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...
Published in: | Ocean Science |
---|---|
Main Authors: | , , , , , , |
Format: | Text |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | https://doi.org/10.5194/os-14-371-2018 https://os.copernicus.org/articles/14/371/2018/ |
id |
ftcopernicus:oai:publications.copernicus.org:os58945 |
---|---|
record_format |
openpolar |
spelling |
ftcopernicus:oai:publications.copernicus.org:os58945 2023-05-15T18:28:25+02:00 Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific Hoshiba, Yasuhiro Hirata, Takafumi Shigemitsu, Masahito Nakano, Hideyuki Hashioka, Taketo Masuda, Yoshio Yamanaka, Yasuhiro 2019-01-07 application/pdf https://doi.org/10.5194/os-14-371-2018 https://os.copernicus.org/articles/14/371/2018/ eng eng doi:10.5194/os-14-371-2018 https://os.copernicus.org/articles/14/371/2018/ eISSN: 1812-0792 Text 2019 ftcopernicus https://doi.org/10.5194/os-14-371-2018 2020-07-20T16:23:16Z 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. Text Subarctic Copernicus Publications: E-Journals Pacific Ocean Science 14 3 371 386 |
institution |
Open Polar |
collection |
Copernicus Publications: E-Journals |
op_collection_id |
ftcopernicus |
language |
English |
description |
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. |
format |
Text |
author |
Hoshiba, Yasuhiro Hirata, Takafumi Shigemitsu, Masahito Nakano, Hideyuki Hashioka, Taketo Masuda, Yoshio Yamanaka, Yasuhiro |
spellingShingle |
Hoshiba, Yasuhiro Hirata, Takafumi Shigemitsu, Masahito Nakano, Hideyuki Hashioka, Taketo Masuda, Yoshio Yamanaka, Yasuhiro Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific |
author_facet |
Hoshiba, Yasuhiro Hirata, Takafumi Shigemitsu, Masahito Nakano, Hideyuki Hashioka, Taketo Masuda, Yoshio Yamanaka, Yasuhiro |
author_sort |
Hoshiba, Yasuhiro |
title |
Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific |
title_short |
Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific |
title_full |
Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific |
title_fullStr |
Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific |
title_full_unstemmed |
Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific |
title_sort |
biological data assimilation for parameter estimation of a phytoplankton functional type model for the western north pacific |
publishDate |
2019 |
url |
https://doi.org/10.5194/os-14-371-2018 https://os.copernicus.org/articles/14/371/2018/ |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
Subarctic |
genre_facet |
Subarctic |
op_source |
eISSN: 1812-0792 |
op_relation |
doi:10.5194/os-14-371-2018 https://os.copernicus.org/articles/14/371/2018/ |
op_doi |
https://doi.org/10.5194/os-14-371-2018 |
container_title |
Ocean Science |
container_volume |
14 |
container_issue |
3 |
container_start_page |
371 |
op_container_end_page |
386 |
_version_ |
1766210892520751104 |