Evolution of Winter Temperature in Toronto, Ontario, Canada: A Case Study of Winters 2013/14 and 2014/15
Bibliographic record
Abstract
Globally, 2014 and 2015 were the two warmest years on record. At odds with these global records, eastern Canada experienced pronounced annual cold anomalies in both 2014 and 2015, especially during the 2013/14 and 2014/15 winters. This study sought to contextualize these cold winters within a larger climate context in Toronto, Ontario, Canada. Toronto winter temperatures (maximum T max , minimum T min , and mean T mean ) for the 2013/14 and 2014/15 seasons were ranked among all winters for three periods: 1840/41–2015 (175 winters), 1955/56–2015 (60 winters), and 1985/86–2015 (30 winters), and the average warming trend for each temperature metric during these three periods was analyzed using the Mann–Kendall test and Thiel–Sen slope estimation. The winters of 2013/14 and 2014/15 were the 34th and 36th coldest winters in Toronto since record-keeping began in 1840; however these events are much rarer, relatively, over shorter periods of history. Overall, Toronto winter temperatures have warmed considerably since winter 1840/41. The Mann–Kendall analysis showed statistically significant monotonic trends in winter T max , T min , and T mean over the last 175 and 60 years. These trends notwithstanding, there has been no clear signal in Toronto winter temperature since 1985/86. However, there was a statistically significant increase in the diurnal temperature range in that period, indicating an expansion of winter extremes. It is proposed that the possible saturation of urban heat island–related warming in Toronto may partially explain this increase in variation. Also, anomalies in the position of the polar jet stream over Toronto during these cold events are identified. No direct influence of major teleconnections on Toronto winter temperature is found.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".