| 11 Sep 2026
Tidal forcing causes bi-decadal variability in dissolved oxygen: clarification with a causal discovery method
Abstract. Identifying the mechanisms behind the trends and variability in observations of ocean oxygen concentration (O2) is key to understanding long-term ocean deoxygenation. But this task remains challenging, as observations contain persistent signals from interacting physical and biogeochemical processes. Here we apply the time series causal discovery method, PCMCI, to a dataset of O2 from the Oyashio region in the North West Pacific to test whether a causal relationship from the nodal cycle in the tidal forcing on to the O2 exists. We identify causal links from the tidal index to O2 and apparent oxygen utilisation (AOU), concentrated between approximately 27.35σθ and 26.75σθ, with a characteristic lag of approximately two years. The tidal effect on O2 is only present at densities greater than the density of the late-winter mixed layer (the base of the mixed layer), with a maximum causal link at around 27.1σθ. Little evidence is found for a causal relationship with the O2 solubility. Compared to lagged correlation, PCMCI detects a reduced strength and density distribution of the tidal effect and removes much of the lag structure while retaining the characteristic 2 year lag. The density and lag structure are consistent with transport and enhanced tidal mixing from within the Sea of Okhotsk system and the Kurils, but we cannot identify a geographical location or the complete physical pathway of the causal effect with the existing observations. It may be the case that several causal models are compatible with the existing data. These results show the promise of causal inference and discovery methods within oceanographic problems.
Review for “Tidal forcing causes bi-decadal variability in dissolved oxygen: clarification with a causal discovery method”
Blackledge et al.
Major Comment: Reliability and validation of the PCMCI-based causal inference
The application of PCMCI to historical O₂ observations is potentially interesting. However, I have fundamental concerns about the reliability of the inferred causal relationships and whether the main objective of this study has been achieved.
The central issue is that the authors have not independently demonstrated that PCMCI can reliably identify the physical causal relationships underlying O₂ variability in the Oyashio region. The causal network includes tidal forcing and climate indices such as PDO, NPGO, and NPI, but excludes important biogeochemical processes, including biological production, organic matter remineralization, and respiratory O₂ consumption. Consequently, the detected Tide → O₂ relationship may reflect either direct physical processes or indirect effects mediated by biological and biogeochemical processes. The present analysis cannot distinguish between these possibilities.
Furthermore, the simplified structural causal model (SCM) constructed from the PCMCI results reproduces the observed lagged correlations, but this does not constitute an independent validation of the inferred causal structure. Different causal mechanisms can produce similar statistical relationships. Therefore, the physical interpretation of the detected causal links remains uncertain.
In my view, the reliability of the PCMCI-based causal inference should be established before applying the method to historical observations and interpreting the results as evidence of physical causality. For example, the authors could first apply PCMCI to numerical ocean model output including the Oyashio region, where causal pathways can be independently evaluated through controlled experiments, and examine whether the method correctly recovers the relevant relationships. The analysis could then be repeated using subsampled model output that mimics the limited variables and sparse temporal coverage of the observations. Such experiments would provide a more convincing assessment of the reliability of the proposed methodology.
I am also concerned about the overall structure of the manuscript. Considerable space is devoted to explaining and justifying the PCMCI methodology, whereas the physical interpretation and independent validation of the main findings remain insufficient. As a result, the manuscript lacks a clear and convincing progression from the research objectives to the results and conclusions. If the reliability of the inferred causal relationships were established, much of the technical description of PCMCI could be moved to an Appendix, allowing the main text to focus more clearly on the scientific findings.
Overall, I do not consider that the manuscript has yet achieved its primary research objective, because the reliability and physical significance of its central causal inference have not been adequately demonstrated. I therefore recommend that the authors first establish the validity of their causal-discovery approach through independent testing before drawing conclusions about the physical mechanisms controlling O₂ variability in the Oyashio region.
I’m not sure if this will be of any use at the moment, but I’ll note down the points I’ve noticed as minor comments.
Minor Comments
I also suggest considering a simpler presentation using time-series plots. For example, Navarra et al. (2026), who also applied PCMCI, presented their results using time-series plots in Figure 4. A seasonally separated time-series presentation might make the results easier to understand and interpret.