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Abstract The performance of global ocean biogeochemical models can be quantified as the misfit between modeled tracer distributions and observations, which is sought to be minimized during parameter optimization. These models are computationally expensive due to the long spin‐up time required to reach equilibrium, and therefore optimization is often laborious. To reduce the required computational time, we investigate whether optimization of a biogeochemical model with shorter spin‐ups provides the same optimized parameters as one with a full‐length, equilibrated spin‐up over several millennia. We use the global ocean biogeochemical model MOPS with a range of lengths of model spin‐up and calibrate the model against synthetic observations derived from previous model runs using a derivative‐free optimization algorithm (DFO‐LS). When initiating the biogeochemical model with tracer distributions that differ from the synthetic observations used for calibration, a minimum spin‐up length of 2,000 years was required for successful optimization due to certain parameters which influence the transport of matter from the surface to the deeper ocean, where timescales are longer. However, preliminary results indicate that successful optimization may occur with an even shorter spin‐up by a judicious choice of initial condition, here the synthetic observations used for calibration, suggesting a fruitful avenue for future research.

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.