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

Vichi, Marcello; Pinardi, Nadia; Masina, Simona

doi:10.1016/j.jmarsys.2006.03.006

Cited by 205 publications

(210 citation statements)

References 73 publications

(95 reference statements)

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“…Some models have a loss term loss of phytoplankton carbon due to nutrient stressed lysis caused by mechanical failure and viruses (Vichi et al, 2007;Mateus, 2012b;Petihakis et al, 2012;Hasumi and Nagata, 2014), frequently staying close to the approach proposed in early models such as the ERSEM model (BarettaBekker et al, 1997;Butenschön et al, 2016). In other cases viruses are assumed to be a minor phytoplankton loss mechanism (Stock et al, 2014), parameterized as weak density-dependent loss term on small phytoplankton, following early and simpler modeling approaches (Suttle, 1994).…”

Section: Current State Of Modelingmentioning

confidence: 94%

“…The inclusion of these "perfect beats" combining several modes of nutrition requires additional inclusion of complexity into models, as shown by Flynn and Mitra (2009); stoichiometric implication must be considered by the model, by which the C-to-nutrient ratios must be explicitly expressed for the mixotrophs and food source, otherwise the model is doomed to fail to reflect the reality of the trophic interactions, both at the mixotrophs level as on the ecosystem level. ERSEM type models that rely on a functional group approach have mixotrophs by combining code from producers and consumers (Baretta-Bekker et al, 1995, 1997Vichi et al, 2007;Mateus, 2012a). However, this method of inclusion of a full description of mixotrophy into models has its own problems, such as an increase in complexity and computational costs.…”

Section: Model Components and Examplesmentioning

confidence: 99%

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Bridging the Gap between Knowing and Modeling Viruses in Marine Systems—An Upcoming Frontier

Mateus

2017

Front. Mar. Sci.

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Viruses are the most abundant biological entities in the world's oceans. Their potential control on the dynamics and diversity of bacterioplankton and some phytoplankton groups, and consequent effect on the flow of energy and matter in food webs, may be argued as beyond dispute. Paradoxically, their importance seems to be persistently underestimated by marine modelers, frequently by exclusion, despite the uninterrupted volume of knowledge advanced during the past decades. Bridging the gap between knowing and modeling the role of viruses is, undoubtedly, one of the upcoming frontiers to be crossed in modeling the plankton. This paper has a two-fold objective: (1) review the knowledge on the roles of viruses in marine systems that has been put forward over the past decades, and (2) see how viruses have been incorporated into marine ecosystem models, along with the factors that are limiting their inclusion.

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Section: Current State Of Modelingmentioning

confidence: 94%

Section: Model Components and Examplesmentioning

confidence: 99%

Bridging the Gap between Knowing and Modeling Viruses in Marine Systems—An Upcoming Frontier

Mateus

2017

Front. Mar. Sci.

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“…The CMCC Carbon Earth System Model (CMCC-CESM) consists of an atmosphereocean-sea ice physical core coupled to models resolving carbon cycle dynamics on land and ocean using the SILVA land surface model (Alessandri 2006), and the PELAGOS model for ocean biogeochemistry (Vichi et al 2007;Vichi and Masina 2009). The technical features of the ESM are fully described in Fogli et al (2009) and Vichi et al (2011).…”

Section: Model Descriptionmentioning

confidence: 99%

“…The carbon cycle components on land and in the ocean are capable to simulate the exchange of carbon in different biological forms by means of a functional group approach. The PELAGOS model (PELAgic biogeochemistry for Global Ocean Simulations) consists of the global ocean version of the Biogeochemical Flux Model (BFM, http://bfm.cmcc.it), with several planktonic groups, multiple nutrient limitations and a complete representation of inorganic ed organic carbon cycling in the marine environment (Vichi et al 2007). The SILVA (Surface Interactive Land VegetAtion) model also includes the biophysical interactions with momentum, heat and water flux exchanges at the interface between land-surface and atmosphere, and dynamical vegetation with different plant functional types (Alessandri 2006;Alessandri et al 2012).…”

