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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 3
Geosci. Model Dev., 8, 697–731, 2015
https://doi.org/10.5194/gmd-8-697-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 8, 697–731, 2015
https://doi.org/10.5194/gmd-8-697-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Methods for assessment of models 23 Mar 2015

Methods for assessment of models | 23 Mar 2015

Mechanistic site-based emulation of a global ocean biogeochemical model (MEDUSA 1.0) for parametric analysis and calibration: an application of the Marine Model Optimization Testbed (MarMOT 1.1)

J. C. P. Hemmings1,2, P. G. Challenor1,3, and A. Yool1 J. C. P. Hemmings et al.
  • 1National Oceanography Centre, Southampton, SO14 3ZH, UK
  • 2Wessex Environmental Associates, Salisbury, UK
  • 3College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, UK

Abstract. Biogeochemical ocean circulation models used to investigate the role of plankton ecosystems in global change rely on adjustable parameters to capture the dominant biogeochemical dynamics of a complex biological system. In principle, optimal parameter values can be estimated by fitting models to observational data, including satellite ocean colour products such as chlorophyll that achieve good spatial and temporal coverage of the surface ocean. However, comprehensive parametric analyses require large ensemble experiments that are computationally infeasible with global 3-D simulations. Site-based simulations provide an efficient alternative but can only be used to make reliable inferences about global model performance if robust quantitative descriptions of their relationships with the corresponding 3-D simulations can be established.

The feasibility of establishing such a relationship is investigated for an intermediate complexity biogeochemistry model (MEDUSA) coupled with a widely used global ocean model (NEMO). A site-based mechanistic emulator is constructed for surface chlorophyll output from this target model as a function of model parameters. The emulator comprises an array of 1-D simulators and a statistical quantification of the uncertainty in their predictions. The unknown parameter-dependent biogeochemical environment, in terms of initial tracer concentrations and lateral flux information required by the simulators, is a significant source of uncertainty. It is approximated by a mean environment derived from a small ensemble of 3-D simulations representing variability of the target model behaviour over the parameter space of interest. The performance of two alternative uncertainty quantification schemes is examined: a direct method based on comparisons between simulator output and a sample of known target model "truths" and an indirect method that is only partially reliant on knowledge of the target model output.

In general, chlorophyll records at a representative array of oceanic sites are well reproduced. The use of lateral flux information reduces the 1-D simulator error considerably, consistent with a major influence of advection at some sites. Emulator robustness is assessed by comparing actual error distributions with those predicted. With the direct uncertainty quantification scheme, the emulator is reasonably robust over all sites. The indirect uncertainty quantification scheme is less reliable at some sites but scope for improving its performance is identified. The results demonstrate the strong potential of the emulation approach to improve the effectiveness of site-based methods. This represents important progress towards establishing a robust site-based capability that will allow comprehensive parametric analyses to be achieved for improving global models and quantifying uncertainty in their predictions.

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Effective calibration of global models is inhibited by the computational demands of 3-D simulations. As a solution for the NEMO-MEDUSA model, we present an efficient emulator of surface chlorophyll as a function of MEDUSA’s biogeochemical parameters. The emulator comprises an array of site-based 1-D simulators and a quantification of uncertainty in their predictions. It is able to produce robust probabilistic estimates of 3-D model output rapidly for comparison with satellite chlorophyll.
Effective calibration of global models is inhibited by the computational demands of 3-D...
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