Impact of Canadian wildfires on aerosol and ice clouds in the early-autumn Arctic
Bibliographic record
Abstract
Cloud particle phase is an important controlling factor for the Earth's surface heat budget, through the radiative balance. Thus, it exerts a strong influence on climate change in the Arctic. Aerosols transported from lower latitudes modify Arctic cloud properties, including cloud phase. In this study, we investigated ice cloud formation and high aerosol concentrations over the Arctic Ocean using a combination of observations obtained by an Arctic voyage, reanalysis data, and backward trajectory analyses. On 12 September 2023, in an atmospheric river over the Arctic Ocean, ice clouds at temperatures warmer than −15 °C were observed in the middle troposphere by a Cloud Particle Sensor sonde. In the lower troposphere, a particle counter onboard a drone detected particle counts two orders of magnitude higher than the voyage average. Backward trajectories indicated that a lower tropospheric air mass with a high concentration of organic carbon (OC) aerosols over northern and coastal western Canada, where wildfire-induced OC emissions were evident, reached the mid-troposphere over the Arctic Ocean. These results suggest that OC aerosols from severe Canadian wildfires in the summer of 2023 acted as ice-nucleating particles for ice cloud formation under high-temperature conditions exceeding −15 °C over the Arctic Ocean. • Vertical profiling of clouds and aerosols was conducted in the Arctic Ocean in 2023. • A particle counter on a drone recorded high-aerosol-concentration events. • Canadian wildfire aerosols would influence the Arctic ice cloud formation.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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".