| Records |
Author  |
Strazzo, S.; Elsner, J.B.; LaRow, T.; Halperin, D.J.; Zhao, M. |
| Title |
Observed versus GCM-Generated Local Tropical Cyclone Frequency: Comparisons Using a Spatial Lattice |
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$loc['typeJournal Article'] |
| Year |
2013 |
Publication |
Journal of Climate |
Abbreviated Journal |
J. Climate |
| Volume |
26 |
Issue |
21 |
Pages |
8257-8268 |
| Keywords |
Tropics; Model comparison; Model evaluation/performance |
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0894-8755 |
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$loc['no'] |
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COAPS @ mfield @ |
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226 |
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Subrahmanyam, B, J.J. O'Brien, V. Manghanani, L. Xie, and J.M. Morrison |
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Estimation of Heat Transports in the Indian Ocean uisng Altimetry and MICOMi |
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$loc['typeConference Article'] |
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2003 |
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WMO WORLD Climate Research Programme, CAS/JSC Working Group on Numerical Experimentation, Research Activities in Atmospheric and Oceanic Modelling |
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$loc['no'] |
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COAPS @ mfield @ |
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866 |
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Sun, J.; Wu, Z. |
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Isolating spatiotemporally local mixed Rossby-gravity waves using multi-dimensional ensemble empirical mode decomposition |
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$loc['typeJournal Article'] |
| Year |
2019 |
Publication |
Climate Dynamics |
Abbreviated Journal |
Clim Dyn |
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3-4 |
Pages |
1383-1405 |
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| 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. |
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0930-7575 |
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$loc['no'] |
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COAPS @ user @ |
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1093 |
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Sura, P.; Hannachi, A. |
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Perspectives of Non-Gaussianity in Atmospheric Synoptic and Low-Frequency Variability |
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| Year |
2015 |
Publication |
Journal of Climate |
Abbreviated Journal |
J. Climate |
| Volume |
28 |
Issue |
13 |
Pages |
5091-5114 |
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Atmospheric circulation; Nonlinear dynamics; Statistics; Climate variability |
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0894-8755 |
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$loc['no'] |
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COAPS @ mfield @ |
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114 |
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Tartaglione, C.A.; Smith, S.R.; O'Brien, J.J. |
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ENSO Impact on Hurricane Landfall Probabilities for the Caribbean |
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$loc['typeJournal Article'] |
| Year |
2003 |
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Journal of Climate |
Abbreviated Journal |
J. Climate |
| Volume |
16 |
Issue |
17 |
Pages |
2925-2931 |
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0894-8755 |
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NOAA |
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$loc['no'] |
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COAPS @ mfield @ |
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473 |
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Verzone, KV; Bourassa, MA; Cocke, SD; LaRow, TE |
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Double ensemble estimates of precipitation in the southeastern United States for extreme ENSO events |
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$loc['typeConference Article'] |
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2001 |
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Symposium on Climate Variability, the Oceans and Societal Impacts |
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167-168 |
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Symposium on Climate Variability, the Oceans and Societal Impacts |
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COAPS @ mfield @ |
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516 |
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Wang, H.; Long, L.; Kumar, A.; Wang, W.; Schemm, J.-K.E.; Zhao, M.; Vecchi, G.A.; Larow, T.E.; Lim, Y.-K.; Schubert, S.D.; Shaevitz, D.A.; Camargo, S.J.; Henderson, N.; Kim, D.; Jonas, J.A.; Walsh, K.J.E. |
| Title |
How Well Do Global Climate Models Simulate the Variability of Atlantic Tropical Cyclones Associated with ENSO? |
Type |
$loc['typeJournal Article'] |
| Year |
2014 |
Publication |
Journal of Climate |
Abbreviated Journal |
J. Climate |
| Volume |
27 |
Issue |
15 |
Pages |
5673-5692 |
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COAPS @ mfield @ |
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149 |
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Wei, J.; Dirmeyer, P.A.; Guo, Z.; Zhang, L.; Misra, V. |
| Title |
How Much Do Different Land Models Matter for Climate Simulation? Part I: Climatology and Variability |
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$loc['typeJournal Article'] |
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2010 |
Publication |
Journal of Climate |
Abbreviated Journal |
J. Climate |
| Volume |
23 |
Issue |
11 |
Pages |
3120-3134 |
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Land surface model; Climatology; Surface fluxes; Climate variability; Model comparison; Precipitation |
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$loc['no'] |
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COAPS @ mfield @ |
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370 |
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Wu, Z.; Chassignet, E.P.; Ji, F.; Huang, J. |
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Reply to 'Spatiotemporal patterns of warming' |
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$loc['typeJournal Article'] |
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2014 |
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Nature Climate Change |
Abbreviated Journal |
Nature Climate change |
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4 |
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10 |
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846-848 |
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1758-678X |
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$loc['no'] |
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COAPS @ mfield @ |
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151 |
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Wu, Z.; Huang, N.E.; Wallace, J.M.; Smoliak, B.V.; Chen, X. |
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On the time-varying trend in global-mean surface temperature |
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$loc['typeJournal Article'] |
| Year |
2011 |
Publication |
Climate Dynamics |
Abbreviated Journal |
Clim Dyn |
| Volume |
37 |
Issue |
3-4 |
Pages |
759-773 |
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Global warming trend; Multidecadal variability; Ensemble empirical mode decomposition; IPCC AR4 |
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0930-7575 |
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COAPS @ mfield @ |
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299 |
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