Articles | Volume 23, issue 12
https://doi.org/10.5194/acp-23-6719-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-6719-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The carbon sink in China as seen from GOSAT with a regional inversion system based on the Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS)
Xingxia Kou
Institute of Urban Meteorology, China Meteorological Administration, Beijing, China
Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing, China
Zhen Peng
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Meigen Zhang
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Fei Hu
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Xiao Han
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Ziming Li
Beijing Meteorological Observatory, Beijing, China
Lili Lei
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Key Laboratory of Mesoscale Severe Weather, Ministry of Education, Nanjing University, Nanjing, China
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Cited articles
Brioude, J., Angevine, W. M., Ahmadov, R., Kim, S.-W., Evan, S., McKeen, S. A., Hsie, E.-Y., Frost, G. J., Neuman, J. A., Pollack, I. B., Peischl, J., Ryerson, T. B., Holloway, J., Brown, S. S., Nowak, J. B., Roberts, J. M., Wofsy, S. C., Santoni, G. W., Oda, T., and Trainer, M.:
Top-down estimate of surface flux in the Los Angeles Basin using a mesoscale inverse modeling technique: assessing anthropogenic emissions of CO, NOx and CO2 and their impacts, Atmos. Chem. Phys., 13, 3661–3677, https://doi.org/10.5194/acp-13-3661-2013, 2013.
Broquet, G., Chevallier, F., Rayner, P., Aulagnier, C., Pison, I., Ramonet, M., Martina, S., Vermeulen, A. T., and Ciais, P. A.:
European summertime CO2 biogenic flux inversion at mesoscale from continuous in situ mixing ratio measurements, J. Geophys. Res.-Atmos., 116, D23303, https://doi.org/10.1029/2011JD016202, 2011.
Byrne, B., Jones, D. B. A., Strong, K., Zeng, Z. C., Deng, F., and Liu, J.:
Sensitivity of CO2 surface flux constraints to observational coverage, J. Geophys. Res.-Atmos., 122, 6672–6694, https://doi.org/10.1002/2016JD026164, 2017.
Byrne, B., Jones, D. B. A., Strong, K., Polavarapu, S. M., Harper, A. B., Baker, D. F., and Maksyutov, S.:
On what scales can GOSAT flux inversions constrain anomalies in terrestrial ecosystems?, Atmos. Chem. Phys., 19, 13017–13035, https://doi.org/10.5194/acp-19-13017-2019, 2019.
Chen, Z. C., Huntzinger, D. N., Liu, J. J., Piao, S. L., Wang, X. H., and Sitch, S.:
Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models, Environ. Res. Lett., 16, 054041, https://doi.org/10.1088/1748-9326/abfac1, 2021.
Chevallier, F.: On the statistical optimality of CO2 atmospheric inversions assimilating CO2 column retrievals, Atmos. Chem. Phys., 15, 11133–11145, https://doi.org/10.5194/acp-15-11133-2015, 2015.
Chevallier, F., Remaud, M., O'Dell, C. W., Baker, D., Peylin, P., and Cozic, A.:
Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions, Atmos. Chem. Phys., 19, 14233–14251, https://doi.org/10.5194/acp-19-14233-2019, 2019.
Ciais, P., Crisp, D., Denier van der Gon, H., Engelen, R., JanssensMaenhout, G., Heimann, M., Rayner, P., and Scholze, M.:
Towards a European operational observing system to monitor fossil CO2 emissions – final report from the expert group, vol. 19, European Commission, Copernicus Climate Change Service, ISBN 978-92-79-53482-9, https://doi.org/10.2788/350433 (last access: 1 November 2022), 2015.
COLA: Grid Analysis and Display System (GrADS), http://cola.gmu.edu/grads/ (last access: 15 June 2023), 2023.
Crowell, S., Baker, D., Schuh, A., Basu, S., Jacobson, A. R., Chevallier, F., Liu, J., Deng, F., Feng, L., McKain. K., Chatterjee, A., Miller, J. B., Stehpens, B. B., Eldering, A., Crisp, D., Schimel, D., Nassar, R., O'Dell, C. W., Oda. T., Sweeny, C., Palmer, P. I., and Jones, D. B. A.: The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network, Atmos. Chem. Phys., 19, 9797–9831, https://doi.org/10.5194/acp-19-9797-2019, 2019.
