Hunting spectro-temporal information in unevenly spaced paleoclimate time series

Here we present some preliminary results of a statistical–computational implementation to estimate the wavelet spectrum of unevenly spaced paleoclimate time series by means of the Morlet Weighted Wavelet Z-Transform (MWWZ). A statistical significance test is performed against an ensemble of first-...

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Bibliographic Details
Main Authors: Josué M. Polanco-Martínez, Sérgio H. Faria
Format: Report
Language:unknown
Subjects:
Greenland
ice core
Online Access:http://www.bc3research.org/index.php?option=com_wpapers&task=downpubli&iddoc=79&repec=1&Itemid=279
Description
Summary:Here we present some preliminary results of a statistical–computational implementation to estimate the wavelet spectrum of unevenly spaced paleoclimate time series by means of the Morlet Weighted Wavelet Z-Transform (MWWZ). A statistical significance test is performed against an ensemble of first-order auto-regressive models (AR1) by means of Monte Carlo simulations. In order to demonstrate the capabilities of this implementation, we apply it to the oxygen isotope ratio (δ18O) data of the GISP2 deep ice core (Greenland). wavelet spectral analysis, continuous wavelet transform, Morlet Weighted Wavelet Z-Transform, unvenly spaced paleoclimate time series, non-stationarity, multi-scale phenomena.

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