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|Stefanova, L., Misra, V., O'Brien, J. J., Chassignet, E. P., & Hameed, S. (2012). Hindcast skill and predictability for precipitation and two-meter air temperature anomalies in global circulation models over the Southeast United States. Clim Dyn, 38(1-2), 161–173.|
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.
|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.|
Xu, X., & Chassignet, E. P., Wang, F. (2018). On the variability of the Atlantic meridional overturning circulation transports in coupled CMIP5 simulations. Clim Dyn., 51(11), 6511–6531.
Abstract: The Atlantic meridional overturning circulation (AMOC) plays a fundamental role in the climate system, and long-term climate simulations are used to understand the AMOC variability and to assess its impact. This study examines the basic characteristics of the AMOC variability in 44 CMIP5 (Phase 5 of the Coupled Model Inter-comparison Project) simulations, using the 18 atmospherically-forced CORE-II (Phase 2 of the Coordinated Ocean-ice Reference Experiment) simulations as a reference. The analysis shows that on interannual and decadal timescales, the AMOC variability in the CMIP5 exhibits a similar magnitude and meridional coherence as in the CORE-II simulations, indicating that the modeled atmospheric variability responsible for AMOC variability in the CMIP5 is in reasonable agreement with the CORE-II forcing. On multidecadal timescales, however, the AMOC variability is weaker by a factor of more than 2 and meridionally less coherent in the CMIP5 than in the CORE-II simulations. The CMIP5 simulations also exhibit a weaker long-term atmospheric variability in the North Atlantic Oscillation (NAO). However, one cannot fully attribute the weaker AMOC variability to the weaker variability in NAO because, unlike the CORE-II simulations, the CMIP5 simulations do not exhibit a robust NAO-AMOC linkage. While the variability of the wintertime heat flux and mixed layer depth in the western subpolar North Atlantic is strongly linked to the AMOC variability, the NAO variability is not.
Keywords: NAO-AMOC; CMIP5; NAO index; AMOC index; meridional pressure gradient; magnitude; structure change of the NAO.