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Record W4378212106 · doi:10.1038/s41558-023-01671-y

Very short-lived halogens amplify ozone depletion trends in the tropical lower stratosphere

2023· article· en· W4378212106 on OpenAlexaboutno aff
Julián Villamayor, Fernando Iglesias‐Suarez, Carlos A. Cuevas, Rafael P. Fernández, Qinyi Li, Marta Ábalos, Ryan Hossaini, Martyn P. Chipperfield, Douglas E. Kinnison, Simone Tilmes, Jean‐François Lamarque, Alfonso Saiz‐Lopez

Bibliographic record

VenueNature Climate Change · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersNatural Environment Research CouncilEuropean CommissionSight Research UKNational Center for Atmospheric ResearchNational Science Foundation
KeywordsStratosphereOzone depletionOzone layerOzoneAtmospheric sciencesClimatologyEnvironmental scienceMontreal ProtocolMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

Abstract In contrast to the general stratospheric ozone recovery following international agreements, recent observations show an ongoing net ozone depletion in the tropical lower stratosphere (LS). This depletion is thought to be driven by dynamical transport accelerated by global warming, while chemical processes have been considered to be unimportant. Here we use a chemistry–climate model to demonstrate that halogenated ozone-depleting very short-lived substances (VSLS) chemistry may account for around a quarter of the observed tropical LS negative ozone trend in 1998–2018. VSLS sources include both natural and anthropogenic emissions. Future projections show the persistence of the currently unaccounted for contribution of VSLS to ozone loss throughout the twenty-first century in the tropical LS, the only region of the global stratosphere not projecting an ozone recovery by 2100. Our results show the need for mitigation strategies of anthropogenic VSLS emissions to preserve the present and future ozone layer in low latitudes.

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 categoriesInsufficient 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 score0.053
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.048
GPT teacher head0.281
Teacher spread0.233 · 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

Citations54
Published2023
Admission routes1
Has abstractyes

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