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INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. 30: 110119 (2010)Published online 23 February 2009 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/joc.1875
Validation of the abrupt change in GPCP precipitation in theCongo River Basin
Xungang Yin* and Arnold GruberCooperative Institute for Climate Studies, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
ABSTRACT: The Global Precipitation Climatology Project (GPCP) monthly precipitation exhibits a significant negative
trend during 19792004 over southern tropical Africa from the Congo River Basin to the east coast. This trend appears
as a more than 20% drop beginning in 1992 in the 6-year and 9-year averages of the areal mean GPCP satellite-gauge
precipitation, whose magnitude is largely determined by the gauge analyses. This papers analysis of satellite precipitation
estimates, gauge precipitation analyses, and gauge coverage information suggests that the negative precipitation trend is
only true in part of southern tropical Africa but the magnitude is much smaller than that calculated from the GPCP. In
the eastern portion of the region, the precipitation drop in the GPCP is confirmed by the satellite-only estimates but thedecrease of more than 16% is amplified by a change in gauge coverage. In the western portion of the region, basically
the southern Congo River Basin, all gauge dependent products show a negative precipitation trend, which is much larger
in the GPCP merged satellite-gauged data set, but not supported by the satellite-only precipitation estimates. In this study
we conclude that for the Congo River Basin, where both the mean precipitation and its spatial gradient are high, the
spurious negative trend detected in the GPCP precipitation is caused by a significant change in local gauge coverage and
the methodology used by the GPCP to merge satellite and gauge data during the analysis period. Copyright 2009 Royal
Meteorological Society
KEY WORDS GPCP; the Congo River Basin; precipitation trend; satellite; gauge
Received 3 September 2007; Revised 15 December 2008; Accepted 20 January 2009
1. IntroductionTrend analysis, particularly for temperature and precipi-
tation, is an important component in the study of global
change (IPCC, 2007). Although it is widely accepted that
the global mean surface temperature has increased by
0.6 C in the twentieth century, our knowledge of pre-
cipitation trends during the same time is still limited.
Since precipitation is highly variable and discontinuous
in space and time, and gauge sampling is frequently inad-
equate, changes in precipitation are difficult to detect.
Nevertheless, efforts have been made on precipitation
data mining to increase our understanding of future pre-
cipitation trends under the scenario of climate change(e.g. Karl and Knight, 1998; Morrissey et al., 1996; Dai
et al., 1997; New et al., 2001).
The Global Precipitation Climatology Project (GPCP)
data set (Huffman et al., 1997; Adler et al., 2003) is
one of the few precipitation products that take advan-
tage of both satellite estimates and gauge analyses to
provide global coverage of monthly mean precipitation
on 2.5 latitude/longitude grids. The GPCP combines
the precipitation information available from each source
(satellite infrared and microwave estimates of rainfall and
gauge observations) into a final merged product, taking
* Correspondence to: Xungang Yin, STG Inc., 151 Patton Ave,Asheville, NC 28801, USA. E-mail: [email protected]
advantage of the strengths of each data type and remov-ing biases based on hierarchical relations in a stepwise
approach (Adler et al., 2003). The last step in the bias
removal is to adjust satellite estimates to the average of
gauge measurements over a 5 5 grid box or a 7 7
grid box depending on the availability of gauges within
the array. In the final step, the gauge adjusted satellite
estimates and the gauge analyses at each grid box are
combined in a weighted average (Huffman et al., 1995).
This data set has been widely used in global change stud-
ies (e.g. New et al., 2001; Hicke et al., 2002; Curtis and
Adler, 2003; Seager et al., 2005; Lau and Wu, 2006;
Smith et al., 2006; Gu et al., 2007) and also in social sci-
ence studies (e.g. Miguel et al., 2004; Funk et al., 2005).Even with both conventional (gauge) and modern
(remote sensing) approaches available for precipitation
measurements, caution should be taken when using the
GPCP data set, especially since it is a combination of var-
ious data inputs. Similar to other analyses and reanalyses
data sets, the GPCP result is only an approximation of
the truth under the current knowledge and technology. At
present, comparison and intercomparison with other pre-
cipitation products are the most effective way to validate
an analysis and reanalysis precipitation data set. In the
past decade analysis and reanalysis, products have been
greatly improved with the help of satellite observations,enhanced computer power, and improved analysis tech-
niques. In a series of studies, the GPCP monthly mean
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GPCP PRECIPITATION TREND IN THE CONGO RIVER BASIN 111
data set has been compared with other precipitation data
(e.g. Janowiak et al., 1998; Gruber et al., 2000; McCol-
lum et al., 2000; Adler et al., 2001; Yin et al., 2004).
