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

• Main Authors: B. Gaubert, L. K. Emmons, K. Raeder, S. Tilmes, K. Miyazaki, A. F. Arellano Jr., N. Elguindi, C. Granier, W. Tang, J. Barré, H. M. Worden, R. R. Buchholz, D. P. Edwards, P. Franke, J. L. Anderson, M. Saunois, J. Schroeder, J.-H. Woo, I. J. Simpson, D. R. Blake, S. Meinardi, P. O. Wennberg, J. Crounse, A. Teng, M. Kim, R. R. Dickerson, H. He, X. Ren, S. E. Pusede, G. S. Diskin
• Format: Article
• Language: English
• Published: Copernicus Publications 2020-12-01
• Series: Atmospheric Chemistry and Physics
• Online Access: https://acp.copernicus.org/articles/20/14617/2020/acp-20-14617-2020.pdf

<p>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 volatile organic compounds (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<span id="page14618"/> with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42&thinsp;% in the control run and by 12&thinsp;% with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80&thinsp;% for northern China, with large increments over the Liaoning Province and the North China Plain (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O<span class="inline-formula">₃</span> datasets and observationally constrained box model simulations of OH and HO<span class="inline-formula">₂</span>. Running a CAM-Chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29&thinsp;% for CO, 18&thinsp;% for ozone, 11&thinsp;% for HO<span class="inline-formula">₂</span>, and 27&thinsp;% 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 O<span class="inline-formula">₃</span>, with an average underestimation of 5.5&thinsp;ppbv, and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of 2 the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to widespread NO<span class="inline-formula">_<i>x</i></span> controls, can improve ozone pollution over East Asia.</p>