1

Bulletin of Glaciological Research ,. (,**1) +ῌ++῍Japanese Society of Snow and Ice

ArticleInterannual variation of summertime precipitation over the Qilian Mountains
in Northwest China
Akiyo YATAGAI
Research Institute for Humanity and Nature, Kamigamo-Motoyama, Kita-ku, Kyoto 0*-ῌ2*.1, Japan(Received September ,*, ,**/; Revised manuscript accepted November +*, ,**0)

Abstract
To better understand the time-space structure of the interannual variation of precipitation in thesummer time, hereafter meaning June to August (JJA), in the Qilian Mountain region, an empiricalorthogonal function (EOF) analysis is applied to the summertime precipitation times for .+ years (+30+ῌ,**+) over there. The atmospheric circulation ﬁeld, the moisture transport and the precipitation
patterns associated with the dominant mode is shown by using European Centre for Medium-range
Weather Forecast (ECMWF) reanalysis (ERA.*, +.+,/ degree grid) dataset and a new precipitation
dataset which expresses an orographic enhancement of precipitation.
The ﬁrst mode of the analysis, which explained ,..2ῌ of the total precipitation variance, had asingle mode centered along the Hexi Corridor region of this mountainous region with a weakincreasing trend of precipitation. The second mode, which explained +/.2ῌ of the total variance, hada dipole structure between the Hexi Corridor (north of -2ῌN) and the region south of this corridor.
This mode was correlated with the Indian Summer Monsoon Rainfall (IMR), and the northern part of
the Qilian Mountains had a negative correlation to IMR. The third mode had a dipole structure
between the Tibetan Plateau area and the northern desert area. The westerly circulation anomaly
was the dominant factor to deﬁne the wet (much precipitation) or dry (less precipitation) years aroundthe Qilian Mountains. The second mode, which had a signiﬁcant negative correlation with the IMR,
was also related to the westerly circulation anomaly. In the southern part of the Qilian Mountains,a pressure anomaly around the western plateau area and the southwesterly moisture ﬂux were also
related to the interannual variation of the precipitation anomaly in this region.

+.

Introduction

Analyses of ice core samples from glaciers in highmountain regions of the mid-latitudes help researchers understand the past climates of these regions. In
particular, many ice core samples from the mountains
in the high-elevation Tibetan Plateau have been analyzed (e.g., Thompson et al., +322, +323, +33*, ,***).
Since summertime is a major accumulation season
with a lot of precipitation and it is easy to make out
inter-annual signals in the ice samples, it is important
to better understand the interannual variation of the
precipitation in the mountain areas. In spite of theimportance, the characteristics and dominant factorof the interannual variation of the precipitation oversuch a region have yet to be clariﬁed.
The summer time precipitation and melting snow
is an important source of water for the Tibetan Pla-teau region, particularly in the arid and semi-arid
areas where the water used for sustaining ecologicaland human society. Therefore, it is crucial to estimate the precipitation quantitatively, and to understand the dynamical processes those a#ect variationsin the precipitation over the mountainous regions of
the Tibetan Plateau. In this study, we focus on the
Qilian Mountains of the Tibetan Plateau. This regionis shown in Fig. +. It is located in the upper reach of
the Yellow River and the Heihe River. Therefore,
this paper aims to clarify the time-space structure of
the interannual variability of summer precipitation
over and around the Qilian Mountains for the hydrologists who study the hydrological budget or hydrological resources management there as well as glaciologists who interpret the past environment fromthe ice cores records etc (Nakawo et al., ,**,; Nakawo
and Sakai, ,**0).
Summertime precipitation in the northwest part2

