Modelling the oceanic partial pressure of carbon dioxide in the North Indian Ocean

Accurate information about sea surface pCO
2
distribution is essential to understand the variability of the ocean’s ability to uptake excess CO
2
in the atmosphere. Here, we present a machine learning algorithm based pCO
2
model customised for the north Indian ocean. The model makes use of Sea Surface Temperature (SST), Sea Surface Salinity (SSS) and Chlorophyll-a (Chl-a) to incorporate the thermodynamic, physical and biological effect that controls the air-sea exchange. Our results show that the model is able to capture the pCO
2
seasonal variations and are consistent with available observations.