La Niña Impacts on Southeastern African Climate: The Influence of Event Duration
Bibliographic record
Abstract
ABSTRACT The multiyear La Niña event of 2020–2023, which brought with it several climate disasters across the globe, sparked both mainstream and scientific interest in La Niña events, which typically have received less attention than El Niño. In southern Africa, there is a general expectation in the scientific community and among user groups that La Niña events result in cool and wet summers. However, such impacts do not always occur and the full diversity of La Niña impacts, including multiyear events, has not been systematically explored. Here, various temperature and rainfall characteristics occurring during three categories of La Niña events—single, double, and triple year events are—investigated for the period 1970–2023. Space‐parameter bubble plots are used to display anomalies in mid‐late summer rainfall, heavy rain and dry spell frequencies, and in mean temperature and extreme heat days across four domains in southeastern Africa. Despite being relatively populated with important port cities and agriculture, not much work has focused on this region. Double‐year La Niña summers were the least consistent in exhibiting expected wet characteristics. During triple‐year events, the subtropical domains, southern Mozambique and eastern South Africa, showed the greatest tendency towards wet conditions, although a few seasons deviated significantly in rainfall distribution from their multiyear counterparts. In contrast, the tropical domains, southern Tanzania and northern Mozambique, more consistently were cooler than average than the subtropical domains. These findings highlight the diversity of La Niña impacts on summer conditions in southeastern Africa and show that La Niña events are not necessarily associated with cooler and wetter than average summers over the region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".