Observationally constrained representation of brown carbon emissions from wildfires in a chemical transport model

The month of August 2015 featured extensive wildfires in the Northwestern U.S. and no significant fires in Alaska and Canada. With the majority of carbonaceous aerosols (CA), including black carbon (BC) and brown carbon (BrC), over the U.S. dominated by emissions from Northwestern wildfires, this month presented a unique opportunity for testing wildfire BrC representation in the Weather Research and Forecasting model with chemistry (WRF-Chem). We performed parallel simulations that (1) did not account for BrC absorption, (2) accounted for BrC absorption, and (3) accounted for BrC absorption as well as its decay due to photobleaching. We used a comprehensive set of extensive and pseudo-intensive optical properties, namely the aerosol optical depth (AOD), aerosol absorption optical depth (AAOD), absorption angstrom ngstrom exponent (AAE), and single scattering albedo (SSA) to constrain the model output against observations from the Aerosol Robotic Network (AERONET). We found that accounting for BrC absorption and photobleaching resulted in the best agreement with observations in terms of aerosol absorption (AAOD and AAE). However, the model severely underestimated AOD and SSA compared to observations. We attributed this discrepancy to missing scattering due to missing secondary organic aerosol (SOA) formation from wildfire emissions in the model. To test this hypothesis, we applied a zeroth-order representation of wildfire SOA, which significantly improved the AOD and SSA model-observation comparison. Our findings indicate that BrC absorption, the decay of its absorption due to photobleaching, as well as SOA formation should be accounted for in chemical transport models in order to accurately represent CA emissions from wildfires.