Tropical Oceanic Rainfall and Sea Surface Temperature Structure: Parsing Causation from Correlation in the MJO
Bibliographic record
Abstract
Based upon on the findings of Y. Li and R. E. Carbone, the association of tropical rainfall with SST structure is further explored, with emphasis on the MJO passband. Analyses include the tropical Indian Ocean, Maritime Continent, and tropical western Pacific regions. The authors examine the anomalies of and correlations between SST structure, the frequency of rainfall events, and rainfall amount. Based on detailed examination of a 49-month time series, all findings are statistical inferences and interpretations consistent with established theory. The statistical inferences are broadly consistent with a pivotal role played by the convergent Laplacian of SST together with an expected, but somewhat indirect, role of SST itself. The main role of SST in the MJO passband appears limited to production of moist static energy, which is highly correlated with cumulative precipitation, yet bears a decidedly conditional relationship to the occurrence of rainfall. If rain occurs, then more rain is likely over warmer SST. The convergent Laplacian of SST is strongly associated with the onset of rainfall, apparently through its capacity to induce vertical air motion with sufficient kinetic energy to overcome convective inhibition in a conditionally unstable troposphere. The convergent Laplacian of SST is directly associated with the location and the variability of rainfall event frequency while having a less direct relationship to cumulative rainfall. These nuanced interpretations of rainfall forcing by the Laplacian of SST, and conditional modulation of cumulative rainfall by SST, may underlie systematic errors in highly parameterized models as a consequence of variable asymmetry in the field of Laplacian anomalies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".