INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 22: 175–195 (2002)
Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.724
SPATIAL AND TEMPORAL VARIABILITIES OF RAINFALL IN TROPICAL
SOUTH AMERICA AS DERIVED FROM CLIMATE PREDICTION CENTER
MERGED ANALYSIS OF PRECIPITATION
H. MATSUYAMA,* J.A. MARENGO, G.O. OBREGON and C.A. NOBRE
Centro de Previsão de Tempo e Estudos Climáticos–Instituto Nacional de Pesquisas Espaciais
Rod. Pres. Dutra, km 40, CEP 12630-000, Cachoeira Paulista, SP, Brazil
Received 10 November 2000
Revised 3 August 2001
Accepted 10 August 2001
ABSTRACT
We investigated the spatial and temporal variabilities of Climate Prediction Center merged analysis of precipitation
(CMAP) in tropical South America from 1979 to 1998. First, we validated CMAP using other hydrometeorological data.
In comparison with the high-density precipitation data of the Global Historical Climatology Network (GHCN) Ver. 2,
CMAP reproduces the spatial pattern well, although it underestimates (overestimates) heavy (light) precipitation. CMAP
also reproduces the interannual variability well, compared with the discharge data of the River Amazon.
Next, we applied the rotated empirical orthogonal function (REOF) to CMAP after subtracting the annual cycle.
Simultaneous and lag correlations were calculated among the scores of REOFs 1 to 4, the southern oscillation index,
and the dipole index of the Atlantic. REOF 1 (15%) represents the north–south pattern that exhibits the maximum
precipitation in the summer hemisphere. REOF 2 (12%) indicates the gradual decrease of precipitation in the northern
part of tropical South America, reflecting the effect of the Atlantic. REOF 3 (11%) exhibits an east–west pattern related
to El Niño. In REOF 4 (7%), the centre of the factor loading is located in Colombia, and the score jumps abruptly around
1985–86.
The Lepage test detected the abrupt increase of CMAP in 1985–86 around Colombia. Since such a jump is not found
in GHCN Ver. 2, the discontinuous changes of CMAP and REOF 4 around 1985–86 are artificial and peculiar to CMAP.
In this region, CMAP should be applied with caution when evaluating recent trends and the interannual variability.
The importance of the abrupt increase of precipitation around Colombia is also addressed. Copyright 2002 Royal
Meteorological Society.
KEY WORDS:
South America; Climate Prediction Center merged analysis of precipitation (CMAP); rotated empirical orthogonal
function (REOF); Lepage test
1. INTRODUCTION
Precipitation data are essential for studying the hydrological cycle at various spatio-temporal scales. In the
past, precipitation measurements were conducted using rain gauges only where humans were living. Recently,
satellites have enabled us to make various estimates of precipitation. These estimates are then merged to
produce global monthly mean fields of precipitation.
Climate Prediction Center (CPC) merged analysis of precipitation (CMAP; Xie and Arkin, 1997) is one
such data set. Several individual sources are merged, including gauge observations, satellite estimates, and
National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR)
re-analyses.
CMAP has been verified by various methods in Xie and Arkin (1997). Although different data sources
were used for different subperiods, no discontinuity occurs in the latitudinal mean precipitation. Also, the
* Correspondence to: H. Matsuyama, Department of Geography, Graduate School of Science, Tokyo Metropolitan University, 1-1.
Minami-Ohsawa, Hachiouji-shi, Tokyo 192-0397, Japan; e-mail: matuyama@geog.metro-u.ac.jp
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H. MATSUYAMA ET AL.
long-term mean global fields and the interannual variability of precipitation in the Pacific seem reasonable.
CMAP is, therefore, regarded as one version of ‘truth’ and is used for the diagnostic study of the global
hydrological cycle (Trenberth and Guillemot, 1998).
Since CMAP is primarily validated on a global scale, it is worthwhile performing continental-scale
validation, such as in South America. This is because precipitation over the land is determined mainly
by gauge observations in CMAP, and gauge networks are relatively sparse in South America (Xie and Arkin,
1996). Another dense gauge observation network, called the Global Historical Climatology Network (GHCN)
Ver. 2 (referred to as GHCN2), compiled by the Carbon Dioxide Information Analysis Center (Peterson and
Vose, 1997) is also valuable for this evaluation, since GHCN2 is not incorporated in CMAP.
CMAP is used to validate general circulation model (GCM) experiments, because the merged analysis with
the observations alone is prepared as well. In tropical South America, the 30 year climatology of precipitation
by Figueroa and Nobre (1990) has been used to evaluate the performance of a GCM before conducting the
deforestation experiments in the Amazon basin (e.g. Nobre et al., 1991; Lean and Rowntree, 1997). Centro de
Previsão de Tempo e Estudos Climaticos–Instituto Nacional de Pesquisas Espaciais, Brazil (CPTEC–INPE)
reproduced the climate of South America by GCM experiments for 1982 to 1991 and compared it with
observations including CMAP (Cavalcanti et al., 2000; Marengo et al., 2000). It is, therefore, worthwhile
validating CMAP using other hydrometeorological data, such as GHCN2 and river discharge data.
In relation to this, Costa and Foley (1998) compared the six different estimates of precipitation data in
the Amazon basin (three based on rain-gauge measurements alone, one based on a combination of raingauge measurements and satellite data, and two based on the reanalysis data). Although these data exhibited
differences in the long-term mean field and the interannual variability, Costa and Foley (1998) did not conclude
which data set was the most reliable. We will demonstrate the reliability of CMAP by comparing the river
discharge data in the Amazon basin, which is an indicator of basin-scale wet and dry conditions.
In addition, CMAP depicts interannual variability, since it is available for 20 years. In this respect, Obregon
and Nobre (1990) applied the rotated empirical orthogonal function (REOF) to the monthly precipitation data
(1951–85) with and without the annual cycle for 28 stations in the Amazon basin. They found that the second
and fourth patterns of the REOF (REOFs 2 and 4) without the annual cycle seem to be related to the El
Niño-southern oscillation (ENSO). Since only 28 stations were used, and these are distributed irregularly
throughout the Amazon basin, it is worthwhile applying the REOF to CMAP, the gridded data, to confirm
the conclusions of Obregon and Nobre (1990). By applying the REOF, we found that REOF 4 is peculiar to
CMAP. The importance of this mode will also be addressed.
