Detecting human influence on extreme temperatures in China
Bibliographic record
Abstract
Abstract This study compares observed and model‐simulated spatiotemporal patterns of changes in Chinese extreme temperatures during 1961–2007 using an optimal detection method. Four extreme indices, namely annual maximum daily maximum (TXx) and daily minimum (TNx) temperatures and annual minimum daily maximum (TXn) and daily minimum (TNn) temperatures, are studied. Model simulations are conducted with the CanESM2, which include six 5‐member ensembles under different historical forcings, i.e., four individual external forcings (greenhouse gases, anthropogenic aerosol, land use change, and solar irradiance), combined effect of natural forcings (solar irradiance and volcanic activity), and combined effect of all external forcings (both natural and anthropogenic forcings). We find that anthropogenic influence is clearly detectable in extreme temperatures over China. Additionally, anthropogenic forcing can also be separated from natural forcing in two‐signal analyses. The influence of natural forcings cannot be detected in any analysis. Moreover, there are indications that the effects of greenhouse gases and/or land use change may be separated from other anthropogenic forcings in warm extremes TXx and TNx in joint two‐signal analyses. These results suggest that further investigations of roles of individual anthropogenic forcing are justified, particularly in studies of extremely warm temperatures over China.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".