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Record W2762881444 · doi:10.1002/2017gl075678

Differential Radiative Heating Drives Tropical Atmospheric Circulation Weakening

2017· article· en· W2762881444 on OpenAlexaff
Yi Huang

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
Fundersnot available
KeywordsForcing (mathematics)ClimatologyRadiative forcingRadiative transferAtmospheric sciencesWalker circulationEnvironmental scienceAtmospheric circulationCirculation (fluid dynamics)General Circulation ModelGCM transcription factorsAtmosphere (unit)Climate changePhysicsMeteorologyGeologyMechanicsSea surface temperatureAerosol

Abstract

fetched live from OpenAlex

Abstract The tropical atmospheric circulation is projected to weaken during global warming, although the mechanisms that cause the weakening remain to be elucidated. We hypothesize that the weakening is related to the inhomogeneous distribution of the radiative forcing and feedback, which heats the tropical atmosphere in the ascending and subsiding regions differentially and thus requires the circulation to weaken due to energetic constraints. We test this hypothesis in a series of numerical experiments using a fully coupled general circulation model (GCM), in which the radiative forcing distribution is controlled using a novel method. The results affirm the effect of inhomogeneous forcing on the tropical circulation weakening, and this effect is greatly amplified by radiative feedback, especially that of clouds. In addition, we find that differential heating explains the intermodel differences in tropical circulation response to CO2 forcing in the GCM ensemble of the Climate Model Intercomparison Project.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

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 science0.0000.001
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 head0.043
GPT teacher head0.322
Teacher spread0.279 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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