Summer Nonconvective Severe Wind Frequency over Ontario and Its Correlation with Tropical Pacific Sea Surface Temperature
Bibliographic record
Abstract
Abstract The summer nonconvective severe surface wind (NCSSW) frequency over Ontario, Canada, in relation to regional climate conditions and tropical Pacific Ocean sea surface temperatures (SSTs) during the period of 1979–2006 is examined using surface wind reports and large-scale analysis data. A statistically robust positive trend in Ontario summer NCSSW frequency is identified using three independent statistical approaches, which include the conventional linear regression that has little disturbance to the original time series, the Mann–Kendall test without a lag-1 autoregressive process, and the Monte Carlo simulation. A composite analysis of the large-scale monthly mean data reveals that the high- (low-) NCSSW occurrence years are linked to stronger (weaker) large-scale horizontal pressure gradients and more (less) intensive vector wind anomalies in the upper troposphere. Unlike the low-event years, anomalous anticyclonic circulations are found at 500 and 250 hPa in the high-event years, which are conducive to downward momentum transport and favorable for severe surface wind development. It is also found that the summer NCSSW occurs more frequently under the conditions of warmer surface air temperature over Ontario. Further analyses indicate that an increase in the summer NCSSW frequency is well correlated with an increase in the previous winter SSTs over the eastern equatorial Pacific, namely, in the Niño-1+2 and Niño-3 areas, through a decrease in sea level pressure over northern Ontario and an increase in surface air temperature over central and southern Ontario.
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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 | 0.000 |
| 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".