Foundations of attribution in climate-change science

Abstract

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.

Regional climate change: consensus, discrepancies, and ways forward

“…In the media, the question whether an event is due to climate change is often asked, but this question does not usually have a clear answer. Framing the question in terms of attribution, e.g., how frequent or how much more intense such events have become due to climate change, is more meaningful (Lloyd and Shepherd, 2023).…”

“…first detect a statistically significant trend in observations, then attribute it to anthropogenic forcing using the consensus from climate models is important. Instead, it would be valuable to articulate physically plausible hypotheses for the observed changes (in terms of causal processes that are well established by the relevant expert groups, e.g., physical drivers), which can be then assessed using models (Lloyd and Shepherd, 2023).…”

“…Communicating regional climate change to the public, media and stakeholders is challenging because of the conflating influences of different forcings, internal variability, and limitations of model fidelity, as discussed above. Storylines of the forced circulation response developed based on physical hypotheses of how different dynamical drivers respond to climate change have emerged as one approach for communicating regional climate change (Lloyd and Shepherd, 2023). In this way, uncertainties around the regional circulation response to climate change can be represented in a physically interpretable fashion which can be reduced as knowledge advances in the future.…”