Multichannel Empirical Orthogonal Teleconnection Analysis: A Method for Space–Time Decomposition of Climate Variability

Multichannel Empirical Orthogonal Teleconnection Analysis: A Method for Space–Time... AbstractWith 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 El Niño–Southern Oscillation (ENSO) phenomenon, a teleconnection with global climate impacts, has 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 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 spatiotemporal patterns that exhibit clear basis vectors in quadrature. MSSA extracted four quadratures, and MEOT extracted three. Findings show that MEOT quadratures are more easily related to climate events corresponding to ENSO, South Atlantic Ocean dipole, and Atlantic meridional mode. MSSA identified one quadrature related to ENSO and one related to the quasi-biennial oscillation. The two remaining MSSA quadratures are mixtures of different indices rather than one climate event. Thus, results indicate that, since it does not suffer from a biorthogonality constraint, MEOT is effective at extracting modes of variability in climate datasets, suggesting its potential use in climate research. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Meteorology and Climatology American Meteorological Society

Multichannel Empirical Orthogonal Teleconnection Analysis: A Method for Space–Time Decomposition of Climate Variability

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1558-8432
eISSN
1558-8432
D.O.I.
10.1175/JAMC-D-16-0072.1
Publisher site
See Article on Publisher Site

Abstract

AbstractWith 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 El Niño–Southern Oscillation (ENSO) phenomenon, a teleconnection with global climate impacts, has 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 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 spatiotemporal patterns that exhibit clear basis vectors in quadrature. MSSA extracted four quadratures, and MEOT extracted three. Findings show that MEOT quadratures are more easily related to climate events corresponding to ENSO, South Atlantic Ocean dipole, and Atlantic meridional mode. MSSA identified one quadrature related to ENSO and one related to the quasi-biennial oscillation. The two remaining MSSA quadratures are mixtures of different indices rather than one climate event. Thus, results indicate that, since it does not suffer from a biorthogonality constraint, MEOT is effective at extracting modes of variability in climate datasets, suggesting its potential use in climate research.

Journal

Journal of Applied Meteorology and ClimatologyAmerican Meteorological Society

Published: Jul 11, 2017

References

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