Chen, X., Zhang, Y., Zhang, M., Feng, Y., Wu, Z., Qiao, F., et al. (2013). Intercomparison between observed and simulated variability in global ocean heat content using empirical mode decomposition, part I: modulated annual cycle.
Clim Dyn, 41(11-12), 2797–2815.
Deng, J., Wu, Z., Zhang, M., Huang, N. E., Wang, S., & Qiao, F. (2018). Using Holo-Hilbert spectral analysis to quantify the modulation of Dansgaard-Oeschger events by obliquity.
Quaternary Science Reviews, 192, 282–299.
Abstract: Astronomical forcing (obliquity and precession) has been thought to modulate Dansgaard-Oeschger (DO) events, yet the detailed quantification of such modulations has not been examined. In this study, we apply the novel Holo-Hilbert Spectral Analysis (HHSA) to five polar ice core records, quantifying astronomical forcing's time-varying amplitude modulation of DO events and identifying the preferred obliquity phases for large amplitude modulations. The unique advantages of HHSA over the widely used windowed Fourier spectral analysis for quantifying astronomical forcing's nonlinear modulations of DO events is first demonstrated with a synthetic data that closely resembles DO events recorded in Greenland ice cores (NGRIP, GRIP, and GISP2 cores on GICC05 modelext timescale). The analysis of paleoclimatic proxies show that statistically significantly more frequent DO events, with larger amplitude modulation in the Greenland region, tend to occur in the decreasing phase of obliquity, especially from its mean value to its minimum value. In the eastern Antarctic, although statistically significantly more DO events tend to occur in the decreasing obliquity phase in general, the preferred phase of obliquity for large amplitude modulation on DO events is a segment of the increasing phase near the maximum obliquity, implying that the physical mechanisms of DO events may be different for the two polar regions. Additionally, by using cross-spectrum and magnitude-squared analyses, Greenland DO mode at a timescale of about 1400 years leads the Antarctic DO mode at the same timescale by about 1000 years. (C) 2018 Elsevier Ltd. All rights reserved.
Fu, C. B., Qian, C., & Wu, Z. H. (2011). Projection of global mean surface air temperature changes in next 40 years: Uncertainties of climate models and an alternative approach.
Sci. China Earth Sci., 54(9), 1400–1406.
Hou, T. Y., Yan, M. P., & Wu, Z. (2009). A Variant Of The Emd Method For Multi-Scale Data.
Adv. Adapt. Data Anal., 01(04), 483–516.
Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., & Blank, K. (2009). On Instantaneous Frequency.
Adv. Adapt. Data Anal., 01(02), 177–229.
Qian, C., Fu, C., Wu, Z., & Yan, Z. (2011). The role of changes in the annual cycle in earlier onset of climatic spring in northern China.
Adv. Atmos. Sci., 28(2), 284–296.
Qian, C., Wu, Z., Fu, C., & Zhou, T. (2010). On multi-timescale variability of temperature in China in modulated annual cycle reference frame.
Adv. Atmos. Sci., 27(5), 1169–1182.
Wu, Z., & Huang, N. E. (2009). Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method.
Adv. Adapt. Data Anal., 01(01), 1–41.
Wu, Z., Feng, J., Qiao, F., & Tan, Z. - M. (2016). Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.
Philos Trans A Math Phys Eng Sci, 374(2065), 20150197.
Abstract: In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders.
Wu, Z., Huang, N. E., & Chen, X. (2009). The Multi-Dimensional Ensemble Empirical Mode Decomposition Method.
Adv. Adapt. Data Anal., 01(03), 339–372.