Investigating the vertical extent of the 2023 summer Canadian wildfire impacts with satellite observations
Bibliographic record
Abstract
Pyrocumulonimbus clouds (pyroCbs) generated by intense wildfires can serve as a direct pathway for the injection of aerosols and gaseous pollutants into the lower stratosphere, resulting in significant chemical, radiative, and dynamical changes. Canada experienced an extremely severe wildfire season in 2023, with a total area burned that substantially exceeded those of previous events known to have impacted the stratosphere (such as the 2020 Australian fires). This season also had record-high pyroCb activity, which raises the question of whether the 2023 Canadian event resulted in significant stratospheric perturbations. Here, we investigate this anomalous wildfire season using retrievals from multiple satellite instruments, ACE-FTS (Atmospheric Chemistry Experiment – Fourier transform spectrometer), OMPS LP (Ozone Mapping and Profiler Suite Limb Profiler), and MLS (Microwave Limb Sounder), to determine the vertical extents of the wildfire smoke along with chemical signatures of biomass burning. These data show that smoke primarily reached the upper troposphere, and only a nominal amount managed to penetrate the tropopause. Only a few ACE-FTS occultations captured elevated abundances of biomass-burning products in the lowermost stratosphere. OMPS LP aerosol measurements also indicate that any smoke that made it past the tropopause did not last long enough or reach high enough to significantly perturb stratospheric composition. While this work focuses on Canadian wildfires given the extensive burned area, pyroCbs at other longitudes (e.g., Siberia) are also captured in the compositional analysis. These results highlight that despite the formation of many pyroCbs in major wildfires, those capable of penetrating the tropopause are extremely rare; this in turn means that even a massive area burned is not necessarily an indicator of stratospheric effects.
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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 | 0.001 | 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".