TY - JOUR
T1 - Sensitivity of Regional WRF-Chem Air Quality and Weather Simulations to Biomass-Burning Emission Data Sets
T2 - A Case Study of the Impact of Canadian Wildfire on the US
AU - Wu, Sicheng
AU - Kumar, Rajesh
AU - Li, Peiyuan
AU - Kotamarthi, Rao
AU - Collis, Scott
AU - Shams, Shima
AU - Sharma, Ashish
N1 - Publisher Copyright:
© 2025. The Author(s).
PY - 2025/11/28
Y1 - 2025/11/28
N2 - This study focuses on the period from June 26 to 29, 2023, when record-breaking Canadian wildfires severely impacted air quality in the Midwest United States. Using the Weather Research and Forecasting Model with Chemistry (WRF-Chem) and four biomass-burning data sets (Fire Inventory from NCAR version 1, Fire Inventory from NCAR version 2.5, Quick Fire Emissions Data set [QFED], and Regional ABI-VIIRS Emission), we analyzed aerosol transport from Canada to the US and assessed the model's accuracy in predicting (Formula presented.), (Formula presented.), (Formula presented.) and aerosol weather feedback. Model simulations were compared with ground-based and remote sensing observations as well as field measurements from the Community Research on Climate and Urban Science (CROCUS) project. 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%, (Formula presented.) by 65%–90% and peak (Formula presented.) concentrations by 50%–55% across four biomass burning data sets. Additionally, CO and (Formula presented.) concentrations were underpredicted. The substantial underestimation of (Formula presented.) led to an overestimation of temperature by up to 3.6 (Formula presented.) 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 data sets, QFED produced the most accurate AOD and (Formula presented.) predictions due to improved wildfire emission estimates, leading to a 1.0 to 1.5 (Formula presented.) 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 weather feedback predictions.
AB - This study focuses on the period from June 26 to 29, 2023, when record-breaking Canadian wildfires severely impacted air quality in the Midwest United States. Using the Weather Research and Forecasting Model with Chemistry (WRF-Chem) and four biomass-burning data sets (Fire Inventory from NCAR version 1, Fire Inventory from NCAR version 2.5, Quick Fire Emissions Data set [QFED], and Regional ABI-VIIRS Emission), we analyzed aerosol transport from Canada to the US and assessed the model's accuracy in predicting (Formula presented.), (Formula presented.), (Formula presented.) and aerosol weather feedback. Model simulations were compared with ground-based and remote sensing observations as well as field measurements from the Community Research on Climate and Urban Science (CROCUS) project. 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%, (Formula presented.) by 65%–90% and peak (Formula presented.) concentrations by 50%–55% across four biomass burning data sets. Additionally, CO and (Formula presented.) concentrations were underpredicted. The substantial underestimation of (Formula presented.) led to an overestimation of temperature by up to 3.6 (Formula presented.) 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 data sets, QFED produced the most accurate AOD and (Formula presented.) predictions due to improved wildfire emission estimates, leading to a 1.0 to 1.5 (Formula presented.) 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 weather feedback predictions.
KW - WRF-chem model
KW - air quality
KW - biomass burning
KW - emissions
KW - wildfires
UR - https://www.scopus.com/pages/publications/105022652018
U2 - 10.1029/2025JD043944
DO - 10.1029/2025JD043944
M3 - Article
AN - SCOPUS:105022652018
SN - 2169-897X
VL - 130
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
IS - 22
M1 - e2025JD043944
ER -