An eddy pathway to marine heatwave predictability off eastern Tasmania
Bibliographic record
Abstract
A systematic analysis of historical and modeled marine heatwaves (MHWs) off eastern Tasmania has been performed based on satellite observations and a high–resolution regional ocean model simulation, over the period from 1994–2016. Our analysis suggests that the distribution of large and intense mesoscale warm core eddies off northeast Tasmania contribute to the development of MHWs further south associated with changes in the circulation and transports. Importantly, we find that eddy distributions in the Tasman Sea can act as predictors of MHWs off eastern Tasmania. We used self-organizing maps to distinguish sea surface height anomalies (SSHA) and MHWs into different, but connected, patterns. We found the statistical model performs best (precision ~ 0.75) in the southern domain off eastern Tasmania. Oceanic mean states and heat budget analysis for true positive and false negative marine heatwave events revealed that the model generally captures ocean advection dominated MHWs. Using SSHA as predictor variable, we find that our statistical model can forecast MHWs off southeast Tasmania up to 7 days in advance above random chance. This study provides improved understanding of the role of circulation anomalies associated with oceanic mesoscale eddies on MHWs off eastern Tasmania and highlights that individual MHWs in this region are potentially predictable up to 7 days in advance using mesoscale eddy-tracking methods.
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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 | 0.001 |
| 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 | 0.001 | 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".