Data-assimilation studies of marine, nitrogen based, ecosystem models in the North Atlantic Ocean

The overall goal of this work is to investigate the performance of ecosystem models and to relate their results to existing observations in the North Atlantic. Different data assimilation methods are applied. A variational adjoint technique and a micro-generic algorithm (mGA) are utilized to estimate model parameters, such that the misfit between model results and observations is minimised. Experiments are performed with nitrogen based ecosystem models, comprising three and four state variables (NPZ- and NPZD models): dissolved inorganic nitrogen (N), phytoplankton (P), herbivorous zooplankton (Z) and detritus (D). First, data assimilation experiments are conducted with observations from the Bermuda Atlantic Time-series Study (BATS) in order to optimise the NPZ-model. While applying the adjoint method different optimal parameter sets are obtained when starting from different initial parameter sets. It is shown that for parameter optimisation of an ecosystem model, the application of the mGA is superior to the performance of the adjoint method. Second, simultaneous assimilation experiments are performed with the NPZD-model using observational data from three locations in the North Atlantic. The parameter set retrieved from the simultaneous optimisations produces substantial differences in the biogeochemical fluxes when compared with model results using previously published parameters. The optimisation yields a best parameter set, which can be utilized for basin wide simulations in coupled physical-biological models of the North Atlantic.