Deng, F., Jones, D. B. A., O'Dell, C. W., Nassar, R., and Parazoo, N. C.:
Combining GOSAT XCO2 observations over land and ocean to improve regional CO2 flux estimates, J. Geophys. Res.-Atmos., 121, 1896–1913, https://doi.org/10.1002/2015JD024157, 2016.
Deng, Z., Ciais, P., Tzompa-Sosa, Z. A., Saunois, M., Qiu, C., Tan, C., Sun, T., Ke, P., Cui, Y., Tanaka, K., Lin, X., Thompson, R. L., Tian, H., Yao, Y., Huang, Y., Lauerwald, R., Jain, A. K., Xu, X., Bastos, A., Sitch, S., Palmer, P. I., Lauvaux, T., d'Aspremont, A., Giron, C., Benoit, A., Poulter, B., Chang, J., Petrescu, A. M. R., Davis, S. J., Liu, Z., Grassi, G., Albergel, C., Tubiello, F. N., Perugini, L., Peters, W., and Chevallier, F.:
Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions, Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, 2022.
Eldering, A., O'Dell, C. W., Wennberg, P. O., Crisp, D., Gunson, M. R., Viatte, C., Avis, C., Braverman, A., Castano, R., Chang, A., Chapsky, L., Cheng, C., Connor, B., Dang, L., Doran, G., Fisher, B., Frankenberg, C., Fu, D., Granat, R., Hobbs, J., Lee, R. A. M., Mandrake, L., McDuffie, J., Miller, C. E., Myers, V., Natraj, V., O'Brien, D., Osterman, G. B., Oyafuso, F., Payne, V. H., Pollock, H. R., Polonsky, I., Roehl, C. M., Rosenberg, R., Schwandner, F., Smyth, M., Tang, V., Taylor, T. E., To, C., Wunch, D., and Yoshimizu, J.:
The Orbiting Carbon Observatory-2: first 18 months of science data products, Atmos. Meas. Tech., 10, 549–563, https://doi.org/10.5194/amt-10-549-2017, 2017a.
Eldering, A., Wennberg, P. O., Crisp, D., Schimel, D. S., Gunson, M. R., Chatterjee, A., Liu, J., Schwandner, F. M., Sun, Y., O'Dell, C. W.:
The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes, Science, 358, eaam5745, https://doi.org/10.1126/science.aam5745, 2017b.
Eldering, A., Taylor, T. E., O'Dell, C. W., and Pavlick, R.: The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data, Atmos. Meas. Tech., 12, 2341–2370, https://doi.org/10.5194/amt-12-2341-2019, 2019.
Enting, I. G., Trudinger, C. M., and Francey, R. J.:
A synthesis inversion of the concentration and δ13C of atmospheric CO2, Tellus B, 47, 35–52, https://doi.org/10.3402/tellusb.v47i1-2.15998, 1995.
Feng, L., Palmer, P. I., Bösch, H., and Dance, S.:
Estimating surface CO2 fluxes from space-borne CO2 dry air mole fraction observations using an ensemble Kalman Filter, Atmos. Chem. Phys., 9, 2619–2633, https://doi.org/10.5194/acp-9-2619-2009, 2009.
Feng, L., Palmer, P. I., Bösch, H., Parker, R. J., Webb, A. J., Correia, C. S. C., Deutscher, N. M., Domingues, L. G., Feist, D. G., Gatti, L. V., Gloor, E., Hase, F., Kivi, R., Liu, Y., Miller, J. B., Morino, I., Sussmann, R., Strong, K., Uchino, O., Wang, J., and Zahn, A.:
Consistent regional fluxes of CH4 and CO2 inferred from GOSAT proxy XCH4:XCO2 retrievals, 2010–2014, Atmos. Chem. Phys., 17, 4781–4797, https://doi.org/10.5194/acp-17-4781-2017, 2017.
Fu, Y., Liao, H., Tian, X. J., Gao, H., Jia, B. H., and Han, R.:
Impact of prior terrestrial carbon fluxes on simulations of atmospheric CO2 concentrations, J. Geophys. Res.-Atmos., 126, e2021JD034794, https://doi.org/10.1029/2021JD034794, 2021.
Gaspari, G. and Cohn S. E.:
Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757. https://doi.org/10.1002/qj.49712555417, 1999.