Bulletin of Glaciological Researchof China is a#ected by both the westerly circulation
system and the southerly monsoon system. Recent
meteorological reanalysis data has revealed that thenortheast part of the Tibetan Plateau and northeast
China receives moisture from the northwest and fromthe south (Yatagai and Yasunari +332; Yatagai, ,**-).
This is supported by a study with an atmosphericgeneral circulation model (GCM) (Numaguti, +333) aswell as column integrated moisture source analysis
that included water isotope partitioning (Yoshimura
et al., ,**.). Further analysis by using more sophisticated models or ﬁner resolution reanalysis dataset
may lead to better understanding of the ice core records. This kind of approach should be e$cient tounderstand the origin of water in the ice cores.
Another way to gain an understanding of theinterannual signals in the ice cores is to know dominant factors of the interannual variation of the precipitation. Over such a complicated region, it is important to investigate the dynamical e#ects on precipitation including tele-connections. The teleconnections and precursor signals are not always explained dynamically. However, precursor signals areused for seasonal predictions and strong simultaneous
correlations helps to project the relationships between
the sporadic proxy data.
Many studies have found that the tele-connections a#ect monsoon rainfall over China (e.g., Ding+33.). However, most analyses have been made forthe plain regions of China, namely east of ++*῍E, notthe mountainous regions, due to the small amount oflocal observation data in the mountains. Over thesemi-arid regions in the north and northwest part ofChina, some studies revealed the relationship between
El-Nino/Southern Oscillation (ENSO) or Indian summer monsoon circulation (Wang and Li, +33*; Yatagai
and Yasunari, +33/; Feng and Hu, ,**.). However, for
the Qilian Mountains in the northeast part of the
Tibetan Plateau, the causes and the characteristics of
the interannual variation of summertime precipitation remains unclear.
Yatagai and Yasunari (+33/) investigated the regionality of the interannual variation of summer time
precipitation over the regions that includes Taklimakan, Jungar, Mongolia, and northern China, andfound that Taklimakan shows a negative correlation
to the Indian summer monsoon rainfall (IMR), whereas Loess Plateau shows a positive correlation to IMR.
Recently, Feng and Hu (,**.) pointed out the role ofthe Indian summer monsoon as a “facilitator” thatconnects ENSO with northern China rainfall. Further, recently, several studies showed that the Indiansummer monsoon-ENSO relationship has been varied(Kumar et al., +333; Wang et al., ,**+). Therefore, it isimportant to investigate the interannual variation ofsummer time precipitation in and around the Qilian
Mountains, and their relation of this variation to theFig. +. The study region. The initial are as follows:Mῌmountains, Pῌplateau, Bῌbasin, Rῌriver.
Inner rectangle in the upper map is the area forthe lower map. The upper map contours are -***
m, whereas those in the lower map are +/** m. In
the lower map, the shaded regions are elevations
exceeding +/**, -***, and ./** with the darkest
being highest, and the open circles mark locationsof the .+ stations used for an empirical orthogonal
function (EOF) analysis.

Indian summer monsoon.
For this study, we used a .+-year (+30+ῌ,**+) precipitation dataset from stations in the northeast partof the Tibetan Plateau and its adjacent regions (Fig. +)to investigate the time-space structure of the variations in the summertime precipitation, meaning thatin June, July, and August (JJA). Then, atmosphericcirculation anomalies related to the interannual variability was investigated. The relationship between
the Indian summer monsoon and the areal precipitation over and around the Qilian Mountains was also
investigated by using a recent rain-gauge-based grid
precipitation dataset that represents the orographic
e#ect of precipitation.

,.

Data
The following data sets were used in this study.

(+) Monthly precipitation data from ++0 stations over
the region [3*ῌ++*῍E/-,ῌ./῍N] of China for the period+30+ῌ,**+. These daily precipitation datasets were3

Yatagaicompiled by the China Meteorological Administration. In this study we used the data after summing
the daily precipitation at each station for the 3, daysof summertime. Among these stations, we used thedata from the -1 stations that are within latitudes 3/ῌ
+*/῍E and longitudes -/ῌ.-῍N (Fig. +) for EOF analysis. The location of the stations and the geographicalfeatures around the study region are also shown in
Fig. +.
(,) Monthly all-India rainfall data for the period +30+
ῌ,**+. These data were obtained from the Indian
Institute of Tropical Meteorology (Parthasarathy et
al. +33/). We calculated the total summertime rainfall in India to examine the correlation in precipitation
between India and the target region (hereafter, IMR).
(-) East Asia gauge-based daily precipitation analysis. Xie et al. (,**., ,**0) describes a new gauge-based
dataset of daily precipitation on a *./῍lat/lon grid
over the East Asia region of 0/ῌ+/*῍E and /ῌ0*῍N,which expresses orographic enhancement of precipitation (hereafter, the Base product). The set of the
product also includes *.+῍ lat/lon grid product overthe Yellow River domain as a derived product (hereafter, the Derived product). From this dataset, we used
the Base product for the period +30+ῌ,**+, and the
Derived product for the period +312ῌ+331. The Baseproduct covers the land area of the domain [0/ῌ+/*῍E//ῌ0*῍N] for +312ῌ,**+. From +30+ῌ+311, the Base product covers only over China. In addition, we useddaily precipitation climatology in a *.*/ degree grid
which was created as an inter-mediate product tomake the Base/Derived product. Yatagai et al. (,**/)
used this climatology dataset to validate a ,* km meshgeneral circulation model precipitation, because theother well-known grid precipitation product have notbeen represented precipitation climatology well enough to validate such hi-resolution model result.
The basic algorithm of creating the above dataset
is a modiﬁcation of Chen et al.’s (,**,) method for
creating monthly grid precipitation data by using an
optimum interpolation (OI) technique. One of the
major improvements of this modiﬁcation is the following. To express the orographic e#ect where thestation data is not available, the new algorithm used
the monthly climatology that was adjusted using theparameter-elevation regressions on independent slopes model (PRISM) (Daly et al. +33.). The model wasapplied to monthly climatology data from China andMongolia in order to express the orographic e#ect
where the station data is not available. Details areshown in Xie et al. (,**0). The data source for theBase/Derived products are from the State Meteorological Administration and the Yellow River Commission, which had data reporting rates that exceeded
3*ῌ. For the earlier period, +30+ῌ+311, we used only