Section: Model Descriptionmentioning

confidence: 99%

Adjustment of the natural ocean carbon cycle to negative emission rates

2013

Self Cite

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Carbon dioxide removal (CDR) is the only geoengineering technique that allows negative emissions and the reduction of anthropogenic carbon in the atmosphere. Since the time scales of the global carbon cycle are largely driven by the exchanges with the natural oceanic stocks, the implementation of CDR actions is anticipated to create outgassing from the ocean that may reduce their efficiency. The adjustment of the natural carbon cycle to CDR was studied with a numerical Earth System Model, focusing on the oceanic component and considering two idealized families of CDR policies, one based on a target atmospheric concentration and one based on planned negative emissions. Results show that both actions are anticipated to release the anthropogenic carbon stored in the surface ocean, effectively increasing the required removal effort. The additional negative emissions are expected to be lower when the CDR policy is driven by planned removal rates without prescribing a target atmospheric CO 2 concentration.

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“…They are also valuable for determining the magnitude and importance of processes that are difficult to measure and observe in the field. Many recent coastal and open ocean biogeochemical ecosystem models have included DOM (Anderson and Pondaven, 2003;Anderson et al, 2007;Anderson andWilliams, 1998, 1999;Aumont et al, 2003;Baklouti et al, 2006a,b;Druon et al, 2010;Faure et al, 2006;Grégoire and Soetaert, 2010;Llebot et al, 2010;Luo et al, 2010;Nakata and Doi, 2006;Pahlow and Vézina, 2003;Pahlow et al, 2008;Polimene et al, 2006Polimene et al, , 2007Ruardij et al, 2005;Salihoglu et al, 2008;Schmittner et al, 2005;Vichi et al, 2007) in order to better simulate and understand biogeochemical cycles. Carbon and nitrogen are typically accounted for in these models, although a few include phosphorus as well.…”

Section: Introductionmentioning

confidence: 99%

Modeling the seasonal autochthonous sources of dissolved organic carbon and nitrogen in the upper Chesapeake Bay

Keller

Hood

2011

Ecological Modelling

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a b s t r a c tIn this paper we investigate the seasonal autochthonous sources of dissolved organic carbon (DOC) and nitrogen (DON) in the euphotic zone at a station in the upper Chesapeake Bay using a new mass-based ecosystem model. Important features of the model are: (1) carbon and nitrogen are incorporated by means of a set of fixed and varying C:N ratios; (2) dissolved organic matter (DOM) is separated into labile, semi-labile, and refractory pools for both C and N; (3) the production and consumption of DOM is treated in detail; and (4) seasonal observations of light, temperature, nutrients, and surface layer circulation are used to physically force the model. The model reasonably reproduces the mean observed seasonal concentrations of nutrients, DOM, plankton biomass, and chlorophyll a. The results suggest that estuarine DOM production is intricately tied to the biomass concentration, ratio, and productivity of phytoplankton, zooplankton, viruses, and bacteria. During peak spring productivity phytoplankton exudation and zooplankton sloppy feeding are the most important autochthonous sources of DOM. In the summer when productivity peaks again, autochthonous sources of DOM are more diverse and, in addition to phytoplankton exudation, important ones include viral lysis and the decay of detritus. The potential importance of viral decay as a source of bioavailable DOM from within the bulk DOM pool is also discussed. The results also highlight the importance of some poorly constrained processes and parameters. Some potential improvements and remedies are suggested. Sensitivity studies on selected parameters are also reported and discussed.

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

Cited by 205 publications

References 73 publications

Bridging the Gap between Knowing and Modeling Viruses in Marine Systems—An Upcoming Frontier

Bridging the Gap between Knowing and Modeling Viruses in Marine Systems—An Upcoming Frontier

Adjustment of the natural ocean carbon cycle to negative emission rates

Modeling the seasonal autochthonous sources of dissolved organic carbon and nitrogen in the upper Chesapeake Bay

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