Disentangling the roles of natural variability and climate change in Canada’s 2023 fire season
Bibliographic record
Abstract
Abstract Canada’s 2023 wildfire season was the most extreme on record, with almost 15 million hectares burned—more than double the previous record. We use an established attribution protocol to examine seasonal and regional changes in weather-related wildfire risk associated with global warming, and also evaluate the extent to which 2023’s unusual level of blocking activity contributed to the severity of the season. We find that the annual accumulated daily severity rating (DSR), a measure of weather-related fire risk) is increasing in most ecozones in response to global warming, with the largest increases in the early months of the fire season; although temperatures are increasing everywhere, this effect is offset in some regions by increased precipitation. Blocking circulation patterns are likewise associated with increased DSR, with the strongest responses in May and September. However, there is wide regional variability, illustrated through two case studies of regions that experienced particularly intense wildfires. In the southern Taiga Plains, the contribution from anthropogenic climate change is unclear, while blocking activity increased the severity of the season by at least 33%; in the East James Bay region, the season was found to be at least 32% more intense due to global warming, and a further 15% more intense due to blocking activity.
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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 | 0.003 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".