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  • Quasi‐Real‐Time and High‐Re...
    Li, B. F.; Watanabe, Y. W.; Hosoda, S.; Sato, K.; Nakano, Y.

    Geophysical research letters, 16 May 2019, Volume: 46, Issue: 9
    Journal Article

    Increasing marine uptake of anthropogenic CO2 (Cant) causes global ocean acidification. To obtain a high‐resolution spatiotemporal distribution of oceanic carbon chemistry, we developed new parameterizations of the seawater total alkalinity, and dissolved inorganic carbon from the ocean's surface to 2,000‐m depth by using dissolved oxygen, water temperature (T), salinity (S), and pressure (P) data. Using the values of total alkalinity and dissolved inorganic carbon predicted by dissolved oxygen, T, S, and P data derived from autonomous biogeochemical Argo floats, we described the distribution of oceanic Cant in the 2000s in the subarctic North Pacific at high spatiotemporal resolution. The Cant was found about 300 m deeper than during the 1990s; its average inventory to 2,000 m was 24.8 ± 10.2 mol/m2, about 20% higher than the 1990s average. Future application of parameterizations to global biogeochemical Argo floats data should allow the detailed global mapping of spatiotemporal distributions of CO2 parameters. Plain Language Summary Ocean absorbs the increasing atmospheric CO2 by human activities from 1750s and encourages global ocean acidification. To obtain the human‐activity‐derived CO2 in the subarctic North Pacific in a high resolution, we applied our empirical ocean carbon chemistry equations using other hydrographic parameters to autonomous biogeochemical Argo floats data. The amount of human‐activity‐derived CO2 in this region was found about 300 m deeper than during the 1990s and about 20% higher than the 1990s average. Our method allows the development of a system for monitoring long‐term trend changes in ocean carbon chemistry similar to other time series stations. Key Points New parameterizations for total alkalinity and dissolved inorganic carbon allow higher‐resolution estimates of oceanic anthropogenic CO2 Applying our parameterizations to autonomous biogeochemical Argo floats data could allow more detailed mapping of parameters influencing global oceanic CO2 cycles