EGU25-15880, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-15880
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Unraveling ocean-atmosphere coupled variability with Transfer Entropy and Information Flow  
Chiara Zelco1, Alberto Carrassi1,2, Michael Ghil3,4, Daniele Marinazzo5, and Stéphane Vannitsem6
Chiara Zelco et al.
  • 1Physics and Astronomy Department, University of Bologna, Bologna, Italy (zelcochiara@gmail.com)
  • 2Department of Meteorology, University of Reading, Reading, United Kingdom (alberto.carrassi@unibo.it)
  • 3Geosciences Department and Laboratoire de Météorologie Dynamique, École Normale Supérieure, Paris, France (ghil@lmd.ipsl.fr)
  • 4Department of Atmospheric Sciences and Institute of Geophysics and Planetary Physics, University of California, Los Angeles, USA (ghil@atmos.ucla.edu)
  • 5Department of Data Analysis, Ghent University, Ghent, Belgium (daniele.marinazzo@ugent.be)
  • 6Royal Meteorological Institute of Belgium, Brussels, Belgium (svn@meteo.be)

Causal questions are fundamental to scientific exploration. The study of causality and its applications has followed a nonlinear trajectory, shaped by diverse methodological developments and debates about their interpretations. Here, we unravel the evolution of these approaches, from Judea Pearl’s formal framework of causal inference (Pearl, Causality, 2009) to methods based on reductions in informational surprise, multivariate probability, and dynamical systems (Kondrashov et al., Physica D, 2015). While principled causal inference ideally relies on Pearl’s framework, its application is often unfeasible. Instead, methods grounded in information theory, combined with prior knowledge of the system, are widely used to assist in the causal inference process. Recent advances include nonlinear, higher-order information-theoretic approaches (Stramaglia et al., Phys. Rev. Res., 2024).

These methods are increasingly applied in Earth and climate sciences to address questions such as the causes of extreme events and global warming, or to explore the mutual influences between the ocean and atmosphere in driving the climate system. A key unresolved question concerns the nature of this interaction: does atmospheric weather drive the ocean, does the ocean steer the atmosphere, or does a coupled mode of variability govern the system? 

In this context, we investigate the reciprocal influences of ocean and atmosphere using a low-order coupled ocean-atmosphere model that includes realistic thermal and mechanical coupling (Vannitsem et al. Physica D, 2015). By applying Transfer Entropy (Schreiber, Phys. Rev. Lett., 2000) and the Liang and Kleeman (Liang, Entropy, 2021) Information Flow, we analyze the dynamical directions within the coupled system. We uncover the directed dynamics of information exchange, adding insight on the emergence of low-frequency variability in the atmosphere. These results offer a new perspective on interannual and decadal-scale climate prediction. 

How to cite: Zelco, C., Carrassi, A., Ghil, M., Marinazzo, D., and Vannitsem, S.: Unraveling ocean-atmosphere coupled variability with Transfer Entropy and Information Flow  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15880, https://doi.org/10.5194/egusphere-egu25-15880, 2025.

Corresponding supplementary materials formerly uploaded have been withdrawn.