A multimodel study of the twentieth‐century simulations of Sahel drought from the 1970s to 1990s
Bibliographic record
Abstract
In this paper, we evaluate the performance of 19 coupled general circulation models (CGCMs) in twentieth‐century simulations of the Sahel during the 1970s to 1990s. Correlation, regression, and cluster analyses are applied to observations and model outputs including Sahel monthly precipitation, evaporation, soil moisture, and sea surface temperature (SST). We find that only eight CGCMs (hit models) produce a reasonable Sahel drought signal, while seven CGCMs (miss models) produce excessive rainfall over the Sahel during the observed drought period. Even the model with the highest prediction skill of the Sahel drought could only predict the increasing trend of severe drought events but not the beginning and duration of the events. From analyses of the statistical characteristics of the hit and miss models, we conclude that a good simulation of the Sahel drought requires (1) a strong coupling between Sahel rainfall and Indian Ocean SST, with warm (cold) SST identified with Sahel drought (flood), (2) a significant coupling between Sahel rainfall and the Atlantic Ocean SST, with a warm equatorial Atlantic and cold extratropical North Atlantic coexisting with Sahel drought, and vice versa, and (3) a robust land surface feedback with strong sensitivity of precipitation and land evaporation to soil moisture. These three characteristics constitute sufficient conditions for a good simulation of Sahel drought in CGCMs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".