Mid-Holocene El Niño Southern Oscillation variability reduced by northern African vegetation changes in climate models
Bibliographic record
Abstract
Several paleoclimatic reconstructions have indicated that the mid-Holocene (6,000 years before present) was characterized by stronger east-west temperature contrast and lower El Niño Southern Oscillation (ENSO) variability relative to the present day. While climate models show a reduction in ENSO variability, they underestimate this reduction compared to paleoclimate reconstructions. Further, the drivers behind these changes remain unclear. Here we use five global climate models to show that incorporating vegetation changes over northern Africa during the mid-Holocene amplifies the orbitally-driven strengthening of the West African Monsoon, warms the tropical north Atlantic, and nudges it to an Atlantic Niño-like mean state. Changes over the Atlantic lead to a La Niña-like mean state over the tropical Pacific, with reductions in interannual variability amplified by up to 18% in the Niño3.4 region due to the Green Sahara alone. Our work highlights the importance of the Atlantic influence on ENSO and provides paleoclimatic evidence for this synergistic teleconnection. Incorporating mid-Holocene vegetation changes over northern Africa amplifies Atlantic-driven teleconnections that shift the tropical Pacific toward a La Niña-like mean state and reduce El Niño-Southern Oscillation variability by up to 18%, according to simulations using global climate models.
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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 | 0.000 |
| 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) | 0.002 | 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".