scholarly journals Principal Modes of Southern Hemisphere Low-Frequency Variability Obtained from NCEP–NCAR Reanalyses

1999 ◽  
Vol 12 (9) ◽  
pp. 2808-2830 ◽  
Author(s):  
John W. Kidson
2003 ◽  
Vol 129 (592) ◽  
pp. 2347-2366 ◽  
Author(s):  
G. Garric ◽  
S. A. Venegas ◽  
C. E. Tansley ◽  
I. N. James

2012 ◽  
Vol 140 (12) ◽  
pp. 3844-3856 ◽  
Author(s):  
Cheng Sun ◽  
Jianping Li

Abstract In this paper the authors use the NCEP–Department of Energy (DOE) Reanalysis 2 (NCEP2) data from 1979 to 2004 to expand the daily 500-hPa geopotential height in the Southern Hemisphere (SH, 90°–20°S) into a double Fourier series, and analyze the temporal frequency characteristics of the expansion coefficients over various spatial scales. For the daily series over the whole year, the coefficient series of the extratropical-mean height is characterized by a significant low-frequency (10–30 day) variation. For zonal waves with (k, l) = (1–5, 1), where k and l are the zonal and meridional wavenumbers, respectively, the low-frequency variability is most pronounced for zonal wavenumbers 3 and 4; while the short wave with zonal wavenumber 5 has significant high-frequency (4–8 day) variability. For meridional waves with (k, l) = (0, 2–6), the meridional dipole (l = 2) makes a major contribution to the low-frequency variability, consistent with the intraseasonal space–time features of the southern annular mode (SAM). The meridional tripole (l = 3) also exhibits low-frequency variability. For two-dimensional waves (k, l) = (1–5, 2–6), the dipole is a preferred meridional structure for intraseasonal modes with large zonal scales, indicating an out-of-phase relationship between low-frequency planetary-scale waves at mid- and high latitudes. The diagnostic results outlined above can be explained, to a certain extent, by the dispersion relation for Rossby waves. Theoretical analysis indicates that zonal wavenumber 3, zonally symmetric flow such as SAM, and planetary-scale waves with meridional dipole structures may be interpreted as low-frequency eigenmodes of the atmosphere.


Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.


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