Remote impacts of the mid-Holocene Green Sahara
Bibliographic record
Abstract
The mid-Holocene (MH: 6,000 years before present) is a key time slice for paleoclimate studies, and is one of the two entry cards for participation in the current Paleoclimate Modelling Intercomparison Project (PMIP4). The MH was characterized by high boreal summer insolation, leading to an intensification of the Northern Hemisphere monsoons. In northern Africa, the strengthening of the West African Monsoon was further amplified by nonlinear feedbacks, resulting in the development of vegetation referred to as the “Green Sahara”. The vegetation and land surface changes over northern Africa had various remote effects impacting the global climate through teleconnections.In this study, we analyse outputs from five fully coupled global climate models to identify the remote impacts of the Green Sahara on global climate. Through the difference of two sets of mid-Holocene simulations – with and without the Green Sahara – we isolate the effect of the northern African vegetation and land cover changes on South American hydroclimate and tropical modes of climate variability such as the El Niño Southern Oscillation and the Atlantic Niño. Using an atmosphere-only climate model, we further investigate the Saharan-Arctic teleconnection invoked to explain the Arctic cooling concurrent with Saharan desertification. We quantify proxy-model agreement through metrics such as the Cohen’s Kappa index and the Root Mean Square Error to assess if the inclusion of the Green Saharan changes leads to greater coherence of model simulations with proxy reconstructions. Our results demonstrate the critical role of the Green Sahara in modulating the MH climate.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".