The SST–Wind Causal Relationship during the Development of the IOD in Observations and Model Simulations
Bibliographic record
Abstract
In this paper, we employ reanalysis data to systematically investigate the development of the Indian Ocean dipole (IOD), thereby distinguishing the SST–wind causal relationship during IOD development. The results indicate that the variations in sea surface temperature anomalies (SSTA) are particularly important during IOD development. SSTAs over the eastern Indian Ocean (EIO) lead to variations in Sumatran coastal winds and equatorial zonal winds, whereas SSTAs over the western Indian Ocean (WIO) lag behind these variations. On this basis, the Community Earth System Model (CESM) is adopted to examine the influences of different atmospheric physical processes and model resolutions on the simulation of the IOD evolution. For this purpose, four sets of sensitivity experiments are carried out involving two versions of the Community Atmospheric Model (CAM4 or CAM5) and two atmospheric model resolutions (0.9° × 1.25° or 1.9° × 2.5°). The CAM5 simulation experiments better capture the detailed characteristics of IOD development, especially the wind–SST causal relationship, than the CAM4 experiments. Moreover, increasing the resolution of the atmospheric model can effectively reduce the simulation bias, thus benefiting the simulation of the SST–wind relationship.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".