We plan to investigate the interannual variability of the hydrological cycle in the Amazon basin using
CMAP and other hydrometeorological data. The study area is, therefore, limited to tropical South America.
In this study, CMAP will be validated first, then characteristics of CMAP will be explored using some
statistical methods including the REOF.
2. DATA AND METHODS
2.1. CMAP
CMAP is monthly precipitation data with 2.5° × 2.5° spatial resolution (Xie and Arkin, 1997). CMAP
collection began in January 1979, and data up to December 1998 are used in this study. CMAP is composed
of two kinds of data: standard precipitation (STD) and enhanced precipitation (ENH). STD consists of gauge
observations (Rudolf et al., 1994; Xie et al., 1996) and five kinds of satellite estimate, namely outgoing
longwave radiation (OLR)-based precipitation index (Xie and Arkin, 1998), infrared-based Geostationary
Operational Environmental Satellite (GOES) precipitation index (GPI; Arkin and Meisner, 1987), microwave
sounding unit (Spencer, 1993), microwave scattering from Special Sensor Microwave/Imager (SSM/I; Grody,
1991; Ferraro et al., 1994), and microwave emission from SSM/I (Wilheit et al., 1991). Since STD has some
missing data at high latitudes, NCEP–NCAR reanalysis (Kalnay et al., 1996) is merged in ENH to supply
missing data points. The merging algorithm (Xie and Arkin, 1996) and the blending algorithm (Reynolds,
1988) are fully documented in Xie and Arkin (1997).
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TROPICAL SOUTH AMERICA RAINFALL VARIABILITIES
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The relative errors in STD and ENH for each grid and for each month have been estimated (Xie and
Arkin, 1996). In the preliminary analysis of the water budget in the Amazon basin, the annual precipitation
for STD and ENH is almost identical, and the monthly difference is small (about ±10 mm/month). Since
relative errors of ENH are generally smaller than those of STD, ENH is employed in this study.
CMAP covers the whole globe with a 144 × 72 array. First, this array is converted to 144 × 73 to match the
basin template of Oki et al. (1995) — some modification is made to the area of the Amazon basin. The data
of the nearest four points are averaged. This does not qualitatively affect the following results. Considering the
length of the analysis period (240 months), data for 240 grid points (11.25° N–18.75° S, and 33.75–83.75° W)
are used.
2.2. GHCN2
GHCN2 (Peterson and Vose, 1997) is rain-gauge data compiled from more than 20 000 stations using 30
different sources. It contains monthly precipitation for each year, and the periods differ from station to station.
GHCN2 has a larger number of stations, better quality control, homogeneity adjustments, and a wider selection
of metadata than GHCN Ver. 1 (Vose et al., 1992). Although a beta release of GHCN2 is used in this study,
the embedded document states that there will not be that many changes made prior to the final release.
GHCN2 is used to evaluate the distribution of CMAP, since it is not incorporated in producing CMAP.
Figure 1(a) depicts the number of GHCN2 stations by year in tropical South America after January 1979.
The total number of stations gradually decreases, and suddenly drops in January 1992. This is common to
the stations in Brazil, which account for a large part of the total. From these facts, GHCN2 can be used for
the analysis until 1991. Since stations are fairly well distributed in Brazil (Figure 1(b)), the distribution of
CMAP will be evaluated by GHCN2.
Stations of GHCN2
(a)
3000
All
Brazil
2500
2000
1500
1000
500
0
1980
1984
1988
1992
1996
2000
Year
(b)
10°N
0°
10°S
80°W
70°W
60°W
50°W
40°W
Figure 1. (a) Number of GHCN2 stations by year after January 1979. The stations located in the area 33.75–83.75 ° W and 11.25 ° N
to 18.75 ° S are selected. Solid line indicates all stations in the region above, and the dashed line corresponds to stations in Brazil.
(b) Observational network of GHCN2 from December 1981 to February 1991. Stations where data are available for periods of 8 years
or more are plotted. SENAMHI stations in Peru are included
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H. MATSUYAMA ET AL.
2.3. Precipitation data in Peru
GHCN2 has many stations in Brazil, but as much precipitation data should be collected in the other countries
as possible. To this end, precipitation data for Peru, compiled by El Servicio Nacional de Meteorologia e
Hidrologia (SENAMHI), are used. One of the authors (HM) archived these data, which mainly cover the
period from 1978 to the middle of 1991 and which are collected from 27 stations.
Matsuyama (1992a) confirmed the accuracy of the SENAMHI precipitation data by comparing it with
the World Monthly Surface Station Climatology (WMSSC; Spangler and Jenne, 1990). In the following,
the term ‘GHCN2’ will be used to mean the combination of the original GHCN2 data and the SENAMHI
precipitation data.
2.4. River discharge data at Obidos
River discharge data at Obidos, located in the lower reaches of the River Amazon (Figure 2), are obtained
from Agéncia Nacional de Energia Elétrica, Brazil (ANEEL). They are daily data from December 1927 to
May 1998. The data from September 1979 to August 1997 are used because there are no missing points.
Discharge is expressed as runoff divided by the drainage area at Obidos (4 618 750 km2 ), which is used to
evaluate the interannual variability of CMAP.
The decreasing number of stations of GHCN2 in recent years makes it difficult to evaluate the interannual
variability of the recent decade (Figure 1(a)). Independent data other than precipitation are therefore necessary
for this validation. This study thus uses river discharge data, such as in Marengo (1995) and Marengo et al.
(1998), as an indicator of the basin-scale wet and dry conditions.
2.5. Southern oscillation index (SOI) and dipole index (DI)
The SOI and the DI (Servain, 1991), characteristic of the tropical Pacific and Atlantic respectively, are
adopted to investigate the relationship between the REOFs and surrounding environments. The former is the
normalized pressure difference between Tahiti and Darwin, and the latter is the difference of the normalized
sea-surface temperature (SST) anomaly between the regions N and S in Figure 2 (N minus S). The DI points
out the high correlation between tropical oceanic and atmospheric circulations (Servain, 1991).
