Reconstructing ocean carbon storage with CMIP6 models and synthetic Argo observations

The ocean carbon store plays a vital role in setting the carbon response to emissions and variability in the carbon cycle. However, due to the ocean’s strong regional and temporal variability, sparse carbon observations limit our understanding of historical carbon changes. We explore how widespread Argo temperature and salinity profiles can provide information to reconstruct ocean carbon inventories with Ensemble Optimal Interpolation. Ensemble Optimal Interpolation draws upon first-order relationships between variables, and high-complexity climate models been used previously to reconstruct interior 5 ocean heat content and salinity changes from Argo profiles. The CMIP6 model ensemble shows coherent relationships between upper-ocean carbon, temperature, salinity, and atmospheric CO 2 that result in solutions that reflect the controls of undersaturation, solubility, and alkalinity. The Ensemble Optimal Interpolation method is tested with synthetic reconstructions of upper-ocean carbon fields using perfect information of temperature and salinity changes, along with atmospheric CO 2 concentrations. Sensitivity tests of show that, in most regions, the trend in ocean carbon and over 60% of detrended variability can 10 be reconstructed using local temperature and salinity measurements. Expanding the synthetic reconstructions to include irregular sampling consistent with Argo profile locations results in a similar capacity to reconstruct ocean carbon variability, as the increased information provided from multiple sampling locations compensates for the propagation of errors within the CMIP6 covariance fields. Overall, our work indicates that hydrographic measurements can provide valuable information about changes in the ocean carbon inventory. The flexibility of optimal interpolation indicates a strong potential for new, independent, and 15 mechanistic estimates of the historical carbon inventory that can be used to compare with other estimates of the ocean carbon system. controls of the Revelle buffer factor, solubility, and alkalinity on upper-ocean carbon. While the correlations between input variables exhibit strong regional variability and structure in their cross-correlations, ocean temperature and salinity observations end up acting in complementary ways. Thus the optimal weight solutions indicate that the information provided by these observations are consistent with our hypothesised first-order controls. Here we present some reconstruction RMSE reductions for the Norwegian ESM model using the CMIP6 covariance fields, Argo profiles consistent with year 2015 sampling, and various search radii. We use the Norwegian ESM as it uses isopycnic depth coordinates as opposed to the z-level coordinates used in the models within the ensemble. We find that the coverage in the reconstructions increases to near-global coverage when allowing profiles within 5 ◦ to be used in the reconstruction and 480 improves the detrended carbon reproduction; however, for regions that are reproduced with high skill using more restrictive search radii, the increase in search radius is accompanied by slight weakenings in the RMSE reductions.