Philosophers of science have paid close attention to climate simulations as means of projecting the severity and effects of climate change, but have neglected the full diversity of methods in climate science. This paper shows the philosophical richness of another method in climate science: the practice of using paleoclimate analogues to inform our climate projections. First, I argue that the use of paleoclimate analogues can offer important insights to philosophers of the historical sciences. Rather than using the present as a (...) guide to the past, as is common in the historical sciences, paleoclimate analogues involve using the past as a guide to the future. I thereby distinguish different methods in the historical sciences and argue that these distinctions bear on debates over whether the historical sciences can produce generalizations or predictions. Second, I suggest that paleoclimate analogues might actually be considered a type of climate model, and, as such, their use expands on common characterizations of models to include those that are full-scale, naturally occurring, and non-manipulable. (shrink)

I examine error diagnosis (model-model disagreement) in climate model intercomparisons including its difficulties, fruitful examples, and prospects for streamlining error diagnosis. I suggest that features of climate model intercomparisons pose a more significant challenge for error diagnosis than do features of individual model construction and complexity. Such features of intercomparisons include, e.g., the number of models involved, how models from different institutions interrelate, and what scientists know about each model. By considering numerous examples in the climate modeling literature, I distill (...) general strategies (e.g., employing physical reasoning and using dimension reduction techniques) used to diagnose model error. Based on these examples, I argue that an error repertoire could be beneficial for improving error diagnosis in climate modeling, although constructing one faces several difficulties. Finally, I suggest that the practice of error diagnosis demonstrates that scientists have a tacit-yet-working understanding of their models which has been under-appreciated by some philosophers. (shrink)

Phylogenetic models traditionally represent the history of life as having a strictly-branching tree structure. However, it is becoming increasingly clear that the history of life is often not strictly-branching; lateral gene transfer, endosymbiosis, and hybridization, for example, can all produce lateral branching events. There is thus motivation to allow phylogenetic models to have a reticulate structure. One proposal involves the reconciliation of genealogical discordance. Briefly, this method uses patterns of disagreement – discordance – between trees of different genes to add (...) lateral branching events to phylogenetic trees of taxa, and to estimate the most likely cause of these events. I use this practice to argue for: (1) a need for expanded accounts of multiple-models idealization, (2) a distinction between automatic and manual de-idealization, and (3) recognition that idealization may serve the meso-level aims of science in a different way than hitherto acknowledged. (shrink)

This article provides an epistemological assessment of climate analogue methods, with specific reference to the use of spatial analogues in the study of the future climate of target locations. Our contention is that, due to formal and conceptual inadequacies of geometrical dissimilarity metrics and the loss of relevant information, especially when reasoning from the physical to the socio-economical level, purported inferences from climate analogues of the spatial kind we consider here prove limited in a number of ways. Indeed, we formulate (...) five outstanding problems concerning the search for best analogues, which we call the problem of non-uniqueness of the source, problem of non-uniqueness of the target, problem of average, problem of non-causal correlations and problem of inferred properties, respectively. In the face of such problems, we then offer two positive recommendations for a fruitful application of this methodology to the assessment of impact, adaptation and vulnerability studies of climate change, especially in the context of what we may prosaically dub “twin cities”. Arguably, such recommendations help decision-makers constrain the set of plausible climate analogues by integrating local knowledge relevant to the locations of interest. (shrink)

A number of philosophers of science have argued that there are important differences between robustness in modeling and experimental contexts, and—in particular—many of them have claimed that the former is non-confirmatory. In this paper, I argue for the opposite conclusion: robust hypotheses are confirmed under conditions that do not depend on the differences between and models and experiments—that is, the degree to which the robust hypothesis is confirmed depends on precisely the same factors in both situations. The positive argument turns (...) on the fact that confirmation theory doesn’t recognize a difference between different sources of evidence. Most of the paper is devoted to rebutting various objections designed to show that it should. I end by explaining why philosophers of science have (often) gone wrong on this point. (shrink)

Lisciandra poses a challenge for robustness analysis as applied to economic models. She argues that substituting tractability assumptions risks altering the main mathematical structure of the model, thereby preventing the possibility of meaningfully evaluating the same model under different assumptions. In such cases RA is argued to be inapplicable. However, Lisciandra is mistaken to take the goal of RA as keeping the mathematical properties of tractability assumptions intact. Instead, RA really aims to keep the modeling component while varying the corresponding (...) mathematical formulation. Thus, her argument concerning whether the associated mathematical properties of certain assumptions can be kept intact is irrelevant to the success of RA. Furthermore, we explicate and develop Lloyd’s account of “model robustness” to provide solutions to Lisciandra’s challenges. Our solutions are, namely, error analysis and independent empirical support. We conclude that although complex economic models do face potential dangers, there are solutions and robustness analysis need not be given up.. (shrink)

In recent publications we have drawn attention to the fact that if the dynamics of a model is structurally unstable, then the presence of structural model error places in-principle limits on the model’s ability to generate decision-relevant probability forecasts. Writing with a varying array of co-authors, Eric Winsberg has now produced at least four publications in which he dismisses our points as unfounded; the most recent of these appeared in this journal. In this paper we respond to the arguments of (...) Winsberg and his co-workers, and we point out that their criticisms fail. We take this as an opportunity to restate and explain our arguments, and to point to fruitful directions for future research. (shrink)

Interpretation plays a central role in using scientific models to explain natural phenomena: Meaning must be bestowed upon a model in terms of what it is and what it represents to be used for model explanations. However, it remains unclear how capacious and complex interpretation in models can be, particularly when conducted by the same group of scientists in the context of one explanatory project. This paper sheds light upon this question by examining modelling and explanatory practices related to the (...) Olami-Feder-Christensen model of earthquakes. This case study shows that various interpretations are intricately connected in the overall meaning of a model used for model explanations. This leads to a manifold picture of interpretation, according to which scientific models are construed as networks of interconnected meanings. As scientists ponder and integrate these various interpretations, guided by locally attended epistemic interests, they achieve model explanations with layers of content, both in their explanantia and explananda. (shrink)