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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 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]

Warming of the Arctic due to climate change means the Arctic Ocean is now ice-free for longer as sea ice melts earlier and refreezes later. It remains unclear how the extended ice-free period will impact carbon dioxide (CO2) fluxes due to scarcity of surface ocean CO2 measurements. Baseline measurements are urgently needed to understand how air−sea CO2 fluxes will spatially and temporally vary in a changing Arctic Ocean. It is uncertain whether the previous basin-wide surveys are representative of the many smaller bays and inlets that make up the Canadian Arctic Archipelago. By using a research vessel that is based in the remote Inuit community of Cambridge Bay (Ikaluqtuutiak, Nunavut), we have been able to reliably survey p CO2 shortly after ice melt and access previously unsampled bays and inlets in the nearby region. We present four years of consecutive summertime p CO2 measurements collected in the Kitikmeot Sea in the southern Canadian Arctic Archipelago. Overall, we found that this region is a sink for atmospheric CO2 in August (average of all calculated fluxes over the four cruises was -8.3 mmol m-2 d-1) but the magnitude of this sink varies substantially between years and locations (average calculated fluxes of 0.41, -7.70, -21.26 and -2.08 mmol m-2 d-1 during the 2016, 2017, 2018 and 2019 cruises respectively). Surface ocean p CO2 varied by up to 142 μatm between years; this highlights the importance of repeat observations in the Arctic as this high interannual variability would not have been captured by sparse and infrequent measurements. We find that the p CO2 value of the surface ocean at the time of ice melt is extremely important in constraining the magnitude of the air−sea flux throughout the ice-free season. Further constraining the flux in the Kitikmeot Sea will require a better understanding of how p CO2 changes outside of the summer season. Surface ocean p CO2 measurements made in the bays and inlets in the Kitikmeot Sea were ~20–40 μatm lower than in the main channels, and p CO2 measurements made close to ice breakup (i.e. within 2 weeks) were 50–100 μatm lower than measurements made >4 weeks after breakup. As basin-wide surveys of the CAA have focused on the deeper shipping channels and rarely measure close to the ice break-up date, we hypothesize that there may be an observational bias in previous studies, leading to an underestimate of the CO2 sink in the Canadian Arctic Archipelago. These high-resolution measurements constitute an important new baseline for gaining a better understanding of the role this region plays in the uptake of atmospheric CO2. [ABSTRACT FROM AUTHOR]

Warming of the Arctic due to climate change means the Arctic Ocean is now ice-free for longer as sea ice melts earlier and refreezes later. It remains unclear how the extended ice-free period will impact carbon dioxide (CO2) fluxes due to scarcity of surface ocean CO2 measurements. Baseline measurements are urgently needed to understand how air-sea CO2 fluxes will spatially and temporally vary in a changing Arctic Ocean. It is uncertain whether the previous basin-wide surveys are representative of the many smaller bays and inlets that make up the Canadian Arctic Archipelago. By using a research vessel that is based in the remote Inuit community of Cambridge Bay (Ikaluqtuutiak, Nunavut), we have been able to reliably survey pCO2 shortly after ice melt and access previously unsampled bays and inlets in the nearby region. We present four years of consecutive summertime pCO2 measurements collected in the Kitikmeot Sea in the southern Canadian Arctic Archipelago. Overall, we found that this region is a sink for atmospheric CO2 in August (average of all calculated fluxes over the four cruises was -8.3 mmol m-2 d-1) but the magnitude of this sink varies substantially between years and locations (average calculated fluxes of 0.41, -7.70, -21.26 and -2.08 mmol m-2 d-1 during the 2016.2017,2018 and 2019 cruises respectively). Surface ocean pCO2 varied by up to 142 μatm between years; this highlights the importance of repeat observations in the Arctic as this high interannual variability would not have been captured by sparse and infrequent measurements. We find that the pCO2 value of the surface ocean at the time of ice melt is extremely important in constraining the magnitude of the air-sea flux throughout the ice-free season. Further constraining the flux in the Kitikmeot Sea will require a better understanding of how pCO2 changes outside of the summer season. Surface ocean pCO2 measurements made in the bays and inlets in the Kitikmeot Sea were ~20-40 μatm lower than in the main channels, and pCO2 measurements made close to ice breakup (i.e. within 2 weeks) were 50-100 μatm lower than measurements made >4 weeks after breakup. As basin-wide surveys of the CAA have focused on the deeper shipping channels and rarely measure close to the ice break-up date, we hypothesize that there may be an observational bias in previous studies, leading to an underestimate of the CO2 sink in the Canadian Arctic Archipelago. These high-resolution measurements constitute an important new baseline for gaining a better understanding of the role this region plays in the uptake of atmospheric CO2. [ABSTRACT FROM AUTHOR]

In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), 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 pCO2, 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 nonin 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 re-analysis datasets to produce a global matchup dataset of carbonate system data, consisting of ~286,000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 days in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is shown. 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 https://doi.org/10.12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931 (Land and Piollé, 2022). [ABSTRACT FROM AUTHOR]