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- W4236510231 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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 modelâdata 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 âobservationsâ 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 âbaselineâ misfit, defined by observational noise, in just 11â14 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." @default.
- W4236510231 created "2022-05-12" @default.
- W4236510231 date "2021-10-15" @default.
- W4236510231 modified "2023-10-16" @default.
- W4236510231 title "Comment on gmd-2021-175" @default.
- W4236510231 doi "https://doi.org/10.5194/gmd-2021-175-rc1" @default.
- W4236510231 hasPublicationYear "2021" @default.
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