Variability of CO and aerosols plumes from wildfires in the Northern Hemisphere in 2008-2022 using satellite observations.
Bibliographic record
Abstract
Wildfires are responsible for significant emissions of greenhouse gases, pollutants and aerosols. In addition to being a large source of carbon monoxide (CO) and carbon dioxide (CO2), they alone account for more than half of black carbon emissions and the majority of primary organic aerosol emissions.Despite proactive fire suppression policies in the Northern Hemisphere (NH), allowing a decrease in fires, especially in Europe, an increase in the number of extreme fires can be noted in recent years. In the NH, this increase is mainly in Western America and boreal regions. The pollution plumes produced during extreme fires can be transported over thousands of kilometers, impacting background pollutant levels on a hemispheric scale. Thus, variability in fire intensity may explain a large part of the spatial and temporal variability of many atmospheric pollutants. For longer lived pollutants, wildfires may significantly increase background levels.In this study, the link between extreme fire weather (high temperature), large fires and background pollution in the Northern Hemisphere is analyzed based on satellite observations. The impact of large wildfires on background levels of CO and aerosols above Europe is studied more specifically. We present the variability of fire frequency in the NH, their intensity and the related emissions using 20 years (2003-2022) of MODIS fire observations analyzed with the APIFLAME model. The link between large events and fire weather is studied using the ERA5 reanalyses and the Canadian Fire Weather Index (FWI). The related impact on the variability of total CO and AOD in the NH is analyzed using 15 years (2008-2022) of satellite observations from IASI/Metop and MODIS/Terra and Aqua, respectively. Finally, plume retro trajectories are computed in order to assess the contribution of the different geographical areas of the NH on the CO and AOD variability.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".