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Iron is represented in biogeochemical ocean models by a variety of structurally different approaches employing generally poorly constrained empirical parameterizations. Increasing the structural complexity of iron modules also increases computational costs and introduces additional uncertainties, with as yet unclear benefits. In order to demonstrate the benefits of explicitly representing iron, we calibrate a hierarchy of iron modules and evaluate the remaining model-data misfit. The first module includes a complex iron cycle with major processes resolved explicitly, the second module applies iron limitation in primary production using prescribed monthly iron concentration fields, and the third module does not explicitly include iron effects at all. All three modules are embedded into the same circulation model. Models are calibrated against global data sets of NO3, PO4 and O-2 applying a state-of-the-art multi-variable constraint parameter optimization. The model with fully resolved iron cycle is marginally (up to 4.8%) better at representing global distributions of NO3, PO4 and O-2 compared to models with implicit or absent parameterizations of iron. We also found a slow down of global surface nutrient cycling by about 30% and a shift of productivity from the tropics to temperate regions for the explicit iron module. The explicit iron model also reduces the otherwise overestimated volume of suboxic waters, yielding results closer to observations.