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Interannual Variability of Summer Water Balance Components in Three Major River
Basins of Northern Eurasia
YOSHIKI FUKUTOMI
Frontier Research System for Global Change, Institute for Global Change Research, JAMSTEC Yokohama Institute for Earth Sciences,
Yokohama, Japan

HIROMICHI IGARASHINational Institute for Environmental Studies, Tsukuba, Japan

KOOITI MASUDA
Frontier Research System for Global Change, Institute for Global Change Research, JAMSTEC Yokohama Institute for Earth Sciences,
Yokohama, JapanTETSUZO YASUNARI
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, and Frontier Research System for Global Change, Institute for
Global Change Research, JAMSTEC Yokohama Institute for Earth Sciences, Yokohama, Japan(Manuscript received 14 January 2002, in final form 26 September 2002)
ABSTRACTThis study investigated water balance components in the three major river basins of Siberia (the Lena, Yenisey,
and Ob) based on the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE)
Atmospheric Model Intercomparison Project II (AMIP-II) reanalysis. The primary focus is the nature of theinterannual signatures of summer precipitation, moisture convergence, and runoff in individual basins, and theirlinkage to the large-scale water transport and circulation fields over northern Eurasia from 1979 to 1995. The
temporal characteristics of the hydrological cycle and the associated large-scale circulation structure are discussed.
Some interesting features are found in the interannual variability of basin-scale hydrometeorological elements.
In the Lena and Ob basins, the temporal signatures of both precipitation and moisture convergence indicate acycle of approximately 6 to 8 yr. The mid- and late-summer runoff variation is significantly correlated withthese two elements. Moreover, the time series of each element for the Lena is almost exactly out of phase withthat for the Ob. The basin-averaged precipitation time series show that the relatively wet phases for the Lena
occurred in the early and late 1980s, during relatively dry phases for the Ob. Similarly, the dry phases for the
Lena and wet phases for the Ob occurred in the mid-1980s and early 1990s.
The structures of the large-scale moisture transport and circulation anomalies responsible for the precipitation
variability for each basin are examined using regression analysis. The space–time fields of precipitation, moistureflux, and geopotential height anomalies are related to the basin-averaged precipitation index. Out-of-phase patterns
of precipitation and circulation anomalies between eastern and western Siberia are evident in the regression
maps. When the Lena basin is unusually wet, an anomalous cyclonic circulation accompanied by high positive
precipitation anomalies is established over the basin, while an anomalous anticyclonic circulation with negative
precipitation anomalies is produced over the Ob; the reverse situation is equally evident at other times. Thus,an east–west dipole structure of circulation and precipitation anomalies is formed over northern Eurasia. The‘‘seesawlike’’ interchange of dry and wet regimes associated with this dipole pattern occurs between eastern
and western Siberia on a timescale of about 6 to 8 yr. Regression patterns of geopotential height anomalies
show good agreement with their summer leading mode derived from a rotated empirical orthogonal function
analysis. These results imply that a large-scale mode of atmospheric teleconnective variability modulates thesummer hydrological cycle in the northern Eurasian domain.

1. Introduction
Evaluation of the water balance and clarification ofhydrological characteristics over the northern EurasianCorresponding author address: Dr. Yoshiki Fukutomi, JAMSTEC
Yokohama Institute for Earth Sciences, 3173-25 Showamachi Kanazawa-ku, Yokohama City, Kanagawa 236-0001 Japan.
E-mail: fukutomi@jamstec.go.jp

q 2003 American Meteorological Society

continental region is one of the important objectives of
the Global Energy and Water Cycle Experiment (GEWEX) Asian Monsoon Experiment (GAME)-Siberia project, which was designed to quantify the interactive processes between the atmosphere and land surface, andtheir relation to patterns of climatic variation. In particular, a study of the nature of the large-scale waterbalance over northern Eurasia on interannual timescales

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is needed to facilitate understanding of physical linkages
with not only regional climatic conditions, but also withvarious climate subsystems.
During the past two decades, there has been considerable interest in the hydrological cycle in high-latitude
land areas. The primary reason is that the water balancein these high-latitude areas is thought to drive the Arctic
climate, as reviewed by Walsh (2000). For instance in
the Mackenzie River basin, which is a portion of the
Arctic drainage area in North America, considerable
progress has been made in identifying the connection
between the response of runoff to basin-wide precipitation and Arctic climatic variation (e.g., Manak andMysak 1989; Mysak et al. 1990; Bjornsson et al. 1995).
In addition, the atmospheric transport of water vapor
into the Arctic basin through this area has been described in several studies (e.g., Walsh et al. 1994; Smirnov and Moore 1999, 2001).
Northern Eurasian hydrology is also considered an
important factor controlling the Arctic climate (e.g.,Peng and Mysak 1993; Bowling et al. 2000; Serreze etal. 2001a, 2002) and that of the neighboring Asian monsoon area (e.g., Yasunari et al. 1991; Kripalani and Kulkarni 1999). The water from major Eurasian rivers is
recognized as an effective source of freshwater flowing
into the Arctic Ocean, [see reviews by Bowling et al.
(2000) and Carmack (2000)]. Atmospheric moisturetransport in high-latitude regions can contribute to thehydrological cycle in the Arctic domain, as discussed
in many previous studies (e.g., Cullather et al. 2000;
Rogers et al. 2001), because the changes in the characteristics of moisture sources and sinks over the continent may be in response to the moisture input to the
Arctic region, and may also play a direct role in the
Arctic climate through surface water and energy balances. Several studies have pointed to the significanceof the interannual variability of precipitation in thenorthern Eurasian domain (e.g., Wang and Cho 1997;
Ye 2001).
Limited efforts have been made to describe the characteristics of the hydrological cycle and their linkage tolarge-scale circulation over northern Eurasia, especiallyin the summer season, when the hydrological cycle isat its most active. Although Ye (2001) identified thevarious timescales of precipitation variation in this region, he focused only on the winter season. As described
below, summer precipitation accounts for about 40% of
the annual precipitation over each river basin. In addition, major Eurasian rivers, such as the Lena, Yenisey,
and Ob, are recognized as large contributors to freshwater input into the Arctic Ocean (Serreze et al. 2001a,
2002). Summer precipitation and its variability over thiscontinent can be considered an important input to the
Arctic freshwater budget.
Peng and Mysak (1993) investigated controls on thelong-term variability of the regional summer water balance over the Eurasian basin, and found evidence thatsummer runoff over western Siberia is strongly affected

