Spatial Variations in Seamless Predictability of Subseasonal Precipitation Over Asian Summer Monsoon Region in S2S Models
Bibliographic record
Abstract
Abstract This study assesses the seamless predictability of subseasonal precipitation over the Asian summer monsoon (ASM) region. The prediction skill of 12 models from the subseasonal‐to‐seasonal (S2S) hindcast database varies considerably, suggesting a large uncertainty in the prediction of ASM precipitation. However, all models show that ASM precipitation is better predicted over ocean than over land, and less reliable in subtropical areas than in tropical areas. The results reveal significant spatial variations in the prediction skill and predictability of ASM precipitation. This study investigates the factors controlling these spatial variations by analyzing the area‐averaged precipitation predictability in three different subregions: the maritime continent (MC), the Indian summer monsoon (ISM) subregion, and the East ASM (EASM) subregion. Precipitation is best predicted over the MC, followed by the ISM and EASM. The distinct disparities in prediction skill among subregions are mainly controlled by differences in potential predictability; that is, the intrinsic limits of predictability among subregions. The high potential predictability of the MC is attributable to small noise due to a strong correlation with the El Niño–Southern Oscillation, whereas the low potential predictability of the EASM is attributable to its small signal. Meanwhile, the comparable signal and noise related to the intraseasonal oscillation in the ISM precipitation, limits the predictability of this subregion. Finally, the evaluation of the ASM precipitation ensemble forecasts in the S2S models demonstrates that the ensemble systems are underdispersive in the three subregions in most S2S models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".