Atmospheric and oceanic drivers behind the 2023 Canadian wildfires
Bibliographic record
Abstract
In the 2023 summer, wildfires in Canada were exceptionally intense, producing the highest carbon emissions recorded since 2003 and affecting much of North America. However, the factors driving the 2023 wildfires and their interactions remain poorly understood. Here we demonstrate that the 2023 Canadian wildfires were primarily fueled by persistent, intense and widespread summer surface warming, along with pronounced reductions in precipitation and soil moisture. These conditions were closely linked to unusually frequent, wide, long-lasting and eastward-moving North American blocking events. We reveal that an unusually strong negative Pacific Decadal Oscillation and an Eastern Pacific El Niño in the 2023 summer play a crucial role in promoting these blocking patterns. Furthermore, we find that abnormally low precipitation and soil moisture during the preceding winter and spring served as critical precursors, compounding summer fire risk by preconditioning the landscape. Our findings offer new insights into the underlying causes of the 2023 Canadian wildfires. The unprecedented intensity of the 2023 Canadian wildfires was primarily driven by persistent summer warming, reduced precipitation and soil moisture due to increased persistence and frequency of North American blocking events linked to oceanic anomalies, according to an analysis of climate data, ocean-atmosphere interactions, and seasonal hydrological conditions.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".