Abstract. Soil and atmospheric water deficits have significant influences on CO₂ and energy exchanges between the atmosphere and terrestrial ecosystems. Model parameterization significantly affects the ability of a model to simulate carbon, water, and energy fluxes. In this study, an ensemble Kalman filter (EnKF) and observations of gross primary productivity (GPP) and latent heat (LE) fluxes were used to optimize model parameters significantly affecting the calculation of these fluxes for a subtropical coniferous plantation in southeastern China. The optimized parameters include the maximum carboxylation rate (Vc_max), the slope in the modified Ball-Berry model (M) and the coefficient determining the sensitivity of stomatal conductance to atmospheric water vapor deficit (D₀). Optimized Vc_max and M showed larger variations than D₀. Seasonal variations of Vc_max and M were more pronounced than the variations between the two years. Vc_max and M were associated with soil water content (SWC). During dry periods, SWC at the 20 cm depth explained 61% and 64% of variations of Vc_max and M, respectively. EnKF parameter optimization improved the simulations of GPP, LE and SH, mainly during dry periods. After parameter optimization using EnKF, the variations of GPP, LE and SH explained by the model increased by 1% to 4% at half-hourly steps and by 3% to 5% at daily time steps. Further efforts are needed to differentiate the real causes of parameter variations and improve the ability of models to describe the change of stomatal conductance with net photosynthesis rate and the sensitivity of photosynthesis capacity to soil water stress under different environmental conditions.