Henry C. Bittig
Papers
1
Total Citations
247
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1
About
Henry C. Bittig is a leading oceanographer specializing in marine biogeochemistry and the development of advanced computational methods for ocean carbon cycle research. His primary contributions lie in creating robust, neural network-based approaches to estimate critical CO2 system variables—including alkalinity, dissolved inorganic carbon, pH, and pCO2—from more readily measured parameters like temperature, salinity, and oxygen. His landmark 2018 paper, cited over 247 times, introduced the CANYON-B algorithm, a Bayesian neural network that accurately reproduces global bottle data from the GLODAPv2 database. This work revolutionized the field by providing an alternative to static climatologies, enabling more precise, high-resolution mapping of ocean carbon chemistry across space and time. Bittig’s innovations have significantly improved our ability to monitor ocean acidification and carbon uptake, directly supporting climate change research and policy. His methods are now widely adopted by the oceanographic community, cementing his reputation as a pioneer in applying machine learning to biogeochemical oceanography.
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