ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels

Butenschön, Momme; Clark, James R.; Aldridge, John; Allen, J. Icarus; Artioli, Yuri; Blackford, Jeremy; Bruggeman, Jorn; Cazenave, Pierre; Ciavatta, Stefano; Kay, Susan; Lessin, Gennadi; Leeuwen, Sonja van; Molen, Johan van der; Mora, L. De; Polimene, Luca; Sailley, Sévrine F.; Stephens, Nicholas; Torres, Ricardo

doi:10.5194/gmdd-8-7063-2015

Cited by 35 publications

(64 citation statements)

References 92 publications

(121 reference statements)

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“…There is a long history of works that demonstrate model validation using static fields, spatial distributions and dynamic variability, including Droop (1973), Fasham et al (1990), Taylor (2001), Blackford (2004), Allen et al (2007), Jolliff et al (2009), Shutler et al (2011), Saux Picart et al (2012, de Mora et al (2013), and Kwiatkowski et al (2014). However, validating a modern biogeochemical model using static fields and spatial distributions may give an appropriate assessment of the coupled biogeochemical and hydrodynamic modelled system, but the performance of the biogeochemical model may be obscured by deficiencies in the modelled circulation.…”

Section: Introductionmentioning

confidence: 99%

“…However, validating a modern biogeochemical model using static fields and spatial distributions may give an appropriate assessment of the coupled biogeochemical and hydrodynamic modelled system, but the performance of the biogeochemical model may be obscured by deficiencies in the modelled circulation. For instance, the point-to-point analysis described in de Mora et al (2013) is vulnerable to discrepancies between the model and the observations in the location of important circulation features such as fronts, coastlines or upwelling regions. These problems in the physical model may needlessly penalise the performance of the biogeochemical model when validating using point-to-point matching.…”

Section: Introductionmentioning

confidence: 99%

“…2 The ERSEM and NEMO models ERSEM is a lower trophic level biogeochemical cycling, carbon-based process model that uses the functional-group approach (Baretta et al, 1995;Blackford, 2004;Butenschön et al, 2015). The carbon, nitrogen, phosphorus, silicon and iron cycles are explicitly resolved.…”

Section: Introductionmentioning

confidence: 99%

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The assessment of a global marine ecosystem model on the basis of emergent properties and ecosystem function: a case study with ERSEM

Mora¹,

Butenschön²,

Allen³

2016

Geosci. Model Dev.

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Abstract. Ecosystem models are often assessed using quantitative metrics of absolute ecosystem state, but these modeldata comparisons are disproportionately vulnerable to discrepancies in the location of important circulation features. An alternative method is to demonstrate the models capacity to represent ecosystem function; the emergence of a coherent natural relationship in a simulation indicates that the model may have an appropriate representation of the ecosystem functions that lead to the emergent relationship. Furthermore, as emergent properties are large-scale properties of the system, model validation with emergent properties is possible even when there is very little or no appropriate data for the region under study, or when the hydrodynamic component of the model differs significantly from that observed in nature at the same location and time.A selection of published meta-analyses are used to establish the validity of a complex marine ecosystem model and to demonstrate the power of validation with emergent properties. These relationships include the phytoplankton community structure, the ratio of carbon to chlorophyll in phytoplankton and particulate organic matter, the ratio of particulate organic carbon to particulate organic nitrogen and the stoichiometric balance of the ecosystem.These metrics can also inform aspects of the marine ecosystem model not available from traditional quantitative and qualitative methods. For instance, these emergent properties can be used to validate the design decisions of the model, such as the range of phytoplankton functional types and their behaviour, the stoichiometric flexibility with regards to each nutrient, and the choice of fixed or variable carbon to nitrogen ratios.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The assessment of a global marine ecosystem model on the basis of emergent properties and ecosystem function: a case study with ERSEM

Mora¹,

Butenschön²,

Allen³

2016

Geosci. Model Dev.

