Summary
This paper presents a global monthly climatology of oceanic total dissolved inorganic carbon derived through feedforward neural network analysis of the GLODAPv2.2019 and LDEO oceanographic datasets. The NNGv2LDEO model integrates relationships between TCO2 and associated oceanographic variables to provide comprehensive, spatially resolved estimates of marine inorganic carbon distribution. This resource supports understanding of natural and anthropogenic influences on the marine carbon cycle and may inform ocean carbon cycle modelling and climate impact assessments.
UK applicability
This global oceanographic dataset has potential relevance to UK marine science and climate policy, particularly for understanding carbon cycling in waters around the British Isles and the North Atlantic. The methodology may support UK-led marine research on ocean acidification and carbon sequestration, which are priorities for UK climate and environmental policy.
Key measures
Total dissolved inorganic carbon (TCO2) concentrations; spatial resolution and temporal coverage of monthly oceanographic climatology; neural network model performance
Outcomes reported
The study developed a global monthly climatology of oceanic total dissolved inorganic carbon (TCO2) using neural network modelling of oceanographic data. This spatially and temporally resolved dataset characterises the distribution and variability of inorganic carbon in the world's oceans.
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