Comparing low-frequency and intermittent variability in comprehensive climate models through nonlinear Laplacian spectral analysis

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Abstract

Nonlinear Laplacian spectral analysis (NLSA) is a recently developed technique for spatiotemporal analysis of high-dimensional data, which represents temporal patterns via natural orthonormal basis functions on the nonlinear data manifold. Through such basis functions, determined efficiently via graph-theoretic algorithms, NLSA captures intermittency, rare events, and other nonlinear dynamical features which are not accessible through linear approaches (e.g., singular spectrum analysis (SSA)). Here, we apply NLSA to study North Pacific SST monthly data from the CCSM3 and ECHAM5/MPI-OM models. Without performing spatial coarse graining (i.e., operating in ambient-space dimensions up to 1.6 × 105after lagged embedding), or seasonal-cycle subtraction, the method reveals families of periodic, low-frequency, and intermittent spatiotemporal modes. The intermittent modes, which describe variability in the Western and Eastern boundary currents, as well as variability in the subtropical gyre with year-to-year reemergence, are not captured by SSA, yet are likely to have high significance in a predictive context and utility in cross-model comparisons.

Original languageEnglish (US)
Article numberL10710
JournalGeophysical Research Letters
Volume39
Issue number10
DOIs
StatePublished - 2012

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climate models
spectral analysis
spectrum analysis
climate modeling
low frequencies
spatiotemporal analysis
eastern boundary current
western boundary current
gyre
sea surface temperature
intermittency
subtraction
embedding
cycles
analysis

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Geophysics

Cite this

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title = "Comparing low-frequency and intermittent variability in comprehensive climate models through nonlinear Laplacian spectral analysis",
abstract = "Nonlinear Laplacian spectral analysis (NLSA) is a recently developed technique for spatiotemporal analysis of high-dimensional data, which represents temporal patterns via natural orthonormal basis functions on the nonlinear data manifold. Through such basis functions, determined efficiently via graph-theoretic algorithms, NLSA captures intermittency, rare events, and other nonlinear dynamical features which are not accessible through linear approaches (e.g., singular spectrum analysis (SSA)). Here, we apply NLSA to study North Pacific SST monthly data from the CCSM3 and ECHAM5/MPI-OM models. Without performing spatial coarse graining (i.e., operating in ambient-space dimensions up to 1.6 × 105after lagged embedding), or seasonal-cycle subtraction, the method reveals families of periodic, low-frequency, and intermittent spatiotemporal modes. The intermittent modes, which describe variability in the Western and Eastern boundary currents, as well as variability in the subtropical gyre with year-to-year reemergence, are not captured by SSA, yet are likely to have high significance in a predictive context and utility in cross-model comparisons.",
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