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Record W4400549714 · doi:10.5194/acp-24-7927-2024

Exploring ozone variability in the upper troposphere and lower stratosphere using dynamical coordinates

2024· article· en· W4400549714 on OpenAlexaff
Luis Millán, Peter Hoor, Michaela I. Hegglin, G. L. Manney, Harald Boenisch, Paul S. Jeffery, Daniel Kunkel, Irina Petropavlovskikh, Hao Ye, Thierry Leblanc, Kaley A. Walker

Bibliographic record

VenueAtmospheric chemistry and physics · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Toronto
FundersInternational Space Science Institute
KeywordsStratosphereTropopauseTroposphereAtmospheric sciencesExtratropical cycloneEnvironmental scienceLatitudeClimatologyAltitude (triangle)Geology

Abstract

fetched live from OpenAlex

Ozone trends in the upper troposphere and lower stratosphere (UTLS) remain highly uncertain because of sharp spatial gradients and high variability caused by competing transport, chemical, and mixing processes near the upper-tropospheric jets and extratropical tropopause as well as inhomogeneous spatially and temporally limited observations of the region. Subtropical jets and the tropopause act as transport barriers, delineating boundaries between atmospheric regimes controlled by different processes; they can thus be used to separate data taken in those different regimes for numerous purposes, including trend assessment. As part of the Observed Composition Trends And Variability in the UTLS (OCTAV-UTLS) Stratosphere-troposphere Processes And their Role in Climate (SPARC) activity, we assess the effectiveness of several coordinate systems in segregating air into different atmospheric regimes. To achieve this, a comprehensive dynamical dataset is used to reference every measurement from various observing systems to the locations of jets and tropopauses in different coordinates (e.g., altitude, pressure, potential temperature, latitude, and equivalent latitude). We assess which coordinate combinations are most useful for dividing the measurements into bins such that the data in each bin are affected by the same processes, thus minimizing the variability induced when combining measurements from different dynamical regimes, each characterized by different physical processes. Such bins will be particularly suitable for combining measurements with different sampling characteristics and for assessing trends and attributing them to changing atmospheric dynamics. Overall, the use of equivalent latitude and potential temperature leads to the most substantial reduction in binned variability across the UTLS. This coordinate pairing uses potential vorticity (PV) on isentropic surfaces, thus aligning with the adiabatic transport of tracers.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.223
Teacher spread0.203 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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