Detection of anthropogenic influence on the intensity of extreme temperatures in China
Bibliographic record
Abstract
ABSTRACT The anthropogenic (ANT) influence on the intensity of temperature extremes in China is detected over the period 1958–2012 using the newest homogenized daily observation data set and multi‐model simulations from the Coupled Model Intercomparison Project Phase 5 ( CMIP5 ). We applied an optimal fingerprinting method to compare spatial–temporal changes in the intensity of temperature extremes, including annual maxima of daily maximum and daily minimum temperatures (warmest day and night, TXx and TNx ) and annual minima of daily maximum and daily minimum temperatures (coldest day and night, TXn and TNn ). For China as a whole, the results show that the ANT influence can be robustly detected in all four extreme indices. The ANT signal is also clearly separable from the response to natural‐only ( NAT ) forcing in the two‐signal analyses. The NAT signal was detectable for the warmest night TNx but not for other indices. At smaller regional scales for Eastern and Western China, the ANT signals were also clearly detected in the changes of temperature extremes. With the use of more observational data and multi‐model simulations, this study updates a previous work and confirms that the human influence can be robustly detected in the changes of extreme temperature intensity in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".