data from the State Meteorological Administration
was used.
(.) European Centre for Medium Range Weather
Forecasts (ECMWF) .*-Year Reanalysis (ERA.*). We
used data on the vertically integrated water vapor
ﬂux and its divergence and the grid spacing is +.+,/
degree grid. The monthly mean upper-air atmospheric data analysis has ,./ degree grid spacing, andthis data was used to diagnose the large-scale atmospheric circulation ﬁeld. The period for +30+ῌ,**+ isused in this study.

-.

Climatology and Trend

Figure , shows the summertime (JJA) mean (+312ῌ,**,) vertically integrated water vapor ﬂux and its

Fig. ,. Moisture patterns for summertime (JJA) +312ῌ,**,. Upper diagram: climatological mean vertically integrated water vapor ﬂux (kg mῌ+ sῌ+) and its
convergence (kg mῌ, per three months). The shading marks regions with convergence. Lower diagram: East Asia gauge-based precipitation climatology (mm per three months). The solid line is
the -***-m elevation contour, which bounds the
Tibetan Plateau.

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Bulletin of Glaciological Research

convergence. The moisture convergence (shaded region) shows where the precipitation exceeds evapotranspiration. The overall characteristics are thesame with those of Yatagai and Yasunari (+332) andYatagai (,**-) (which showed the climatological moisture transport ﬁeld by using ECMWF +/ years dataset(ERA+/) in ,./ degree horizontal resolution). Over the
Qilian Mountains, the summer time mean moistureﬂows from the west. A day-to-day moisture ﬂow pattern varies by the atmospheric circulation ﬁeld, sometimes it comes from the northwest and sometimes itcomes from the southwest according to the position oftrough/ridge in mid-latitude (Yatagai and Yasunari,+332). It is interesting to see that a maximum convergence is observed at +**ῌE/.*ῌN, where is the basin of
the Heihe River (Fig. +). The East Asia gauge-based
precipitation climatology (Fig. ,, lower diagram)shows a clear precipitation maximum over the Qilian
Mountains (3/ῌ+*,ῌE/-0ῌ.*ῌN). Comparing the convergence pattern with that of precipitation, places of
convergence maxima and precipitation maxima are
not always the same. Since a convergence means
total area averaged and seasonal (JJA) averaged precipitation exceeds evapotranspiration. In arid/semiarid region, most precipitation may evaporate in ashort period. This results in convergence of * mm.
If we have solid precipitation in mountain areas and/or precipitation is stored as snow or in a water reserver, seasonal maxima of precipitation and convergence do not appear simultaneously (Yatagai, ,**-).
Although recent earth observation systems reveal global precipitation pattern including mountainareas and in various times scales including diurnalchange, observed precipitation patterns and its variability of the Qilian Mountains has not been clariﬁed.
Since our target region is out of Tropical Rainfall
Measuring Mission (TRMM) observation area, a gestational meteorological satellite provides us information on diurnal convection, which is a dominant circulation characters in mountain areas. A clear diurnal
variation is observed in upper tropospheric humidity(UTH) pattern over this area (Yatagai, ,**+). More
UTC is observed in the evening (+, UTC) than themorning (** UTC) over the Qilian Mountains and less
UTC is observed around the Tibetan Plateau (higher
than -*** m, above sea level) within the distanceabout /** km. The southern foot of the Qilian Mountians is still on the Tibetan Plateau and is not as low
as that of northern foot. Therefore, major moisturesource of the precipitation in both northern and southern slopes of the Qilian Mountains may di#er depends on the atmospheric circulation ﬁeld including
diurnal change. Detail analysis between precipitation and moisture transport is necessary in the future
by using datasets with enough resolution in time andspace, and isotope observation. In this paper, we
compare the characteristics of interannual variation

