A derivative-free optimisation method for global ocean biogeochemical models

[摘要] The skill of global ocean biogeochemical models, and the earth system models in which they areembedded, 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 irregularmodel–data misfit landscapes typical of biogeochemical models. We use DFO-LS to calibrate six parameters of a relatively complexglobal ocean biogeochemical model (MOPS) against synthetic dissolved oxygen, phosphate andnitrate “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 thatwas applied in a previous study to the same model in one of the first successful attempts at calibrating aglobal model of this complexity. We find that DFO-LS successfully recovers five of the six parameters in approximately 40evaluations of the misfit function (each one requiring a 3000-year run of MOPS to equilibrium), while CMA-ESneeds 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 heresuggest that DFO-LS is sufficiently inexpensive and robust to apply to the calibration of complex, global oceanbiogeochemical models.

[学科分类] 土木及结构工程学