Abstract
Abstract. Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical interactions, which are described by many ( ≈ 10 to ≈ 100) parameters. The values of many of these parameters are empirically difficult to constrain, due to the fact that in the models they represent processes for a range of different groups of organisms at the same time, while even for single species parameter values are often difficult to determine in situ. Therefore, these models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing the relevant features of the present ocean, as well as their sensitivity to possible environmental changes. We here present a framework for the calibration of global biogeochemical ocean models on short and long timescales. The framework combines an offline approach for transport of biogeochemical tracers with an estimation of distribution algorithm (Covariance Matrix Adaption Evolution Strategy, CMA-ES). We explore the performance and capability of this framework by five different optimizations of six biogeochemical parameters of a global biogeochemical model, simulated over 3000 years. First, a twin experiment explores the feasibility of this approach. Four optimizations against a climatology of observations of annual mean dissolved nutrients and oxygen determine the extent to which different setups of the optimization influence model fit and parameter estimates. Because the misfit function applied focuses on the large-scale distribution of inorganic biogeochemical tracers, parameters that act on large spatial and temporal scales are determined earliest, and with the least spread. Parameters more closely tied to surface biology, which act on shorter timescales, are more difficult to determine. In particular, the search for optimum zooplankton parameters can benefit from a sound knowledge of maximum and minimum parameter values, leading to a more efficient optimization. It is encouraging that, although the misfit function does not contain any direct information about biogeochemical turnover, the optimized models nevertheless provide a better fit to observed global biogeochemical fluxes.
Highlights
Global ocean models that simulate biogeochemical interactions are subject to many uncertainties, among them those related to initial conditions, forcing, and parameterizations of physical and biological processes, as well as the adequacy of the chosen model complexity with respect to the scientific problem under investigation
Because optimization OBS-NARR showed the best results with respect to misfit function, biogeochemical fluxes, and optimization performance, in experiment “OBS-NARR-R” we evaluate the robustness of optimization OBS-NARR by repeating this optimization with a different random selection of the parameter values from the distribution calculated by Covariance Matrix Adaption Evolution Strategy (CMA-ES)
The trajectory of transient average parameter values and their variance depend strongly on the parameter itself: while the two parameters associated with rather long timescales, namely the stoichiometric ratio R−O2 : P and exponent b describing particle sinking, approach their target values quite early, parameters associated with surface biogeochemistry stay far away from their target value for ≈ 80 generations (Ic, KPHY, κZOO) or oscillate around it
Summary
Global ocean models that simulate biogeochemical interactions are subject to many uncertainties, among them those related to initial conditions, forcing, and parameterizations of physical and biological processes, as well as the adequacy of the chosen model complexity with respect to the scientific problem under investigation. Quantitative estimates of the effect of model uncertainty on model residuals are generally obtained from individual sensitivity studies and model intercomparison or model ensemble studies, where the spread of model results is regarded as a measure of model uncertainty. This procedure is, for example, followed in the assessment reports of the Intergovernmental Project of Climate Change (IPCC). The diversity of biogeochemical models ranges from simple, “nutrient-only” models to far more complex ones, comprising different elemental cycles and biological components
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