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Record W3016407951 · doi:10.5194/acp-20-10611-2020

Global-scale distribution of ozone in the remote troposphere from the ATom and HIPPO airborne field missions

2020· article· en· W3016407951 on OpenAlexaff
Ilann Bourgeois, Jeff Peischl, Chelsea R. Thompson, K. C. Aikin, T. Campos, Hannah Clark, R. Commane, Bruce C. Daube, Glenn S. Diskin, James W. Elkins, R. S. Gao, Audrey Gaudel, Eric J. Hintsa, B. J. Johnson, Rigel Kivi, Kathryn McKain, F. L. Moore, D. D. Parrish, Richard Querel, Eric Ray, Ricardo Sánchez, Colm Sweeney, D. W. Tarasick, Anne M. Thompson, V. Thouret, J. C. Witte, S. C. Wofsy, Thomas B. Ryerson

Bibliographic record

VenueAtmospheric chemistry and physics · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
FundersCentre National de la Recherche ScientifiqueEuropean CommissionNational Oceanic and Atmospheric AdministrationUniversity of ManchesterNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsTroposphereEnvironmental scienceTropospheric ozoneAtmospheric sciencesStratosphereClimatologyOzoneNorthern HemisphereAtmosphere (unit)MeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract. Ozone is a key constituent of the troposphere, where it drives photochemical processes, impacts air quality, and acts as a climate forcer. Large-scale in situ observations of ozone commensurate with the grid resolution of current Earth system models are necessary to validate model outputs and satellite retrievals. In this paper, we examine measurements from the Atmospheric Tomography (ATom; four deployments in 2016–2018) and the HIAPER Pole-to-Pole Observations (HIPPO; five deployments in 2009–2011) experiments, two global-scale airborne campaigns covering the Pacific and Atlantic basins. ATom and HIPPO represent the first global-scale, vertically resolved measurements of O3 distributions throughout the troposphere, with HIPPO sampling the atmosphere over the Pacific and ATom sampling both the Pacific and Atlantic. Given the relatively limited temporal resolution of these two campaigns, we first compare ATom and HIPPO ozone data to longer-term observational records to establish the representativeness of our dataset. We show that these two airborne campaigns captured on average 53 %, 54 %, and 38 % of the ozone variability in the marine boundary layer, free troposphere, and upper troposphere–lower stratosphere (UTLS), respectively, at nine well-established ozonesonde sites. Additionally, ATom captured the most frequent ozone concentrations measured by regular commercial aircraft flights in the northern Atlantic UTLS. We then use the repeated vertical profiles from these two campaigns to confirm and extend the existing knowledge of tropospheric ozone spatial and vertical distributions throughout the remote troposphere. We highlight a clear hemispheric gradient, with greater ozone in the Northern Hemisphere, consistent with greater precursor emissions and consistent with previous modeling and satellite studies. We also show that the ozone distribution below 8 km was similar in the extra-tropics of the Atlantic and Pacific basins, likely due to zonal circulation patterns. However, twice as much ozone was found in the tropical Atlantic as in the tropical Pacific, due to well-documented dynamical patterns transporting continental air masses over the Atlantic. Finally, we show that the seasonal variability of tropospheric ozone over the Pacific and the Atlantic basins is driven year-round by transported continental plumes and photochemistry, and the vertical distribution is driven by photochemistry and mixing with stratospheric air. This new dataset provides additional constraints for global climate and chemistry models to improve our understanding of both ozone production and loss processes in remote regions, as well as the influence of anthropogenic emissions on baseline ozone.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.208
Teacher spread0.200 · 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 teacher head, 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

Citations111
Published2020
Admission routes1
Has abstractyes

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