Thesis (MComm)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: The Southern Ocean is a complex system, where the relationship between CO2
concentrations and its drivers varies intra- and inter-annually. Due to the lack
of readily available in situ data in the Southern Ocean, a model approach
was required which could predict the CO2 concentration proxy variable, fCO2.
This must be done using predictor variables available via remote measurements
to ensure the usefulness of the model in the future. These predictor
variables were sea surface temperature, log transformed chlorophyll-a concentration,
mixed layer depth and at a later stage altimetry. Initial exploratory
analysis indicated that a non-parametric approach to the model should be
taken. A parametric multiple linear regression model was developed to use as
a comparison to previous studies in the North Atlantic Ocean as well as to
compare with the results of the non-parametric approach. A non-parametric
kernel regression model was then used to predict fCO2 and nally a combination
of the parametric and non-parametric regression models was developed,
referred to as the mixed regression model. The results indicated, as expected
from exploratory analyses, that the non-parametric approach produced more
accurate estimates based on an independent test data set. These more accurate
estimates, however, were coupled with zero estimates, caused by the
curse of dimensionality. It was also found that the inclusion of salinity (not
available remotely) improved the model and therefore altimetry was chosen
to attempt to capture this e ect in the model. The mixed model displayed
reduced errors as well as removing the zero estimates and hence reducing
the variance of the error rates. The results indicated that the mixed model
is the best approach to use to predict fCO2 in the Southern Ocean and that
altimetry's inclusion did improve the prediction accuracy. / AFRIKAANSE OPSOMMING: Die Suidelike Oseaan is 'n komplekse sisteem waar die verhouding tussen CO2
konsentrasies en die drywers daarvoor intra- en interjaarliks varieer. 'n Tekort
aan maklik verkrygbare in situ data van die Suidelike Oseaan het daartoe gelei
dat 'n model benadering nodig was wat die CO2 konsentrasie plaasvervangerveranderlike,
fCO2, kon voorspel. Dié moet gedoen word deur om gebruik te
maak van voorspellende veranderlikes, beskikbaar deur middel van afgeleë metings,
om die bruikbaarheid van die model in die toekoms te verseker. Hierdie
voorspellende veranderlikes het ingesluit see-oppervlaktetemperatuur, log getransformeerde
chloro l-a konsentrasie, gemengde laag diepte en op 'n latere
stadium, hoogtemeting. 'n Aanvanklike, ondersoekende analise het aangedui
dat 'n nie-parametriese benadering tot die data geneem moet word. 'n Parametriese
meerfoudige lineêre regressie model is ontwikkel om met die vorige
studies in die Noord-Atlantiese Oseaan asook met die resultate van die nieparametriese
benadering te vergelyk. 'n Nie-parametriese kern regressie model
is toe ingespan om die fCO2 te voorspel en uiteindelik is 'n kombinasie van
die parametriese en nie-parametriese regressie modelle ontwikkel vir dieselfde
doel, wat na verwys word as die gemengde regressie model. Die resultate het
aangetoon, soos verwag uit die ondersoekende analise, dat die nie-parametriese
benadering meer akkurate beramings lewer, gebaseer op 'n onafhanklike toets
datastel. Dié meer akkurate beramings het egter met "nul"beramings gepaartgegaan
wat veroorsaak word deur die vloek van dimensionaliteit. Daar is ook
gevind dat die insluiting van soutgehalte (nie beskikbaar oor via sateliet nie)
die model verbeter en juis daarom is hoogtemeting gekies om te poog om hierdie
e ek in die model vas te vang. Die gemengde model het kleiner foute
getoon asook die "nul"beramings verwyder en sodoende die variasie van die
foutkoerse verminder. Die resultate het dus aangetoon dat dat die gemengde
model die beste benadering is om te gebruik om die fCO2 in die Suidelike Oseaan
te beraam en dat die insluiting van altimetry die akkuraatheid van hierdie
beraming verbeter.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/71630 |
| Date | 12 1900 |
| Creators | Pretorius, Wesley Byron |
| Contributors | Mostert, Paul J., Das, Sonali, Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 161 p. : ill., maps |
| Rights | Stellenbosch University |
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