| The El Nino/Southern Oscillation (ENSO) represents the most important natural part of global climate variability. The Southern Oscillation Index (SOI) and the Nino Regional Sea Surface Temperature (SST) Indices measure ENSO fluctuations over the atmosphere and the ocean, respectively. Scientists recognize that relying on the unchanged seasonal cycles as an annual reference is a weak assumption for anomaly estimation and, thus, the accuracy of these two climate indicators is affected. Hence, several seasonal adjustment methodologies to estimate a modulated seasonal cycle have been studied and employed.One weakness of modulated seasonal adjustment methodologies, however, is their inability to quantify the separation of seasonal variation from the original data due to the intrinsic ambiguity of the definition for seasonal variation. Moreover, no previous study has characterized these methodologies, evaluated their performance, or compared and contrasted their impact on the description of the ENSO phenomenon.Based on rigorous statistical analysis and comparison, this thesis: (1) develops a tool to separate the seasonal and inter-annual variations quantitatively and also extends the implementation of modulated seasonal adjustment methods to spatiotemporal data sets, (2) provides guidelines on which seasonal adjustment methodology one should choose when handling seasonal variability in climate data-processing for different circumstances, and (3) highlights the impact of the seasonal adjustment methodology on the interpretation of ENSO phenomena. |
