A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO<sub>2</sub> fluxes and 3-D atmospheric CO<sub>2</sub> concentrations from observations

Abstract

Abstract.We have developed a novel framework ("TanTracker") for assimilating observations of atmospheric CO 2 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 CO 2 concentrations and the surface CO 2 fluxes as part of the large state vector to be simultaneously estimated from assimilation of atmospheric CO 2 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 CO 2 surface fluxes (CFs) and the atmospheric CO 2 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 CO 2 exchanges are prescribed by using a persistent forecasting model for the scaling factors of the first-guess net CO 2 surface fluxes and (2) the atmospheric CO 2 transport is simulated by using the GEOSChem three-dimensional global chemistry transport model. Observing system simulation experiments (OSSEs) for assimilating synthetic in situ observations of surface CO 2 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 CO 2 concentrations and CO 2 fluxes, mainly due to the simultaneous estimation of CO 2 concentrations and CFs in our TanTracker data assimilation system. A experiment for assimilating the real dry-air column CO 2 retrievals (XCO 2 ) from the Japanese Greenhouse Gases Observation Satellite (GOSAT) further demonstrates its potential wide applications.

A regional carbon data assimilation system and its preliminary evaluation in East Asia

“…Recently, Tian et al (2014) developed a new surface CO 2 flux data assimilation system, Tan-Tracker, by incorporating a joint PODEn4DVar assimilation framework into the GEOS-Chem model on the basis of Peters et al (2005Peters et al ( , 2007 and Kang et al (2011Kang et al ( , 2012. They discussed in detail that the assimilation of CO 2 surface fluxes could be improved through the use of simultaneous assimilation of CO 2 concentrations and CO 2 surface fluxes.…”

“…Miyazaki, 2009;Liu et al, 2011Liu et al, , 2012Feng et al, 2011;Tangborn et al, 2013;Huang et al, 2014). In addition, previous studies showed that the simultaneous assimilation of CO 2 concentrations and surface CO 2 fluxes can largely eliminate the uncertainty in initial CO 2 concentrations on the CO 2 evolution Tian et al, 2014). Therefore, we also use the simultaneous assimilation framework; the ensemble Kalman filter (EnKF) was used to constrain CO 2 concentrations and the ensemble Kalman smoother (EnKS) was used to optimize surface CO 2 fluxes.…”

“…As an ensemble-based assimilation system, CFI-CMAQ was also developed by applying a set of linear multiplication factors, similar to the approach by Peters et al (2007) and Tian et al (2014). The ith ensemble member of the surface fluxes, F i (x, y, z, t), from an N-member ensemble can be described by…”

“…where λ i (x, y, z, t) represents the ith ensemble member of the linear scaling factors (Peters et al, 2007;Tian et al, 2014) for each time and each grid to be optimized in the assimilation. The notations are standard: the subscript i refers to the ith ensemble member.…”

“…However, the "signal-to-noise" problem has not yet been resolved, and thus CarbonTracker also has to assimilate natural CO 2 emissions at the ecological scale only. In Tan-Tracker (Tian et al, 2014), a fourdimensional (4-D) moving sampling strategy (Wang et al, 2010) is used to generate the flux ensemble members, and so the surface CO 2 fluxes can be optimized as a whole at the grid scale. In this work, the persistence dynamical model taken by Peters et al (2005) was further developed for the purpose of resolving the "signal-to-noise" problem, to optimize the surface CO 2 fluxes as a whole at the grid scale.…”