The role of Rossby wave breaking in coral bleaching on the Great Barrier Reef
Bibliographic record
Abstract
The frequencies of thermal coral bleaching events (CBEs) over the Great Barrier Reef (GBR) continue to increase with five mass CBEs reported since 2016. While changes in the local meteorology, such as reduced wind speeds and decreased cloud cover, are known to heat the shallow reef waters, little consideration has been given to the overriding synoptic meteorology. The 2022 CBE, occurring under La Niña conditions, saw ocean temperatures at Davies Reef increase 1.9℃ over 19-days and subsequently cool 2.1℃ back to seasonal norms over eight days. This event was found to be triggered by repeated Rossby wave breaking disrupting the local trade winds. As the trades broke down, calm winds and clear skies covered the reef, thus inhibiting the latent heat flux, allowing for the build-up of ocean heat to at least 18m. Following the re-establishment of the trade winds via coastal ridging, latent heat fluxes, the primary driver of the event, tripled allowing the ocean to rapidly cool. Concurrent to the mass bleaching event, these are the same Rossby wave breaking events found to cause the historic Lismore flooding located hundreds of kilometres south of the GBR. This case study notes the first reported linkage between mass coral bleaching and a severe flooding event.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".