Escalating threat of human-perceived heatwaves in Brazil
Bibliographic record
Abstract
Abstract Heatwaves pose significant threats to socioeconomic and environmental systems, with their intensity and frequency expected to increase due to climate change. Despite their critical impacts, future heatwaves in Brazil remain underexplored, especially from a human-perceived perspective, which is crucial for assessing potential public health impacts. Here, we propose a method to assess heatwaves using the humidex ( H )—a climate index that combines temperature and relative humidity to indicate human-perceived heat - alongside traditional temperature-based measures. Using bias-corrected simulations from 10 CMIP6 models under SSP2-4.5 and SSP5-8.5 scenarios, we quantified projected changes in heatwaves across Brazil. The results indicate that heatwaves will become more severe and prolonged, with greater changes under the SSP5-8.5 scenario by the end of the century, particularly in the North, Northeast, and Central regions. The magnitude of human-perceived heatwaves is expected to rise faster than temperature-based ones, underscoring the need for public health-focused assessments. CMIP6 models strongly agree on increased future heatwaves, potentially tripling population exposure in most Brazilian states, with the Southeast experiencing greater changes due to its larger population. These events are expected not only to affect more people but also to be more severe, exceeding over 60 days per year of serious danger ( H > 45 °C) by the end of the century under SSP5-8.5. Record-shattering events in the historical period are projected to become the norm by mid-century, highlighting the accelerating nature of these extreme events. Our findings emphasize the importance of considering human-perceived heat in climate impact studies and public health planning to mitigate potential impacts. Significance Statement Despite the increasing threat of heatwaves, most studies focus on their climate properties, overlooking human-perceived aspects. This is especially true for Brazil, where heatwaves receive limited attention. This study introduces a novel approach, coupling heatwaves with a heat stress index (H) to evaluate them from a human-perceived perspective. Our results suggest more intense and prolonged heatwaves in the future, with record-breaking events becoming the norm by mid-century. Human-perceived heatwaves are projected to rise faster than climate-based ones, emphasizing the need for public health-focused assessments. These increases are expected to more than triple population exposure in most Brazilian states, with severe events (H > 45 °C) exceeding 60 days per year by the end of the century under the pessimistic scenario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".