INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 36: 355–368 (2016)
Published online 13 May 2015 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/joc.4352

Trend and interannual variation in summer precipitationin eastern Siberia in recent decades
Hatsuki Fujinami,a* Tetsuzo Yasunarib and Tatsuro WatanabecaHydrospheric Atmospheric Research Center, Nagoya University, Japanb
Research Institute for Humanity and Nature, Kyoto, Japanc Graduate School of Environmental Sciences, Nagoya University, Japan

ABSTRACT: We analysed the long-term trend and interannual variability in summer (JJA) precipitation over eastern Siberia(90∘ –140∘ E, 50∘ –70∘ N) for the period 1979–2007. An increasing trend in summer precipitation was observed over large areas(Yenisei and Lena river basins) of eastern Siberia. Summer mean 850-hPa geopotential height decreased from Siberia to themarginal seas north of Siberia, with the largest decreases occurring over the northern part of eastern Siberia, while increasing
trends were found around Mongolia, inducing a significant increase in westerly moisture flux and its convergence over eastern
Siberia. Empirical orthogonal function (EOF) analysis of detrended summer precipitation revealed the leading modes of theinterannual variability. The first leading mode (EOF1) represents interannual variation around the central Siberian Plateau andthe middle reaches of the Lena river basin, related to the enhancement of a low-level cyclonic circulation centred on 65∘ N,
100∘ E around eastern Siberia. The circulation pattern of EOF1 closely matches the trend pattern over Siberia, indicating that a
deeper quasi-stationary trough appears with increasing frequency in recent decades. The second leading mode (EOF2) showsa dipole pattern between the middle reaches of the Lena river basin and the western part of the Central Siberian Plateau. Wet
summers in the Lena river basin are related to a stationary trough centred at around 60∘ N, 130∘ E. These leading patterns are
related to the change in position of the stationary trough and the ridge of the wave train structure along the Arctic polar jet
across the northern Eurasian continent.
KEY WORDS

Eastern Siberia; summer precipitation; hydroclimate; trend; interannual variation; stationary trough; polar jet

Received 1 August 2014; Revised 25 February 2015; Accepted 27 March 2015

1. Introduction
Eastern Siberia possesses a vast region of coniferous
forest known as taiga. The forest is maintained undervery dry conditions with a total annual precipitation of∼200–300 mm (Fukutomi et al., 2003; Ohta et al., 2008).
In eastern Siberia, the larch-dominated taiga has a spatially homogeneous distribution over the continuous anddiscontinuous permafrost. In summer, the surface layer(i.e. the active layer) of permafrost thaws and provides liquid water to tree roots. The boundary between the surface
active layer and permafrost acts as an impermeable layer,
and a small amount of rainfall and meltwater is retainedin the surface layer, without infiltration into deeper layers. Thus, the existence of permafrost allows a vast areaof forest to be maintained under dry conditions, forming aunique taiga–permafrost coupled system in eastern Siberia(Osawa et al., 2010; Zhang et al., 2011).
The taiga plays an important role in the hydroclimateof eastern Siberia (Ohta et al., 2001; Saito et al., 2006;
Ohta et al., 2008). Ohta et al. (2001) reported fromin situ observations that the total evapotranspiration* Correspondence to: Dr H. Fujinami, Hydrospheric AtmosphericResearch Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya
464-8601, Japan. E-mail: hatsuki@hyarc.nagoya-u.ac.jp© 2015 Royal Meteorological Society

from the forest (151 mm) exceeded the precipitation(106 mm) and was equal to 73% of the total water input(211 mm), including snow-melt water during the forest growing season (mid-April to early September) in
1998. Precipitation reaches a maximum (minimum) in
July (February) in the seasonal cycle (Fukutomi et al.,
2003; Serreze and Etringer, 2003). Summer precipitation(June–August; JJA) accounts for more than 40% of thetotal annual precipitation, indicating its importance tothe hydroclimate in eastern Siberia. Atmospheric water
budget analysis can estimate the contribution of evaporation/evapotranspiration and large-scale moisture flux
divergence to precipitation (e.g. Fukutomi et al., 2003;
Serreze and Etringer, 2003; Yatagai, 2003). If we assume
that the tendency of precipitable water in the atmosphereis negligible, the water budget equation can be simplified
to state that precipitation equals the sum of evaporation/evapotranspiration and moisture flux convergence:⟨P⟩ = ⟨E⟩ − ⟨∇ · Q⟩, where P is precipitation, E is evaporation/evapotranspiration, ⟨∇ · Q⟩ is the divergence ofvertically integrated moisture flux, and angled brackets
denote the area average. According to the atmosphericwater budget, summer precipitation over east Siberia originates predominantly from evaporation/evapotranspirationfrom the land surface, whereas the contribution of thelarge-scale moisture flux convergence is small (Fukutomi

