Modeling the Black and Brown Carbon Absorption and Their Radiative Impact: The June 2023 Intense Canadian Boreal Wildfires Case Study
Bibliographic record
Abstract
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 chemical and transport model. The study focused on a domain extending from North America to Eastern Europe, including the Arctic up to 85°N. The enhanced model includes an update treatment for BC absorption enhancement and a BrC aging scheme accounting for browning and blanching through oxidation. Validation against Aerosol Robotic Network and satellite data showed the model accurately reproduced aerosol optical depth (AOD) at multiple wavelengths, both near wildfire sources and during transoceanic transport to Europe. Improvements were observed in simulations of absorbing aerosol optical depth (AAOD) compared with the baseline model. Significant enhancements were achieved in capturing the spatial distribution of aerosol absorption in areas affected by wildfire emissions. For June 2023, the regional all‐sky DRE attributed to Canadian wildfires was reduced from −2.1 W/m2 in the control model to −1.9 W/m2 in the enhanced model. This corresponded to an additional warming effect of +0.2 W/m2 (+10%) due to the advanced treatment of BC and BrC absorption. These results indicate the importance of accurate aerosol absorption modeling in regional climate predictions, during large‐scale biomass burning events. They also highlight potential overestimations of cooling effects in traditional models, emphasizing the need of improved aerosol parameterization to better simulate the DRE and for evaluating the impacts of mitigation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".