Assimilation of JGOFS EqPac and SeaWiFS data into a marine ecosystem model of the Central Equatorial Pacific Ocean

Abstract

Abstract A five-component (phytoplankton, zooplankton, ammonium, nitrate and detritus) ecosystem model developed for the central equatorial Pacific is reformulated in a data assimilative mode, using the variational adjoint technique. This method minimizes model/data misfits by adjusting six model parameters that were selected by assessing parameter co-dependencies and model sensitivity to parameter variations. Through the assimilation of cruise data from the US Joint Global Ocean Flux Study (JGOFS) Equatorial Pacific Process Study (EqPac), and ocean color data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), it is possible to reduce model/data misfit by estimating optimal parameters governing processes such as phytoplankton and zooplankton mortality, zooplankton grazing, phytoplankton growth, and the recycling of nutrients from detritus remineralization. The success of this approach is evident in that similar parameter sets are obtained even when independent data sets are assimilated. For example, the assimilation of in situ EqPac (depth-resolved) data from the 1991–1992 El Nino produces a parameter set that is nearly identical to that estimated via the assimilation of remotely sensed (surface) SeaWiFS data collected during the 1997–1998 El Nino. The assimilation of biological data also allows objective determination of whether or not a given model structure is consistent with a specific set of observations. For example, the assimilation process demonstrates that data collected during and after the 1991–1992 El Nino are consistent with the same single-species ecosystem model, thereby suggesting that El Nino conditions may not necessarily be associated with shifts in species composition. In contrast, the increased abundance of diatoms associated with the passage of a tropical instability wave in October 1992 as well as a brief period of macronutrient limitation observed from November 1997 through January 1998 violate key assumptions of the model. Assimilation of data that include these dynamics results in unrealistic simulations of the lower trophic levels. The successful simulation of these particular data sets will require that the model dynamics allow for species composition changes and alternation between macro- and micronutrient limitation. In this way, assimilation of biological data into marine ecosystem models cannot necessarily overcome inappropriate model dynamics and structure; rather, it can serve to guide model reformulation.