A data assimilation technique applied to estimate parameters for the NEMURO marine ecosystem model

2004 ◽  
Vol 172 (1) ◽  
pp. 69-85 ◽  
Author(s):  
Hiroshi Kuroda ◽  
Michio J. Kishi
Keyword(s):  
Data Assimilation ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Marine Ecosystem Model ◽  
Data Assimilation Technique

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 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 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.

scholarly journals Skillful prediction of tropical Pacific fisheries provided by Atlantic Niños

2021 ◽  
Author(s):  
Iñigo Gómara ◽  
Belén Rodríguez-Fonseca ◽  
Elsa Mohino ◽  
Teresa Losada ◽  
Irene Polo ◽  
...  
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Tropical Pacific ◽  
Equatorial Atlantic ◽  
Climate Conditions ◽  
Marine Food Web ◽  
Marine Ecosystem Model ◽  
The Tropical Pacific

AbstractTropical Pacific upwelling-dependent ecosystems are the most productive and variable worldwide, mainly due to the influence of El Niño Southern Oscillation (ENSO). ENSO can be forecasted seasons ahead thanks to assorted climate precursors (local-Pacific processes, pantropical interactions). However, owing to observational data scarcity and bias-related issues in earth system models, little is known about the importance of these precursors for marine ecosystem prediction. With recently released reanalysis-nudged global marine ecosystem simulations, these constraints can be sidestepped, allowing full examination of tropical Pacific ecosystem predictability. By complementing historical fishing records with marine ecosystem model data, we show herein that equatorial Atlantic Sea Surface Temperatures (SSTs) constitute a superlative predictability source for tropical Pacific marine yields, which can be forecasted over large-scale areas up to 2 years in advance. A detailed physical-biological mechanism is proposed whereby Atlantic SSTs modulate upwelling of nutrient-rich waters in the tropical Pacific, leading to a bottom-up propagation of the climate-related signal across the marine food web. Our results represent historical and near-future climate conditions and provide a useful springboard for implementing a marine ecosystem prediction system in the tropical Pacific.

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