Reduction in ENSO variability during the mid-Holocene: a multi-model perspective
Bibliographic record
Abstract
Paleoclimatic reconstructions have suggested a reduction inthe variability of the El Niño Southern Oscillation (ENSO) during the mid-Holocene (MH). Model simulations have largely failed to capture thisreduction, potentially due to the inadequate representation of the Green Sahara.The presence of a vegetated Sahara has been shown to have significant impacts on both regional and remote climate but remains inadequately addressed in Paleoclimate Modelling Intercomparison Project / Coupled Model Intercomparison Project (PMIP/CMIP) boundary conditions. Specifically, the incorporation of a Green Sahara has been shown to impact ENSO variability through perturbations to the Walker Circulation. In this study, we evaluate the MH (6,000 years BP) ENSO signatures of simulations from four models, namely —EC-Earth 3.1, iCESM 1.2, University of Toronto version of CCSM4 and GISS Model E2.1-G. Two simulations are considered for each model—a standard PMIP simulation (MHPMIP) with the mid-Holocene orbital parameters and greenhouse gas concentrations with vegetation prescribed to preindustrial conditions, as well as a Green Sahara simulation (MHGS) which additionally incorporates factors such as enhanced vegetation, reduced dust, presence of lakes, and land and soil feedbacks. All models show a reduction in ENSO variability due to the incorporation of Green Sahara conditions. This variability is interpreted in the context of perturbations to the Walker Circulation, triggered by the strengthening of the West African Monsoon.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".