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Record W2110630607 · doi:10.1002/qj.844

Dust impact on the West African heat low in summertime

2011· article· en· W2110630607 on OpenAlexaff
Christophe Lavaysse, Jean‐Pierre Chaboureau, Cyrille Flamant

Bibliographic record

VenueQuarterly Journal of the Royal Meteorological Society · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
FundersGrand Équipement National De Calcul Intensif
KeywordsAdvectionMineral dustForcing (mathematics)Environmental scienceAtmospheric sciencesClimatologyTropical waveRadiative forcingAfrican easterly jetDaytimeConvectionMeteorologyAerosolGeologyGeographyPhysics

Abstract

fetched live from OpenAlex

Abstract The impact of dust on a six‐day pulsation of the West African heat low (WAHL) in summertime (14–20 July 2006) is investigated, with convective rainfall and dust bursts being observed over the Sahel at the beginning and end of the episode. Three Meso‐NH simulations were designed which differed in their dust representation. All the simulations capture the variation in the WAHL intensity well, including the simulation without any dust effects. This shows the primary role of large‐scale forcing on the WAHL pulsation. In spite of additional daytime heating and night‐time cooling effects over the Sahara, the simulation with dust climatology resembles the simulation without any dust effects. In contrast, the simulation using a prognostic dust scheme enhances alternating northward advection of warm and dry air and southward advection of cold and wet air associated with the propagation of an African easterly wave, leading to a strengthening of the WAHL variabilities. This study highlights two effects of dust on the WAHL over the Sahara: a so‐called direct effect associated with dust radiative heating, which increases the WAHL thickness, and a so‐called indirect effect that intensifies both the African easterly jet and a related African easterly wave. Copyright © 2011 Royal Meteorological Society

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0050.000

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 head0.029
GPT teacher head0.235
Teacher spread0.206 · 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.

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

Citations79
Published2011
Admission routes1
Has abstractyes

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