Sahel precipitation and regional teleconnections with the Indian Ocean
Bibliographic record
Abstract
Abstract The drought in the Sahel in the 1980s has been associated with Indian Ocean warming, although the Sahel has experienced a recovery in precipitation since the 1990s, despite continued warming in the Indian Ocean. Using the Community Earth System Model (CESM), we examined the linkages between the pattern of Indian Ocean warming and changes in atmospheric circulation over the Indian Ocean and North Africa to determine how they impact Sahel precipitation. The influence of the Indian Ocean on Sahel precipitation was investigated using a series of sea surface temperature (SST) sensitivity experiments. We identified two mechanisms by which the Indian Ocean can alter Sahel precipitation. The first mechanism is associated with perturbations in SSTs on the equator that alter Sahel precipitation by modulating the Asian monsoon circulation and driving changes in descent in North Africa. The second mechanism is associated with SST perturbations that cover more of the basin and alter the overturning circulation between the Indian and Atlantic Oceans. These two mechanisms result in different precipitation responses in the Sahel: the first induces an increase in precipitation as a result of warming in the Indian Ocean, whereas the second produces a decrease in Sahel precipitation in response to warming. Our results suggest that obtaining robust projections of precipitation in the Sahel will require reliably capturing the scale and spatial patterns of Indian Ocean warming.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".