Fig. -. Interannual variation of summertime (JJA)
precipitation from +313 to +331at stations /,/--,/-0--, and /,1-1, which are marked “A”, “B”, and“C” in Fig. +, respectively. Straight lines with
open circles are the original time series. Dottedlines with closed circles are the time series of the*.+ῌ box of the derived product, where the station
belongs to. Dashed lines with open squares are
the time series of the *./ῌ box of the base product,
where the station belongs to. The range of thevertical axis are di#erent each other.

of precipitation over the Qilian Mountains as well as
its surrounding as areas shown in Fig. +.
Figure - shows sample time series of the summer
time precipitation at /,/-- (+*../*ῌE, .*.1/ῌN, +.11., m),/,0-- (32..,ῌE, -2.2ῌ0N, --01.* m) and /,1-1 (31.-1ῌE,-1.-1ῌN, ,32+./ m). Time series of the Base and the
Derived product where the three stations are included
are also plotted in each panel of Fig. -. In the Xie etal. (,**., ,**0) algorithm, the orographic e#ect was
expressed by adjusting monthly climatology against
PRISM in *.*/ῌ increments. The daily ratio to thedaily climatology at each station was interpolated byusing OI in *.*/ῌ increments. Then, base (*./ῌ) andthe Derived (*.+ῌ) products are made from the *.*/ῌ
daily grid data. Therefore, the time series of theBase/Derived products are very similar to the originaltime series but are sometimes larger in magnitude,especially where the original rain-gauge data camefrom measurements in the valley (relatively lower
elevation).

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Yatagai

The /,/-- station is located in the Hexi Corridor(Fig. +), and it is on the north foot of the Qilian Mountains. Stations /,0-- and the /,1-1 are located in the
Qilian Mountains, but the former is on the northernslopes and latter is on the southern slopes of the Qilian
Mountains (Fig. +). Station /,/-- has the smallest
amount in precipitation among the three stations, but
it shows the largest interannual variation.
The precipitation maxima of /,/-- that match
maxima of /,0-- occur in the years +313, +323, +33*,+33- and +331 and these two time series show decreasing trends for the year +312ῌ+331. In contrast, /,1-1
shows the simultaneous maxima with the other twotime series in +313, +323, and +331, but it shows peaksof opposite direction (i.e., maximum instead of minimum or vice-versa) with the other two in +32-, +321,
and +33-. These stations thus show a complex interrelationship. To address this complexity, we apply
an empirical orthogonal function analysis to grasp thedominant mode of the interannual variation of thesummer precipitation over this region. This is described in the next section.
A map of the linear trend pattern is shown in Fig. ..
Signs of the trends are sometimes depend on the
period of the time series. To determine the impact of
global climate change to the local change of the cryosphere or environmental change in historical time series, longer period analysis or dataset should be more
important. In this sense, we show the trend during+30+ to ,**+ where we got a consistent dataset. The
Qilian Mountains and its adjacent Hexi Corridor region show an increasing trend during the .+ years.
To the south of the Qilian Mountains, around theregion 3/῍Eῌ+*῍/E and -,῍Nῌ-1῍N, a decreasing trend
is dominant. The general pattern of Fig. . is consistent with those of the trend pattern in summer precipitation in China for almost the same period (Zai etal., ,**/; Endo et al., ,**/).

Fig. .. Linear trends of the summer time precipitationfrom +30+ to ,**+. Black circles indicate an increasing trend, whereas gray indicates decreasing.
The magnitude of the increasing or decreasing
trends are shown by the size of the circles: small
size indicates * mm yearῌ+, medium indicates *./mm yearῌ+, and large indicates +.* mm yearῌ+.

..