These studies have provided important feedback to the
GPCP on both global and regional scales.
Gauge observations are considered the most reliable
estimates of precipitation reaching the ground. Precip-itation is discrete and highly variable over both time
and space, so spatial density and distribution of gauge
population and continuity of gauge network operation
are fundamental for precipitation analysis. Extended pre-
cipitation analyses in space based on a limited number
of gauge records can sometimes yield highly biased or
even completely unrepresentative results if the precipita-
tion has very low spatial homogeneity. Clearly, there is a
dependency of the results on how well gauges sample the
precipitation (e.g. Dai et al., 2004). Satellite estimates of
precipitation provide fairly complete sampling, but over
land areas the GPCP merging procedure, as previously
described, adjusts the satellite estimates to the large-scalegauge analysis. In satellite-gauge merged precipitation
data sets such as the GPCP poor and inconsistent gauge
coverage can easily result in time-dependent biases and
false trends.
The African continent has a wide variability of precip-
itation regimes ranging from extremely dry in the north
to extremely wet in the central (Nicholson, 2000). Eco-
nomic and political stability in Africa is closely linked
to rainfall variability (Miguel et al., 2004; Funk et al.,
2005). However, the study of precipitation in Africa suf-
fers from fragmentary and incomplete gauge observations
over many parts of the continent. The eastern portionof the southern tropical Africa referenced in this study
comprises a large part of the Congo River Basin, which
occupies an area roughly between 15 E and 30 E, and
12 S and 10N. Because of the importance of precipi-
tation for the threatened ecosystem of the Congo Basin
rainforest, we are motivated to analyze the precipitation
change in this region. The objective of this study is to
assess the validity of the calculated GPCP precipitation
trend in the Congo River Basin. We will present our work
in the next three sections. Section 2 describes the data and
method used in this work. Section 3 contains the results
and discussions of this study. Summary and conclusions
are given in Section 4.
2. Data and method
All precipitation products used in this study are monthly
means (in units of mm/day, unless otherwise noted),
analyzed on 0.5 0.5 or 2.5 2.5 latitudelongitude
grids. The base period for this study is 19792004
but the data availability during the base period varies
for each data set. Trend analysis and comparison are
mainly based on precipitation anomalies but monthly or
annual precipitation means are used when precipitationmagnitude is a concern. Precipitation anomalies are the
monthly precipitation with annual cycle removed. They
are derived by first calculating the base period average for
each of the 12 calendar months and then subtracting the
corresponding calendar-month averages from the monthly
precipitation. A least square analysis method is used to
calculate the precipitation trends, whose significance is
determined by Student t-test. A cross-validation of the
calculated GPCP trend is carried out by analyzing thesatellite-only and gauge-only precipitation products. By
looking at the contribution of each data set we are able
to assess the validity of the trend calculated from the
GPCP data. If gauge and satellite data independently
agree on a trend, we then have more confidence on
the corresponding GPCP trend. For the convenience
of using gridded data a geographic box (12.5 S-Eq,
17.5 E40 E) representing southern tropical Africa is
selected as the study area (hereafter referred to as SA
box).
The GPCP data set used in this study is the version
2 satellite-gauge combined precipitation product (Adler
et al., 2003) available for 1979 present. Two satellite-only and three gauge-only precipitation data sets are uti-
lized to verify the GPCP trend. The two satellite products
are the outgoing longwave radiation (OLR) precipita-
tion index (OPI) (Xie and Arkin, 1998) available for
1979 present and the Geostationary Operational Envi-
ronmental Satellite (GOES) precipitation index (GPI)
(Janowiak and Arkin, 1991) available for 1986present.
