Ward, Ben Andrew
Marine ecosystem model analysis using data assimilation.
University of Southampton, School of Ocean and Earth Science,
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 quantied.
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 sucient 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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