Evidence from the Historical Record to Support Projection of Future Wind Regimes: An Application to Canada
Bibliographic record
Abstract
In the field of climate change impact analysis, bidirectional changes in projections of future wind regimes varying among studies, locations, and models have been described in the literature, which is understandable from a global perspective. However, we should attempt to find evidence in the historical record to support these projections. This paper attempts to address this issue by analyzing historical wind gust observations for up to 57 years (1953–2009) over Canada. Two wind gust analysis techniques were used: the speed of daily wind gust events ≥50 km h−1 was compared with (1) the climatological daily temperature anomaly and (2) the climatological daily sea level air pressure anomaly. In addition, the frequency of daily wind gust events ≥90 km h−1 was compared with both daily temperature and pressure anomalies. The results indicate that during the past five decades gust wind speed over Canada increased significantly as the daily temperature anomaly increased and the daily pressure anomaly decreased. About 50–60% of daily wind gust events ≥90 km h−1 occurred with positive daily temperature anomalies and negative daily pressure anomalies. One major conclusion is that the methods used in and results derived from this study might be applied to climate change impact analysis to support projections of future wind regimes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".