Extreme Coastal Waves due to Australian East Coast Lows in a Warming Climate
Bibliographic record
Abstract
The southeast coastline of Australia is frequently impacted by East Coast Lows (ECLs) - hybrid storms with both tropical and extratropical characteristics. Although typically short-lived and spatially limited, ECLs can rapidly intensify and generate extreme waves that can cause severe coastal erosion and its associated hazards. Their small scale and transient nature make ECLs difficult to resolve in conventional global climate models. This study utilizes the HiRes-MESECA atmospheric dataset, comprising 12 historically significant ECL events between 2001 and 2016 that are re-simulated under an RCP8.5 climate scenario using a pseudo-global warming approach. Triple-nested WaveWatchIII modelling, that resolves waves to a spatial resolution of 100 m near the coast, was used to simulate ECL-driven waves at the 10 m isobath coastal boundary. The results of this near-coast wave modelling indicate that, contrary to prevailing expectations, future manifestations of these events may generate reduced peak wave heights, wave periods and overall cumulative wave power along southeast Australia, even considering an extreme (RCP8.5) climate change scenario. These findings challenge assumptions that climate change will necessarily intensify wave extremes globally and highlight the importance of incorporating regional-scale specifics into coastal hazard assessments and planning.
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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 | 0.001 |
| Research integrity | 0.001 | 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".