Possible Impacts of Climate Change on Wind Gusts under Downscaled Future Climate Conditions: Updated for Canada
Bibliographic record
Abstract
Abstract The methods used in earlier research focusing on the province of Ontario, Canada, were adapted for the current paper to expand the study area over the entire nation of Canada where various industries (e.g., transportation, agriculture, energy, and commerce) and infrastructure are at risk of being impacted by extreme wind gust events. The possible impacts of climate change on future wind gust events across Canada were assessed using a three-step process: 1) development and validation of hourly and daily wind gust simulation models, 2) statistical downscaling to derive future station-scale hourly wind speed data, and 3) projection of changes in the frequency of future wind gust events. The wind gust simulation models could capture the historically observed daily and hourly wind gust events. For example, the percentage of excellent and good validations for hourly wind gust events ≥90 km h−1 ranges from 62% to 85% across Canada; the corresponding percentage for wind gust events ≥40 km h−1 is about 90%. For future projection, the modeled results indicated that the frequencies of the wind gust events could increase late this century over Canada using the ensemble of the downscaled eight-GCM simulations [Special Report on Emissions Scenarios (SRES) A2 and B1]. For example, the percentage increases in future daily wind gust events ≥70 km h−1 from the current condition could be 10%–20% in most of the regions across Canada; the corresponding increases in future hourly wind gust events ≥70 km h−1 are projected to be 20%–30%. In addition, the inter-GCM and interscenario uncertainties of future wind gust projections were quantitatively assessed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".