Boreal Forest Fire Causes Daytime Surface Warming During Summer to Exceed Surface Cooling During Winter in North America
Bibliographic record
Abstract
Abstract Boreal wildfires modify surface climates affecting plant physiology, permafrost thaw, and carbon fluxes. Post‐fire temperatures vary over decades because of successional vegetation changes. Yet, the underlying biophysical drivers remain uncertain. Here, we quantify surface climate changes following fire disturbances in the North American boreal forest and identify its dominant biophysical drivers. We analyze multi‐year land‐atmosphere energy exchange and satellite observations from across North America and find post‐fire daytime surface temperatures to be substantially warmer for about five decades while winter temperatures are slightly cooler. Post‐fire decadal changes are characterized by decreasing leaf area index during the first decade, by sharply increasing surface albedo during the snow cover period, and by a less efficient heat exchange between the forest and the atmosphere caused by decreasing surface roughness for about 2–3 decades. Over the first three decades, the amount of energy used for evapotranspiration increases before returning to lower values. We find that surface warming is mainly explained by less efficient forest‐atmosphere heat exchange while cooling is additionally explained by increasing surface albedo. We estimate that biome‐wide daytime surface temperatures of the Canadian boreal forest in 2024 are 0.27°C warmer in the summer and 0.02°C cooler during the winter because of fire. For a scenario with a strong increase in burned area, we estimate annual warming from fire to increase by a third until 2050. Our study highlights the potential for accelerated surface warming in the boreal biome with increasing wildfire activity and disentangles the biophysical drivers of fire‐related surface 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".