Online tuning of ocean biogeochemical model parameters using ensemble estimation techniques: Application to a one-dimensional model in the North Atlantic

• Published in: Journal of Marine Systems
• Main Authors: Gharamti, M.E., Samuelsen, A., Bertino, L., Simon, Ehouarn, Korosov, A., Daewel, Ute
• Other Authors: Nansen Environmental and Remote Sensing Center Bergen (NERSC), National Center for Atmospheric Research Boulder (NCAR), Algorithmes Parallèles et Optimisation (IRIT-APO), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut National Polytechnique (Toulouse) (Toulouse INP)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2017
• Subjects: Biogeochemical parameters; State-parameters estimation; Nutrients profiles and chlorophyll-a; Filtering and smoothing; OSA-EnKF; [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere; [SDU.STU.GC]Sciences of the Universe [physics]/Earth Sciences/Geochemistry; [INFO]Computer Science [cs]; North Atlantic
• Online Access: https://hal.archives-ouvertes.fr/hal-02960626; https://doi.org/10.1016/j.jmarsys.2016.12.003

International audience 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) 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%.