Extreme Marine Warming Across Tropical Australia During Austral Summer 2015–2016
Bibliographic record
Abstract
Abstract During austral summer 2015–2016, prolonged extreme ocean warming events, known as marine heatwaves (MHWs), occurred in the waters around tropical Australia. MHWs arose first in the southeast tropical Indian Ocean in November 2015, emerging progressively east until March 2016, when all waters from the North West Shelf to the Coral Sea were affected. The MHW maximum intensity tended to occur in March, coinciding with the timing of the maximum sea surface temperature (SST). Large areas were in a MHW state for 3–4 months continuously with maximum intensities over 2°C. In 2016, the Indonesian‐Australian Basin and areas including the Timor Sea and Kimberley shelf experienced the longest and most intense MHW from remotely sensed SST dating back to 1982. In situ temperature data from temperature loggers at coastal sites revealed a consistent picture, with MHWs appearing from west to east and peaking in March 2016. Temperature data from moorings, an Argo float, and Slocum gliders showed the extent of warming with depth. The events occurred during a strong El Niño and weakened monsoon activity, enhanced by the extended suppressed phase of the Madden‐Julian Oscillation. Reduced cloud cover in January and February 2016 led to positive air‐sea heat flux anomalies into the ocean, predominantly due to the shortwave radiation contribution with a smaller additional contribution from the latent heat flux anomalies. A data‐assimilating ocean model showed regional changes in the upper ocean circulation and a change in summer surface mixed layer depths and barrier layer thicknesses consistent with past El Niño events.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".