Decadal variation of the rainfall predictability over the maritime continent in the wet season
Bibliographic record
Abstract
Abstract Maritime continent (MC) rainfall plays an important role in global climate variability, but its prediction remains extremely challenging. Based on a long-term state-of-the-art hindcast product recently completed by the authors’ group, this work investigates the decadal variation of the MC rainfall predictability in the wet season for the first time. The prediction skills were relatively high before 1940 and after 1980, but relatively low between these years. In a diagnostic analysis of the controlling factors of the decadal variation, the signal strength represented by the variance of the rainfall variability was identified as the dominant factor. Further analysis concluded that the El Niño Southern Oscillation (ENSO) phases are the key controlling sources. The MC rainfall was more predictable during periods dominated by El Niño events than during periods dominated by La Niña events because El Niño elicits stronger ocean–atmosphere interactions in the tropics, providing a stronger signal of MC rainfall than La Niña events.
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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 | 0.000 |
| 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".