Modelling the spatial and temporal variability of the Cretan Sea ecosystem

Petihakis, George; Triantafyllou, G.; Allen, Icarus; Ibrahim, Hoteit; Dounas, C.

doi:10.1016/s0924-7963(02)00186-0

Cited by 67 publications

(69 citation statements)

References 39 publications

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“…On the other hand several implementations of this model have shown the skill of this approach, both in coastal areas with large land-derived inputs but also in the oligotrophic Mediterranean regions and in the subtropical Atlantic Ocean (Ruardij et al, 1997;Allen et al, 1998;Vichi et al, 1998;Zavatarelli et al, 2000;Obernosterer et al, 2001;Allen et al, 2001;Petihakis et al, 2002;Vichi et al, 2004;Raick et al, 2005). The same approach has also been used in the context of climate studies, particularly to capture and analyze climate variability in the North Sea and in the Adriatic Sea (Taylor et al, 2002;Vichi et al, 2003a).…”

Section: Introductionmentioning

confidence: 99%

A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory

Vichi

Pinardi

Masina

2007

Journal of Marine Systems

205

204

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Section: Introductionmentioning

confidence: 99%

A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory

Vichi

Pinardi

Masina

2007

Journal of Marine Systems

205

204

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“…The model is based on the European Regional Seas Ecosystem Model (ERSEM, Baretta et al, 1995), a generic comprehensive model that has been successfully implemented across a wide range of coastal and open ocean ecosystems, including the Mediterranean Petihakis et al, 2002Petihakis et al, , 2015Tsiaras et al, 2014). ERSEM follows a functional group approach, describing the marine ecosystem with different groups based on their functional role and size classes.…”

Section: Model Description/setupmentioning

confidence: 99%

“…The uptake of dissolved inorganic nutrients by phytoplankton is regulated based on the difference between external and internal nutrient pools, following a Droop kinetics formulation (Droop, 1974). The model configuration and parameter set have been adopted from Petihakis et al (2002). The initial food web matrix and plankton maximum growth rates were slightly modified by Tsiaras et al (2014), while the bacteria sub-model has also been revised (Petihakis et al, 2015) from the one in Petihakis et al (2002), allowing for a more realistic representation of the dissolved organic matter (DOM) pool.…”

Section: Model Description/setupmentioning

confidence: 99%

“…The model configuration and parameter set have been adopted from Petihakis et al (2002). The initial food web matrix and plankton maximum growth rates were slightly modified by Tsiaras et al (2014), while the bacteria sub-model has also been revised (Petihakis et al, 2015) from the one in Petihakis et al (2002), allowing for a more realistic representation of the dissolved organic matter (DOM) pool. In the present study, a few additional changes in the model parameter set and formulations were needed, as further hereafter described, to achieve a better fit of the model simulations with in situ measurements from the mesocosm experiment.…”

Section: Model Description/setupmentioning

confidence: 99%

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Model Simulations of a Mesocosm Experiment Investigating the Response of a Low Nutrient Low Chlorophyll (LNLC) Marine Ecosystem to Atmospheric Deposition Events

et al. 2017

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Atmospheric deposition of nitrogen and phosphorus represents an important source of nutrients, enhancing the marine productivity in oligotrophic areas, e.g., the Mediterranean. A comprehensive biogeochemical model (ERSEM) was setup and customized to simulate a mesocosm experiment, where dissolved inorganic nitrogen and phosphorus by means of atmospheric dust (single addition/SA and repetitive addition/RA in three successive doses) was added in controlled tanks and compared with a control (blank), all with Cretan Sea (Eastern Mediterranean) water. Observations on almost all components of the pelagic ecosystem in a ten-day period allowed investigating the effect of atmospheric deposition and the pathways of the added nutrients. The model was able to reasonably capture the observed variability of different ecosystem components and reproduce the main features of the experiment. An enhancement of primary production and phytoplankton biomass with added nutrients was simulated, in agreement with observations. A significant increase of bacterial production was also reproduced, while the model underestimated the observed increase and variability in bacterial biomass, but this deviation could be partly removed considering a lower carbon conversion factor from cell abundance data. A slightly stronger overall response was simulated with the single dust addition, compared to the repetitive that showed a few days delay. The simulated carbon pathways indicated that nutrient additions did not modify the microbial food web structure, but just increased its trophic status. Changes in model assumptions and parameter set that were necessary to reproduce the observed variability in the mesocosm experiment were discussed through a series of sensitivity simulations. Bacterial production was assumed to be mostly affected by the in situ produced labile organic matter, while it was further stimulated by the addition of inorganic nutrients, adopting a function of external nutrient concentrations for bacteria nutrient limitation. The effective increase in phytoplankton nutrient uptake rate was Tsiaras et al.Mesocosm Simulations of Atmospheric Deposition necessary, in order to reproduce the observed primary production, under such low nutrient concentrations, as also the increase of the grazers growth rate. The model was thus tuned to better work under very low nutrient concentrations, such as those found in the Eastern Mediterranean.

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“…Both sub models have been customed tuned and validated for the Cretan Sea, as described in detail in Petihakis et al (2002) and Triantafyllou et al (2003a), producing a rather efficient representation of both physical and biological components. Although, as mentioned before, both models have been already described elsewhere, a brief reference on the key features is considered to be useful for the readership.…”

Section: The Ecosystem Modelmentioning

confidence: 99%

Efficient data assimilation into a complex, 3-D physical-biogeochemical model using partially-local Kalman filters

2005

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Abstract. Advanced Kalman filtering techniques were used to assimilate pseudo ocean color and profile data into a complex, three-dimensional coupled physical (POM)-biogeochemical (ERSEM) model of the Cretan Sea ecosystem. The assimilation schemes, the Singular Evolutive Partially-Local Extended Kalman (SEPLEK) filter and its variant called SFPLEK, are based on the standard SEEK filter in which the Kalman correction is made along a set of "global" and "local" directions, determined via a so-called "global-local EOF analysis". The global functions are used to represent the ecosystem large-scale variability. They are allowed to evolve in time in the SEPLEK filter to follow changes in the model dynamics, while they remain invariant in the SFPLEK filter. The local functions always remain invariant and are determined in such a way as to independently represent the different spatial regimes of the ecological model. This helps to improve the estimation of finescale variations while requiring significantly less computational time compared to the SEEK filter.Several assimilation experiments were performed to assess the relevance of the assimilation system and to study its sensitivity to different choices of global/local EOFs. The SFPLEK filter was used in all the sensitivity experiments in order to efficiently measure the representativeness of the different set of correction directions, as well as to save computational time. Assimilation results suggest that the use of global-local correction directions clearly enhances the filter's performance under different assimilation setups. The choice of the local directions should, however, be carefully considered, taking into account the model regional variability and the characteristics of the observational system.

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Modelling the spatial and temporal variability of the Cretan Sea ecosystem

Cited by 67 publications

References 39 publications

A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory

A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory

Model Simulations of a Mesocosm Experiment Investigating the Response of a Low Nutrient Low Chlorophyll (LNLC) Marine Ecosystem to Atmospheric Deposition Events

Efficient data assimilation into a complex, 3-D physical-biogeochemical model using partially-local Kalman filters

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