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“…In these models, the zooplankton community ranges from unicellular fastgrowing microorganisms to multicellular meso-and macrozooplankton (e.g. copepods, krill) with longer generation times, but the community is often reduced to one or a few zoo-PFTs , Butenschön et al 2016). Mesozooplankton (carnivorous or omnivorous) represents, in most cases, the highest trophic level and mortality on this group represents a closure term for nutrient and carbon fluxes.…”

Section: Introductionmentioning

confidence: 99%

Responses of summer phytoplankton biomass to changes in top-down forcing: Insights from comparative modelling

Maar

Butenschön

Daewel

et al. 2018

Ecological Modelling

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The present study describes the responses of summer phytoplankton biomass to changes in top-down forcing (expressed as zooplankton mortality) in three ecosystems (the North Sea, the Baltic Sea and the Nordic Seas) across different 3D ecosystem models. In each of the model set-ups, we applied the same changes in the magnitude of mortality (±20%) of the highest trophic zooplankton level (Z1). Model results showed overall dampened responses of phytoplankton relative to Z1 biomass. Phytoplankton responses varied depending on the food web structure and trophic coupling represented in the models.Hence, a priori model assumptions were found to influence cascades and pathways in model estimates and, thus, become highly relevant when examining ecosystem pressures such as fishing and climate change. Especially, the different roles and parameterizations of additional zooplankton groups grazed by Z1, and their importance for the outcome, emphasized the need for better calibration data. Spatial variability was high within each model indicating that physics (hydrodynamics and temperature) and nutrient dynamics also play vital roles for ecosystem responses to top-down effects. In conclusion, the model comparison indicated that changes in top-down forcing in combination with the modelled foodweb structure affect summer phytoplankton biomass and, thereby, indirectly influence water quality of the systems.3

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“…In this regard, PFT models may be more generally applicable because they resolve rel-105 atively more fundamental ecological processes that may be less sensitive to environmental variability (Friedrichs et al, 2007). These are the key factors that have motivated the development of more complex models, in which the broad ecological guilds of NPZD models are replaced with more specific groups based on ecological and/or biogeochemical function (Aumont et al, 2015;Butenschön et al, 2016). It is argued that resolving more components of the ecosystem allows the representation of 110 important climate feedbacks that cannot be accounted for in simpler models (Le Quéré, 2006).…”

mentioning

confidence: 99%

EcoGEnIE 0.1: Plankton Ecology in the cGENIE Earth system model

Ward¹,

Wilson²,

Death³

et al. 2017

Preprint

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Abstract. We present an extension to the cGENIE Earth System model that explicitly accounts for the growth and interaction of an arbitrary number of plankton species. The new package ('ECOGEM') replaces the implicit, flux-based, parameterisation of the plankton community currently employed, with explicitly resolved plankton populations and ecological dynamics. In ECOGEM, any number of plankton species, with ecophysiological traits (e.g. growth and grazing rates) assigned according 5 to organism size and functional group (e.g. phytoplankton and zooplankton) can be incorporated at run-time. We illustrate the capability of the marine ecology enabled Earth system model ('EcoGE-NIE') by comparing results from one configuration of ECOGEM (with eight generic phytoplankton and zooplankton size classes) to climatological and seasonal observations. We find that the new ecological components of the model show reasonable agreement with both global-scale climatological 10 and local-scale seasonal data. We also compare EcoGENIE results to a the existing biogeochemical incarnation of cGENIE. We find that the resulting global-scale distributions of phosphate, iron, dissolved inorganic carbon, alkalinity and oxygen are similar for both iterations of the model. A slight deterioration in some fields in EcoGENIE (relative to the data) is observed, although we make no attempt to re-tune the overall marine cycling of carbon and nutrients here. The increased capa-15 bilities of EcoGENIE in this regard will enable future exploration of the ecological community on much longer timescales than have previously been examined in global ocean ecosystem models and particularly for past climates and global biogeochemical cycles.

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ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels

Cited by 35 publications

References 92 publications

The assessment of a global marine ecosystem model on the basis of emergent properties and ecosystem function: a case study with ERSEM

The assessment of a global marine ecosystem model on the basis of emergent properties and ecosystem function: a case study with ERSEM

Responses of summer phytoplankton biomass to changes in top-down forcing: Insights from comparative modelling

EcoGEnIE 0.1: Plankton Ecology in the cGENIE Earth system model

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