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Record W3089105490 · doi:10.5194/acp-2020-761

Influence of Saharan dust on Atlantic tropical cyclones

2020· article· en· W3089105490 on OpenAlexfundno aff
Zhenxi Zhang, Wen Zhou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaMcGill UniversityCity University of Hong KongNational Aeronautics and Space Administration
KeywordsTropical cycloneTropical AtlanticEnvironmental sciencePlumeClimatologyAtmospheric sciencesMineral dustIntertropical Convergence ZoneLatitudeAfrican easterly jetAtmosphere (unit)Forcing (mathematics)Tropical waveAerosolGeographySea surface temperatureGeologyMeteorologyPrecipitation

Abstract

fetched live from OpenAlex

Abstract. The influence of Saharan dust outbreaks on summertime Atlantic tropical cyclone (TC) activity is explored using continuous atmospheric reanalysis products and TC track data from 1980 to 2019. Analyses reveal that the Saharan dust plume over the tropical Atlantic can affect TC activity by affecting the atmospheric hydrology and radiation absorbed by the earth's surface, which can be classified into three mechanisms. (1) A strong Saharan dust plume indirectly induces the reduction of atmospheric moisture, which further suppresses TC track, number of TC days, and intensity, with the influence covering the whole tropical Atlantic. (2) A strong Saharan dust plume enhances atmospheric moisture just along the North Atlantic ITCZ through the dust microphysical effect, which further promotes TC activity along 10º N latitude in June. (3) The climatological influence of dust on TC activity is caused by the strong radiative forcing of Saharan dust over the eastern tropical Atlantic in June, which produces an evident reduction in SST and lessens the duration and intensity of regional TC activity in June, according to the 40-yr average from 1980 to 2019.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

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)0.0030.001

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 head0.025
GPT teacher head0.237
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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