Temporal and spatial variations

The -1 stations marked in Fig. + were selected forthe empirical orthogonal function analysis (Fig. +).
The summer time (JJA) precipitation time-series forthe .+ years (+30+ῌ,**+) are used as samples. A correlation matrix of the variables was used for computing
the eigenvalues and eigenvectors of the EOFs because
we are concerned with the interannual variability ofthe precipitation, not the absolute amounts. The
contributions to the total variance (ῌ) of the ﬁrst ﬁveeigenvalues are ,..11, +/.2., +,.+3, 1..1, and /.+/ῌ, respectively. The ﬁrst three components account for
more than /*ῌ of the total variance.
Figure / shows the eigenvector patterns of theseﬁrst three components. The time sequence of thescore for each EOF is presented in Fig. 0. The ﬁrst
EOF (hereafter EOF+) shows a single sign over the

Fig. /. Eigenvector patterns of the ﬁrst three EOFs ofsummer time (JJA) precipitation. Positive values
are black circles, negative values are gray circles.

6

Bulletin of Glaciological Researchwhole domain, and stations along the Hexi Corridor
and those around +*-῍E, -0῍N have higher values thanother areas. The time series of the score of the EOF+
shows gradual increasing trend and a particularly
large value in the year +313. The eigenvector patternof EOF+ (Fig. / upper diagram) is similar to that oflinear trend of this area (Fig. .). However, the scoreof the EOF+ shows decreasing trend during +33/ῌ,**+,
we need to care about the decadal change in the
future.
The second EOF (EOF,) shows a clear dipole
mode between northwest part of the domain and southeast part of the domain. Eigenvalues around themountains are not large but they have positive values,
which show a simultaneous signal with that of theHexi Corridor regions in this mode. The region
where EOF, is negative corresponds to a part of theregion where positive correlation was observed between the summer precipitation there and the Indian
monsoon rainfall (Guo and Wang, +322; Wang and Li,+33*; Yatagai and Yasunari, +33/). The time sequence of the score of the EOF, (black line with open
circles in the middle panel of Fig. 0) shows a clear
oscillation with a period of ,ῌ- years. The all-India
JJA time series (IMR) is also plotted in the middle
panel of Fig. 0. The simultaneous correlation coe$cient between the two time series is ῌ*..0, which
exceeds the +ῌ signiﬁcant level. A clear negative
relationship exists between the two time series, except
for the years of +320, +321, and +331. It is said that theFig. 0. Time series of the scores of the ﬁrst three
EOFs. The line with black circles in the middle
diagram is a time series of the all India monsoon
rainfall (JJA, mm).

region where eigenvector of EOF,, that is along theHexi Corridor and a part of the Qilian Mountains,show positive values has a negative correlation between the Indian summer monsoon rainfall for JJA.
Further analysis of the correlation with Indian monsoon rainfall will be discussed in the discussion section.
The third EOF shows a dominant pattern aroundthe Qilian Mountains. Some of the Hexi Corridor
stations (32῍E-+*,῍E) show a positive sign. In contrast, most stations below lower than -*** m show
negative values. This mode roughly shows a seesaw
pattern between the Tibetan Plateau area and thelower (desert) area. The time series of EOFs shows
an oscillation with a period between several years toseveral decades.

/.

Relationship with the atmospheric circulation

The EOF analysis of this study clearly showedthat three modes can explain the interannual precipitation variations around the Qilian Mountains. As
described above, there are some dominant factors thata#ect the interannual variation of the precipitation
anomaly of the region. Therefore, we show composite maps of the atmospheric circulation ﬁelds that
correspond to each EOF. We chose the ﬁrst ﬁve highand low years of EOFs (scores) to make composite
charts, respectively.
Figure 1 shows the composite charts of the /**hPa geopotential height (Z/**) for the ﬁve high years(+313, +32-, +33-, +33. and +330) and the ﬁve low dry
years (+30,, +30/, +32,, +33+ and ,**+) of EOF+. As
shown in Figs. / and 0, the high years of EOF+ meanswet (much precipitation) years, while the low years of
EOF+ means dry (less precipitation) years. The di#erence of the two is also presented in Fig. 1c. Figure 1
d shows the high-low year di#erence of the verticallyintegrated water vapor ﬂux and precipitation, which
are compared to the climatological ﬁelds in Fig. ,.
EOF+ is related to the positive anomaly in west
Siberia (2*῍E, 0,῍N) and the negative anomaly around
Lake Baikal. The moisture ﬂux is strengthened around the Qilian Mountains due to moisture from thewest converging around this region.
Figure 2 shows the similar composite charts with
Fig. 1 but for EOF,. In this mode, high years (+300,+303, +313, +32, and +321) mean much precipitation in
Hexi Corridor area and less precipitation around +*-῍E, -0῍N, and low years (+30+, +30., +301, +312 and +323)
shows opposite signals of the high years. A clear
di#erence is observed in the pattern of the westerly
circulation. Negative anomalies in the /** hPa geopotential height are found at around //῍E, 0/῍N and+,*῍E, .*῍N in the di#erence map (Fig. 2c). As a result of the circulation anomaly, the northerly moisture ﬂux was strengthened in the wet years of the

