Abstract

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.

Highlights

  • Carbon monoxide (CO) has long been considered an excellent tracer for air mass origin

  • While the improved method does not allow us to distinguish between production from different precursor nonmethane volatile organic compounds (NMVOCs) or types of NMVOC emissions, we find that by combining these regional secondary CO tracers with the primary CO tracers we are usually able to infer the likely source of secondary enhancements

  • We have implemented a major improvement to the representation of secondary CO production in the GEOS-Chem linear “tagged” CO-only simulation, which is frequently used for emission inversion, data interpretation, and chemical forecasting

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Summary

Introduction

Carbon monoxide (CO) has long been considered an excellent tracer for air mass origin. Emitted CO can be “tagged” by source region or type and followed as it is transported to receptor regions downwind, providing a quantitative metric for source–receptor influence This capability to determine air mass origin, along with the efficiency of CO-only simulation, means the linear CO capability has frequently been applied in a variety of CTMs and CCMs to interpret in situ observations of CO and co-varying species (e.g., Staudt et al, 2001; Liang et al, 2004; Fisher et al, 2010; Pfister et al, 2011), analyze satellite data (e.g., Park et al, 2009; Kumar et al, 2013), improve emission estimates (e.g., Kopacz et al, 2010; Jiang et al, 2011), disentangle multi-model ensembles (e.g., Monks et al, 2015; Zeng et al, 2015), and forecast expected chemical conditions for field campaigns. We use the Australasian region as a case study, comparing the base and improved simulations against observations from the Total Carbon Column Observing Network (TCCON) to explore the benefits of the improved source attribution capability for regions with limited impact from primary CO emissions (Sect. 5)

Model description
CO-only simulation in GEOS-Chem
Improved CO-only simulation
Source attribution capability
Implications for global CO distribution
Implications for global evaluation with observations
Implications for source attribution: case study for Australasia
Findings
Conclusions

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