Glumb, R., Davis, G., and Lietzke, C.: The tanso-fts-2 instrument for the gosat-2 greenhouse gas monitoring mission, in: 2014 IEEE Geoscience and Remote Sensing Symposium, 13–18 July 2014, Quebec City, Canada, 1238–1240, https://doi.org/10.1109/IGARSS.2014.6946656, 2014.
He, H. L., Wang, S. Q., Zhang, L., Wang, J. B., Ren, X. L., Zhou, L., Piao, S. L., Yan, H., Ju, W. M., Gu, F. X., Yu, S. Y., Yang, Y. H., Wang, M. M., Niu, Z. G., Ge, R., Yan, H. M., Huang, M., Zhou, G. Y., Bai, Y. F., Xie, Z. Q., Tang, Z. Y., Wu, B. F., Zhang, L. M., He, N. P., Wang, Q. F., and Yu, G. R.:
Altered trends in carbon uptake in China's terrestrial ecosystems under the enhanced summer monsoon and warming hiatus, Natl. Sci. Rev., 6, 505–514, https://doi.org/10.1093/nsr/nwz021, 2019.
He, W., Jiang, F., Wu, M., Ju, W., Scholze, M., Chen, J. M., Byrne, B., Liu, J. J., Wang, H. M., Wang, J., Wang, S. H., Zhou, Y. L., Zhang, C. H., Nguyen, N. T., Shen, Y., and Chen, Z.:
China's terrestrial carbon sink over 2010–2015 constrained by satellite observations of atmospheric CO2 and land surface variables, J. Geophys. Res.-Biogeo., 127, e2021JG006644, https://doi.org/10.1029/2021JG006644, 2022.
Houtekamer, P. L., and Mitchell, H. L.:
A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 129, 123–137, https://doi.org/10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2, 2001.
Houweling, S., Baker, D., Basu, S., Boesch, H., Butz, A. Chevallier, F., Deng, F., Dlugokencky, E. J., Feng, L., Ganshin, A., Hasekamp, O., Jones, D., Maksyutov, S., Marshall, J., Oda, T., O'Dell, C. W., Oshchepkov, S., Palmer, P. I., Peylin, P., Poussi, Z., Reum, F., Takagi, H., Yoshida, Y., Zhuravlev, R.:
An intercomparison of inverse models for estimating sources and sinks of CO2 using GOSAT measurements, J. Geophys. Res.-Atmos., 120, 5253–5266, https://doi.org/10.1002/2014JD022962, 2015.
Huang, Z. K., Peng, Z., Liu, H. N., Zhang, M. G., Ma, X. G., Yang, S. C., Lee, S. D., Kim, S. Y.:
Development of CMAQ for East Asia CO2 data assimilation under an EnKF framework: a first result, Chin. Sci. Bull., 59, 3200–3208, https://doi.org/10.1007/s11434-014-0348-9, 2014.
IPCC: 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventory, edited by: Buendia, C. E., Guendehou, S., Limmeechokchai, B., Pipatti, R., Rojas, Y., and Sturgiss, R., considered in May 2019 during the IPCC's 49th Session (Kyoto, Japan), accepted, 12 May 2019.
Jacobson, A. R., Schuldt, K. N., Miller, J. B., Oda, T., Tans, P., Andrews, A., Mund, J., Ott, L., Collatz, G. J., Aalto, T., Afshar, S., Aikin, K., Aoki, S., Apadula, F., Baier, B., Bergamaschi, P., Beyersdorf, A., Biraud, S. C., Bollenbacher, A., and Bowling, D.: CarbonTracker CT2019B, model published by NOAA Global Monitoring Laboratory, https://doi.org/10.25925/20201008 (last access: 1 November 2022), 2020.
Jiang, F., Chen, J. M., Zhou, L. X., Ju, W. M., Zhang, H. F., Machida, T., Ciais, P., Peters, W., Wang, H. M., Chen, B. Z., Liu, L. X., Zhang, C. H., Matsueda, H., and Sawa, Y.:
A comprehensive estimate of recent carbon sinks in China using both top-down and bottom-up approaches, Sci. Rep., 6, 22130, https://doi.org/10.1038/srep22130, 2016.
Jiang, F., Wang, H., Chen, J. M., Ju, W., Tian, X., Feng, S., Li, G., Chen, Z., Zhang, S., Lu, X., Liu, J., Wang, H., Wang, J., He, W., and Wu, M.:
Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System, Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, 2021.
