Phillip Moore, Ashley Berryman, Diaminatou Goudiaby, & Dr. Jinchun Yuan

1. Oceanography TeamA Multiple Linear Regression of pCO2 Against Sea Surface Temperature, Salinity, and Chlorophyll a at Station ALOHA and its potential for Estimating pCO2 from Satellite Data Phillip Moore, Ashley Berryman, Diaminatou Goudiaby, & Dr. Jinchun Yuan

2. Abstract 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, and it is not clear whether 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 Oligotrophic Habitat Assessment; 22° 45'N, 158° 00'W) Specifically, we will: (1) Obtain field data of alkalinity, DIC, temperature, salinity, and Chlorophyll a determined at station ALOHA in the last two decades; (2) Calculate pCO2 from alkalinity and DIC; (3) Apply the correlation method to test the applicability of the method in the central North Pacific Ocean; (4) Apply 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.

3. Early Method Limitations • Expensive • Time Consuming • Poor Spatial Coverage

4. Lohrenz & Cai Foundation Wei-Jun Cai

5. Remote Sensing Method Lohrenz & Cai • Less Expensive • Less Time Consuming • More spatial coverage • Only applies at Northern Gulf of Mexico

6. Linear Regression What is a Linear Regression? A process for determining the statistical relationship between a random variable and one or more independent variables that is used to predict the value of the random variable.

7. Linear Regression Graphs

8. P-Value What is P-Value? Possibility of coming up with a regression by random chance

9. Station ALOHA Station ALOHA is located in Kaena, Hawaii. This is the area where all of the fields measurements related to calculating pCO2 came from.

10. Our Linear Regressions pCO2-Temperature, Salinity, and Chlorophyll a pCO2- Temperature, Salinity, and Dissolved Organic Carbon (DOC) pCO2- Temperature, Salinity, and Particular Carbon (PC)

11. Our Work vs. Lohrenz & CaiComparison • Spatial Coverage • Amount of Data • Area of Study • Explanations of Regressions

12. Results: pCO2- Temperature, Salinity, Chlorophyll a

13. Results: pCO2- Temperature, Salinity, DOC

14. Results: pCO2- Temperature, Salinity, PC

15. Conclusion A multiple-linear correlation between pCO2, temperature, salinity, and chlorophyll a obtained for the surface water in the Central-North Pacific Ocean is the correlation that can be use to estimate pCO2 from parameters that can be determined from satellite-remote sensing. We used p-value to confirm that temperature, salinity, and chlorophyll a are significant factors.

16. Improvements & Future Work Use of Model 2 Regressions Conduct the same study in the other oceans (i.e. Atlantic Ocean) Use satellite data to predict pCO2

17. Photos P H O T O G R A P H S

18. References • Maritorena, S., D.A. Siegel and A.R. Peterson, (2002). Optimization of a semianalytical ocean color model for global-scale applications. Applied Optics-LP, 41, 2705-2714; • McClain E.P.,Pichel, W. and Walton C.C. (1985). Comparative performance of AVHRR-based multichannel sea surface temperature. Journal of Geophysical Resear, 90(C6):11587-11601; • O'Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L. Garder, S.A. Garver, M. Kahru, and C. McClain, (1998). Ocean Color chlorophyll algorithms for SeaWiFS, Journal of Geophysical Research, 103 (C11), 24937-24953. • [4] Lohrenz, S.E.,W.J. Cai, (2006). Satellite ocean color assessment of air-sea fluxes of CO2 in a river-dominated coastal margin, Geophysical Research Letters, VOL. 33, L01601, doi:10.1029/2005GL023942, 2006 • http://www.soest.hawaii.edu/HOT_WOCE/index.html • http://kela.soest.hawaii.edu/ALOHA/index.html • http://modis.gsfc.nasa.gov/about/http://ocean.otr.usm.edu/%7Ew301130/research/research_bioocean.htm • http://ocean.otr.usm.edu/%7Ew301130/index.html#Researchhttp://www.marsci.uga.edu/facultypages/cai/ • http://en.wikipedia.org/wiki/P-valuehttp://en.wikipedia.org/wiki/Linear_regression

19. Questions ?