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Record W4396951049 · doi:10.1088/2752-5295/ad3fdc

Effectiveness of wind-constrained sea-ice momentum on formation of sea-ice distribution and upper halocline of Arctic Ocean in climate model

2024· article· en· W4396951049 on OpenAlexaboutno aff
Jun Ono, Yoshiki Komuro, Hiroaki Tatebe, Noriaki Kimura

Bibliographic record

VenueEnvironmental Research Climate · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology
KeywordsHaloclineSea iceOceanographyArctic ice packDrift iceGeologyClimatologyArcticCryosphereThe arcticAntarctic sea iceEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Initialization of sea ice and the upper halocline in the Arctic Ocean is crucial for sea-ice prediction, but their representation in climate models still remains biased. Here, using historical and four different simulations by a single climate model, we find that constraining the sea-ice momentum by surface wind stress contributes to a better representation of the sea-ice velocity, area, and concentration. Moreover, the wind-constrained sea-ice drift modifies the underlying ocean structure via ice-ocean stress, leading to an improved climatological halocline’s vertical structure in the Canada Basin. This is because the excessively represented negative wind and ice-ocean stress curl in the climate model is weakened when constraining the sea-ice momentum and consequently the downward vertical speed, including the Ekman pumping, is also weakened at depths of 0–500 m, alleviating the deepening of isohalines. From these results, the improvement of sea-ice and ocean states by constraining sea-ice momentum is expected to make sea-ice prediction more accurate.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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 teacher head, 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

Citations0
Published2024
Admission routes1
Has abstractyes

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