| dc.description.abstract | ith the increasing availability of Earth observation datasets, developing methods for the identification of modes of variability is becoming crucial in Earth System Science. These modes, also referred as teleconnections, are useful to understand the global climate system and to predict short term climate and climate variability. For example, the ENSO phenomenon is a teleconnection with global climate impacts that have been associated with major social, economic, and ecological consequences. In this study, a novel procedure called Multichannel Empirical Orthogonal Teleconnection (MEOT) analysis is introduced as a simple extension of the logic of Empirical Orthogonal Teleconnections (EOT) to uncover the temporal evolution of recurrent space-time patterns. A global monthly sea surface temperature dataset (1982-2007 time series) is used to explore the MEOT method and its differences and similarities with the Multichannel Singular Spectrum Analysis (MSSA). Both methods are applied with a 13-month embedding dimension to extract spatio-temporal patterns that exhibit clear basis vectors in quadrature. MSSA extracted four quadratures and MEOT three. Findings show that MEOT quadratures are more easily related to climate events corresponding to El Niño Southern Oscillation (ENSO), South Atlantic Ocean Dipole (SAOD) and Atlantic Meridional Mode (AMM). MSSA identified one quadrature related to ENSO and one to Quasi Biennial Oscillation. The two remaining MSSA quadratures are mixtures of different indices rather than one climate event. Thus, results indicate that since MEOT does not suffer from a bi-orthogonality constraint, it is effective at extracting modes of variability in climate datasets suggesting its potential use in climate research. | |
