05 Oct 2023
 | 05 Oct 2023
Status: this preprint is open for discussion.

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.

David Docquier et al.

Status: open (until 30 Dec 2023)

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 reply

David Docquier et al.

Model code and software

Codes to compute Liang index and correlation for comparison study David Docquier

David Docquier et al.


Total article views: 175 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
115 48 12 175 6 6
  • HTML: 115
  • PDF: 48
  • XML: 12
  • Total: 175
  • BibTeX: 6
  • EndNote: 6
Views and downloads (calculated since 05 Oct 2023)
Cumulative views and downloads (calculated since 05 Oct 2023)

Viewed (geographical distribution)

Total article views: 170 (including HTML, PDF, and XML) Thereof 170 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 29 Nov 2023
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.