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Record W2518940097 · doi:10.5194/acp-17-2631-2017

Global carbonyl sulfide (OCS) measured by MIPAS/Envisat during 2002–2012

2017· article· en· W2518940097 on OpenAlexafffund
N. Glatthor, M. Höpfner, Adrian Leyser, G. P. Stiller, T. von Clarmann, Udo Grabowski, S. Kellmann, A. Linden, Björn‐Martin Sinnhuber, Gisèle Krysztofiak, Kaley A. Walker

Bibliographic record

VenueAtmospheric chemistry and physics · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationCanadian Space AgencyDeutsche ForschungsgemeinschaftEuropean Space AgencyBundesministerium für Wirtschaft und EnergieKarlsruhe Institute of Technology
KeywordsAltitude (triangle)Environmental scienceAtmospheric sciencesLatitudeTroposphereNorthern HemisphereClimatologyCarbonyl sulfideAtmospheric soundingMixing ratioGeologyChemistrySulfurGeodesy

Abstract

fetched live from OpenAlex

Abstract. We present a global carbonyl sulfide (OCS) data set covering the period June 2002 to April 2012, derived from FTIR (Fourier transform infrared) limb emission spectra measured with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on the ENVISAT satellite. The vertical resolution is 4–5 km in the height region 6–15 km and 15 at 40 km altitude. The total estimated error amounts to 40–50 pptv between 10 and 20 km and to 120 pptv at 40 km altitude. MIPAS OCS data show no systematic bias with respect to balloon observations, with deviations mostly below ±50 pptv. However, they are systematically higher than the OCS volume mixing ratios of the ACE-FTS instrument on SCISAT, with maximum deviations of up to 100 pptv in the altitude region 13–16 km. The data set of MIPAS OCS exhibits only moderate interannual variations and low interhemispheric differences. Average concentrations at 10 km altitude range from 480 pptv at high latitudes to 500–510 pptv in the tropics and at northern mid-latitudes. Seasonal variations at 10 km altitude amount to up to 35 pptv in the Northern and up to 15 pptv in the Southern Hemisphere. Northern hemispheric OCS abundances at 10 km altitude peak in June in the tropics and around October at high latitudes, while the respective southern hemispheric maxima were observed in July and in November. Global OCS distributions at 250 hPa (∼ 10–11 km) show enhanced values at low latitudes, peaking during boreal summer above the western Pacific and the Indian Ocean, which indicates oceanic release. Further, a region of depleted OCS amounts extending from Brazil to central and southern Africa was detected at this altitude, which is most pronounced in austral summer. This depletion is related to seasonally varying vegetative uptake by the tropical forests. Typical signatures of biomass burning like the southern hemispheric biomass burning plume are not visible in MIPAS data, indicating that this process is only a minor source of upper tropospheric OCS. At the 150 hPa level (∼ 13–14 km) enhanced amounts of OCS were also observed inside the Asian monsoon anticyclone, but this enhancement is not especially outstanding compared to other low latitude regions at the same altitude. At the 80 hPa level (∼ 17–18 km), equatorward transport of mid-latitude air masses containing lower OCS amounts around the summertime anticyclones was observed. A significant trend could not be detected in upper tropospheric MIPAS OCS amounts, which points to globally balanced sources and sinks. Simulations with the ECHAM-MESSy model reproduce the observed latitudinal cross sections fairly well.

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 score0.012
Threshold uncertainty score0.024

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 communication0.0000.000
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 head0.005
GPT teacher head0.192
Teacher spread0.188 · 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

Citations52
Published2017
Admission routes2
Has abstractyes

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