Summary
This paper presents a gridded global monthly climatology of seawater total alkalinity derived from the GLODAPv2 dataset using neural network modelling (NNGv2). Total alkalinity is a fundamental variable in seawater carbonate chemistry and essential for quantifying ocean acidification and the marine carbon cycle. The resulting climatology, with an RMSE of 5.3 µmol kg⁻¹, provides a reference dataset for marine biogeochemical research and climate model validation.
UK applicability
The global alkalinity climatology provides baseline reference data relevant to UK marine policy and research on ocean acidification impacts in North Atlantic and surrounding waters. The methodology and dataset support UK participation in international ocean observing systems and inform marine environmental monitoring and conservation strategies.
Key measures
Total alkalinity (µmol kg⁻¹), root mean square error (RMSE = 5.3 µmol kg⁻¹), seasonal and spatial variability patterns
Outcomes reported
The study produced a global monthly climatology of seawater total alkalinity derived from quality-controlled oceanographic data using neural network modelling. The climatology quantifies seasonal variability and spatial patterns in ocean alkalinity, a key parameter for understanding marine carbon cycling and ocean acidification.
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