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On the Role of Wind-Driven Sea Ice Motion on Ocean Ventilation

2002· article· en· W2054858626 on OpenAlexafffund
Oleg A. Saenko, Andreas Schmittner, Andrew J. Weaver

Bibliographic record

VenueJournal of Physical Oceanography · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Victoria
FundersCanadian Foundation for Climate and Atmospheric Sciences
KeywordsGeologySea iceClimatologyOceanographyMeteorologyEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

Simulations with a coupled ocean-atmosphere-sea ice model are used to investigate the role of wind-driven sea ice motion on ocean ventilation. Two model experiments are analyzed in detail: one including and the other excluding wind-driven sea ice transport. Model-simulated concentrations of chlorofluorocarbons (CFCs) are compared with observations from the Weddell Sea, the southeastern Pacific, and the North Atlantic. The authors show that the buoyancy fluxes associated with sea ice divergence control the sites and rates of deep- and intermediate-water formation in the Southern Ocean. Divergence of sea ice along the Antarctic perimeter facilitates bottom-water formation in the Weddell and Ross Seas. Neglecting wind-driven sea ice transport results in unrealistic bottom- water formation in Drake Passage and too-strong convection along the Southern Ocean sea ice margin, whereas convection in the Weddell and Ross Seas is suppressed. The freshwater fluxes implicitly associated with sea ice export also determine the intensity of the gyre circulation and the rate of downwelling in the Weddell Sea. In the North Atlantic, the increased sea ice export from the Arctic weakens and shallows the meridional overturning cell. This results in a decreased surface flux of CFCs around 65degreesN by about a factor of 2. At steady state, convection in the North Atlantic is found to be less affected by the buoyancy fluxes associated with sea ice divergence when compared with that in the Southern 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 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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.009
GPT teacher head0.191
Teacher spread0.183 · 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

Citations42
Published2002
Admission routes2
Has abstractyes

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