Decomposing Barotropic Transport Variability in a High-Resolution Model of the North Atlantic Ocean
Bibliographic record
Abstract
A method using a linear shallow water model is presented for decomposing the temporal variability of the barotropic stream function in a high-resolution ocean model. The method is based on the vertically averaged momentum equations and is applied to the time series of annual mean stream function from the model configuration VIKING20 for the northern North Atlantic. An important result is the role played by the nonlinear advection terms in VIKING20 for driving transport. The method is illustrated by examining how the Gulf Stream transport in the recirculation region responds to the winter North Atlantic Oscillation (NAO). While no statistically significant response is found in the year overlapping with the winter NAO index, there is a tendency for the Gulf Stream transport to increase as the NAO becomes more positive. This becomes significant in lead years 1 and 2 when the mean flow advection and eddy momentum flux contributions, associated with nonlinear momentum advection, dominate. Only after 2 years, does the potential energy term, associated with the density field, start to play a role and it is only after 5 years that the transport dependence on the NAO ceases to be significant. It is also shown that the potential energy contribution to the transport stream function has significant memory of up to 5 years in the Labrador and Irminger Seas. However, it is only around the northern rim of these seas that VIKING20 and the transport reconstruction exhibit similar memory. This is due to masking by the mean flow advection and eddy momentum flux contributions.
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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 | 0.001 |
| 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".