On the climate impacts of four different Great Green Wall scenarios on the northern Africa and the Atlantic Tropical Cyclones variability.
Bibliographic record
Abstract
The Great Green Wall (GGW) is a multibillion-dollar African initiative to combat desertification in the Sahel by restoring 100 million hectares of degraded land. The idea of a physical green wall of trees has now been developed into the implementation of scattered green zones throughout arid areas, providing sustainable reforestation, revegetation, and land management. In West Africa, the most important climate feature is the West African Monsoon (WAM), which brings rainfall over the Sahel during the Northern Hemisphere summer. Climate dynamics associated with WAM changes could also play a role on the Atlantic Tropical Cyclones (ATCs) formation and variability. The potential climate impacts of the most recent GGW plan on northern Africa and tropical Atlantic have not yet been adequately evaluated, raising concerns about unforeseen climate ramifications that could affect stability in northern Africa and impact on the ATC variability. Here, we use a high-resolution (~13 km) regional climate model to evaluate the climate impacts of four GGW scenarios with varying vegetation densities under two extreme emission pathways (low and high). Higher vegetation density GGW scenarios under both emission pathways show enhanced rainfall, reduced drought lengths and decreased summer temperatures beyond the GGW region relative to the cases with no GGW. However, all GGW scenarios show more extreme hot days and heat indices in the pre-monsoonal season. Furthermore, in spite of a strong variation in the African Easterly Waves activity, no significant changes are found in terms of ATCs frequency, intensity, meridional motion and translation speed over the North Atlantic area. Small changes in the TC densities are found in front of the cost of West Africa, in the eastern side of the Main Development Region. These findings highlight the GGW's contrasting climatic effects, emphasizing the need for comprehensive assessments in shaping future policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".