsberg 2018;Odenbaugh 2018;O'Loughlin 2021) and also interpretations and statistical evaluations of climate model ensembles (Annan andHargreaves 2010, 2017;Jebeile and Barberousse 2021;Dethier 2022). Yet not many have discussed climate model error diagnosis.…”