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On the interannual wintertime rainfall variability in the Southern Andes
Author(s) -
González M. H.,
Vera C. S.
Publication year - 2010
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.1910
Subject(s) - climatology , predictability , rossby wave , environmental science , atmospheric circulation , el niño southern oscillation , indian ocean dipole , sea surface temperature , teleconnection , geology , physics , quantum mechanics
The paper concentrates on the analysis of the interannual variability of wintertime rainfall in the Southern Andes. Besides the socio‐economic relevance of the region, mainly associated with hydroelectric energy production, the study of the climate variability in that area has not received as much attention as others along the Andes. The results show that winter rainfall explains the largest percentage of regional total annuals. A principal component analysis (PCA) of the winter rainfall anomalies showed that the regional year‐to‐year variability is mostly explained by three leading patterns. While one of them is significantly associated with both the El Niño Southern Oscillation (ENSO), and the Southern Annular Mode (SAM), the other two patterns are significantly related to interannual changes of the sea surface temperature (SST) anomalies in the tropical Indian Ocean. Specifically, changes in the ocean surface conditions at both tropical basins induce in the atmospheric circulation the generation of Rossby wave trains that extend along the South Pacific towards South America, and alter the circulation at the region under study. The relationship between variability in the Indian Ocean and the Andes climate variability has not been previously addressed. Therefore, this result makes a significant contribution to the identification of the sources of predictability in South America with relevant consequences for future applications in seasonal predictions. Copyright © 2009 Royal Meteorological Society