Eddies and the Distribution of Eddy Kinetic Energy in the Arctic Ocean
Bibliographic record
Abstract
Mesoscale eddies are important to many aspects of the dynamics of the Arctic Ocean. Among others, they maintain the halocline and interact with the Atlantic Water circumpolar boundary current through lateral eddy fluxes and shelf-basin exchanges. Mesoscale eddies are also important for transporting biological material and for modifying sea ice distribution. Here, we review what is known about eddies and their impacts in the Arctic Ocean in the context of rapid climate change. Eddy kinetic energy (EKE) is a proxy for mesoscale variability in the ocean due to eddies. We present the first quantification of EKE from moored observations across the entire Arctic Ocean and compare those results to output from an eddy resolving numerical model. We show that EKE is largest in the northern Nordic Seas/Fram Strait and it is also elevated along the shelf break of the Arctic Circumpolar Boundary Current, especially in the Beaufort Sea. In the central basins, EKE is 100–1,000 times lower. Generally, EKE is stronger when sea ice concentration is low versus times of dense ice cover. As sea ice declines, we anticipate that areas in the Arctic Ocean where conditions typical of the North Atlantic and North Pacific prevail will increase. We conclude that the future Arctic Ocean will feature more energetic mesoscale variability.
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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 | 0.001 |
| 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) | 0.000 | 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".