Effects of Wave-Induced Sea Ice Break-Up and Mixing in a High-Resolution Coupled Ice-Ocean Model
Bibliographic record
Abstract
Arctic sea ice plays a vital role in modulating the global climate. In the most recent decades, the rapid decline of the Arctic summer sea ice cover has exposed increasing areas of ice-free ocean, with sufficient fetch for waves to develop. This has highlighted the complex and not well-understood nature of wave-ice interactions, requiring modeling effort. Here, we introduce two independent parameterizations in a high-resolution coupled ice-ocean model to investigate the effects of wave-induced sea ice break-up (through albedo change) and mixing on the Arctic sea ice simulation. Our results show that wave-induced sea ice break-up leads to increases in sea ice concentration and thickness in the Bering Sea, the Baffin Sea and the Barents Sea during the ice growth season, but accelerates the sea ice melt in the Chukchi Sea and the East Siberian Sea in summer. Further, wave-induced mixing can decelerate the sea ice formation in winter and the sea ice melt in summer by exchanging the heat fluxes between the surface and subsurface layer. As our baseline model underestimates sea ice cover in winter and produces more sea ice in summer, wave-induced sea ice break-up plays a positive role in improving the sea ice simulation. This study provides two independent parameterizations to directly include the wave effects into the sea ice models, with important implications for the future sea ice model development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".