Heat-amplifying boreal forests exacerbate snowmelt, fuel availability and wildfires in sub-Arctic regions
Bibliographic record
Abstract
Abstract Forests, once the largest terrestrial carbon sink, are increasingly becoming significant sources of carbon emissions worldwide due to large wildfires and the accumulation of fire fuels in warming environments that deplete soil and vegetation moisture. Despite growing needs such as Nature-Based Solutions, there is a lack of operationalized near-real-time satellite observations of forest fuel conditions to assess whether forests are acting as carbon sinks or emitters. Most existing satellite products focus on chlorophyll content or vegetation cover rather than directly measuring hydrological or thermal variations that influence carbon flux. From Soil Moisture and Ocean Salinity (SMOS) L-band microwave brightness temperature, we retrieved forest (or canopy) temperatures over the 2023 Canadian and 2021 Sakha Republic wildfires that generated some of the world’s largest carbon emissions. We propose forest canopy temperature as a predictor of natural carbon emissions from mega-wildfires, in comparison with Soil Moisture Active Passive vegetation water content and European Centre for Medium-Range Weather Forecasts ERA5 land surface temperature products, which fail to capture the tipping points of thermal development in cold forests. The heat-amplifying feedback between forests and pre-fire sensible heat further accelerates fuel dryness through evapotranspiration driven by snow-melt water and forest warming, leading to the large-scale spread of wildfires. Under such dry conditions, forestation policies may inadvertently increase fuel availability and wildfire risk, potentially leading to an increase in net carbon emissions rather than achieving the intended benefits of carbon sequestration.
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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) | 0.001 | 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".