Jiang, F., Ju, W., He, W., Wu, M., Wang, H., Wang, J., Jia, M., Feng, S., Zhang, L., and Chen, J. M.:
A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021), Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, 2022.
JPL – Jet Propulsion Laboratory: oco2.gesdisc.eosdis.nasa.gov, https://oco2.gesdisc.eosdis.nasa.gov/data/GOSAT_TANSO_Level2/ (last access: 15 June 2023), 2023.
Kou, X. X., Zhang, M. G., and Peng, Z.:
Numerical simulation of CO2 concentrations in East Asia with RAMS-CMAQ, Atmos. Ocean. Sci. Lett., 6, 179–184, https://doi.org/10.3878/j.issn.1674-2834.13.0022, 2013.
Kou, X. X., Zhang, M. G., Peng, Z., and Wang, Y. H.:
Assessment of the biospheric contribution to surface atmospheric CO2 concentrations over East Asia with a regional chemical transport model, Adv. Atmos. Sci., 32, 287–300, https://doi.org/10.1007/s00376-014-4059-6, 2015.
Kou, X. X., Tian, X. J., Zhang, M. G., Peng, Z., and Zhang, X. L.:
Accounting for CO2 variability over East Asia with a regional joint inversion system and its preliminary evaluation, J. Meteorol. Res.-PRC, 31, 834–851, https://doi.org/10.1007/s13351-017-6149-8, 2017.
Kou, X. X., Peng, Z., Zhang, M. G., Zhang, N., Lei, L., Zhao, X., Miao, S. G., Li, Z. M., and Ding, Q. J.:
Assessment of the meteorological impact on improved PM2.5 air quality over North China during 2016–2019 based on a regional joint atmospheric composition reanalysis data-set, J. Geophys. Res.-Atmos., 126, e2020JD034382, https://doi.org/10.1029/2020JD034382, 2021.
Kountouris, P., Gerbig, C., Rödenbeck, C., Karstens, U., Koch, T. F., and Heimann, M.:
Atmospheric CO2 inversions on the mesoscale using data-driven prior uncertainties: quantification of the European terrestrial CO2 fluxes, Atmos. Chem. Phys., 18, 3047–3064, https://doi.org/10.5194/acp-18-3047-2018, 2018.
Kurokawa, J. and Ohara, T.: Long-term historical trends in air pollutant emissions in Asia: Regional Emission inventory in ASia (REAS) version 3, Atmos. Chem. Phys., 20, 12761–12793, https://doi.org/10.5194/acp-20-12761-2020, 2020.
Kuze, A., Suto, H., Nakajima, M., and Hamazaki, T.:
Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring, Appl. Optics, 48, 6716–6733, https://doi.org/10.1364/AO.48.006716, 2009.
Lauvaux, T., Miles, N. L., Deng, A., Richardson, S. J., Cambaliza, M. O., Davis, K. J., Gaudet, B., Gurney, K. R., Huang, J. H., O'keefe, D., Song, Y., Karion, A., Oda, T., Patarasuk, R., Razlivanov, I., Sarmiento, D., Shepson, P., Sweeney, C., Turnbull, J., Wu, K.:
High-resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX), J. Geophys. Res.-Atmos., 121, 5213–5236, https://doi.org/10.1002/2015JD024473, 2016.
Lei, L., Guan, X., Zeng, Z., Zhang, B., Ru, F., and Bu, R.:
A comparison of atmospheric CO2 concentration GOSAT-based observations and model simulations, Sci. China Earth Sci., 57, 1393–1402, https://doi.org/10.1007/s11430-013-4807-y, 2014.
Li, R., Zhang, M. G., Chen, L. F., Kou, X. X., and Skorokhod, A.:
CMAQ simulation of atmospheric CO2 concentration in East Asia: comparison with GOSAT observations and ground measurements, Atmos. Environ., 160, 176–185, https://doi.org/10.1016/j.atmosenv.2017.03.056, 2017.
Liang, M., Zhang, Y., Ma, Q., L., Yu, D. J., Chen, X. J., Cohen, J. B.:
Dramatic decline of observed atmospheric CO2 and CH4 during the COVID-19 lockdown over the Yangtze River Delta of China, J. Environ. Sci., 124, 712–722, https://doi.org/10.1016/j.jes.2021.09.034, 2023.
