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Exploring the impact of the Great Green Wall on Atlantic Tropical Cyclone activity

2025· article· en· W4413641129 on OpenAlexafffund
Roberto Ingrosso, Francesco S. R. Pausata, Katja Winger, Suzana J. Camargo

Bibliographic record

VenueGlobal and Planetary Change · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaNova
KeywordsTropical cycloneAtlantic hurricaneClimatologyTropical cyclone scalesTropical AtlanticOceanographyCyclone (programming language)GeologyEnvironmental scienceSea surface temperature

Abstract

fetched live from OpenAlex

The Great Green Wall (GGW) initiative aims to restore 100 million hectares of degraded land over the Sahel. However, its potential impacts on the far-afield climate have hitherto not been evaluated. Here, we use a high-resolution regional climate model to evaluate the potential impacts on Atlantic tropical cyclone (ATC) activity in four different GGW scenarios under two emission pathways. The results reveal a shift in ATC genesis from the subtropical and western Atlantic to the eastern Main Development Region under medium to extreme vegetation density scenarios, compared to cases without the GGW. An increase in genesis is also observed off the coasts of eastern Florida and the Carolinas under the high-concentration pathway. However, no significant change in basin-wide TC frequency is found. Instead, the main impact of GGW appears to be a redistribution of tropical cyclogenesis within the basin. Our analysis highlights the primary role of African Easterly Waves in driving changes in TC genesis across the eastern MDR, while large-scale environmental factors – though secondary overall - primarily influence changes off the southeastern U.S. coast. No GGW-induced changes are found in the ATC intensity, translation speed or other TC metrics across the entire basin.

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 score0.133
Threshold uncertainty score0.976

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 head0.091
GPT teacher head0.273
Teacher spread0.182 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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