Abstract

Given the recent focus on developing new data assimilation systems for biological models, we present in this study the application of a newly developed state-parameters estimation tool for a marine ecosystem model. The data assimilation scheme is based on the original Ensemble Kalman Filter (EnKF... Show moreGiven the recent focus on developing new data assimilation systems for biological models, we present in this study the application of a newly developed state-parameters estimation tool for a marine ecosystem model. The data assimilation scheme is based on the original Ensemble Kalman Filter (EnKF) algorithm and further applies a One-Step-Ahead smoothing to the state variables. The state-parameters estimation scheme, referred to as OSA-EnKF, further transforms the state variables, parameters and observations to a Gaussian space using a predefined anamorphosis formulation, before applying the update. The performance of the OSA-EnKF is tested against the standard Joint- and Dual-EnKF schemes using a one-dimensional configuration of the coupled General Ocean Turbulence Model and the Norwegian Ecological Model (GOTM-NORWECOM) in the North Atlantic. Nutrient profile data (up to 2000 m deep) and surface chlorophyll-a measurements at Mike weather station (station M: 66° N, 2° E) are used to estimate selected biogeochemical parameters for phytoplankton and zooplankton. The filters are analyzed in terms of computational complexity and accuracy of the state and parameters estimates. Assimilation results suggest that the OSA-EnKF is capable of providing more accurate and dynamically consistent state and parameters estimates compared to the two other ensemble schemes. Convergence and interdependence features of the estimated parameters in relation to the major biological processes are thoroughly discussed. The optimized parameters are assessed and found useful, enhancing the prediction capability and the seasonal variability of the coupled GOTM-NORWECOM system by up to 30%. Show less