Lindqvist, H., O'Dell, C. W., Basu, S., Boesch, H., Chevallier, F., Deutscher, N., Feng, L., Fisher, B., Hase, F., Inoue, M., Kivi, R., Morino, I., Palmer, P. I., Parker, R., Schneider, M., Sussmann, R., and Yoshida, Y.:
Does GOSAT capture the true seasonal cycle of carbon dioxide?, Atmos. Chem. Phys., 15, 13023–13040, https://doi.org/10.5194/acp-15-13023-2015, 2015.
Liu, J., Baskaran, L., Bowman, K., Schimel, D., Bloom, A. A., Parazoo, N. C., Oda, T., Carroll, D., Menemenlis, D., Joiner, J., Commane, R., Daube, B., Gatti, L. V., McKain, K., Miller, J., Stephens, B. B., Sweeney, C., and Wofsy, S.:
Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020), Earth Syst. Sci. Data, 13, 299–330, https://doi.org/10.5194/essd-13-299-2021, 2021.
Liu, Y., Wang, J., Yao, L., Chen, X., Cai, Z. N., Yang, D. X., Yin, Z. S., Gu, S. Y., Tian, L. F., Lu, N. M., and Lyu, D. R.:
The TanSat mission: Preliminary global observations, Sci. Bull., 63, 1200–1207, https://doi.org/10.1016/j.scib.2018.08.004, 2018.
Liu, Z., Bambha, R. P., Pinto, J. P., Zeng, T., Boylan, J., Huang, M. Y., Lei, H. M., Zhao, C., Liu, S. S., Mao, J. F., Schwalm, C. R., Shi, X. Y., Wei, Y. X., Michelsenet, H. A.:
Toward verifying fossil fuel CO2 emissions with the Community Multi-scale Air Quality (CMAQ) model: motivation, model description and initial simulation, J. Air Waste Manage., 64, 419–435, https://doi.org/10.1080/10962247.2013.816642, 2013.
Maksyutov, S., Takagi, H., Valsala, V. K., Saito, M., Oda, T., Saeki, T., Belikov, D. A., Saito, R., Ito, A., Yoshida, Y., Morino, I., Uchino, O., Andres, R. J., and Yokota, T.:
Regional CO2 flux estimates for 2009–2010 based on GOSAT and ground-based CO2 observations, Atmos. Chem. Phys., 13, 9351–9373, https://doi.org/10.5194/acp-13-9351-2013, 2013.
MATLAB: MATLAB and Statistics Toolbox Release, https://www.mathworks.com/ (last access: 15 June 2023), 2019.
Monteil, G. and Scholze, M.: Regional CO2 inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0, Geosci. Model Dev., 14, 3383–3406, https://doi.org/10.5194/gmd-14-3383-2021, 2021.
Monteil, G., Broquet, G., Scholze, M., Lang, M., Karstens, U., Gerbig, C., Koch, F.-T., Smith, N. E., Thompson, R. L., Luijkx, I. T., White, E., Meesters, A., Ciais, P., Ganesan, A. L., Manning, A., Mischurow, M., Peters, W., Peylin, P., Tarniewicz, J., Rigby, M., Rödenbeck, C., Vermeulen, A., and Walton, E. M.: The regional European atmospheric transport inversion comparison, EUROCOM: first results on European-wide terrestrial carbon fluxes for the period 2006–2015, Atmos. Chem. Phys., 20, 12063–12091, https://doi.org/10.5194/acp-20-12063-2020, 2020.
National Climate Center, China Meteorological Administration: China Climate Bulletin 2016, edited by Chao, Q. C., Jia, X. L., and Li, W., Beijing, China, 2016.
NOAA ESRL: CarbonTracker CT2022, http://carbontracker.noaa.gov (last access: 15 June 2023), 2023.
