Improved estimation of global surface ocean total alkalinity fields using in-situ and satellite observations

Total Alkalinity (TA) is identified as one of the Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) for climate change impact assessment studies. This also plays an important role in understanding the oceanic carbon cycle, and ocean acidification. The existing global algorithms are Sea Surface Salinity (SSS) based and inadequate for precise estimation of TA fields and their dynamics due to precluding physical and biological parameters such as Sea Surface Temperature (SST), Chlorophyll-a concentration (Chl-a) and its influencing parameters (nutrients). In the present study, a novel algorithm known as the Improved Single Linear Regression (ISLR) was devised based on SSS and Nitrate (Ni) concentration. The ISLR was formulated using simultaneous in-situ measurements of SSS and Ni acquired across the global oceans. The primary objective was to mitigate uncertainties and generate consistent TA fields for the global surface ocean. ISLR performance was assessed with independent satellite and in-situ TA observations. The validation results demonstrate the ISLR approach superior performance, characterized by significant low errors (mean relative error (MRE) = 0.04 mu mol kg(-1); mean normalized bias (MNB) = -0.0003 mu mol kg(-1); and root mean square error (RMSE) = 10.08 mu mol kg(-1)) with a high correlation coefficient (R-2 = 0.96). The ISLR derived TA fields were used to study the spatiotemporal variability and seasonal dynamics of the global oceans.