AIRS Satellite CO Data Confirm the Increase in Wildfires in the Northern Hemisphere Over the Past Two Decades
Bibliographic record
Abstract
Biomass burning is an important and changing component of the global and hemispheric carbon cycles. In particular, boreal forest fires in Russia and Canada are important sources of greenhouse gases carbon dioxide (CO2) and methane (CH4). The influence of carbon monoxide (CO) on the climate is insignificant: its main absorption bands of 4.6 and 2.3 μm are far away from the climatically important regions of the spectrum. Meanwhile, CO concentrations in fire plumes are closely related to CO2 and CH4 emissions from fires. On the other hand, satellite measurements of CO are much simpler than those for the aforementioned gases. The Atmospheric Infrared Sounder (AIRS) provides a long satellite-based CO data set. This article presents estimates of CO emissions from biomass burning north of 30° N using a simple two-box model. These results correlate closely with independently estimated CO emissions from the GFED4 bottom-up data base. Both ones reported record high emissions in 2021 throughout two decades, double the annual emissions comparing to the previous a few years. There have been several years with extreme emissions, but for the rest of data upward trend with a rate of 3.7 ± 2.3 Tg CO yr-2 (4.4 ± 2.8% per year), was found.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 0.001 |
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".