Potential benefits of limiting global warming for the mitigation of temperature extremes in China
Bibliographic record
Abstract
Abstract In this study, we attempt to quantify the potential impacts of two global warming levels (i.e., 1.5 °C and 2.0 °C) on extreme temperature indices across China. The CMIP6 dataset is first evaluated against the CN05.1 observation for the historical period of 1995–2014. Then, future spatiotemporal patterns of changes in extreme temperature at two global warming levels under two shared socio-economic pathway scenarios (SSP245 and SSP585) are further analyzed. Overall, China will experience more frequent and intense high temperature events, such as summer days (SU), tropical nights (TR), warm days (TX90p) and nights (TN90p). On the other hand, under the SSP585, the number of icing days and frost days is projected to decrease at two global warming levels, with the maximal days of decrease (exceeding 20 days) seen in the west of China. Our results suggest that limiting global warming to 1.5 °C rather than 2.0 °C is beneficial to reduce extreme temperature risks. As temperature increases to 1.5 °C and then 2.0 °C above preindustrial levels, the most extreme temperature indices are expected to increase proportionately more during the final 0.5° than during the first 1.5° across most regions of China. For some warm indices, such as the warmest day (TXx), summer days (SU), and warm days (TX90p), the largest incremental changes (from 1.5° to 2.0°) tend to be found in the southwest. Under the SSP585, the incremental changes are similar to the change in the SSP245, but smaller magnitude and spatial extent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".