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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

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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 Geosci. Model Dev., 9, 59–76, 2016 www.geosci-model-dev.net/9/59/2016/ doi:10.5194/gmd-9-59-2016 © Author(s) 2016. CC Attribution 3.0 License. The assessment of a global marine ecosystem model on the basis of emergent properties and ecosystem function: a case study with ERSEM L. de Mora, M. Butenschön, and J. I. Allen Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL1 3DH, UK Correspondence to: L. de Mora ([email protected]) Received: 26 June 2015 – Published in Geosci. Model Dev. Discuss.: 5 August 2015 Revised: 7 December 2015 – Accepted: 11 December 2015 – Published: 15 January 2016 Abstract. Ecosystem models are often assessed using quan- 1 Introduction titative metrics of absolute ecosystem state, but these model– data comparisons are disproportionately vulnerable to dis- Numerical models of the environment are used frequently for crepancies in the location of important circulation features. informing policy decisions, for forecasting the impact of cli- An alternative method is to demonstrate the models capac- mate change, and to obtain a deeper understanding of nature. ity to represent ecosystem function; the emergence of a co- In order for a model to be used for any of these purposes, herent natural relationship in a simulation indicates that the the model must first be shown to be a valid representation model may have an appropriate representation of the ecosys- of the system under study. There are two objective strate- tem functions that lead to the emergent relationship. Further- gies available to demonstrate that the model is a valid repre- more, as emergent properties are large-scale properties of the sentation of the system under study. The first strategy is to system, model validation with emergent properties is possi- reproduce the spatial and temporal distributions of historic ble even when there is very little or no appropriate data for observations, and the second is to reproduce the functional the region under study, or when the hydrodynamic compo- relationships. nent of the model differs significantly from that observed in There is a long history of works that demonstrate model nature at the same location and time. validation using static fields, spatial distributions and dy- A selection of published meta-analyses are used to estab- namic variability, including Droop(1973), Fasham et al. lish the validity of a complex marine ecosystem model and (1990), Taylor(2001), Blackford(2004), Allen et al.(2007), to demonstrate the power of validation with emergent prop- Jolliff et al.(2009), Shutler et al.(2011), Saux Picart et al. erties. These relationships include the phytoplankton com- (2012), de Mora et al.(2013), and Kwiatkowski et al.(2014). munity structure, the ratio of carbon to chlorophyll in phy- However, validating a modern biogeochemical model using toplankton and particulate organic matter, the ratio of partic- static fields and spatial distributions may give an appropri- ulate organic carbon to particulate organic nitrogen and the ate assessment of the coupled biogeochemical and hydrody- stoichiometric balance of the ecosystem. namic modelled system, but the performance of the biogeo- These metrics can also inform aspects of the marine chemical model may be obscured by deficiencies in the mod- ecosystem model not available from traditional quantitative elled circulation. For instance, the point-to-point analysis de- and qualitative methods. For instance, these emergent proper- scribed in de Mora et al.(2013) is vulnerable to discrepancies ties can be used to validate the design decisions of the model, between the model and the observations in the location of im- such as the range of phytoplankton functional types and their portant circulation features such as fronts, coastlines or up- behaviour, the stoichiometric flexibility with regards to each welling regions. These problems in the physical model may nutrient, and the choice of fixed or variable carbon to nitro- needlessly penalise the performance of the biogeochemical gen ratios. model when validating using point-to-point matching. Val- idation methods that use historic data are also sensitive to Published by Copernicus Publications on behalf of the European Geosciences Union. 60 L. de Mora et al.: The assessment of a global marine ecosystem model using emergent properties initial conditions and the boundary conditions of the model. occur without being explicitly parameterised. The interplay These problems are amplified for models with coarse spatial of multiple ecosystem functions can result in the emergence and temporal resolution, such as the monthly mean of a 1◦ of observable relationships. The link between diatom chloro- global model. The disentanglement of the performance of the phyll and total community chlorophyll, as shown by Holt biogeochemical model from that of the physics is a major et al.