INTERDECADAL OSCILLATIONS OF THE SOUTH AMERICAN MONSOON AND THEIR RELATIONSHIP WITH SEA SURFACE TEMPERATURE João Paulo Jankowski Saboia Alice Marlene Grimm Universidade Federal do Paraná INTRODUCTION Climate variability is of great interest to very important sectors such as agriculture and hydroelectric power generation. The present work focuses on low frequency climate variability. Our objective is to characterize the interdecadal oscillations of the precipitation over South America. Before identifying hydroclimate change of anthropogenic origin in South America, a comprehensive diagnostic of the interdecadal variability in observed time-series needs to be carried out. MATERIALS AND METHODS Data: totals of monthly precipitation from approximately 10,000 stations of South American meteorological services. They were submitted to a process of elimination of spurious data in the Laboratory of Meteorology of UFPR. Missing data, if not numerous, were replaced with values computed from data of neighbouring stations that presented significant correlation. The data were gridded to 2,5º x 2,5º. Discontinuities in the time series of each grid point, resulting from great variation in the number of contributing stations were eliminated (Fig. 2). Finally, the grid point series were submitted to a filling process, similar to that applied to the station data. Filtered and non-filtered series: for each grid point, series of monthly, seasonal and annual precipitation were prepared for three periods of study ( , e ; Fig. 1). These series were submitted to a gaussian filter, resulting in new series with just the interdecadal variability - defined here as the variability with periods above 7 years. Principal Components Analysis (PCA): was applied to the filtered series, in order to obtain the interdecadal variability modes of the monthly, seasonal and annual totals of precipitation. The modes were also rotated to isolate regional patterns that appear combined in the non-rotated modes. Spectral Analysis: Cycles of precipitation were identified in the non-filtered series, using the Blackman-Tuckey method. Correlation with SST: the correlation of the interdecadal principal components with SST was investigated. All correlations were submitted to a Monte Carlo based significance test. CONCLUSIONS The first South American modes of interdecadal variability of summer and spring precipitation show a dipole with components in central-East and southeast South America. These modes are significantly related (Table 1), with tendency to reversal of polarity from spring to summer. The SST anomalies associated with them persist from spring and summer, except in the South Atlantic Convergence Zone. This anomalies are distributed in the Pacific Ocean so as to modulate the intensity and spatial coverage of the anomalies associated with El Niño/La Niña. This inverse relationship between the anomalies in central-east Brazil in spring and summer is present also in the interannual variability. The summer first mode also shows a strong signal in northern Argentina, which appears in the second rotated mode (not shown). The interdecadal oscillations founds with PCA were confirmed by Spectral Analysis (Figures 7 and 8). In Summer, for instance there are singnificant interdecadal cycles in central-east Brazil and Argentina, in agreement with the strong component found in the first mode for this season. In southern Brazil the influence of shorter cycles is stronger. There are significant relationships between these modes and SST both in the Pacific and Atlantic Oceans. Figure 1: Spatial coverage of core data to each period of study: , e Figure 2: Comparison between the series before the correction of the discontinuity (red line) and after (green line). Figure 7: Cycles in spring total rainfall. Figure 8: Cycles in summer total rainfall. RESULTS Figures 3 to 6 show the first non-rotated and rotated modes for spring and summer, the explained variance, their linear correlation with global SST, with its significance level. Figures 7 and 8 show the significant cycles present in the seasonal series. The results are relative to the period Figure 3: First mode of spring total rainfall, explained variance, and correlation with global SST. Figure 4: First rotated mode of spring total rainfall, explained variance, and correlation with global SST. Explained Variance: 24,95 % Explained Variance: 18,62 % Explained Variance: 26,95 % Explained Variance: 17,97 % Figure 5: First mode of summer total rainfall, explained variance, and correlation with global SST. Figure 6: First rotated mode of summer total rainfall, explained variance, and correlation with global SST. Table 1: Correlation coeficient and significance levels between principal components of spring and summer (NR = non-rotated; R = rotated). Correlation Summer NR1Summer R1 Spring NR1 0,55 (0,04) - Spring R1 - 0,89 (0,00)