OceanSODA-UNEXE: a multi-year gridded Amazon and Congo River outflow surface ocean carbonate system dataset
Authors: Sims Richard P. , Holding Thomas M. , Land Peter E. , Piolle Jean-Francois , Green Hannah L. , Shutler Jamie D. 
Published: June 2023

Abstract. Large rivers play an important role in transferring water and all of its constituents, including carbon in its various forms, from the land to the
ocean, but the seasonal and inter-annual variations in these riverine flows remain unclear. Satellite Earth observation datasets and reanalysis
products can now be used to observe synoptic-scale spatial and temporal variations in the carbonate system within large river outflows. Here, we
present the University of Exeter (UNEXE) Satellite Oceanographic Datasets for Acidification (OceanSODA) dataset (OceanSODA-UNEXE) time series, a dataset of the full carbonate system in the surface water outflows of the Amazon (2010–2020) and Congo (2002–2016) rivers. Optimal empirical approaches were used to generate gridded total alkalinity (TA) and dissolved inorganic carbon (DIC) fields in
the outflow regions. These combinations were determined by equitably evaluating all combinations of algorithms and inputs against a reference
matchup database of in situ observations. Gridded TA and DIC along with gridded temperature and salinity data enable the calculation of the full
carbonate system in the surface ocean (which includes pH and the partial pressure of carbon dioxide, pCO2). The algorithm evaluation
constitutes a Type-A uncertainty evaluation for TA and DIC, in which model, input and sampling uncertainties are considered. Total combined
uncertainties for TA and DIC were propagated through the carbonate system calculation, allowing all variables to be provided with an associated
uncertainty estimate. In the Amazon outflow, the total combined uncertainty for TA was 36 µmol kg−1 (weighted root-mean-squared difference, RMSD, of
35 µmol kg−1 and weighted bias of 8 µmol kg−1 for n = 82), whereas it was 44 µmol kg−1 for DIC (weighted
RMSD of 44 µmol kg−1 and weighted bias of −6 µmol kg−1 for n = 70). The spatially averaged propagated combined
uncertainties for the pCO2 and pH were 85 µatm and 0.08, respectively, where the pH uncertainty was relative to an
average pH of 8.19. In the Congo outflow, the combined uncertainty for TA was identified as 29 µmol kg−1 (weighted RMSD of
28 µmol kg−1 and weighted bias of 6 µmol kg−1 for n = 102), whereas it was 40 µmol kg−1 for DIC (weighted
RMSD of 37 µmol kg−1 and weighted bias of −16 µmol kg−1 for n = 77). The spatially averaged propagated combined
uncertainties for pCO2 and pH were 74 µatm and 0.08, respectively, where the pH uncertainty was relative to an
average pH of 8.21. The combined uncertainties in TA and DIC in the Amazon and Congo outflows are lower than the natural variability within their
respective regions, allowing the time-varying regional variability to be evaluated. Potential uses of these data would be the assessment of the spatial
and temporal flow of carbon from the Amazon and Congo rivers into the Atlantic and the assessment of the riverine-driven carbonate system variations
experienced by tropical reefs within the outflow regions. The data presented in this work are available at https://doi.org/10.1594/PANGAEA.946888 (Sims et al., 2023).