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The long‐term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). The Arctic is a challenging region to gather enough data to examine the changes in carbonate chemistry over sufficient scales. However, algorithms that calculate carbonate chemistry parameters from more frequently measured parameters, such as temperature and salinity, can be used to fill in data gaps. Here, these published algorithms were evaluated against in situ measurements using different data input types (data from satellites or in situ re‐analysis climatologies) across the Arctic Ocean. With the lowest uncertainties in the Atlantic influenced Seas (AiS), where re‐analysis inputs achieved total alkalinity estimates with Root Mean Squared Deviation (RMSD) of 21 μmol kg−1 and a bias of 2 μmol kg−1 (n = 162) and dissolved inorganic carbon RMSD of 24 μmol kg−1 and bias of −14 μmol kg−1 (n = 262). AiS results using satellite observation inputs show similar bias but larger RMSD, although due to the shorter time span of available satellite observations, more contemporary in situ data would provide further assessment and improvement. Synoptic‐scale observations of surface water carbonate conditions in the Arctic are now possible to monitor OA, but targeted in situ data collection is needed to enable the full exploitation of satellite observation‐based approaches. Plain Language Summary: The long‐term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). Sea surface salinity and temperature measurements from satellites or in situ re‐analysis products can be used as input to empirical algorithms to calculate OA parameters. This paper provides a first analysis of published Arctic Ocean empirical algorithms to estimate surface water OA parameters using observation‐based data sets. Results show promise in the Atlantic influenced Seas using both in situ re‐analysis and satellite products, but satellite salinity is relatively recent, and a paucity of in situ data in the satellite salinity era precludes a robust assessment. To fully exploit satellite‐based approaches, efforts need to focus on collecting in situ data while these satellites are overhead and operating in orbit. Key Points: Observation‐based data sets enable synoptic spatio‐temporal assessments of Arctic Ocean carbonate system parametersRe‐analysis and satellite products predict total alkalinity with accuracies of ∼21 μmol kg−1Targeted in situ data collection is needed to fully exploit satellite observations in the Arctic to enable carbonate system monitoring [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 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]

In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO 2 (pCO 2), total alkalinity, dissolved inorganic carbon and pH) have been collated, quality-controlled and made publicly available. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO 2 , which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example, for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude- and projection-dependent (e.g. become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (e.g. a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching surface in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into "regions of interest": spatiotemporal cylinders consisting of circles on the Earth's surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All surface in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is applied to each of the non-in situ datasets in turn, producing a dataset of coincident matchups that are consistent in space and time. About 35 million in situ data points were matched with data from five satellite sources and five model and reanalysis datasets to produce a global matchup dataset of carbonate system data, consisting of ∼286000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 d in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is presented. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example, to enable regional studies. The matchup dataset provides users with a large multi-parameter carbonate system dataset containing data from different sources, in one consistent, collated and standardised format suitable for model–data intercomparisons and model evaluations. The OceanSODA-MDB data can be downloaded from 10.12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931 (Land and Piollé, 2022). [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]