Polar lows and their effects on sea ice and the upper ocean in the Iceland, Greenland and Labrador Seas
Bibliographic record
Abstract
Based on two case studies, we show for the first time that explicitly resolving polar lows in a global climate model (ICON-Sapphire) with a high resolution of 2.5 km in all components (atmosphere, ocean, sea ice and land) leads to strong heat loss from the ocean near the sea ice edge and from leads and polynyas in the ice cover. Heat losses during marine cold air outbreaks triggered by polar lows lead to the formation of dense water in the Iceland and Greenland Seas that replenishes the climatically important Denmark Strait Overflow Water (DSOW). Further heat losses and the rejection of brine during ice formation in polynyas, such as the Sirius Water Polynya in northeast Greenland, contribute to the formation of dense water over the Greenland shelf. In the Labrador Sea, polar lows intensify cold air outbreaks from the sea ice and quickly deepen the ocean mixed layer by 100 m within two days. If mesoscale polar lows and kinematic features in the sea ice are not resolved in global climate models, heat loss and dense water formation in (sub-)polar regions will be underestimated.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".