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Record W2591616909 · doi:10.1007/s11430-016-8315-5

Genesis of the 2014–2016 El Niño events

2017· article· es· W2591616909 on OpenAlexaff
Tao Lian, Dake Chen, Youmin Tang

Bibliographic record

VenueScience China Earth Sciences · 2017
Typearticle
Languagees
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Northern British Columbia
FundersNational Natural Science Foundation of China
KeywordsDownwellingAdvectionKelvin waveGeologyClimatologyAtmospheric sciencesEnvironmental scienceOceanographyPhysicsUpwellingThermodynamics

Abstract

fetched live from OpenAlex

The 2015/2016 El Niño was one of the strongest El Niño events in history, and this strong event was preceded by a weak El Niño in 2014. This study systematically analyzed the dynamical processes responsible for the genesis of these events. It was found that the weak 2014 El Niño had two warming phases, the spring-summer warming was produced by zonal advection and downwelling Kelvin waves driven by westerly wind bursts (WWBs), and the autumn-winter warming was produced by meridional advection, surface heating as well as downwelling Kelvin waves. The 2015/2016 extreme El Niño, on the other hand, was primarily a result of sustained zonal advection and downwelling Kelvin waves driven by a series of WWBs, with enhancement from the Bjerknes positive feedback. The vast difference between these two El Niño events mainly came from the different amount of WWBs in 2014 and 2015. As compared to the 1982/1983 and 1997/1998 extreme El Niño events, the 2015/2016 El Niño exhibited some distinctive characteristics in its genesis and spatial pattern. We need to include the effects of WWBs to the theoretical framework of El Niño to explain these characteristics, and to improve our understanding and prediction of El Niño.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.025
Scholarly communication0.0010.003
Open science0.0080.003
Research integrity0.0000.000
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 head0.024
GPT teacher head0.287
Teacher spread0.263 · 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; both teacher heads agree on what is shown here.

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

Citations59
Published2017
Admission routes1
Has abstractyes

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