Triggers of the Record‐Breaking 2023 Canadian Wildfires: Extreme Heat Waves and Droughts Driven by Abnormally High Sea Surface Temperatures
Bibliographic record
Abstract
Abstract The 2023 Canadian wildfires burnt the areas across the Northwest Territories and British Columbia during May–September with record‐breaking intensity. However, the near‐surface meteorological conditions, atmospheric circulation, and climate drivers behind these exceptional events remain insufficiently understood. Using multisource data sets, dynamic diagnostics, and numerical modeling, this study identified unprecedented 2023 heat waves and droughts in wildfire‐prone regions as key contributors to the wildfire severity. These extreme conditions were closely associated with abnormally high sea surface temperatures (SSTs) in the northwestern North Pacific (NWNP) in 2023. Acting as a strong heat source, the elevated SSTs triggered a Rossby wave train that mainly propagated southeastward across the North Pacific and then northeastward into North America. This wave activity contributed to the weakest upper‐level westerly over central North America and the strongest high‐pressure ridge over northwestern Canada in 2023, thereby facilitating the favorable near‐surface fire weather. As NWNP SSTs continue to rise, their influence on wildfire activity in northwestern Canada is expected to grow. Our findings highlight the critical role of sea‐atmosphere interactions in wildfire behavior.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".