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Record W2171781543 · doi:10.1002/joc.4288

Influence of the November Arctic Oscillation on the subsequent tropical Pacific sea surface temperature

2015· article· en· W2171781543 on OpenAlexaff
Shangfeng Chen, Renguang Wu, Wen Chen, Bin Yu

Bibliographic record

VenueInternational Journal of Climatology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersJiangsu Collaborative Innovation Center for Climate ChangeChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsClimatologySea surface temperatureWalker circulationSubtropicsAtmospheric circulationArctic oscillationPacific decadal oscillationEnvironmental scienceSubtropical ridgeGeologySpring (device)OceanographyAtmospheric sciencesPrecipitationGeographyNorthern Hemisphere

Abstract

fetched live from OpenAlex

ABSTRACT Previous studies suggested that the variability of boreal spring Arctic Oscillation ( AO ) can exert a significant influence on the sea surface temperature ( SST ) anomalies in the Niño‐3.4 region during the following winter. This study further reveals that AO in November can have a pronounced influence on the tropical central‐eastern Pacific SST anomalies during the following spring and summer. When the November AO is in its positive (negative) phase, SST anomalies tend to be positive (negative) during the following spring and summer in the tropical central‐eastern Pacific. The influence of the AO is accomplished by atmospheric circulation anomalies over the subtropical North Pacific through an interaction between the synoptic‐scale eddy and the low‐frequency mean flow. In the positive November AO years, pronounced cyclonic circulation and atmospheric heating anomalies are observed over the subtropical North Pacific. The atmospheric heating anomalies sustain westerly wind anomalies over the tropical western North Pacific through a Gill‐like atmospheric response. The westerly wind anomalies extend eastward subsequently through positive air–sea feedback mechanism, and result in SST warming during the following spring and summer in the tropical central‐eastern Pacific. Results of this study imply that the November AO index can be used as an effective predictor of SST anomalies in the Niño‐3.4 region during the following spring and summer.

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)0.001
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 score0.156
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.270
Teacher spread0.246 · 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

Citations40
Published2015
Admission routes1
Has abstractyes

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