Constraining parameters in marine pelagic ecosystem models – is it actually feasible with typical observations of standing stocks?

Abstract

In a changing climate, marine pelagic biogeochemistry may modulate the atmospheric concentrations of climate-relevant species such as CO2 and N2O. To date, projections rely on earth system models, featuring simple pelagic biogeochemical model components, embedded into 3-D ocean circulation models. Most of these biogeochemical model components rely on the hyperbolic Michaelis–Menten (MM) formulation which specifies the limiting effect of light and nutrients on carbon assimilation by autotrophic phytoplankton. The respective MM constants, along with other model parameters, of 3-D coupled biogeochemical ocean-circulation models are usually tuned; the parameters are changed until a "reasonable" similarity to observed standing stocks is achieved. Here, we explore with twin experiments (or synthetic "observations") the demands on observations that allow for a more objective estimation of model parameters. We start with parameter retrieval experiments based on "perfect" (synthetic) observations which we distort, step by step, by low-frequency noise to approach realistic conditions. Finally, we confirm our findings with real-world observations. In summary, we find that MM constants are especially hard to constrain because even modest noise (10 %) inherent to observations may hinder the parameter retrieval already. This is of concern since the MM parameters are key to the model's sensitivity to anticipated changes in the external conditions. Furthermore, we illustrate problems caused by high-order parameter dependencies when parameter estimation is based on sparse observations of standing stocks. Somewhat counter to intuition, we find that more observational data can sometimes degrade the ability to constrain certain parameters.