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Volume 14, issue 23
Atmos. Chem. Phys., 14, 13281–13293, 2014
https://doi.org/10.5194/acp-14-13281-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: 9th International Carbon Dioxide Conference (ICDC9) (ESD/ACP/AMT/BG...

Atmos. Chem. Phys., 14, 13281–13293, 2014
https://doi.org/10.5194/acp-14-13281-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Dec 2014

Research article | 12 Dec 2014

A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO2 fluxes and 3-D atmospheric CO2 concentrations from observations

X. Tian1,2, Z. Xie3, Y. Liu4, Z. Cai4, Y. Fu5, H. Zhang6, and L. Feng7 X. Tian et al.
  • 1ICCES, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 2Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, 210044, China
  • 3LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 4LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 5Climate Change Research Center (CCRC), Chinese Academy of Sciences, Beijing, China
  • 6Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, China
  • 7School of GeoSciences, University of Edinburgh, King's Buildings, Edinburgh EH9 3JN, UK

Abstract. We have developed a novel framework ("Tan-Tracker") for assimilating observations of atmospheric CO2 concentrations, based on the POD-based (proper orthogonal decomposition) ensemble four-dimensional variational data assimilation method (PODEn4DVar). The high flexibility and the high computational efficiency of the PODEn4DVar approach allow us to include both the atmospheric CO2 concentrations and the surface CO2 fluxes as part of the large state vector to be simultaneously estimated from assimilation of atmospheric CO2 observations. Compared to most modern top-down flux inversion approaches, where only surface fluxes are considered as control variables, one major advantage of our joint data assimilation system is that, in principle, no assumption on perfect transport models is needed. In addition, the possibility for Tan-Tracker to use a complete dynamic model to consistently describe the time evolution of CO2 surface fluxes (CFs) and the atmospheric CO2 concentrations represents a better use of observation information for recycling the analyses at each assimilation step in order to improve the forecasts for the following assimilations. An experimental Tan-Tracker system has been built based on a complete augmented dynamical model, where (1) the surface atmosphere CO2 exchanges are prescribed by using a persistent forecasting model for the scaling factors of the first-guess net CO2 surface fluxes and (2) the atmospheric CO2 transport is simulated by using the GEOS-Chem three-dimensional global chemistry transport model. Observing system simulation experiments (OSSEs) for assimilating synthetic in situ observations of surface CO2 concentrations are carefully designed to evaluate the effectiveness of the Tan-Tracker system. In particular, detailed comparisons are made with its simplified version (referred to as TT-S) with only CFs taken as the prognostic variables. It is found that our Tan-Tracker system is capable of outperforming TT-S with higher assimilation precision for both CO2 concentrations and CO2 fluxes, mainly due to the simultaneous estimation of CO2 concentrations and CFs in our Tan-Tracker data assimilation system. A experiment for assimilating the real dry-air column CO2 retrievals (XCO2) from the Japanese Greenhouse Gases Observation Satellite (GOSAT) further demonstrates its potential wide applications.

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Short summary
A new carbon cycle data assimilation system (Tan-Tracker) is developed based on an advanced hybrid assimilation approach, as a part of the preparation for the launch of the Chinese carbon dioxide observation satellite (TanSat). Tan-Tracker adopts a joint data assimilation framework to simultaneously estimate CO2 concentrations and CFs and thus gradually reduce the uncertainty in the CO2 concentration evolution through continuously fitting model CO2 concentration simulations to the observations.
A new carbon cycle data assimilation system (Tan-Tracker) is developed based on an advanced...
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