Participation of off-equatorial wave energy for the Atlantic Niño events identified by wave energy flux in case studies
Bibliographic record
Abstract
In the tropical Atlantic Ocean, extreme climate events with anomalous sea surface temperature, current, and precipitations are often referred to as the Atlantic Niño. It has many similarities with the EI Niño including the analogous mechanism that winds above the western equatorial ocean will excite oceanic waves to amply the temperature anomaly in the east. However, the Atlantic Niño presents more diversity in its intensity and occurrence time, especially in recent years, eg. 2019 and 2021, in which the classic theory becomes insufficient to explain. This study focuses on ocean responses to atmospheric forcing, manipulating the wind forcing in both equatorial and off-equatorial regions to excite linear ocean models for three types of events that occurred in 1999, 2019, and 2021 respectively. This study has found those extraordinary Atlantic Niños may owe to the wind in the off-equatorial region, where the winds can also excite oceanic waves that transfer energy to the western boundary and reflect back to the equatorial Atlantic. The interaction between the energy from the equatorial and the off-equatorial region makes the event less predictable. The participation of off-equatorial wave energy leads to the diversity of the Atlantic Niños. Hence, for the Atlantic Niño forecast, more concerns about ocean dynamics to cover a wider latitude range should be required.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".