Projected changes in hail damage potential in Australian cities under climate change
Bibliographic record
Abstract
Hailstorms are a leading contributor to insured losses in cities in Australia and are expected to be affected by climate change, yet changes to hailstorm damage potential under climate change are not well quantified. Hail damage grows with the size of the hailstones produced by a storm, and is exacerbated by the co-occurrence of strong winds. Here, we show the first projections for hail size and co-occurring wind strength for Australian cities under a climate change scenario. Convection-permitting downscaled simulations were run for major cities and a remote region, in total covering 65% of Australia's population, for a historical period and a scenario with ~2.8 degrees Celsius of global warming over pre-industrial temperatures. Using extreme value analysis, we show that in the future scenario hail damage potential increased in some regions. In particular, overall hail frequency was projected to increase around Sydney/Canberra and Brisbane, while there were projected increases in maximum hail size for domains covering Melbourne, Sydney/Canberra, a remote region in Western Australia, and Perth. Strong winds coincident with hail were projected to decrease around Melbourne, Sydney/Canberra, and Perth. These results are important for urban design and city planning in a changing climate and can inform future climate risk planning in Australian cities.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".