Data-Driven Modeling of 4D Ocean and Coastal Acidification in the Massachusetts and Cape Cod Bays From Surface Measurements

Abstract

A significant portion of atmospheric (Formula presented.) emissions is absorbed by the ocean, resulting in acidified seawater and altered carbonate composition that is harmful to marine life. Despite detrimental effects, assessing ocean and coastal acidification (OCA) is difficult due to the scarcity of in situ measurements and the high costs of computational modeling. We develop a parsimonious data-driven framework to model indicators of OCA and test it in the Massachusetts Bay and Stellwagen Bank, a region with fishing and tourism industries affected by OCA. First, we trained a neural network to predict in-depth fields for temperature and salinity (Formula presented.) using surface quantities from satellites and in situ measurements (Formula presented.). The relationship between 2D surface and 3D properties is captured through the in-depth modes and coefficients obtained from principal component analysis applied to a high-resolution historical reanalysis data set. Next, we used Bayesian regression methods to estimate region-specific relationships for in-depth total alkalinity (TA), dissolved inorganic carbon (DIC), and aragonite saturation state (Formula presented.) as functions of temperature, salinity, and chlorophyll. Lastly, 4D daily field predictions are generated from surface measurements with a spatial resolution of 4 km horizontally and 45 sigma levels vertically. The model's performance is evaluated using withheld measurements across depths, locations, and seasons with RMSEs of 1.59°C, 0.31 PSU, 37.54 (Formula presented.) mol (Formula presented.), 39.40 (Formula presented.) mol (Formula presented.), and 0.42 for temperature, salinity, TA, DIC, and (Formula presented.), respectively, at one withheld location. The framework is useful for understanding OCA and includes uncertainty quantification for future planning and optimal sensor placement.