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Relationships linking primary production, sea ice melting, and biogenic aerosol in the Arctic

2016· article· en· W2334497792 on OpenAlexaff
Silvia Becagli, Luigi Lazzara, Christian Marchese, Uri Dayan, S. E. Ascanius, Marco Cacciani, Laura Caiazzo, Claudia Di Biagio, Tatiana Di Iorio, Alcide di Sarra, P. Eriksen, Fabiola Fani, Fabio Giardi, Daniela Meloni, Giovanni Muscari, Giandomenico Pace, Mirko Severi, Rita Traversi, R. Udisti

Bibliographic record

VenueAtmospheric Environment · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversité du Québec à Rimouski
FundersNational Oceanic and Atmospheric AdministrationMinistero dell’Istruzione, dell’Università e della RicercaNational Aeronautics and Space Administration
KeywordsBayOceanographyArcticSea icePhytoplanktonChlorophyll aArctic ice packEnvironmental scienceAerosolPhotic zoneClimatologyGeologyGeographyEcologyChemistryMeteorologyBiology

Abstract

fetched live from OpenAlex

This study examines the relationships linking methanesulfonic acid (MSA, arising from the atmospheric oxidation of the biogenic dimethylsulfide, DMS) in atmospheric aerosol, satellite-derived chlorophyll a (Chl-a), and oceanic primary production (PP), also as a function of sea ice melting (SIM) and extension of the ice free area in the marginal ice zone (IF-MIZ) in the Arctic. MSA was determined in PM10 samples collected over the period 2010–2012 at two Arctic sites, Ny Ålesund (78.9°N, 11.9°E), Svalbard islands, and Thule Air Base (76.5°N, 68.8°W), Greenland. PP is calculated by means of a bio-optical, physiologically based, semi-analytical model in the potential source areas located in the surrounding oceanic regions (Barents and Greenland Seas for Ny Ålesund, and Baffin Bay for Thule). Chl-a peaks in May in the Barents sea and in the Baffin Bay, and has maxima in June in the Greenland sea; PP follows the same seasonal pattern of Chl-a, although the differences in absolute values of PP in the three seas during the blooms are less marked than for Chl-a. MSA shows a better correlation with PP than with Chl-a, besides, the source intensity (expressed by PP) is able to explain more than 30% of the MSA variability at the two sites; the other factors explaining the MSA variability are taxonomic differences in the phytoplanktonic assemblages, and transport processes from the DMS source areas to the sampling sites. The taxonomic differences are also evident from the slopes of the correlation plots between MSA and PP: similar slopes (in the range 34.2–36.2 ng m−3of MSA/(gC m−2 d−1)) are found for the correlation between MSA at Ny Ålesund and PP in Barents Sea, and between MSA at Thule and PP in the Baffin Bay; conversely, the slope of the correlation between MSA at Ny Ålesund and PP in the Greenland Sea in summer is smaller (16.7 ng m−3of MSA/(gC m−2 d−1)). This is due to the fact that DMS emission from the Barents Sea and Baffin Bay is mainly related to the MIZ diatoms, which are prolific DMS producers, whereas in the Greenland Sea the DMS peak is related to an offshore pelagic bloom where low-DMS producer species are present. The sea ice dynamic plays a key role in determining MSA concentration in the Arctic, and a good correlation between MSA and SIM (slope = 39 ng m−3 of MSA/106 km2 SIM) and between MSA and IF-MIZ (slope = 56 ng m−3 of MSA/106 km2 IF-MIZ) is found for the cases attributable to bloomings of diatoms in the MIZ. Such relationships are calculated by combining the data sets from the two sites and suggest that PP is related to sea ice melting and to the extension of marginal sea ice areas, and that these factors are the main drivers for MSA concentrations at the considered Arctic sites.

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 score0.011
Threshold uncertainty score0.707

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 science0.0000.000
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 head0.013
GPT teacher head0.178
Teacher spread0.165 · 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

Citations113
Published2016
Admission routes1
Has abstractyes

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