Global surface ocean calcite saturation (Ωcal) estimation using satellite and in-situ observations

Abstract

Calcite saturation (Ω_cal) in global surface ocean waters is a crucial parameter for assessing marine ecosystem health. This study presents a multiparametric linear regression (MLR) model integrating satellite and in-situ observations to estimate global surface ocean Ω_cal. The model was developed using in-situ measurements of sea surface temperature (SST), sea surface salinity (SSS), total alkalinity (TA), dissolved inorganic carbon (DIC) and Ω_cal obtained from the National Center for Environmental Information (NCEI), combined with satellite derived chlorophyll concentrations (Chla). Model validation demonstrated strong agreement with in-situ data, indicating high accuracy of estimation. Satellite derived Ω_cal estimates also showed robust correlations with in-situ measurements, confirming the MLR model reliability. Sensitivity analysis highlighted the model resilience to variations in input parameters. This study reveals significant spatiotemporal variability in Ω_cal, driven by physical, chemical, and biological processes, including seasonal patterns and climate phenomena like the El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Analysis of interannual trends and the rate of change in Ω_cal emphasize the impacts of ocean acidification, revealing a declining trend that poses challenges to marine ecosystems. The proposed approach offers a valuable tool for monitoring global ocean carbonate chemistry, providing insights into the long term impacts of environmental changes on marine health and enabling informed decision making for ecosystem management.