Of the three gauge analyses, one is based on the
Global Historical Climatology Network (GHCN) and the
Climate Anomaly Monitoring System (CAMS) named
as GHCN+ CAMS (Xie et al., 1996), and the other
two from the Global Precipitation Climatology Center(GPCC) are the GPCC monitoring (Rudolf and Schnei-
der, 2005) and the GPCC 50-year (19512000) clima-
tology (Beck et al., 2005). Both the GHCN+ CAMS
(19791985) and the GPCC monitoring (1986present)
have varying gauge population and are used as the input
data by the version 2 GPCP. The GPCC 50-year clima-
tology only incorporates gauges with at least 90% data
availability, which can be considered as a gauge den-
sity invariant product. The grid size is 0.5 0.5 for the
GPCC 50-year and 2.5 2.5 for the rest.
Because the SA box is over tropical land, there are two
other satellite-only precipitation estimates for the GPCP
project. One is the Special Sensor Microwave Imager(SSM/I) scattering (Ferraro, 1997) available since July
1987 and the other is the GPCP multisatellite precipi-
tation estimates available since January 1979. However,
these two satellite estimates are not used in this study
because of their obvious flaws in the early 1990s. For
the SSM/I between June 1990 and December 1991, the
normally used 85.5 GHz channel was unavailable and an
alternative retrieval algorithm based on the 37 GHz chan-
nel was used (Adler et al., 2003). Although an adjustment
was attempted for this channel change, unusually high
SSM/I estimates appeared in southern tropical Africa dur-
ing this period (Figure 1(a)). Because the precipitationtrend studied in this paper occurred around this time, the
SSM/I estimates are not suitable for the cross-validation
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112 X. YIN AND A. GRUBER
Figure 1. The SA box mean precipitation anomaly calculated from (a) SSM/I scattering estimates and (b) multisatellite estimates for the period
19792004 (filled lines). The GPCP satellite-gauge precipitation anomaly (simple line) is also displayed for contrast.
of the GPCP trend. The GPCP multisatellite precipitation
estimates are SSM/I adjusted GPI (AGPI) precipitation
estimates (see Huffman et al., 1997). Figure 1(b) shows
that the multisatellite estimates behave the same as the
SSM/I estimates throughout the SSM/I era including theperiod between June 1990 and December 1991. This is
not surprising since the GPI is adjusted to whatever the
SSM/I estimates provide. There is also an overall magni-
tude jump from the pre-SSM/I (before July 1987) to the
SSM/I period in the multisatellite time series (Takahashi
et al., 2006) shown in Figure 1(b). Thus, the multisatel-
lite estimates are also dropped from this study. (After
this study was completed, the GPCP has recomputed
the multisatellite precipitation for the span 1987 2006
to eliminate the inhomogeneity across the 1986/1987
(OPI/SSMI) data boundary over land (see GPCP docu-
mentation: http://www1.ncdc.noaa.gov/pub/data/gpcp/v2/
documentation/V2 doc.pdf). The recomputed MS data setwas just recently released to researchers. However, this
adjustment to the MS data set does not affect the conclu-
sions of this study.)
In this paper, a term called gauge grid is introduced
for the discussion of gauge precipitation analysis. At any
time, if a 2.5 2.5 grid box has at least one gauge
with record, it is defined as a gauge grid. So a grid box
can be one gauge grid at one time but not at another
time, depending on the availability of gauge observation
in that grid box. The usage of gauge grid number instead
of total gauge number is because, in general, precipitation
is highly variable in space thus the greater the numberof gauge grids, the better the precipitation pattern is
represented by the gauge observations.
3. Results and discussion
The precipitation trend in Africa during 1979 2004 is
computed from the GPCP monthly precipitation anoma-
lies using the method of least squares. Since the mag-
nitude of the trend is very small when expressed in
its original unit of mm/day/month, an alternative unit
of mm/year/decade, meaning annual total precipitation
change at 10-year scale, is used to present the trend result.
Figure 2(a) shows the grids that have trends significant at
the 95% level based on the Students t-test. Small trends
less than 50 mm/year/decade are not shown because they
are much smaller than those unusual large trends, which
are the concern of this study.
In this result, the most prominent feature is the large-
scale negative trend in southern tropical Africa repre-
sented by the SA box. For comparison, the same trend
calculation is done for the OPI precipitation estimates(Figure 2(b)). The OPI estimates are derived from a
simple algorithm and are the only consistent satellite
precipitation product which covers the GPCP period
1979present over land and ocean. It performs best in
the tropics where convection is the dominant form of
precipitation (Xie and Arkin, 1998). So for the SA box
area selected in this study, the OPI estimates can provide
a reasonably good indicator of spatial patterns and low-
frequency changes in precipitation. As seen in Figure 2,
in the SA box the computed precipitation trends based
on the GPCP data are significantly negative in all grids
except a few, and are nearly negligible when computedwith OPI estimates of precipitation. To further analyze
the precipitation trends, it is necessary to look into the
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GPCP PRECIPITATION TREND IN THE CONGO RIVER BASIN 113
Figure 2. Precipitation trend (mm/year/decade) in 1979 2004 computed from (a) GPCP satellite-gauge precipitation analysis and (b) OPI
precipitation estimates. All trends are displayed in their absolute values. A triangle in a grid indicates that the precipitation trend is negative.
