A management oriented 1-D ecosystem model: Implementation in the Gulf of Trieste (Adriatic Sea)

Mussap, Giulia; Zavatarelli, Marco; Pinardi, Nadia; Celio, Massimo

doi:10.1016/j.rsma.2016.03.015

Cited by 9 publications

(13 citation statements)

References 42 publications

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“…To obtain the complete 1D biophysical model, BFM17 has been coupled with a modification of the three-dimensional Princeton Ocean Model (POM) (Blumberg and Mellor, 1987) that considers only the vertical and time dimensions; that is, the evolution of the system in the (z, t) space. It is well known that the primary calibration dimension in marine ocean biogeochemistry is along the vertical direction, as shown in several previous calibration and validation exercises (Vichi et al, 2003;Triantafyllou et al, 2003;Mussap et al, 2016).…”

Section: Coupled Physical-biogeochemical Flux Modelmentioning

confidence: 94%

“…assumed that the constant mortality term was sufficient for this model, similar to other reduced-order models (Fasham et al, 1990;Lawson et al, 1996;Clainche et al, 2004). Additionally, the benthic system within BFM56 (Mussap et al, 2016) has been removed. It is assumed that within the upper thermocline of the open ocean, the ecosystem is not substantially influenced by a benthic system and any water-column influences from depth can be taken into account using boundary conditions (such as those discussed in Section 4).…”

Section: Biogeochemical Flux Model 17 (Bfm17)mentioning

confidence: 99%

“…(53) for each of the 17 species in BFM17 are discussed in Section 2.1, and the specific forms of this equation for each of the 56 species in BFM56 were previously discussed in Vichi et al (2007). The parameters used in BFM56 correspond to the values provided in Tables 4-6, with the remaining undefined parameters (since BFM56 includes many more model parameters than BFM17) based on values from Mussap et al (2016).…”

Section: Coupled Physical-biogeochemical Flux Modelmentioning

confidence: 99%

“…In this model adaptation, vertical temperature and salinity profiles are imposed from given climatological monthly profiles, as previously done in Mussap et al (2016) and Bianchi et al (2005). The model computes only the time evolution of the horizontal velocity components, the turbulent kinetic energy and the mixing length scale, all of which are used to compute the turbulent diffusivity term, K H , required in Eq.…”

Section: Coupled Physical-biogeochemical Flux Modelmentioning

confidence: 99%

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BFM17 v1.0: Reduced-Order Biogeochemical Flux Model for Upper Ocean Biophysical Simulations

Smith

Kern

Hamlington

et al. 2020

Preprint

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Abstract. We present a newly developed reduced-order biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics, but reduced enough to incorporate into highly resolved numerical simulations with limited additional computational cost. The reduced-order model, which is derived from the full 56 state variable Biogeochemical Flux Model (BFM56; Vichi et al. (2007)), follows a biological and chemical functional group approach and allows for the development of critical non-Redfield nutrient ratios. Matter is expressed in units of carbon, nitrogen, and phosphate, following techniques used in more complex models. To reduce the overall computational cost and to focus on open-ocean conditions, the reduced model eliminates certain processes, such as benthic, silicate, and iron influences, and parameterizes others, such as the bacterial loop. The model explicitly tracks 17 state variables, divided into phytoplankton, zooplankton, dissolved organic matter, particulate organic matter, and nutrient groups. It is correspondingly called the Biogeochemical Flux Model 17 (BFM17). After providing a detailed description of BFM17, we couple it with the one-dimensional Princeton Ocean Model (POM) for validation using observational data from the Sargasso Sea. Results show good agreement with the observational data and with corresponding results from BFM56, including the ability to capture the subsurface chlorophyll maximum and bloom intensity. In comparison to previous reduced-order models of similar size, BFM17 provides improved correlations between model output and field data, indicating that significant improvements in the reproduction of in situ data can be achieved with a low number of variables, while maintaining the functional group approach.

