Modelling the black and brown carbon absorption and their radiative impact: the June 2023 intense Canadian boreal wildfires case study
Bibliographic record
Abstract
Black carbon (BC) and brown carbon (BrC) are light-absorbing aerosols with significant climate impacts, but their absorption properties and direct radiative effect (DRE) remain uncertain. We simulated BC and BrC absorption during the intense Canadian boreal wildfires in June 2023 using an enhanced version of CHIMERE model. The study focused on a domain extending from North America to Eastern Europe, including a significant portion of the Arctic up to 85°N. The enhanced model includes an updated treatment for the BC absorption enhancement and a BrC ageing scheme accounting for both browning and blanching through oxidation. When compared to observations, the updated model accurately captured aerosol optical depth (AOD) at multiple wavelengths, both near the wildfires and during transoceanic transport to Europe. Improvements were observed in the simulation of absorbing aerosol optical depth (AAOD) compared to the control model. The all-sky regional direct radiative effect (DRE) for June 2023 attributed to the intense Canadian wildfires, was reduced from -2.1 W/m² in the control model to -1.9 W/m² (-2.0/-1.8 W/m², ±5%), in the enhanced model, indicating an additional warming effect of +0.2 W/m² (about +10%) due to advanced schemes used for the BC and BrC absorption. The results indicate the importance of an accurate simulation of aerosol absorption in regional climate predictions, especially during large-scale biomass burning events. They also suggest that traditional models could overestimate the cooling effect of boreal wildfires, highlighting the need for improvement of aerosol parameterization to better predict the DRE and develop effective mitigation strategies.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".