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Record W4238802165 · doi:10.5194/acp-2019-243

Stratospheric ozone trends for 1985–2018: sensitivity to recent large variability

2019· preprint· en· W4238802165 on OpenAlexaboutno aff
William T. Ball, Justin Alsing, J. Staehelin, Sean Davis, L. Froidevaux, Thomas Peter

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsStratosphereMontreal ProtocolEnvironmental scienceOzoneOzone layerAtmospheric sciencesOzone depletionClimatologyLatitudeMeteorologyGeographyPhysics

Abstract

fetched live from OpenAlex

Abstract. The Montreal Protocol has successfully prevented catastrophic losses of stratospheric ozone, and signs of recovery are now evident. Nevertheless, recent work suggests that ozone in the lower stratosphere ( 95 %, 30° S–30° N) decreases dominate the quasi-global integrated decrease (99 % probability); the integrated tropical stratospheric column (1–100 hPa, 30° S–30° N) displays a significant overall decrease, with 95 % probability. These decreases do not reveal an inefficacy of the Montreal Protocol. Rather, they suggest other effects to be at work, mainly dynamical variability on long or short timescale, counteracting the protocol's regulation of halogenated ozone depleting substances (hODS). We demonstrate that large inter-annual mid-latitude variations (30° –60° ), such as the 2017 resurgence, are driven by non-linear QBO phase-dependent seasonal variability. However, this variability is not represented in current regression analyses. To understand if observed lower stratospheric decreases are a transient or long-term phenomenon, progress needs to be made in accounting for this dynamically-driven variability.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.256
Teacher spread0.232 · 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 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

Citations8
Published2019
Admission routes1
Has abstractyes

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