Preprints
https://doi.org/10.5194/gmd-2021-175
https://doi.org/10.5194/gmd-2021-175

Submitted as: methods for assessment of models 30 Sep 2021

Submitted as: methods for assessment of models | 30 Sep 2021

Review status: this preprint is currently under review for the journal GMD.

A derivative-free optimisation method for global ocean biogeochemical models

Sophy Elizabeth Oliver1, Coralia Cartis2, Iris Kriest3, Simon F. B. Tett4, and Samar Khatiwala1 Sophy Elizabeth Oliver et al.
  • 1Department of Earth Sciences, University of Oxford, South Parks Road, Oxford OX1 3AN, UK
  • 2Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
  • 3GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
  • 4School of GeoSciences, University of Edinburgh, Edinburgh, UK

Abstract. The performance of global ocean biogeochemical models, and the Earth System Models in which they are embedded, can be improved by systematic calibration of the parameter values against observations. However, such tuning is seldom undertaken as these models are computationally very expensive. Here we investigate the performance of DFO-LS, a local, derivative-free optimisation algorithm which has been designed for computationally expensive models with irregular model-data misfit landscapes typical of biogeochemical models. We use DFO-LS to calibrate six parameters of a relatively complex global ocean biogeochemical model (MOPS) against synthetic dissolved oxygen, inorganic phosphate and inorganic nitrate observations from a reference run of the same model with a known parameter configuration. The performance of DFO-LS is compared with that of CMA-ES, another derivative-free algorithm that was applied in a previous study to the same model in one of the first successful attempts at calibrating a global model of this complexity. We find that DFO-LS successfully recovers 5 of the 6 parameters in approximately 40 evaluations of the misfit function (each one requiring a 3000 year run of MOPS to equilibrium), while CMA-ES needs over 1200 evaluations. Moreover, DFO-LS reached a baseline misfit, defined by observational noise, in just 11–14 evaluations, whereas CMA-ES required approximately 340 evaluations. We also find that the performance of DFO-LS is not significantly affected by observational sparsity, however fewer parameters were successfully optimised in the presence of observational uncertainty. The results presented here suggest that DFO-LS is sufficiently inexpensive and robust to apply to the calibration of complex, global ocean biogeochemical models.

Sophy Elizabeth Oliver et al.

Status: open (until 27 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-175', Anonymous Referee #1, 15 Oct 2021 reply
    • AC1: 'Reply on RC1', Sophy Oliver, 21 Oct 2021 reply

Sophy Elizabeth Oliver et al.

Sophy Elizabeth Oliver et al.

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Short summary
Global ocean biogeochemical models are used within Earth System Models which are used to predict future climate change. However, these are very computationally expensive to run, therefore are rarely routinely improved/calibrated to real oceanic observations. Here we apply a new, fast optimisation algorithm to one such model, and show that it can calibrate the model much faster than previously managed, therefore encouraging further ocean biogeochemical model improvements.