Abstract
Lack of sufficient observations has been an impediment for understanding the spatial and temporal variability of sea-surface pCO(2) 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 pCO(2) maps using a significant number of open and coastal ocean observations of pCO(2) and associated variables regulating pCO(2) variability in BoB. We employ four advanced ML algorithms to predict pCO(2). We use the best ML model to produce a high-resolution climatological product (INCOIS-ReML). The comparison of INCOIS-ReML pCO(2) with RAMA buoy-based sea-surface pCO(2) observations indicates INCOIS-ReML's satisfactory performance. Further, the comparison of INCOIS-ReML pCO(2) with existing ML products establishes the superiority of INCOIS-ReML. The high-resolution INCOIS-ReML greatly captures the spatial variability of pCO(2) and associated air-sea CO(2) flux compared to other ML products in the coastal BoB and the northern BoB.