Advancements in Carbon Dioxide Modeling: An Algorithm Incorporating In-Situ and Satellite Data for Improved Understanding of pCO Dynamics in the Bay of Bengal

Estimation of the partial pressure of carbon dioxide (pCO2) in the Bay of Bengal (BoB) region plays a crucial role in better understanding the air-sea CO2 fluxes. Complex physical and biogeochemical processes such as physical mixing, stratification, thermodynamic, and biological effects dominate the spatiotemporal variability of pCO2 concentration over the BoB. This is difficult to estimate through in-situ platforms alone due to the time-consuming, cost-effective, and intricacies involved in water sample collection during rough oceanic weather conditions. Alternatively, remote sensing technology provides governing control parameters with high spatiotemporal resolution over large synoptic scales. Since the BoB region is influenced by the Indian monsoon system and other complex processes, existing regional and global pCO2 algorithms are not adequate to estimate more accurate pCO2 fields. Hence, there is a need to develop a regional pCO2 algorithm over the BoB. To resolve this problem, in the present study, a Multi Parametric Regional Regression (MPRR) approach was developed over the BoB using satellite data such as sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-
${a}$
(Chl
$a$
) concentration. To train and validate the MPRR approach, required in-situ measurements were obtained from the open and coastal waters of the BoB. The validation results revealed that the present MPRR approach showed better performance with significant low errors (mean relative error (MRE) = 0.012, mean normalized bias (MNB) = 0.022, and root mean square error (RMSE) = 4.75
$\mu $
atm) and a high correlation coefficient (R2 = 0.92). Furthermore, the study demonstrated the spatiotemporal variability of pCO2and generated monthly, seasonal, and annual pCO2 maps over the BoB.