ENSO Impacts on Jamaican Rainfall Patterns: Insights from CHIRPS High-Resolution Data for Disaster Risk Management
Bibliographic record
Abstract
This study examines the influence of the El Niño Southern Oscillation (ENSO) on Jamaica’s rainfall patterns, leveraging CHIRPS data from 1981 to 2021 in 370 locations. Our analysis reveals a distinct ENSO imprint on rainfall, with La Niña phases showing a consistently higher probability of exceeding various rainfall thresholds compared to El Niño. Notably, La Niña increases the likelihood of heavier rainfall, particularly in the wet seasons, with probabilities of exceeding 200 mm reaching up to 50% during wet season II. Spatially, the probability of total monthly rainfall (TMR) during La Niña is elevated in the northeastern regions, suggesting regional vulnerability to excess rainfall. Additionally, during El Niño, the correlation between TMR and the maximum air temperature (Tmax) is significantly stronger, indicating a positive and more pronounced relationship between higher temperatures and rainfall, with correlation coefficients ranging from 0.39 to 0.80. Wind speed and evapotranspiration show a negligible influence on TMR during both ENSO phases, maintaining stable correlation patterns with only slight variations. The results of this study underscore the necessity for differentiated regional strategies in water resource management and disaster preparedness, tailored to the unique climatic characteristics imposed by ENSO variability. These insights contribute to a refined understanding of climate impacts, essential for enhancing resilience and adaptive capacity in Jamaica and other small island developing states.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".