气候变化科学归因的基础

E. Lloyd, T. Shepherd
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引用次数: 0

摘要

归因——用多个因果因素来解释观察到的变化——是气候变化科学的基石。对于人为气候变化(ACC),中心原因显然是ACC本身,而揭示ACC的主要工具之一是数据的汇总或分组,例如全球平均地表温度。虽然这种方法很好地服务于气候变化科学,但景观正在迅速变化。首先,人们越来越关注气候变化的区域或局部方面,以及需要不同程度分解的单一或前所未有的事件。与此相关,气候变化在局地尺度观测中越来越明显,这对气候模式模拟的首要地位提出了挑战。最后,气候数据的爆炸式增长正在催生更多现象分析方法,比如机器学习。所有这些都要求我们重新思考归因是如何进行的,以及因果解释是如何构建的。在这里,我们使用劳埃德的“研究问题的逻辑”框架来展示归因问题的框架如何强烈地限制其可能的和响应性的答案。为了解决研究问题“ACC对X的影响是什么?”(RQ1),科学家们通常考虑的问题是“导致X的原因是什么,ACC在其中吗?”如果因果因素只包括外部强迫和内部变率(RQ2),那么回答RQ2也回答RQ1。然而,这种无条件归因并不总是可能的。在这种情况下,允许因果因素包括气候系统本身的要素(RQ3)——有条件的故事线方法——被证明比RQ2允许更广泛的可能和响应性答案,包括单一因果关系。当不确定性很高时,这种灵活性很重要。因此,条件RQ3减轻了可能由无条件RQ2引起的认知不公正。
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Foundations of attribution in climate-change science
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.
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