Linkage between Interannual Variation of the East Asian Intraseasonal Oscillation and Mei-Yu Onset
Bibliographic record
Abstract
Abstract The mei-yu onset over the middle to lower reaches of the Yangtze River Valley (MLYRV) varies considerably from early June to mid-July, which leads to large interannual changes in rainy-season length, total summer rainfall, and flooding potential. Previous studies have investigated the impact of El Niño–Southern Oscillation (ENSO) on the mei-yu onset. This study shows that a strong (weak) East Asian and western North Pacific (EAWNP) intraseasonal oscillation (ISO) in spring leads to an early (late) onset of the mei-yu over the MLYRV, and this ISO–mei-yu relationship is attributed to different types of ENSO in the preceding winter. A strong EAWNP ISO in spring is related to an eastern Pacific El Niño (EP El Niño) in the previous winter, and negative sea surface temperature (SST) anomalies in the eastern Indian Ocean and the South China Sea (SCS) in May, which can cause an early onset of the South China Sea summer monsoon that also favors an early mei-yu onset. In contrast, a weak EAWNP ISO in spring is associated with a central Pacific El Niño (CP El Niño) before April, but with an EP El Niño after April, and positive SST anomalies in both the eastern Indian Ocean and the SCS in May. A statistical forecast model combining the intensity of spring EAWNP ISO, CP ENSO, and EP ENSO indices shows a high prediction skill of the observed mei-yu onset date.
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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 | 0.001 | 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".