%0 Journal Article
%A Sims, Richard P.
%A Holding, Thomas M.
%A Land, Peter E.
%A Piolle, Jean-Francois
%A Green, Hannah L.
%A Shutler, Jamie D.
%+ Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
%T OceanSODA-UNEXE: a multi-year gridded Amazon and Congo River outflow surface ocean carbonate system dataset : 
%G eng
%U https://hdl.handle.net/21.11116/0000-000D-715F-A
%R 10.5194/essd-15-2499-2023
%D 2023
%* Review method: peer-reviewed
%X 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).
%J Earth System Science Data
%V 15
%N 6
%& 2499
%P 2499 - 2516
%I Copernicus
%@ 1866-35161866-3508