Modeling of carbonaceous particles emitted by boreal and temperate wildfires at northern latitudes
Bibliographic record
Abstract
For the first time, a spatial and monthly inventory has been constructed for carbonaceous particles emitted by boreal and temperate wildfires in forests, shrublands, and grasslands, with burned area data statistics, fuel load maps, fire characteristics, and particle emission factors. The time period considered is 1960–1997, and an important year‐to‐year variability was observed. On average, boreal and temperate vegetation fires represent 4% of global biomass burning, but during extreme years, their contribution may reach 12%, producing 9% and 20% of black carbon (BC) and particulate organic matter (POM), respectively, emitted by worldwide fires. The North American component of the boreal forest fires (Canada and Alaska) represents 4 to 122 Gg C yr‐1 of BC and 0.07 to 2.4 Tg yr‐1 of POM emitted, whereas the Eurasiatic component (Russia and northern Mongolia) may vary in the 16 to 474 Gg C yr‐1 range for BC and between 0.3 and 9.4 Tg yr‐1 for POM, with however great uncertainty. Temperate forests in conterminous United States and Europe have a much lower contribution with an average of 11 Gg C yr‐1 of BC and 0.2 Tg yr‐1 of POM. Grassland fires in Mongolia represent significant BC and POM sources which may reach 62 Gg C and 0.4 Tg, respectively. Finally, an annual average of BC emissions for shrubland fires in both the Mediterranean region and California is 20 Gg C yr‐1, with average POM emissions of 0.1 Tg yr‐1. These source maps obtained with a high spatial resolution (lox lo) can now be added to previous ones developed for other global carbonaceous aerosol sources (fossil fuel combustion, tropical biomass burning, agricultural and domestic fires) in order to provide global maps of particulate carbon emissions. Taking into account particle injection height in relation with each type of fire, our source map is a useful tool for studying the atmospheric transport and the impact of carbonaceous aerosols in three‐dimensional transport and climate models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".