Numerical Analysis of Storm Surges on Canada’s Western Arctic Coastline
Bibliographic record
Abstract
A numerical study was conducted to characterize the probability and intensity of storm surge hazards in Canada’s western Arctic. The utility of the European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5) dataset to force numerical simulations of storm surges was explored. Fifty historical storm surge events that were captured on a tide gauge near Tuktoyaktuk, Northwest Territories, were simulated using a two-dimensional (depth-averaged) hydrodynamic model accounting for the influence of sea ice on air-sea momentum transfer. The extent of sea ice and the duration of the ice season has been reducing in the Arctic region, which may contribute to increasing risk from storm surge-driven hazards. Comparisons between winter storm events under present-day ice concentrations and future open-water scenarios revealed that the decline in ice cover has potential to result in storm surges that are up to three times higher. The numerical model was also used to hindcast a significant surge event that was not recorded by the tide gauge, but for which driftwood lines along the coast provided insights to the high-water marks. Compared to measurements at proximate meteorological stations, the ERA5 reanalysis dataset provided reasonable estimates of atmospheric pressure but did not accurately capture peak wind speeds during storm surge events. By adjusting the wind drag coefficients to compensate, reasonably accurate predictions of storm surges were attained for most of the simulated events. The extreme value probability distributions (i.e., return periods and values) of the storm surges were significantly altered when events absent from the tide gauge record were included in the frequency analysis, demonstrating the value of non-conventional data sources, such as driftwood line surveys, in supporting coastal hazard assessments in remote regions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".