Using biogeochemical data assimilation to assess the relative skill of multiple ecosystem models in the Mid-Atlantic Bight: effects of increasing the complexity of the planktonic food web

Now that regional circulation patterns can be reasonably well reproduced by ocean circulation models, significant effort is being directed toward incorporating complex food webs into these models, many of which now routinely include multiple phytoplankton (P) and zooplankton (Z) compartments. This study quantitatively assesses how the number of phytoplankton and zooplankton compartments affects the ability of a lower-trophic-level ecosystem model to reproduce and predict observed patterns in surface chlorophyll and particulate organic carbon. Five ecosystem model variants are implemented in a one-dimensional assimilative (variational adjoint) model testbed in the Mid-Atlantic Bight. The five models are identical except for variations in the level of complexity included in the lower trophic levels, which range from a simple 1P1Z food web to a considerably more complex 3P2Z food web. The five models assimilated satellite-derived chlorophyll and particulate organic carbon concentrations at four continental shelf sites, and the resulting optimal parameters were tested at five independent sites in a cross-validation experiment. Although all five models showed improvements in model-data misfits after assimilation, overall the moderately complex 2P2Z model was associated with the highest model skill. Additional experiments were conducted in which 20% random noise was added to the satellite data prior to assimilation. The 1P and 2P models successfully reproduced nearly identical optimal parameters regardless of whether or not noise was added to the assimilated data, suggesting that random noise inherent in satellite-derived data does not pose a significant problem to the assimilation of satellite data into these models. However, the most complex model tested (3P2Z) was sensitive to the level of random noise added to the data prior to assimilation, highlighting the potential danger of over-tuning inherent in such complex models.