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Record W2136851207 · doi:10.1175/jpo-d-13-0116.1

Bottom-Enhanced Diapycnal Mixing Driven by Mesoscale Eddies: Sensitivity to Wind Energy Supply

2013· article· en· W2136851207 on OpenAlexaff
Geoffrey J. Stanley, Oleg A. Saenko

Bibliographic record

VenueJournal of Physical Oceanography · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Victoria
Fundersnot available
KeywordsEddyGeologyWind stressAbyssal zoneBaroclinityClimatologyMixing (physics)Mesoscale meteorologyOcean currentEnergy budgetEnvironmental scienceAtmospheric sciencesOceanographyMeteorologyTurbulencePhysics

Abstract

fetched live from OpenAlex

Abstract It has been estimated that much of the wind energy input to the ocean general circulation is removed by mesoscale eddies via baroclinic instability. While the fate of this energy remains a subject of research, arguments have been presented suggesting that a fraction of it may get transferred to lee waves that, upon breaking, result in bottom-enhanced diapycnal mixing. Here the authors propose several parameterizations of this process and explore their impact in a low-resolution ocean–climate model, focusing on their impact on the abyssal meridional overturning circulation (MOC) of Antarctic Bottom Water. This study shows that, when the eddy energy is allowed to maintain diapycnal mixing, the abyssal MOC generally intensifies with increasing wind energy input to the ocean. In such a case, the whole system is driven by the wind: wind steepens isopycnals and generates eddies, and the (parameterized) eddies generate small-scale mixing, driving the MOC. It is also demonstrated that if the model diapycnal diffusivity, eddy transfer coefficient, and surface climate are decoupled from the winds, then stronger wind stress in the Southern Ocean may lead to a weaker MOC in the abyss—in line with previous results. A simple scaling theory, describing the response of the abyssal MOC strength to wind energy input, is developed, providing a better insight on the numerical results.

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 score0.131
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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

Citations16
Published2013
Admission routes1
Has abstractyes

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