SIMPLE

Spatially-Implicit Modelling of Plankton Ecosystems

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 Obiettivo del progetto (Objective)

'The aim of SIMPLE is to investigate and parameterise the influence of small-scale spatial variability in plankton concentrations on the behaviour of large-scale mean fields. This is necessary for accurate plankton ecosystem models as biogeochemical subunits of predictive climate models, which must account for the influence of plankton ecosystems on the biosphere, and as tools to predict the large-scale effects of climate change on sensitive marine ecosystems, which support economically-critical resources. SIMPLE will achieve this aim in two stages. First, we use spatially-resolved numerical simulation to investigate the influence of small-scale spatial variability on mean field behaviour (the ‘biological Reynolds fluxes’), and how these fluxes may be parameterised in coarse-resolution models using second moment closure (SMC) methods. Second, we groundtruth the SMC method by fitting coarse-resolution models to in situ and satellite data from the BATS (Bermuda Atlantic Time Series) region and attempting to predict independent data. The objectives are to quantify the ecosystem fluxes due to unresolved biological variability, develop SMC parameterisations of these fluxes, and assess the predictive benefits in a context applicable to large areas of the oceans. SIMPLE will fulfil FP7 directives for collaborative research into the Environment (‘Cooperation’ block) as well as frontier science (‘Ideas’) and training/career development for the applicant (‘People’).'

Introduzione (Teaser)

New research could help scientists and policymakers assess the long-term role of marine plankton in the biosphere.

Descrizione progetto (Article)

Marine ecosystems support a number of economically critical resources, and scientists are working on predicting the effects of climate change on these environments. The EU-funded project, 'Spatially-implicit modelling of plankton ecosystems' (SIMPLE), modelled the effects of different plankton concentrations in different locations. This helped assess the impact of plankton spatial variability on the levels of carbon and nutrients in marine ecosystems.

Project members found that low-resolution models like those used for Intergovernmental Panel on Climate Change (IPCC) simulations, often make poor predictions of marine ecosystem fluxes. This is because they do not take ocean weather into account.

High-resolution systems can, however, be quite expensive. The researchers therefore assessed how well spatial variability effects could be predicted by a particular mathematical modelling approach.

In order to match their model to observational data from the Bermuda Atlantic time series (BATS), the team collaborated with BATS on a statistical study. They discovered that carbon biomass could be accurately inferred from the measurement of variables like pigment and irradiance using transform-linear multiple regression models. This allowed the researchers to use raw data from 2004 to 2012 to reconstruct the biomass measurements from 1989 to 2012.

SIMPLE findings should be of interest to climate modellers and policymakers who currently rely on low-resolution models to address climate-related questions. These include issues like carbon absorption by the ocean or how climate change influences plankton abundance, and hence fishery productivities.