A global carbon assimilation system using a modified ensemble Kalman filter

Abstract

A Global Carbon Assimilation System based on the ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO 2 data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO 2 distribution. This assimilation approach is similar to CarbonTracker, but with several new developments, including inclusion of atmospheric CO 2 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 CO 2 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.