A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part II: Numerical simulations

This paper presents a global ocean implementation of a multi-component model of marine pelagic biogeochemistry coupled on-line with an ocean general circulation model forced with climatological surface fields (PELAgic biogeochemistry for Global Ocean Simulations, PELAGOS). The final objective is the inclusion of this model as a component in an Earth System model for climate studies. The pelagic model is based on a functional stoichiometric representation of marine biogeochemical cycles and allows simulating the dynamics of C, N, P, Si, O and Fe taking into account the variation of their elemental ratios in the functional groups. The model also includes a parameterization of variable chlorophyll/carbon ratio in phytoplankton, carrying chl as a prognostic variable. The first part of the paper analyzes the contribution of non-local advective–diffusive terms and local vertical processes to the simulated chl distributions. The comparison of the three experiments shows that the mean chl distribution at higher latitudes is largely determined by mixing processes, while vertical advection controls the distribution in the equatorial upwelling regions. Horizontal advective and diffusive processes are necessary mechanisms for the shape of chl distribution in the sub-tropical Pacific. In the second part, the results have been compared with existing datasets of satellite-derived chlorophyll, surface nutrients, estimates of phytoplankton community composition and primary production data. The agreement is reasonable both in terms of the spatial distribution of annual means and of the seasonal variability in different dynamical oceanographic regions. Results indicate that some of the model biases in chl and surface nutrients distributions can be related to deficiencies in the simulation of physical processes such as advection and mixing. Other discrepancies are attributed to inadequate parameterizations of phytoplankton functional groups. The model has skill in reproducing the overall distribution of large and small phytoplankton but tends to underestimate diatoms in the northern higher latitudes and overestimate nanophytoplankton with respect to picoautotrophs in oligotrophic regions. The performance of the model is discussed in the context of its use in climate studies and an approach for improving the parameterization of functional groups in deterministic models is outlined.     

Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models

Marine ecosystem models are becoming increasingly complex and sophisticated, and are being used to estimate the effects of future changes in the earth system with a view to informing important policy decisions. Despite their potential importance, far too little attention has been, and is generally, paid to model errors and the extent to which model outputs actually relate to real-world processes. With the increasing complexity of the models themselves comes an increasing complexity among model results. If we are to develop useful modelling tools for the marine environment we need to be able to understand and quantify the uncertainties inherent in the simulations. Analysing errors within highly multivariate model outputs, and relating them to even more complex and multivariate observational data, are not trivial tasks. Here we describe the application of a series of techniques, including a 2-stage self-organising map (SOM), non-parametric multivariate analysis, and error statistics, to a complex spatio-temporal model run for the period 1988–1989 in the Southern North Sea, coinciding with the North Sea Project which collected a wealth of observational data. We use model output, large spatio-temporally resolved data sets and a combination of methodologies (SOM, MDS, uncertainty metrics) to simplify the problem and to provide tractable information on model performance. The use of a SOM as a clustering tool allows us to simplify the dimensions of the problem while the use of MDS on independent data grouped according to the SOM classification allows us to validate the SOM. The combination of classification and uncertainty metrics allows us to pinpoint the variables and associated processes which require attention in each region. We recommend the use of this combination of techniques for simplifying complex comparisons of model outputs with real data, and analysis of error distributions.     

Error quantification of a high-resolution coupled hydrodynamic-ecosystem coastal-ocean model: Part 2. Chlorophyll-a, nutrients and SPM

Marine systems models are becoming increasingly complex and sophisticated, but far too little attention has been paid to model errors and the extent to which model outputs actually relate to ecosystem processes. Here we describe the application of summary error statistics to a complex 3D model (POLCOMS-ERSEM) run for the period 1988–1989 in the southern North Sea utilising information from the North Sea Project, which collected a wealth of observational data. We demonstrate that to understand model data misfit and the mechanisms creating errors, we need to use a hierarchy of techniques, including simple correlations, model bias, model efficiency, binary discriminator analysis and the distribution of model errors to assess model errors spatially and temporally. We also demonstrate that a linear cost function is an inappropriate measure of misfit. This analysis indicates that the model has some skill for all variables analysed. A summary plot of model performance indicates that model performance deteriorates as we move through the ecosystem from the physics, to the nutrients and plankton.     

Error quantification of a high resolution coupled hydrodynamic-ecosystem coastal-ocean model: Part3, validation with Continuous Plankton Recorder data

Data collected during the Continuous Plankton Recorder (CPR) survey has been used to validate a three-dimensional hydrodynamic ecosystem model simulation of the North-west European Shelf for the years 1988–89. The CPR time series is unique to the North Atlantic region as a validation tool. Data were extracted from the model to correspond with those collected by the CPR survey, and both the model and survey plankton data were standardised to allow the comparison of model biomass with survey counts. Simple linear regression and absolute error maps provide a qualitative evaluation of spatio-temporal model performance of simulated diatoms, flagellates, total phytoplankton and omnivorous mesozooplankton. Comparisons of z-scores indicate that the model reproduces the main pelagic seasonal features, and there is good correlation between magnitudes of these features with respect to standard deviations from a long-term mean. The model is replicating up to 62% of the mesozooplankton seasonality across the domain, with variable results for the phytoplankton. There are, however, differences in the timing of patterns in plankton seasonality. The validation exercise has highlighted that the spring diatom bloom in the model is too early, suggesting the need to reparameterise the response of phytoplankton to changing light levels in the model. Errors in the north and west of the domain imply that model turbulence and vertical density structure need to be improved to more accurately capture plankton dynamics.     

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

The set of equations for global ocean biogeochemistry deterministic models have been formulated in a comprehensive and unified form in order to use them in numerical simulations of the marine ecosystem for climate change studies (PELAGOS, PELAgic biogeochemistry for Global Ocean Simulations). The fundamental approach stems from the representation of marine trophic interactions and major biogeochemical cycles introduced in the European Regional Seas Ecosystem Model (ERSEM). Our theoretical formulation revisits and generalizes the stoichiometric approach of ERSEM by defining the state variables as Chemical Functional Families (CFF). CFFs are further subdivided into living, non-living and inorganic components. Living CFFs are the basis for the definition of Living Functional Groups, the biomass-based functional prototype of the real organisms. Both CFFs and LFGs are theoretical constructs which allow us to relate measurable properties of marine biogeochemistry to the state variables used in deterministic models. This approach is sufficiently generic that may be used to describe other existing biomass-based ecosystem model.