Dynamics of Extreme Wind Speeds in Canada and Their Response to Climate Change
Bibliographic record
Abstract
The damaging impacts of extreme near-surface wind speeds in the midlatitudes makes it important to understand how they may be affected by anthropogenic climate change. Unfortunately, the magnitude, sign, and drivers of potential changes to strong wind events are highly uncertain. The combined effect of changes to several atmospheric processes that cause strong winds is unclear, and the typically coarse spatial resolution of climate models inhibits their ability to simulate extreme winds. This thesis investigates how a more finely resolved climate model represents the physical processes that drive synoptic-scale extreme wind events in the midlatitude regions of Canada, how these processes explain model-projected changes to extreme winds, and how these factors depend on refined spatial resolution. Model representation of the synoptic-scale drivers of extreme winds is assessed by comparing composite extratropical cyclones (ETC) and upper-level jet streaks for simulated extreme events to reanalysis composites for observed events. While the coarse resolution model shows good skill for common event regimes in non-mountainous regions, refined spatial resolution offers improvements for other extreme event regimes and for wind speed itself. The model projections of extreme winds under climate change reveal a discrepancy between the coarse and fine resolution models regarding the sign of the change to extreme wind intensity. The discrepancy is explained by identifying the physical mechanisms responsible for the changes. The coarse resolution model projects less intense strong winds related to weaker ETC intensity, and the refined model projects stronger extreme winds over land due to increased local turbulent mixing of momentum. The initial focus is on the North American Great Lakes region, but refined resolution simulations centred on the western and Atlantic regions of Canada confirm the resolution-dependence of the sign of the extreme wind response. The turbulent mixing mechanism partially explains the discrepancy in the additional study regions, but the separation of the synoptic-scale and local-scale mechanisms is less clear because of regional differences in the wind climate and the large-scale circulation response. The findings of this thesis improve understanding of the physical mechanisms that cause extreme winds to respond to climate change and demonstrate the importance of using high-resolution models for studying extreme wind speeds.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".