VOLUME 4

by precipitation during the preceding winter, which inturn is affected by sea surface temperatures in the North
Atlantic and associated teleconnections at interannual
timecales. A more recent investigation by Rogers et al.
(2001) addressed the importance of water transport into
the Arctic region over western Siberia and related atmospheric patterns during summer. In addition, the effort by Serreze et al. (2002) clarified various aspects of
the land–atmosphere hydroclimatology in Siberia for allseasons. However, few studies have examined and discussed specific relationships between the basin-scalewater balance, including both western and eastern Siberia, and larger-scale circulations for summer. Moisturetransport and precipitation during the warm season constitute an integral part of the Eurasian hydrological cycle.
This paper examines the space–time characteristics ofthe atmospheric hydrological cycle over the northernEurasian basin during summer (June through August:
JJA) employing the National Centers for Environmental
Prediction (NCEP)–Department of Energy (DOE) Atmospheric Model Intercomparison Project II (AMIP-II)
reanalysis dataset (Kanamitsu et al. 2002). The basinscale water balance is estimated using a simple budget
equation involving precipitation and runoff data. The
results are used to discuss the accuracy of water balance
estimation using reanalysis products, and to identify climate signals that affect the summer hydrological cycle
over Eurasia. We attempt to detect the dominant temporal signals in the water balance components for eachof the Lena, Yenisey, and Ob basins, and discuss thecoherent atmospheric systems that regulate the basinscale variation patterns.
The structure of this paper is as follows. Section 2
describes the datasets and the computational procedures
used in this study. In section 3, we document the basic
features of the annual cycle of water balance components. Seasonal mean and interannual variance of summer precipitation are given. Section 4 presents the dominant temporal signals in the water balance elements forthe basins, and the atmospheric sources of those signals,
by examining their spatial structure and that of largescale circulation anomalies occurring simultaneously.
Section 5 presents a discussion of the results, and ourmain conclusions are addressed in section 6.
2. Data and methods
Three observational datasets are used to characterize
the water budgets for the individual river basins in thisstudy. One is the NCEP–DOE AMIP-II reanalysis (hereafter referred to as R2) dataset for 1979–99, which has
recently been released by NCEP. This data is itemized
at 6-h intervals, at T62 horizontal resolution, and 28
sigma levels in the vertical. The horizontal wind andspecific humidity from this archive have been used inwater budget calculations. Kanamitsu et al. (2002) briefly surveyed this dataset. As they described, the R2 da-APRIL 2003

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taset resulted from an attempt to improve the quality of
the atmospheric and surface components; it is recognized that the former NCEP–National Center for Atmospheric Research (NCAR) reanalysis data (referredto here as R1) contained some errors in atmospheric andsurface physical quantities (Kalnay et al. 1996; Kistler
et al. 2001). The R2 dataset is believed to estimate thewater-related elements more accurately, not only in tropical regions, but also in mid- and high latitudes. Recently, Maurer et al. (2001) derived and compared land
surface water budgets for the Mississippi River basinusing both the R1 and R2 datasets.
Global precipitation data from surface gauging andsatellite-based estimation was presented by Xie and Arkin (1996, 1997); this dataset, called the Climate Prediction Center (CPC) Merged Analysis of Precipitation(CMAP), has also been used to calculate the atmosphere–land water balance and describe the spatial andtemporal characteristics of climate signals. This dataset
may have advantages for evaluating these componentsof the continental-scale hydrological cycle. Trenberth(1999) used the CMAP data to estimate precipitation
recycling ratio over land on a global scale. Using the
combined CMAP and R1 data, a grid-scale atmosphere–
land water balance was calculated that suggests thatthese gauge–satellite merged precipitation data are also
applicable to assessments of basin-scale hydrologicalcycles over an entire continent. The CMAP data has
2.58 3 2.58 resolution on a monthly timescale.
The Bodo-enhanced Russian river flow data were obtained from the NCAR Data Support Section (DSS).
This dataset includes data from more than 2400 gauges
covering rivers in Russia and the former Soviet Union(FSU) for about 180 yr. We use only the record of discharge and the drainage area information for gauges at
the mouths of individual rivers.
In addition to aforementioned datasets, anothermonthly gridded precipitation dataset for 54 yr from1948 to 2001 is taken from NCEP–CPC to examine thelong-term tendencies of basin-scale precipitation variability. This new product is called PREC/L, which isthe precipitation reconstruction over land (Chen et al.
2002). The basic format of this data is almost the sameas CMAP data, but is only available over the land area.
The NCEP–NCAR monthly reanalysis product is also
used for the purpose of additional analysis.
Actual water budget calculations and data analyses
were performed for the 17-yr period from January 1979
through December 1995; later runoff data were not available. The available period for runoff data differs for different river basins. The atmospheric water budget equation (e.g., Peixoto and Oort 1992), can be written as

