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Record W2941924519 · doi:10.1038/s41598-019-42922-3

The changing relationship between ENSO and its extratropical response patterns

2019· article· en· W2941924519 on OpenAlexafffund
Nicholas Soulard, Hai Lin, Bin Yu

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change CanadaMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExtratropical cycloneClimatologyNorthern HemisphereAnomaly (physics)Jet streamSea surface temperatureJet (fluid)El Niño Southern OscillationOscillation (cell signaling)Atmospheric circulationEnvironmental scienceGeologyAtmospheric sciencesPhysicsBiology

Abstract

fetched live from OpenAlex

El Niño-Southern Oscillation (ENSO) can influence Northern Hemisphere seasonal conditions through its interaction with the Pacific-North American pattern (PNA) and the Tropical Northern Hemisphere pattern (TNH). Possibly due to Earth's changing climate, the variability of ENSO, as well as the zonal location of its sea-surface temperature (SST) anomaly, is changing. Along with this, the strength and location of the jet, in which these atmospheric patterns are embedded, are changing. Using a simple tracking algorithm we create a continuous time series for the zonal location of ENSO's SST anomaly, and show that the relationship between ENSO and the PNA is linearly sensitive to this location, while its relationship with the TNH is not. ENSO's relationship with both the TNH and PNA is shown to be strongly influenced by the position of the Pacific jet stream. The ENSO-TNH relationship is found to be linked to phase changes of the Atlantic Multidecadal Oscillation, and the resulting changes in SST and jet speed.

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)0.003
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 score0.018
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.263
Teacher spread0.229 · 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

Citations67
Published2019
Admission routes2
Has abstractyes

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