Long-lasting impacts of winds on Arctic sea ice through the ocean’s memory
Bibliographic record
Abstract
This modelling study reveals that the changes in the ocean state induced by wind perturbations can significantly influence the Arctic sea ice drift, thickness, concentration and deformation rates even after the wind perturbations have been eliminated for years. Wind perturbations can change the Arctic Ocean liquid freshwater content locally or basin-wide, thus changing the sea surface height and ocean surface geostrophic current accordingly. Such changes in the ocean can last for many years, which enforces long-lasting and strong imprint on sea ice. Both the changes in sea surface height gradient force (due to changes in sea surface height) and ocean-ice stress (due to changes in ocean geostrophic velocity) are found to be important in determining the overall impacts on sea ice. Depending on the preceding atmospheric mode driving the ocean, the ocean’s memory of wind forcing can lead to changes in Arctic sea ice characteristics with very different spatial patterns. We identified these spatial patterns associated with Arctic Oscillation, Arctic Dipole Anomaly and Beaufort High modes through dedicated numerical simulations in this study. Our results suggest the importance of initial ocean state in sea ice prediction on subseasonal to decadal time scales.
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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) | 0.002 | 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".