The WMSSC is used from March 1978 to February 1999 to calculate the SOI. The 2° × 2° monthly-mean
SST of Reynolds (1988) for the same period is used for the DI. The regions for calculating DI are the same
as employed in Wagner (1996) and differ slightly from the original definition of Servain (1991) to avoid the
effect of the upwelling around the equator and the African coast.
30°N
20°N
N
10°N
0°
O
10°S
S
20°S
80°W
70°W
60°W
50°W
40°W
30°W
20°W
10°W
0°
Figure 2. Orientation map. The grey zone indicates the Amazon basin. River networks and position of Obidos (O) are also displayed.
Thick lines illustrate the location of regions N (10–30 ° N, 10–50 ° W) and S (0–20 ° S, 0–40 ° W)
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TROPICAL SOUTH AMERICA RAINFALL VARIABILITIES
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3. VALIDATION OF CMAP
3.1. Distribution of precipitation
Figure 3 depicts the December–February (DJF) total precipitation. We derived 10 year averages from
December 1981 to February 1991 from GHCN2 and CMAP (Figure 3(a) and (b)); the differences are shown
in Figure 3(c). In GHCN2, stations that have no missing data are selected for each year. These irregularly
distributed station data are converted to 2.5° × 2.5° gridded data for each year, using adjustable-tension,
continuous-curvature surface gridding (Smith and Wessel, 1990); the 10 year average is then calculated.
Also, we plotted stations where GHCN2 data are available for eight or more years in Figure 3(a). Brazilian
(a)
10°N
90
0
600
DJF (81/82-90/91)
0°
90
10°S
80°W
(b)
10°N
70°W
60°W
70°W
60°W
0
50°W
40°W
50°W
40°W
300
600
0°
10°S
80°W
(c)
10°N
1%
5%
-6
00
0
0°
10°S
150
80°W
70°W
60°W
50°W
40°W
Figure 3. The 3 month total precipitation (millimetres) from December to February. The 10 year average from December 1981 to
February 1991 is displayed. (a) Derived from GHCN2. Contours are drawn at 30, 60, 150, 300, 450, 600, 750, 900, 1050 and 1200 mm
to match Figueroa and Nobre (1990). Precipitation exceeding 900 mm is shaded. (b) Derived from CMAP. (c) (b)–(a). Contours are
drawn every 150 mm. Negative values are shaded and drawn by dashed lines. Significant differences at the 5% (1%) level depicted by
the Lepage test are displayed by small (large) circles
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H. MATSUYAMA ET AL.
Amazonia has a fairly large number of stations, but many stations are concentrated in the northeastern part
of Brazil (Nordeste).
A DJF precipitation maximum appears in the southeastern part of the Amazon basin, with good agreement
between GHCN2 and CMAP. Though some contours of 900 mm appear in GHCN2 (Figure 3(a)), such
as in Figueroa and Nobre (1990), CMAP lacks this feature (Figure 3(b)). CMAP thus underestimates the
heavy precipitation around the South Atlantic convergence zone (SACZ; Figure 3(c)). In contrast, CMAP
overestimates the precipitation on the western side of the Andes, where the absolute values are small.
We confirmed the difference statistically by the Lepage test (Lepage, 1971) — see Appendix A. Many grid
points around the Andes display significant differences at the 1% level, which may come from the interpolation
scheme used in this study. The root-mean-square error (RMSE) in Figure 3(c) is 132 mm (1.5 mm/day).
Figure 4 is the same as Figure 3, but for June–August (JJA) 1982 to 1991. In this season, the intertropical
convergence zone (ITCZ) shifts northward, which causes maximum precipitation above 900 mm north of the
(a)
10°N
600
JJA (1982-1991)
00
12
90
0
0°
30
0
60
10°S
80°W
70°W
60°W
50°W
40°W
50°W
40°W
(b)
10°N
600
0°
10°S
80°W
70°W
60°W
(c)
50
0
0
-300
-4
0°
-15
10°N
1%
5%
0
-150
10°S
80°W
70°W
60°W
50°W
40°W
Figure 4. As Figure 3, but from June 1982 to August 1991
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TROPICAL SOUTH AMERICA RAINFALL VARIABILITIES
181
equator. In contrast, minimum precipitation is found around inner Nordeste. Both GHCN2 and CMAP exhibit
these features, although minor differences exist between them.
In JJA, CMAP underestimates heavy precipitation in the northern part of tropical South America
(Figure 4(c)). The underestimation is common for DJF (Figure 3(c)). The only exception is that CMAP
overestimates the precipitation of mountainous regions around Colombia. The reason for this will be discussed
later.
The significant differences at the 1% level are distributed everywhere in Figure 4(c). They are generally
found where the absolute differences are large. In addition, some grid points reach the 1% level where the
absolute differences are small, due to the non-linearity of the Lepage test. In the southern part of Peru and
Nordeste, where there is less precipitation, the overestimation of CMAP is remarkable, and it also occurs in
DJF. In JJA, the RMSE in Figure 4(c) is estimated as 125 mm (1.4 mm/day). This is almost the same as
for DJF.
The pattern correlation between CMAP and GHCN2 is calculated and displayed in Figure 5. The monthly
anomaly data from September 1981 to August 1991 are used. Most grid points reach the 5% level except
for those around the Andes, where significant differences are found both in Figures 3(c) and 4(c). We should
keep this in mind for the following analyses.
From these results, CMAP reproduces the distribution fairly well, although it underestimates (overestimates)
heavy (light) precipitation. The reasons for the differences are (1) the relatively sparse gauge networks
in South America in CMAP (Xie and Arkin, 1996), and (2) the merged analysis itself. For (1), it
is worthwhile comparing GHCN2 and the gauge observations used for CMAP (Rudolf et al., 1994;
Xie et al., 1996), whereas comparing the latter and CMAP itself will eliminate (2). Unfortunately, the
gauge observations used for CMAP are not open to the public so far (C. Smith of CPC, personal
communication).
