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Record W2029927744 · doi:10.1175/2007jcli2063.1

Direct and Indirect Shortwave Radiative Effects of Sea Salt Aerosols

2008· article· en· W2029927744 on OpenAlexaffabout
Tarek Ayash, Sunling Gong, Charles Q. Jia

Bibliographic record

VenueJournal of Climate · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSea saltShortwaveEnvironmental scienceCloud condensation nucleiShortwave radiationAtmospheric sciencesSea salt aerosolRadiative transferClimatologySouthern HemisphereNorthern HemisphereEarth's energy budgetAerosolRadiative fluxMeteorologyRadiationGeologyGeographyPhysics

Abstract

fetched live from OpenAlex

Abstract Sea salt aerosols play a dual role in affecting the atmospheric radiative balance. Directly, sea salt particles scatter the incoming solar radiation and absorb the outgoing terrestrial radiation. By acting as cloud condensation nuclei, sea salt aerosols indirectly modulate the atmospheric radiative budget through their effective contribution to cloud formation. Using the Canadian Aerosol Module (CAM)–Canadian Centre for Climate Modelling and Analysis (CCCma) GCM, version 3 (GCM3) framework, the direct as well as the indirect shortwave (SW) radiative effects of sea salt aerosols are simulated. The model results herein suggest that sea salt aerosols exert a significant direct radiative effect over oceanic regions, with seasonal means in the range from −2 to −3 W m−2 over the Southern Ocean. Globally, sea salt’s SW indirect effect (annual mean −0.38 W m−2) is found to be less than its direct effect (annual mean −0.65 W m−2). However, sea salt’s indirect effect is found to be far stronger over the Southern Hemisphere than over the Northern Hemisphere, especially over the Southern Ocean with seasonal means around −4 W m−2, which exceed its direct effect. The model results herein suggest that sea salt aerosols significantly modulate the atmospheric radiation budget over oceanic regions and need to be accounted for in global climate models.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.326

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)0.0000.000

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 head0.010
GPT teacher head0.213
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 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

Citations51
Published2008
Admission routes2
Has abstractyes

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