The Causal Interaction between Complex Subsystems

Liang, X. San

doi:10.3390/e24010003

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…It is publicly available at a monthly scale in the form of time series of 'anomalies' (defined as differences from long-term means) for several parts of the Earth, as well as in map form. Here we use only the global average (noting that Liang [19] disputes the use of averages as he claims that generally they conceal regional patterns of change) on the monthly scale for the lowest level, referred to as the lower troposphere. For the CO 2 concentration, we use the most famous dataset, that of the Mauna Loa Observatory [20].…”

Section: (C) Atmospheric Temperature and Carbon Dioxide Concentrationmentioning

confidence: 99%

Revisiting causality using stochastics: 2. Applications

et al. 2022

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In a companion paper, we develop the theoretical background of a stochastic approach to causality with the objective of formulating necessary conditions that are operationally useful in identifying or falsifying causality claims. Starting from the idea of stochastic causal systems, the approach extends it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. The framework developed is applicable to large-scale open systems, which are neither controllable nor repeatable. In this paper, we illustrate and showcase the proposed framework in a number of case studies. Some of them are controlled synthetic examples and are conducted as a proof of applicability of the theoretical concept, to test the methodology with a priori known system properties. Others are real-world studies on interesting scientific problems in geophysics, and in particular hydrology and climatology.

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Section: (C) Atmospheric Temperature and Carbon Dioxide Concentrationmentioning

confidence: 99%

Revisiting causality using stochastics: 2. Applications

et al. 2022

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“…Traditionally it is usually formulated as a statistical testing problem, with only qualitative results offered (e.g., Granger, 1969;Pearl, 2009). During the past 17 years, it has been realized as a physical problem, and can be rigorously established from first principles, rather than axiomatically proposed as ansatz (Liang, 2008(Liang, , 2013(Liang, , 2014(Liang, , 2015(Liang, , 2016(Liang, , 2018a(Liang, , 2019(Liang, , 2021(Liang, , 2022Liang & Kleeman, 2005). That is information flow and the information flow-based causality analysis.…”

Section: Quantitative Causality Analysis-liang-kleeman Information Flowmentioning

confidence: 99%

The Kuroshio Intrusion Into the South China Sea at Luzon Strait Can Be Remotely Influenced by the Downstream Intrusion Into the East China Sea

2023

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The intrusion of Kuroshio into the South China Sea through Luzon Strait has been of enormous interest in western boundary current studies. Here we show that this intrusion can be remotely influenced by the downstream intrusion of the Kuroshio northeast of Taiwan into the East China Sea, at a lead time of about 220 days. This remarkable finding is first revealed by a quantitative causality analysis which is rigorously established from first principles in physics; it is originally motivated by the observation of a wake southeast of Taiwan, whose influence on the loop current, also revealed by causality analysis, is traced downstream to the Kuroshio intrusion into the East China Sea. Further analysis suggests that the two important Kuroshio intrusions are connected via a coastal trapped wave mode with a period of 1.5 years propagating southward along the eastern Taiwan coast, as supported by both theoretical and numerical models. Upon approaching the Luzon Strait, its negative (positive) phase functions to intensify (reduce) the loop current. At a speed of 1.37 km day−1, the mode takes about 220 days for the disturbances northeast of Taiwan to travel along the coast to Luzon Strait, leading to the same time delay for the downstream‐upstream causal relation.

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“…If what we want to know is the causal relation between two subsystems/subnetworks, rather than two individual components/nodes, the information flow can also be derived, in a way as above, and estimated in the maximum likelihood sense. The results are referred to Liang (2022).…”

Section: It Reads Thatmentioning

confidence: 99%

Quantitative Causality, Causality-Aided Discovery, and Causal Machine Learning

Liang,

Chen,

Zhang

2023

Ocean-Land-Atmos Res

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It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence algorithms, however, is challenged with its vagueness, nonquantitativeness, computational inefficiency, etc. During the past 18 years, these challenges have been essentially resolved, with the establishment of a rigorous formalism of causality analysis initially motivated from atmospheric predictability. This not only opens a new field in the atmosphere-ocean science, namely, information flow, but also has led to scientific discoveries in other disciplines, such as quantum mechanics, neuroscience, financial economics, etc., through various applications. This note provides a brief review of the decade-long effort, including a list of major theoretical results, a sketch of the causal deep learning framework, and some representative real-world applications pertaining to this journal, such as those on the anthropogenic cause of global warming, the decadal prediction of El Niño Modoki, the forecasting of an extreme drought in China, among others.

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The Causal Interaction between Complex Subsystems

Cited by 7 publications

References 26 publications

Revisiting causality using stochastics: 2. Applications

Revisiting causality using stochastics: 2. Applications

The Kuroshio Intrusion Into the South China Sea at Luzon Strait Can Be Remotely Influenced by the Downstream Intrusion Into the East China Sea

Quantitative Causality, Causality-Aided Discovery, and Causal Machine Learning

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