Revisiting causality using stochastics: 2. Applications

D. Koutsoyiannis, C. Onof, A. Christofides, and Z. W. Kundzewicz, Revisiting causality using stochastics: 2. Applications, Proceedings of The Royal Society A, 478 (2261), 20210836, doi:10.1098/rspa.2021.0836, 2022.

[doc_id=2194]

[English]

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.

Full text is only available to the NTUA network due to copyright restrictions

PDF Additional material:

Our works referenced by this work:

1. D. Koutsoyiannis, A random walk on water, Hydrology and Earth System Sciences, 14, 585–601, doi:10.5194/hess-14-585-2010, 2010.
2. A. Montanari, and D. Koutsoyiannis, A blueprint for process-based modeling of uncertain hydrological systems, Water Resources Research, 48, W09555, doi:10.1029/2011WR011412, 2012.
3. D. Koutsoyiannis, Entropy: from thermodynamics to hydrology, Entropy, 16 (3), 1287–1314, doi:10.3390/e16031287, 2014.
4. D. Koutsoyiannis, Generic and parsimonious stochastic modelling for hydrology and beyond, Hydrological Sciences Journal, 61 (2), 225–244, doi:10.1080/02626667.2015.1016950, 2016.
5. D. Koutsoyiannis, Entropy production in stochastics, Entropy, 19 (11), 581, doi:10.3390/e19110581, 2017.
6. D. Koutsoyiannis, Time’s arrow in stochastic characterization and simulation of atmospheric and hydrological processes, Hydrological Sciences Journal, 64 (9), 1013–1037, doi:10.1080/02626667.2019.1600700, 2019.
7. D. Koutsoyiannis, Simple stochastic simulation of time irreversible and reversible processes, Hydrological Sciences Journal, 65 (4), 536–551, doi:10.1080/02626667.2019.1705302, 2020.
8. D. Koutsoyiannis, Revisiting the global hydrological cycle: is it intensifying?, Hydrology and Earth System Sciences, 24, 3899–3932, doi:10.5194/hess-24-3899-2020, 2020.
9. Z. W. Kundzewicz, I. Pińskwar, and D. Koutsoyiannis, Variability of global mean annual temperature is significantly influenced by the rhythm of ocean-atmosphere oscillations, Science of the Total Environment, 747, 141256, doi:10.1016/j.scitotenv.2020.141256, 2020.
10. D. Koutsoyiannis, and Z. W. Kundzewicz, Atmospheric temperature and CO₂: Hen-or-egg causality?, Sci, 2 (4), 83, doi:10.3390/sci2040083, 2020.
11. D. Koutsoyiannis, Rethinking climate, climate change, and their relationship with water, Water, 13 (6), 849, doi:10.3390/w13060849, 2021.
12. D. Koutsoyiannis, and A. Montanari, Bluecat: A local uncertainty estimator for deterministic simulations and predictions, Water Resources Research, 58 (1), e2021WR031215, doi:10.1029/2021WR031215, 2022.
13. D. Koutsoyiannis, C. Onof, A. Christofides, and Z. W. Kundzewicz, Revisiting causality using stochastics: 1.Theory, Proceedings of The Royal Society A, 478 (2261), 20210835, doi:10.1098/rspa.2021.0835, 2022.

Our works that reference this work:

1. D. Koutsoyiannis, C. Onof, A. Christofides, and Z. W. Kundzewicz, Revisiting causality using stochastics: 1.Theory, Proceedings of The Royal Society A, 478 (2261), 20210835, doi:10.1098/rspa.2021.0835, 2022.

Tagged under: Climate stochastics, Determinism vs. stochasticity, Most recent works, Stochastics, Uncertainty