Warming and cooling influences of North American boreal fires
Bibliographic record
Abstract
The Arctic-boreal region is warming rapidly, with consequences for northern ecosystems and global climate. Fires across the Arctic-boreal region are a major natural disturbance mechanism that initiate climate warming (positive) and cooling (negative) feedbacks. Understanding the net forcing effect from boreal fire on climate is crucial in managing and mitigating climate change impacts of boreal fires. Here we report radiative forcing estimates from boreal forest fires across Alaska and Western Canada (Arctic Boreal Vulnerability Experiment-domain). Our results integrate the effect of greenhouse gas emissions (warming) and aerosols emission (net cooling) have through direct combustion, post-fire vegetation recovery sequestering carbon (cooling), fire-induced permafrost degradation emitting CO2 and CH4 (warming), and changes in surface albedo (cooling). Alaskan fires are on average climate warming (1.34±2.95 W/m2 per burned area) – uncertainty given as spatial standard deviation, while Canadian fires show on average a climate cooling (‑2.26±2.48 W/m2 per burned area) effect. The emissions from the combustion of organic soils and post-fire permafrost thaw dominate the positive feedback for Alaskan fires, whereas the cooling effect of post-fire changes in surface albedo because of prolonged spring snow cover dominate for the western Canadian fires. Our work demonstrates large-scale spatial variability in the climate feedbacks from North American boreal forest fires. Such fine-scale spatial information on the warming and cooling influences of forest fires could be useful in designing forest management and fire suppression activities informed by climate impacts.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".