7 8]W
1 ^= · Q& 5 ^E 2 P&,]t(1)

where P is precipitation, E is evaporation, and angled
brackets denote the area average. Here, the precipitable

FIG. 1. Three major river basins and the gauge stations measuring
the discharge at the river mouths in northern Eurasia.

water content (W), vertically integrated moisture flux
vector (Q), and its divergence (= · Q) are defined by
W5
Q5

ps
g
ps
g

^= · Q& 5 2

E
E
R
1

q ds

(2)

qv ds

(3)

Q · n dl,

(4)

0

1

0

1
A

where q is the specific humidity, v is the horizontal windvector, p s is surface pressure, g is gravitational acceleration, and s is the vertical sigma coordinate (s 5 p/
p s ; p is pressure). Vertical integration is performed fromthe top of the atmosphere (s 5 0) to ground level (s5 1). The moisture flux divergence that is the second
term in the left-hand side of (1) is computed using theGauss divergence theorem. The value l represents theboundary of area A, and n is the unit vector outward
normal to A.
The atmospheric water balance was evaluated at bothbasin and grid scales. The moisture flux divergence atgrid scale was computed by applying the spherical harmonic transform method (Adams and Swarztrauber1999). Evaporation at the land surface was estimated as
the residual from the earlier budget equation. Before
calculating the moisture flux and other associated elements, a mass correction of horizontal wind fields toeliminate dry airmass imbalances was made, using themethod of Trenberth (1991).
The outlines of the three major river basins for which
the moisture budget components were computed areshown in Fig. 1. The locations of the gauge sites nearest
to the river mouths are also shown. The boundaries ofthe basins were defined using the gridpoint locations ofreanalysis data (2.58 interval), similar to the basin definitions of Oki et al. (1995). Oki et al. (1999) recently
produced the Total River Integrated Pathway (TRIP),
digitized 18 and 0.58 streamflow basin definitions on a
global basis. For this analysis, the perimeters of basins
were determined with reference to the TRIP 18 version.
The information on the climatological water balance foreach basin using these datasets is given in the appendix.

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3. Climatological features
a. Annual cycle
Area-averaged water budget estimates were made forthree basins, the Lena, Yenisey, and Ob, over a 17-yr
period, and the characteristics of the mean annual cycleof their water budget terms are presented before focusing on the summer hydrological cycle. Figure 2 shows
the monthly climatological components for each basin;
temporal variation is similar to the results presented in
the NCEP–NCAR reanalysis (Serreze et al. 2001a,
2002).
The precipitation maximum occurs in July and theminimum in the period from February through March.
It is interesting to note that two maxima and two minimaof the moisture flux convergence are evident for the
Lena and Yenisey. A significant decrease, retaining a
low positive value, is evident during the summer. On
the other hand, the moisture convergence becomes negative, indicating divergence over the Ob during the summer (June–August); this basin is a moisture-source region during that period. Despite the decrease in moisture
convergence, evaporation increases and reaches a maximum in summer over each basin, as does precipitation,emphasizing the importance of surface evaporation relative to the regional water input from precipitation during the summer. Particularly over the Ob, evaporation
greatly exceeds the peak value of precipitation during
the warm season. These features are similar to the resultsof Serreze et al. (2002). They discussed different characteristics between basins in the annual cycle of eachcomponent, and concluded that larger evaporation andnegative P 2 E for the Ob during summer was due tothe specific land cover characteristics and lower atmospheric thermal conditions.
Runoff remains less than 10 mm month 21 at eachlocation during the winter and spring; however, it
abruptly increases in May and peaks in June, one monthearlier than the precipitation maximum in July. Thisrunoff maximum is definitely due to surface dischargeof water caused by snowmelt. For the Ob basin, therunoff peak is smaller than those for the other two basins, and the runoff curve indicates smoother temporalchanges than in the other two river basins. Serreze etal. (2002) explained this smoother runoff peak by lessavailability of snow meltwater for runoff, which is
caused by the larger evaporation from spring to summer.
The time series of P 2 E was examined in the samemanner (Fig. 2). On a monthly basis, P 2 E has a value
very close to the moisture convergence, but it is adjusted
by the W tendency in the budget equation. In addition,
the negative values of E derived during the cold season
may be caused by underestimating snowfall and rainfall
at the rain gauge sites (Serreze et al. 2001a).
b. Climatological fields for summer
The 17-yr climatological fields for the summer (JJA
1979–95), mean precipitation, moisture flux, and its

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FIG. 2. Annual cycle of precipitation, moisture flux convergence,
runoff, and evaporation (estimated as a residual) over the (a) Lena,(b) Yenisey, and (c) Ob River basins (mm month 21 ). All components
except for runoff are 17-yr averages. Runoff records for the Lena
and Ob (Yenisey) are available for 16 (17) yr from 1979–94 (1995).

convergence, are shown in Fig. 3a. One of the dominantfeatures is a prominent mid- and high-latitude precipitation band over a broad part of Eurasia. A zonallyoriented band of precipitation exceeding 2 mm day 21
extending from the east of Lake Baikal to the Far East
Asian coast is clearly seen. This band lies along 458–
608N, which corresponds closely to the area of maximum frontal activity during summer (e.g., Chen et al.

