Granger causal inference for climate change attribution
Climate and Ecosystem Sciences Division, LBNL（劳伦斯伯克利国家实验室气候与生态系统科学部）；College of Engineering, University of Michigan（密歇根大学工程学院）；Applied Mathematics and Computational Research Division, LBNL（劳伦斯伯克利国家实验室应用数学与计算研究部）
Mark D. Risser, Mohammed Ombadi, Michael F. Wehner
本文探讨了格兰杰因果推断在气候变迁归因中的应用，对比了传统方法，强调其快速性和有效性，主张融合格兰杰与 Pearl 因果方法以提高归因准确性。
摘要：
气候变迁检测与归因（D&A）旨在确定人为活动对全球气候系统特定方面的影响力程度。D&A 属于更广泛的因果推断领域，即识别因果关系的统计方法。归因陈述有多种方法，每种方法需要不同类型的数据输入，并在不同程度上具有条件性。一些方法基于 Pearl 因果（实验干预），而另一些则利用格兰杰（预测）因果，因果框架为如何解释归因结论提供了重要背景。然而，尽管格兰杰因果归因分析变得更为常见，但尚无明确说明其优势和劣势，也无明确共识表明何时何地使用格兰杰因果视角是合适的。在本文前瞻性论文中，我们为基于格兰杰的方法的趋势和事件归因提供正式定义，并与更传统的方法进行清晰比较，以评估人类对极端天气和气候事件的影响。广义而言，格兰杰因果归因陈述可以从观测中快速构建，不需要计算密集的动力学实验。这些分析还允许快速归因，在严重天气事件之后非常有用，并且与 Pearl 因果归因结合时能提供多个证据线以支持人为气候变化。当不同方法得出相似结论时，归因陈述的可信度会提高。未来，我们鼓励 D&A 社区采用融合格兰杰和 Pearl 因果方法的混合方法，以提高归因准确性。
英文摘要：
Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and each of which are conditional to varying extents. Some methods are based on Pearl causality (experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality.
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Swiss Data Science Center（瑞士数据科学中心）；ETH Zurich（苏黎世联邦理工学院）；Institute for Atmospheric and Climate Science（大气与气候科学研究所）
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