Canadian wildfire in a changing climate from the 2023 wildfire season to the 2100s
Bibliographic record
Abstract
Wildfire influences the carbon cycle and impacts property, harvestable timber, and public health. The year 2023 saw a record area burned of 14.9 Mha in Canada, compared to an average of ~2 Mha between 1959 and 2015. Boreal wildfire is a critical process that is difficult to represent in land surface models. To enhance our understanding of historical and future wildfire regimes in Canada and their impact on carbon cycling we implement two methods of representing boreal wildfire in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC). These include a new dynamic wildfire model that represents fire weather and lightning ignitions as well as a fire model which is forced by historical observations of burned area. We find that in 2023 simulated wildfire emissions were eight times their 1985 - 2022 mean with consequences for the annual net carbon balance in Canada. Moving into the future we find that climate change below a 2°C global target (shared socioeconomic pathway [SSP] 126) yields burned area near modern (2004 - 2014) norms by end-century (2090 - 2100). However, under rapid climate change (SSP370/585), the end-century mean annual burned area increases 2 - 4 times, compared to present-day values, approaching the burned area seen in Canada in 2023. This work illustrates the historical implications of Canadian wildfires on the carbon cycle and the future implications of climate change for area burned in Canada.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".