(2014), is an example of such an emergent relationship. challenge in marine ecosystem modelling (Holt et al., 2014). These emergent relationships can be used to characterise and Direct comparisons of model to data inform only about validate the ecosystem and its functioning. As in the exam- how similar the model is to the observations. Such methods ple from Holt et al.(2014), emergent relationships are impor- also risk compartmentalising the validation of ecosystems tant because they occur independently of the hydrodynamic and may not cover the interaction of their parts. The ability model, and because they reflect the functioning of the mod- of a model to represent present-day measurements is impor- elled ecosystem in a way that would not be visible in a simple tant, but it does not inform about the models representation point-to-point comparison of ecosystem state. of the behaviour of the ecosystem as a whole. Furthermore, A selection of historically published large-scale emer- historical static fields may not necessarily validate a model, gent relationships are proposed to illustrate the validation of which is subjected to a changing climate due to the scarce a complex ecosystem model and demonstrate the power of availability of long-term time-series data sets. emergent property validation. The example ecosystem model As a solution to the problem of the absence of data and used here is European Regional Seas Ecosystem Model presence of poorly constrained physics, Holt et al.(2014) (ERSEM), and it is run coupled with the Nucleus for Eu- wrote “there is a need for metrics that assess the fidelity of ropean Modelling of the Ocean (NEMO) in a global hind- the biogeochemical processes independently of the physics”. cast scenario. The emergent relationships shown here are the In that work, they identify one such functional relationship: community structure, the carbon to chlorophyll ratio, the ra- the link between diatom chlorophyll and total community tio of particulate organic carbon against particulate organic chlorophyll. They demonstrated that the fraction of the com- nitrogen and stoichiometric balance. munity chlorophyll that originates in diatoms increases with After this introductory section, Sect.2 contains a brief total chlorophyll in multiple models. A relationship between description of the circulation model, NEMO, the ecosys- diatom concentration and total chlorophyll was also observed tem model, ERSEM, and the sea ice model, CICE, used in in nature in Hirata et al.(2011). In effect, a relationship this study. Section3 is a non-exhaustive list of examples of seen in in situ observations also appeared in multiple bio- ecosystem relationships that have been investigated in the geochemical models. In addition, the relationship observed ERSEM ecosystem model. An expanded version of the com- in Holt et al.(2014) was a widespread general response in all munity structure relationship described by Holt et al.(2014) the plankton models that was independent of local hydrody- is included in Sect. 3.1. Section 3.2 shows the ratio of carbon namic conditions. Furthermore, this relationship is a large- to chlorophyll in phytoplankton and particulate organic mat- scale property of the marine ecosystem, and is expected to ter. Section 3.3 demonstrates how the model reproduces the hold true even in regions with few historical measurements. ratio of particulate organic carbon and nitrogen as described This relationship is important because it occurred indepen- by Redfield(1934), Martiny et al.(2013). Section 3.4 illus- dently of the hydrodynamic model, and because it reflected trates the internal stoichiometric relationships for ERSEM the functioning of the modelled ecosystem in a way that for each of the nutrient currencies modelled. Finally, Sect.4 would not be visible in a simple point-to-point comparison discusses the successes, potential and limitations of these of ecosystem state. methods. Beyond Hirata et al.(2011), there are many works that link a large phytoplankton size class with the community structure: Devred et al.(2011), Brewin et al.(2012, 2014, 2 The ERSEM and NEMO models 2015). Many features of an ecosystem can affect the balance of large phytoplankton chlorophyll against the total commu- ERSEM is a lower trophic level biogeochemical cycling, nity chlorophyll, such as the large phytoplankton response carbon-based process model that uses the functional-group to nutrients, light and temperature, competition for light and approach (Baretta et al., 1995; Blackford, 2004; Butenschön nutrients from other phytoplankton, and predation on large et al., 2015). The carbon, nitrogen, phosphorus, silicon and phytoplankton relative to other phytoplankton
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