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Record W2603493125 · doi:10.1002/2017gl072754

The role of sulfur dioxide in stratospheric aerosol formation evaluated by using in situ measurements in the tropical lower stratosphere

2017· article· en· W2603493125 on OpenAlexafffund
Andrew W. Rollins, Troy Thornberry, L. A. Watts, Pengfei Yu, Karen H. Rosenlof, Michael Mills, Esther Baumann, Fabrizio R. Giorgetta, T. P. Bui, M. Höpfner, Kaley A. Walker, C. D. Boone, P. F. Bernath, Peter R. Colarco, Paul A. Newman, D. W. Fahey, R. S. Gao

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of TorontoUniversity of Waterloo
FundersCanadian Space AgencyNational Oceanic and Atmospheric AdministrationLangley Research CenterUniversity of WaterlooNational Aeronautics and Space Administration
KeywordsStratosphereTropopauseEnvironmental scienceAtmospheric sciencesTroposphereClimatologyAerosolMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract Stratospheric aerosols (SAs) are a variable component of the Earth's albedo that may be intentionally enhanced in the future to offset greenhouse gases (geoengineering). The role of tropospheric‐sourced sulfur dioxide (SO 2 ) in maintaining background SAs has been debated for decades without in situ measurements of SO 2 at the tropical tropopause to inform this issue. Here we clarify the role of SO 2 in maintaining SAs by using new in situ SO 2 measurements to evaluate climate models and satellite retrievals. We then use the observed tropical tropopause SO 2 mixing ratios to estimate the global flux of SO 2 across the tropical tropopause. These analyses show that the tropopause background SO 2 is about 5 times smaller than reported by the average satellite observations that have been used recently to test atmospheric models. This shifts the view of SO 2 as a dominant source of SAs to a near‐negligible one, possibly revealing a significant gap in the SA budget.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.312
Teacher spread0.256 · 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

Citations41
Published2017
Admission routes2
Has abstractyes

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