As computational resources have increased in availability and capability, so has the complexity of the models used to represent biogeochemical (BGC) processes in ocean simulations. To effectively calibrate the increasingly large number of uncertain parameters in these models, efficient parameter estimation methods are needed to ensure that the models can accurately represent the BGC processes under investigation. In this study, we address this challenge using a multistage automatic parameter estimation methodology that sequentially applies global sampling and local optimization to calibrate both the BGC model parameters and the parameters associated with the mathematical representation of physical ocean dynamics. We quantitatively compare the accuracy of sequential and simultaneous parameter estimations of moderately complex BGC and physical models at locations corresponding to the Bermuda Atlantic time series and the Hawaii Ocean time series. The results show that the best overall agreement with the observational data is obtained when the BGC and physical model parameters are estimated simultaneously, rather than sequentially. In particular, simultaneous estimation results in significantly improved predictions of oxygen and particulate organic nitrogen. Moreover, the agreement is improved in general when the physical model is included in the estimation, as opposed to calibrating the BGC model alone. This study also serves as a demonstration of a meta-algorithm for performing parameter estimation in high-dimensional models with local optimization approaches.