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 rea- sonably well reproduced by ocean circulation models, sig- nificant effort is being directed toward incorporating com- plex food webs into these models, many of which now rou- tinely include multiple phytoplankton (P) and zooplankton (Z) compartments. This study quantitatively assesses how the number of phytoplankton and zooplankton compartments af- fects the ability of a lower-trophic-level ecosystem model to reproduce and predict observed patterns in surface chloro- phyll 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 result- ing optimal parameters were tested at five independent sites in a cross-validation experiment. Although all five models showed improvements in model-data misfits after assimila- tion, overall the moderately complex 2P2Z model was asso- ciated 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 assimi- lated data, suggesting that random noise inherent in satellite- derived data does not pose a significant problem to the as- similation 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, high-

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