Abstract
Abstract. To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling. We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time- and space-dependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.
Highlights
The growth, decay, and interaction of planktonic organisms drive the transformation and cycling of chemical elements in the ocean
In some problems, assimilating all the data at once from all available sampling times can be computationally impractical. This is likely for models with stochastic dynamics (η = 0 in Eq 1), if the data are clustered in time, or if model states need to be repeatedly updated as new data come in
The increased parsimony of the simpler models led to improved performance in terms of the likelihood ratio test (LRT), and the AIC and Bayesian information criterion (BIC)
Summary
The growth, decay, and interaction of planktonic organisms drive the transformation and cycling of chemical elements in the ocean. Understanding the interconnected and complex nature of these processes is critical to understanding the ecological and biogeochemical function of the system as a whole. The development of biogeochemical models requires accurate mathematical descriptions of key physiological and ecological processes, and their sensitivity to changes in the chemical and physical environment. Such mathematical descriptions form the basis of integrated dynamical models, typically composed of a set of differential equations that allow credible computations of the flux and transformation. Schartau et al.: Parameter identification in planktonic ecosystem modelling of energy (light) and mass (nutrients) within the ecosystem (US Joint Global Ocean Flux Study Planning Report Number 14, Modeling and Data Assimilation, 1992)
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