Ocean Science
Ocean Science
OS
Articles | Volume 14, issue 3
Articles | Volume 14, issue 3
https://doi.org/10.5194/os-14-371-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-14-371-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 14, issue 3
Research article
 | 
01 Jun 2018
Research article |  | 01 Jun 2018

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

Yasuhiro Hoshiba, Takafumi Hirata, Masahito Shigemitsu, Hideyuki Nakano, Taketo Hashioka, Yoshio Masuda, and Yasuhiro Yamanaka

Related authors

Long-term response of oceanic carbon uptake to global warming via physical and biological pumps
Akitomo Yamamoto, Ayako Abe-Ouchi, and Yasuhiro Yamanaka
Biogeosciences, 15, 4163–4180, https://doi.org/10.5194/bg-15-4163-2018,https://doi.org/10.5194/bg-15-4163-2018, 2018
Short summary
Seasonal effects of photophysiology and chlorophyll a abundance on phytoplankton group-specific primary production in the Kuroshio region as revealed by SeaStar/SeaWiFS
Takafumi Hirata and Koji Suzuki
Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-164,https://doi.org/10.5194/bg-2017-164, 2017
Preprint withdrawn
Short summary
Drivers and uncertainties of future global marine primary production in marine ecosystem models
C. Laufkötter, M. Vogt, N. Gruber, M. Aita-Noguchi, O. Aumont, L. Bopp, E. Buitenhuis, S. C. Doney, J. Dunne, T. Hashioka, J. Hauck, T. Hirata, J. John, C. Le Quéré, I. D. Lima, H. Nakano, R. Seferian, I. Totterdell, M. Vichi, and C. Völker
Biogeosciences, 12, 6955–6984, https://doi.org/10.5194/bg-12-6955-2015,https://doi.org/10.5194/bg-12-6955-2015, 2015
Short summary
Distribution of phytoplankton functional types in high-nitrate, low-chlorophyll waters in a new diagnostic ecological indicator model
A. P. Palacz, M. A. St. John, R. J. W. Brewin, T. Hirata, and W. W. Gregg
Biogeosciences, 10, 7553–7574, https://doi.org/10.5194/bg-10-7553-2013,https://doi.org/10.5194/bg-10-7553-2013, 2013
The distribution, dominance patterns and ecological niches of plankton functional types in Dynamic Green Ocean Models and satellite estimates
M. Vogt, T. Hashioka, M. R. Payne, E. T. Buitenhuis, C. Le Quéré, S. Alvain, M. N. Aita, L. Bopp, S. C. Doney, T. Hirata, I. Lima, S. Sailley, and Y. Yamanaka
Biogeosciences Discuss., https://doi.org/10.5194/bgd-10-17193-2013,https://doi.org/10.5194/bgd-10-17193-2013, 2013
Revised manuscript has not been submitted
More articles (2)

Related subject area

Approach: Numerical Models | Depth range: Mixed Layer | Geographical range: All Geographic Regions | Phenomena: Biological Processes
Constraining parameters in marine pelagic ecosystem models – is it actually feasible with typical observations of standing stocks?
U. Löptien and H. Dietze
Ocean Sci., 11, 573–590, https://doi.org/10.5194/os-11-573-2015,https://doi.org/10.5194/os-11-573-2015, 2015
Short summary

Cited articles

Aumont, O. and Bopp, L.: Globalizing results from ocean in situ iron fertilization studies, Global Biogeochem. Cy., 20, GB2017, https://doi.org/10.1029/2005GB002591, 2006. 
Blauw, A. N., Los, H. F. J., Bokhorst, M., and Erftemeijer, P. L. A.: GEM: a generic ecological model for estuaries and coastal waters, Hydrobiologia, 618, 175–198, 2009. 
Buitenhuis, E. T., Rivkin, R. B., Sailley, S., and Le Quéré, C.: Biogeochemical fluxes through microzooplankton, Global Biogeochem. Cy., 24, GB4015, https://doi.org/10.1029/2009GB003601, 2010. 
Chai, F., Dugdale, R., Peng, T., Wilkerson, F., and Barber, R.: One-dimensional ecosystem model of the equatorial Pacific upwelling system, Part I: model development and silicon and nitrogen cycle, Deep-Sea Res. Pt. II, 49, 2713–2745, 2002. 
Chelton, D. B., Schlax, M. G., and Samelson, R. M.: Global observations of nonlinear mesoscale eddies, Prog. Oceanogr., 91, 167–216, 2011. 
More articles (47)
Download
Short summary
We developed a three-dimensional lower-trophic-level marine ecosystem model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate physiological parameters for two phytoplankton functional types in the western North Pacific. The 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.
Read more