Indexing, mode definition, and signal extraction in climate research: Analysis and applications involving the MJO, the AO, and ENSO
There are two objectives of the present study. The primary objective is to undertake following research projects involving the Arctic Oscillation (AO), the El Niño Southern Oscillation (ENSO), and the Madden Julian Oscillation (MJO): (1) an assessment of the utility of using Cyclo-stationary empirical orthogonal function (CSEOF) analysis to define the AO, (2) an empirical analysis of ENSO impacts based on varying indicator and impact regions, (3) detection and extraction of the MJO signal from QuikSCAT, and (4) the development of a general algorithm for determining optimal filter weights for time series endpoints. A secondary objective is to enumerate the statistical and analytical treatments of the AO, ENSO, and the MJO. This will include comparisons of how these three modes are defined (including their indices) and extracted from geophysical data sets.
The AO is defined using empirical orthogonal function (EOF) analysis of sea level pressure north of 20°N. The resulting spatial pattern and time series captures the regional influence of its precursor, the North Atlantic Oscillation (NAO), which is a measure of mid-latitude zonal winds over the North Atlantic. ENSO was originally defined as the pressure difference between Tahiti and Darwin, Australia: the Southern Oscillation Index. Scientists now primarily use sea surface temperature (SST) anomalies averaged over one of the Niño regions as ENSO indices. The MJO was originally observed using spectral analysis of zonal wind time series in the Indian Ocean and Western Pacific. Present day researchers use extensions of EOF analysis to construct MJO time series. For all three climate modes, the creation of high quality space-time data sets has allowed for more sophisticated indices, supplanting the simpler point-based metrics.
For the AO project, the cyclo-stationarity of Northern Hemisphere sea level pressure variability is considered. CSEOF analysis is an extension of EOF analysis that allows multiple spatial maps per mode. It accomplishes this by cyclically extending the covariance matrix based on a parameter called the nested period. (Abstract shortened by UMI.)