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Record W4416134236 · doi:10.1029/2025gl117957

Mean Kinetic Energy and Its Projected Changes Dominate Over Eddy Kinetic Energy in the Arctic Ocean

2025· article· en· W4416134236 on OpenAlexafffund
Jan Klaus Rieck, Josué Martínez‐Moreno, Camille Lique, Carolina O. Dufour, Claude Talandier

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAgence Nationale de la Recherche
KeywordsBaroclinityKinetic energyArcticSea iceForcing (mathematics)Sea ice thicknessArctic ice packOcean currentThe arctic

Abstract

fetched live from OpenAlex

Abstract As sea ice retreats in a warming climate, the Arctic Ocean is becoming more energetic; yet little is known about this additional energy's distribution in the water column. We use a high‐resolution (3–4 km) pan‐Arctic ocean‐sea ice model forced by present day and future scenarios to examine changes in mean kinetic energy (MKE) and eddy kinetic energy (EKE). Our study suggests that both the mean and eddy fields are becoming more energetic under anthropogenic forcing but changes in the mean circulation dominate the increase, concurrent with a spin‐up of the large‐scale circulation, concentrated in the top 200 m and along boundaries. The increase in EKE is strongest in the upper 50 m and is linked to enhanced baroclinic instability within the mean boundary currents. A better grasp of the distribution of this energy surplus helps to understand projected changes to stratification, mixing, and circulation in a future Arctic Ocean.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.258
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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