Assessing the sources of discrepancies in top-down CO emission estimates from wildfires

Emissions from wildfires have a large impact on the carbon cycle and air quality. Robust estimates of these emissions are important as they inform policy decisions. Bottom-up or top-down approaches are commonly used to estimate these emissions. Bottom-up inventories represent emissions as a product of a biome-specific emission factor and the amount of fuel burned. Top-down estimates utilize observations of trace gas from satellites or ground-based sensors to estimate emissions through an inverse modeling approach. A chemical transport model is used in conjunction with observations and a prior estimate (from a bottom-up inventory) to provide constraints on the emission sources. Bottom-up inventories typically have large uncertainties arising from variations in emission factors and discrepancies in the estimated mass of burned vegetation. While top-down estimates have the potential to mitigate these errors and provide more constrained emissions data, they still possess large biases and uncertainties. Atmospheric carbon monoxide (CO) is widely used as a tracer of wildfire emissions, and although various CO inversion studies have been conducted over the past two decades, there are still large discrepancies in reported top-down CO emission estimates. Here, we conduct a series of CO inversion analyses, focusing on the 2023 wildfires, to quantify the impact on the inferred CO emissions of the choice of data assimilation scheme employed, the specific observations being assimilated, the prior emissions inventory used, as well as the assumptions about the modeled chemical processes. Specifically, we compare the impact on the top-down emission estimates of using an ensemble Kalman filter and a four-dimensional variational data assimilation scheme to conduct the inversion. We also compare the impact of observations from the TROPOspheric Monitoring Instrument (TROPOMI) and the Measurement Of Pollution In The Troposphere (MOPITT) instrument, prior biomass burning emissions from the Global Fire Assimilation System (GFAS) and the Quick Fire Emissions Dataset (QFED), and examine the influence of the distribution of the modeled OH fields on the CO wildfire emission estimates.