Articles | Volume 22, issue 16
https://doi.org/10.5194/acp-22-10769-2022
https://doi.org/10.5194/acp-22-10769-2022
Research article
 | 
25 Aug 2022
Research article |  | 25 Aug 2022

Global and regional carbon budget for 2015–2020 inferred from OCO-2 based on an ensemble Kalman filter coupled with GEOS-Chem

Yawen Kong, Bo Zheng, Qiang Zhang, and Kebin He

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Subject: Gases | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
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Cited articles

Baker, D. F., Doney, S. C., and Schimel, D. S.: Variational data assimilation for atmospheric CO2, Tellus B, 58, 359–365, https://doi.org/10.1111/j.1600-0889.2006.00218.x, 2006. 
Basu, S., Baker, D. F., Chevallier, F., Patra, P. K., Liu, J., and Miller, J. B.: The impact of transport model differences on CO2 surface flux estimates from OCO-2 retrievals of column average CO2, Atmos. Chem. Phys., 18, 7189–7215, https://doi.org/10.5194/acp-18-7189-2018, 2018. 
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. 
Bruhwiler, L. M. P., Michalak, A. M., Peters, W., Baker, D. F., and Tans, P.: An improved Kalman Smoother for atmospheric inversions, Atmos. Chem. Phys., 5, 2691–2702, https://doi.org/10.5194/acp-5-2691-2005, 2005. 
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We developed a Bayesian atmospheric inversion system based on the 4D local ensemble transform Kalman filter (4D-LETKF) algorithm coupled with GEOS-Chem from the latest Orbiting Carbon Observatory-2 (OCO-2) V10r XCO2 retrievals. This is the first adaptation of 4D-LETKF to an OCO-2-based global carbon inversion system. We inferred global gridded carbon fluxes and investigated their magnitudes, variations, and partitioning schemes to understand the global and regional carbon budgets for 2015–2020.
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