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

Gaubert, Benjamin and Emmons, Louisa K. and Raeder, Kevin and Tilmes, Simone and Miyazaki, Kazuyuki and Arellano, Avelino F., Jr. and Elguindi, Nellie and Granier, Claire and Tang, Wenfu and Barré, Jérôme and Worden, Helen M. and Buchholz, Rebecca R. and Edwards, David P. and Franke, Philipp and Anderson, Jeffrey L. and Saunois, Marielle and Schroeder, Jason R. and Woo, Jung-Hun and Simpson, Isobel J. and Blake, Donald R. and Meinardi, Simone and Wennberg, Paul O. and Crounse, John and Teng, Alex and Kim, Michelle and Dickerson, Russell R. and He, Hao and Ren, Xinrong and Pusede, Sally E. and Diskin, Glenn S. (2020) Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ. Atmospheric Chemistry and Physics, 20 (23). pp. 14617-14647. ISSN 1680-7324. PMCID PMC7786812. https://resolver.caltech.edu/CaltechAUTHORS:20210114-164620773

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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 NO_x controls, can improve ozone pollution over East Asia.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.5194/acp-20-14617-2020DOIArticle
https://acp.copernicus.org/articles/20/14617/2020/acp-20-14617-2020-supplement.pdfPublisherSupporting Information
https://www.cesm.ucar.eduRelated ItemCommunity Earth System Model
https://doi.org/10.5065/D6WQ0202DOIThe Data Assimilation Research Testbed
https://dart.ucar.eduRelated ItemCode and Documentation
https://doi.org/10.5067/Suborbital/KORUSAQ/DATA01DOIKorea–United States Air Quality Field Study dataset
https://www-air.larc.nasa.gov/cgi-bin/ArcView/korusaq?OTHER=1#topRelated ItemARIAs observational dataset
https://www2.acom.ucar.eduRelated ItemMOPITT data
https://tes.jpl.nasa.gov/chemical-reanalysis/products/monthly-meanRelated ItemTropospheric Chemistry Reanalysis version 2
https://eccad3.sedoo.frRelated ItemCopernicus Atmosphere Monitoring Service (CAMS)
http://www.ncbi.nlm.nih.gov/pmc/articles/pmc7786812/PubMed CentralArticle
ORCID:
AuthorORCID
Gaubert, Benjamin0000-0002-6595-0686
Emmons, Louisa K.0000-0003-2325-6212
Tilmes, Simone0000-0002-6557-3569
Miyazaki, Kazuyuki0000-0002-1466-4655
Arellano, Avelino F., Jr.0000-0002-2615-5831
Elguindi, Nellie0000-0002-9211-4173
Granier, Claire0000-0002-3777-6078
Tang, Wenfu0000-0002-0107-4496
Worden, Helen M.0000-0002-5949-9307
Buchholz, Rebecca R.0000-0001-8124-2455
Edwards, David P.0000-0003-4342-1190
Franke, Philipp0000-0001-6298-164X
Saunois, Marielle0000-0003-3983-2931
Simpson, Isobel J.0000-0002-4211-1126
Blake, Donald R.0000-0002-8283-5014
Meinardi, Simone0000-0003-4828-9889
Wennberg, Paul O.0000-0002-6126-3854
Crounse, John0000-0001-5443-729X
Teng, Alex0000-0002-6434-0501
Kim, Michelle0000-0002-4922-4334
Dickerson, Russell R.0000-0003-0206-3083
He, Hao0000-0002-6823-9603
Ren, Xinrong0000-0001-9974-1666
Pusede, Sally E.0000-0002-3041-0209
Diskin, Glenn S.0000-0002-3617-0269
Additional Information:© Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Received: 15 Jun 2020 – Discussion started: 30 Jun 2020 – Revised: 19 Oct 2020 – Accepted: 19 Oct 2020 – Published: 01 Dec 2020. We thank the editor Tim Butler and two anonymous reviewers for the constructive comments and useful suggestions. We would like to acknowledge high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. Neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains. We thank Yi Yin, Arjo Segers, Aki Tsuruta, Peter Bergamaschi, and Bo Zheng for sharing their CH4 inversions. We acknowledge James H. Crawford, Alan Fried, Andrew Weinheimer, and everybody that contributed to the KORUS-AQ campaign. The PTR-MS instrument team (Philipp Eichler, Lisa Kaser, Toms Mikoviny, Markus Müller, Armin Wisthaler) is acknowledged for providing the PTR-MS data for this study. We also thank Duseong Jo for reading the paper. This research has been supported by the National Aeronautics and Space Administration (grant no. NNX16AD96G) and the National Oceanic and Atmospheric Administration (grant no. NA18OAR4310283). Author contributions. BG and LE designed the study. BG performed all the simulations with help from LE, ST, KR and JB. BG and LE analyzed the results with contributions from all authors. BG, LE, and AFA wrote the original draft. All authors participated in paper review, comments, and editing. Code and data availability. CESM2.1.0 is a publicly released version of the Community Earth System Model that is freely available online (at https://www.cesm.ucar.edu/, last access: 2 April 2020). The Data Assimilation Research Testbed is open-source software (version Manhattan; Boulder, Colorado: UCAR/NCAR/CISL/DAReS, https://doi.org/10.5065/D6WQ0202); code and documentation are available at https://dart.ucar.edu/ (last access: 22 November 2020). The Korea–United States Air Quality Field Study (KORUS-AQ) dataset is available at https://doi.org/10.5067/Suborbital/KORUSAQ/DATA01. The ARIAs observational dataset is available at https://www-air.larc.nasa.gov/cgi-bin/ArcView/korusaq?OTHER=1#top (last access: 24 November 2020). MOPITT data are available at https://www2.acom.ucar.edu/mopitt (last access: 24 November 2020, UCAR, 2020). The Tropospheric Chemistry Reanalysis version 2 is available for download at https://tes.jpl.nasa.gov/chemical-reanalysis/products/monthly-mean/ (last access: 24 November 2020, JPL, 2020). The Copernicus Atmosphere Monitoring Service (CAMS) global bottom-up emission inventory is available on the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) website (https://eccad3.sedoo.fr, last access: 24 November 2020, AERIS, 2020). Supplement. The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-14617-2020-supplement. The authors declare that they have no conflict of interest. Review statement. This paper was edited by Tim Butler and reviewed by two anonymous referees.
Funders:
Funding AgencyGrant Number
NASANNX16AD96G
National Oceanic and Atmospheric Administration (NOAA)NA18OAR4310283
Issue or Number:23
PubMed Central ID:PMC7786812
Record Number:CaltechAUTHORS:20210114-164620773
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210114-164620773
Official Citation:Gaubert, B., Emmons, L. K., Raeder, K., Tilmes, S., Miyazaki, K., Arellano Jr., A. F., Elguindi, N., Granier, C., Tang, W., Barré, J., Worden, H. M., Buchholz, R. R., Edwards, D. P., Franke, P., Anderson, J. L., Saunois, M., Schroeder, J., Woo, J.-H., Simpson, I. J., Blake, D. R., Meinardi, S., Wennberg, P. O., Crounse, J., Teng, A., Kim, M., Dickerson, R. R., He, H., Ren, X., Pusede, S. E., and Diskin, G. S.: Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ, Atmos. Chem. Phys., 20, 14617–14647, https://doi.org/10.5194/acp-20-14617-2020, 2020.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107503
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:15 Jan 2021 17:45
Last Modified:11 Feb 2021 00:06

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