Dissecting the Barotropic Transport in a High-resolution ocean model
Bibliographic record
Abstract
We present a method to decompose the time mean vertically averaged transport, as simulated by an high-resolution ocean model, into its four dominant components. These components are driven by the gradient of potential energy per unit area (PE), the divergence of the flux of time mean momentum (MMF) and eddy momentum (EMF), and the wind stress. Since the local vorticity budget and the bathymetry are noisy and dominated by small spatial scales, a barotropic shallow water model is used as a filter to diagnose the respective transports instead of integrating along lines of constant f/H. Applying this method to the output of a high-resolution model of the North Atlantic we find that PE is the most important driver, including the northwest corner. MMF is an important driver of transport around the Labrador Sea continental slope and, together with the EMF, it drives significant transport along the path of the Gulf Stream and North Atlantic current. Additionally, the circulation patterns driven by the EMF compares well with an estimate based on a satellite product. Hence, the presented method provides insights into the relative importance of the different dynamical processes that may drive barotropic transport in an ocean model. But it may also be used to isolate potential issues if a model misrepresents the barotropic transport.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".