Predictability of Indian Ocean Dipole Over 138 Years Using a CESM Ensemble‐Prediction System
Bibliographic record
Abstract
Abstract In this study, we performed a long‐term ensemble hindcast from 1880 to 2017 (138 years) using the Community Earth System Model (CESM) and conducted a comprehensive investigation of the Indian Ocean Dipole (IOD) predictability. We found that the CESM can produce the IOD prediction skill comparable to that produced by some of the best state‐of‐the‐art coupled general circulation models, achieving a correlation skill of 0.5 for a lead time of one season over the 138 years. The Brier skill score shows one season of the effective probability prediction skill for the below‐ and above‐normal events and no probability prediction skill for the near‐normal events. The potential predictability of the IOD is much higher than the actual prediction skill; for example, the information‐based potential correlation is as high as 0.8 at a 6‐month lead time, suggesting a large scope for improvement in current IOD predictions. Compared with the dispersion component, the signal component dominates the variation in the relative entropy and the relationship between the potential predictability and deterministic prediction skill. An analysis of the IOD prediction skills suggests that the strength of IOD events plays an important role in the IOD prediction skills, regardless of the measurement metrics used to evaluate the prediction skills. Our study also suggested that the remote forcing from the tropical Pacific and the local sea‐air interaction in the tropical Indian Ocean would be two major sources of IOD 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".