Interplay between subsurface eddies and sea ice over the Arctic Ocean
Bibliographic record
Abstract
The paucity of observations over the Arctic Ocean prevents us from fully understanding the interaction between sea ice and mesoscale dynamics. Previous studies on this interplay have documented the interaction between surface eddies and sea ice, omitting the subsurface eddies. This work focuses on the possible role of these subsurface eddies in shaping the sea ice distribution. First, we perform an extensive eddy census over the period 2004-2020 over the Arctic Basin, based on data from Ice Tethered Profilers (ITP) and moorings from the Beaufort Gyre Exploration Project. About 500 subsurface eddies are detected, including both submesoscale (radius between 2-10 km) and mesoscale (up to 80 km) structures. Second, we investigate the dynamical or thermodynamical signature that these eddies may imprint at the surface. On average, these eddies do not cause significant variations in either the temperature of the mixed layer or the melting of sea ice. However, we estimate that subsurface eddies induce a dynamic height anomaly of the order of a few centimetres, leading to a surface vorticity anomaly of O(10^{-5} - 10^{-4}) s^{-1}, suggesting that they may be a significant local forcing for the sea ice momentum balance. Our results suggest that there is no link between the sea ice evolution and the energy level associated with the presence of subsurface eddies. It suggests that once formed, these structures may evolve at depth independently of the presence of sea ice.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".