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Climate Dynamics

, Volume 51, Issue 5–6, pp 1991–2001 | Cite as

Seasonal cycle of precipitation variability in South America on intraseasonal timescales

  • Carolina S. Vera
  • Mariano S. AlvarezEmail author
  • Paula L. M. Gonzalez
  • Brant Liebmann
  • George N. Kiladis
Article

Abstract

The seasonal cycle of the intraseasonal (IS) variability of precipitation in South America is described through the analysis of bandpass filtered outgoing longwave radiation (OLR) anomalies. The analysis is discriminated between short (10–30 days) and long (30–90 days) intraseasonal timescales. The seasonal cycle of the 30–90-day IS variability can be well described by the activity of first leading pattern (EOF1) computed separately for the wet season (October–April) and the dry season (May–September). In agreement with previous works, the EOF1 spatial distribution during the wet season is that of a dipole with centers of actions in the South Atlantic Convergence Zone (SACZ) and southeastern South America (SESA), while during the dry season, only the last center is discernible. In both seasons, the pattern is highly influenced by the activity of the Madden–Julian Oscillation (MJO). Moreover, EOF1 is related with a tropical zonal-wavenumber-1 structure superposed with coherent wave trains extended along the South Pacific during the wet season, while during the dry season the wavenumber-1 structure is not observed. The 10–30-day IS variability of OLR in South America can be well represented by the activity of the EOF1 computed through considering all seasons together, a dipole but with the stronger center located over SESA. While the convection activity at the tropical band does not seem to influence its activity, there are evidences that the atmospheric variability at subtropical-extratropical regions might have a role. Subpolar wavetrains are observed in the Pacific throughout the year and less intense during DJF, while a path of wave energy dispersion along a subtropical wavetrain also characterizes the other seasons. Further work is needed to identify the sources of the 10–30-day-IS variability in South America.

Keywords

Subseasonal OLR SACZ Teleconnections 

Notes

Acknowledgements

The research was supported by Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) PIP 112-20120100626CO, UBACyT 20020130100489BA, PIDDEF 2014/2017 Nro 15, Belmont Forum/ANR-15-JCL/-0002-01 “CLIMAX”. M.S.A. was supported by a Postdoctoral grant from CONICET, Argentina.

Supplementary material

382_2017_3994_MOESM1_ESM.gif (21 mb)
Online Resource 1 (Left column) Maps of linear lagged regressions between OLR anomalies and the standardized PC1 30-90 for each season, for lags -30 to 0. The values enclosed by the black contour are significant. Units in Wm-2. (Right column) Local linear lagged regression between OLR anomalies and the standardized PC1 30-90 for each season, for lags -30 to 0, in Wm-2. The green (brown) line corresponds to a point within the SESA (SACZ) center of action. First three rows correspond to the wet season, divided in ON, DJF and MA. The fourth row corresponds to the dry season (GIFF 21,529 KB)
382_2017_3994_MOESM2_ESM.gif (19.5 mb)
Online Resource 2 (Left column) Maps of linear lagged regressions between 0.21 σ-level streamfunction anomalies and the standardized PC1 30-90 for each season, for lags -30 to 0. The values enclosed by the black contour are significant. Units in 10-5m2s-1. Vectors represent the linear lagged regression of the wave activity fluxes for the 0.21 σ-level. The reference magnitude is shown below the first map and its units are m2s2. (Right column) Local linear lagged regression between OLR anomalies and the standardized PC1 30-90 for each season, for lags -30 to 0, in Wm2. The green (brown) line corresponds to a point within the SESA (SACZ) center of action. First three rows correspond to the wet season, divided in ON, DJF and MA. The fourth row corresponds to the dry season (GIFF 19,994 KB)
382_2017_3994_MOESM3_ESM.gif (8.9 mb)
Online Resource 3 (Left column) Maps of linear lagged regressions between OLR anomalies and the standardized PC1 10-30 for each season, for lags -15 to 0. The values enclosed by the black contour are significant. Units in Wm-2. (Right column) Local linear lagged regression between OLR anomalies and the standardized PC1 10-30 for each season, for lags -15 to 0, in Wm-2. The green (brown) line corresponds to a point within the SESA (SACZ) center of action. From upper to lower row, SON, DJF, MAM and JJA (GIFF 9,104 KB)
382_2017_3994_MOESM4_ESM.gif (8.7 mb)
Online Resource 4 (Left column) Maps of linear lagged regressions between 0.21 σ-level streamfunction anomalies and the standardized PC1 10-30 for each season, for lags -15 to 0. The values enclosed by the black contour are significant. Units in 10-5m2s-1. Vectors represent the linear lagged regression of the wave activity fluxes for the 0.21 σ-level. The reference magnitude is shown below the first map and its units are m2s-2. (Right column) Local linear lagged regression between OLR anomalies and the standardized PC1 10-30 for each season, for lags -15 to 0, in Wm-2. The green (brown) line corresponds to a point within the SESA (SACZ) center of action. From upper to lower row, SON, DJF, MAM and JJA (GIFF 8,957 KB)

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Carolina S. Vera
    • 1
    • 2
  • Mariano S. Alvarez
    • 1
    • 2
    Email author
  • Paula L. M. Gonzalez
    • 3
    • 4
  • Brant Liebmann
    • 5
    • 6
  • George N. Kiladis
    • 6
  1. 1.Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y NaturalesUniversidad de Buenos AiresBuenos AiresArgentina
  2. 2.Centro de Investigaciones del Mar y la Atmósfera (CIMA), Instituto Franco-Argentino del Clima y sus Impactos (UMI-IFAECI)/CNRSCONICET-Universidad de Buenos AiresBuenos AiresArgentina
  3. 3.Department of MeteorologyUniversity of ReadingReadingUK
  4. 4.National Centre for Atmospheric ScienceUniversity of ReadingReadingUK
  5. 5.CIRESUniversity of Colorado BoulderBoulderUSA
  6. 6.Physical Sciences DivisionNOAA Earth System Research LaboratoryBoulderUSA

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