Causal mechanisms of subpolar gyre variability in CMIP6 models

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Conflicts of Interest

Identified Weaknesses

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Paper Summary

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Ocean Tipping Points: Climate Models Still Can't Agree How the North Atlantic Gyre Might Break Down

This study used causal inference to investigate how well CMIP6 climate models represent the mechanisms of subpolar gyre variability and potential tipping points in the North Atlantic. While basic links between surface conditions and deep convection are generally captured, key feedback loops involving subsurface temperature, ocean density, and gyre strength often show inconsistent or even contradictory signs across models, or are completely absent. Crucially, models that do capture more of the proposed mechanisms are also the ones that predict abrupt shifts in the subpolar gyre.

Possible Conflicts of Interest

None identified. The authors declared no competing interests, and the funding sources are public research councils.

Identified Weaknesses

Causal Sufficiency Violation (Mechanism A)

The analysis of local convection mechanisms (A1, A2) was potentially hindered by not including all common drivers (e.g., ocean circulation), meaning critical confounding factors might have been missed, leading to less robust identification of links.

Inconsistent/Missing Feedback Loops (Mechanism B)

Many essential links in the larger feedback loop for subpolar gyre variability, particularly those related to density and gyre strength, were either not found or showed conflicting signs across models, directly contradicting theoretical understanding. This indicates fundamental issues in how these models represent crucial oceanic processes.

Model Resolution and Eddy Transport

CMIP6 models typically have too coarse a resolution to explicitly resolve ocean eddies, which are vital for heat and salt transport. This parameterization likely affects the accurate representation of the link between density and gyre strength.

Barotropic Streamfunction as Gyre Measure

The use of barotropic streamfunction as an indicator of gyre strength may not fully capture the baroclinic effects that are theoretically crucial for the positive feedback loop, potentially obscuring important interactions.

Linearity Assumption in Causal Inference

The PCMCI algorithm uses partial correlation, which assumes linearity of links. While deemed a good first-order approximation, this assumption may not fully capture the complex, non-linear dynamics inherent in climate systems, potentially affecting the accuracy of identified causal effects.

Atmospheric Forcing/Dampening

The theoretical model and current analysis do not fully account for the influence of the atmosphere (e.g., atmospheric dampening of surface signals), which can act as a confounding factor and influence the observed ocean dynamics.

Rating Explanation

This paper provides a strong diagnostic analysis of CMIP6 models using robust causal inference methods to evaluate their representation of critical subpolar gyre variability mechanisms. It clearly identifies significant inconsistencies and missing links in how models represent key feedback loops, particularly concerning density and gyre strength, which are vital for understanding potential tipping points. The finding that models showing more complete mechanisms are also those predicting abrupt shifts is a valuable insight. The limitations highlighted primarily reflect issues within the CMIP6 models themselves rather than fundamental flaws in the study's methodology.

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