Impact of Cold Tongue Bias on Indian Ocean Dipole Prediction Skills
Bibliographic record
Abstract
Abstract In this study, we employ the Model‐based Analog Forecast approach to conduct Indian Ocean Dipole (IOD) hindcasts from 1982 to 2017, using 18 CMIP6 models. We focus on the skill diversity among different climate models, with particular attention to how the cold tongue bias affects IOD predictions. Our findings reveal a significant diversity in IOD prediction skills across the CMIP6 models. Skillful predictions are observed for lead times ranging from 1 to 4 months, depending on the model. Also, this study identifies a direct relationship between cold tongue bias and IOD prediction skills. Models that exhibit a more pronounced cold tongue bias tend to show weaker El Niño‐Southern Oscillation influences over the tropical Indian Ocean, which in turn leads to a reduction in IOD prediction skills. This study provides valuable insights into the factors driving the diversity in IOD predictions and highlights the potential for improving IOD forecasting skills.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".