Influence of the South China Sea Biweekly Sea Surface Temperature on the South China Sea Summer Monsoon Especially during the Indian Ocean Dipole
Bibliographic record
Abstract
The influence of the biweekly sea surface temperature (SST) in the South China Sea (SCS) on the SCS summer monsoon, especially during the Indian Ocean Dipole (IOD) is presented using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST and rainfall data for April to June from 1999 to 2013. During positive IOD (PIOD) years the biweekly SST anomalies over the SCS lead the rain anomalies by three days, with a significant correlation (r = 0.8, at the 99% confidence level), whereas during negative IOD (NIOD) years, the correlation is only 0.2. The biweekly SST is observed to influence the westward and northward propagating rainfall anomalies over the SCS and, hence, affect the SCS summer monsoon, especially during PIOD years. No such propagation was seen during NIOD years. The biweekly intraseasonal oscillation of SST in the SCS results in enhanced sea level pressure and surface shortwave radiation, especially during PIOD years. The potential findings here indicate that the biweekly SST in the SCS is strongly (weakly) influenced during PIOD (NIOD) years. Further, it is observed that SST in the SCS has a strong (weak) effect on the SCS summer monsoon by westward and northward propagation of rainfall, especially during PIOD (NIOD) years. When a PIOD or NIOD exists over the tropical Indian Ocean, the SCS SST will be strongly (r = 0.6, at the 99% confidence level) or weakly correlated with the residual index, respectively.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".