High-latitude cooling associated with landscape changes from North American boreal forest fires
Bibliographic record
Abstract
Abstract. Fires in the boreal forests of North America are generally stand-replacing, killing the majority of trees and initiating succession that may last over a century. Functional variation during succession can affect local surface energy budgets and, potentially, regional climate. Burn area across Alaska and Canada has increased in the last few decades and is projected to be substantially higher by the end of the 21st century because of a warmer climate with longer growing seasons. Here we simulated changes in forest composition due to altered burn area using a stochastic model of fire occurrence, historical fire data from national inventories, and succession trajectories derived from remote sensing. When coupled to an Earth system model, younger vegetation from increased burning cooled the high-latitude atmosphere, primarily in the winter and spring, with noticeable feedbacks from the ocean and sea ice. Results from multiple scenarios suggest that a doubling of burn area would cool the surface by 0.23 ± 0.09 °C across boreal North America during winter and spring months (December through May). This could provide a negative feedback to winter warming on the order of 3–5% for a doubling, and 14–23% for a quadrupling, of burn area. Maximum cooling occurs in the areas of greatest burning, and between February and April when albedo changes are largest and solar insolation is moderate. Further work is needed to integrate all the climate drivers from boreal forest fires, including aerosols and greenhouse gasses.
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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 | 0.000 |
| 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) | 0.001 | 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".