Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ

Abstract

Abstract. Global coupled chemistry-climate models underestimate carbon monoxide (CO) in the Northern Hemisphere, exhibiting a pervasive, negative bias against measurements peaking in late winter and early spring. While this bias has been commonly attributed to underestimation of direct anthropogenic and biomass burning emissions, chemical production and loss via OH reaction from emissions of anthropogenic and biogenic VOCs play an important role. Here we investigate the reasons for this underestimation using aircraft measurements taken in May and June 2016 from the Korea United States Air Quality (KORUS‐AQ) experiment in South Korea and the Air chemistry Research In Asia (ARIAs) in the North China Plain (NCP). For reference, multispectral CO retrievals (V8J) from the Measurements of Pollution in the Troposphere (MOPITT) are jointly assimilated with meteorological observations using an Ensemble Adjustment Kalman Filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42 % in the Control-Run and by 12 % with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80 % for Northern China, with large increments over the Liaoning province and the North China Plains (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O3 datasets, and observationally constrained box model simulations of OH and HO2. Running a CAM-chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29 % for CO, 18 % for ozone, 11 % for HO2 and 27 % for OH. Longer lived anthropogenic VOCs whose model errors are correlated with CO are also improved while short-lived VOCs, including formaldehyde, are difficult to constrain solely by assimilating satellite retrievals of CO. During an anticyclonic episode, better simulation of O3, with an average underestimation of 5.5 ppbv and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of two the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to wide spread NOx controls, can improve pollution ozone over East Asia.

Process-based and observation-constrained SOA simulations in China: the role of semivolatile and intermediate-volatility organic compounds and OH levels

“…Consistently, top-down estimates suggest greater CO emissions than those in the MEIC inventory (X. Feng et al, 2020;Gaubert et al, 2020). On the other hand, recent measurements of SOA formation potential show a wide range of reference values for EF SOAP /EF CO (Table S10 in the Supplement).…”

Global Changes in Secondary Atmospheric Pollutants During the 2020 COVID‐19 Pandemic

“…The distribution of chemical species presented in our study are derived from the Community Earth System Model (CESM) version 2.2 (Danabasoglu et al., 2020). The atmospheric component of CESM, the Community Atmosphere Model (CAM‐Chem) (Emmons et al., 2020; Gaubert et al., 2020; Gettelman et al., 2019; Tilmes et al., 2020) that is described in the supplementary information, provides a comprehensive description of atmospheric chemistry and aerosol processes with 221 gas phase and aerosol species, and 528 chemical and photochemical reactions (Emmons et al., 2020). Aerosols are represented by the four‐mode Modal Aerosol Model (MAM4, Liu et al., 2016; Mills et al., 2016).…”

On the relationship between tropospheric CO and CO<sub>2</sub> during KORUS-AQ and its role in constraining anthropogenic CO<sub>2</sub>

“…We expect that anthropogenic sources of CO in this region is already overestimated. This systematic bias has been reported in Tang et al (2019a), which implies considering optimizing secondary CO and indirectly constraining CO loss due to OH together with primary CO emissions (Gaubert et al, 2020). Relative to CAMS CO, the overall mean bias against KORUS-AQ in CAM-chem (-24 ppb) is also comparable to CAMS (-20 to -25 ppb).…”