The SA box (17.5 E40 E, 12.5 S-Eq) is drawn in black solid line. The black dashed line box (10 E40 E, 17.5 S 5N) will be used as the
extended area for gauge coverage analysis in Figure 6.
regional precipitation pattern first. The annual mean pre-
cipitation (mm/year) map shown in Figure 3 is calculated
based on the monthly GPCP precipitation in 1979 2004.
As can be seen from this map, the Congo River Basin,
which is considered to be the worlds second ecological
lung after the Amazon rainforest, composes the largest
heavy precipitation area over the African continent. Since
the western portion of the SA box is located in the Congo
River Basin, a detailed analysis of precipitation varia-
tion in this region is thus important for the study of the
regional ecosystem.
In order to validate the computed GPCP trends shown
in Figure 2, both gauge and satellite precipitation prod-
ucts are used for a cross-validation. The time series of
the SA box mean precipitation anomaly and its 12-month
running mean are shown in Figure 4 for each of the four
precipitation products, including the GPCP, GPCC 50-
year climatology, OPI, and GPI. In addition, for each
product its multiyear averaged SA box mean precipitation
is calculated for both a pair of 6-year and a pair of 9-year periods separated by the 1991 1992 year boundary,
which is roughly when the GPCP precipitation decline
appeared. The selection of the 6-year and 9-year peri-
ods is to maximally utilize the GPI estimates (starting in
1986) and the GPCC 50-year climatology (available up
to 2000). The resultant numbers are printed on each panel
of Figure 4. These numbers show that the GPI estimates
are much higher than the other products. This magnitude
discrepancy was addressed by Xie and Arkin (1997), and
a discussion on the overestimation of satellite (basically
the GPI and the SSM/I) estimates of precipitation over
Equatorial Africa was given by McCollum et al. (2000).Since only the relative change matters in trend analy-
sis, the magnitude issue will not be discussed further in
Figure 3. Annual mean precipitation (mm/year) in Africa calculated
from the 19792004 GPCP satellite-gauge monthly precipitation.
this paper. Despite the above uncertainties, because of
the spatio-temporal consistency in satellite measurements
the OPI and the GPI estimates are robust enough to pro-
vide supporting evidence for precipitation trend analysis
in areas where the number of gauge grids is insufficient
for a reliable precipitation analysis.In Figure 4, the running average time series of the
GPCP precipitation exhibits an apparent negative trend
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114 X. YIN AND A. GRUBER
Figure 4. The SA box mean precipitation anomaly calculated from (a) GPCP, (b) GPCC 50-year climatology, (c) OPI, and (d) GPI. Gray line:
monthly precipitation anomaly; black line: 12-month running average of monthly precipitation anomaly. The numbers on each panel are time
span (e.g. 19831991), followed by multiyear average of the box mean precipitation, and then the change (in percentage) of the two multiyear
means in each group.
but at the same time this trend is much weaker or even
reversed in the other data sets. Since 1992, the SA box
mean GPCP precipitation has decreased by more than
22% in both the 9-year and the 6-year averages. The
GPCC 50-year climatology also shows a decrease in
the SA box mean but at a much smaller rate, which is
only 4.5% and 7.4%, respectively, for the 9-year and 6-year periods. In comparison, the SA box means of both
the OPI and GPI estimates have increased for the two
periods. For the OPI, the increase amount is 3.4% and
2.3%, respectively, for the 9- and 6-year periods. For the
GPI, the 6-year mean has increased by 0.7%. In fact,
as will be shown in Table I, the increase in the satellite
estimates is weighted by the increase in the Congo River
Basin where precipitation is much heavier (see Figure 3)
and thus dominates the variation of the whole SA boxmean, even though the trend in the eastern SA box is
negative.
