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Attribution—the explanation of an observed change in terms of multiple causal factors—is the cornerstone of climate-change science. For anthropogenic climate change (ACC), the central causal factor is evidently ACC itself, and one of the primary tools used to reveal ACC is aggregation, or grouping together, of data, e.g. global mean surface temperature. Whilst this approach has served climate-change science well, the landscape is changing rapidly. First, there is an increasing focus on regional or local aspects of climate change, and on singular or unprecedented events, which require varying degrees of disaggregation. Relatedly, climate change is increasingly apparent in observations at the local scale, which is challenging the primacy of climate model simulations. Finally, the explosion of climate data is leading to more phenomena-laden methodologies such as machine learning. All this demands a re-think of how attribution is performed and causal explanations are constructed. Here we use Lloyd’s ‘Logic of Research Questions’ framework to show how the way in which the attribution question is framed can strongly constrain its possible and responsive answers. To address the Research Question ‘What was the effect of ACC on X?’ (RQ1), scientists generally consider the question ‘What were the causal factors leading to X, and was ACC among them?’. If the causal factors include only external forcing and internal variability (RQ2), then answering RQ2 also answers RQ1. However, this unconditional attribution is not always possible. In such cases, allowing the causal factors to include elements of the climate system itself (RQ3)—the conditional, storyline approach—is shown to allow for a wider range of possible and responsive answers than RQ2, including that of singular causation. This flexibility is important when uncertainties are high. As a result, the conditional RQ3 mitigates against the sort of epistemic injustice that can arise from the unconditional RQ2.

 

Abstract In a recent very influential court case, Juliana v. United States , climate scientist Kevin Trenberth used the “storyline” approach to extreme event attribution to argue that greenhouse warming had affected and will affect extreme events in their regions to such an extent that the plaintiffs already had been or will be harmed. The storyline approach to attribution is deterministic rather than probabilistic, taking certain factors as contingent and assessing the role of climate change conditional on those factors. The US Government’s opposing expert witness argued that Trenberth had failed to make his case because “all his conclusions of the injuries to Plaintiffs suffer from the same failure to connect his conditional approach to Plaintiffs’ local circumstances.” The issue is whether it is possible to make statements about individual events based on general knowledge. A similar question is sometimes debated within the climate science community. We argue here that proceeding from the general to the specific is a process of deduction and is an entirely legitimate form of scientific reasoning. We further argue that it is well aligned with the concept of legal evidence, much more so than the more usual inductive form of scientific reasoning, which proceeds from the specific to the general. This has implications for how attribution science can be used to support climate change litigation. “The question is”, said Alice, “whether you can make words mean different things.” “The question is”, said Humpty Dumpty, “which is to be master — that’s all.” (Lewis Carroll, Alice’s Adventures in Wonderland). 

Abstract Standards of proof for attributing real world events/damage to global warming should be the same as in clinical or environmental lawsuits, argue Lloyd et al. The central question that we raise is effective communication. How can climate scientists best and effectively communicate their findings to crucial non-expert audiences, including public policy makers and civil society? To address this question, we look at the mismatch between what courts require and what climate scientists are setting as a bar of proof. Our first point is that scientists typically demand too much of themselves in terms of evidence, in comparison with the level of evidence required in a legal, regulatory, or public policy context. Our second point is to recommend that the Intergovernmental Panel on Climate Change recommend more prominently the use of the category “more likely than not” as a level of proof in their reports, as this corresponds to the standard of proof most frequently required in civil court rooms. This has also implications for public policy and the public communication of climate evidence. 

Abstract In this paper we consider some questions surrounding whether or not regional climate models “add value,” a controversial issue in climate science today. We highlight some objections frequently made about regional climate models both within and outside the community of modelers, including several claims that regional climate models do not “add value.” We show that there are a number of issues involved in the latter claims, the primary ones centering on the fact that different research questions are being pursued by the modelers making the complaints against regional climate models. Further issues focus on historical deficiencies of particular—but not generalizable—failures of individual regional models. We provide tools to sort out these different research questions and particular failures, and to improve communication and understanding surrounding added value in climate modeling and philosophy of climate science.