Presenter: Jann Paul Mattern (UC Santa Cruz)

Description:
Advanced marine ecosystem models can contain more than 100 biogeochemical variables, making data assimilation for these models a challenging prospect. Traditional variational data assimilation techniques like 4dVar rely on tangent linear and adjoint code, which can be difficult to create and maintain for complex ecosystem models with more than a few dozen variables. More recent hybrid ensemble-variational data assimilation techniques use ensembles of model forecasts to produce model statistics and can thus avoid the need for tangent linear or adjoint code. We present a new implementation of a four-dimensional ensemble optimal interpolation (4dEnOI) technique for use with coupled physical-ecosystem models. Our 4dEnOI implementation uses a small ensemble, and spatial and variable localization to create reliable flow-dependent statistics. The technique is easy to implement, requires no tangent linear or adjoint code, and is suitable computationally for advanced ecosystem models. We initially test the 4dEnOI implementation in comparison to a 4dVar technique for a simple marine ecosystem model with 4 biogeochemical variables. We then apply it to the Darwin ecosystem model, which tracks 6 nutrients and can be configured to have upwards of 35 phytoplankton variables.