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GPCP PRECIPITATION TREND IN THE CONGO RIVER BASIN 115
Table I. Six-year mean precipitation change in the west and east
sub-boxes of the SA box. The two sub-boxes are divided by
the 30 E longitude. The number for a product in each sub-box
represents the precipitation change (in percentage) from the
first period (Jan 1986 Dec 1991) to the second period (Jan
1992Dec 1997).
Products West East
GPCP merged 25.5 16.4
GPCC 50-year 5.9 9.5
OPI estimates +4.4 1.7
GPI estimates +3.3 6.2
In Figure 4, all the products show a precipitation mini-
mum in 19911992. This is an El Nino induced drought,
which was the most severe one in the twentieth century
for Southern Africa (UNEP, 2002). The anomalously low
precipitation during the southern summer of 19911992
lasted for only a few months in the two satellite esti-mates but has been sustained in the GPCP throughout the
following decade, which is unusual and doubtful. Consid-
ering the fact that the GPCP is a merged satellite-gauge
data set comprising several data sources, none of which is
used for the entire period, any discontinuities in the satel-
lite input and artifacts in the input gauge analyses may be
reflected in the final GPCP analysis. Therefore, we will
investigate the GPCP abrupt change in greater detail by
analyzing the GPCP components. In particular, we will
look closely at the gauge data input, whose gauge cov-
erage has varied considerably throughout the time, and
to which the satellite data are adjusted to achieve the
merged GPCP satellite-gauge precipitation.
Figure 5(a) displays the 19792004 time series of the
SA box mean anomaly for the GPCP, GHCN+ CAMS,
and GPCC monitoring. Because over land, the GPCP
uses gauge measurements for large-scale adjustment on
the multisatellite precipitation estimates created in thefirst step of the merging process, the GPCP is close
to the GHCN+ CAMS (1979 1985) and the GPCC
(1986 2004) gauge analyses, as shown in the figure.
The time series in Figure 5(b) is the total number of
gauge grids in the SA box for the two gauge data sets.
Based upon a comparison of the time series in Figure 5,
it seems that in southern tropical Africa there is a loose
relationship between the gauge dependent precipitation
and the number of gauge grids. In the GPCC period
1986 2004, the gauge coverage is high between late
1986 and 1992, but has remained low since 1994. In
the GHCN+ CAMS period 19791985, the number of
gauge grids is relatively invariant and on an average is
higher than that in the GPCC period after 1994. To some
degree, except during the 19911992 El Nino year, the
precipitation variation proportionally follows the change
of the number of gauge grids. For example, the areal
mean precipitation anomaly is higher during 19861991
(period of higher number of gauge grids), lower after
1993 (period of lower number of gauge grids), and near
zero before 1986 (period of moderate number of gauge
grids). For the SA box, the relationship between the
mean GPCP precipitation anomaly (Figure 5(a)) and the
number of gauge grids (Figure 5(b)) is also analyzed in
Figure 5. Mean precipitation anomaly and number of gauge grids in the SA box during 1979 2004: (a) precipitation anomaly calculated from theGPCP, GHCN+CAMS (19791985). and GPCC monitoring (19862004); and (b) number of gauge grids for the GHCN+CAMS (19791985)
and GPCC monitoring (19862004). This figure is available in colour online at www.interscience.wiley.com/ijoc
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116 X. YIN AND A. GRUBER
a statistical approach. For the base period 19792004,
the calculated Spearmans rank correlation coefficient is
= 0.34, which is significant at the 99% level, indicating
a possible dependency of precipitation magnitude on the
number of gauge grids. This result is consistent with the
work of Hulme and New (1997) and Huff (1970), who
showed that systematic errors being dependent on gaugedensity.
