Estimation of Spatiotemporal Variability of Global Surface Ocean DIC Fields Using Ocean Color Remote Sensing Data

The estimation of dissolved inorganic carbon (DIC) in global surface ocean waters is crucial for understanding air-sea CO ₂ flux rates, ocean acidification, and climate change. DIC magnitude and spatiotemporal variability are influenced by various physical and biogeochemical processes. Due to dynamic variations in ocean surface water, estimating DIC through in-situ data alone is challenging. Ocean color remote sensing offers high spatial and temporal resolution data with extensive synoptic views. Over decades, multiple DIC approaches have emerged using in-situ and satellite observations but are limited to specific regions due to improper model parameter selection and sparse in-situ measurements. To address this, we propose a novel Multi-Parametric Regression (MPR) approach that relates DIC as a function of sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chla) concentration. Utilizing in-situ data from the Global Ocean Data Analysis Project (GLODAP), trends of DIC with SST, SSS, and Chla were analyzed to develop MPR regression equations. The validation results indicated that the proposed regression approach accurately estimates DIC in global surface ocean waters. This approach offers benefits such as DIC estimates at any spatiotemporal resolutions, easy implementation, and cost-effective alternatives to in-situ measurements. Additionally, seasonal and inter-annual variations of global DIC fields were demonstrated through satellite oceanographic data, enhancing monitoring of ocean acidification and climate change scenarios.