The remote response of the South Asian Monsoon to reduced dust emissions and Sahara greening during the middle Holocene
Bibliographic record
Abstract
Previous studies based on multiple paleoclimate archives suggested a prominent intensification of the South Asian Monsoon (SAM) during the mid-Holocene (MH, ∼6000 years before present). The main forcing that contributed to this intensification is related to changes in the Earth's orbital parameters. Nonetheless, other key factors likely played important roles, including remote changes in vegetation cover and airborne dust emission. In particular, northern Africa also experienced much wetter conditions and a more mesic landscape than today during the MH (the so-called African Humid Period), leading to a large decrease in airborne dust globally. However, most modeling studies investigating the SAM changes during the Holocene overlooked the potential impacts of the vegetation and dust emission changes that took place over northern Africa. Here, we use a set of simulations for the MH climate, in which vegetation over the Sahara and reduced dust concentrations are considered. Our results show that SAM rainfall is strongly affected by Saharan vegetation and dust concentrations, with a large increase in particular over northwestern India and a lengthening of the monsoon season. We propose that this remote influence is mediated by anomalies in Indian Ocean sea surface temperatures and may have shaped the evolution of the SAM during the termination of the African Humid Period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".