7

YatagaiFig. 1. (a) Composite height charts at /** hPa for the years of largest ﬁve scores of EOF +. The years are shown in the
top of the ﬁgure. Contour interval is ,* gpm. Dark shading indicates regions in which the anomaly of the heights
exceeds ,* gpm, whereas light shading indicates the anomaly negatively exceeds ῌ,* gpm.
(b) The same as (a) but for the smallest ﬁve years. (c) Geopotential height at /** hPa in (a) minus that in (b).
Contour interval is / gpm. (d) Di#erence of the vertically integrated water vapor ﬂux and precipitation (Baseproduct) between the years used in (a) and (b). Shading indicates regions in which the magnitude of the precipitation
di#erence exceeds +** mm.

EOF,. The moisture ﬂux shows a divergence, corresponding to the negative precipitation anomaly (+*,῍E, -0῍N and east of +*/῍E). It is known that the EOF,
negative region (+*,῍E, -0῍N) has more moisture fromthe south in the wet years for the region.
Figure 3 shows the composite charts for the EOF(high years: +301, +32-, +322, +323 and +332; low years:+300, +302, +312, +313 and +33/). The previous two
charts of precipitation anomaly show a clear di#erence in the northern part of the Qilian Mountains.
Here, the di#erence for the EOF- shows a clear precipitation anomaly in the south of the Qilian Mountains. The di#erence of the Z/** ﬁeld (Fig. 3c) showsa similar pattern as that of EOF+ (Fig. 1c), with apositive anomaly around 0*῍E, 1,῍N and a negative
anomaly around the Lake Baikal. However, thismode (Fig. 3c) also shows a negative anomaly in thewestern part of the Tibetan Plateau. As a result, themoisture ﬂux vector of southwesterly ﬂow is strengthened over the Tibetan Plateau, and it converges inthe southerly slopes of the Qilian Mountains (Fig. 3d)in the high years.

0.

Disucssion

Figure +* shows simultaneous correlation between the time series JJA precipitation of the Baseproduct and the all India JJA precipitation. Recently, several studies pointed out that the Indiansummer monsoon-ENSO relationship has been changing (Kumar et al., +333) and the impact of these signals
on the East Asian monsoon has been changing (Wanget al., ,**+; Feng and Hu, ,**.). Therefore, we presented the correlation pattern for the .+ year period(+30+ῌ,**+) and the recent ,/ year period (+312ῌ,**,) to
IMR. Both patterns are similar to the eigenvector
pattern of the EOF , (Fig. /, middle panel).
Interestingly, the signiﬁcant positive correlationin the northern China region of Fig. +*a (+*-῍E,-0῍N;++*῍E, .*῍N) has disappeared in Fig. +*b. This is consistent with the ﬁndings in Feng and Hu (,**.). In
contrast, a signiﬁcant negative correlation to the8Bulletin of Glaciological Research

Fig. 2.

The same as Fig. 2 but for EOF ,.

