On the Role of Sea Ice and Convection in a Global Ocean Model
Bibliographic record
Abstract
An earlier estimate regarding the possible impact of sea ice on deep-ocean water mass properties and the global thermohaline circulation in a coupled sea ice–ocean general circulation model (OGCM) is updated. Compared to the earlier application, the main upgrade is a subgrid-scale plume-convection parameterization that replaces conventional grid-cell-wide convective adjustment. The different treatment of convection leads to some noticeable differences in some of the repeated sensitivity experiments. For example, in an experiment where sea ice salinity is assumed to be that of the upper ocean, thus neglecting the primary effect of sea ice formation and melting on the ocean's buoyancy forcing, Antarctic Bottom Water formation comes essentially to a halt, the global deep-ocean properties and thermohaline circulation thus being almost solely determined by North Atlantic Deep Water. The much weaker impact in the earlier estimate turns out to be mainly due to excessive open-ocean convection in the Southern Ocean, rendering that region susceptible to the open-ocean polynya mode. The associated melting in such polynyas leads to an enhancement of convection in a salty sea ice experiment, thus obscuring the effect of neglected brine release in coastal polynyas. Besides underscoring the necessity of a careful treatment of sea ice and convection in the Southern Ocean of a global OGCM, this study indicates that new-ice formation around Antarctica has a much larger effect on global deep-ocean properties and circulation than previously estimated.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".