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FIG. 4. The time series of annual precipitation (mm), JJA accumulated precipitation (mm), and its ratio to annual precipitation (%)
for the Lena River basin.

FIG. 3. (a) Average JJA precipitation, moisture flux, and its convergence (1979–95). Precipitation values over 1 mm day 21 are shaded. Contour intervals of moisture flux convergence are 0.5 mm day 21 .
Negative values (for moisture flux divergence) are drawn with dashedcontours. (b) Variance in JJA precipitation (1979–95). Contour intervals are 0.1 mm 2 day 22 (0.1–0.5) and 1 mm 2 day 22 (0.6–2).

1991; Serreze et al. 2001b). This precipitation pattern
may be a primary source of water, particularly for the
Lena basin during summer. Another band of precipitation extends from northeastern Europe to western Siberia; this constitutes a major water input to the Ob
basin. Along these precipitation bands, eastward-directed water transport is prominent, and the moisture
convergence maxima almost coincide with the precipitation maxima.
The distribution of interannual variance of JJA precipitation is illustrated in Fig. 3b. Two dominant variance maxima with more than 0.2 mm 22 day 22 are located over northwestern and eastern Eurasia. One approximately corresponds to the upper Lena basin, andthe other covers the Ob basin and the Ural Mountains.
The locations of these high-variance centers coincidewith those of the mean precipitation maxima shown in
Fig. 3a. Also note that the mean and variance of precipitation over the Yenisey basin are both weaker thanthose over the other two basins. No center of variability
for precipitation appears to be dominant over this basin.
Substantial interannual variability in the activity of precipitation systems is concentrated in western and eastern
Siberia.
4. Interannual variability
a. Interannual signals in hydrometeorologicalcomponents
This section examines the evidence for a dominanttimescale of interannual variability. Figure 4 shows aseries of annual precipitation, summer (JJA)-accumulated precipitation, and its ratio to the annual value forthe Lena River basin. The annual precipitation is definedas the total amount over the hydrological year from
October through September. The ratio for individualyears exceeds 40%, emphasizing the importance of summer precipitation. On the other hand, the ratio for the
Ob (not shown) is a slightly smaller value, although it
still exceeds 30% of the annual precipitation throughout
the period of analysis. The percentage for the Yenisey(not shown) is 40%. Another notable feature is the similarity between the long-term changes in JJA precipitation and those of annual precipitation. Temporal-phasecharacteristics of both time series agree quite well. Thissuggests that the interannual variation of JJA precipitation contributes significantly to that of the annual precipitation. We note that the JJA precipitation time series
appears to have 6–8-yr cycles during our analysis period.
The simultaneous correlation coefficients between theannual and JJA precipitation amounts for individual basins are presented in Table 1. The correlation coefficientfor the Lena is 0.95, which considerably exceeds the
95% confidence level (0.45) based on the standard t test.
This emphasizes the considerable effect of interannual
variation in summer precipitation on that of annual precipitation for this basin. For the Yenisey, the correlation
coefficient (0.81) is also significant. The value of 0.54
for the Ob is smaller, but still significant.
Figure 5 illustrates interannual variation in the summer hydrometeorological conditions, mean precipitaTABLE 1. Simultaneous correlation coefficients of summer (JJA)
precipitation with annual (Oct–Sep) precipitation in each river basin
for 1979–95 (4). The correlation values are above the 95% significance level.
Annual precipitation vs JJA precipitation
River basin

Correlation coef

Lena
Yenisey
Ob

0.95
0.81
0.54

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TABLE 2. Simultaneous correlation coefficients of summer (Jun–
Aug) precipitation with summer (Jul–Sep) runoff in each river basin
for 1979–95 (4). The correlation values above the 95% significancelevel are in boldface.
JJA precipitation vs JAS runoff
JJA precipitation

JAS runoff

Lena
Yenisey
Ob

0.89
0.32
0.68

Relatively dry and wet periods over each basin are
also strongly evident in these time series. In the Lenabasin (Fig. 5a), wet years clearly occurred during theearly 1980s (1980–83) and late 1980s (1988–89) fromthe temporal changes of precipitation and moisture convergence. On the other hand, dry periods occurred during the middle 1980s (1984–87) and early 1990s (1990–
93). Extreme wet events occurred in the Lena basin in
1981 and 1988, and an extreme dry event in 1986. Interestingly, the precipitation time series for the Ob basin(Fig. 5b) appears to be negatively correlated with thatfor the Lena basin, with a correlation coefficient of
20.53, which is significant at the 95% confidence level.
Relatively dry phases occur in the early and late 1980sin the Ob basin, and wet phases appear in the middle
1980s and early 1990s. Another notable feature in Fig.
5 is the marked difference in the temporal characteristicsof the time series for the Yenisey from those of the othertwo basins. The longer timescale signals seen in the
Lena and Ob are not dominant in this basin.
Finally, in this section, we note the temporal variationin residual evaporation derived from observed variables
for the basins. Evaporation decreases during the wetphase of precipitation (early and late 1980s) and increases during the dry phase (mid-1980s) in the Lenabasin. However, in the Ob and Yenisey basins, such
temporal characteristics are not displayed.
FIG. 5. (a) The time series of JJA averaged precipitation, moisture
convergence, evaporation, and JAS averaged runoff for the Lena
River basin (mm month 21 ). (b) As in (a) except for the Yenisey basin.
(c) As in (a) except for the Ob basin.