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et al., 2003; Serreze and Etringer, 2003; Serreze et al.,2003). Thus, summer precipitation is controlled mainly by
the contribution of recycled water from the land surface(Kurita et al., 2004).
In contrast, on interannual timescales, the variation of
the large-scale moisture convergence becomes the main
driver of summer precipitation variability over eastern
Siberia (Fukutomi et al., 2003) because the interannual
variation of evaporation/evapotranspiration is small compared with the large fluctuations in precipitation in eastern Siberia during summer (Fukutomi et al., 2003; Ohta
et al., 2008). The taiga–permafrost coupling maintains a
constant amount of evapotranspiration (Sugimoto et al.,
2002; Ohta et al., 2008). These observations suggest thatknowledge of the variability of large-scale summer precipitation is essential in understanding the hydroclimate of theregion.
Fukutomi et al. (2003) reported that the dominant modeof interannual variation in the large-scale precipitation
anomaly over Siberia is an east–west seesaw oscillation
between eastern and western Siberia, with a timescaleof ∼6–8 years. Cyclone tracks are closely clustered inrelation to the seesaw oscillation: cyclones frequently passover eastern (western) Siberia in wet years in eastern(western) Siberia, while a significant decrease in cyclone
tracks is observed in western (eastern) Siberia. Thus, theeast–west seesaw oscillation is strongly related to theeast–west shift of storm tracks across Siberia (Fukutomi
et al., 2004, 2007). Iwao and Takahashi (2006) showedthat the dominant mode of the interannual fluctuation in
July precipitation is a north–south seesaw pattern between
northeast Asia and Siberia.
However, these previous studies did not focus on thevariation in summer precipitation over eastern Siberia,
where the larch taiga–climate interaction through thewater cycle is dominant. Few studies have investigatedthe variability of summer precipitation in eastern Siberia,or the precipitation trend in recent decades. Therefore,
this study examines the spatial patterns in the trend andinterannual variation of summer precipitation in eastern
Siberia, and the associated atmospheric circulation fields.
The remainder of the manuscript is organized as follows.
Section 2 introduces datasets and methods. The results, interms of precipitation trends, the leading modes of interannual precipitation variability in eastern Siberia, and theirassociated atmospheric circulation patterns, are given in
Section 3. Section 4 discusses the long-term trend andthe recent change in hydroclimate over eastern Siberia.
Finally, the conclusions are presented in Section 5.

2.

Data and methods

The analysis of precipitation variability was based on
the APHRODITE V1101 dataset for northern Eurasia(hereafter, APHRODITE-RU) (Yatagai et al., 2012).
APHRODITE-RU consists of daily gridded precipitation datasets, derived from quality-controlled rain gauge
data. The number of valid rain gauge stations is much
© 2015 Royal Meteorological Society

greater than that available from the Global Telecommunication System (GTS). The daily precipitation data
enable the analysis of precipitation characteristics, suchas the frequency and intensity, on a daily basis. Thus,
APHRODITE-RU has an advantage over other long-term
precipitation datasets that provide data on a monthly basis.
The precipitation frequency was defined as the total number of days with more than 1.0 mm day−1 of precipitation
during the summer (JJA). Daily precipitation intensity
was expressed as the total summer rainfall divided by thefrequency. The dataset is on a 0.5∘ latitude–longitudegrid. We also used the Japanese 25-year (1979–2004)
reanalysis (JRA25)/Japan Meteorological Agency (JMA)
Climate Data Assimilation system (JCDAS) datasets on
a 1.25∘ latitude–longitude grid to examine large-scaleatmospheric circulation associated with rainfall variability(Onogi et al., 2007). The JRA25/JCDAS dataset covers
the period 1979–2013. The dataset variables include
horizontal wind (u and v), specific humidity (q), andgeopotential height (Z). The vertically integrated water
vapour flux from the surface to 100 hPa was also calculated. The data were monthly averaged from the original
6 hourly data. We used both datasets from 1979 to 2007
(29 years), during the summer season (JJA). Monthly
global Normalised Difference Vegetation Index (NDVI)
data obtained from the Pathfinder advanced very highresolution radiometer (AVHRR) Land (PAL) dataset, with1∘ × 1∘ resolution, were also used to show the spatial distribution of vegetation over eastern Siberia for the period
1980–2000 (Smith et al., 1997; Suzuki et al., 2007).
To investigate the variability of precipitation in eastern
Siberia, we define a rectangular domain of 50∘ –70∘ N,
90∘ –140∘ E (Figure 1), characterised by higher elevation
than the West Siberian Plain to the west of 90∘ E. The
Central Siberian Plateau extends over the northwesternpart of the domain, and high mountain ranges are present
in its southern and eastern parts. The Lena river basin is
located between these ranges. The domain also contains
parts of the Yenisei river and Amur river basins in its
western and southeastern-most areas, respectively.
First, we calculated the long-term trend of JJA-total precipitation over eastern Siberia during 1979–2007. The
non-parametric Mann–Kendall test was applied to assess
the trend. Then, we used empirical orthogonal function(EOF) analysis to examine the leading modes of interannual variability in JJA precipitation over eastern Siberia.
The linear trend was removed from the original time series
at each grid point before applying EOF analysis. The total
precipitation in summer varies from less than 100 mm in
the northern part of the analysis domain to more than
300 mm in the southeastern part (Figure 2(a)). Therefore,
to detect the spatial pattern of the leading mode, which
includes an area of relatively low precipitation, EOF analysis was applied to normalised and detrended JJA precipitation data from 1979 to 2007 in the domain, providing
similar results to EOF analysis using a correlation matrix(Wilks, 2011). The spatial structure of each EOF represents the spatial distribution of the correlation coefficient
between the precipitation value at each grid point andInt. J. Climatol. 36: 355–368 (2016)

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TREND AND INTERANNUAL VARIATION IN SUMMER HYDROCLIMATE IN SIBERIA
80°N
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1200 2400 (m)Figure 1. Shaded relief map of Siberia. The domain enclosed by the rectangle is the key domain used to examine precipitation variability in thisstudy and is referred to as eastern Siberia.
JJA Climatology (1979–2007)(a) Precipitation
80°N

70°N

JJA Climatology

NDVI and moisture flux divergence

75°N
70°N

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50

80°N

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(b) Moisture flux vectors (sfc-100 hPa) and its divergence

