Stratospheric impact of the anomalous 2023 Canadian wildfires
Bibliographic record
Abstract
The frequency of extreme wildfires has increased as a response to the regional and global warming trends and there is an emerging realization of their impact on climate through emissions of smoke aerosols into the stratosphere. The 2023 wildfire season in Canada was unprecedented in terms of its duration, burned area and cumulative fire power, rendering it the most destructive ever recorded. Here we use various satellite observations (TROPOMI, OMPS-LP, OMPS-NM, MLS, CALIPSO, SAGE III) to quantify the stratospheric emissions of smoke aerosols and carbon monoxide by the 2023 Canadian wildfires and to characterize the long-range transport of smoke plumes in the stratosphere. Using multiwavelength lidar observations in Northern France, we show systematically distinct microphysical properties of UTLS smoke aerosols compared to their free-tropospheric counterparts.The analysis of satellite data reveals multiple episodes of smoke intrusions into the stratosphere through pyroconvection (PyroCb) and synoptic-scale processes (warm conveyor belt, WCB). Model simulations using MOCAGE chemistry-transport model, which included emission data from GFAS (Global Fire Assimilation System) are shown to accurately capture the synoptic-scale uplift of smoke into the UTLS and reproduce the spatial evolution of the aerosol plumes.We show that the multiple episodes of wildfire-driven stratospheric intrusions during Boreal Summer 2023 through PyroCb and WCB mechanisms are altogether responsible for the record-high and persistent season-wide smoke pollution at the commercial aircraft cruising altitudes and the lowermost stratosphere.
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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 | 0.000 | 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".