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Record W2120254366 · doi:10.1002/2013gl059079

The implication of radiative forcing and feedback for meridional energy transport

2014· article· en· W2120254366 on OpenAlexafffund
Yi Huang, Minghong Zhang

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEquatorCloud forcingCloud feedbackRadiative forcingZonal and meridionalAtmospheric sciencesEnvironmental scienceForcing (mathematics)ShortwaveLongwaveShortwave radiationClimatologyRadiative equilibriumAlbedo (alchemy)Northern HemisphereRadiative transferLatitudeCoupled model intercomparison projectClimate modelGeologyPhysicsClimate sensitivityAerosolRadiationClimate changeMeteorology

Abstract

fetched live from OpenAlex

Abstract The distributions of radiative forcing and feedback in the Coupled Model Intercomparison Project phase 5 abrupt4xCO2 and Historical experiments are diagnosed, with a focus on their effects on the zonal mean structure of the top‐of‐the‐atmosphere radiation anomalies and implications for the meridional energy transport. It is found that because the greenhouse gas longwave forcing peaks in the low latitudes, it reinforces the equator‐to‐pole net radiation gradient and accounts for the increase in the poleward energy transport in both hemispheres under global warming. The shortwave forcing by aerosol, ozone, etc. peaks in the Northern Hemisphere and instead implies an interhemispheric energy transport. Although the water vapor feedback also reinforces the equator‐to‐pole gradient of the net radiation, the temperature and albedo feedback act against it. The feedback tend to offset the zonal mean radiation anomaly caused by the forcing, although the overall feedback effect on the energy transport is rather uncertain, mainly due to the uncertainty in the cloud feedback.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.235

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 head0.020
GPT teacher head0.270
Teacher spread0.250 · 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

Citations35
Published2014
Admission routes2
Has abstractyes

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