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Record W1964451770 · doi:10.1175/2009jpo4170.1

Sea Level Variations in the Tropical Pacific Ocean and the Madden–Julian Oscillation

2009· article· en· W1964451770 on OpenAlexaff
Xu Zhang, Youyu Lu, Keith R. Thompson

Bibliographic record

VenueJournal of Physical Oceanography · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsDalhousie UniversityBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsMadden–Julian oscillationKelvin waveEquatorEquatorial wavesClimatologyGeologyForcing (mathematics)Oscillation (cell signaling)Wind stressSwellSea surface temperatureLatitudeMeteorologyOceanographyPhysicsGeodesyConvection

Abstract

fetched live from OpenAlex

Abstract Satellite observations of sea level and surface wind from the tropical Pacific Ocean, and their relationship to the Madden–Julian oscillation (MJO), are analyzed using a combination of statistical techniques and a simple, physically based model. Wavenumber–frequency analysis reveals that sea level variations at the equator contain prominent eastward-propagating signals as the intraseasonal Kelvin waves. The component of sea level variation that is coherent with the MJO (ηMJO) is concentrated in a narrow strip along the equator between 150°E and 110°W. To explain the physical forcing of ηMJO, the component of zonal wind stress that is coherent with the MJO is also calculated. It is shown that is strongest in the western Pacific, but the MJO accounts for a higher percentage of the wind variance in the central equatorial Pacific. A simple linear model of the Kelvin waves, based on a first-order wave equation forced by and with a linear damping term included, successfully reproduces ηMJO. It is also shown that zonal wind variations to the east of the date line act to increase the apparent propagation speeds of the Kelvin waves.

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 score0.027
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.213
Teacher spread0.201 · 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

Citations12
Published2009
Admission routes1
Has abstractyes

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