Quantifying Causal Pathways of Teleconnections
Quantifying Causal Pathways of Teleconnections
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DOI：
10.1175/bams-d-20-0117.1
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发表时间：
2021-12-01
影响因子：
8
通讯作者：
Shepherd, Theodore G.
中科院分区：
文献类型：
--
作者：
Kretschmer, Marlene;Adams, Samantha, V;Shepherd, Theodore G.
遥相关是区域天气和气候可预测性的来源，但不同遥相关对区域异常的相对贡献通常不为人所知。虽然有关所涉及机制的物理知识通常是可用的，但如何从数据中量化特定的因果路径通常尚不清楚。在这里，我们主张在远程连接的统计分析中采用基于因果推理的框架来克服这一挑战。因果方法要求在统计分析中明确包含专业知识，这使得人们能够得出定量结论。我们用著名的大气遥相关的具体例子来说明该理论的一些关键概念。我们进一步讨论了这些对气候科学意味着的特殊挑战和优势，并认为统计推断的系统因果方法应该成为遥相关研究的标准实践。
Teleconnections are sources of predictability for regional weather and climate, but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data are usually unclear. Here, we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections.