Improved method for linear carbon monoxide simulation and source attribution in atmospheric chemistry models illustrated using GEOS-Chem v9
- 1Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, NSW, Australia
- 2School of Earth and Environmental Sciences, University of Wollongong, Wollongong, NSW, Australia
- 3Department of Earth and Environmental Sciences, University of Rochester, Rochester, NY, USA
- 4Department of Physics, University of Toronto, Toronto, ON, Canada
- 5Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, USA
Abstract. Carbon monoxide (CO) simulation in atmospheric chemistry models is frequently used for source–receptor analysis, emission inversion, interpretation of observations, and chemical forecasting due to its computational efficiency and ability to quantitatively link simulated CO burdens to sources. While several methods exist for modeling CO source attribution, most are inappropriate for regions where the CO budget is dominated by secondary production rather than direct emissions. Here, we introduce a major update to the linear CO-only capability in the GEOS-Chem chemical transport model that for the first time allows source–region tagging of secondary CO produced from oxidation of non-methane volatile organic compounds. Our updates also remove fundamental inconsistencies between the CO-only simulation and the standard full chemistry simulation by using consistent CO production rates in both. We find that relative to the standard chemistry simulation, CO in the original CO-only simulation was overestimated by more than 100 ppb in the model surface layer and underestimated in outflow regions. The improved CO-only simulation largely resolves these discrepancies by improving both the magnitude and location of secondary production. Despite large differences between the original and improved simulations, however, model evaluation with the global dataset used to benchmark GEOS-Chem shows negligible change to the model's ability to match the observations. This suggests that the current GEOS-Chem benchmark is not well suited to evaluate model changes in regions influenced by biogenic emissions and chemistry, and expanding the dataset to include observations from biogenic source regions (including those from recent aircraft campaigns) should be a priority for the GEOS-Chem community. Using Australasia as a case study, we show that the new ability to geographically tag secondary CO production provides significant added value for interpreting observations and model results in regions where primary CO emissions are low. Secondary production dominates the CO budget across much of the world, especially in the Southern Hemisphere, and we recommend future model–observation and multi-model comparisons implement this capability to provide a more complete understanding of CO sources and their variability.