b. Large-scale moisture transport and circulation
anomaliestion, moisture convergence, evaporation as a residual ofwater balance components, and runoff for each basin.
The time series show well-defined interannual timescale
signatures. Note that the runoff time series are definedover mid- and late summer from July through September(JAS) in order to reduce the effect of snow meltwater
response to runoff.
There is close agreement in the variation among thesecomponents, particularly over the Lena (Fig. 5a) andOb (Fig. 5c). As described above, the time series of thethree components has a similar temporal signature. The
cross-correlation coefficient between JJA precipitation
and JAS runoff is 0.89 for the Lena, and 0.68 for theOb, as summarized in Table 2. These values are statistically significant at the 95% confidence level.

This section examines the large-scale moisture transport anomalies and atmospheric circulation patterns associated with the interannual signals in precipitation, as
presented earlier. Of particular interest are the spatial
structure of moisture transport in the summer precipitation regimes and the coherent pattern of hemisphericcirculation anomalies. As shown previously, the centersof action of interannual precipitation variability are located over the Lena and Ob basins. Therefore, we performed correlation and regression analysis between the
precipitation in these basins and the large-scale atmospheric fields.
Figure 6a shows the geographical distribution of regressed precipitation and moisture flux anomalies of theJune through August seasons when there was a large
amount of precipitation over the Lena basin. This anal-

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FIG. 6. (a) The regression map of the precipitation (mm day 21 )and moisture flux (kg m 21 s 21 ) over northern Eurasia on the precipitation index for the Lena River basin. Contour intervals are 0.1 mm
day 21 . (b) As in (a) except for the Ob index. Negative values areindicated by dashed contours.

ysis proceeded as follows: A precipitation index was
defined as the JJA-averaged precipitation time series(Fig. 5a) with the 17-yr mean subtracted. Linear regression coefficients to estimate precipitation and moisture flux values at each grid point from the precipitationindex were then evaluated. A value equal to 11.0 standard deviation of the precipitation index was input tothe regression equation to produce the gridpoint values.
A test of local statistical significance for the spatial correlation coefficient was performed using the standard t
test. Only moisture flux vectors with 95% significancewere plotted. Positive anomaly patterns on this map
correspond to the times when the precipitation index
was positive. Therefore, this map represents relatively
wet conditions over the Lena basin.
The overall pattern of anomalies in Fig. 6a is characterized by an east–west dipole structure. The positive
precipitation anomalies coupled with the cyclonic circulation centered over the upper Lena River basin are
well defined. Anomalous eastward and northeastward
moisture transport along 458–558N appear to contributeto an increased water vapor charge into this basin, andreinforce the precipitation anomalies. On the other hand,negative precipitation and strong anticyclonic moisture
flux anomalies entirely cover western Siberia, representing relatively dry conditions in the Ob River basin.
It should be noted that the regression pattern of moisture
flux convergence (not shown) is similar to that of precipitation. Convergent anomalies prevail over eastern
Siberia, while divergent anomalies extend over western
Siberia. This suggests that in this situation the Lenabasin plays the role of an anomalous moisture sink region, and the Ob a source.

289

A regression map based on the precipitation index
for the Ob (Fig. 6b) was produced, and indicates conditions of increased precipitation in this basin. This map
clearly shows a pattern almost completely out of phasewith that in Fig. 6a. Compared with Fig. 6a, anomalous
wet and dry regions are completely interchanged. Strong
positive precipitation anomalies accompanied by cyclonic circulating moisture flux anomalies that are zonally elongated broadly cover western Siberia, and negative precipitation and anticyclonic moisture flux anomalies lie over eastern Siberia.
In order to establish the basic pattern of large-scale
circulation anomalies associated with basin-scale precipitation variability, a regression map between the precipitation index for the Lena and 500-hPa geopotential
height anomalies over the entire Northern Hemisphereis given in Fig. 7a. This map was constructed in thesame manner as Fig. 6a. A clear wave train along 408–
708N extends from western Eurasia into the North Pacific–Alaska. The spatial scale of these hemisphericwavelike anomalies is at about zonal wavenumber 3 to
4. It is obvious that a significant pair of anomalous
cyclone and anticyclone patterns over Eurasia, part ofthis Eurasian wave pattern, coincides with the east–west
dipolelike anomalies shown in Fig. 6a. Additionally, theregression map of geopotential height anomalies basedon the Ob index (Fig. 7b) shows a phase signature nearly
opposite to the pattern in Fig. 7a, as in the case of Fig.
6b. A series of features found in Figs. 6 and 7 strongly
suggest that an east–west ‘‘seesawlike’’ replacement of
the relative dry and wet regimes occurs on an interannual timescale over the northern Eurasian region.
It is interesting to note that Rogers et al. (2001) also
identified the summer wave pattern over northern Eurasia in association with the interannual variation of July
poleward moisture transport across 708N. Comparing
our regressed patterns (Fig. 7) with their composite 500hPa height patterns, one can notice that there are somedifferences. The most significant difference is the location of Eurasian cyclonic–anticyclonic couplet. Whilethe centers of cyclonic and anticyclonic anomalies intheir Fig. 17 are positioned over the Arctic coastal region (708–808N), those in our maps lie over the inlandarea (558–658N). The patterns in Fig. 7 rather resemble
the composite maps constructed based on P–E time series for the Lena River basin by Serreze et al. (2002).
Finally, we present results from a rotated EOF(REOF) analysis using the covariance method performed on the 17-yr 500-hPa geopotential height timeseries. Its purpose was to establish whether the largescale anomalies shown by Fig. 7 are directly connectedto the atmospheric principal mode for summer on aninterannual timescale. Before undertaking the REOF
analysis, we carried out an EOF analysis and the firstsix EOF modes were rotated by the varimax method to
obtain the REOF modes. The first REOF (REOF 1; Fig.
8a) pattern is characterized by a zonally extending wave
train over mid- and high-latitude Eurasia and the North

