Impact of Tropopause Folds on Regional Extreme Cold Events in Yunnan of China
Bibliographic record
Abstract
ABSTRACT The impact of tropopause folds (TFs) on the regional extreme cold events (RECEs) in Yunnan Province is examined. This province is situated in China's Low‐Latitude Highlands (CLLH). In the 60‐year period 1961–2020, 140 RECEs in Yunnan are identified from the air temperature measurements at 125 meteorological observation sites. Among these RECEs, 115 events are accompanied by TFs in the CLLH and referred to as TF‐type events and the remaining 25 are not accompanied by TFs and termed as NTF‐type events. In comparison with the NTF‐type events, TF‐type RECEs are generally more severe and last longer with wider areas impacted over southern China. On average, TF‐type (NTF‐type) events last about 2.5 (1.3) days, have a daily intensity of about 1.7 (1.2)°C, a maximum daily intensity of about 4.2 (3.2)°C and affect 54 (37) sites. For both types of RECEs, the tropopause generally exhibits a lower altitude over northeastern China, showing an intensified East Asian trough and an anomalous cyclonic circulation in this region. During the TF‐type events, however, cold air is transported downward from the stratosphere to the troposphere over southwestern China. The anomalous cyclonic circulation over East Asia extends farther to the southwest, accompanied by an anomalous anticyclonic circulation over India. Thus, compared to NTF‐type events, the northerly wind anomalies over northeast Yunnan tend to be stronger in TF‐type events, enhancing the cold air advection in the region. Our results show that the RECEs in Yunnan are significantly affected by TFs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".