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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 µmolkg-1 (weighted root-mean-squared difference, RMSD, of 35 µmolkg-1 and weighted bias of 8 µmolkg-1 for n = 82), whereas it was 44 µmolkg-1 for DIC (weighted RMSD of 44 µmolkg-1 and weighted bias of - 6 µmolkg-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 µmolkg-1 (weighted RMSD of 28 µmolkg-1 and weighted bias of 6 µmolkg-1 for n = 102), whereas it was 40 µmolkg-1 for DIC (weighted RMSD of 37 µmolkg-1 and weighted bias of - 16 µmolkg-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 10.1594/PANGAEA.946888 (Sims et al., 2023). [ABSTRACT FROM AUTHOR]

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 OceanSODA-UNEXE time series, a dataset of the full carbonate system in the surface water outflows of the Amazon (2010-2020) and Congo Rivers (2002-2016). 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 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. The algorithm evaluation constitutes a Type A uncertainty evaluation for TA and DIC where 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 identified as 36 µmol kg-1 (weighted RMSD 35 µmol kg-1 and weighted bias 8 µmol kg-1 for n=82) and for DIC was 44 µmol kg-1 (weighted RMSD 44 µmol kg-1 and weighted bias -6 µmol kg-1 for n=70). The spatially averaged propagated uncertainties for the partial pressure of carbon dioxide (pCO2) and pH are 85 µatm and 0.08 respectively, where the pH uncertainty is 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 28 µmol kg-1 and weighted bias 6 µmol kg-1 for n=102) and for DIC was 40 µmol kg-1 (weighted RMSD 37 µmol kg-1and weighted bias -16 µmol kg-1 for n=77). The spatially averaged propagated uncertainties for pCO2 and pH are 74 µatm and 0.08 respectively, where the pH uncertainty is 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 their respective regions allowing the time varying regional variability to be evaluated. Potential uses of these data would be for assessing the spatial and temporal flow of carbon from the Amazon and Congo rivers into the Atlantic and for assessing the riverine driven carbonate system variations experienced by tropical reefs within the outflow regions. [ABSTRACT FROM AUTHOR]

Improving our ability to monitor ocean carbonate chemistry has become a priority as the ocean continues to absorb carbon dioxide from the atmosphere. This long-term uptake is reducing the ocean pH; a process commonly known as ocean acidification. The use of satellite Earth Observation has not yet been thoroughly explored as an option for routinely observing surface ocean carbonate chemistry, although its potential has been highlighted. We demonstrate the suitability of using empirical algorithms to calculate total alkalinity (A T) and total dissolved inorganic carbon (C T), assessing the relative performance of satellite, interpolated in situ , and climatology datasets in reproducing the wider spatial patterns of these two variables. Both A T and C T in situ data are reproducible, both regionally and globally, using salinity and temperature datasets, with satellite observed salinity from Aquarius and SMOS providing performance comparable to other datasets for the majority of case studies. Global root mean squared difference (RMSD) between in situ validation data and satellite estimates is 17 μmol kg−1 with bias < 5 μmol kg−1 for A T and 30 μmol kg−1 with bias < 10 μmol kg−1 for C T. This analysis demonstrates that satellite sensors provide a credible solution for monitoring surface synoptic scale A T and C T. It also enables the first demonstration of observation-based synoptic scale A T and C T temporal mixing in the Amazon plume for 2010–2016, complete with a robust estimation of their uncertainty. • Satellite salinity measurements enable estimation of surface carbonate parameters. • Uncertainties within these observation-based estimates are well characterized. • Monthly satellite salinity and temperature allows synoptic monitoring. • Satellite observations allow study of seasonal, interannual and episodic variations. [ABSTRACT FROM AUTHOR]