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Record W1647518391 · doi:10.1029/2004pa001020

Modeling evidence for enhanced El Niño–Southern Oscillation amplitude during the Last Glacial Maximum

2004· article· en· W1647518391 on OpenAlexaff
Soon‐Il An, Axel Timmermann, Luís Bejarano, Fei‐Fei Jin, Flávio Justino, Zhengyu Liu, Alexander W. Tudhope

Bibliographic record

VenuePaleoceanography · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
FundersJapan Agency for Marine-Earth Science and TechnologyNatural Environment Research CouncilDeutsche ForschungsgemeinschaftNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsLast Glacial MaximumClimatologyGeologyThermoclineEl Niño Southern OscillationShoaling and schoolingAmplitudeOscillation (cell signaling)Pacific decadal oscillationHoloceneAtmospheric sciencesOceanographyPhysics

Abstract

fetched live from OpenAlex

We present a numerical eigenmode analysis of an intermediate El Niño–Southern Oscillation (ENSO) model which is driven by present‐day observed background conditions as well as by simulated background conditions for the Last Glacial Maximum (LGM) about 21,000 years ago. The background conditions are obtained from two LGM simulations which were performed with the National Center for Atmospheric Research climate system model (CSM1.4) and an Earth system model of intermediate complexity (ECBilt‐CLIO). Our analysis clearly shows that the leading present‐day unstable recharge‐discharge mode changes its stability as well as its frequency during LGM conditions. Simulated LGM background conditions were favorable to support large‐amplitude self‐sustained interannual ENSO variations in the tropical Pacific. Our analysis indicates that off‐equatorial climate conditions as well as a shoaling of the thermocline play a crucial role in amplifying the LGM ENSO mode.

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 score0.009
Threshold uncertainty score0.017

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 communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.276
Teacher spread0.242 · 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

Citations76
Published2004
Admission routes1
Has abstractyes

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