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Record W3158867456 · doi:10.1029/2020jc017001

What Role Does the Barrier Layer Play During Extreme El Niño Events?

2021· article· en· W3158867456 on OpenAlexfundno aff
Xiaolin Zhang, Janet Sprintall, Lili Zeng

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationNational Natural Science Foundation of ChinaAcademic Medical Organization of Southwestern OntarioNational Aeronautics and Space Administration
KeywordsEquatorFlux (metallurgy)AdvectionClimatologyOceanographySalinityGeologyMixed layerBarrier layerAtmospheric sciencesEnvironmental scienceLayer (electronics)LatitudeMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract Intensive air‐sea interaction and the formation of the salinity barrier layer (BL) in the Pacific has fundamental importance to the El Niño evolution. The structure and formation of the BL in the equatorial Pacific Ocean during moderate and extreme El Niños over the past 30 years are investigated using in situ temperature and salinity data measured by the TAO/TRITON array and the data‐assimilating ECCO2 product. In the western and central Pacific Ocean, the BL is thicker during moderate El Niños compared to extreme El Niños due to a deeper isothermal layer depth compared to the density defined mixed layer depth. Moreover, in the western and central Pacific Ocean, the anomalous zonal eastward current related to the westerly wind event that initiates El Niños is found to be stronger during extreme El Niños, advecting the thicker BLs from west to east. A salinity budget suggests that during both moderate and extreme El Niño events, surface freshwater flux dominates at the equator. During extreme El Niños, the change in the freshwater flux drives a strong surface jet in the far western Pacific at 1°S, 156°E. North of the equator, the surface freshwater flux largely dampens this advective impact. Thus during the different El Niño strengths, the BL distribution, evolution and impact are also different. This suggests that climate models need to better distinguish different types of El Niño events in order to simulate the ENSO dynamics correctly.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.290
Teacher spread0.260 · 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 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

Citations10
Published2021
Admission routes1
Has abstractyes

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