Sea-surface pCO₂ maps for the Bay of Bengal based on advanced machine learning algorithms

Lack of sufficient observations has been an impediment for understanding the spatial and temporal variability of sea-surface pCO₂ for the Bay of Bengal (BoB). The limited number of observations into existing machine learning (ML) products from BoB often results in high prediction errors. This study develops climatological sea-surface pCO₂ maps using a significant number of open and coastal ocean observations of pCO₂ and associated variables regulating pCO₂ variability in BoB. We employ four advanced ML algorithms to predict pCO₂. We use the best ML model to produce a high-resolution climatological product (INCOIS-ReML). The comparison of INCOIS-ReML pCO₂ with RAMA buoy-based sea-surface pCO₂ observations indicates INCOIS-ReML's satisfactory performance. Further, the comparison of INCOIS-ReML pCO₂ with existing ML products establishes the superiority of INCOIS-ReML. The high-resolution INCOIS-ReML greatly captures the spatial variability of pCO₂ and associated air-sea CO₂ flux compared to other ML products in the coastal BoB and the northern BoB.