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Record W4205571217 · doi:10.1088/1748-9326/ac4a31

Stronger Arctic amplification from ozone-depleting substances than from carbon dioxide

2022· article· en· W4205571217 on OpenAlexaffabout
Yu‐Chiao Liang, Lorenzo M. Polvani, Michael Previdi, Karen L. Smith, Mark England, Gabriel Chiodo

Bibliographic record

VenueEnvironmental Research Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
FundersEidgenössische Technische Hochschule ZürichSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsEnvironmental scienceCarbon dioxideClimatologyGlobal warmingArcticOzoneAtmospheric sciencesAlbedo (alchemy)Arctic ice packArctic geoengineeringThe arcticArctic sea ice declineGreenhouse gasClimate changeSea iceOceanographyChemistryMeteorologyGeologyGeographyDrift ice

Abstract

fetched live from OpenAlex

Abstract Arctic amplification (AA)—the greater warming of the Arctic near-surface temperature relative to its global mean value—is a prominent feature of the climate response to increasing greenhouse gases. Recent work has revealed the importance of ozone-depleting substances (ODS) in contributing to Arctic warming and sea-ice loss. Here, using ensembles of climate model integrations, we expand on that work and directly contrast Arctic warming from ODS to that from carbon dioxide (CO 2 ), over the 1955–2005 period when ODS loading peaked. We find that the Arctic warming and sea-ice loss from ODS are slightly more than half (52%–59%) those from CO 2 . We further show that the strength of AA for ODS is 1.44 times larger than that for CO 2 , and that this mainly stems from more positive Planck, albedo, lapse-rate, and cloud feedbacks. Our results suggest that AA would be considerably stronger than presently observed had the Montreal Protocol not been signed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.019
GPT teacher head0.245
Teacher spread0.226 · 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.

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

Citations20
Published2022
Admission routes2
Has abstractyes

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