55°N

300 [mm (3 month)–1]
JJA Climatology

50°N
90°E

100°E

110°E

120°E

130°E

140°E

75°N

0.1

70°N
65°N

0.2

0.3

0.4
(NDVI)

0.5

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0.7

60°N
55°N
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40°N
20°E 30°E 40°E 50°E 60°E 70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E 150°E 160°E
0150 (kg m–1 s–1)

Figure 2. (a) Climatological (1979–2007) mean distribution of summer(JJA) total precipitation. (b) As in (a), but for vertically integrated (fromthe surface to 100 hPa) moisture flux vectors and divergence. Shading
denotes areas of divergent moisture flux.

the principal component (PC), because the PCs are normalised. Before EOF analysis, we multiplied the data by aweighting factor: the square root of the cosine of latitude.
The statistical significance of the composite and regression fields at each grid point was estimated using Student’s
t-test.

3. Results
3.1. Climatological features
Climatological mean summer total precipitation displays
a zonal distribution over Siberia (Figure 2(a)). Precipitation is less than 200 mm, except over the southern partof the domain. In eastern Siberia, the precipitation distribution is characterised by a large meridional gradientfrom south to north, partly due to the existence of mountains located around the southeastern part of the analysis© 2015 Royal Meteorological Society

Figure 3. Climatological (1980–2000) seasonal (JJA) mean distribution
of NDVI over eastern Siberia (shading) and moisture flux divergence(1.0 × 10−6 kg m−2 s−1 ; contours). Contours are drawn only in areas ofdivergent moisture flux, at 0, 5, and 10 × 10−6 kg m−2 s−1 .

domain (Yoshida et al., 2011). Westerly moisture flux vectors predominate over Siberia, and the area of moisture flux
divergence extends zonally along 60∘ N. This means that
evaporation/evapotranspiration from the surface is a major
source of summer precipitation over the area, which is inagreement with the results of an atmospheric water budget analysis for the Lena, Obi, and Yenisei river basins by
Fukutomi et al. (2003) and Serreze and Etringer (2003).
Note that the dense vegetation area, as determined by
NDVI, corresponds well to the area of moisture flux divergence in JJA (Figure 3), suggesting that boreal forest (i.e.
taiga) is an important source of moisture through evapotranspiration during summer.
3.2.

Trend in summer precipitation over eastern Siberia

A significant trend in summer precipitation is observed
over eastern Siberia (Figure 4). An increasing trend isfound over most of eastern Siberia (e.g. the Yenisei river
basin and Lena river basin) except in the southeasternpart of the domain where there is a decreasing trend.
The time series of area-averaged JJA total precipitationfor 55∘ –70∘ N, 90∘ –130∘ E from APHRODITE-RU showsa remarkable increasing trend over eastern Siberia with
Int. J. Climatol. 36: 355–368 (2016)

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Linear trend of precipitation

JJA (1979–2007)

70°N

85°N

Trend of Z (850-hPa) and moisture flux

JJA (1979–2007)

80°N
75°N

65°N

70°N
65°N
60°N

60°N

55°N
50°N
45°N

55°N

40°N
20°E 30°E 40°E 50°E 60°E 70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E 150°E 160°E
–8

50°N
90°E

100°E
–30

110°E
–15

120°E

–5

5

130°E
15

140°E

30

Figure 4. Linear trend (per decade) of summer total precipitation [mm(3 months)−1 ] over eastern Siberia for 1979–2007. Solid dots denotegrid points with trends that are statistically significant at the 95%
confidence level.

240
210
180

[mm (3 month)–1]150
APHRODITE V1101
PREC/L
GPCC V6
Delaware

120
90
60
30
0

Moisture flux convergence

–30
1980

1985

1990

1995
(Year)

2000

2005

2010

Figure 5. Time series of area-averaged (55∘ –70∘ N, 90∘ –130∘ E)
3-month (JJA) total precipitation in eastern Siberia from four different
datasets (APHRODITE, GPCC, PREC/L, and Delaware). The timeseries of moisture flux convergence from JRA25 is also shown. The
unit of moisture flux convergence (kg m−2 s−1 ) is converted into mm(3 month)−1 for the comparison with the JJA total precipitation.

interannual fluctuation on a 3–5-year period (Figure 5).
There is a linear trend in the time series with a 13.9-mm
increase per decade during 1979–2007, which is significant at the 99% level (Mann–Kendall test).
The increasing trend can also be observed until recently
in other monthly precipitation datasets such as the Global
Precipitation Climatology Centre (GPCC) (Schneider
et al., 2014), Precipitation Reconstruction over Land
(PREC/L) (Chen et al., 2002), and the University of
Delaware precipitation (Willmott and Matsuura, 1995),
with 0.5∘ × 0.5∘ latitude–longitude grids (Figure 5).
The time series of these four datasets show a similar
increasing trend and interannual variation from 1979 to
2007, although the precipitation in APHRODITE is thelowest of all the datasets. Yatagai et al. (2012) reported
that APHRODITE data underestimated precipitation© 2015 Royal Meteorological Society

–4

–1

1

4

8

50 (kg m–1 s–1)

(m)

Figure 6. Linear trend (per decade) of 850-hPa geopotential height (Z)and vertically integrated moisture flux vectors for 1979–2007. Solid(open) dots denote grid points with trends in geopotential height that are
statistically significant at the 95% (90%) confidence level. Moisture flux
vectors are plotted where either the u or v component exceeds the 95%
confidence level.

