Polynyas in a high-resolution dynamic–thermodynamic sea ice model and their parameterization using flux models
Bibliographic record
Abstract
This paper presents an analysis of the solutions for a steady state latent heat polynya generatedby an applied wind stress acting over a semi-enclosed channel using: (a) a dynamic–thermodynamicsea ice model, and (b) a steady state flux model. We examine what processes in the seaice model are responsible for the maintenance of the polynya and how sensitive the results areto the choice of rheological parameters. We find that when the ice is driven onshore by anapplied wind stress, a consolidated ice pack forms downwind of a zone of strong convergencein the ice velocities. The build-up of internal stresses within the consolidated ice pack becomesa crucial factor in the formation of this zone and results in a distinct polynya edge. Furthermore, within the ice pack the across-channel ice velocity varies with the across-channel distance. It isdemonstrated that provided this velocity is well represented, the steady state polynya flux modelsolutions are in close agreement with those of the sea ice model. Experiments with the sea icemodel also show that the polynya shape and area are insensitive to (a) the sea ice rheology;(b) the imposition of either free-slip or no-slip boundary conditions. These findings are usedin the development of a simplified model of the consolidated ice pack dynamics, the output ofwhich is then compared with the sea ice model results. Finally, we discuss the relevance of thisstudy for the modelling of the North Water Polynya in northern Baffin Bay.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".