rainfall amounts result in higher crop yields. This region is a major agricultural area, with state-of-the-art technology; weather effects on crops influence world market prices
of several agricultural products. The organization of this paper is as follows. In Section 2, data set and methods are
described. In Section 3, the excitations of amplifying fluctuation at interdecadal timescale, and trend in time series of annual rainfall are described and analyzed. In Section 4 the
seasonal variability of amplifying fluctuation and trend in rainfall amount is analyzed. Finally, Section 5 contains the conclusions.
2. Data and method
Fig. 1 shows the geographical locations of the rain gauges whose time series of rainfall amount are used in this research. These are Cordoba astronomical observatory,
´
Laboulaye, and Pilar, located in the province of Cordoba. These time series are of
´
excellent quality. Monthly rainfall sequences cover the spans: 1873–1987 for Cordoba
´
Observatory, and 1905–1984 for Laboulaye and Pilar. The main period of analysis is 1905–1983.
In this study, the main time series is the sequence of areal average of annual rainfall Ž
. AAvAR . Areal averages are averages of annual rainfall values in the three rain gauges
indicated above. Fig. 2 shows the time series of areal average of AAvAR. The Global Ž
. Historical Climatology Network GHCN was the data source. GHCN is a joint effort of
Ž .
Fig. 1. Left: location of the province of Cordoba Argentina . Right: locations of rain gauges whose monthly
´
rainfall data are used in this research. Dashed line: locations of the mountains chain ‘‘Sierras de Cordoba’’;
´
rain gauges are on the plain.
Ž .
Fig. 2. Time series of AAvAR. Averages calculated using annual rainfall amounts hydrologic year in Cordoba, Laboulaye, and Pilar. Dashed lines are regression lines for 1905–1934, and for 1935–1983.
´
the Carbon Dioxide Information Analysis Center at Oak Ridge National Laboratory and Ž
. the National Climatic Data Center USA . To extend analyses back in time beyond
1905, some of them were carried out using only data from Cordoba’s astronomical
´
observatory. Fig. 3 shows the time series of annual rainfall in Cordoba Observatory,
´
spanning 115 years. Regression lines in Figs. 2 and 3 are described in Section 3.
Fig. 3. Time series of annual rainfall measured at Cordoba astronomical observatory. Dashed lines are
´
regression lines for 1873–1934, and for 1935–1983.
Ž .
This region has a dry season and a rainy season see Fig. 4 . The dry season extends from June to August. The rainy season extends from October to April. Transition
months are May and September. Hailstorms and intense convective cells spawning small tornadoes are common in late spring and summer of years with abundant rainfall. In this
Ž region, the extremes of the Southern Oscillation ENSO and La Nina — Southern
˜
. Oscillation produce statistically significant effects on monthly rainfall amounts during
Ž .
April, August, November and December Lucero, 1998b . During austral summer, this region is in the southern margin of the monsoon circulation induced by the Bolivian
Ž .
Altiplano Plateau Zhou and Lau, 1998 . Time-varying components, in time series of annual rainfall, are individualized using
the Morlet continuous wavelet transform. Wavelet analysis is increasingly being used in Ž
atmospheric sciences to analyze time series of several types of variables Meyers et al., .
1993; Weng and Lau, 1994; Mak, 1995; Hu et al., 1998, among others . Wavelet analysis decomposes the annual rainfall time series into features of similar
wavelet timescale; thereby constituent parts can be recognized, and their evolutions can Ž
. be traced Morlet, 1982; Grossman and Morlet, 1984; Daubechies, 1994; Mallat, 1998 .
This aptitude of wavelet analysis is particularly convenient for identifying non-periodic signals.
Ž .
Following Torrence and Compo 1998 , let x be a time series of rainfall values,
n
where n s 0 . . . N y 1; measurements are equally spaced at time interval d t. Let s be
j
the wavelet scale, defined by s s s 2
Jd j
, where s is a starting scale, which is equal 1.8
j o
o
in this research; j s 0, 1, . . . , J; and the highest index J is given by: J s d j
y1
Ž .
log Nd trs . d j controls the separation between wavelet scales. In this research d j is
2 o
equal to 0.1.
Fig. 4. Box diagram of the mean values of areal average of monthly rainfall. This variable is equal to the average along the record of monthly rainfall amounts in Cordoba, Pilar, and Laboulaye. Box diagrams show
´
the median value, quartiles of 25 and 75, and observed minimum and maximum values for each month, in the period 1905–1984.
After wavelet decomposition of the time series of annual rainfall values, x , the
n
reconstruction is given by:
1r2
J
d jd t R W s
Ž .
n j
x s ;
1
Ž .
Ý
n 1r2
C c 0 s
Ž .
d j
js0
Ž . symbols have the following values: c 0 removes a scaling introduced during wavelet
y1 r4
w Ž .x
transform; its value is equal to p . C is a constant with value 0.776. R W s
is
d n
j
the real component of the complex wavelet transform. To avoid unnecessary repetition, Ž
. readers are referred to Torrence and Compo 1998 for further details of the Morlet
continuous wavelet transform.
3. Description of decadal and interdecadal fluctuations