compared with monthly precipitation datasets. As in
APHRODITE-RU, the increasing trends observed in
GPCC and the Delaware dataset from 1979 to 2010 are
statistically significant at the 99% level. The increasing
trend in PREC/L from 1979 to 2011 is statistically significant at the 95% level. Thus, the increasing trend is a robustfeature of precipitation over eastern Siberia, although theabsolute values of precipitation and their linear trends
differ slightly between datasets.
Figure 6 shows the linear trend of summer mean 850-hPa
geopotential height (Z850) and vertically integrated moisture flux vectors. Summer mean Z850 decreases from
Siberia to the marginal seas north of Siberia, with thelargest decreases occurring over the northern part of eastern Siberia. The spatial structure of the trend in Z850
around Siberia is consistent with that in surface pressureduring the period 1979–2007 shown by Ogi and Wallace(2007, their Figure 4(c)). In contrast, Z850 around Mongolia shows a significant increase. The spatially varying
feature of trends in geopotential height over and around
Siberia suggests that atmospheric circulation changes contribute to the increasing trend in precipitation over eastern Siberia. The structure of the trend in Z850 induces
a significant strengthening in westerly moisture flux overeastern Siberia. The area of increasing precipitation trend
over eastern Siberia is consistent with the area where thedecreasing Z850 trend and the increasing trend of westerly moisture flux are observed. An increasing trend in
the moisture flux convergence is also found over eastern
Siberia (Figure 5), consistent with the increasing trend in
precipitation, and this is statistically significant at the 90%
level for the period 1979–2011. This means that moisture
flux convergence is likely to be a key factor causing thetrend of precipitation over eastern Siberia.
3.3. Leading modes of interannual variability insummer precipitation over eastern Siberia
The three leading EOFs and PCs of summer precipitation
are shown in Figure 7. The first three EOFs account for
49.6% of the total variance, with the first EOF explaining
20.2%. Positive values of EOF1 are spread widely over theInt. J. Climatol. 36: 355–368 (2016)

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TREND AND INTERANNUAL VARIATION IN SUMMER HYDROCLIMATE IN SIBERIA

3

65°N
60°N
55°N
50°N
90°E

(d) PC1 and precipitation (55°N–70°N, 90°E–130°E)
150

2

100

1

50

0

0

–1

–50

–2

–100
–150

–3
100°E

110°E

120°E

(b) EOF2 (17.5%) precipitation

130°E

140°E

JJA 1979–2007

70°N
65°N
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1980
3

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2005

(e) PC2 and precipitation (55°N–65°N, 120°E–140°E)
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2

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–100

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50°N

–150

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100°E

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(c) EOF3 (11.9%) precipitation

130°E

1980

140°E

[mm (3 month)–1]

JJA 1979–2007[mm (3 month)–1](a) EOF1 (20.2%) precipitation
70°N

1985

1990

1995

2000

2005

1985

1990

1995

2000

2005

(f) PC3JJA 1979–2007
3

70°N

2
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1
0

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–1
55°N
–2
50°N

–3
90°E

100°E

110°E

120°E

–0.8 –0.6 –0.4 –0.2 0.2

0.4

130°E
0.6

140°E

1980

0.8

Figure 7. Spatial patterns of the first three leading EOF modes of summer precipitation over eastern Siberia: (a) EOF1, (b) EOF2, and (c) EOF3, andtime series of the first three PCs (solid line with solid circles): (d) PC1, (e) PC2, and (f) PC3. The solid line with open triangles in (d) indicates thearea-averaged precipitation for 55∘ –70∘ N, 90∘ –130∘ E, and in (e) for 55∘ –65∘ N, 120∘ –140∘ E.

Central Siberian Plateau, including the middle reaches of
the Yenisei river basin, and the upper to lower reaches of
the Lena river basin, while negative values occur aroundthe lower reaches of the Amur river basin. The spatial pattern of EOF1 is similar to that of the precipitation trend
shown in Figure 4. The time series of PC1 reveals interannual fluctuations with a 3–5-year period (Figure 7(d)). The
time variation of the detrended rainfall averaged over thearea where positive values exist in Figure 7(a) (55∘ –70∘ N,
90∘ –130∘ E) is almost identical to PC1 (Figure 7(d)).
EOF2 represents a dipole pattern between the middle
reaches of the Lena river basin with a positive centre
around 60∘ N, 130∘ E and a negative area around the westernmost part of the analysis domain. This mode explains
17.5% of the total variance. PC2 displays interannual variability with a timescale of ∼6–8 years (Figure 7(e)). The
spatiotemporal structure from EOF2 and PC2 resembles
the precipitation seesaw pattern between the Lena andObi river basins at a timescale of ∼6–8 years as reported
by Fukutomi et al. (2003) and Fukutomi et al. (2007).
The correlation between PC2 and the precipitation seesaw© 2015 Royal Meteorological Society

index (defined by Fukutomi et al. 2007) is 0.75, which issignificant at the 99% level. A time series of area-averaged
precipitation anomaly around the positive area of EOF2
(i.e. 55∘ –65∘ N, 120∘ –140∘ E) corresponds closely to PC2,
which displays large positive values from 2005 to 2007.
In EOF3 (Figure 7(c)), the positive areas extend over themountainous regions of the northeast (around the Verkhoyansk Mountains) and the southern parts of eastern Siberia(around the boundary with Mongolia). However, these values are small compared with those seen in EOF1 andEOF2. Values are negative along 60∘ N, between the positive areas, but the values are very small. Positive values
generally continued from the mid-1980s to 2000, while
negative values occurred in other years (Figure 7(f)).
3.4. Distribution of precipitation and large-scaleatmospheric circulation related to the leading modes
Figure 8 shows the regression patterns in Z850 and thevertically integrated moisture flux vectors based on thefirst three PCs. Note that the figures cover a broader area
than the domain used for the EOF analysis. In EOF1,
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85°N(a) Regression (PC1) 850-hPa Z and moisture flux vectors

