In this dissertation, the aim is to investigate the empirical relationship between the partial pressure
of CO2 (pCO2) and other ocean variables in the Southern Ocean, by using a small percentage of the
available data.
CO2 is one of the main greenhouse gases that contributes to global warming and climate change.
The concentration of anthropogenic CO2 in the atmosphere, however, would have been much higher
if some of it was not absorbed by oceanic and terrestrial sinks. The oceans absorb and release CO2
from and to the atmosphere. Large regions in the Southern Ocean are expected to be a CO2 sink.
However, the measurements of CO2 concentrations in the ocean are sparse in the Southern Ocean,
and accurate values for the sinks and sources cannot be determined. In addition, it is difficult
to develop accurate oceanic and ocean-atmosphere models of the Southern Ocean with the sparse
observations of CO2 concentrations in this part of the ocean.
In this dissertation classical techniques are investigated to determine the empirical relationship between
pCO2 and other oceanic variables using in situ measurements. Additionally, sampling techniques
are investigated in order to make a judicious selection of a small percentage of the total
available data points in order to develop an accurate empirical relationship.
Data from the SANAE49 cruise stretching between Antarctica and Cape Town are used in this dissertation.
The complete data set contains 6103 data points. The maximum pCO2 value in this stretch
is 436.0 μatm, the minimum is 251.2 μatm and the mean is 360.2 μatm. An empirical relationship is
investigated between pCO2 and the variables Temperature (T), chlorophyll-a concentration (Chl),
Mixed Layer Depth (MLD) and latitude (Lat). The methods are repeated with latitude included
and excluded as variable respectively. D-optimal sampling is used to select a small percentage of
the available data for determining the empirical relationship. Least squares optimization is used as
one method to determine the empirical relationship. For 200 D-optimally sampled points, the pCO2
prediction with the fourth order equation yields a Root Mean Square (RMS) error of 15.39 μatm
(on the estimation of pCO2) with latitude excluded as variable and a RMS error of 8.797 μatm with
latitude included as variable. Radial basis function (RBF) interpolation is another method that is
used to determine the empirical relationship between the variables. The RBF interpolation with
200 D-optimally sampled points yields a RMS error of 9.617 μatm with latitude excluded as variable
and a RMS error of 6.716 μatm with latitude included as variable. Optimal scaling is applied to
the variables in the RBF interpolation, yielding a RMS error of 9.012 μatm with latitude excluded
as variable and a RMS error of 4.065 μatm with latitude included as variable for 200 D-optimally sampled points. / Thesis (MSc (Applied Mathematics))--North-West University, Potchefstroom Campus, 2012
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nwu/oai:dspace.nwu.ac.za:10394/9536 |
| Date | January 2011 |
| Creators | Van der Walt, Marizelle |
| Publisher | North-West University |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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