Preprints
https://doi.org/10.5194/gmd-2021-375
https://doi.org/10.5194/gmd-2021-375

Submitted as: development and technical paper 11 Nov 2021

Submitted as: development and technical paper | 11 Nov 2021

Review status: this preprint is currently under review for the journal GMD.

Improving the joint estimation of CO2 and surface carbon fluxes using a Constrained Ensemble Kalman Filter in COLA (v1.0)

Zhiqiang Liu1,2, Ning Zeng3,4,1, Yun Liu5,6, Eugenia Kalnay3, Ghassem Asrar7, Bo Wu1, Qixiang Cai1, Di Liu8, and Pengfei Han9,1 Zhiqiang Liu et al.
  • 1State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 2College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
  • 3Dept. of Atmospheric and Oceanic Science, University of Maryland – College Park, Maryland, USA
  • 4Earth System Science Interdisciplinary Center, University of Maryland, USA
  • 5International Laboratory for High-Resolution Earth System Model and Prediction (iHESP), Texas A&M University, College Station, Texas, USA
  • 6Dept. of Oceanography, Texas A & M University, College Station, TX, USA
  • 7Universities Space Research Association, Columbia, MD, USA
  • 8Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
  • 9Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Abstract. Atmospheric inversion of carbon dioxide (CO2) measurements to understand carbon sources and sinks has made great progress over the last two decades. However, most of the studies, including four-dimension variational (4D-Var), Ensemble Kalman filter (EnKF), and Bayesian synthesis approaches, obtains directly only fluxes while CO2 concentration is derived with the forward model as post-analysis. Kang et al. (2012) used the Local Ensemble Transform Kalman Filter (LETKF) that updates the CO2, surface carbon fluxes (SCF), and meteorology field simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this system faces the challenge of maintaining global carbon mass. To overcome this shortcoming, here we introduce a Constrained Ensemble Kalman Filter (CEnKF) approach to ensure the conservation of global CO2 mass. After a standard LETKF procedure, an additional assimilation process is applied to adjust CO2 at each model grid point and to ensure the consistency between the analysis and the first guess of global CO2 mass. In the context of observing system simulation experiments (OSSEs), we show that the CEnKF can significantly reduce the annual global SCF bias from ~0.2 gigaton to less than 0.06 gigaton by comparing between experiments with and without it. Moreover, the annual bias over most continental regions is also reduced. At the seasonal scale, the improved system reduced the flux root-mean-square error from priori to analysis by 48–90 %, depending on the continental region. Moreover, the 2015–2016 El Nino impact is well captured with anomalies mainly in the tropics.

Zhiqiang Liu et al.

Status: open (until 06 Jan 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-375', Juan Antonio Añel, 26 Nov 2021 reply

Zhiqiang Liu et al.

Zhiqiang Liu et al.

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Short summary
We described the application of a Constrained Ensemble Kalman Filter (CEnKF) in the joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is constrained without disruption but improving the LETKF estimation of both CO2 and fluxes.