Size-dependent wet removal of black carbon in Canadian biomass burning plumes
Bibliographic record
Abstract
Abstract. Wet deposition is the dominant mechanism for removing black carbon (BC) from the atmosphere, and is key in determining its atmospheric lifetime, vertical gradient and global transport. Despite the importance of BC in the climate system, especially in terms of its ability to modulate the radiative energy budget, there are few quantitative case studies of wet removal in ambient environments. We present a case study of BC wet removal by examining aerosol size distributions and BC coating properties sampled in three Canadian boreal biomass burning plumes, one of which passed through a precipitating cloud. In this plume, the largest and most coated BC particles were found to be preferentially removed, suggesting that nucleation scavenging was the likely dominant mechanism. Calculated mass absorption coefficient (MAC) in the plumes showed no significant variation, as the shifts to smaller BC cores and thinner coatings had opposing effects. Similarly, calculated single-scatter albedo (SSA) showed little variation, as a large number of non-BC particles were also present in the precipitation-affected plume. The remaining BC cores were smaller than those observed in previous studies of BC in post-precipitation outflow over Asia, possibly due to the thick coatings associated with the biomass burning particles. This study provides important constraints to model parameterisations of BC wet removal in biomass burning regions, which will help to reduce uncertainty in radiative forcing calculations.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".