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Team Members: Phillip Moore, Amanda Bland, Diaminatou Goudiaby
Team Mentor: Dr. Jinchun Yuan
[Team Website]
A Multiple Linear Regression of pCO2 Against Sea Surface Temperature, Salinity and Chlorophyll a at Station ALOHA and its Potential for Estimate pCO2 From Satellite Data


The 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 systems. 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 th2e main source of data for characterizing oceanic CO2 system. Recently Lohrenz and Cai (2006) conducted a field study of pCO2, 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 Spectrophotometer (MODIS) remote sensing data. Although it showed great potential, the correlation is based on field data with a small temperature variation and typical salinity, and it is not clear if it can be applied elsewhere. Here, we propose to extend the applicability of the method by conducting a data analysis study of field observations conducted at station ALOHA (A Long-Term Oligarchic Habitat Assessments)
Our primary goal of the research project was to develop a multiple linear correlation between pCO2 and a combination of temperature, salinity, dissolved organic carbon, particular carbon, and chlorophyll a. Specifically, we: (1) Obtained field data of alkalinity, DIC, temperature, salinity, and Chlorophyll a determined at station ALOHA in the last two decades; (2) Calculated pCO2 from alkalinity and DIC; (3) Applied the correlation method to test the applicability of the method in the central North Pacific Ocean; (4) Applied the correlation method and predict the distributions of partial-pressure and air-sea fluxes of carbon dioxide in the central Pacific Ocean from MODIS data.

Comiing Soon.......More Work!!!!!