Tobias Steinhoff
Papers
1
Total Citations
247
H-Index
1
About
Tobias Steinhoff is a leading figure in marine biogeochemistry, whose work has fundamentally advanced how we estimate the ocean’s role in the carbon cycle. His primary research focuses on developing robust computational methods to derive critical carbon system parameters—such as alkalinity, dissolved inorganic carbon, pH, and pCO₂—from more readily measured oceanographic variables. Steinhoff’s most impactful contribution is the CANYON-B neural network, a Bayesian approach introduced in his landmark 2018 paper (247 citations). This method provides a powerful alternative to static climatologies, enabling accurate, high-resolution estimates of open ocean CO₂ variables and nutrients using only temperature, salinity, oxygen, and location data. By effectively mapping the complex, non-linear relationships between these variables, Steinhoff’s work has allowed the scientific community to vastly expand the spatial and temporal coverage of ocean carbon observations, turning sparse bottle data into a continuous, reliable dataset. This innovation is critical for understanding ocean acidification, air-sea CO₂ fluxes, and the marine carbon sink, cementing his reputation as a pioneer in computational biogeochemistry.
Research Focus
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Top Papers
- 1