Impact of Eddy-Induced Mixing on AMOC: A Model Study
Bibliographic record
Abstract
Representing mesoscale eddies and understanding their impact on large-scale ocean circulation are critical challenges in climate research. High-resolution models are necessary to resolve eddies explicitly, while parameterisations, most commonly those of Gent and McWilliams (1990) and Redi (1982), are used in lower-resolution models to simulate their effects. Mesoscale eddies can affect the Atlantic Meridional Overturning Circulation (AMOC) by flattening isopycnals. Additionally, mesoscale eddies can also affect AMOC by modifying turbulent diapycnal mixing, which is parameterised by e.g. the mixing scheme of Gaspar et al. (1990). The modification of turbulent mixing can occur when horizontal density gradients and with them, vertical velocity shear and the turbulent shear production are reduced as a result of flattened isopycnals.In this study, we analyse the sensitivity of AMOC to parameterised eddy diffusivity using a coupled model. To isolate the role of eddies in diapycnal mixing, we separate the buoyancy tendency forcing produced by the eddy parameterisations (GM-Redi) from that produced by diapycnal mixing. Previous studies with uncoupled models have shown how eddy-induced isopycnal flattening affects the upwelling of North Atlantic Deep Water (NADW) in the Southern Ocean (Marshall et al., 2017). Our results from a coupled model show that the impact on diapycnal mixing is most pronounced in the downwelling regions in the Subpolar North Atlantic, with increasing eddy diffusivity causing a shift in the dominant location of deep water formation from the Labrador Sea northeastward to the Irminger and Iceland Basins.By investigating the interplay between eddy-induced mixing and the AMOC, this work provides new insights into how spatially variable mixing processes shape large-scale ocean circulation patterns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".