Oceanography Team
A 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
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 airsea fluxes of carbon dioxide in the central Pacific Ocean from MODIS data.
Early Method Limitations



Expensive
Time Consuming
Poor Spatial
Coverage
Lohrenz & Cai
Foundation
Wei-Jun Cai
Remote Sensing Method
Lohrenz & Cai
 Less Expensive
 Less Time Consuming
 More spatial coverage
 Only applies at Northern Gulf of
Mexico
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.
Linear Regression Graphs
P-Value
What is P-Value?
Possibility of coming up with a
regression by random chance
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.
Our Linear Regressions
pCO2- Temperature, Salinity, and
Chlorophyll a
pCO2- Temperature, Salinity, and
Dissolved Organic Carbon (DOC)
pCO2- Temperature, Salinity, and
Particular Carbon (PC)
Our Work vs. Lohrenz & Cai
Comparison




Spatial Coverage
Amount of Data
Area of Study
Explanations of Regressions
Results: pCO2- Temperature,
Salinity, Chlorophyll a
R-Square
0.59257356
Coefficients
P-Value
Intercept
-62.00983918
0.757804313
X-Variable 1
-7.225784671
8.8441E-14
X-Variable 2
17.25222807
0.00399179
X-Variable 3
-91.9544452
0.011177371
Results: pCO2- Temperature,
Salinity, DOC
R-Square
0.63326581
Coefficients
P-Value
Intercept
123.906899
0.548744
X-Variable 1
-7.127532
1.32E-13
X-Variable 2
12.2405364
0.0406
X-Variable 3
-0.2267359
0.001751
Results: pCO2- Temperature,
Salinity, PC
R-Square
0.480164
Coefficients
P-Value
Intercept
69.21939541
0.877669739
X-Variable 1
-5.236379355
0.030147492
X-Variable 2
11.7757827
0.353379684
X-Variable 3
2.612531754
0.698314739
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.
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
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Photos
References
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Maritorena, S., D.A. Siegel and A.R. Peterson, (2002). Optimization of a semianalytical
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McClain E.P.,Pichel, W. and Walton C.C. (1985). Comparative performance of AVHRRbased multichannel sea surface temperature. Journal of Geophysical Resear,
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[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/re
search_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
Questions