Heatwave Dynamics in Bangladesh: Long-Term Trends and Contributing Factors.
Bibliographic record
Abstract
Bangladesh, with 170 million people, faces deadly heatwaves due to high temperatures, humidity, poor socioeconomic conditions, and lack of air conditioning. Heatwaves significantly impact public health and the economy, yet the long-term trends and mechanisms of heatwaves over Bangladesh remain poorly understood. This study aims to investigate all heatwave events from 1971 to 2023, analyzing their trends and identifying the major driving mechanisms behind them. We utilized ERA5 reanalysis data to study heatwave events. A heatwave was defined as occurring when the maximum daily temperature or heat index exceeded the 95 th percentile for at least three consecutive days. For major heatwave events, we performed a composite analysis of geopotential and wind anomalies at 500 hPa. Our study finds a distinct uptrend in annual heatwave days. While pre-monsoon heatwave days show no significant trend, monsoon heatwave days have markedly increased since 2005. Insufficient post-monsoon events prevent trend assessment. While the pre-monsoon period showed no significant trend in average maximum temperatures, daily maximum temperatures in the monsoon and post-monsoon seasons clearly increased. The composite analysis indicates the presence of positive geopotential anomalies and anomalous anticyclonic flow over Bangladesh during the pre-monsoon period, but no clear pattern was observed for heatwave events during the monsoon. Significant soil moisture deficits and positive net radiation anomalies were identified over western Bangladesh during pre-monsoon and monsoon heatwaves. These deficits increase sensible heat flux, creating a warmer, drier atmospheric boundary layer and cloudless skies, thereby intensifying heatwave conditions. Our findings enhance understanding of heatwave characteristics in Bangladesh and aid policymakers in making informed decisions to mitigate the future impacts of deadly heatwaves.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".