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

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 o...

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Bibliographic Details
Published in:Ocean Science
Main Authors: Hoshiba, Yasuhiro, Hirata, Takafumi, Shigemitsu, Masahito, Nakano, Hideyuki, Hashioka, Taketo, Masuda, Yoshio, Yamanaka, Yasuhiro
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2018
Subjects:
article
Verlagsveröffentlichung
Pacific
Subarctic
Online Access:https://doi.org/10.5194/os-14-371-2018
https://noa.gwlb.de/receive/cop_mods_00005674
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00005631/os-14-371-2018.pdf
https://os.copernicus.org/articles/14/371/2018/os-14-371-2018.pdf
Description
Summary: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.

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