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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

Copyright 2009 Royal Meteorological Society


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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

Copyright 2009 Royal Meteorological Society Int. J. Climatol. 30: 110119 (2010)

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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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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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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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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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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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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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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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