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Record W4245107562 · doi:10.5194/acp-2017-1075

Stratospheric ozone loss in the Arctic winters between 2005 and2013 derived with ACE-FTS measurements

2018· preprint· en· W4245107562 on OpenAlexafffund
Debora Griffin, Kaley A. Walker, Ingo Wohltmann, Sandip Dhomse, Markus Rex, Martyn P. Chipperfield, Wuhu Feng, G. L. Manney, Jane Liu, D. W. Tarasick

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change CanadaUniversity of WaterlooUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyNational Centre for Earth ObservationNatural Environment Research CouncilUniversity of LeedsSight Research UK
KeywordsTRACEROzonePolar vortexOzone depletionAtmospheric sciencesArcticMixing ratioStratospherePolarOzone layerVortexEnvironmental scienceChemistryMeteorologyPhysicsGeologyNuclear physicsOceanography

Abstract

fetched live from OpenAlex

Abstract. Stratospheric ozone loss inside the Arctic polar vortex for the winters between 2004/2005 and 2012/2013 has been quantified using measurements from the space-borne Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS). Six different methods, including tracer-tracer correlation, artificial tracer correlation, average vortex profile descent, and passive subtraction with model output from both Lagrangian and Eulerian chemical transport models (CTMs), have been employed to determine the Arctic ozone loss (mixing ratio loss profiles and the partial column ozone losses between 380 and 550 K). For the tracer-tracer, the artificial tracer, and the average vortex profile descent approaches, various tracers have been used. Here, we show that CH4, N2O, HF, and CFC-12 are suitable tracers for investigating polar stratospheric ozone depletion with ACE-FTS. The ozone loss estimates (in terms of the mixing ratio as well as total column ozone) are generally in good agreement between the different methods and among the different tracers. However, the tracer-tracer correlation method does not agree with the other estimation methods in March 2005 and using the average vortex profile descent technique typically leads to smaller maximum losses compared to all other methods. The passive subtraction method using output from CTMs generally results in smaller uncertainties and slightly larger losses compared to the techniques that use ACE-FTS measurements only. The ozone loss computed, using both measurements and models, shows the greatest loss during the 2010/2011 Arctic winter. For that year, our results show that maximum ozone loss (2.1–2.7 ppmv) occurred at 460 K. The estimated partial column ozone loss inside the polar vortex (between 380 K and 550 K) is 66–103 DU, 61–95 DU, 59–96 DU, 41–89 DU, and 85–122 DU for March 2005, 2007, 2008, 2010, and 2011, respectively. Ozone loss is difficult to diagnose during 2005/2006, 2008/2009, 2011/2012, and 2012/2013 because strong polar vortex disturbance or major sudden stratospheric warming events significantly perturbed the polar vortex thereby limiting the number of measurements available for the analysis.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.099
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.242
Teacher spread0.202 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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