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Record W4322210048 · doi:10.5194/egusphere-egu23-14758

Sensitivity of ozone return dates to shared socioeconomic pathway in CMIP6 models

2023· preprint· en· W4322210048 on OpenAlexaboutno aff
James Keeble, Birgit Haßler, Manuel Schlund

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsOzoneOzone layerMontreal ProtocolOzone depletionEnvironmental scienceAtmospheric sciencesClimatologySocioeconomic statusMeteorologyGeographyGeologyDemography

Abstract

fetched live from OpenAlex

Following the success of the Montreal Protocol, stratospheric ozone is projected to recover over the coming decades as halogenated ozone depleting substances decline. However, future projections of stratospheric ozone recovery are also dependent on assumptions made about the emissions of other gases such as CO2, CH4, and N2O. As a result, the pathway of ozone recovery is sensitive to the choice of future emissions scenario. Here we explore ozone recovery under different Shared Socioeconomic Pathways (SSPs) in 6 CMIP6 models that include interactive chemistry schemes: CESM2-WACCM, CNRM-ESM2-1, GFDL-ESM4, GISS-E2-1-G, MRI-ESM2-0, UKESM1-O-LL. We explore the impact of different SSP scenarios on projections of ozone recovery, the date at which total column ozone returns to historic values, and the healing of the ozone hole. Additionally, we compare global mean return dates with regional return dates and explore the different processes affecting the timing of ozone recovery in these different regions.

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)0.003
metaresearch head score (Gemma)0.007
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.241
Teacher spread0.200 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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