O'Dell, C. W., Eldering, A., Wennberg, P. O., Crisp, D., Gunson, M. R., Fisher, B., Frankenberg, C., Kiel, M., Lindqvist, H., Mandrake, L., Merrelli, A., Natraj, V., Nelson, R. R., Osterman, G. B., Payne, V. H., Taylor, T. E., Wunch, D., Drouin, B. J., Oyafuso, F., Chang, A., McDuffie, J., Smyth, M., Baker, D. F., Basu, S., Chevallier, F., Crowell, S. M. R., Feng, L., Palmer, P. I., Dubey, M., García, O. E., Griffith, D. W. T., Hase, F., Iraci, L. T., Kivi, R., Morino, I., Notholt, J., Ohyama, H., Petri, C., Roehl, C. M., Sha, M. K., Strong, K., Sussmann, R., Te, Y., Uchino, O., and Velazco, V. A.:
Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm, Atmos. Meas. Tech., 11, 6539–6576, https://doi.org/10.5194/amt-11-6539-2018, 2018.
Peng, Z., Zhang, M., Kou, X., Tian, X., and Ma, X.:
A regional carbon data assimilation system and its preliminary evaluation in East Asia, Atmos. Chem. Phys., 15, 1087–1104, https://doi.org/10.5194/acp-15-1087-2015, 2015.
Peng, Z., Liu, Z., Chen, D., and Ban, J.:
Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter, Atmos. Chem. Phys., 17, 4837–4855, https://doi.org/10.5194/acp-17-4837-2017, 2017.
Peng, Z., Lei, L., Liu, Z., Sun, J., Ding, A., Ban, J., Chen, D., Kou, X., and Chu, K.:
The impact of multi-species surface chemical observation assimilation on air quality forecasts in China, Atmos. Chem. Phys., 18, 17387–17404, https://doi.org/10.5194/acp-18-17387-2018, 2018.
Peng, Z., Lei, L. L., Liu, Z., Liu, H. N., Chu, K. K., and Kou, X. X.:
Impact of assimilating meteorological observations on source emissions estimate and chemical simulations, Geophys. Res. Lett., 47, e2020GL089030, https://doi.org/10.1029/2020GL089030, 2020.
Peng, Z., Kou, X. X., Zhang, M. G., Lei, L. L., Miao, S. G., Wang, H. M., Jiang, F., Han, X., and Fang, S. X.:
CO2 flux inversion with a regional joint data assimilation system based on CMAQ, EnKS, and surface observations, J. Geophys. Res.-Atmos., 128, e2022JD037154, https://doi.org/10.1029/2022JD037154, 2023.
Peters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway, T. J., Masarie, K., Miller, J. B., Bruhwiler, L. M. P., Petron, G., Hirsch, A., Worthy, D. E. J., van der Werf, G. R., Randerson, J. T., Wennberg, P. O., Krol, M. C., Tans, P. P.:
An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker, P. Natl. Acad. Sci. USA, 104, 18925–18930, https://doi.org/10.1073/pnas.0708986104, 2007.
Piao, S. L., Fang, J. Y., Ciais, P., Peylin, P. Huang, Y., Sitch, S., and Wang, T.:
The carbon balance of terrestrial ecosystems in China, Nature, 458, 23, 1009–1013, https://doi.org/10.1038/nature07944, 2009.
Piao, S., He, Y., Wang, X., and Chen F.:
Estimation of China's terrestrial ecosystem carbon sink: methods, progress and prospects, Sci. China Earth Sci., 65, 641–651, https://doi.org/10.1007/s11430-021-9892-6, 2022.
Pillai, D., Buchwitz, M., Gerbig, C., Koch, T., Reuter, M., Bovensmann, H., Marshall, J., and Burrows, J. P.:
Tracking city CO2 emissions from space using a high-resolution inverse modelling approach: a case study for Berlin, Germany, Atmos. Chem. Phys., 16, 9591–9610, https://doi.org/10.5194/acp-16-9591-2016, 2016.
Pinty, B., Janssens-Maenhout, G., Dowell, M., Zunker, H., Brunhes, T., Ciais, P., Holmlund, G. Janssens-Maenhout, Y. Meijer, P., and Palmer, M. S.:
An Operational Anthropogenic CO2 Emissions Monitoring & Verification Support capacity – Baseline Requirements, Model Components and Functional Architecture, European Commission Joint Research Centre, EUR 28736 EN, https://doi.org/10.2760/39384, 2017.
Reuter, M., Buchwitz, M., Hilker, M., Heymann, J., Bovensmann, H., Burrow, J. P., Houweling, S., Liu, Y. Y., Nassar, M. R., Chevallier, F., Ciais, P., Marshall, J., and Reichstein, M.:
How much CO2 is taken up by the European terrestrial biosphere?, B. Am. Meteorol. Soc., 665–671, https://doi.org/10.1175/BAMS-D-15-00310.1, 2017.