To further illustrate the association of precipitation
trend and gauge grid number change, precipitation spatial
patterns and gauge spatial distributions averaged over
19791986, 19871991, and 19922000 are displayed
in Figure 6. For this figure the map range, being the
dashed line box shown in Figure 2, is at least 5 larger
than the SA box on each side, as long as it is still
over land. This extended box area is selected because, as
previously described in the data section, the adjustment
of the multisatellite estimates to the gauge analyses is
done with weighted averages computed on a 5 5 grid
box centered on the box of interest, or a 7 7 grid box
area if there are too few gauge observations. Among
the 3 3 panels in Figure 6, the first two columns areprecipitation spatial patterns, respectively, for the OPI
and the GPCP. The gauge information of the GPCP gauge
input, including the GHCN+ CAMS and the GPCC
monitoring, is shown in the third column with average
gauge number per grid more than 0.5/month denoted. For
comparison, the actual gauge positions of the GPCC 50-
year climatology are also denoted on the third column
panels. In this case, the spatial distributions of the OPI
Figure 6. Multiyear average results of precipitation and gauge distribution. The three columns from left to right are, respectively, for OPI
estimates, GPCP monthly precipitation, and average gauge number in each grid (0.5) for GHCN+ CAMS and GPCC monitoring. Gaugelocations denoted by filled circles for GPCC 50-year climatology are also shown on the third column maps. The three rows from top to bottom
are, respectively, for averaging periods 19791986, 19871991, and 19922000.
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GPCP PRECIPITATION TREND IN THE CONGO RIVER BASIN 117
estimates are used as a reference base for detecting
the GPCP errors induced by gauge coverage change.
According to the OPI estimates in all the three periods,
precipitation in this extended box area is the highest in
the northwest centered at about 24 E over the equator,
and the lowest in equatorial East Africa in the northeast
corner of the box.Throughout 19792000, the spatial patterns of mean
precipitation represented by the OPI estimates in the three
periods are very similar, as shown in Figure 6. For the
GPCP gauge input, the majority of the gauge records
are located in the east, southeast, and northwest of the
extended box area at all times. As shown by the three
gauge maps, the period 19871991 has the highest num-
ber of gauge grids with additional gauges from the Congo
River Basin in the center of the box as compared to the
other two periods, making it the best gauge coverage
period. For the other two periods, the Congo River Basin
lacks gauge records to represent its relatively higher
precipitation. Meanwhile, the number of gauge grids in19791986 is slightly higher than in 19922000, as can
be seen in the mideast and the south of the extended box.
In connection to the general spatial pattern of the precip-
itation in the extended box, more gauge grids available
in the Congo River Basin means a higher contribution
of heavy precipitation available for the gauge analyses
and thus the GPCP gauge input. For the northern central
area of the extended box, during the first and the third
periods when there are hardly any grids with gauges,
the local precipitation for the GPCP input is interpo-
lated from the surrounding areas including East Africa,
which has much lower precipitation. As a consequence,for the GPCP over the Congo River Basin, the real pre-
cipitation is underestimated in 1979 1986, even more
underestimated in 19922000, but adequately estimated
in 19871991. In comparison, the gauge coverage of the
GPCC 50-year climatology is constant and nearly the
same as the GHCN+ CAMS in the first period and the
GPCC monitoring in the third period as seen in Figure 6.
So over the Congo River Basin there is no gauge cover-
age change induced precipitation variation in the GPCC
50-year climatology as has occurred in the GPCP gauge
input data. Thus for the SA box mean precipitation, as
shown in Figure 4, the GPCC 50-year climatology analy-
sis only shows moderate precipitation drop as comparedto the GPCP precipitation, which shows a much larger
drop because of the imposed negative trend caused by
the gauge coverage change in the Congo River Basin.
On the basis of the precipitation patterns and the gauge
distributions shown in Figure 6, the SA box is divided
into two sub-boxes using the 30 E longitude as a dividing
line. The west sub-box is basically the southern portion of
the Congo River Basin with heavy rainfall. Then for each
sub-box, the changes between two 6-year (1986 1991
and 19921997) averaged areal mean precipitation are
calculated for the GPCP, GPCC 50-year, OPI, and GPI
precipitation products. The result in Table I shows thatthe changes are inconsistent between the gauge dependent
analyses (GPCP merged and GPCC 50-year) and the
satellite-only estimates (OPI and GPI). While the two
gauge dependent analyses show negative trends in both
the east and the west, the two satellite-only estimates
show a decrease in the east but an increase in the west.
