Data assimilation in a simple marine ecosystem model based on spatial biological parameterizations

Abstract. 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 (3D) 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 approach used a one-dimensional emulator that referenced satellite data. The 3D NSI-MEM with biological parameters optimised by assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to models without data assimilation. Furthermore, the model was able to simulate not only surface concentrations of phytoplankton but also subsurface maximum concentrations of phytoplankton. Our results show that surface data assimilation of biological parameters from two observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.

Download Full-text

An Ensemble Kalman Filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea

Annales Geophysicae ◽

10.5194/angeo-21-399-2003 ◽

2003 ◽

Vol 21 (1) ◽

pp. 399-411 ◽

Cited By ~ 45

Author(s):

J. I. Allen ◽

M. Eknes ◽

G. Evensen

Keyword(s):

Data Assimilation ◽

Marine Ecosystem ◽

Three Dimensional ◽

Ecosystem Model ◽

Sequential Data ◽

Model Errors ◽

In Situ Data ◽

Marine Ecosystem Model ◽

Cretan Sea

Abstract. The purpose of this paper is to examine the use of a complex ecosystem model along with near real-time in situ data and a sequential data assimilation method for state estimation. The ecosystem model used is the European Regional Seas Ecosystem Model (ERSEM; Baretta et al., 1995) and the assimilation method chosen is the Ensemble Kalman Filer (EnKF). Previously, it has been shown that this method captures the nonlinear error evolution in time and is capable of both tracking the observations and providing realistic error estimates for the estimated state. This system has been used to assimilate long time series of in situ chlorophyll taken from a data buoy in the Cretan Sea. The assimilation of this data using the EnKF method results in a marked improvement in the ability of ERSEM to hindcast chlorophyll. The sensitivity of this system to the type of data used for assimilation, the frequency of assimilation, ensemble size and model errors is discussed. The predictability window of the EnKF appears to be at least 2 days. This is an indication that the methodology might be suitable for future operational data assimilation systems using more complex three-dimensional models. Key words. Oceanography: general (numerical modelling; ocean prediction) – Oceanography: biological and chemical (plankton)

Download Full-text

SPRAT: A spatially-explicit marine ecosystem model based on population balance equations

Ecological Modelling ◽

10.1016/j.ecolmodel.2017.01.020 ◽

2017 ◽

Vol 349 ◽

pp. 11-25 ◽

Cited By ~ 2

Author(s):

Arne N. Johanson ◽

Andreas Oschlies ◽

Wilhelm Hasselbring ◽

Boris Worm

Keyword(s):

Marine Ecosystem ◽

Population Balance ◽

Ecosystem Model ◽

Spatially Explicit ◽

Balance Equations ◽

Population Balance Equations ◽

Model Based ◽

Marine Ecosystem Model

Download Full-text

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

Ocean Science ◽

10.5194/os-14-371-2018 ◽

2018 ◽

Vol 14 (3) ◽

pp. 371-386 ◽

Cited By ~ 3

Author(s):

Yasuhiro Hoshiba ◽

Takafumi Hirata ◽

Masahito Shigemitsu ◽

Hideyuki Nakano ◽

Taketo Hashioka ◽

...

Keyword(s):

Data Assimilation ◽

North Pacific ◽

Western North Pacific ◽

Marine Ecosystem ◽

Ecosystem Model ◽

Biological Data ◽

Physiological Parameters ◽

Ecosystem Dynamics ◽

Ecosystem Models ◽

Marine Ecosystem Model

Abstract. 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.

Download Full-text