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