Indo-Pacific Variability on Seasonal to Multidecadal Timescales. Part I: Intrinsic SST Modes in Models and Observations

Indo-Pacific Variability on Seasonal to Multidecadal Timescales. Part I: Intrinsic SST Modes in... AbstractThe variability of Indo-Pacific SST on seasonal to multidecadal timescales is investigated using a recently introduced technique called nonlinear Laplacian spectral analysis (NLSA). Through this technique, drawbacks associated with ad hoc pre-filtering of the input data are avoided, enabling recovery of low-frequency and intermittent modes not previously accessible via classical approaches. Here, a multiscale hierarchy of spatiotemporal modes is identified for Indo-Pacific SST in millennial control runs of CCSM4 and CM3 and in HadISST data. On interannual timescales, a mode with spatiotemporal patterns corresponding to the fundamental component of ENSO emerges, along with ENSO-modulated annual modes consistent with combination mode theory. The ENSO combination modes also feature prominent activity in the Indian Ocean, explaining a significant fraction of the SST variance in regions associated with the Indian Ocean dipole, and suggesting a deterministic relationship between these patterns. A pattern representing the tropospheric biennial oscillation also emerges along with its associated annual cycle combination modes. On multidecadal timescales, the dominant NLSA mode in the model data is predominantly active in the western tropical Pacific; we call this pattern west Pacific multidecadal mode. The interdecadal Pacific oscillation also emerges as a distinct NLSA mode, though with smaller explained variance than the western Pacific multidecadal mode. Analogous modes on interannual and decadal timescales are also identified in HadISST data for the industrial era, as well as in model data of comparable timespan, though decadal modes are either absent or of degraded quality in these datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Climate American Meteorological Society

Indo-Pacific Variability on Seasonal to Multidecadal Timescales. Part I: Intrinsic SST Modes in Models and Observations

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0442
eISSN
1520-0442
D.O.I.
10.1175/JCLI-D-16-0176.1
Publisher site
See Article on Publisher Site

Abstract

AbstractThe variability of Indo-Pacific SST on seasonal to multidecadal timescales is investigated using a recently introduced technique called nonlinear Laplacian spectral analysis (NLSA). Through this technique, drawbacks associated with ad hoc pre-filtering of the input data are avoided, enabling recovery of low-frequency and intermittent modes not previously accessible via classical approaches. Here, a multiscale hierarchy of spatiotemporal modes is identified for Indo-Pacific SST in millennial control runs of CCSM4 and CM3 and in HadISST data. On interannual timescales, a mode with spatiotemporal patterns corresponding to the fundamental component of ENSO emerges, along with ENSO-modulated annual modes consistent with combination mode theory. The ENSO combination modes also feature prominent activity in the Indian Ocean, explaining a significant fraction of the SST variance in regions associated with the Indian Ocean dipole, and suggesting a deterministic relationship between these patterns. A pattern representing the tropospheric biennial oscillation also emerges along with its associated annual cycle combination modes. On multidecadal timescales, the dominant NLSA mode in the model data is predominantly active in the western tropical Pacific; we call this pattern west Pacific multidecadal mode. The interdecadal Pacific oscillation also emerges as a distinct NLSA mode, though with smaller explained variance than the western Pacific multidecadal mode. Analogous modes on interannual and decadal timescales are also identified in HadISST data for the industrial era, as well as in model data of comparable timespan, though decadal modes are either absent or of degraded quality in these datasets.

Journal

Journal of ClimateAmerican Meteorological Society

Published: Mar 23, 2017

References

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