3.2. Interannual variability of the water budget in the Amazon basin
Figure 6 displays the interannual variability of the runoff at Obidos and the precipitation in the Amazon
basin as evaluated by CMAP. Considering the number of GHCN2 stations (Figure 1(a)), the precipitation
derived from GHCN2 is also plotted until the water year 1991. Note that the water year 1991 starts in
September 1990 and ends in August 1991 (Marengo, 1992; Costa and Foley, 1998). In general, the runoff
and precipitation have similar variations. The correlation coefficient between CMAP and runoff from 1980
to 1997 is calculated as 0.50 (significant at the 5% level).
As mentioned above, the absolute values of GHCN2 from 1982 to 1991 are systematically larger than
those of CMAP. In order to analyse this feature further, we tabulate the year-to-year variation of the total
precipitation in the Amazon basin (Table I). The tabulation reveals that DJF total precipitation is systematically
larger in GHCN2 than in CMAP. In contrast, CMAP overestimates JJA total precipitation from 1980 to
0.2
0.6
10°N
0.
6
0°
0.4
10°S
0.6
80°W
70°W
60°W
50°W
40°W
Figure 5. Pattern correlation between CMAP and GHCN2 from September 1981 to August 1991. Monthly anomaly data are used for
this calculation. Contours are drawn every 0.2. The shaded area is significant at the 5% level
Copyright 2002 Royal Meteorological Society
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H. MATSUYAMA ET AL.
P, Ro (mm/y)
3000
GHCN2 (2153 mm/y, 80-91)
2500
2000
CMAP (2014 mm/y, 80-97)
1500
1000
Runoff at Obidos (1139 mm/y, 80-97)
500
r = 0.50* (CMAP & Ro, 80-97)
0
1980
1984
1988
1992
1996
2000
Water Year (from previous September to August)
Figure 6. Interannual variability of precipitation in the Amazon basin as evaluated by CMAP (thick solid line), GHCN2 (thin solid
line), and runoff at Obidos (dashed line), integrated according to the water year. Owing to the rapid decrease of the number of GHCN2
stations (Figure 1(a)), GHCN2 is calculated until 1991. The correlation coefficient (significant at the 5% level) between CMAP and
runoff for 1980–97 is also shown
Table I. Year-to-year variation of the total precipitation in the Amazon basin as evaluated from CMAP and GHCN2
(unit: mm)a
Water year
DJF total
JJA total
Annual total
CMAP
GHCN2
CMAP
GHCN2
CMAP
GHCN2
Ro
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
712.5
660.3
754.0
581.0
661.8
648.6
655.9
606.8
692.4
725.4
718.7
663.9
557.9
668.7
672.3
614.4
728.5
658.0
541.0
730.4
707.1
803.3
612.1
760.1
708.9
742.7
690.6
748.6
788.0
729.8
711.3
—
—
—
—
—
—
—
292.0
328.9
362.3
232.5
304.0
321.0
297.8
264.3
264.0
317.8
320.0
286.1
284.2
277.6
329.8
273.3
300.2
232.5
284.7
305.8
309.0
319.7
257.6
328.2
349.5
362.1
295.6
318.7
375.8
346.9
328.8
—
—
—
—
—
—
—
2103.8
2092.3
2252.6
1774.9
2022.8
1980.8
2019.4
1942.3
2010.2
2159.0
2044.4
1965.6
1722.9
1904.3
2112.7
2034.2
2143.8
1973.3
1690.5
2103.7
2119.2
2280.6
1910.0
2233.7
2104.2
2216.7
2089.2
2107.4
2343.8
2135.3
2193.7
—
—
—
—
—
—
—
968.9
1035.8
1200.5
1014.8
1120.7
1088.6
1204.1
1186.9
1112.7
1293.5
1193.6
1156.9
946.3
1171.2
1314.7
1123.6
1147.1
1221.8
—
Average
1980–91
1980–97
673.4
665.6
727.7
—
299.2
293.8
324.8
—
2030.7
2014.4
2153.1
—
1131.4
1139.0
a Note that the water year 1980 (DJF 1980) corresponds to the period from September 1979 to August 1980 (from December 1979 to
February 1980). The column ‘Ro’ shows the annual runoff at Obidos for that water year.
1982. An almost identical annual precipitation from 1980 to 1982 (Figure 6) will be derived from this
compensation.
In the Amazon basin, precipitation and runoff decrease during ENSO periods (e.g. Richey et al., 1989;
Marengo, 1992). In Figure 6, ENSO episodes of 1982–83 and 1991–93 are captured well, although the
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Int. J. Climatol. 22: 175–195 (2002)
TROPICAL SOUTH AMERICA RAINFALL VARIABILITIES
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1987–88 event is unclear. From 1980 to 1997, the annual runoff of Obidos (CMAP precipitation) is
calculated as 1139 mm/year (2014 mm/year). In the Amazon basin, about half of the annual precipitation
drains to the Atlantic, and the remaining half is recycled over the basin (e.g. Salati, 1987; Matsuyama,
1992b). In the light of this observation, the ratio obtained, 0.57 (1139/2014), agrees well with these previous
studies.
The interannual variability of precipitation in the Amazon basin was also investigated by Costa and Foley
(1998). In comparison with their study, we concluded that CMAP is quantitatively reliable for both the
long-term mean and the interannual variability. This advantage is derived from the analysis of the water
budget in the Amazon basin, using the river discharge data. Since CMAP was found reliable, spatio-temporal
characteristics of CMAP will be explored by REOF next.
4. PREVAILING PATTERNS OF CMAP AS REVEALED BY REOF
4.1. Spatial and temporal characteristics of REOFs
This section describes the spatio-temporal characteristics of REOFs 1 to 4. The physical interpretation of
each REOF will be presented in Section 4.3.
The REOF starts from a correlation matrix of 240 × 240 grid points. To this end, we prepared a time series
without the annual cycle for each grid point by subtracting the 20 year mean precipitation (from January 1979
to December 1998) of each month from the respective monthly value. The first six components are rotated
by the varimax method, such as in Obregon and Nobre (1990). In this study, we discuss the characteristics
of REOFs 1 to 4 to compare them with those derived by Obregon and Nobre (1990), then we investigate the
trend of the scores of REOFs 1 to 4 by the two-sided version of the Mann–Kendall rank statistic (Press et al.,
1989). The scores are prewhitened by the ‘AR(1) filter’ of Kulkarni and von Storch (1995) to be regarded as
independent samples before applying the Mann–Kendall rank statistic.
