argopy
New classes to predict nutrients and carbonates in the global ocean, with their uncertainties.
Two new extensions were implemented in argopy to compute nutrient and carbonate system variables.
CANYON-B is a Bayesian neural network approach that estimates water-column nutrient concentrations (NO3, PO4, SiOH4) and carbonate system variables (AT, DIC, pHT, pCO2). It provides more robust neural networks than CANYON-MED and includes a local uncertainty estimate for each predicted parameter.
CONTENT is a combination of CANYON-B Bayesian neural network mappings of AT, CT, pH and pCO2 made consistent with carbonate chemistry constraints for any set of water column P, T, S, O2 location data as an alternative to (spatial) climatological interpolation.
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