Predictability of Sea Surface Temperature Anomaly of the Eastern Indian Ocean Over Past 142 Years
Bibliographic record
Abstract
Abstract This study studies the predictability of the sea surface temperature anomaly (SSTA) for the eastern Indian Ocean (EIO) over the past 142 years from 1880–2021 by exploring its fastest error growth. This is implemented by applying the climate‐relevant singular vector (CSV) analysis onto a tropical intermediate coupled model. Emphasis places on identifying the optimal perturbation that can result in the fastest prediction error growth of the EIO SSTA. It is found that, with the 6‐month lead prediction, the spatial pattern of the optimal initial perturbations is characterized by a negative SSTA pole with the diverged surface wind in the southern Indian Ocean and a positive SSTA pole with the converged surface wind in the eastern Pacific. The growth rate of optimal initial perturbation, as measured by the leading singular value, exhibits significant interannual variation consistent with the variation of the SSTA in EIO. Specifically, when the SSTA in the EIO is colder, the prediction error grows more rapidly, and conversely, when the SSTA is warmer, the error grows more slowly. The results of the dynamical analysis show that a cold SSTA background strengthens Ekman feedback through both atmospheric and oceanic responses, leading to faster growth of prediction errors. This study enhances our understanding to the predictability in the Indian Ocean, in particular the role of EIO in the Indian Ocean dipole 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".