Sensitivity of regional WRF-Chem air quality and climate simulations to biomass-burning emission datasets: a case study of the impact of Canadian wildfire on the US
Bibliographic record
Abstract
This study focuses on the period from June 26 to 29, 2023, when record-breaking Canadian wildfires severely impacted air quality in the Midwest U.S. Using the Weather Research and Forecasting Model with Chemistry (WRF-Chem) and four biomass-burning datasets (FINN v1, FINN v2.5, QFED, and RAVE), we analyzed aerosol transport from Canada to the US and assessed the model’s accuracy in predicting PM2.5, O3, CO and aerosol climate feedback. Model simulations were compared with ground-based and remote sensing observations, as well as field measurements. Our findings show that the movement of a low-pressure system from the Great Lakes to the Atlantic, combined with the high-pressure system over the Atlantic, caused the transport of aerosols from Canadian wildfires to the US. Results show WRF-Chem significantly underestimated key atmospheric components: aerosol optical depth (AOD) by over 50%, PM2.5 by 65-90%, and peak O3 concentrations by 50-55% across four biomass burning datasets. Additionally, CO and NO2 concentrations were underpredicted. The substantial underestimation of PM2.5 led to an overestimation of temperature by up to 3.6°C, primarily due to excessive downward shortwave radiation, which resulted from the underestimation of direct aerosol effects and an increase in sensible heat flux. Among the biomass-burning datasets, QFED produced the most accurate AOD and PM2.5 predictions due to improved wildfire emission estimates, leading to a 1.0 to 1.5°C reduction in temperature overestimation during the daytime. These findings underscore the need for improving wildfire emission estimates for trace gases and aerosols to enhance air quality and climate feedback predictions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".