Differences in Pre-Flood Season Rainfall in South China between Spring and Summer El Niño Events
Bibliographic record
Abstract
The El Niño–Southern Oscillation (ENSO) plays an important role in pre-flood season (PFS) precipitation over South China. In this work, the analysis of observational and reanalysis data shows that PFS precipitation is closely related to the onset time of El Niño events. The PFS precipitation tended to be higher (lower) than normal for spring (summer) El Niño events during the 1979–2016 period. Our composite analyses reveal that, for spring El Niño events, the sea surface temperature (SST) anomaly in the central-east equatorial Pacific (CEEP) Ocean provided favourable large-scale circulation for abundant PFS precipitation, where the Hadley cell served as a bridge. In the year following an El Niño event, SST anomalies in the CEEP persist from January to June, while for April-May-June (AMJ) positive SST anomalies are seen offshore near China. These anomalies are associated with the AMJ–enhanced convective instability over South China through a weakened Walker circulation and a zonal teleconnection wavetrain pattern at 700 hPa in the northern hemisphere. Meanwhile, a weakened 200 hPa anticyclonic shear was seen over the Indochina Peninsula. There was a southwestward shift of the 500 hPa western Pacific subtropical high, and anomalous 850 hPa southwesterly wind-enhanced water vapour and warm advection toward South China. Therefore, the circulation-induced moisture environment and dynamical conditions both facilitated enhanced PFS precipitation over South China. For summer El Niño events, the moisture environment and dynamical conditions were unfavourable for producing precipitation, which resulted in below-normal PFS precipitation levels. Categorizing El Niño events by the onset time is very important because it provides useful information for predicting PFS precipitation with lead times of two or three seasons.
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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 | 0.001 |
| 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) | 0.001 | 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".