the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Oct 2024
Nonlinear causal dependencies as a signature of the complexity of the climate dynamics
Abstract. Nonlinear quadratic dynamical dependencies of large-scale climate modes are disentangled through the analysis of the rate of information transfer. Eight dominant climate modes are investigated covering the tropics and extratropics over the North Pacific and Atlantic. A clear signature of nonlinear influences at low-frequencies (time scales larger than a year) are emerging, while high-frequencies are only affected by linear dependencies. These results point to the complex nonlinear collective behavior at global scale of the climate system at low-frequencies, supporting earlier views that regional climate modes are local expressions of a global intricate low-frequency variability dynamics, which is still to be fully uncovered.
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RC1: 'Comment on egusphere-2024-3308', Anonymous Referee #1, 05 Nov 2024
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The manuscript by Stéphane Vannitsem et al. provides a significant contribution to our understanding of complex nonlinear interactions among climate modes, employing the Liang-Kleeman information flow technique. Through the examination of eight key climate indices, the authors offer valuable insights into low-frequency climate variability and nonlinear causal dependencies that shape the collective dynamics of the climate system. The study is distinguished by its rigorous methodology and its implications for advancing climate science. Overall, the paper is well-structured, clearly written, and holds considerable scientific merit. I recommend it for publication following minor revisions to address a few specific comments and clarifications.Specific Comments
The authors have made an impressive extension of Liang’s method by incorporating nonlinear predictors, which allows for isolating specific quadratic nonlinearities among climate indices. This thoughtful approach offers a fresh perspective on capturing joint influences across multiple indices, greatly enriching our understanding of complex climate interactions. Additionally, the use of Singular Spectrum Analysis (SSA) to separate low- and high-frequency components for each climate mode adds valuable clarity to the dynamic relationships within the data. To further enhance the paper, a brief discussion on the effects of factors such as data length and predictor count on the accuracy and robustness of the results would be highly informative. A qualitative exploration of these potential limitations would add context to the findings and guide future applications of this methodology.
The distinctions between low- and high-frequency influences are skillfully captured, particularly in the analysis of the NAO and El Niño indices. Interestingly, while low-frequency components reveal inter-dependencies among indices not evident in the original data, this phenomenon might stem from high-frequency components masking or "overwriting" lower-frequency signals. A brief clarification on this point would deepen readers' appreciation of the novelty and importance of the frequency filtering method used here, highlighting its role in uncovering hidden relationships otherwise obscured in unfiltered data.
The figures are generally clear and highly informative. To further support the study’s central conclusion—that "nonlinear causal dependencies are a hallmark of the complexity of climate dynamics"—a comprehensive visualization would be a valuable addition. For instance, a general illustration showing the overall inter-dependencies between each mode could provide a clearer comparison of how nonlinear dependencies statistically differ from those in a linear or null model. This would enhance the manuscript’s narrative, offering readers an intuitive grasp of the paper's findings and reinforcing the significance of the nonlinear dependencies identified.
Overall, this manuscript is a thoughtful and impactful piece of research, with a few refinements adding further depth and clarity.
Citation: https://doi.org/10.5194/egusphere-2024-3308-RC1
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