28 Mar 2022
28 Mar 2022
Status: this preprint is currently under review for the journal ACP.

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

Yawen Kong1, Bo Zheng2,3, Qiang Zhang1, and Kebin He3,4 Yawen Kong et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
  • 2Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
  • 3State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
  • 4State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China

Abstract. Understanding carbon sources and sinks across the Earth’s surface is fundamental in climate science and policy; thus, these topics have been extensively studied but have yet to be fully resolved and are associated with massive debate regarding the sign and magnitude of the carbon budget from global to regional scales. Developing new models and estimates based on state-of-the-art algorithms and data constraints can provide valuable knowledge and contribute to a final ensemble model in which various optimal carbon budget estimates are integrated, such as the annual Global Carbon Budget paper. Here, we develop a new atmospheric inversion system based on the four-dimensional local ensemble transform Kalman filter (4D-LETKF) coupled with the GEOS-Chem global transport model to infer surface-to-atmosphere net carbon fluxes from Orbiting Carbon Observatory-2 (OCO-2) V10r XCO2 retrievals. The 4D-LETKF algorithm is adapted to an OCO-2-based global carbon inversion system for the first time in this work. On average, the mean annual terrestrial and oceanic fluxes between 2015 and 2020 are estimated as −2.02 GtC yr−1 and −2.34 GtC yr−1, respectively, compensating for 21 % and 24 %, respectively, of global fossil CO2 emissions (9.80 GtC yr−1). Our inversion results agree with the CO2 atmospheric growth rates reported by the National Oceanic and Atmospheric Administration (NOAA) and reduce the modelled CO2 concentration biases relative to the prior fluxes against surface and aircraft measurements. Our inversion-based carbon fluxes are broadly consistent with those provided by other global atmospheric inversion models, although discrepancies still occur in the land-ocean flux partitioning schemes and seasonal flux amplitudes over boreal and tropical regions, possibly due to the sparse observational constraints of the OCO-2 satellite and the divergent prior fluxes used in different inversion models. Four sensitivity experiments are performed herein to vary the prior fluxes and uncertainties in our inversion system, suggesting that regions that lack OCO-2 coverage are sensitive to the priors, especially over the tropics and high latitudes. In the further development of our inversion system, we will optimize the data-assimilation configuration to fully utilize current observations and increase the spatial and seasonal representativeness of the prior fluxes over regions that lack observations.

Yawen Kong et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-183', Anonymous Referee #1, 18 May 2022
  • RC2: 'comment on acp-2022-183', Anonymous Referee #2, 19 May 2022

Yawen Kong et al.


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Short summary
We developed a Bayesian atmospheric inversion system based on the 4D-LETKF algorithm coupled with GEOS-Chem, constrained by OCO-2 XCO2. This work represents the first time the 4D-LETKF algorithm was adapted to a global carbon inversion system that assimilated OCO-2 data. We inferred global gridded carbon fluxes from the latest OCO-2 V10r retrievals and investigated their magnitudes, variations, and partitioning schemes to understand the global and regional carbon budgets between 2015 and 2020.