Figure 7(a) shows the spatial pattern of the factor loading of REOF 1. The negative (positive) sign prevails
south (north) of central Amazonia. Apparently, REOF 1 exhibits a north–south pattern, which explains 15%
of the total variance. The score has no significant trend at the 5% level (Figure 7(b)).
The spatial patterns of REOF 2 and REOF 1 look similar (Figures 8(a) and 7(a)), in that the negative
sign covers Nordeste and the eastern part of the Amazon basin. Also, the positive sign prevails over the
northern part of Amazonia, although the center is located in the Atlantic. This pattern explains 12% of the
total variance, and the score has a decreasing trend, significant at the 5% level (Figure 8(b)).
For REOF 3 (Figure 9(a)), the positive sign covers most of the studied area, while a strong negative sign
is found in the Pacific. REOF 3 thus displays an east–west pattern, which explains 11% of the total variance.
The score (Figure 9(b)) has no significant trend and looks like that of the SOI (figure not shown); negative
values prevail in 1982–83, 1987–88, and 1991–93 during ENSO warm events.
Figure 10 is the same as Figure 7 but for REOF 4. The negative sign prevails from northwest to southeast
in the tropical area, and the centre is found in Colombia. This northwest–southeast pattern explains 7% of the
total variance, and the score has some conspicuous features (Figure 10(b)). It shows an abrupt jump around
1985–86, but this decreasing trend is not significant at the 5% level.
4.2. Relationships among the scores of REOFs, SOI, and DI
Next, we investigate the relationships among REOFs, SOI and DI because of the interannual variability
of precipitation in accordance with ENSO in this region (e.g. Ropelewski and Halpert, 1987; Richey et al.,
1989). Also, the effect of the Atlantic is important (e.g. Nobre and Shukla, 1996; Wagner, 1996; Uvo et al.,
1998). To explore which components are related to SOI and which to DI, we calculated simultaneous and lag
correlations with the scores of REOFs 1 to 4 and illustrated them as in Zeng (1999).
Figure 11(a) is the result for SOI. Here, positive lags indicate SOI leading. The maximum correlation,
which is significant at the 5% level, appears when SOI leads by 3 months. This feature is also found in
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H. MATSUYAMA ET AL.
(a)
10°N
0.2
0
0°
0.2
-0
.8
10°S
80°W
70°W
60°W
50°W
40°W
Score
(b)
4
3
2
1
0
−1
−2
−3
−4
−5
NS
1980
1984
1988
1992
1996
2000
Year
Figure 7. (a) Distribution of the factor loading of REOF 1. Contours are drawn every 0.1. The hatched area indicates the Amazon basin.
(b) Score of REOF 1. Raw value (thin line), 13 month running mean (thick line), and linear trend (thick dotted line) are displayed.
Significant level of the Mann–Kendall rank statistic, applied to the prewhitened time series (Kulkarni and von Storch, 1995) is also
indicated. NS means that it is not significant at the 5% level
the relationship between SOI and CMAP itself (Zeng, 1999). In contrast, scores of the other REOFs do not
exhibit any significance.
For DI, none of the correlation coefficients reach the significance level (Figure 11(b)). This may be explained
as follows. DI decreases from 1979 to 1984 (Figure 11(c)), reflecting the decadal-scale variation (Servain,
1991). After that, it increases steadily except for the sudden drop and recovery from 1993 to 1994. The
differences of the variation for the recent decades thus lead to the low correlation for 1979–98 among DI
and REOFs 1 to 4.
Although the absolute values of the correlation coefficients are low, the 1 month lag correlation with
REOF 2 and the simultaneous correlation with REOF 3, have local extrema (Figure 11(b)). This will be
discussed in Section 4.3, in relation to the interpretation of REOFs.
4.3. Physical interpretation of each REOF
Based on the above results, we will present the physical interpretation of each REOF here, with the aid of
important findings of previous studies.
The north–south pattern of REOF 1 is also depicted as REOF 1 by Obregon and Nobre (1990). They stated
that their REOF 1 represents the maximum precipitation in the summer hemisphere, derived by applying
REOF to the precipitation series with the annual cycle. They found that the factor loading and scores of their
REOF 1 with and without the annual cycle are quite similar, which is also confirmed by this study (results not
shown). Obregon and Nobre (1990) also pointed out that some seasonality still remains in the precipitation
series without the annual cycle. Specifically, the variance is larger in the rainy season than in the dry season.
From these facts, REOF 1 of this study also represents the maximum precipitation in the summer hemisphere.
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(a)
10°N
0.2
0°
10°S
-0.
0
80°W
70°W
2
60°W
50°W
40°W
Score
(b)
4
3
2
1
0
−1
−2
−3
−4
−5
5%
1980
1984
1988
1992
1996
2000
Year
Figure 8. As Figure 7, but for REOF 2. The decreasing trend of the prewhitened time series is significant at the 5% level
(a)
10°N
2
0.4
0.
0.6
0°
10°S
0
80°W
70°W
60°W
50°W
40°W
Score
(b)
4
3
2
1
0
−1
−2
−3
−4
−5
NS
1980
1984
1988
1992
1996
2000
Year
Figure 9. As Figure 7, but for REOF 3
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H. MATSUYAMA ET AL.
(a)
10°N
-0.6
.4
-0
0°
-0.2
0
10°S
80°W
70°W
60°W
50°W
40°W
Score
(b)
4
3
2
1
0
−1
−2
−3
−4
−5
NS
1980
1984
1988
1992
1996
2000
Year
Figure 10. As Figure 7, but for REOF 4
As mentioned later, abrupt changes do not occur in the score of REOF 1. Also, the score has no significant
trend (Figure 7(b)). The low correlation among the scores of REOF 1, SOI, and DI (Figure 11(a) and (b))
also indicates that REOF 1 is not related to the long-term variability or periodic phenomena.
