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

Z Peng,X Tian,X Ma,X Kou,M Zhang

doi:10.5194/acp-15-1087-2015

Abstract

Abstract. In order to optimize surface CO2 fluxes at grid scales, a regional surface CO2 flux inversion system (Carbon Flux Inversion system and Community Multi-scale Air Quality, CFI-CMAQ) has been developed by applying the ensemble Kalman filter (EnKF) to constrain the CO2 concentrations and applying the ensemble Kalman smoother (EnKS) to optimize the surface CO2 fluxes. The smoothing operator is associated with the atmospheric transport model to constitute a persistence dynamical model to forecast the surface CO2 flux scaling factors. In this implementation, the "signal-to-noise" problem can be avoided; plus, any useful observed information achieved by the current assimilation cycle can be transferred into the next assimilation cycle. Thus, the surface CO2 fluxes can be optimized as a whole at the grid scale in CFI-CMAQ. The performance of CFI-CMAQ was quantitatively evaluated through a set of Observing System Simulation Experiments (OSSEs) by assimilating CO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The results showed that the CO2 concentration assimilation using EnKF could constrain the CO2 concentration effectively, illustrating that the simultaneous assimilation of CO2 concentrations can provide convincing CO2 initial analysis fields for CO2 flux inversion. In addition, the CO2 flux optimization using EnKS demonstrated that CFI-CMAQ could, in general, reproduce true fluxes at grid scales with acceptable bias. Two further sets of numerical experiments were conducted to investigate the sensitivities of the inflation factor of scaling factors and the smoother window. The results showed that the ability of CFI-CMAQ to optimize CO2 fluxes greatly relied on the choice of the inflation factor. However, the smoother window had a slight influence on the optimized results. CFI-CMAQ performed very well even with a short lag-window (e.g. 3 days).

Highlights

Peters et al (2005, 2007, 2009) developed a surface CO2 flux inversion system, CarbonTracker, by incorporating the ensemble square-root filter (EnSRF) into the atmospheric transport TM5 model; the inversion results obtained by assimilating in situ surface CO2 observations are in excellent agreement with a wide collection of carbon inventories that form the basis of the first North American State of the Carbon Cycle Report (SOCCR) (Peters et al, 2007)
The performance of the ensemble Kalman filter (EnKF) subsection will be greatly influenced by the validation of the ensemble Kalman smoother (EnKS) subsection, or vice versa
A regional surface CO2 flux inversion system, CFI-CMAQ, has been developed to optimize CO2 fluxes at grid scales. It operates under a joint data assimilation framework by applying EnKF to constrain the CO2 concentrations and applying EnKS to optimize the surface CO2 flux, which is similar to Kang et al (2011, 2012) and Tian et al (2014)

Summary

Introduction

Considerable progress has been made in recent years to reduce the uncertainties of surface CO2 flux estimates through the use of an advanced data assimilation technique (e.g. Chevallier, 2007; Chevallier et al, 2005, 2007; Baker et al, 2006; Engelen et al, 2009; Liu et al, 2012). Feng et al (2009) showed that the uncertainties of surface CO2 flux estimates can be reduced significantly by assimilating OCO XCO2 measurements. Peters et al (2005, 2007, 2009) developed a surface CO2 flux inversion system, CarbonTracker, by incorporating the ensemble square-root filter (EnSRF) into the atmospheric transport TM5 model; the inversion results obtained by assimilating in situ surface CO2 observations are in excellent agreement with a wide collection of carbon inventories that form the basis of the first North American State of the Carbon Cycle Report (SOCCR) (Peters et al, 2007). Considerable progress has been made in recent years to reduce the uncertainties of surface CO2 flux estimates through the use of an advanced data assimilation technique Feng et al (2009) showed that the uncertainties of surface CO2 flux estimates can be reduced significantly by assimilating OCO XCO2 measurements. Kang et al (2012) presented a simultaneous data assimilation of surface CO2 fluxes and atmospheric CO2 concentrations along with meteorological variables using a local ensemble transform Kalman filter (LETKF).

Methods

Results

Conclusion