Assimilation of ocean colour data into a biochemical model of the North Atlantic: Part 1. Data assimilation experiments

An advanced multivariate sequential data assimilation method, the ensemble Kalman filter (EnKF), has been investigated with a three-dimensional biochemical model of the North Atlantic, utilizing real chlorophyll data from the from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The approach chosen here differs significantly from conventional parameter estimation techniques. We keep the parameters fixed, and instead update the actual model state, allowing for unknown errors in the dynamical formulation. In the ensemble Kalman filter, estimates of the true dynamical error covariances are provided from an ensemble of model states. The physical ocean is described through the Miami Isopycnic Coordinate Ocean Model (MICOM). Its output, e.g. fields of temperature, velocities and layer thicknesses, is used to force the ecosystem model, which contains 11 biochemical components. The system is driven by realistic ECMWF atmospheric forcing fields. A simple demonstration experiment was performed for April and May 1998, that is, the early part of the North Atlantic spring bloom is included. It is shown that the ensemble Kalman filter analysis estimate is consistent with the choice of prior error variances. Furthermore, it is illustrated that the multivariate analysis scheme also affects the unobserved model compartments, and that the variance fields for all the variables decrease during the assimilation. A discussion of the number of ensemble members needed to ensure proper representations of the true error covariances is also given. In a companion paper [J. Mar. Syst. 40/41 (2003)], hereafter referred to as the Part 2 paper, we illustrate some useful approaches to monitor the evolution (i.e., time series) of the ensemble of model states.