80°N
75°N
70°N
65°N
60°N
55°N
50°N
45°N
40°N
20°E 30°E 40°E 50°E 60°E 70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E 150°E 160°E(b) Regression (PC2) 850-hPa Z and moisture flux vectors
85°N
80°N
75°N
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55°N
50°N
45°N
40°N
20°E 30°E 40°E 50°E 60°E 70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E 150°E 160°E(c) Regression (PC3) 850-hPa Z and moisture flux vectors
85°N
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20°E 30°E 40°E 50°E 60°E 70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E 150°E 160°E
99%

95%

90%

90%

95%

99%

30 (kg m–1 s–1)

Figure 8. Regression patterns of the 850-hPa geopotential height (contours, interval 2 gpm), and vertically integrated moisture flux vectors based on(a) PC1, (b) PC2, and (c) PC3. The colour shading represents the confidence level of the correlation coefficient between each PC and the 850-hPa
geopotential height. Blue (red) shading indicates negative (positive) correlation. Only moisture flux vectors statistically significant at the 95% levelare plotted. The topographic contour for 1500 m is shown as a thick solid line.

a cyclonic circulation anomaly centred at around 65∘ N,
100∘ E (Figure 8(a)) is associated with wet summers. The
cyclonic circulation anomaly extends over a broad areafrom eastern Siberia to the marginal seas to the north (i.e.
the Laptev and Kara seas). It enhances westerly moisture
flux anomalies along latitudes from 50∘ to 65∘ N toward
eastern Siberia. South and southwesterly moisture flux
vectors also dominate over the eastern part of eastern
Siberia. Note that the regression pattern in EOF1 is similar to the trend pattern of Z850 shown in Figure 6 except
around Mongolia, indicating a physical linkage between
the trend and the interannual variability in Z850 related to© 2015 Royal Meteorological Society

EOF1. The regression pattern for EOF2 has a dipole pattern between the Obi and Lena river basins (Figure 8(b)).
High summer precipitation around the middle reaches of
the Lena river basin is related to the cyclonic circulation
anomaly centred around 65∘ N, 130∘ E. The structure of theanomaly is similar to that of the seesaw mode between
the two river basins as reported by Fukutomi et al. (2003).
Circulation anomalies in EOF3 do not exhibit large signals over eastern Siberia (Figure 8(c)). Weak and relatively
local cyclonic anomalies are located around the positive
values seen in EOF3 (Figure 7(c)). Herein, we focus on theatmospheric structure in relation to EOF1 and 2, because
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TREND AND INTERANNUAL VARIATION IN SUMMER HYDROCLIMATE IN SIBERIA

85°N(a) Precipitation, 850-hPa Z and moisture flux(a) Moisture flux vectors (sfc-100 hPa) and its divergence

Dry summer (PC1)85°N

Dry summer (PC1)

80°N

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75°N

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40°N
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50

85°N

100

150

150 (kg m–1 s–1)

250(b) Precipitation, 850-hPa Z and moisture flux

Wet summer (PC1)

150 (kg m–1 s–1)0(b) Moisture flux vectors (sfc-100 hPa) and its divergence
85°N

Wet summer (PC1)

80°N

80°N

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40°N
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150 (kg m–1 s–1)0(c) Difference (wet-dry)

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–2

–1

–0.3

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1

2 (1.0 x 10–5 kg m–2 s–1)

150 (kg m–1 s–1)

Figure 9. (a) Composite of 3-month total precipitation (shading,
mm (3 months)−1 ), the 850-hPa geopotential height (contours, interval
15 gpm), and vertically integrated moisture flux vectors for dry summers
based on PC1. (b) As in (a) but for wet summers. (c) As in (a) but for thedifference between wet and dry summers, and with a contour interval for
geopotential height of 5 gpm. Only moisture flux vectors that are statistically significant difference at the 95% level are plotted. Shaded areas
enclosed by thick solid lines indicate a significant difference at the 95%
confidence level in precipitation.

Figure 10. (a) Composite of vertically integrated moisture flux vectors
and its divergence (contours, interval 2.0 × 10−5 kg m−2 s−1 ) for dry
summers based on PC1. Areas of divergence are shaded. (b) As in (a)but for wet summers. (c) As in (a) but for the difference between wet anddry summers. Only moisture flux vectors that are statistically significant
difference at the 95% level are plotted. Shaded areas enclosed by thicksolid lines indicate a significant difference at the 95% confidence levelin moisture flux divergence. The unit for moisture flux divergence is
1.0 × 10−5 kg m−2 s−1 .

