A high-resolution biogeochemical model (ROMS 3.4 + bio_Fennel) of the East Australian Current system
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Published:2019-01-25
Issue:1
Volume:12
Page:441-456
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Rocha CarlosORCID, Edwards Christopher A., Roughan Moninya, Cetina-Heredia PaulinaORCID, Kerry Colette
Abstract
Abstract. Understanding phytoplankton dynamics is critical across a range
of topics, spanning from fishery management to climate change mitigation. It is
particularly interesting in the East Australian Current (EAC) system, as the region's
eddy field strongly conditions nutrient availability and therefore phytoplankton growth.
Numerical models provide unparalleled insight into these biogeochemical dynamics. Yet, to
date, modelling efforts off southeastern Australia have either targeted case studies
(small spatial and temporal scales) or encompassed the whole EAC system but focused on
climate change effects at the mesoscale (with a spatial resolution of 1/10∘).
Here we couple a model of the pelagic nitrogen cycle (bio_Fennel) to a 10-year
high-resolution (2.5–5 km horizontal) three-dimensional ocean model (ROMS) to resolve
both regional and finer-scale biogeochemical processes occurring in the EAC system. We
use several statistical metrics to compare the simulated surface chlorophyll to an ocean
colour dataset (Copernicus-GlobColour) for the 2003–2011 period and show that the model
can reproduce the observed phytoplankton surface patterns with a domain-wide RMSE of
approximately 0.2 mg Chl a m−3 and a correlation coefficient of 0.76.
This coupled configuration will provide a much-needed framework to examine phytoplankton
variability in the EAC system providing insight into important ecosystem dynamics such as
regional nutrient supply mechanisms and biogeochemical cycling occurring in EAC eddies.
Publisher
Copernicus GmbH
Reference70 articles.
1. Alvera-Azcárate, A., Barth, A., Sirjacobs, D., Lenartz, F., and Beckers,
J. M.: Data Interpolating Empirical Orthogonal Functions (DINEOF): a tool for
geophysical data analyses, Mediterr. Mar. Sci., 12, 5–11,
https://doi.org/10.12681/mms.64, 2010. 2. Andersen, V., Nival, P., and Harris, R. P.: Modelling of a planktonic
ecosystem in an enclosed water column, J. Mar. Biol. Assoc. UK., 67,
407–430, 1987. 3. Anderson, T. R.: Plankton functional type modelling: running before we can
walk?, J. Plankton Res., 27, 1073–1081, https://doi.org/10.1093/plankt/fbi076, 2005. 4. Armbrecht, L. H., Roughan, M., Rossi, V., Schaeffer, A., Davies, P. L.,
Waite, A. M., and Armand, L. K.: Phytoplankton composition under contrasting
oceanographic conditions: Upwelling and downwelling (Eastern Australia),
Cont. Shelf Res., 75, 54–67, https://doi.org/10.1016/j.csr.2013.11.024, 2013. 5. Baird, M. E., Timko, P. G., Suthers, I. M., and Middleton, J. H.: Coupled
physical-biological modelling study of the East Australian Current with
idealised wind forcing, Part I: Biological model intercomparison, J. Mar.
Syst., 59, 249–270, 2006a.
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