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

2018 ◽  
Author(s):  
Hagen Radtke ◽  
Marko Lipka ◽  
Dennis Bunke ◽  
Claudia Morys ◽  
Bronwyn Cahill ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Marine Ecosystem ◽  
Nutrient Recycling ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Predictive Capacity ◽  
Formation Pathway ◽  
Sediment Types ◽  
Marine Ecosystem Model ◽  
Reasonable Description

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 parametrisations 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 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 and bioturbation rates collected within the framework of the SECOS project.

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 Assimilation of ocean colour data into a Biochemical Flux Model of the Eastern Mediterranean Sea

2006 ◽  
Vol 3 (5) ◽  
pp. 1569-1608 ◽  
Author(s):  
G. Triantafyllou ◽  
G. Korres ◽  
I. Hoteit ◽  
G. Petihakis ◽  
A. C. Banks
Keyword(s):  
Eastern Mediterranean ◽  
Ocean Model ◽  
Empirical Orthogonal Functions ◽  
Wide Field ◽  
Sequential Data ◽  
Error Statistics ◽  
Orthogonal Functions ◽  
Flux Model ◽  
The Impact ◽  
Assimilation System

Abstract. Within the framework of the European MFSTEP project, an advanced multivariate sequential data assimilation system has been implemented to assimilate real chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) into a three-dimensional biochemical model of the Eastern Mediterranean. The physical ocean is described through the Princeton Ocean Model (POM) while the biochemistry of the ecosystem is tackled with the Biochemical Flux Model (BFM). The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter, in which the error statistics were parameterized by means of a suitable set of Empirical Orthogonal Functions (EOFs). A radius of influence was further selected around every data point to limit the range of the EOFs spatial correlations. The assimilation experiment was performed for one year over 1999 and forced with ECMWF 6 hour atmospheric fields. The accuracy of the ecological state identification by the assimilation system is assessed by the relevance of the system in fitting the data, and through the impact of the assimilation on non-observed biochemical processes. Assimilation of SeaWiFS data significantly improves the forecasting capability of the BFM model. Results, however, indicate the necessity of subsurface data to enhance the controllability of the ecosystem model in the deep layers.

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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