From mild to extreme heatwaves: Examining trends in North America
Bibliographic record
Abstract
Extreme heat is associated with negative consequences. Previous research has shown an increase in the frequency and intensity of extreme heat in the 20 th and early 21 st centuries in most of North America. Similar trends are expected in the next decades. These increases are primarily driven by a shift of the temperature distribution towards warmer temperatures. Despite this rich literature, few studies have considered how past trends of milder heatwaves may differ from those of more extreme heatwaves. Here we quantify recent intensity and duration trends of North American heatwaves according to their severity, a novel metric which measures the intensity of a heatwave relative to other local contemporaneous heatwaves. We measure heatwave intensity using three different metrics (cumulative, average and maximum). These metrics are based on the anomaly of the daily maximum temperature relative to the local non-stationary 90 th percentile. Heatwaves are then categorized as either mild, moderate or extreme in their severity. Our findings indicate that heatwave temperatures have been increasing in most of North America between 1940 and 2019 for every season. However, heatwave temperature anomalies have remained stable over this same period. Additionally, higher heatwave severity is linked to less noisy intensity trends. This cannot be explained solely by changes in the mean of the temperature distribution over time. Our results have important implications for the current estimation of heatwave intensity trends and suggest that the impact of their severity should be considered.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".