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Record W2563121250 · doi:10.1175/jcli-d-16-0347.1

A Central Indian Ocean Mode and Heavy Precipitation during the Indian Summer Monsoon

2016· article· en· W2563121250 on OpenAlexaff
Lei Zhou, Raghu Murtugudde, Dake Chen, Youmin Tang

Bibliographic record

VenueJournal of Climate · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Northern British Columbia
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMonsoonClimatologyPrecipitationMesoscale meteorologyMode (computer interface)Indian oceanSea surface temperatureMonsoon of South AsiaEnvironmental scienceGeologyOceanographyMeteorologyGeography

Abstract

fetched live from OpenAlex

A central Indian Ocean (CIO) mode is found to play a critical role in driving the heavy precipitation during the Indian summer monsoon (ISM). It is typically denoted with a combination of intraseasonal sea surface temperature (SST) anomalies and intraseasonal wind anomalies over the central Indian Ocean, and it preserves the mechanistic links among various dynamic and thermodynamic fields. Like a T junction, it controls the propagation direction of the intraseasonal variabilities (ISVs) originating in the western Indian Ocean. During the ISM, the CIO mode creates an environment favorable for the northward-propagating mesoscale variabilities. These results unveil the relation between the subseasonal monsoonal precipitation and the CIO mode in the ocean–atmosphere system in the Indian Ocean. The identification of the CIO mode deepens our understanding of the coupled monsoon system and brightens the prospects for better simulation and prediction of monsoonal precipitation in the affected countries.

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 score0.039
Threshold uncertainty score0.261

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 communication0.0000.000
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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

Citations44
Published2016
Admission routes1
Has abstractyes

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