Iron is a key micronutrient controlling phytoplankton growth in vast regions of the global ocean. Despite its importance, uncertainties remain high regarding external iron source fluxes and internal cycling on a global scale. In this study, we used a global dissolved iron dataset, including GEOTRACES measurements, to constrain source and scavenging fluxes in the marine iron component of a global ocean biogeochemical model. Our model simulations tested three key uncertainties: source inputs of atmospheric soluble iron deposition (varying from 1.4–3.4 Gmol/yr), reductive sedimentary iron release (14–117 Gmol/yr), and compared a variable ligand parameterization to a constant distribution. In each simulation, scavenging rates were tuned to reproduce the observed global mean iron inventory for consistency. The variable ligand parameterization improved the global model-data misfit the most, suggesting that heterotrophic bacteria are an important source of ligands to the ocean. Model simulations containing high source fluxes of atmospheric soluble iron deposition (3.4 Gmol/yr) and reductive sedimentary iron release (114 Gmol/yr) further improved the model most notably in the surface ocean. High scavenging rates were then required to maintain the iron inventory resulting in relatively short surface and global ocean residence times of 0.83 and 7.5 years, respectively. The model simulates a tight spatial coupling between source inputs and scavenging rates, which may be too strong due to underrepresented ligands near source inputs, contributing to large uncertainties when constraining individual fluxes with dissolved iron concentrations. Model biases remain high and are discussed to help improve global marine iron cycle models.

Model intercomparison studies of coupled carbon-climate simulations have the potential to improve our understanding of the processes explaining the pCO2 drawdown at the Last Glacial Maximum (LGM) and to identify related model biases. Models participating in the Paleoclimate Modelling Intercomparison Project (PMIP) now frequently include the carbon cycle. The ongoing PMIP-carbon project provides the first opportunity to conduct multimodel comparisons of simulated carbon content for the LGM time window. However, such a study remains challenging due to differing implementation of ocean boundary conditions (e.g. bathymetry and coastlines reflecting the low sea level) and to various associated adjustments of biogeochemical variables (i.e. alkalinity, nutrients, dissolved inorganic carbon). After assessing the ocean volume of PMIP models at the pre-industrial and LGM, we investigate the impact of these modelling choices on the simulated carbon at the global scale, using both PMIP-carbon model outputs and sensitivity tests with the iLOVECLIM model. We show that the carbon distribution in reservoirs is significantly affected by the choice of ocean boundary conditions in iLOVECLIM. In particular, our simulations demonstrate a ~250 GtC effect of an alkalinity adjustment on carbon sequestration in the ocean. Finally, we observe that PMIP-carbon models with a freely evolving CO2 and no additional glacial mechanisms do not simulate the pCO2 drawdown at the LGM (with concentrations as high as 313, 331 and 315 ppm), especially if they use a low ocean volume. Our findings suggest that great care should be taken on accounting for large bathymetry changes in models including the carbon cycle.