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|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.|
|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.
Keywords: Empirical Mode Decomposition (EMD); ensemble empirical mode decompositions; noise-assisted data analysis (NADA); Intrinsic Mode Function (IMF); shifting stoppage criteria; end effect reduction Read More: http://www.worldscientific.com/doi/abs/10.1142/S1793536909000047
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.
Keywords: adaptive and local data analysis; data compression; empirical orthogonal function; fast algorithm; multidimensional ensemble empirical mode decomposition; principal component analysis
|Wu, Z., Huang, N. E., & Chen, X. (2009). The Multi-Dimensional Ensemble Empirical Mode Decomposition Method. Adv. Adapt. Data Anal., 01(03), 339–372.|
|Wu, Z., Huang, N. E., Wallace, J. M., Smoliak, B. V., & Chen, X. (2011). On the time-varying trend in global-mean surface temperature. Clim Dyn, 37(3-4), 759–773.|
Zhang, M., Zhang, Y., Shu, Q., Zhao, C., Wang, G., Wu, Z., et al. (2018). Spatiotemporal evolution of the chlorophyll a trend in the North Atlantic Ocean. Sci Total Environ, 612, 1141–1148.
Abstract: Analyses of the chlorophyll a concentration (chla) from satellite ocean color products have suggested the decadal-scale variability of chla linked to the climate change. The decadal-scale variability in chla is both spatially and temporally non-uniform. We need to understand the spatiotemporal evolution of chla in decadal or multi-decadal timescales to better evaluate its linkage to climate variability. Here, the spatiotemporal evolution of the chla trend in the North Atlantic Ocean for the period 1997-2016 is analyzed using the multidimensional ensemble empirical mode decomposition method. We find that this variable trend signal of chla shows a dipole pattern between the subpolar gyre and along the Gulf Stream path, and propagation along the opposite direction of the North Atlantic Current. This propagation signal has an overlapping variability of approximately twenty years. Our findings suggest that the spatiotemporal evolution of chla during the two most recent decades is part of the multidecadal variations and possibly regulated by the changes of Atlantic Meridional Overturning Circulation, whereas the mechanisms of such evolution patterns still need to be explored.
Keywords: Chlorophyll a; Dipole pattern; Multidimensional ensemble empirical mode decomposition; Propagation; Spatiotemporal evolution; The variable trend