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FIG. 7. (a) The regression map of the Northern Hemisphere 500-hPa geopotential height field (m) based on the precipitationindex for the Lena River basin. Contour interval is 2 m. Negative values are drawn with dashed contours. Zero contours areomitted. The 95% statistically significant areas are shaded. (b) As in (a) except for the Ob index.

FIG. 8. (a) The REOF1 of the 500-hPa geopotential height field for 17 summers (JJA 1979–95). Contour
intervals are 0.1. Negative values are drawn with dashed contours. Zero contours are omitted. Values greater(smaller) than 0.4 are lightly (darkly) shaded. (b) Amplitude time series for the RPC 1 (normalized) andarea-averaged precipitation anomalies (precipitation index) for the river basins (mm day 21 ).

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291

The time series of the REOF 1 score (denoted as RPC
1) is shown in Fig. 8b, combined with the precipitationindex for each basin. Although biennial-like signals are
dominant during the early 1980s and early 1990s in
REOF 1, it also appears to contain longer-timescale(more than 6-yr period) components. In fact, the RPC
1 time series is most highly correlated with the precipitation index for the Lena and Ob of all the RPC scores.
The significant positive correlation between RPC 1 andthe Lena index (0.52) is most important, as is the significant negative correlation between RPC 1 and the Ob
index (20.52). From the time series plots (Fig. 8b), thenearly in-phase relationship between RPC 1 and the
Lena index, and the out-of-phase relationship for theOb, are evident.
c. Robustness of the seesawlike dry and wet regimes

FIG. 9. (a) The time series of area-averaged precipitation anomalies
for JJA 1948–2001. Thick solid (thin dashed) line indicates the series
for the Lena (Ob) basin. (b) The same as (a) except for the east (508–
708N, 958–1358E) and west (508–708N, 558–958E) Siberian domain.
(c) The same as (a) except for the river runoff for JAS 1948–94. The
river gauge station for the Lena is located at Kusur.

Pacific that corresponds well with the pattern in Fig. 7,
in that it shows large-amplitude anomalies appearing
over the east Pacific, North America, and Atlantic regions. This REOF 1 mode explains 15.7% of the total
variance. Here, note that this Eurasian wave pattern was
also determined as a second EOF mode derived before
the REOF analysis.

FIG. 10. A 21-yr moving cross correlation between the Lena andOb precipitation index for JJA during 1948–2001. The dashed line
indicates the 95% significance level.

The results presented in this paper suggest the existence of an east–west seesawlike alternation of summer
dry and wet regimes on the interannual timescale in the
Siberian domain. As aforementioned, it appears to havea 6–8-yr cycle. However, our results have not completely determined the 6–8-yr quasi-periodical signals
in precipitation. Fundamental questions still remain. Is
the summer precipitation seesaw on this longer interannual timescale a robust signal? Our main analyses in
the previous sections are limited for the period after
1979. Here, we further examined long-term changes ofbasin-scale precipitation and runoff for this issue. Therefore, the monthly precipitation record spanning the 54yr period (see section 2) was used for the additional
analysis that follows.
Figure 9 displays simple comparisons of summer precipitation (JJA 1948–2001) and runoff (JAS 1948–94)
index between the Lena and Ob area. In the basin-averaged precipitation index (Fig. 9a), an out-of-phase relationship with a nearly 6–8-yr cycle and associatedreplacement of wet and dry regimes are evident fromthe middle 1970s through 1980s. In contrast, this characteristic is not found in the period from the middle
1950s through 1960s. Similar variation features are
identified also in the area-averaged precipitation series
for east and west Siberia (Fig. 9b). The Lena and Ob
runoff time series (Fig. 9c) appear to well reflect the
precipitation tendencies on interannual and decadal
timescales. While these two tend to be out of phase witheach other, particularly for the 15-yr period from the
middle 1970s, they appear to keep nearly in-phase variations from the middle 1950s through late 1960s. Obviously, the 6–8-yr cycle signal after 1979 shown in theprevious section constitute a part of about 2.5 cycles ofthis that last from the 1970s through the 1980s in Fig.
9. A changing relationship between the Lena and Ob
precipitation index can be statistically identified by the
21-yr moving cross correlation (Fig. 10). The negative
correlation well exceeds the 95% significance level after
the early 1970s, which supports the emergence of the292

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FIG. 11. Real part of Morlet wavelet transform of monthly precipitation index for (a) the Lena River basin and (b) the Ob River basin
during 54-yr period from 1948–2001 (648 months). The real part of wavelet coefficient is normalized by the std dev. The solid and dashedcontours indicate the 95% and 90% significance levels, respectively.

