Preprints
https://doi.org/10.5194/egusphere-2023-2212
https://doi.org/10.5194/egusphere-2023-2212
05 Oct 2023
 | 05 Oct 2023

A comparison of two causal methods in the context of climate analyses

David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem

Abstract. Correlation does not necessarily imply causation, and this is why causal methods have been developed to try to disentangle true causal links from spurious relationships. In our study, we use two causal methods, namely the Liang-Kleeman information flow (LKIF) and the Peter and Clark momentary conditional independence (PCMCI) algorithm, and apply them to four different artificial models of increasing complexity and one real-case study based on climate indices in the North Atlantic and North Pacific. We show that both methods are superior to the classical correlation analysis, especially in removing spurious links. LKIF and PCMCI display some strengths and weaknesses for the three simplest models, with LKIF performing better with a smaller number of variables, and PCMCI being best with a larger number of variables. Detecting causal links from the fourth model is more challenging as the system is nonlinear and chaotic. For the real-case study with climate indices, both methods present some similarities and differences at monthly time scale. One of the key differences is that LKIF identifies the Arctic Oscillation (AO) as the largest driver, while El Niño-Southern Oscillation (ENSO) is the main influencing variable for PCMCI. More research is needed to confirm these links, in particular including nonlinear causal methods.

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Journal article(s) based on this preprint

27 Feb 2024
A comparison of two causal methods in the context of climate analyses
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136, https://doi.org/10.5194/npg-31-115-2024,https://doi.org/10.5194/npg-31-115-2024, 2024
Short summary
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2212', Anonymous Referee #1, 13 Nov 2023
  • RC2: 'Comment on egusphere-2023-2212', Anonymous Referee #2, 18 Dec 2023
  • AC1: 'Reply to both reviewers', David Docquier, 12 Jan 2024

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2212', Anonymous Referee #1, 13 Nov 2023
  • RC2: 'Comment on egusphere-2023-2212', Anonymous Referee #2, 18 Dec 2023
  • AC1: 'Reply to both reviewers', David Docquier, 12 Jan 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by David Docquier on behalf of the Authors (12 Jan 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (17 Jan 2024) by Stefano Pierini
AR by David Docquier on behalf of the Authors (18 Jan 2024)

Journal article(s) based on this preprint

27 Feb 2024
A comparison of two causal methods in the context of climate analyses
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136, https://doi.org/10.5194/npg-31-115-2024,https://doi.org/10.5194/npg-31-115-2024, 2024
Short summary
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem

Model code and software

Codes to compute Liang index and correlation for comparison study David Docquier https://doi.org/10.5281/zenodo.8383534

David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem

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Latest update: 06 Sep 2024
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Short summary
Identifying causes of specific processes is crucial in order to better understand our climate system. Traditionally, correlation analyses have been used to identify cause-effect relationships in climate studies. However, correlation does not imply causation, which justifies the need to use causal methods. We compare two independent causal methods and show that these are superior to classical correlation analyses. We also find some interesting differences between the two methods.