Impact of internal wave drag on Arctic sea ice
Bibliographic record
Abstract
Abstract A parameterization of the impact of internal waves on momentum transfer at the sea-ice–ocean interface based on previous work by McPhee has been implemented in a sea-ice model for the first time. The ice–ocean drag from internal waves is relevant for shallow mixed layer depth and the presence of a density jump at the pycnocline and is also a function of the strength of the stratification beneath the ocean mixed layer and geometry of the ice interface. We present results from a coupled sea-ice–ocean model where the parameterization of internal wave drag has been implemented. We conducted simulations spanning the years from 2000 to 2017. We find a deceleration of ice drift by 5–8% in both winter and summer, but with significant spatial and temporal variation reaching seasonal average values of ~10%. The spatial variation of ice transport leads to local impacts on deformed ice of magnitude ~0.05 m (2–5%), and reductions in ocean-to-ice heat fluxes of ~1 W m−2, and a decrease in bottom melt of ~0.02–0.04 cm d−1. There is an increase of up to 15% in thickness and ice concentration in the Canadian Arctic and a 10% overall impact on the total sea-ice volume.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".