URE OMPS Summer 2009
Team Members: Thaddeus T. Fairley, Candy M. Graves, Kadarice J. Joyce
Team Mentor: Dr. Jinchun Yuan
Estimating the distribution of CO2 parameters in surface water of the Indian Ocean from temperature and salinity

Keywords: carbon dioxide, temperature, salinity, alkalinity, multiple linear regression


The distribution of CO2 parameters in the ocean is important for understanding the fate of anthropogenic carbon emission and its effects on global climate change. Among the four essential parameters, pH, alkalinity (TA), pCO2, and total inorganic carbon (Tco2), any two of them are sufficient to fully define the aquatic CO2 system. Traditionally, each CO2 parameters has to be determined using either field sampling or in situ sensors which are inefficient. As a result, temporal and spatial variations of CO2 system are poorly understood. Recently, linear correlations between CO2 parameters and temperature, salinity, and concentrations of dissolved organic carbon (DOC) and particulate organic carbon (POC) of various surface waters have been developed (Lohronze and Cai 2006, Berryman et al. 2007, Small and Reid 2007, Yuan 2009). Since sea surface temperature (SST) can be determined from satellite sensors, concentrations of DOC and POC can be estimated from satellite data, and the satellite sensor for sea surface salinity will be launched soon, these correlations will enable estimation of global distribution of CO2 parameters from satellite data. We have tested these linear equations by predicting CO2 parameters from sea surface temperature and salinity along cruise transects in the Indian Ocean. We have compared our prediction with field measurements of CO2 parameters and evaluated the potential of these linear equations for estimating CO2 parameters. The final research paper presents our final results, which shows which formula could possibly be future ways of estimating the distribution of CO2.