Net Modulation of Upper Ocean Thermal Structure by Typhoon Kalmaegi (2014)
Bibliographic record
Abstract
Abstract In situ observation of a buoys/moorings array and a model simulation were used to study the modulation of upper ocean thermal structure by Typhoon Kalmaegi in September 2014. The inertial period signals were significant after forcing of Kalmaegi, but they did not account for the net heat change. Removing the inertial period signals showed that the net thermal response biased to the right of Kalmaegi's track. Vertical mixing caused surface cooling with an inverted‐cone structure and subsurface warming with a double‐wing structure. Net upwelling converted the left wing of the subsurface warming to cooling, while net downwelling warmed the upper ocean in front and on both sides of the net upwelling zone. Horizontal advection was not as important as vertical mixing and vertical advection in modulating the thermal structure but contributed to the net outward advection of thermal anomaly in the mixed layer during the forced stage and also in the net along‐track recovery of subsurface anomaly during the relaxation stage. In general, horizontal and vertical advection modulated thermal anomalies in the upper ocean across a broader horizontal range and into the deeper ocean compared with the effect of vertical mixing. Our results indicate the need to consider both mixing and advection (rather than only mixing) when studying the effects of tropical cyclones on local ocean heat uptake and global ocean heat transport.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".