Sequential weak constraint parameter estimation in an ecosystem model

Abstract

A Sequential Importance Resampling filter (SIR) is applied to assimilate data of the Bermuda Atlantic Time-Series Study for the period December 1988 to January 1994 into a nine-compartment ecosystem model. The filter provides an opportunity to combine state and parameter estimations. We detected notable seasonality of some model parameters. A filtered solution is in close agreement with the data and is superior to that obtained with fixed model parameters. The seasonal dependence of the initial slope of the P– I curve is similar to other known estimates. The seasonality of the phytoplankton specific mortality rate obtained can point out that either the phytoplankton mortality parameterization has to be improved or the Chl:C ratio varies in time. Being of the same computational cost as the Ensemble Kalman filter, the data assimilation approach used can be implemented for on-line tuning and operational prediction the ecosystem dynamics with a coupled hydrodynamical–ecosystem model.