A derivative-free optimisation method for global ocean biogeochemical models

Abstract

Abstract. The skill 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, phosphate and 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 five of the six 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.

Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency

“…A 367 rather simplistic answer would be that measuring 368 fluxes at many locations and depths and throughout 369 the whole year for many years, and using this data to 370 calibrate state-of-the-art ocean-biogeochemical mod-371 els, may solve the issue. Unfortunately, such a large-372 scale, high-frequency sampling and optimisation ap-373 proach would be costly and is logistically unfeasible 374 in the near future, although BGC-Argo floats [40,41] 375 and derivative-free computational optimisers [42] offer 376 some hope.…”

A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research

“…Calibration can increase the reliability of Earth system models (e.g., Oliver et al, 2022). For this purpose, a metric calculates the difference between simulated model output and measured field data.…”

“…In comparison, field data are usually sparsely available only. Interpolating such sparse field data can introduce high uncertainty (e.g., Oliver et al, 2022). PDFs provide a useful approach to investigate data independent of the number of data points available (Thorarinsdottir et al, 2013).…”

A CMA‐ES Algorithm Allowing for Random Parameters in Model Calibration

“…(2017) and also in Oliver et al. (2021), we apply monthly mean transport matrices from a 2.8° global configuration of the MIT general circulation model (MITgcm), having 15 depth levels (Marshall et al., 1997). MOPS coupled to the TMM simulates globally the concentrations and biogeochemical turnover of seven tracer components, namely phyto‐ and zooplankton, dissolved and particulate organic matter, phosphate, nitrate and oxygen.…”