robust large-scale atmospheric circulation signals are discernible in the two modes from the regression analysis.
Figure 9 shows composites of precipitation, Z850,
and the integrated moisture flux vectors for dry and wet
summers in EOF1. We selected the six driest summers(1984, 1990, 1992, 1994, 2003, and 2006) and the sixwettest summers (1980, 1988, 1991, 1997, 2001, and2007) for each composite, based on the PC1 time series.
Figure 10 is the same as Figure 9, but for the moisture flux
vectors and divergence. In a dry summer, an anti-cyclonic
circulation dominates over eastern Siberia, with the ridgeline along 105∘ E (Figure 9(a)). The divergence area ofthe moisture flux vectors extends widely over eastern
Siberia under the anti-cyclonic circulation (Figure 10(a)).
In contrast, in a wet summer, the ridge disappears overeastern Siberia (Figure 9(b)), replaced by a cyclonic
circulation with a trough line along 80∘ E that influences
eastern Siberia. Strong southwesterly/westerly moisture
fluxes dominate over eastern Siberia. Areas of moisture
convergence expand over eastern Siberia, especiallyaround the central Siberian Plateau where precipitationshows a large increase (Figure 9(b)). The composite difference reveals a similar spatial structure to the regression
pattern in Figure 8(a) (Figure 9(c)). The positive precipitation anomaly over eastern Siberia is associated with a
low-level cyclonic circulation anomaly centred at around65∘ N, 90∘ E. The positions of the positive and negative
rainfall anomalies correspond to convergent and divergent
anomalies of the moisture flux, respectively (Figures 9(c)and 10(c)).
Based on PC2, we selected the six driest (1985, 1986,
1991, 1998, 2001, and 2002) and six wettest (1981, 1982,
1994, 2005, 2006, and 2007) summers for the EOF2
mode. Note that the wet and dry years for the EOF2
mode represent high and low precipitation years in theeastern part of eastern Siberia (i.e. the Lena river basin),
respectively. Dry summers in EOF2 are characterisedby a deep trough along 80∘ E over the West Siberian
Plain (Figure 11(a)). In contrast, in a wet summer, westerly moisture flux vectors are observed from the West© 2015 Royal Meteorological SocietyInt. J. Climatol. 36: 355–368 (2016)

362
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H. FUJINAMI et al.
(a) Precipitation, 850-hPa Z and moisture flux

Dry summer (PC2)85°N(a) Moisture flux vectors (sfc-100 hPa) and its divergence

Dry summer (PC2)

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250(b) Precipitation, 850-hPa Z and moisture flux

150 (kg m–1 s–1)

0

Wet summer (PC2)85°N(b) Moisture flux vectors (sfc-100 hPa) and its divergence

Wet summer (PC2)

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150 (kg m–1 s–1)

–2

–1

–0.3

0.3

1

2 (1.0 x10–5 kg m–2 s–1)

150 (kg m–1 s–1)

Figure 11. (a) Composite of 3-month total precipitation (shading,
mm (3 months)−1 ), the 850-hPa geopotential height (contours, interval
15 gpm), and vertically integrated moisture flux vectors for dry summers
based on PC2. (b) As in (a) but for wet summers. (c) As in (a) but for thedifference between wet and dry summers, and with a contour interval for
geopotential height of 5 gpm. Only moisture flux vectors that are statistically significant difference at the 95% level are plotted. Shaded areas
enclosed by thick solid lines indicate a significant difference at the 95%
confidence level in precipitation.

Figure 12. (a) Composite of vertically integrated moisture flux vectors
and its divergence (contours, interval 2.0 × 10−5 kg m−2 s−1 ) for dry
summers based on PC2. Areas of divergence are shaded. (b) As in (a)but for wet summers. (c) As in (a) but for the difference between wet anddry summers. Only moisture flux vectors that are statistically significant
difference at the 95% level are plotted. Shaded areas enclosed by thicksolid lines indicate a significant difference at the 95% confidence levelin moisture flux divergence. The unit for moisture flux divergence is
1.0 × 10−5 kg m−2 s−1 .

Siberian Plain to the western part of eastern Siberia,
where the deep trough lies in dry summers, with nodistinct cyclonic or anti-cyclonic circulation. Of note, adeep trough extends from 55∘ N, 115∘ E to 65∘ N, 140∘ E
over the upper/middle reaches of the Lena river basin(Figure 11(b)). Westerly/southwesterly moisture fluxes areenhanced and converge over the southeastern side of thetrough (Figure 12(b)). The trough results in enhanced
westerly moisture flow over Siberia, and the southwesterly moisture flux from the Asian monsoon region south of50∘ N seems to converge over the easternmost part of eastern Siberia. The composite difference shows an east–west
dipole of circulation anomalies between the Obi and Lenariver basins (Figure 11(c)), similar to the regression pattern
in Figure 8(b). The difference in summer precipitation ismore than 100 mm around the middle reaches of the Lenariver basin (Figure 11(c)). The distribution of precipitation
anomalies corresponds to the distribution of moisture flux
divergence between the two basins (Figure 12(c)), suggesting that the difference in precipitation is induced bychanges in the atmospheric circulation and the associatedmoisture flux divergence.
The precipitation anomalies in the eastern Siberia
domain (Figures 9(c) and 11(c)) are accompanied
by changes in precipitation characteristics, including
frequency and intensity. Climatologically, both thefrequency and intensity of precipitation are high in thesouthern part of the domain (Figure 13(a) and (e)). A large
increasing trend in frequency occurs over and around theCentral Siberian Plateau and the northern part of eastern
Siberia (Figure 13(b)), resulting in the increasing trend
in summer precipitation seen in Figure 4. In contrast, thedecreasing precipitation around the lower reaches of theAmur river basin is attributed to a decrease in both precipitation frequency and intensity. In EOF1, the area of thepositive precipitation anomaly around the Central Siberian
Plateau corresponds to the area with high precipitation
frequency (Figures 9(c) and 13(c)). The spatial patternof the precipitation frequency is also similar to that of
EOF1 (Figure 7(a)). Of note, there is no marked change in© 2015 Royal Meteorological SocietyInt. J. Climatol. 36: 355–368 (2016)

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TREND AND INTERANNUAL VARIATION IN SUMMER HYDROCLIMATE IN SIBERIA(a) Precipitation frequency(e) Precipitation intensity