The score of REOF 2 has a significant decreasing trend (Figure 8(b)). This does not contradict the decreasing
(increasing) trend of precipitation in the northern (southern) part of the Amazon basin after the 1960s (Marengo
and Nobre, 2001). Also, an increase of precipitation is reported in Nordeste after 1980 (Marengo et al., 1998).
The southward shift of the ITCZ in the Atlantic after the 1950s (Wagner, 1996) is responsible for these
phenomena. REOF 2 thus represents the effect of the Atlantic.
In order to support this idea, the 1 month lag correlation between the score of REOF 2 and SST is calculated
(SST leads 1 month), based on the results of Figure 11(b). The dipole pattern appears in Figure 12, where the
equatorial northern (southern) Atlantic shows positive (negative) correlations. It is found that a few grid points
reach the 5% significant level. Tanimoto and Xie (1999) illustrated that the dipole pattern, represented by DI,
appears not only on a decadal time scale (8 to 16 years) but also on an interannual time scale (<5 years).
In this case, the 1 month lag correlation in Figure 11(b) reflects the interannual time scale phenomenon.
The cooling (warming) of SST in region N (S) in Figure 2 will thus decrease (increase) the moisture flux
convergence in the northern (southern) part of the Amazon basin through evaporation, decreasing (increasing)
the precipitation in these parts.
REOF 3 is related to ENSO, as confirmed statistically by the lag correlation with SOI (Figure 11(a)). This
pattern is also depicted as REOF 4 by Obregon and Nobre (1990). It is reported that precipitation in the
Amazon basin and Nordeste decreases during ENSO periods (e.g. Ropelewski and Halpert, 1987; Richey
et al., 1989; Marengo, 1992), when SOI changes 3 or 4 months before precipitation decreases (Zeng, 1999).
This feature is also captured by Figure 11(a).
ENSO affects the formation of the SST anomaly in the northern Atlantic through the atmospheric
teleconnection (e.g. Nobre and Shukla, 1996; Uvo et al., 1998). A significant lag correlation, therefore,
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Correlation Coefficients
(a)
0.6
1%
REOF3
5%
0.4
REOF2
0.2
REOF4
0.0
REOF1
−0.2
0
−2
2
4
6
8
10
Lag (months of leading SOI)
Correlation Coefficients
(b)
5%
0.4
0.2
REOF2
0.0
REOF1
REOF4
−0.2
REOF3
0
−2
2
4
6
8
10
Lag (months of leading Dipole Index)
Normalized SSTA (N-S)
(c)
4
3
2
1
0
−1
−2
−3
−4
NS
1980
1984
1988
1992
1996
2000
Year
Figure 11. (a) Simultaneous and lag correlations between SOI and the scores of REOFs 1 to 4, calculated from January 1979 to December
1998. Positive lag indicates SOI leading. Significant levels (5%, 1%) of the correlation coefficients are indicated by the dashed lines.
(b) Same as (a), but for DI from January 1979 to December 1998. Only the 5% level is displayed that differs from (a), considering the
autocorrelation of SOI, DI and the scores of REOFs (Leith, 1973). (c) As Figure 7(b), but for DI without the linear trend
exists between SOI and DI, in which the changes of SOI lead those of DI by a few months (Servain, 1991).
In Figure 11(b), the simultaneous correlation between DI and the score of REOF 3 has local extrema, which
reflects the lag relation between SOI and DI.
The spatial pattern of the factor loading of REOF 4 looks like that of REOF 2 obtained by Obregon and
Nobre (1990), although an ordered difference is found between them. Since the rotation procedure is the
same, this difference arises from the analysis years and different data sets. Obregon and Nobre (1990) pointed
out that their REOF 2 is related to ENSO. However, the score of REOF 4 in this study has little relation to
SOI or to DI (Figure 11(a) and (b)). The physical interpretation of REOFs 1 to 3 is thus successful, but does
not apply to REOF 4. We will discuss what REOF 4 represents in Section 5.
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H. MATSUYAMA ET AL.
30°N
0.1
0.2
20°N
0.2
10°N
0.3
2
0.1
0
0°
3
0.
0.
0
10°S
20°S
80°W
70°W
60°W
50°W
40°W
30°W
20°W
10°W
0°
Figure 12. The 1 month lag correlation between monthly anomaly of SST and the score of REOF2 (SST leads 1 month). Contours are
drawn every 0.05. Negative values are drawn by dashed lines. The grid points that reach the 5% significance level are shaded
5. DISCUSSION
5.1. Detection of the abrupt jump in the score of REOF 4 around 1985–86
REOF 4 is characterized by an abrupt jump of the score around 1985–86. Here, we will show objectively
that this jump is the largest of all scores. The Lepage test is again used for this evaluation.
In this case, we searched the sample size m in multiples of 12 months. As is found in the interpretation
of REOF 1, some seasonality still remains in the precipitation series without the annual cycle. Therefore,
the selection of m = 12 is not suitable in this case. Also, when m is set to 36 or larger, the analysis period
becomes too short. As a compromise, m is selected as 24. In the following, all Lepage tests are conducted
with m = 24. Before this test, all time series are prewhitened by the ‘AR(1) filter’ of Kulkarni and von Storch
(1995), such as in the case of the Mann–Kendall rank statistic.
The results are displayed in Figure 13. None of the HK values (the Lepage statistics) reach the 5% level in
REOF 1 (Figure 13(a)), although some HK values pass the 5% level in REOF 2 (Figure 13(b)). The situation
changes in REOF 3. Statistically significant peaks appear every 2 or 3 years in Figure 13(c). El Niño and La
Niña events are responsible for these sharp peaks, since the Lepage test can detect cyclic variations (Yonetani,
1993). Some El Niño events persist for 2 years (Tomita and Yasunari, 1993); hence, the Lepage test with
m = 24 clearly detects the El Niño–La Niña cycle.
A peak is also apparent in REOF 4 (Figure 13(d)). Although the HK value in 1996–97 almost reaches the
1% level, it is found that the peak in 1985–86 is the largest of all the HK values in Figure 13. The abrupt
jump of the score of REOF 4 (Figure 10(b)) is thus objectively detected by the Lepage test with m = 24.
