New assessment of the global carbon monoxide source
The hydroxyl radical (OH) is the main detergent in the atmosphere and its abundance controls the concentrations of carbon monoxide (CO), a primary pollutant gas. As a result, the carbon monoxide emissions determined through inverse modeling constrained by satellite data depend crucially upon the OH levels simulated with models. Here we have combined observation-based constraints on OH levels, satellite CO data from the IASI sensor, and the IMAGES atmospheric model to provide improved global estimates of CO emissions.
The sources of CO include direct emissions and photochemical production due to the oxidation of hydrocarbons. The dominant CO sink is its reaction with OH, CO has therefore a strong influence on the oxidative capacity of the atmosphere and on the removal of air pollutants. However, most chemistry transport models fall short of reproducing constraints on OH levels derived from methyl chloroform (MCF) observations. In this study, we prescribe different OH fields compatible with MCF data in the IMAGES global model to infer CO fluxes constrained by IASI CO data. Each OH field leads to a different set of optimized emissions. Differences of 40% in the top‐down global anthropogenic CO emissions are found when varying the OH levels, and even larger differences are found regionally, e.g. in China and the United States. Comparisons with independent surface and aircraft observations indicate that the inversion adopting the lowest average OH level in the Northern Hemisphere (18% lower than the best estimate based on MCF measurements) provides the best overall agreement with all tested observation datasets.
Fig.1. Mean annual updates (optimized/prior) of anthropogenic, biomass burning, and biogenic emissions in the optimization HN and LN, assuming either very high and very low OH in the Northern Hemisphere, from Müller et al. Top‐Down CO Emissions Based On IASI Observations and Hemispheric Constraints on OH Levels. Geophys. Res. Lett., https://doi.org/10.1002/2017GL076697, 2018.