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Record W3196909956 · doi:10.1029/2021gl094008

Significant Contribution of Stratospheric Water Vapor to the Poleward Expansion of the Hadley Circulation in Autumn Under Greenhouse Warming

2021· article· en· W3196909956 on OpenAlexafffund
Yuwei Wang, Yi Huang, Yongyun Hu, Jianchun Bian, Chuanfeng Zhao, Cheng Sun

Bibliographic record

VenueGeophysical Research Letters · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
FundersFundamental Research Funds for the Central UniversitiesCanadian Space AgencyNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsHadley cellStratosphereAtmospheric sciencesEnvironmental scienceWater vaporClimatologyGreenhouse gasPolar vortexAtmospheric circulationSubtropicsGeneral Circulation ModelClimate changeGeologyPhysicsMeteorology

Abstract

fetched live from OpenAlex

Abstract Widening of the Hadley circulation and associated poleward shifts of subtropical dry zones have drawn extensive studies in the past decade. It is found that the poleward expansion of the Hadley circulation has strong seasonality and peaks in autumn in both Hemispheres in response to quadrupling CO2. Here we find that the poleward expansion in autumn is closely related to the increase of stratospheric water vapor (SWV). The SWV increase radiatively cools the stratosphere especially in the polar lower stratosphere, which consequently leads to widening of the Hadley cell in autumn. The SWV effect is affirmed in a set of “SWV‐locking” experiments. It is found that the SWV increase leads to a poleward expansion of the Hadley circulation in autumn in both Hemispheres, which contributes about 30% of the total expansion due to quadrupling CO2 in autumn.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.037
GPT teacher head0.291
Teacher spread0.254 · 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

Citations30
Published2021
Admission routes2
Has abstractyes

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