Modulation of the ENSO on Winter Aerosol Pollution in the Eastern Region of China
Bibliographic record
Abstract
Abstract The modulation of the El Niño/Southern Oscillation (ENSO) on winter aerosol pollution (WAP) in the eastern region of China (ERC) was investigated from a climatological perspective. The results show that the weakened East Asian winter monsoon during El Niño events has an important influence on increased WAP in ERC, especially over northern China. The enhancement of the East Asian winter monsoon related to La Niña events reduces WAP in northeast, east, and south China but increases it in central and western China. Compared to northern China, anomalies in dynamic and thermodynamic conditions related to ENSO lead to more uncertainties in its effect on aerosol pollution over southern China. El Niño (La Niña) events tend to cause southwesterly (northeasterly) wind anomalies in winter southern China, along with the blocking effect of Nanling Mountains on southward (northward) air pollution, which is not (is) beneficial for the transport outside or dilution in local aerosols over southern China. Meanwhile, positive (negative) water vapor convergence and rainfall anomalies caused by El Niño (La Niña) are (are not) beneficial for the local removal of aerosols. The dominance of both factors determines the ENSO modulation on aerosols over southern China. An atmospheric global climate model largely reproduces the observed findings. Consistent with the observation, El Niño events exacerbate WAP in most of ERC, whereas La Niña ones reduce it except for western 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 | 0.000 | 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) | 0.001 | 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".