Summer Predictability Barrier of Indian Ocean Dipole Events and Corresponding Error Growth Dynamics
Bibliographic record
Abstract
Abstract The effects of sea temperature errors in both tropical Pacific and Indian oceans on the predictability of positive Indian Ocean dipole (IOD) events are explored by using the GFDL CM2p1 coupled model. The results show that the positive IOD events tend to occur with a “winter predictability barrier” (WPB) and a “summer predictability barrier” (SPB). More is known about the WPB, while less is known about the SPB. This study focuses on the SPB. The results demonstrate that two types of initial errors are more likely to cause a significant SPB. One type is of large and negative sea surface temperature anomalies (SSTAs) in the central‐eastern Pacific and a dipole mode‐structured subsurface sea temperature that has negative anomalies in the upper layers of the eastern equatorial Pacific and positive anomalies in the lower layers of the western equatorial Pacific; the other type shows a pattern almost opposite of the former type. By tracking evolutions of both types of initial errors, it is found that their Pacific Ocean (PO) component‐induced northwesterly wind in the east pole of IOD is significantly suppressed by the summer strongest climatological southeasterly and positive IOD‐induced southeasterly wind there, finally causing the considerable suppression of the loss of latent heat flux in the east pole and then favors the fastest growth of a positive SST error in this region during summer. Then a significant SPB for the IOD occurs.
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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 | 0.002 |
| 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 | 0.001 |
| 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".