dominant out-of-phase relationship between them during the last 25–30 yr. In contrast, these two precipitation
time series are actually incoherent with respect to eachother before this epoch. Both the in-phase and out-ofphase relationship is not robust from the 1950s throughthe 1960s.
To clarify the quasi periodicity of 6–8 yr in the precipitation variation for these two basins, wavelet analysis is applied based on the method developed by Torrence and Compo (1998). Figure 11 displays the real partof the Morlet wavelet coefficients for the entire 54-yr
record (648 months). Prior to this analysis, monthly
climatological annual cycle and linear trend are subtracted from each precipitation index. The upper panel(Fig. 11a) clearly shows the strong signal in the 6–8-yr
range of the Lena precipitation index, and this signal is
90% statistically significant from 1975 through 1998.
A corresponding signal is also seen in the Ob index(Fig. 11b), which has a 90% significant area spanning1971 through 1993. These two signals apparently exhibit an out-of-phase pattern with each other after 1972.
Furthermore, we examined a wavelet spectrum of eachseasonal mean precipitation time series over the 54-yr
period (not shown). The seasons were simply defined
as JJA, SON (September–November), DJF (December–
February), and MAM (March–May). As a result, the 6–
8-yr signals were evident only in the wavelet spectrum
for the summer mean (JJA) precipitation indices. Statistical properties in the analysis results verify the ro-bustness of the out-of-phase summer precipitation variation between the Lena and Ob since the early 1970s.
Here, we can also notice that the Ob spectrum (Fig.
11b) includes a decadal signal during 1950–76 and a
4–5-yr signal during 1978–92; however, it is beyond
the scope of this paper to discuss these variabilities.
Finally in this section, the summer atmospheric patterns are examined to ensure the robustness of the Eurasian wave structure during the period when the pronounced precipitation seesaw is observed. The last 30yr (1972–2001) record of the NCEP–NCAR reanalysis
and PREC/L-based precipitation index are used. Notethat each precipitation index was detrended. The resulting regression maps of 500-hPa geopotential heightanomalies against the Lena and Ob precipitation index
are given in Fig. 12. The basic features, especially theeast–west dipole structures are strongly similar to thosein Fig. 7. This fact gives us reasonable confidence inthe presence of this type of atmospheric pattern associated with the northern Eurasian precipitation seesaw.
5. Discussion
It is interesting to note that similar timescale fluctuations in Eurasian hydrometeorological components
have been detected by several past studies. Semiletov
et al. (2000) and Savelieva and Semiletov (2001) confirmed the existence of 6–8-yr-period variations of annual runoff, precipitation, and temperature in Siberian

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293

FIG. 12. (a) The regression map of the Northern Hemisphere 500-hPa geopotential height field (m) based upon the JJA
precipitation index for the Lena River basin during 30-yr period from 1972 to 2001. Contour interval is 2 m. Negative values
are drawn with dashed contours. Zero contours are omitted. The 95% statistically significant areas are shaded. (b) As in (a)except for the Ob index.

river basins. One can also find a similar signal of Ob
runoff after 1970 in the results of Johnson et al. (1999),
which is based on the 3-yr smoothed monthly record(in their Fig. 5). As shown in our Figs. 9, 10, and 11
and some previous results, the 6–8-yr variations with
out-of-phase signatures in summer precipitation andrunoff are likely to have been initiated in the early
1970s. Savelieva et al. (2000) pointed out that the decadal climate regime shift over northern Eurasia and theNorth Pacific in the 1970s had significant influences on
the hydrological variations for Siberia. This wintertime
climate shift with a pan-Pacific scale has been reported
by many previous studies as noted by Minobe (2000).
Besides, the impact of this on the summer monsoon–
ENSO relationship is proposed by Chang et al. (2001)
and Kinter et al. (2002). However, the direct link between this regime shift and the appearance of the 6–8yr signals (Fig. 9) is still unclear.
As described at the beginning of this paper, the factor
to regulate the northern Eurasian dry and wet regimes
is a primary concern. It is natural to ask what factors
excite the Eurasian wave pattern that alternates the summer precipitation and associated hydrological cycles? A
relation with the large-scale mode of atmospheric variations is an unresolved issue. Park and Schubert (1997)
discussed the causes for summer Eurasian stationary
waves in terms of orographic forcing and remote response to the monsoon. They also noted the possibleroles of atmospheric conditions in the preceding season

on generating the midlatitude wave anomalies. Yasunari
et al. (1991) give an insight into the effect of the atmosphere–land interactions and land memory in the preceding seasons on the summertime mid- and high-latitude circulations over Eurasia. However, the generationof the 6–8-yr cycles observed in northern Eurasia itself
could not be explained by only these factors. We needto examine the land-memory effect during the spring toearly-summer season on the summer atmospheric conditions and hydrological cycle in current GCM results.
For example, outputs reproduced by AMIP-II models
can be useful to diagnose these factors.
Now, another possibility may be a close association
with the subdecadal atmospheric mode on a 6–7-yr timescale obtained by Venegas and Mysak (2000). They isolated this timescale mode by coupling sea ice anomalies
in the North Atlantic–Arctic Ocean (Greenland and Irminger Seas) to hemispheric sea level pressure anomalies. In their Fig. 8, there is an evident recurrent cycleof sea level pressure anomalies that forms a hemisphericwavelike pattern. In addition, variation on a similar
timescale has been noticed in global temperature (Mannand Park 1994), Arctic sea ice anomalies (Arfeuille etal. 2000), and the North Atlantic Oscillation (e.g., Hurrell and van Loon 1997; Pozo-Vázquez et al. 2001; Costa and de Verdiere 2002). These earlier studies imply
that the atmosphere–ocean coupling system in the North
Atlantic domain may be an effective driving force forthe hemispheric atmospheric mode at this timescale. The

