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

2003 ◽  
Vol 21 (1) ◽  
pp. 399-411 ◽  
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)

scholarly journals A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem model

2003 ◽  
Vol 21 (1) ◽  
pp. 389-397 ◽  
Author(s):  
I. Hoteit ◽  
G. Triantafyllou ◽  
G. Petihakis ◽  
J. I. Allen
Keyword(s):  
Estimation Error ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Empirical Orthogonal Functions ◽  
Error Statistics ◽  
Orthogonal Functions ◽  
In Situ Data ◽  
Marine Ecosystem Model

Abstract. A singular evolutive extended Kalman (SEEK) filter is used to assimilate real in situ data in a water column marine ecosystem model. The biogeochemistry of the ecosystem is described by the European Regional Sea Ecosystem Model (ERSEM), while the physical forcing is described by the Princeton Ocean Model (POM). In the SEEK filter, the error statistics are parameterized by means of a suitable basis of empirical orthogonal functions (EOFs). The purpose of this contribution is to track the possibility of using data assimilation techniques for state estimation in marine ecosystem models. In the experiments, real oxygen and nitrate data are used and the results evaluated against independent chlorophyll data. These data were collected from an offshore station at three different depths for the needs of the MFSPP project. The assimilation results show a continuous decrease in the estimation error and a clear improvement in the model behavior. Key words. Oceanography: general (ocean prediction; numerical modelling) – Oceanography: biological and chemical (ecosystems and ecology)

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

2017 ◽  
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 ◽  
Biological Parameters ◽  
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 (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.

scholarly journals Ecological ReGional Ocean Model with vertically resolved sediments (ERGOM SED 1.0): coupling benthic and pelagic biogeochemistry of the south-western Baltic Sea

2019 ◽  
Vol 12 (1) ◽  
pp. 275-320 ◽  
Author(s):  
Hagen Radtke ◽  
Marko Lipka ◽  
Dennis Bunke ◽  
Claudia Morys ◽  
Jana Woelfel ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Marine Ecosystem ◽  
Nutrient Recycling ◽  
Three Dimensional ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Ecosystem Models ◽  
Predictive Capacity ◽  
Sediment Types ◽  
Marine Ecosystem Model

Abstract. Sediments play an important role in organic matter mineralisation and nutrient recycling, especially in shallow marine systems. Marine ecosystem models, however, often only include a coarse representation of processes beneath the sea floor. While these parameterisations may give a reasonable description of the present ecosystem state, they lack predictive capacity for possible future changes, which can only be obtained from mechanistic modelling. This paper describes an integrated benthic–pelagic ecosystem model developed for the German Exclusive Economic Zone (EEZ) in the western Baltic Sea. The model is a hybrid of two existing models: the pelagic part of the marine ecosystem model ERGOM and an early diagenetic model by Reed et al. (2011). The latter one was extended to include the carbon cycle, a determination of precipitation and dissolution reactions which accounts for salinity differences, an explicit description of the adsorption of clay minerals, and an alternative pyrite formation pathway. We present a one-dimensional application of the model to seven sites with different sediment types. The model was calibrated with observed pore water profiles and validated with results of sediment composition, bioturbation rates and bentho-pelagic fluxes gathered by in situ incubations of sediments (benthic chambers). The model results generally give a reasonable fit to the observations, even if some deviations are observed, e.g. an overestimation of sulfide concentrations in the sandy sediments. We therefore consider it a good first step towards a three-dimensional representation of sedimentary processes in coupled pelagic–benthic ecosystem models of the Baltic Sea.

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

Ocean Science ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 371-386 ◽  
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.

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