Rödenbeck, C., Zaehle, S., Keeling, R., and Heimann, M.:
How does the terrestrial carbon exchange respond to inter-annual climatic variations? A quantification based on atmospheric CO2 data, Biogeosciences, 15, 2481–2498, https://doi.org/10.5194/bg-15-2481-2018, 2018.
Schuh, A. E., Byrne, B., Jacobson, A. R., Crowell, S. M. R., Deng, F., Baker, D. F., Johnson, M. S., Philip, S., and Weir, B.: On the role of atmospheric model transport uncertainty in estimating the Chinese land carbon sink, Nature, 603, 40 E13–E16, https://doi.org/10.1038/s41586-021-04258-9, 2022.
Staufer, J., Broquet, G., Bréon, F.-M., Puygrenier, V., Chevallier, F., Xueref-Rémy, I., Dieudonné, E., Lopez, M., Schmidt, M., Ramonet, M., Perrussel, O., Lac, C., Wu, L., and Ciais, P.:
The first 1-year-long estimate of the Paris region fossil fuel CO2 emissions based on atmospheric inversion, Atmos. Chem. Phys., 16, 14703–14726, https://doi.org/10.5194/acp-16-14703-2016, 2016.
Takagi, H., Houweling, S., Andres, R. J., Belikov, D., Bril, A., Boesch, H., Butz, A., Guerlet, S., Hasekamp, O., Maksyutov, S., Morino, I., Oda, T., O'Dell, C., Oshchepkov, S., Parker, R., Saito, M., Uchino, O., Yokota, T., Yoshida, Y., Valsala, V.:
Influence of differences in current GOSAT XCO2 retrievals on surface flux estimation, Geophys. Res. Lett., 41, 2598–2605, https://doi.org/10.1002/2013GL059174, 2014.
Thompson, R. L. and Stohl, A.:
FLEXINVERT: an atmospheric Bayesian inversion framework for determining surface fluxes of trace species using an optimized grid, Geosci. Model Dev., 7, 2223–2242, https://doi.org/10.5194/gmd-7-2223-2014, 2014.
Thompson, R. L., Patra, P. K., Chevallier, F., Maksyutov, S., Law, R. M., Ziehn, T., van der Laan-Luijkx, I. T., Peters, W., Ganshin, A., Zhuravlev, R., Maki, T., Nakamura, T., Shirai, T., Ishizawa, M., Saeki, T., Machida, T., Poulter, B., Canadell, J. G., and Ciais, P.:
Top–down assessment of the Asian carbon budget since the mid 1990s, Nat. Commun., 7, 10724, https://doi.org/10.1038/ncomms10724, 2016.
Tian, X., Xie, Z., Liu, Y., Cai, Z., Fu, Y., Zhang, H., and Feng, L.:
A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO2 fluxes and 3-D atmospheric CO2 concentrations from observations, Atmos. Chem. Phys., 14, 13281–13293, https://doi.org/10.5194/acp-14-13281-2014, 2014.
UNFCCC: The Paris Agreement on Climate Change, adopted by 196 Parties at the UN Climate Change Conference in Paris, France, on 12 December 2015 and entered into force on 4 November 2016, https://www.nrdc.org/sites/default/files/paris-climate-agreement-IB.pdf (last access: 15 June 2023), 2015.
van der Laan-Luijkx, I. T., van der Velde, I. R., van der Veen, E., Tsuruta, A., Stanislawska, K., Babenhauserheide, A., Zhang, H. F., Liu, Y., He, W., Chen, H., Masarie, K. A., Krol, M. C., and Peters, W.:
The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance 2001–2015, Geosci. Model Dev., 10, 2785–2800, https://doi.org/10.5194/gmd-10-2785-2017, 2017.
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.:
Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017.
Wang, H., Jiang, F., Wang, J., Ju, W., and Chen, J. M.:
Terrestrial ecosystem carbon flux estimated using GOSAT and OCO-2 XCO2 retrievals, Atmos. Chem. Phys., 19, 12067–12082, https://doi.org/10.5194/acp-19-12067-2019, 2019.
