Projected changes in extreme warm and cold temperatures in China from 1.5 to 5°C global warming
Bibliographic record
Abstract
Abstract Linking regional extreme temperature changes to global warming levels is important for understanding the impacts of global emission targets on regional climate. Here, we investigate how the temperature extremes in China change with different global warming levels using large ensemble runs from Canadian Earth System Model version 2. With the global mean near‐surface temperature increasing from 1.5 to 5°C above the preindustrial level, the absolute intensity of the warmest and coldest temperatures in China will change linearly, while the percentile‐based frequency of warm and cold temperatures will change nonlinearly. All the changes in the intensity and the frequency show clear regional differences, with the most obvious changes observed in northeastern China. The probability distribution functions (PDFs) for the intensity indices show clear shifts but with little change in shape, while the PDFs for the frequency indices show changes in both position and shape. Quantified analyses of risk ratio show that the risk changes in the frequency of temperature extremes will be larger than those for the intensity indices. The changes in the nighttime extremes are faster than those in the daytime extremes. The rarer the event is, the larger the change in the risk ratio. At the 2°C warming level, the cold days and nights with return periods of 5, 10, and 50 years in the current climate will become almost disappeared. At the 3°C level and beyond, the once‐in‐5‐year, 10‐year and 50‐year warm events in the current climate will occur every year.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".