north of the Qilian Mountains does not disappear in
Fig. +*b, rather it was strengthened. Although EOF,
shows a dipole structure in the spatial pattern, thephysical mechanism between the IMR the local precipitation seems to be di#erent from each other.
Yatagai and Yasunari (+33/) pointed out that a region
that includes the Taklimakan Desert and the EOF,positive region of this study showed a negative correlation to IMR, whereas a region that includes theLoess Plateau and the EOF,-negative region of thisstudy showed a positive correlation to IMR. However, the former correlation was related to the anomaly of the westerly circulation and the latter was
related to the southerly monsoon circulation.
Therefore, the correlation of ENSO with IMR andthe correlation of ENSO with the precipitation anomaly of northern China (North China and the EOF,
-negative region of this study) have both weakened
recently (Kumar et al., +333, Feng and Hu, ,**.).
However, there is another mechanism with a negative
correlation between IMR and around Qilian Mountains (EOF,-positive region of this study), and thisrelationship has not been weakening.
We need to care that a positive correlation between an area and IMR does not mean much moisturecomes from India to the area in wet years. IMR has a
global (or at least continental scale) signal and IMR
has signiﬁcant relationship with mid-latitude circulation in interannual variation as well as intra-seasonalvariation such as active/break cycle. It is consideredthat the mid-latitude circulation which relates to the
IMR a#ects the interannual variation of precipitationand the moisture transport over the Qilian Mountains.
The basic method that we used in this study (anEOF analysis and composite analysis of atmosphericcirculation ﬁeld) was a conventional analysis method.
However, both precipitation data of the target region(the Qilian Mountains) and a long-term high resolution reanalysis data have not been available till recent
years. In addition, most these conventional analyses
has not estimated the impact of global/continental
climatic signals to local water resources, because
there has not been available global/continental precipitation dataset with expressing orographic enhancement of precipitation although it is inevitably
important in terms of water management and estimating accumulation on snow/glaciers. Therefore, it iskeen issue to develop daily grid precipitation datasetwith expressing orographic e#ect to achieve these
purposes (Xie et al., ,**0).

9YatagaiFig. 3.

The same as Fig. 2 but for EOF -.

Recently, many attempts have been made of combining use of general circulation models and proxy
data to know the past environment. However, horizontal resolution of general circulation models are
generally coarse and the precipitation simulation islargely depends on the convection schemes in themodels. Therefore, to understand the point proxydata, it is required to understand the regionality of
precipitation variation and relationship between local
precipitation and global signals such as ENSO and
IMR.
However, we should be careful in change of therelationship between global signals and local precipitation as we have shown in the beginning of thissection. The EOF-+ showed weak increasing trend, apeak in +313, and decreasing trend for +33/ῌ,**+. The
dominant factors of ,ῌ- years oscillation, decadal variation and linear trend related to the global warming
are di#erent each other. Therefore, we need to pay
attention with the periodicity of the precipitation variation of this region in the future by using the updated
time series.
In arid regions, precipitation case is rare. Therefore, moisture transport that related to the heavy
precipitation in such region di#ers from the meanﬁeld (Yatagai and Yasunari, +332). As for the target

mountain region of this study which is adjacent to thearid region, diurnal cycle and orographic enhancement of precipitation should be taken into account inorder to understand the variation of the precipitation
record in the ice cores. Since atmospheric circulationis complicated in the target region, stable isotope
technique will give us better understanding of the
precipitation mechanism of this region. Combined
use of isotope technique and objective reanalysis data
is important for understanding the recent record of anice core. Meteorologically, the precipitation case israre and geography is complicated in this region,
analysis of isotope in the water vapor should be essential (Yatagai et al., ,**.).

1.

Conclusion and remarks

In order to know the time-space structure of theinterannual variation of the summer time (JJA) precipitation around the Qilian Mountains, an empiricalorthogonal function (EOF) analysis was applied to the
JJA precipitation time of .+ years (+30+ῌ,**+). The
ﬁrst mode showed a single mode centered along theHexi Corridor and it shows a weakly increasing trendof precipitation. The second mode had a dipole structure between the Hexi Corridor (north of -2ῌN) and10

Bulletin of Glaciological Research

Acknowledgments
This is a contribution from the OASIS Project (Project .ῌ+), being promoted by the Research Institute for
Humanity and Nature. The Global Environment Research Fund (FS*/+ and B*0,) by Ministry of Environment, Japan also supported this study.

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Fig. +*. (a) The correlation between summer time
(JJA) precipitation and all-Indian summer monsoon (JJA). (a) For the period +30+ῌ,**+, (b) forthe period +312ῌ,**,. Dark shading indicates 3/ῌ
conﬁdence level of positive correlation, whereas
light shading indicates the same level for negative
correlation.

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Although there is a large altitude di#erence in thealtitude between the Qilian Mountains and Hexi Corridor stations, the ﬁrst two modes do not show a clear
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The northern part of the Qilian Mountains had asigniﬁcant negative correlation with the IMR, and it isalso related to the westerly circulation anomaly. In
the southern part of the Qilian Mountains, pressurearound the western part of the Tibetan Plateau andsouthwesterly moisture ﬂux was also related to theinterannual variation of the precipitation there.

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