Wang, J., Feng, L., Palmer, P. I., Liu, Y., Fang, S. X., Bösch, H., O'Dell, C. W., Tang, X. P., Yang, D. X., Liu, L. X., and Xia, C. Z.:
Large Chinese land carbon sink estimated from atmospheric carbon dioxide data, Nature, 586, 720–735, https://doi.org/10.1038/s41586-020-2849-9, 2020.
Wang, J. S., Kawa, S. R., Collatz, G. J., Sasakawa, M., Gatti, L. V., Machida, T., Liu, Y., and Manyin, M. E.:
A global synthesis inversion analysis of recent variability in CO2 fluxes using GOSAT and in situ observations, Atmos. Chem. Phys., 18, 11097–11124, https://doi.org/10.5194/acp-18-11097-2018, 2018.
Wang, Y. L., Wang, X. H., Wang, K., Chevallier, F., Zhu, D., Lian, J., Yue, H., Tian, H. Q., Li, J. S., Zhu, J. X., Jeong, S. J., and Canadell, J. G.: The size of the land carbon sink in China, Nature, 603, E7–E12, https://doi.org/10.1038/s41586-021-04255-y, 2022.
Wunch, D., Wennberg, P. O., Osterman, G., Fisher, B., Naylor, B., Roehl, C. M., O'Dell, C., Mandrake, L., Viatte, C., Kiel, M., Griffith, D. W. T., Deutscher, N. M., Velazco, V. A., Notholt, J., Warneke, T., Petri, C., De Maziere, M., Sha, M. K., Sussmann, R., Rettinger, M., Pollard, D., Robinson, J., Morino, I., Uchino, O., Hase, F., Blumenstock, T., Feist, D. G., Arnold, S. G., Strong, K., Mendonca, J., Kivi, R., Heikkinen, P., Iraci, L., Podolske, J., Hillyard, P. W., Kawakami, S., Dubey, M. K., Parker, H. A., Sepulveda, E., García, O. E., Te, Y., Jeseck, P., Gunson, M. R., Crisp, D., and Eldering, A.:
Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) measurements with TCCON, Atmos. Meas. Tech., 10, 2209–2238, https://doi.org/10.5194/amt-10-2209-2017, 2017.
Yang, D. X., Liu, Y., Cai, Z. N., Chen, X., Yao, L., and Lyu, D. R.:
First global carbon dioxide maps produced from TanSat measurements, Adv. Atmos. Sci., 35, 621–623, https://doi.org/10.1007/s00376-018-7312-6, 2018.
Zhang, H. F., Chen, B. Z., van der Laan-Luijkx, I. T., Chen, J., Xu, G., Yan, J. W., Zhou, L. X., Fukuyama, Y., Tans, P. P., and Peters, W.:
Net terrestrial CO2 exchange over China during 2001–2010 estimated with an ensemble data assimilation system for atmospheric CO2, J. Geophys. Res.-Atmos., 119, 3500–3515, https://doi.org/10.1002/2013JD021297, 2014.
Zhang, M. G., Uno, I., Sugata, S., Wang, Z. F., Byun, D., and Akimoto, H.:
Numerical study of boundary layer ozone transport and photochemical production in East Asia in the wintertime, Geophys. Res. Lett., 29, 40–43, https://doi.org/10.1029/20001GL014368, 2002.
Zhang, Q. W., Li, M. Q., Wei, C., Mizzi, A. P., Huang, Y. J., and Gu, Q. R.:
Assimilation of OCO-2 retrievals with WRF-Chem/DART: A case study for the Midwestern United States, Atmos. Environ., 246, 118106, https://doi.org/10.1016/j.atmosenv.2020.118106, 2021.
Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.:
Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111, https://doi.org/10.5194/acp-18-14095-2018, 2018a.
Zheng, T., French, N. H. F., and Baxter, M.: Development of the WRF-CO2 4D-Var assimilation system v1.0, Geosci. Model Dev., 11, 1725–1752, https://doi.org/10.5194/gmd-11-1725-2018, 2018b.
Short summary
A CMAQ EnSRF-based regional inversion system was extended to resolve satellite retrievals into biogenic source–sink changes. The size of the assimilated biosphere sink in China inferred from GOSAT was −0.47 Pg C yr−1. The biosphere flux at the provincial scale was re-estimated following the refined description in the regional inversion.
A CMAQ EnSRF-based regional inversion system was extended to resolve satellite retrievals into...
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