Articles | Volume 8, issue 3
Geosci. Model Dev., 8, 805–816, 2015
https://doi.org/10.5194/gmd-8-805-2015
Geosci. Model Dev., 8, 805–816, 2015
https://doi.org/10.5194/gmd-8-805-2015
Model description paper
25 Mar 2015
Model description paper | 25 Mar 2015

A global carbon assimilation system using a modified ensemble Kalman filter

S. Zhang1,2, X. Zheng1,2, J. M. Chen2,3,4, Z. Chen1,2, B. Dan1,2, X. Yi1,2, L. Wang5, and G. Wu1,2 S. Zhang et al.
  • 1College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
  • 2Joint Center for Global Change Studies, Beijing, China
  • 3Department of Geography, University of Toronto, Toronto, Canada
  • 4International Institute for Earth System Science, Nanjing University, Nanjing, China
  • 5Department of Statistics, University of Manitoba, Winnipeg, Canada

Abstract. A Global Carbon Assimilation System based on the ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is similar to CarbonTracker, but with several new developments, including inclusion of atmospheric CO2 concentration in state vectors, using the ensemble Kalman filter (EnKF) with 1-week assimilation windows, using analysis states to iteratively estimate ensemble forecast errors, and a maximum likelihood estimation of the inflation factors of the forecast and observation errors. The proposed assimilation approach is used to estimate the terrestrial ecosystem carbon fluxes and atmospheric CO2 distributions from 2002 to 2008. The results show that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.

Download
Short summary
A Global Carbon Assimilation System based on the Ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is similar to CarbonTracker, but with several new developments. The results showed that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.