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., Chassignet, E. P., Ji, F., & Huang, J. (2014). Reply to 'Spatiotemporal patterns of warming'.
Nature Climate change, 4(10), 846–848.
Ji, F., Wu, Z., Huang, J., & Chassignet, E. P. (2014). Evolution of land surface air temperature trend.
Nature Climate change, 4(6), 462–466.
Wdowinski, S., Bray, R., Kirtman, B. P., & Wu, Z. (2016). Increasing flooding hazard in coastal communities due to rising sea level: Case study of Miami Beach, Florida.
Ocean & Coastal Management, 126, 1–8.
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.
Sun, J., & Wu, Z. (2019). Isolating spatiotemporally local mixed Rossby-gravity waves using multi-dimensional ensemble empirical mode decomposition.
Clim Dyn, (3-4), 1383–1405.
Abstract: Tropical waves have relatively large amplitudes in and near convective systems, attenuating as they propagate away from the area where they are generated due to the dissipative nature of the atmosphere. Traditionally, nonlocal analysis methods, such as those based on the Fourier transform, are applied to identify tropical waves. However, these methods have the potential to lead to the misidentification of local wavenumbers and spatial locations of local wave activities. To address this problem, we propose a new method for analyzing tropical waves, with particular focus placed on equatorial mixed Rossby-gravity (MRG) waves. The new tropical wave analysis method is based on the multi-dimensional ensemble empirical mode decomposition and a novel spectral representation based on spatiotemporally local wavenumber, frequency, and amplitude of waves. We first apply this new method to synthetic data to demonstrate the advantages of the method in revealing characteristics of MRG waves. We further apply the method to reanalysis data (1) to identify and isolate the spatiotemporally heterogeneous MRG waves event by event, and (2) to quantify the spatial inhomogeneity of these waves in a wavenumber-frequency-energy diagram. In this way, we reveal the climatology of spatiotemporal inhomogeneity of MRG waves and summarize it in wavenumber-frequency domain: The Indian Ocean is dominated by MRG waves in the period range of 8–12 days; the western Pacific Ocean consists of almost equal energy distribution of MRG waves in the period ranges of 3–6 and 8–12 days, respectively; and the eastern tropical Pacific Ocean and the tropical Atlantic Ocean are dominated by MRG waves in the period range of 3–6 days. The zonal wavenumbers mostly fall within the band of 4–15, with Indian Ocean has larger portion of higher wavenumber (smaller wavelength components) MRG waves.
Hu, X., Cai, M., Yang, S., & Wu, Z. (2018). Delineation of thermodynamic and dynamic responses to sea surface temperature forcing associated with El Niño.
Clim Dyn, 51(11-12), 4329–4344.
Abstract: A new framework is proposed to gain a better understanding of the response of the atmosphere over the tropical Pacific to the radiative heating anomaly associated with the sea surface temperature (SST) anomaly in canonical El Niño winters. The new framework is based on the equilibrium balance between thermal radiative cooling anomalies associated with air temperature response to SST anomalies and other thermodynamic and dynamic processes. The air temperature anomalies in the lower troposphere are mainly in response to radiative heating anomalies associated with SST, atmospheric water vapor, and cloud anomalies that all exhibit similar spatial patterns. As a result, air temperature induced thermal radiative cooling anomalies would balance out most of the radiative heating anomalies in the lower troposphere. The remaining part of the radiative heating anomalies is then taken away by an enhancement (a reduction) of upward energy transport in the central-eastern (western) Pacific basin, a secondary contribution to the air temperature anomalies in the lower troposphere. Above the middle troposphere, radiative effect due to water vapor feedback is weak. Thermal radiative cooling anomalies are mainly in balance with the sum of latent heating anomalies, vertical and horizontal energy transport anomalies associated with atmospheric dynamic response and the radiative heating anomalies due to changes in cloud. The pattern of Gill-type response is attributed mainly to the non-radiative heating anomalies associated with convective and large-scale energy transport. The radiative heating anomalies associated with the anomalies of high clouds also contribute positively to the Gill-type response. This sheds some light on why the Gill-type atmospheric response can be easily identifiable in the upper atmosphere.
Misra, V., Li, H., Wu, Z., & DiNapoli, S. (2014). Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons.
Clim Dyn, 42(5-6), 1425–1448.
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.
Huang, B., Hu, Z. - Z., Schneider, E. K., Wu, Z., Xue, Y., & Klinger, B. (2012). Influences of tropical-extratropical interaction on the multidecadal AMOC variability in the NCEP climate forecast system.
Clim Dyn, 39(3-4), 531–555.