Marine ecosystem model analysis using data assimilation

Abstract

Numerical modelling of the marine ecosystem requires the aggregation of diverse chemical and biological species into broad categories. To avoid large bias errors it is preferable to resolve as many explicit state variables and processes as possible. The cost of this increased complexity is greater uncertainty in model parameters and output. When comparing models, the importance of quantifying both bias error and the variability of unconstrained solutions was revealed as two marine ecosystem models were calibrated to data. Results demonstrated that all prior parameter information must include realistic error estimates if model uncertainty is to be quantified. Five simple ecosystem models were calibrated to observations from two North Atlantic sites; the Bermuda Atlantic Time-series Study (BATS) and the North Atlantic Bloom Experiment (NABE). Model-data mists were reduced by between 45 and 50%. The addition of model complexity (a parameterised microbial loop, a variable chlorophyll a to nitrogen ratio and dissolved organic nitrogen) led to larger improvements in model performance at BATS relative to NABE. Calibrated parameter values developed at NABE performed better than the default parameter values when applied at BATS. Solutions developed at BATS performed worse than the default values at NABE. The models lacked sufficient ecological complexity to function well at BATS. Errors in the model were masked by errors in the calibrated parameters and the models did not perform well with regard to independent data. The models were well suited to reproducing the NABE data, and the calibrated models performed relatively well at BATS. The models were sensitive to the underlying physical forcing. Although the ecosystem models were originally calibrated within a poor representation of the physical environment at BATS, results from experiments using an improved physical model support the conclusion that the ecosystem models lacked the required complexity at that site.

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