Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ
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Published:2020-12-01
Issue:23
Volume:20
Page:14617-14647
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ISSN:1680-7324
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Container-title:Atmospheric Chemistry and Physics
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language:en
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Short-container-title:Atmos. Chem. Phys.
Author:
Gaubert BenjaminORCID, Emmons Louisa K.ORCID, Raeder Kevin, Tilmes Simone, Miyazaki KazuyukiORCID, Arellano Jr. Avelino F.ORCID, Elguindi Nellie, Granier Claire, Tang WenfuORCID, Barré Jérôme, Worden Helen M.ORCID, Buchholz Rebecca R.ORCID, Edwards David P., Franke PhilippORCID, Anderson Jeffrey L., Saunois Marielle, Schroeder Jason, Woo Jung-Hun, Simpson Isobel J., Blake Donald R., Meinardi Simone, Wennberg Paul O.ORCID, Crounse JohnORCID, Teng Alex, Kim Michelle, Dickerson Russell R.ORCID, He HaoORCID, Ren XinrongORCID, Pusede Sally E., Diskin Glenn S.ORCID
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 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 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 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 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 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 NOx controls,
can improve ozone pollution over East Asia.
Funder
National Aeronautics and Space Administration National Oceanic and Atmospheric Administration
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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