Attribution of extreme winds during 2022 post-tropical cyclone Fiona in Atlantic Canada
Bibliographic record
Abstract
Abstract Hurricane Fiona, which made landfall as a post-tropical cyclone in Atlantic Canada, on the 24th of September 2022, became the costliest natural disaster in Atlantic Canada causing $900 million in insured losses in the region. Fiona set the new Canadian record for the lowest atmospheric pressure ever recorded on land, and caused extensive wind damage and coastal flooding, driven by large waves and destructive storm surge. In this study, we show that the maximum wind speeds observed on the 24th of September 2022 were roughly a once-in-a-century event for the hurricane season in Atlantic Canada, based on the ERA5 reanalysis data. Using the wind speed data obtained from high resolution model intercomparison project (HighResMIP) simulations, we assess whether human-caused climate change has influenced the probability of extreme winds comparable to those observed in Atlantic Canada following Fiona’s landfall. We find that differences in how daily mean wind speed is derived across the CMIP6 models prevent this variable from being combined into a multi-model ensemble. Instead, we use daily maximum wind speed, which is more consistently derived. Our analysis indicates that HighResMIP models simulate a statistically significant increase in the likelihood of maximum wind speeds during the hurricane season (June-October) in Atlantic Canada in the current climate compared to the climate of 1950-1979.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".