So in the west sub-box the gauge dependent analyses
and satellite-only estimates exhibit opposite trends. For
the east sub-box, because the gauge grids have beensufficient in the base period, we have confidence in the
gauge dependent precipitation drop which is supported by
the satellite estimates. However, for the west sub-box, the
negative trend shown only in the two gauge dependent
precipitation analyses is in fact an artifact. Similar to our
previous analysis, two sources have contributed to this
false trend. For the west sub-box, when the local gauge
coverage is very low, the gauges from the east dominate
the gauge analysis result. Thus the negative precipitation
trend in the east sub-box can be reflected in the west sub-
box. This is evidenced in the GPCP 50-year climatology
data set, which shows comparable trends in both the sub-
boxes. For the GPCP, the decreased number of gaugegrids in the high precipitation area from the first to the
second period is another source of precipitation drop in
both the sub-boxes, in particular the west one. So for
the GPCP satellite-gauge precipitation in the west sub-
box the two sources of negative trend together result in a
significant large precipitation drop (25.5%). For the east
sub-box, the smaller number of GPCC monitoring gauges
in the high precipitation area in the second period results
in a calculated GPCP precipitation drop (16.4%) much
larger than what the other three data sets show.
4. Summary and conclusions
The Congo River Basin is the second largest rainforest
on earth and its precipitation change can have an impor-
tant impact on the global ecosystems. On the basis of the
GPCP satellite-gauge monthly precipitation data, a signif-
icant and sustained precipitation drop starting in 1992 is
detected in southern tropical Africa. The western portion
of the study area is basically the southern Congo River
Basin whose mean precipitation is much higher than that
of the surrounding areas. For both the 6-year and 9-year
averages of the areal mean precipitation, the calculated
GPCP decrease is more than 22%. The observational datashow that the availability of rain gauges in the southern
Congo River Basin has been extremely low most of the
time since 1979 except the period from the late 1980s to
the early 1990s, during which the gauge coverage experi-
enced a moderate increase. For southern tropical Africa,
the areal mean GPCP precipitation anomaly is found to
be significantly correlated with the total number of gauge
grids in the base period 19792004.
This study confirms the existence of a negative precip-
itation trend in the eastern portion of southern tropical
Africa as found by both the gauge dependent analyses
and satellite-only precipitation estimates, but the formeroverestimate the trend because of the varying number of
gauge grids. Over the Congo River Basin, the GPCP areal
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118 X. YIN AND A. GRUBER
mean precipitation exhibits a drop more than 25% in its 6-
year average, while the satellite-only estimates show the
opposite. This GPCP spurious trend in the Congo River
Basin can be explained by a change in the total gauge grid
number in combination with the GPCP method of merg-
ing the gauges with satellite estimates. In the GPCP gauge
adjustment process, when the available gauge number istoo low in a standard 5 5 grid array, a broader area of
7 7 grid array is used. The number of gauge grids in the
Congo River Basin within the SA box was high during
the late 1980s to the early 1990s but became extremely
low after the early 1990s. During the low gauge cov-
erage time the GPCP merging procedure has introduced
both the lower precipitation values and the slight neg-
ative precipitation trend from southern East Africa into
the local precipitation analysis. As a consequence, over
the Congo River Basin a large precipitation drop appears
in the GPCP satellite-gauge precipitation. In comparison,
for the GPCP 50-year climatology over the Congo River
Basin, the extremely low but invariant gauge coverage
only results in a moderate precipitation drop mainly intro-
duced from southern East Africa by analysis procedure.
Unarguably the GPCP, together with several other pre-
cipitation analysis and reanalysis products, represents the
latest knowledge of past precipitation change today, but
they are simply not perfect and should not be treated as
such. Each of the analysis and reanalysis products has
its own problems caused by various limitations that can
be both objective (e.g. input data availability, contem-
porary analysis methods) and subjective (e.g. personal
opinions in algorithm design and data usage). Therefore,
users should use the data cautiously, particularly whenstudying sensitive topics such as trends.
Finally, it should be noted that GPCP is an ongoing
project and changes are made as new knowledge and
techniques are developed. One change that is being
tested is a new climatology/anomaly analysis scheme that
will apparently have a greater number of gauges and
provide a more homogeneous gauge analysis. However,
since varying gauge data over time will still exist in
the new analysis it remains to be seen if the problem
identified here will be entirely solved. Another change
being planned for several years in the future will involve
improved satellite data, finer space/time resolution andparallel observation only and combined observation and
numerical model output products. (G. Huffman, personal
comment)
Acknowledgements
This study was supported and monitored by the Office
of Research and Applications of the National Oceanic
and Atmospheric Administration (NOAA) under Grant
NA17EC1483. The authors would like to thank Drs
Rudolf, Grieser, and Beck for providing the GPCC data.We would also like to thank George Huffman for updates
on the GPCP data set.
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