Abstract
To overcome the limitations due to sporadic carbonate parameter data, this study developed and evaluated empirical multiple linear regression (MLR) models for dissolved inorganic carbon (DIC), pH in total scale (pHT), and aragonite carbonate saturation state (ΩAr) using hydrographic data (temperature, salinity, and oxygen) measured during 2007–2018 in the South Atlantic Bight (SAB) and Mid‐Atlantic Bight (MAB) along the U.S. East Coast. We first reviewed the assumptions and routines of MLR models and then generated MLR models for each cruise for all three carbonate parameters in each region and assessed model performance. Models derived from measured spectrophotometric pH have smaller uncertainties than pHT models based on pH calculated from total alkalinity (TA) and DIC. The regional differences of carbonate parameters between MAB and SAB are reflected in the coefficients of the empirical models. The MLR model temporal consistency indicates that the effect of the atmospheric CO2 increase on seawater carbonate parameters cannot be unequivocally resolved for the period of this study in the regions. Therefore, we combined different cruises to build composite models for each region. The composite models can capture the key features in the SAB and MAB. To further assess the model applicability, we applied our models to Biogeochemical‐Argo data to reconstruct carbonate parameters. The algorithm in this study helps to reconstruct seawater carbonate chemistry using proxy data of high spatial and temporal resolution, which will enhance our understanding of physical and biological processes on carbon cycle and the long‐term anthropogenic carbon inputs in coastal oceans.
Plain Language Summary
Coastal ocean carbon cycling is a complex process that is influenced by various physical and biological processes. Sporadic carbonate data challenges our understanding of carbon cycling in coastal areas. We first reviewed the assumptions and routines in developing coastal empirical models, and then built linear regression models with frequently measured seawater properties, such as temperature, salinity, and O2, to estimate the carbonate variables along the U.S. East Coast. The key features of seawater carbonate parameters are captured by the empirical models. The sub‐regional differences are reflected in the coefficients of the empirical models. We also found that the effect of anthropogenic carbon dioxide increase on the DIC is limited over 10 years. This study helps to reconstruct seawater carbonate chemistry where data are limited, predict future changes in coastal carbonate chemistry, and enhance our understanding of long‐term anthropogenic carbon inputs in the coastal ocean.
Key Points
pH estimation models based on measured pH have smaller uncertainties than those based on pH calculated from other carbonate parameters
Models differ between the Mid and South Atlantic Bights, and their temporal changes due to atmospheric CO2 are limited over 10 years
Multiple linear regression models provide a promising tool for reconstructing carbonate parameters using data from autonomous platforms
