Abstract

Australian fires are a primary driver of variability in Australian atmospheric composition and contribute significantly to regional and global carbon budgets. However, biomass burning emissions from Australia remain highly uncertain. In this work, we use surface in situ, ground-based total column... Show moreAustralian fires are a primary driver of variability in Australian atmospheric composition and contribute significantly to regional and global carbon budgets. However, biomass burning emissions from Australia remain highly uncertain. In this work, we use surface in situ, ground-based total column and satellite total column observations to evaluate the ability of two global models (GEOS-Chem and ACCESS-UKCA) and three global biomass burning emission inventories (FINN1.5, GFED4s, and QFED2.4) to simulate carbon monoxide (CO) in the Australian atmosphere. We find that emissions from northern Australia savanna fires are substantially lower in FINN1.5 than in the other inventories. Model simulations driven by FINN1.5 are unable to reproduce either the magnitude or the variability of observed CO in northern Australia. The remaining two inventories perform similarly in reproducing the observed variability, although the larger emissions in QFED2.4 combined with an existing high bias in the southern hemisphere background lead to large CO biases. We therefore recommend GFED4s as the best option of the three for global modeling studies with focus on Australia or the Southern Hemisphere. Near fresh fire emissions, the higher resolution ACCESS-UKCA model is better able to simulate surface CO than GEOS-Chem, while GEOS-Chem captures more of the observed variability in the total column and remote surface air measurements. We also show that existing observations in Australia can only partially constrain global model estimates of biomass burning. Continuous measurements in fire-prone parts of Australia are needed, along with updates to global biomass burning inventories that are validated with Australian data. Show less