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Record W2990856752 · doi:10.1007/s00382-019-05034-2

A study of the effects of westerly wind bursts on ENSO based on CESM

2019· article· en· W2990856752 on OpenAlexaff
Xiaoxiao Tan, Youmin Tang, Tao Lian, Zhixiong Yao, Xiaojing Li, Dake Chen

Bibliographic record

VenueClimate Dynamics · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Northern British Columbia
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsClimatologyEl Niño Southern OscillationAdvectionEnvironmental scienceMultivariate ENSO indexAtmospheric sciencesGeologySouthern oscillationPhysics

Abstract

fetched live from OpenAlex

Abstract Numerous works have indicated that westerly wind bursts (WWBs) have a significant contribution to the development of El Niño events. However, the simulation of WWBs commonly suffers from large biases in the current generation of coupled general circulation models (CGCMs), limiting our ability to predict El Niño events. In this study, we introduce a WWBs parameterization scheme into the global coupled Community Earth System Model (CESM) to improve the representation of WWBs and to study the impacts of WWBs on El Niño-Southern Oscillation (ENSO) characteristics. It is found that CESM with the WWBs parameterization scheme can generate more realistic characteristics of WWBs, in particular their location and seasonal variation of occurrence. With the parameterized WWBs, the skewness of the Niño 3 index is increased, in better agreement with observation. Eastern Pacific El Niño and central Pacific El Niño events could be successfully reproduced in the model run with WWBs parameterization. Further diagnoses show that the enhanced horizontal advection in the central Pacific and vertical advection in the eastern Pacific, both of which are triggered by WWBs, are crucial factors responsible for the improvements in ENSO simulation. Clearly, WWBs have important effects on ENSO asymmetry and ENSO diversity.

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)0.000
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 score0.113
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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

Citations34
Published2019
Admission routes1
Has abstractyes

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