Southern Indian Ocean SST variability and its relationship with Indian summer monsoon
Bibliographic record
Abstract
Sea surface temperature (SST) variability in the Southern Indian Ocean (SIO) region and its relationship to Indian summer monsoon rainfall is investigated. A correlation analysis is used to examine the relation between SIO SST variability and Indian monsoons with lag‐lead times of up to two years (eight seasons). A significant positive correlation is found between tropical SIO SST and the All India Rainfall Index (AIRI) in the March‐May and December‐February seasons before the onset of monsoon. The SST in the region south of 35°S is positively correlated with AIRI six, seven, and eight seasons before the onset of monsoon. A maximum correlation of 0.47 is found for the region south of 35°S, with a confidence level of 99%. Based on this correlation, we have defined SST indices for the central SIO (CSIO), the northwest of Australia (NWA), the SIO, and the Antarctic Circumpolar Current (ACC). These indices seem to be early predictors of Indian monsoons when their relationship with AIRI is examined. The predictive skill of these indices is also tested by multivariate linear regression. The consistency of this relationship is verified by the removal of the El Niño Southern Oscillation (ENSO) signal from SST data and is found to be unaffected by the ENSO signal, except in the region west of Australia.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".