Understanding the Mechanisms behind the Northward Extension of the West African Monsoon during the Mid-Holocene
Bibliographic record
Abstract
Abstract Understanding the West African monsoon (WAM) dynamics in the mid-Holocene (MH) is a crucial issue in climate modeling, because numerical models typically fail to reproduce the extensive precipitation suggested by proxy evidence. This discrepancy may be largely due to the assumption of both unrealistic land surface cover and atmospheric aerosol concentration. In this study, the MH environment is simulated in numerical experiments by imposing extensive vegetation over the Sahara and the consequent reduction in airborne dust concentration. A dramatic increase in precipitation is simulated across the whole of West Africa, up to the Mediterranean coast. This precipitation response is in better agreement with proxy data, in comparison with the case in which only changes in orbital forcing are considered. Results show a substantial modification of the monsoonal circulation, characterized by an intensification of large-scale deep convection through the entire Sahara, and a weakening and northward shift (~6.5°) of the African easterly jet. The greening of the Sahara also leads to a substantial reduction in the African easterly wave activity and associated precipitation. The reorganization of the regional atmospheric circulation is driven by the vegetation effect on radiative forcing and associated heat fluxes, with the reduction in dust concentration to enhance this response. The results for the WAM in the MH present important implications for understanding future climate scenarios in the region and in teleconnected areas, in the context of projected wetter conditions in West Africa.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".