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best way to understand this is an examination from theviewpoint of the teleconnections between North Atlantic
sea surface conditions (sea surface temperatures and sea
ice extent) and subsequent Eurasian atmospheric conditions (e.g., Palmer and Sun 1985; Peng and Mysak
1993; Peng et al. 1995; Ting and Peng 1995). However,
we would note that representative teleconnection indiceshave no significant correlation with Siberian hydrometeorological components for summer (e.g., Rogers etal. 2001; Serreze et al. 2002).
6. Conclusions
We investigated the characteristics of the summer water balance components over northern Eurasia, and revealed noticeable interannual signals. The variance inthe summer (JJA) precipitation suggests that two centersof action for the hydrological cycle exist, one over eastern Siberia (including the Lena basin), and the otherover western Siberia (including the Ob basin). The JJA
precipitation correlates strongly with the mid- to latesummer runoff (JAS) in these two basins. In addition,
their fluctuations in precipitation, moisture convergence,and runoff indicate the presence of 6–8-yr cycles. Dry
and wet regimes are associated with these signals. Moreover, basin-scale precipitation variability indicates thatthe relatively wet and dry regimes in the Lena basin are
essentially out of phase with those in the Ob basin.
We then examined the large-scale atmospheric moisture transport and circulation fields associated with basin-scale precipitation variability. The regression analysis results based on the precipitation index for these
two basins (Lena and Ob Rivers) show contrasting features. When the Lena basin is wet, a cyclonic circulation(moisture flux) coupled with large positive precipitation
anomalies entirely covers this basin, while at these times
anticyclonic circulation collocated with negative precipitation anomalies prevail over the Ob basin. The east–west dipole structures of the circulation and precipitation anomalies are clearly evident. A seesawlike replacement of the dry and wet regimes associated withthis dipole circulation structure occurs between eastern
and western Siberia at this timescale.
The Eurasian wave patterns that emerge, correlatedwith the basin-scale precipitation variability, appear to
be closely linked to the interannual leading mode ofsummer geopotential height anomalies over the Northern Hemisphere. The first REOF mode, which shows ahemispheric wavelike pattern, closely resembles the regression pattern of geopotential height anomalies in the
precipitation index for each basin. This suggests thatlarge-scale circulation anomalies with a hemispheric teleconnective pattern may be one of the causes for modulating the regional moisture transport and atmosphericwater flow into these basins, and also the resulting precipitation and runoff. This is also strongly supported by
the significant cross correlation between the precipita-

VOLUME 4

tion index and the leading principal component timeseries.
Acknowledgments. We thank the three anonymous reviewers for their insightful comments on an earlier version of manuscript. We benefited from critical comments
by John Walsh for improving the quality of this paper.
Mark Serreze and David Bromwich kindly sent us theirforthcoming paper. Part of the NCEP reanalysis-2 dataset was obtained from the NCEP. We would like tothank M. Kanamitsu (now at Scripps Institute of Oceanography), W. Ebisuzaki, and E. Yarosh who kindly
helped us to access the data. The rest of this dataset wasprovided by the NCAR Data Support Section (DSS).
The CMAP and PREC/L datasets were obtained fromthe NCEP–CPC (ftp://ftp.cpc.ncep.noaa.gov/precip).
The Bodo-enhanced Russian river flow dataset is available at NCAR/DSS (ftp://dss.ucar.edu/ds553.2). The
wavelet analysis tools developed by Torrence and Compo (1998) was taken from their Web site at the University of Colorado (http://monsoon.colorado.edu/research/wavelet). The Generic Mapping Tools (GMT)
graphics package developed by Wessel and Smith(1995) was used in preparing all the figures.
APPENDIX
Climatology of Annual Water Balance for ThreeRiver Basins
Long-term annual means of water balance components estimated from the NCEP–DOE AMIP-II reanalysis and CMAP data are provided for the purpose ofchecking their accuracy. The main focus is whether the
climatological precipitation minus evaporation (P 2 E)
derived from reanalysis is close to runoff (R) or not ineach river basin. In general, the long-term mean of P
2 E and R should be equal in a theoretical sense. The
values of these components are summarized in Table
A1. The mean annual values are defined based on thehydrological year from October 1979 through September 1994 for the Lena and Ob (15 yr), and from October
1979 through September 1995 for the Yenisey (16 yr).
Comparing P 2 E with R, these terms almost agreewith each other for the Lena and Yenisey. On the other
hand, the difference between these two components ismuch larger for the Ob (50 mm yr 21 ). One of the posTABLE A1. Climatological annual water budget for three river basins in Siberian domain (mm y21 ). P 2 E is computed from R2, andP is derived from CMAP; E is estimated as the residual from theseproducts.
Annual P, P 2 E, E, and R
Lena
Yenisey
Ob

P

P2E

E

R

350
410
430

190
231
110

160
179
320

197
241
164

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sible reasons for this large imbalance may be caused byunderestimation of moisture convergence for the Ob.
In addition, the area-averaged precipitation for eachbasin computed from CMAP data is much smaller thanthat obtained from the gauge-corrected precipitation by
Serreze et al. (2002). The major factor for this is presumably caused by the snow undercatchments at rain
gauges during the cold season.
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