Predictable Mode of Tropical Intraseasonal Variability in Boreal Summer
Bibliographic record
Abstract
Abstract This work uses a 19-yr ensemble hindcast of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the average predictable time (APT) method to detect the most predictable tropical intraseasonal variability (ISV) mode. The first and most predictable mode (APT1) of tropical ISV is similar to a joint merger of the two Madden–Julian oscillation (MJO) modes with more weight on the second mode and is characterized by a tripole pattern with two positive centers in the equatorial western Indian Ocean and central Pacific Ocean and a negative center over the Maritime Continent. The APT1 doubles the skillful prediction period made by the MJO defined by a correlation skill of 0.5 (approximately 25 days in the ECMWF model), demonstrating its potential to become a skillful prediction target and to offer powerful subseasonal prediction sources. The underlying physical process and predictability source of the APT1 are further analyzed. The APT1 is very similar to the pattern triggered by the most predictable tropical intraseasonal sea surface temperature (SST) anomalies mode, suggesting its oceanic origin. Tropical ocean–atmosphere interaction plays a critical role in the APT1 by enhancing the evolution of tropical convection cells under WES (wind–evaporation–SST) and Bjerknes feedbacks. The internal atmospheric processes also have an important impact on the formation and maintenance of the APT1.
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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 | 0.000 |
| 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) | 0.001 | 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".