Calibrating a global three-dimensional biogeochemical ocean model (MOPS-1.0)

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.

Evaluation of the transport matrix method for simulation of ocean biogeochemical tracers

“…A recent study (Séférian et al, 2016) highlighting the importance of adequate spin-ups suggests that this could be beneficial even for earth system models that are already parallelized, especially with the advent of many-core hardware architectures, such as general purpose graphics processing units (GPGPUs) to which the TMM has been recently ported (Siewertsen et al, 2013). Moreover, the speed-up opens up the possibility of systematically testing different parameterizations in complex, global biogeochemical models, or even optimizing such models against data as has been recently accomplished for a slightly simpler model by Kriest et al (2017). While the results presented here are for a particular model, they should be broadly applicable to other global models of similar complexity.…”

“…The TMM has been applied to a wide range of problems, including simulating anthropogenic carbon uptake and radiocarbon by the ocean (Graven et al, 2012), simulating noble gases to improve the parameterization of air-sea gas transfer (Nicholson et al, 2011;Liang et al, 2013) and investigate ocean ventilation (Nicholson et al, 2016), studying ocean proxy (Siberlin and Wunsch, 2011) and radiocarbon (Koeve et al, 2015) timescales, investigating the mechanisms controlling nutrient ratios (Weber and Deutsch, 2010), modeling the cycling of particle reactive geochemical tracers Siddall et al, 2008;Vance et al, 2017), estimating respiration in the ocean from oxygen utilization rates (Duteil et al, 2013;Koeve and Kähler, 2016), demonstrating the utility of atmospheric potential oxygen measurements to constrain ocean heat transport , modeling the ocean's CaCO 3 (Koeve et al, 2014) and nitrogen (Kriest and Oschlies, 2015) cycles; studying the impact of the Southern Ocean on global ocean oxygen (Keller et al, 2016), estimating the flux of organic matter (Wilson et al, 2015), and biogeochemical parameter sensitivity (Khatiwala, 2007;Kriest et al, 2010Kriest et al, , 2012 and optimization (Priess et al, 2013b, a;Kriest et al, 2017).…”

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

“…Plankton parameters that act on seasonal scale within the upper, near surface layers are more difficult to identify, if annual mean climatological data are used. Figure 8 exemplifies this difficulty, based on results from Kriest et al (2017), who optimized six biogeochemical parameters in total. The example reveals differences in the sensitivity of the cost function with respect to variations of two contrasting parameters, the zooplankton mortality (κ zoo ) and b respectively.…”

“…In a recent study of Kriest et al (2017) the export parameter b turned out to be well identifiable, with an optimal value of ≈ 1.3, based on annual mean climatologies of dissolved nutrients and oxygen. As in Kwon and Primeau (2006) their biogeochemical model explicitly resolves seasonal cycles.…”

Error assessment of biogeochemical models by lower bound methods (NOMMA-1.0)

“…Thus, other studies use fast approximations (Kennedy et al, 2006;Khatiwala, 2007) to be able to optimize certain parameters for full three-dimensional models. A drawback is that the latter approaches are generally restricted to the estimation of few parameters only (e.g., Mattern et al, 2012;Kriest et al, 2017;Prieß et al, 2013a, b;Piwonski and Slawig, 2016;Rückelt et al, 2010). In addition, limited data availability (e.g., Lawson et al, 1996) and a deficient representation of certain processes in the underlying ocean circulation model (e.g., Dietze and Löptien, 2013) encumber the optimization process.…”