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Record W4309785120 · doi:10.5194/acp-22-14709-2022

Water vapour and ozone in the upper troposphere–lower stratosphere: global climatologies from three Canadian limb-viewing instruments

2022· article· en· W4309785120 on OpenAlexafffundabout
Paul S. Jeffery, Kaley A. Walker, Christopher E. Sioris, C. D. Boone, D. A. Degenstein, G. L. Manney, C. T. McElroy, Luis Millán, David A. Plummer, Niall J Ryan, Patrick E. Sheese, Jiansheng Zou

Bibliographic record

VenueAtmospheric chemistry and physics · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of SaskatchewanYork UniversityUniversity of WaterlooEnvironment and Climate Change CanadaUniversity of Toronto
FundersCanadian Space AgencyEuropean Space Agency
KeywordsStratosphereTroposphereWater vaporTropopauseAtmospheric sciencesEnvironmental scienceMicrowave Limb SounderOccultationOzoneClimatologyAtmosphere (unit)LatitudeSatelliteMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

This study presents upper troposphere–lower stratosphere (UTLS) water vapour and ozone climatologies generated from 14 years (June 2004 to May 2018) of measurements made by three Canadian limb-viewing satellite instruments: the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), the Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (MAESTRO), and the Optical Spectrograph and InfraRed Imaging System (OSIRIS; ozone only). This selection of instruments was chosen to explore the capability of these Canadian instruments in representing the UTLS and to enable analysis of the impact of different measurement sampling patterns. The water vapour and ozone climatologies have been constructed using tropopause-relative potential temperature and equivalent-latitude coordinates in an effort to best represent the distribution of these two gases in the UTLS, which is characterized by a high degree of dynamic and geophysical variability. Zonal-mean multiyear-mean climatologies are provided with 5 ∘ equivalent latitude and 10 K potential temperature spacing and have been constructed on a monthly, seasonal (3-month), and yearly basis. These climatologies are examined in-depth for two 3-month periods, December–January–February and June–July–August, and are compared to reference climatologies constructed from the Canadian Middle Atmosphere Model 39-year specified dynamics (CMAM39-SD) run, subsampled to the times and locations of the satellite measurements, in order to evaluate the consistency of water vapour and ozone between the datasets. Specifically, this method of using a subsampled model addresses the impact of each instrument's measuring pattern and allows for the quantification of the influence of different measurement patterns on multiyear climatologies. This in turn permits a more consistent evaluation of the distributions of these two gas species, as assessed through the differences between the model and measurement climatologies. For water vapour, the average absolute relative difference between CMAM39-SD and ACE-FTS differed between the two versions of ACE-FTS by less than 8 %, while the MAESTRO climatologies were found to differ by 15 %–41 % from ACE-FTS, depending on the version of ACE-FTS and the season. When considering the ozone climatologies, those constructed from the two ACE-FTS versions agreed to within 2 % overall, and the OSIRIS ozone climatologies agreed with these to within 10 %. The MAESTRO ozone climatologies differ from those from ACE-FTS and OSIRIS by 30 %–35 % and 25 %, respectively, albeit with regions of better agreement within the UTLS. These findings indicate that this set of Canadian limb sounders yields generally similar water vapour and ozone distributions in the UTLS, with some exceptions for MAESTRO depending on the season and gas species.

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)0.001
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 score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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

Citations12
Published2022
Admission routes3
Has abstractyes

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