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Record W2064639645 · doi:10.1175/jcli-d-14-00564.1

The Influence of High-Frequency Atmospheric Forcing on the Circulation and Deep Convection of the Labrador Sea

2015· article· en· W2064639645 on OpenAlexaffabout
Amber M. Holdsworth, Paul G. Myers

Bibliographic record

VenueJournal of Climate · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOcean gyreForcing (mathematics)ClimatologyDeep convectionConvectionAtmospheric circulationAtmospheric sciencesMixed layerCirculation (fluid dynamics)Environmental scienceAtmospheric convectionGeologyMeteorologyTroposphereGeographyMechanicsSubtropicsPhysics

Abstract

fetched live from OpenAlex

Abstract The influence of high-frequency atmospheric forcing on the circulation of the North Atlantic Ocean with emphasis on the deep convection of the Labrador Sea was investigated by comparing simulations of a coupled ocean–ice model with hourly atmospheric data to simulations in which the high-frequency phenomena were filtered from the air temperature and wind fields. In the absence of high-frequency atmospheric forcing, the strength of the Atlantic meridional overturning circulation and subpolar gyres was found to decrease by 25%. In the Labrador Sea, the eddy kinetic energy decreased by 75% and the average maximum mixed layer depth decreased by between 20% and 110% depending on the climatology. In particular, high-frequency forcing was found to have a greater impact on mixed layer deepening in moderate to warm years whereas in relatively cold years the temperatures alone were enough to facilitate deep convection. Additional simulations in which either the wind or temperature was filtered revealed that the wind, through its impact on the bulk formulas for latent and sensible heat, had a greater impact on deep convection than the temperature.

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)0.001
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 score0.032
Threshold uncertainty score0.127

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.201
Teacher spread0.192 · 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

Citations59
Published2015
Admission routes2
Has abstractyes

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