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Record W3159717243 · doi:10.3390/jmse9050457

The Influence of Wind-Induced Waves on ENSO Simulations

2021· article· en· W3159717243 on OpenAlexaff
Yao Hu, Xiaoxiao Tan, Youmin Tang, Zheqi Shen, Ying Bao

Bibliographic record

VenueJournal of Marine Science and Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Northern British Columbia
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsAdvectionAmplitudeClimatologyGCM transcription factorsWave modelZonal and meridionalForcing (mathematics)Mixing (physics)Oscillation (cell signaling)Ocean general circulation modelAtmospheric sciencesGeneral Circulation ModelGeologyEnvironmental scienceMeteorologyPhysicsOceanographyClimate change

Abstract

fetched live from OpenAlex

We evaluated the influence of wind-induced waves on El Niño-Southern Oscillation (ENSO) simulations based on the First Institute of Oceanography-Earth System Model version 2 (FIO-ESM 2.0), a global coupled general circulation model (GCM) with a wave component. Two sets of experiments, the GCM, with and without a wave model, respectively, were conducted in parallel. The simulated sea surface temperature (SST) was cooled by introducing the wave model via the enhancement of the vertical mixing in the ocean upper layer. The strength of ENSO was intensified and better simulated with the inclusion of wave-induced mixing, particularly the La Niña amplitude. Furthermore, the simulated amplitude and spatial pattern of El Niño events were slightly altered with the wave model. Heat budget analyses revealed the intensification of La Niña events to be generally attributed to wave-induced vertical advection, followed by the zonal and meridional advection terms.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.229
Teacher spread0.216 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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