Effect of a Variable Eddy Transfer Coefficient in an Eddy-Permitting Model of the Subpolar North Atlantic Ocean
Bibliographic record
Abstract
Abstract The effect of using a variable eddy transfer coefficient for the Gent–McWilliams (GM) parameterization in a (1/3)°-resolution ocean model of the subpolar North Atlantic Ocean is investigated. Results from four experiments with different implementations of this coefficient are compared among themselves as well as with two control experiments. A series of improvements have been obtained in all of the experiments that use a low level of explicit horizontal tracer diffusion. These include a better representation of the overflow waters originating from the Nordic seas, leading to a more realistic deep western boundary current and to increased eddy activity in the deep ocean in the eastern North Atlantic. In the same experiments, the GM velocities “help” the Labrador Sea Water to spread from the deep convection region to the currents that surround it without incurring significant spurious diapycnal mixing. Thus, two classical pathways for the spreading of this water are established. Moreover, the simulated Labrador Current and the near-surface circulation in the eastern North Atlantic are in better agreement with flow patterns inferred from observations. The increased release of available potential energy obtained in the experiments with variable eddy transfer coefficients is responsible for the simulation of a flow that varies less in time. An overly strong countercurrent still occurs in the Labrador Sea in these experiments, and it has a negative impact on the pathway of the North Atlantic Current in the “Northwest Corner” and on the hydrography of the Labrador Sea. Nonetheless and overall, the use of the variable eddy transfer coefficient has led to better representations of the general circulation and hydrography in the subpolar North Atlantic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".