Characteristics of ocean mesoscale vortices in the Amerasian Basin from a high resolution pan-Arctic model
Bibliographic record
Abstract
Observations and numerical models reveal that mesoscale eddies are ubiquitous in the Arctic Ocean. These eddies are thought to play an important role in particular in the transport of heat, salt and nutrients from the shelves to the deep basins, in the modulation of the sea ice cover, and in the dynamical equilibrium of the Beaufort gyre. However, the characteristics of these eddies are poorly documented. Here, an eddy detection and tracking method is applied to the output of a high resolution (1/12°) regional model of the Arctic - North Atlantic over the period 1995-2020 to investigate mesoscale eddies in the Amerasian Basin. Over that period, about 6000 eddies per year and per depth level are found distributed about equally between cyclones and anticyclones. On average, these eddies last 7 days, travel 5 km and have a radius of 12.4 km, with strong regional and temporal disparities that exist within the eddy population studied. Down to 250 m (i.e. the second pycnocline), eddy characteristics show a strong asymmetry between the shelf and the central basin with more numerous and larger eddies that travels longer distances with the mean flow along the shelf break. In the top 70 m, the mean characteristics of detected eddies display a strong seasonality following that of the sea ice cover. Below the first pycnocline at 70 m, the number of eddies shows little seasonality but a transient increase in response to the recent acceleration of the gyre. Deeper, within the Atlantic Waters, eddies are generated everywhere across the basin and present little interannual variability.Finally, this eddy census helps interpret some discrepancies found between previous studies that use different datasets and approaches to examine the eddy field in the Arctic. In particular, our analysis show that the anticyclone dominance within the Beaufort Gyre that arises from the analysis of eddies from the Ice Tethered Profilers is partly due a regional sampling bias.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".