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Record W2791590178 · doi:10.1002/2017jd027221

On the pattern of CO<sub>2</sub> radiative forcing and poleward energy transport

2017· article· en· W2791590178 on OpenAlexaff
Yi Huang, Xiaoxiao Tan

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsRadiative forcingForcing (mathematics)Cloud forcingCloud feedbackEnvironmental scienceAtmospheric sciencesClimatologyAlbedo (alchemy)Radiative transferAtmosphere (unit)Climate modelClimate changePhysicsMeteorologyGeologyClimate sensitivityAerosol

Abstract

fetched live from OpenAlex

Abstract A set of general circulation model experiments are conducted to analyze how the poleward energy transport (PET) is related to the spatial pattern of CO 2 radiative forcing. The effects of forcing pattern are affirmed by comparing the conventional doubling CO 2 experiment, in which the forcing pattern is inhomogeneous, to a set of forcing homogenization experiments, in which the top of atmosphere (TOA), surface, or atmospheric forcing distribution is homogenized respectively. In addition, we separate and compare the effects of CO 2 forcing to various feedbacks on atmospheric and oceanic PETs, by using a set of radiative kernels that we have developed for both TOA and surface radiation fluxes. The results here show that both the enhancement of atmospheric PET and weakening of oceanic PET during global warming are directly driven by the meridional gradients of the CO 2 forcing. Interestingly, the overall feedback effect is to reinforce the forcing effect, mainly through the cloud feedback in the case of atmospheric PET and the albedo feedback in the case of the oceanic PET. Contrary to previous studies, we find that the water vapor feedback only has a weak effect on atmospheric PET. The Arctic warming amplification, which strongly affects atmospheric PET, is sensitive to the CO 2 forcing pattern.

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)0.001
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 score0.612
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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

Citations146
Published2017
Admission routes1
Has abstractyes

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