Information on the spatial and temporal patterns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents th...Information on the spatial and temporal patterns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents the construction and implementation of a terrestrial carbon cycle data assimilation system based on a dynamic vegetation and terrestrial carbon model Vegetation-Global-Atmosphere-Soil(VEGAS) with an advanced assimilation algorithm, the local ensemble transform Kalman filter(LETKF, hereafter LETKF-VEGAS). An observing system simulation experiment(OSSE) framework was designed to evaluate the reliability of this system, and numerical experiments conducted by the OSSE using leaf area index(LAI) observations suggest that the LETKF-VEGAS can improve the estimations of leaf carbon pool and LAI significantly, with reduced root mean square errors and increased correlation coefficients with true values, as compared to a control run without assimilation. Furthermore, the LETKF-VEGAS has the potential to provide more accurate estimations of the net primary productivity(NPP) and carbon flux to atmosphere(CFta).展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 41305066)the Special Funds for Public Welfare of China (Grant No. GYHY201306045)the National Basic Research Program of China (Grant Nos. 2010CB951101 and 2010CB428403)
文摘Information on the spatial and temporal patterns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents the construction and implementation of a terrestrial carbon cycle data assimilation system based on a dynamic vegetation and terrestrial carbon model Vegetation-Global-Atmosphere-Soil(VEGAS) with an advanced assimilation algorithm, the local ensemble transform Kalman filter(LETKF, hereafter LETKF-VEGAS). An observing system simulation experiment(OSSE) framework was designed to evaluate the reliability of this system, and numerical experiments conducted by the OSSE using leaf area index(LAI) observations suggest that the LETKF-VEGAS can improve the estimations of leaf carbon pool and LAI significantly, with reduced root mean square errors and increased correlation coefficients with true values, as compared to a control run without assimilation. Furthermore, the LETKF-VEGAS has the potential to provide more accurate estimations of the net primary productivity(NPP) and carbon flux to atmosphere(CFta).
