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Record W2017814059 · doi:10.1029/2008jd011096

Influence of Indian Ocean Dipole on boreal summer intraseasonal oscillations in a coupled general circulation model

2009· article· en· W2017814059 on OpenAlexaff
R. S. Ajayamohan, Suryachandra A. Rao, Jing‐Jia Luo, Toshio Yamagata

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsClimatologyGeneral Circulation ModelIndian Ocean DipoleOcean currentIndian oceanOcean general circulation modelCirculation (fluid dynamics)BorealEnvironmental scienceAtmospheric sciencesDipoleMadden–Julian oscillationOceanographyEl Niño Southern OscillationGeologyMeteorologyPhysicsClimate changeMechanicsConvection

Abstract

fetched live from OpenAlex

The role of Indian Ocean Dipole (IOD) on poleward propagation of boreal summer intraseasonal oscillations (BSISO) is examined using long simulation of a coupled ocean‐atmosphere general circulation model. The model (SINTEX‐F1) simulates the salient features of BSISO realistically. It is found that coherent (incoherent) poleward propagation of precipitation anomalies from 5°S to 25°N are observed during negative (positive) IOD years. The probable mechanisms behind such an anomaly in poleward propagation of BSISO in contrasting IOD years are identified. We find that the mean structure of meridional specific humidity distribution undergoes cardinal changes in contrasting IOD years, which in turn influences the meridional propagation of BSISO. Enhanced (decreased) air‐sea interaction in negative (positive) IOD years also supports coherent (incoherent) poleward propagation of BSISO anomalies. This study has important implications, considering the critical role of BSISO in modulating the seasonal mean summer monsoon rainfall.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.326
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations29
Published2009
Admission routes1
Has abstractyes

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