Climatology JJA

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Climatology JJA

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2535(b) Precipitation frequency

2

45

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95°E 100°E 105°E 110°E 115°E 120°E 125°E 130°E 135°E 140°E–0.4

95°E 100°E 105°E 110°E 115°E 120°E 125°E 130°E 135°E 140°E

20Diff. (wet–dry)(d) Precipitation frequency (PC2)–0.6(g) Precipitation intensity (PC1)

70°N

–20

6

Linear trend (1979–2007)

95°E 100°E 105°E 110°E 115°E 120°E 125°E 130°E 135°E 140°E

5Diff. (wet–dry)(c) Precipitation frequency (PC1)4(f) Precipitation intensity

Linear trend (1979–2007)

95°E 100°E 105°E 110°E 115°E 120°E 125°E 130°E 135°E 140°E
–2

–1.5

–1

–0.5

0.5

1

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2

(mm day–1)

Figure 13. (a) Climatological mean (1979–2007) precipitation frequency in summer (JJA), defined as the average number of days per JJA periodwith precipitation exceeding 1 mm day−1 . The contour interval is five days. (b) Linear trend (per decade) of precipitation frequency for 1979–2007.
Shaded areas enclosed by thick solid lines indicate significance at the 95% level (Mann-Kendall test). (c) Difference in the frequency of precipitation
during JJA between wet and dry summers based on PC1. Shaded areas enclosed by a thick solid line indicate a significant difference at the 95%
confidence level. (d) As in (c), but for PC2. (e)-(h) As in (a)-(d), but for precipitation intensity. The contour interval for (e) is 1 mm day−1 . The unit
is mm day−1 in (f), (g) and (h).

precipitation intensity (Figure 13(g)). The spatial patternof precipitation frequency for EOF2 resembles that of the
precipitation itself (Figures 13(d) and 7(b)), indicating thatthe change in the distribution of precipitation is caused
by a change in precipitation frequency. Additionally, theincrease in precipitation intensity also contributed to thehigher precipitation around the middle reaches of the Lenariver basin (Figure 13(h)).
© 2015 Royal Meteorological Society

The patterns of atmospheric structure in both the
EOF1 and EOF2 modes are related to the changes inquasi-stationary wave patterns (i.e. troughs and ridges)
across northern Eurasia along the Arctic frontal zone. In
EOF1, the wave train structure is observed in both thelower and upper troposphere across Siberia from western
Europe to east of the Verkhoyansk Mountains, where
the jet exit region of the North Atlantic and the polar jet
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H. FUJINAMI et al.
850-hPa geopotential height (Z)(a) Regression (PC1)(c) Regression (PC2)

80°N

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(d) Regression (PC2)

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300-hPa geopotential height (Z)(b) Regression (PC1)099%850-hPa geopotential height (Z)

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300-hPa geopotential height (Z)

40°E
7

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60°E 100°E 120°E 140°E 160°E
8

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180

(m s–1)Figure 14. Regression patterns of geopotential height at (a) 850 hPa and (b) 300 hPa against PC1. (a) The contour interval is 2 gpm and the zero
contour is omitted. The colour shading represents the confidence level of the correlation coefficient between PC1 and the geopotential height at
850 hPa. The topographic contour for 1500 m is shown as a thick solid line. (b) The contour interval is 6 gpm, and the contours start from ±3 gpm.
The zero contour is omitted. Shading denotes areas of zonal wind speed greater than 7 m s−1 . (c) and (d) As in (a) and (b), but for PC2.

are located (Figure 14(a) and (b)). The cyclonic circulation related to wet summers in eastern Siberia is partof this wave train. The vertical structure of geopotential
anomalies is barotropic in the upstream area over England.
However, it appears to change into a more vertically baroclinic structure over eastern Siberia. Another wave patternis also found along the Asian subtropical jet south of thenorthern wave train along the polar jet (Figure 14(b)). The
anomaly pattern seen in Figure 14(b) is similar to that
presented by Iwao and Takahashi (2006), who interpreted
it as the eastward energy dispersion of a quasi-stationary
Rossby wave along the Arctic polar and Asian jets. In
EOF2, the wave pattern tends to be confined along the jetexit region of the Atlantic Ocean and the Arctic polar jet(Figure 14(c) and (d)). Generally, the vertical structure of
the wave is barotropic except in eastern Siberia where thevertical structure tilts westward. Note that there are two
significant geopotential anomalies: over the jet exit region
in the Northern Atlantic and the entrance region of thepolar jet to the north of the Scandinavian Peninsula. This
is indicative of a dynamic link in the fluctuation between
the east–west dipole over Siberia and the two upstream
anomalies.

4.

Discussion

4.1. Increasing trend in summer precipitation overeastern Siberia
The increasing trend in summer precipitation is remarkable over eastern Siberia (Figure 4). The raw precipitation
time series over eastern Siberia shows a large interannual
variability superimposed on the long-term trend (Figure 5).
The trend in atmospheric circulation at 850 hPa is marked© 2015 Royal Meteorological Society

