Impact of model resolution on the representation of the air–sea interaction associated with the North Water Polynya
Bibliographic record
Abstract
The North Water Polynya (NOW), one of the largest and most productive of the Arctic polynyas, is situated just downwind of Smith Sound, the southern terminus of Nares Strait. The high topography along the narrow strait results in common occurrences of high‐speed northerly flow that is accelerated through Smith Sound. The resulting divergence of the surface wind field contributes to the formation of the polynya. Within the NOW, the combination of high winds, cold and dry Arctic air, and reduced ice cover can result in the transfer of heat, moisture and momentum from the ocean to the atmosphere. Much of our knowledge of the air–sea interaction over the NOW comes from atmospheric models, many of which have horizontal resolutions greater than 75 km. As such, there is concern that they may under‐represent the characteristics of the flow in the region, impacting the representation of the resulting air–sea interaction. In this study we use a set of atmospheric analyses with a common lineage but with horizontal resolutions that range from ∼75 to ∼9 km to characterize this interaction. We show that increasing the model resolution leads to an improved representation of the kinematics of the flow along the strait. However, details of the thermodynamics are more nuanced and, as a result, the intensity of the air–sea interaction over the NOW does not simply increase with increasing resolution. The results suggest that a horizontal atmospheric model resolution lower than ∼30 km is needed to represent the air–sea interaction over the NOW and that a re‐evaluation of previous modelling efforts in the region is needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".