Information‐based potential predictability of the Asian summer monsoon in a coupled model
Bibliographic record
Abstract
In this study, we applied two information‐based measures, relative entropy (RE) and mutual information (MI), to explore the potential predictability of the Asian summer monsoon (ASM) at seasonal time scales using the hindcasts of the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). Several important issues related to ASM predictability were addressed, including the dominant precursors of forecast skill and the degree of confidence that can be placed in an individual forecast. We found that the MI‐based average potential predictability can explain, to a large extent, the variation in overall prediction skill, especially anomaly correlation skill. Compared with the conventional signal‐to‐noise ratio approach, the MI‐based measures can characterize more potential prediction utility. As a potential predictability measure for an individual prediction, RE has a good relationship with C, the contribution of an individual forecast to overall anomaly correlation skill, and a poor relationship with the absolute error (AE). Further analyses revealed that RE is highly related to sea surface temperature (SST) and SST‐ASM correlation patterns in the model resemble the typical El Niño‐Southern Oscillation (ENSO) structure. The different impacts of ENSO on the South Asian summer monsoon and the East Asian summer monsoon, the two main components of the ASM, well explain the different potential predictability features in the two regions, in particular, in terms of their interannual variability. Thus, ENSO is the main source of ASM seasonal predictability.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".