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Record W2770027345 · doi:10.5194/acp-17-13645-2017

Overview and preliminary results of the Surface Ocean Aerosol Production (SOAP) campaign

2017· article· en· W2770027345 on OpenAlexafffund
Cliff S. Law, M. J. Smith, Mike Harvey, Thomas G. Bell, Luke T. Cravigan, Fiona Elliott, Sarah Lawson, Martine Lizotte, Andrew Marriner, John L. McGregor, Zoran Ristovski, Karl Safi, E. S. Saltzman, P. Vaattovaara, Carolyn F. Walker

Bibliographic record

VenueAtmospheric chemistry and physics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaAcademy of FinlandNational Science FoundationScience Foundation IrelandSight Research UKNatural Environment Research CouncilCommonwealth Scientific and Industrial Research OrganisationMet Office
KeywordsBiogeochemistryAerosolEnvironmental scienceFlux (metallurgy)Mixed layerAtmospheric sciencesOceanographyParticulatesGeologyChemistryMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract. Establishing the relationship between marine boundary layer (MBL) aerosols and surface water biogeochemistry is required to understand aerosol and cloud production processes over the remote ocean and represent them more accurately in earth system models and global climate projections. This was addressed by the SOAP (Surface Ocean Aerosol Production) campaign, which examined air–sea interaction over biologically productive frontal waters east of New Zealand. This overview details the objectives, regional context, sampling strategy and provisional findings of a pilot study, PreSOAP, in austral summer 2011 and the following SOAP voyage in late austral summer 2012. Both voyages characterized surface water and MBL composition in three phytoplankton blooms of differing species composition and biogeochemistry, with significant regional correlation observed between chlorophyll a and DMSsw. Surface seawater dimethylsulfide (DMSsw) and associated air–sea DMS flux showed spatial variation during the SOAP voyage, with maxima of 25 nmol L−1 and 100 µmol m−2 d−1, respectively, recorded in a dinoflagellate bloom. Inclusion of SOAP data in a regional DMSsw compilation indicates that the current climatological mean is an underestimate for this region of the southwest Pacific. Estimation of the DMS gas transfer velocity (kDMS) by independent techniques of eddy covariance and gradient flux showed good agreement, although both exhibited periodic deviations from model estimates. Flux anomalies were related to surface warming and sea surface microlayer enrichment and also reflected the heterogeneous distribution of DMSsw and the associated flux footprint. Other aerosol precursors measured included the halides and various volatile organic carbon compounds, with first measurements of the short-lived gases glyoxal and methylglyoxal in pristine Southern Ocean marine air indicating an unidentified local source. The application of a real-time clean sector, contaminant markers and a common aerosol inlet facilitated multi-sensor measurement of uncontaminated air. Aerosol characterization identified variable Aitken mode and consistent submicron-sized accumulation and coarse modes. Submicron aerosol mass was dominated by secondary particles containing ammonium sulfate/bisulfate under light winds, with an increase in sea salt under higher wind speeds. MBL measurements and chamber experiments identified a significant organic component in primary and secondary aerosols. Comparison of SOAP aerosol number and size distributions reveals an underprediction in GLOMAP (GLObal Model of Aerosol Processes)-mode aerosol number in clean marine air masses, suggesting a missing marine aerosol source in the model. The SOAP data will be further examined for evidence of nucleation events and also to identify relationships between MBL composition and surface ocean biogeochemistry that may provide potential proxies for aerosol precursors and production.

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)0.002
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: none
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.003

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 head0.015
GPT teacher head0.218
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 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

Citations61
Published2017
Admission routes2
Has abstractyes

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