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Record W2886656324 · doi:10.1175/jcli-d-18-0186.1

Impact of Mesoscale Eddy Transfer on Heat Uptake in an Eddy-Parameterizing Ocean Model

2018· article· en· W2886656324 on OpenAlexafffund
Oleg A. Saenko, Duo Yang, Jonathan M. Gregory

Bibliographic record

VenueJournal of Climate · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsPacific Institute for Climate Solutions
FundersEnvironment and Climate Change Canada
KeywordsMesoscale meteorologyAdvectionClimatologyHeat transferSea surface temperatureEnvironmental scienceOcean currentZonal and meridionalScalingHeat fluxGeologyMixed layerAtmospheric sciencesMechanicsPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Using a set of experiments with an eddy-parameterizing ocean model, it is found that the strength of the Atlantic meridional overturning circulation (AMOC) intensifies with the decrease of the density-dependent mesoscale eddy transfer. However, the intensification is weaker than that suggested by simple scaling relationships previously applied. Perturbing the model control sea surface temperature (SST) to mimic its change in response to doubling of CO 2 , it is shown that the associated ocean heat uptake (OHU) increases and penetrates deeper with the decrease of the mesoscale eddy transfer. It is shown that the OHU correlates with the AMOC strength, and both these quantities are affected by the mesoscale eddy transfer. Passive tracer experiments in the ocean model provide a possible explanation for the finding in coupled-model climate simulations that the ocean heat uptake efficiency (OHUE) increases with the AMOC strength and decreases with the eddy energy generated from the mean state. It is also found that the OHU in the SST-perturbation experiments scales with the net downward advection of heat. The contribution of the AMOC to the downward heat flux is illustrated using a streamfunction in depth–temperature space.

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 score0.157
Threshold uncertainty score0.379

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 communication0.0000.001
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 head0.021
GPT teacher head0.277
Teacher spread0.255 · 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

Citations25
Published2018
Admission routes2
Has abstractyes

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