Substantial Increase in Heat Wave Risks in China in a Future Warmer World
Bibliographic record
Abstract
Abstract Increases in the frequency and intensity of heat waves have serious impacts on human health, agriculture, energy and infrastructure. Here we use three simple metrics including the number of heat wave days, the length of heat wave season, and the annual hottest day temperature to characterize future changes in heat wave severity in China, based on large ensemble simulations conducted with the Canadian Earth System Model Version 2 (CanESM2) in the context of emergency preparedness. A heat wave day is defined as a day with daily maximum temperature reaching heat alert level (35 °C). We find that global warming is associated with more severe heat waves including more heat wave days, longer heat wave season and higher hottest day temperature, and expansion of regions impacted by heat waves. While the increase in the magnitude of extremes in heat wave metrics with global warming level is close to linear, the increase in the frequency of extremes is much faster. For example, the historically hottest summer in 2013 in Eastern China, which occurs about one in 5 years in the 2013 climate, is projected to become more frequent than one in 2 years under 1.5 °C global warming and almost every year would be worse than 2013 under 2 °C warming. Additionally, the increase in the frequency of the extreme events is larger for rarer extremes. The frequencies for once‐in‐5‐year, once‐in‐10‐year, and once‐in‐50‐year events increase by 2.5, 3.5, and 5.5 times under 1.5 °C global warming, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".