Next, we seek to determine the distribution of the abrupt change around 1985–86.
5.2. Distribution of the abrupt jump around 1985–86 in CMAP and GHCN2
Here, the Lepage test is applied to CMAP without the annual cycle for each grid point. These anomaly
data are also prewhitened by the ‘AR(1) filter’.
Figure 14(a) shows the distribution of the abrupt change of CMAP from 1985 to 1986, where HK values
exceed the 5% and 1% levels. The abrupt increases are concentrated in the mountainous regions of Panama,
Colombia, Ecuador and Peru, as well as in central Amazonia and Nordeste. In contrast, the abrupt decreases
are mainly found in the equatorial Atlantic and the South Pacific. Over land, this figure is similar to the
distribution of the factor loading of REOF 4 (Figure 10(a)), in that the strong signals appear in Colombia and
the adjacent regions.
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TROPICAL SOUTH AMERICA RAINFALL VARIABILITIES
189
HK (dimensionless)
(a)
20
REOF 1 (m=24)
15
10
1%
5%
5
0
1980
1984
1988
1992
1996
2000
HK (dimensionless)
(b)
20
REOF 2 (m=24)
15
10
1%
5%
5
0
1980
1984
1988
1992
1996
2000
HK (dimensionless)
(c)
20
REOF 3 (m=24)
15
EN
10
EN
EN
1%
5%
5
0
1980
1984
1988
1992
1996
2000
HK (dimensionless)
(d)
20
REOF 4 (m=24)
15
10
1%
5%
5
0
1980
1984
1988
1992
1996
2000
Year
Figure 13. Time series of the Lepage statistic (HK, dimensionless) applied to the prewhitened scores of REOFs 1 to 4, when the
averaging period m is set to 24 months. Significance levels (5% and 1%) are indicated by the dashed lines. (a) REOF 1. (b) REOF 2.
(c) REOF 3; EN roughly indicates the warm periods of ENSO. (d) REOF 4
To validate Figure 14(a), we performed the same analysis with GHCN2. We selected 775 stations without
missing data from January 1983 to December 1988. The stations where HK values exceed the 5% and 1%
levels during 1985–86 are displayed in Figure 14(b). Although some stations in Colombia show the abrupt
increase at the 5% level, many stations in Colombia show abrupt decreases during 1985–86. Note that many
Copyright 2002 Royal Meteorological Society
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H. MATSUYAMA ET AL.
(a)
10°N
0°
10°S
80°W
70°W
60°W
50°W
40°W
80°W
70°W
60°W
50°W
40°W
(b)
10°N
0°
10°S
Figure 14. Distribution of the abrupt change of precipitation in 1985–86, detected by applying the Lepage test to the prewhitened time
series of precipitation anomaly. The averaging period m is set to 24 months. Black (white) circles indicate the increase (decrease) in
1985–86. Significant changes at the 5% (1%) level are displayed as small (large) circles. (a) The results of CMAP. (b) The results of
GHCN2
other stations have no significant changes. This feature apparently differs from Figure 14(a), where the abrupt
increases prevail in and around Colombia.
We calculated the time series of the precipitation anomaly in Colombia and Panama by CMAP and GHCN2
respectively. An average of six grid points of CMAP (Figure 15(a)), where the precipitation increase rate from
January 1979 to December 1998 exceeds 1 mm/year, is shown in Figure 15(b). The average of the monthly
precipitation anomaly as evaluated by GHCN2 is depicted in Figure 15(c). Since the number of stations of
GHCN2 in Colombia and Panama suddenly drops in January 1991, the anomaly is calculated until the end
of 1990.
An abrupt increase is found at the beginning of 1986 in CMAP (Figure 15(b)), whereas no such
discontinuous change is found in GHCN2 (Figure 15(c)). Around 1985–86, no abrupt changes of OLR
(Chu et al., 1994) or river discharge (Poveda and Mesa, 1997; Restrepo and Kjerfve, 2000) are reported
in Colombia. Furthermore, the precipitation in the Amazon basin shows no such discontinuous increase in
1985–86 (Marengo and Nobre, 2001). From these facts, we concluded that the abrupt change of CMAP
around 1985–86 (Figure 15(b)) is artificial.
Judging from the description of Xie and Arkin (1997), causes of the abrupt change of CMAP are (1) the
introduction of GPI in 1986, and (2) the change of the gauge observations from Xie et al. (1996) to Rudolf
et al. (1994) in 1986. Cause (1) does not contradict Figure 15(b), since GPI overestimates the precipitation
over tropical land (Arkin and Meisner, 1987; Janowiak, 1992), and cumulus clouds frequently cover the
mountainous region around Colombia. The distribution of the factor loading of REOF 4 (Figure 10(a)) does
not contradict (1) either, since the strong signals mainly appear over tropical land. In order to evaluate the
effect of introducing GPI, it would be better to prepare CMAP without the GPI version, and to compare it
with the original CMAP.
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TROPICAL SOUTH AMERICA RAINFALL VARIABILITIES
(a)
10°N
0
0°
0
-1
0
10°S
80°W
70°W
60°W
50°W
40°W
Anomaly of P (mm/mon)
(b)
150
n=6
100
50
0
−50
−100
CMAP
−150
1980
1984
1988
1992
1996
2000
200
200
150
150
100
100
50
50
0
0
−50
GHCN2
Number of Stations
Anomaly of P (mm/mon)
(c)
−100
1980
1984
1988
1992
1996
2000
Year
Figure 15. (a) Distribution of the linear trend of precipitation (mm/year) from January 1979 to December 1998. Contour interval is
1.0 mm/year. Solid (dashed) lines denote positive (negative) trends. Grid points with small (large) circles indicate that the prewhitened
time series reach the 5% (1%) significance level. (b) Monthly precipitation anomaly of CMAP, averaged for the six grid points in
Colombia and Panama, where the linear trend is larger than 1.0 mm/year. The linear trend is depicted as the dashed line. (c) Same as
(b) but for GHCN2 from 1979 to 1990 in Colombia and Panama (solid line), calculated with all stations available at that month (dashed
line). Annual cycle is removed by subtracting the average (January 1979–December 1990) from the respective monthly value
For (2), Xie et al. (1996) compared their precipitation data with Rudolf et al. (1994) from 1986 to 1992.
