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Record W4311517397 · doi:10.5194/acp-22-15333-2022

The historical ozone trends simulated with the SOCOLv4 and their comparison with observations and reanalyses

2022· article· en· W4311517397 on OpenAlexaboutno aff
Arseniy Karagodin‐Doyennel, Eugene Rozanov, Timofei Sukhodolov, ‪Tatiana Egorova, Jan Sedláčék, William T. Ball, Thomas Peter

Bibliographic record

VenueAtmospheric chemistry and physics · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersCentro Svizzero di Calcolo ScientificoMinistry of Science and Higher Education of the Russian FederationChina Scholarship CouncilEidgenössische Technische Hochschule ZürichSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsOzoneStratosphereTropospheric ozoneAtmospheric sciencesMontreal ProtocolOzone layerEnvironmental scienceClimatologyOzone depletionNOxTroposphereMeteorologyChemistryGeographyGeology

Abstract

fetched live from OpenAlex

There is evidence that the ozone layer has begun to recover owing to the ban on the production of halogenated ozone-depleting substances (hODS) accomplished by the Montreal Protocol and its amendments and adjustments (MPA). However, recent studies, while reporting an increase in tropospheric ozone from the anthropogenic NO x and CH 4 and confirming the ozone recovery in the upper stratosphere from the effects of hODS, also indicate a continuing decline in the lower tropical and mid-latitudinal stratospheric ozone. While these are indications derived from observations, they are not reproduced by current global chemistry–climate models (CCMs), which show positive or near-zero trends for ozone in the lower stratosphere. This makes it difficult to robustly establish ozone evolution and has sparked debate about the ability of contemporary CCMs to simulate future ozone trends. We applied the new Earth system model (ESM) SOCOLv4 (SOlar Climate Ozone Links, version 4) to calculate long-term ozone trends between 1985–2018 and compare them with trends derived from the BAyeSian Integrated and Consolidated (BASIC) ozone composite and MERRA-2, ERA-5, and MSRv2 reanalyses. We designed the model experiment with a six-member ensemble to account for the uncertainty of the natural variability. The trend analysis is performed separately for the ozone depletion (1985–1997) and ozone recovery (1998–2018) phases of the ozone evolution. Within the 1998–2018 period, SOCOLv4 shows statistically significant positive ozone trends in the mesosphere, upper and middle stratosphere, and a steady increase in the tropospheric ozone. The SOCOLv4 results also suggest slightly negative trends in the extra-polar lower stratosphere, yet they barely agree with the BASIC ozone composite in terms of magnitude and statistical significance. However, in some realizations of the SOCOLv4 experiment, the pattern of ozone trends in the lower stratosphere resembles much of what is observed, suggesting that SOCOLv4 may be able to reproduce the observed trends in this region. Thus, the model results reveal marginally significant negative ozone changes in parts of the low-latitude lower stratosphere, which agrees in general with the negative tendencies extracted from the satellite data composite. Despite the slightly smaller significance and magnitude of the simulated ensemble mean, we confirm that modern CCMs such as SOCOLv4 are generally capable of simulating the observed ozone changes, justifying their use to project the future evolution of the ozone layer.

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)0.001
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 score0.105
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.206
Teacher spread0.190 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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