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

• Published in: Nonlinear Processes in Geophysics
• Main Authors: Docquier, David, Di Capua, Giorgia, Donner, Reik, V, Pires, Carlos, a L, Simon, Amélie, Vannitsem, Stéphane
• Other Authors: Institut Royal Météorologique de Belgique Bruxelles - Royal Meteorological Institute of Belgium (IRM), HSMD Hochschule Magdeburg-Stendal, Potsdam Institute for Climate Impact Research (PIK), Universidade de Lisboa = University of Lisbon = Université de Lisbonne (ULISBOA), Département Mathematical and Electrical Engineering (IMT Atlantique - MEE), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Equipe Observations Signal & Environnement (Lab-STICC_OSE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT), David Docquier, Giorgia Di Capua,Reik Donner, Carlos Pires, Amélie Simon, and Stéphane Vannitsemwere supported by ROADMAP (Role of ocean dynamicsand Ocean-Atmosphere interactions in Driving cliMAte variationsand future Projections of impact-relevant extreme events;https://jpi-climate.eu/project/roadmap/, last access: 21 February2024), a coordinated JPI-Climate/JPI-Oceans project.David Docquier and Stéphane Vannitsem received fundingfrom the Belgian Federal Science Policy Office under contractB2/20E/P1/ROADMAP. Giorgia Di Capua and Reik Donnerwere supported by the German Federal Ministry for Educationand Research (BMBF) via the ROADMAP project (grant no.01LP2002B). Amélie Simon and Carlos Pires were supportedby Portuguese funds: Fundação para a Ciência e a Tecnologia(FCT) I.P./MCTES through national funds (PIDDAC) –UIDB/50019/2020 (https://doi.org/10.54499/UIDB/50019/2020),UIDP/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020)and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020),and the project JPIOCEANS/0001/2019 (ROADMAP).
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2024
• Subjects: [SPI]Engineering Sciences [physics]; Arctic; Pacific
• Online Access: https://imt-atlantique.hal.science/hal-04591649; https://imt-atlantique.hal.science/hal-04591649/document; https://imt-atlantique.hal.science/hal-04591649/file/npg-31-115-2024.pdf; https://doi.org/10.5194/npg-31-115-2024

International audience Correlation does not necessarily imply causation, and this is why causal methods have been developedto 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 we apply them to four different artificial models of increasing complexity and onereal-world case study based on climate indices in the Atlantic and Pacific regions. We show that both methodsare superior to the classical correlation analysis, especially in removing spurious links. LKIF and PCMCI displaysome strengths and weaknesses for the three simplest models, with LKIF performing better with a smallernumber of variables and with PCMCI being best with a larger number of variables. Detecting causal links fromthe fourth model is more challenging as the system is nonlinear and chaotic. For the real-world case study withclimate indices, both methods present some similarities and differences at monthly timescale. One of the keydifferences is that LKIF identifies the Arctic Oscillation (AO) as the largest driver, while the El Niño–SouthernOscillation (ENSO) is the main influencing variable for PCMCI. More research is needed to confirm these links,in particular including nonlinear causal methods.