Boreal forests on fire - Decadal wildfire impacts on boreal forest microclimate
Bibliographic record
Abstract
Wildfire is the most important disturbance agent in boreal forests. These disturbances play a major role in the boreal forest carbon cycle. They lead to direct CO2 and CH4 emissions during the active fire phase and to long-lasting post-fire impacts on net CO2 and CH4 fluxes through changes in forest structure and in microclimatic conditions. For example, a forest’s ability to minimise differences between land surface and air temperature can preserve permafrost and can lower soil temperatures and thus soil respiration. Fire and post-fire succession are linked to diverse changes in ecosystem function and structure shaping land-atmosphere interactions and, thus, forest microclimate for decades after the disturbance event. However, the mechanisms behind changes in boreal forest microclimate remain uncertain hampering our understanding of carbon cycle impacts of wildfires. Here, we analyse surface energy balance observations from 17 eddy covariance flux tower sites across fire disturbance chronosequences in the North American boreal biome to identify the main drivers of post-fire changes in land surface-air temperature gradients. We use 102 years of observations to quantify winter and summer changes in important ecosystem properties such as evaporative fraction, aerodynamic conductance, and albedo following stand-replacing fire disturbances. Then, we link changes in ecosystem properties to decadal changes in surface-air temperature gradients.We find that the summer daytime surface-air temperature gradient increases after the fire disturbance indicating reduced ability to cool the land surface during the warm summer months. However, in the winter, the daytime temperature gradient becomes smaller. Decreased aerodynamic conductance contributes mainly to the post-fire surface heating in the summer while increasing albedo mainly explains winter cooling. Evaporative fraction increases initially in the first few decades after the post-fire disturbance. However, during drought years, the evaporative fraction declines rapidly. Our results provide important insights into fire impacts on microclimatic conditions and ground thermal regimes in boreal forests and highlight the reduced capacity of post-fire forests to reduce land surface temperatures during heatwave events. The findings have the potential to contribute to a better mechanistic understanding of post-fire permafrost thaw and soil respiration changes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".