RESUMO
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.