A Multiple Linear Regression of pCO2 against Sea Surface Temperature, Salinity, and Chlorophyll a at Station BATS and its Potential for Estimate pCO2 from Satellite Data

Ocean is one of the major reservoirs of carbon and can be a major sink of anthropogenic carbon dioxide. Together with pH, alkalinity, and total dissolved inorganic carbon (DIC), partial pressure of carbon dioxide (pCO2) is one of the four essential parameters for determining aquatic CO2 system. These four CO2 parameters are interrelated through chemical equilibrium and the determination of any two is sufficient for calculating the other two parameters. Ship-based oceanographic research cruise, that is expensive to operate and inefficient to provide global coverage, has long been the main source of data for characterizing oceanic CO2 system. Recently, Lohrenz and Cai (2006) conducted a field study of partial pressure of carbon dioxide, temperature, salinity, and Chlorophyll a in surface waters of the Northern Gulf of Mexico and developed a correlation method for estimating carbon dioxide distribution from the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. Although it showed great potential, the correlation is based on field data with a small temperature variation and atypical salinity for open ocean waters, and it is not clear whether it can be applied elsewhere in the ocean. Here, we propose to extend the applicability of the method by conducting a data analysis study of field observations conducted at station BATS (Bermuda Atlantic Time-Series) Specifically, we: (1) Obtain field data o