Sea-surface pCO 2 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 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.