Linear trends in temperature extremes in China, with an emphasis on non-Gaussian and serially dependent characteristics
Bibliographic record
Abstract
Record-breaking hot and cold extremes have occurred in China in recent years and, therefore, it is compelling to investigate the long-term trend in temperature extremes at individual stations to see whether they have become more frequent. Many previous studies on the linear trend analysis of temperaure extremes in China have used oridinary least squares (OLS) regression, without consideration of non-Gaussian and/or serially dependent characteristics, or nonparametric methods, again not considering the latter, thus leaving some uncertainty in the significance testing. The present study examines in detail these characteristics in eight commonly used extreme temperature indices, on the basis of both station data and gridded data across China. The results show that 71–100% of the stations or grids cannot directly use standard OLS regression to analyze the statistical significance of the linear trend, because of either non-Gaussian or Gaussian but serially dependent characteristics in the regression residuals. Also, more than 43% of the stations and more than 54% of the grid boxes for annual indies cannot directly use the original Sen’s slope estimator and Mann–Kendall test because of serial dependence. Based on a nonparamtric method that takes into account serial dependence, the spatial patterns of the linear trend on an annual basis, as well as in hot and cold extremes, are examined for the period 1960–2017. The results show that hot extremes at most stations have increased, more than 57% of which are statistically significant; whereas, cold extremes at almost all stations have decreased, more than 32% (85%) of which are statistically significant during daytime (at night).
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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".