Constraints on CO Emissions from Wildfire Inventories Using a Receptor-oriented Lagrangian Particle Dispersion Model

Constraints on CO Emissions from Wildfire Inventories Using a Receptor-oriented Lagrangian Particle Dispersion Model

Dien Wu, Derek V. Mallia, Shawn P. Urbanski, and John C. Lin

3rd Conference on Atmospheric Biogeosciences: Emission and uptake/deposition of trace gases and aerosols, 20-24 June 2016, Salt Lake City, UT

Although wildfire strength varies annually, much of the Western U.S. is undergoing an increasing trend in wildfire activity. Fires release large amounts of CO2, CO and other pollutants into the atmosphere, which can impact the regional and global carbon cycle and climate, along with adverse effects on human health. Since fires are strong emitters of CO, especially over forested regions, constraints on CO emissions can naturally lead to more accurate emissions of CO2 and other species. We simulated enhancements of CO concentrations at three selected sites for summer 2012, using a receptor-oriented Lagrangian particle model (STILT), with the meteorological fields driven by a mesoscale numerical weather prediction model (WRF). Two fire inventories — the Wildland Fire Emission Inventory (WFEI) and the Global Fire Emissions Database (GFED Version 4.1s) — were evaluated separately. Simulations using both inventories resulted in lower CO values at the Niwot Ridge site in Colorado compared to NOAA's flask observations, and higher values at a tall tower site near Fairbanks, Alaska. But, on average, the deviation of simulations from observations using WFEI is smaller than that using GFED4.1s. Additionally, transport errors within WRF-STILT can be significant regarding uncertainty analysis.