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Section: Coupled Physical-biogeochemical Flux Modelmentioning

confidence: 94%

Section: Biogeochemical Flux Model 17 (Bfm17)mentioning

confidence: 99%

Section: Coupled Physical-biogeochemical Flux Modelmentioning

confidence: 99%

Section: Coupled Physical-biogeochemical Flux Modelmentioning

confidence: 99%

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BFM17 v1.0: Reduced-Order Biogeochemical Flux Model for Upper Ocean Biophysical Simulations

Smith

Kern

Hamlington

et al. 2020

Preprint

Self Cite

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“…Lastly, remineralization of nutrients is provided by parameterized bacterial closure terms, thereby reducing complexity while still maintaining critical nutrient recycling. The related parameter values (see Table A5 in Appendix A) were chosen according to Mussap et al (2016), who carried out sensitivity tests to evaluate the many parameter values found in the literature.…”

Section: Biogeochemical Flux Model 17 (Bfm17)mentioning

confidence: 99%

BFM17 v1.0: a reduced biogeochemical flux model for upper-ocean biophysical simulations

et al. 2021

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Abstract. We present a newly developed upper-thermocline, open-ocean biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics but reduced enough to incorporate into highly resolved numerical simulations and parameter optimization studies with limited additional computational cost. The model, which is derived from the full 56-state-variable Biogeochemical Flux Model (BFM56; Vichi et al., 2007), follows a biological and chemical functional group approach and allows for the development of critical non-Redfield nutrient ratios. Matter is expressed in units of carbon, nitrogen, and phosphate, following techniques used in more complex models. To reduce the overall computational cost and to focus on upper-thermocline, open-ocean, and non-iron-limited or non-silicate-limited conditions, the reduced model eliminates certain processes, such as benthic, silicate, and iron influences, and parameterizes others, such as the bacterial loop. The model explicitly tracks 17 state variables, divided into phytoplankton, zooplankton, dissolved organic matter, particulate organic matter, and nutrient groups. It is correspondingly called the Biogeochemical Flux Model 17 (BFM17). After describing BFM17, we couple it with the one-dimensional Princeton Ocean Model for validation using observational data from the Sargasso Sea. The results agree closely with observational data, giving correlations above 0.85, except for chlorophyll (0.63) and oxygen (0.37), as well as with corresponding results from BFM56, with correlations above 0.85, except for oxygen (0.56), including the ability to capture the subsurface chlorophyll maximum and bloom intensity. In comparison to previous models of similar size, BFM17 provides improved correlations between several model output fields and observational data, indicating that reproduction of in situ data can be achieved with a low number of variables, while maintaining the functional group approach. Notable additions to BFM17 over similar complexity models are the explicit tracking of dissolved oxygen, allowance for non-Redfield nutrient ratios, and both dissolved and particulate organic matter, all within the functional group framework.

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Linking 1D coastal ocean modelling to environmental management: an ensemble approach

2017

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The use of a one-dimensional interdisciplinary numerical model of the coastal ocean as a tool contributing to the formulation of ecosystem-based management (EBM) is explored. The focus is on the definition of an experimental design based on ensemble simulations, integrating variability linked to scenarios (characterised by changes in the system forcing) and to the concurrent variation of selected, and poorly constrained, model parameters. The modelling system used was previously specifically designed for the use in "data-rich" areas, so that horizontal dynamics can be resolved by a diagnostic approach and external inputs can be parameterised by nudging schemes properly calibrated. Ensembles determined by changes in the simulated environmental (physical and biogeochemical) dynamics, under joint forcing and parameterisation variations, highlight the uncertainties associated to the application of specific scenarios that are relevant to EBM, providing an assessment of the reliability of the predicted changes. The work has been carried out by implementing the coupled modelling Dipartimento di Fisica e Astronomia, Alma Mater Studiorum Università di Bologna, Viale Berti Pichat 6/2, Bologna, Italy system BFM-POM1D in an area of Gulf of Trieste (northern Adriatic Sea), considered homogeneous from the point of view of hydrological properties, and forcing it by changing climatic (warming) and anthropogenic (reduction of the land-based nutrient input) pressure. Model parameters affected by considerable uncertainties (due to the lack of relevant observations) were varied jointly with the scenarios of change. The resulting large set of ensemble simulations provided a general estimation of the model uncertainties related to the joint variation of pressures and model parameters. The information of the model result variability aimed at conveying efficiently and comprehensibly the information on the uncertainties/reliability of the model results to non-technical EBM planners and stakeholders, in order to have the model-based information effectively contributing to EBM.

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A management oriented 1-D ecosystem model: Implementation in the Gulf of Trieste (Adriatic Sea)

Cited by 9 publications

References 42 publications

BFM17 v1.0: Reduced-Order Biogeochemical Flux Model for Upper Ocean Biophysical Simulations

BFM17 v1.0: Reduced-Order Biogeochemical Flux Model for Upper Ocean Biophysical Simulations

BFM17 v1.0: a reduced biogeochemical flux model for upper-ocean biophysical simulations

Linking 1D coastal ocean modelling to environmental management: an ensemble approach

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