Multiple Linear Regression Models for Reconstructing and Exploring Processes Controlling the Carbonate System of the Northeast US From Basic Hydrographic Data

In the coastal ocean, local carbonate system variability is determined by the interaction between ocean acidification and local processes. Sporadic observations indicate that biological metabolism, river input, and water mass mixing are dominant local processes driving carbonate system variability in northeast US shelf waters. These processes are also reflected in the variability of observed temperature (T), salinity (S), oxygen concentration (O-2), and nitrate concentration (NO3-). Therefore, regionally specific empirical models can be developed, which relate carbonate system parameters to a combination of basic hydrographic parameters. Here, we develop multiple linear regression models that represent the processes that drive carbonate system variability in the Mid-Atlantic Bight and Gulf of Maine using observations obtained on three hydrographic surveys in summers between 2007 and 2015. The empirical model equations reveal the observation-based relationships between carbonate parameters and basic hydrographic variables. Unlike other regions where empirical models have been developed, salinity appears in all models. T is the most important parameter for predicting aragonite saturation state (omega(AR)), while S and O-2 are most important for predicting pH on total scale (pH(T)). The basic hydrographic variables explain over 98% of the variability in total alkalinity (TA), dissolved inorganic carbon (DIC), and omega(AR) and 89% of the variability in pH(T) in the calibration data. We recommend applying models that depend on T, S, O-2, and NO3- as predictors, which reproduce TA and DIC with R-2 > 0.97, omega(AR) with R-2 > 0.93, and pH(T) with R-2 > 0.77, to reconstruct carbonate system parameters in the region.