They found the difference to be small, but the effect of changing the gauge data in 1986 has not yet been
investigated (P. Xie of NCEP, personal communication). To solve this problem, we must apply the Lepage
test to the time series of Xie et al. (1996) and Rudolf et al. (1994). As mentioned in Section 3, however, the
data set of Xie et al. (1996) is not open to the public.
5.3. Comparison with related studies
We will next discuss the problem of applying CMAP to evaluate precipitation trends in tropical South
America, based on the findings of Section 5.2.
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H. MATSUYAMA ET AL.
Chu et al. (1994) investigated the recent trend of OLR in the Amazon basin from 1974 to 1990. They
found that the convective activity in the Amazon basin as a whole has an increasing trend. They also found
that the Mann–Kendall rank statistic of OLR reaches the 5% level in some grid points of Colombia.
Dias de Paiva and Clarke (1995) validated the result of Chu et al. (1994) using the precipitation data
of 48 stations in Brazilian Amazonia that had collected data for 15 years or more up to the early 1990s.
They spatially extrapolated the results for Brazilian Amazonia to the adjacent countries, including Colombia.
Although the decreasing trend covers Colombia, the procedure of Dias de Paiva and Clarke (1995) is not a
true validation.
CMAP can be utilized to evaluate the recent trend of precipitation, considering the length of the data
collection period used by Chu et al. (1994), and by Dias de Paiva and Clarke (1995). Figure 15(a) shows the
distribution of the linear trend of precipitation from January 1979 to December 1998. The increasing trend of
the six grid points in Colombia and Panama exceeds 1 mm/year. We would like to use this figure to validate
Figure 4 of Chu et al. (1994).
Here, we have to remember that the significant increasing trends of Figure 15(a) result from the abrupt
jump around 1985–86 (Figure 15(b)). Since this discontinuous change is artificial, we must be careful when
applying CMAP to evaluate precipitation trends in this region and the interannual variability.
6. CONCLUSIONS
We validated CMAP using other hydrometeorological data, then investigated the spatial and temporal
variabilities of CMAP in tropical South America. The important and new findings of the present study
are summarized below.
(1) CMAP reproduces a spatial pattern in agreement with GHCN2, although it underestimates (overestimates)
heavy (light) precipitation. CMAP also reproduces the interannual variability, in agreement with the river
discharge near the mouth of the Amazon river.
(2) We applied the REOF to CMAP after subtracting the annual cycle and calculated simultaneous and lag
correlations among SOI, DI, and the scores of REOFs 1 to 4. REOF 1 (15%) represents the north–south
pattern and indicates the maximum precipitation in the summer hemisphere. The score of REOF 2 (12%)
indicates a gradual decrease of precipitation in the northern part, which reflects the effect of the Atlantic.
REOF 3 (11%) exhibits an east–west pattern related to ENSO. In REOF 4 (7%), the centre of the factor
loading is located in Colombia, and the score displays an abrupt jump around 1985–86 that cannot be
explained well on a physical basis.
(3) CMAP rainfall shows an abrupt increase in 1985–86 around Colombia. Since such a jump is not found
in GHCN2, the abrupt increase of CMAP is artificial. REOF 4 above is thus also artificial and peculiar
to CMAP. In this region, CMAP should be applied with caution when evaluating recent trends and the
interannual variability.
ACKNOWLEDGEMENTS
This study was conducted when the corresponding author (HM) was a visiting scientist at CPTEC/INPE
as a Japan Society for the Promotion of Science Postdoctoral Fellow for Research Abroad (1998 to
2000). Comments and English correction by Dr P. Satyamurty of CPTEC/INPE are sincerely appreciated.
Constructive comments by an anonymous reviewer have been valuable for revising the original manuscript.
APPENDIX A
A.1. The Lepage test, modified after Yonetani (1992)
The Lepage (1971) test is a non-parametric test that investigates significant differences between two samples,
even if the distributions of the parent populations are unknown. When the size of each sample m is equal to or
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193
greater than ten, the Lepage statistic (HK) follows the χ 2 distribution with two degrees of freedom. Hirakawa
(1974) demonstrated that the Lepage test is more statistically powerful than other similar non-parametric tests.
The Lepage statistic (HK) is a sum of the squares of the standardized Wilcoxon’s and Ansari–Bradley’s
statistics (Equation(1)). When HK exceeds 5.991 (9.210), the difference between two samples is judged at
the 5% (1%) significance level.
HK = [W − E(W )]2 /V (W ) + [A − E(A)]2 /V (A)
(1)
HK is calculated as follows. Let x = (x1 , x2 , . . . , xn1 ) and y = (y1 , y2 , . . . , yn2 ) be two independent samples
of size n1 and n2 respectively. Assume that ui = 1 if the ith smallest observation in a combined sample of
the size (n1 + n2 ) belongs to x, and ui = 0 if it belongs to y. The terms of Equation (1) are expressed as
follows.
W =
n
1 +n2
iui
(2)
i=1
1
n1 (n1 + n2 + 1)
2
1
n1 n2 (n1 + n2 + 1)
V (W ) =
12
n
n1
1 +n2
(n1 + n2 − i + 1)ui
iui +
A=
E(W ) =
i=1
(3)
(4)
(5)
i=n1 +1
When n1 + n2 is even, E(A) and V (A) are estimated as follows:
n1 (n1 + n2 + 2)
4
n1 n2 (n1 + n2 − 2)(n1 + n2 + 2)
V (A) =
48(n1 + n2 − 1)
(6)
E(A) =
(7)
When n1 + n2 is odd, these equations become
E(A) =
n1 (n1 + n2 + 1)2
4(n1 + n2 )
(8)
and
V (A) =
n1 n2 (n1 + n2 + 1)[(n1 + n2 )2 + 3]
48(n1 + n2 )2
(9)
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