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Growth of initial condition errors in a pelagic ecosystem model

The growth of initial condition errors in a size-resolved pelagic ecosystem are investigated using initial condition perturbation experiments. Numerical experiments show that perturbations of 1, 0.1, 0.01, 0.001 and 0.0001 % of the initial biomass of individual size-classes leads to equal spread of model trajectories after 120 days. Thus, predictive skill cannot be improved by the reduction of initial conditions errors beyond 1 %, but can be improved with the use of the mean of an ensemble of runs, when compared to a single, unperturbed control simulation. The greatest divergence of trajectories occurs between 125 and 140 days when a 2.68 μm phytoplankton size-class blooms. This divergence has a finite-time Lyapunov exponent of 0.210 d and a prediction time of 33 days for a precision of 10 mol N m. For initial condition perturbations > 1 % of the biomass of individual size-classes: (1) the model output after 125 days is sensitive to the magnitude of initial condition perturbations, (2) the divergence of trajectories between 125-140 days is less than 0.210 d as system constraints prevent exponential growth of perturbations and (3) the ensemble mean has no better predictive skill than the control run. Initial condition experiments, such as those conducted in this paper, have been critical for establishing realistic goals in the development of numerical weather models and could play a similar role in ecological models.