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Record W2094927735 · doi:10.1175/jcli4229.1

PDO-Related Heat and Temperature Budget Changes in a Model of the North Pacific

2007· article· en· W2094927735 on OpenAlexaff
J. T. Dawe, LuAnne Thompson

Bibliographic record

VenueJournal of Climate · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of British Columbia
FundersNational Aeronautics and Space Administration
KeywordsOcean gyreOcean heat contentAdvectionClimatologyHeat fluxGeostrophic windEnvironmental sciencePacific decadal oscillationEntrainment (biomusicology)Boundary currentSea surface temperatureAtmospheric sciencesOcean currentGeologyHeat transferSubtropicsPhysics

Abstract

fetched live from OpenAlex

Abstract Heat and temperature budget changes in a ⅓° model of the North Pacific driven by an idealized Pacific decadal oscillation (PDO) atmospheric forcing are diagnosed to determine the roles of atmospheric heat flux and ocean dynamics in upper-ocean heat content and mixed layer temperature (MLT) changes. Changes in MLT and heat content during the transition between negative and positive PDOs are driven primarily by atmospheric heat fluxes, with contributions from ageostrophic advection and entrainment. Once the new PDO state is established, atmospheric heat flux in the central North Pacific works to mitigate the MLT change while vertical entrainment and ageostrophic advection act to enhance it. Upper-ocean heat content is affected in a similar matter, except that vertical processes are not important in the heat budget balance. At the same time, changes in wind stress curl cause the subtropical gyre to spin up and the subpolar gyre boundary to migrate southward. These circulation changes cause a large increase in the geostrophic advective heat flux in the Kuroshio region. This increase results in more heat flux to the atmosphere, demonstrating an active role for ocean dynamics in the upper-ocean heat budget. Eddy heat flux divergence along the Kuroshio Extension doubles after the transition, due to stronger eddy activity related to increased Kuroshio transport.

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 score0.173
Threshold uncertainty score0.212

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 head0.011
GPT teacher head0.229
Teacher spread0.217 · 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
Published2007
Admission routes1
Has abstractyes

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