We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. Daily-differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. In addition, we evaluated different models and optimization methods in influencing estimation of key parameters and CO2 flux components. The results show that: (1) Random flux error more closely follows a dou-ble-exponential (Laplace), rather than a normal (Gaussian) distribution. (2) Different optimization meth-ods result in different estimates of model parameters. Uncertainties of parameters estimated by the maximum likelihood estimation (MLE) are lower than those derived from ordinary least square method (OLS). (3) The differences between simulated Reco, NEE and GEE derived from MLE and those derived from OLS are 12.18% (176 g C·m-2·a-1), 34.33% (79 g C·m-2·a-1) and 5.4% (92 g C·m-2·a-1). However, for a given parameter optimization method, a temperature-dependent model (T_model) and the models derived from a temperature and water-dependent model (TW_model) are 1.31% (17.8 g C·m-2·a-1), 2.1% (5.7 g C·m-2·a-1), and 0.26% (4.3 g C·m-2·a-1), respectively, which suggested that the optimization methods are more important than the ecological models in influencing uncertainty in estimated carbon fluxes. (4) The relative uncertainty of CO2 flux derived from OLS is higher than that from MLE, and the uncertainty is related to timescale, that is, the larger the timescale, the smaller the uncertainty. The relative uncertainties of Reco, NEE and GEE are 4%-8%, 7%-22% and 2%-4% respectively at annual timescale. We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. -differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. The results show that: (1) Random flux error more closely follows a dou-ble-exponential (Laplace), rather than a normal (Gaussian) distribution. (2) Different optimization meth-ods result in different estimates of model parameters. Uncertainties of parameters estimated by the maximum likelihood estimation (MLE) are lower than those derived from ordinary least square method (OLS) ) The differences between simulated Reco, NEE and GEE derived from MLE and those derived from OLS are 12.18% (176 g C · m -2 · a -1), 34.33% (79 g C m -2 · a -1) and 5.4% (92 g C · m -2 · a -1). However, for a given parameter optimization method, a temperature-dependent model (T_model) and the models derived from a temperature and water-dependent model (TW_model) are 1.31% (17.8 g C · m -2 · a -1), 2.1% (5.7 g C m -2 · a -1) and 0.26% (4.3 g C m -2 · a -1) , which suggested that the optimization methods are more important than the ecological models in influencing uncertainty in estimated carbon fluxes. (4) The relative uncertainty of CO2 flux derived from OLS is higher than that from MLE, and the uncertainty is related to timescale, that is, the larger the timescale, the smaller the uncertainty. The relative uncertainties of Reco, NEE and GEE are 4% -8%, 7% -22% and 2% -4% respectively at annual timescale.