| Summary: | This study aims to generate from a three-dimensional data set of carbon dioxide ux in the Southern Ocean, a sample set for use with Kriging in order to generate estimated carbon dioxide ux values across the complete three-dimensional data set. In order to determine which sampling strategies are to be used with the three-dimensional data set, a number of a-priori and a-posteriori sampling methods are tested on a two-dimensional subset. These various sampling methods are used to determine whether or not the estimated error variance generated by Kriging is a good substitute for the true error as a measure of error. Carbon dioxide is a well known "greenhouse gas" and is partially responsible for climate change. However, some anthropogenic carbon dioxide is absorbed by the oceans and as such, the oceans currently play a mitigating role in climate change by acting as a sink for carbon dioxide. It has been suggested that if the production of carbon dioxide continues unabated that the oceans may become a source rather than a sink for carbon dioxide. This would mean that the oceanic carbon dioxide ux (exchange of carbon dioxide between the atmosphere and the surface of the ocean) would invert. As such, modelling of the carbon dioxide ux is of clear importance. Additionally as the Southern Ocean is highly undersampled, a sampling strategy for this ocean which would allow for high levels of accuracy with small sample sizes would be ideal. Kriging is a geostatistical weighted interpolation technique. The weights are based on the covariance structure of the data and the distances between points. In addition to an estimate at a point, Kriging also produces an estimated error variance which can be used as an indication of uncertainty. This study made use of model data for carbon dioxide ux in the Southern i Ocean. This data covers a year by making use of averaged data for 5 day intervals. This results in a three-dimensional data set covering latitude, longitude and time. This study used this data to generate a covariance ...
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