Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

• Published in: Ocean science Vol. 14; no. 3; pp. 371 - 386
• Main Authors: Hoshiba, Yasuhiro, Hirata, Takafumi, Shigemitsu, Masahito, Nakano, Hideyuki, Hashioka, Taketo, Masuda, Yoshio, Yamanaka, Yasuhiro
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 01-06-2018; Copernicus Publications
• Subjects: Biogeochemical cycle; Biogeochemical cycles; Biogeochemistry; Biological assimilation; Biological data; Computer simulation; Data assimilation; Data collection; Dynamics; Ecosystem dynamics; Ecosystem models; Environment models; Environmental aspects; Genetic algorithms; Iron; Marine ecosystems; Mathematical models; Methods; Observations; Ocean models; Parameter estimation; Parameters; Physiology; Phytoplankton; Plankton; Plankton blooms; Satellite data; Satellites; Three dimensional models; Vertical distribution
• ISSN: 1812-0792; 1812-0784; 1812-0792
• DOI: 10.5194/os-14-371-2018

Ecosystem models are used to understand ecosystem dynamics and ocean biogeochemical cycles and require optimum physiological parameters to best represent biological behaviours. These physiological parameters are often tuned up empirically, while ecosystem models have evolved to increase the number of physiological parameters. We developed a three-dimensional (3-D) lower-trophic-level marine ecosystem model known as the Nitrogen, Silicon and Iron regulated Marine Ecosystem Model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate 23 physiological parameters for two phytoplankton functional types in the western North Pacific. The estimation of the parameters was based on a one-dimensional simulation that referenced satellite data for constraining the physiological parameters. The 3-D NSI-MEM optimized by the data assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to the model without data assimilation. Furthermore, the model was able to improve not only surface concentrations of phytoplankton but also their subsurface maximum concentrations. Our results showed that surface data assimilation of physiological parameters from two contrasting observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.