Attribution of climate impacts to natural and anthropogenic source forcings is essential for understanding and addressing climate effects. While standard methods like optimal fingerprinting have been effective for long-term changes, they often struggle in low signal-to-noise regimes typical of short-term forcings or with climate variables loosely related to the forcing. Single-step approaches fail to leverage additional climate information to enhance attribution certainty. To overcome these limitations, we propose a formal statistical framework that incorporates hypothesized physical pathways linking source forcings to downstream impacts. By establishing relationships based on scalar features and simple forcing response models, we create a series of conditional probabilities that describe the likelihood of the final impact. This method captures both primary and secondary processes by which the downstream impact evolves. Through hypothesis testing in a likelihood ratio framework, we demonstrate improved attribution confidence for source magnitudes in low signal-to-noise scenarios. Using the 1991 eruption of Mt. Pinatubo as a case study, we show that incorporating near-surface temperature and stratospheric radiative flux measurements enhances attribution certainty compared to analyses based solely on temperature, even at seasonal and regional scales. This framework holds promise for improving climate attribution assessments for unknown source magnitudes and low signal-to-noise impacts, where traditional methods may falter. Additionally, the formal inclusion of pathways allows for a deeper exploration of complex, multivariate relationships influencing source attribution.
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）
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。
GCMagicc v1: a fast generative emulator for multivariate climate-impact ensembles
The University of Melbourne（墨尔本大学）；Climate Resource（气候资源公司）；University of Washington（华盛顿大学）；International Institute for Applied Systems Analysis（国际应用系统分析研究所）；ETH Zürich（苏黎世联邦理工学院）