by decreasing geopotential height over and around Siberia,
and increasing geopotential height around Mongolia andwestern Russia (Figure 6). The spatial variation of trends ingeopotential height over and around Siberia suggests that
atmospheric circulation changes contribute to the increasing trend in precipitation over eastern Siberia (Figure 6).
The circulation changes indeed enhance eastward moisture transport and its convergence over eastern Siberia(Figures 5 and 6). Thus, moisture flux divergence is probably a key factor that causes variability in precipitation overeastern Siberia.
APHRODITE and the other monthly precipitation
datasets use the data available through the GTS network.
The APHRODITE-RU dataset also includes individual data correction additional to that used for the GTS
gauge network (Yatagai et al., 2012). Rain gauges often
measure rainfall with large errors, especially for solid
precipitation (i.e. snowfall), and wind-induced negative
biases for snowfall measurements are higher than forother precipitation types. Warming around Siberia mightcause an artificial increase in the measured precipitationin winter (Groisman et al., 2014). In summer, the trend insurface temperature around the region with a significant
increasing trend of summer precipitation does not show
any clear warming during 1979–2012 (Ding et al., 2014;
their extended data Figure 2(c)). This suggests that where
solid precipitation falls in summer, any artificial increase
related to warming due to reduced wind-induced negative
biases for solid precipitation is small over eastern Siberia.
Instead, the circulation changes can explain the increasing
trend in precipitation: moisture flux convergence has been
increasing there in recent decades. However, we stillneed to examine the effects of systematic bias of gauge
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TREND AND INTERANNUAL VARIATION IN SUMMER HYDROCLIMATE IN SIBERIA

measurements in order to assess the precipitation trend
more quantitatively.
The regression pattern of Z850 based on PC1 showsthat the interannual variations are associated with thequasi-stationary trough centred around the Central
Siberian Plateau (Figure 8(a)). The regression pattern
is similar to the trend pattern of Z850 around Siberia(Figures 6 and 8(a)). This means that a combinationof the trend and EOF1 are responsible for the raw
precipitation time series seen in Figure 5. It also suggests that a deeper quasi-stationary trough has probably
appeared with growing frequency around eastern Siberia
in recent decades. It is important to determine the precipitation systems that generate the increase in precipitation
under this quasi-stationary trough, for which cyclones area possible candidate. The east–west seesaw oscillationin precipitation and wave pattern, which corresponds toEOF2 (Figures 8(b) and 11(c)), is clearly related to theeast–west shifting of storm tracks across Siberia (Fukutomi et al., 2007). The decreasing trend of Z850 and theinterannual variation of circulation anomalies over eastern
Siberia might indicate a change in cyclone tracks andfrequency in recent decades. Further study is required to
fully resolve this issue.
4.2. Large-scale atmospheric circulation related toprecipitation variability in eastern Siberia
It is clear that the quasi-stationary troughs related to
EOF1 and EOF2 are part of the wave-train-like structures
along the polar jet, and this is indicative of a dynamic
linkage between the windward regions through Rossby
wave dispersion. The polar jet acts as a waveguide for the
Rossby wave, as does the Asian subtropical jet (Ding andWang, 2005). Both wave trains can be traced back to the
Atlantic Ocean region. However, the wave sources of thesewave trains remain unclear. It has been reported that the
recent decline in sea-ice extent in the Arctic Ocean may
change atmospheric circulation patterns (Screen, 2013;
Cassano et al., 2014). Alternatively, a Rossby wave train
originating from the tropics, driven by sea surface temperature (SST) forcing, may affect the Arctic region (Dinget al., 2014). The variation in summer monsoon rainfall
over South Asia could also affect the recent rapid ice meltover the Arctic Ocean through a teleconnecion (Krishnamurti et al., 2015). Further studies are required to determine the sources of the atmospheric circulation changes
around eastern Siberia, related to both the interannual variation and the long-term trend.
A continued decline in summer sea ice cover has been
observed in the Arctic (Ogi and Wallace, 2007; Comiso
et al., 2008) and may be an important factor in climatechange in the region. Of note, a remarkable decline has
been observed over marginal seas such as the Kara Sea andLaptev Sea north of eastern Siberia. Figure 15 shows theregression patterns of the geopotential height at 925 hPa
against the time series of normalised raw precipitationfrom APHRODITE-RU over eastern Siberia (Figure 5)
for the circum-Arctic region. The regression pattern in© 2015 Royal Meteorological Society

925-hPa Z and wind vectors

Regression

90°E

90°W

1 (m s–1)

180
99% 95%90%90%

95% 99%Figure 15. Regression pattern of geopotential height and wind vectors
at 925 hPa based on normalised area-averaged (55∘ –70∘ N, 90–130∘ E)
time series of precipitation. The colour shading represents the confidence
level of the correlation coefficient between the time series and thegeopotential height at 925 hPa. Blue (red) shading indicates negative(positive) correlation. The contour interval for geopotential height is
2 gpm. Only wind vectors that are statistically significant at the 90%
level are plotted. The topographic contour for 750 m is shown as a thicksolid line.

Figure 15 resembles that of sea level pressure against thetime series of the extent of September Arctic sea ice,
which has declined substantially since 1979, especiallynorth of eastern Siberia and Alaska (Figure 4(a) of Ogi
and Wallace, 2007). The pattern is characterised by acyclonic circulation anomaly around eastern Siberia and ananti-cyclonic anomaly over the Arctic Ocean. The atmospheric circulation anomaly can induce an easterly wind
anomaly that could reduce Arctic sea ice cover (Ogi andWallace, 2007; Ogi et al., 2008). Thus, the increasing trend
and interannual variation in summer precipitation overeastern Siberia, and the related atmospheric circulation,
may have a dynamic connection with the recent decline
in Arctic ice during late summer.
4.3. Recent wet years in eastern Siberia and theirimpact on the hydroclimate
Recent in situ observations at Spasskaya Pad (62.2∘ N,
129.1∘ E) near Yakutsk, eastern Siberia, where the action
centre of precipitation in EOF2 is located (Figure 7(b)),
indicate that wet years occurred from 2005 to 2008 anddry years from 2001 to 2004 (Hiyama et al., 2013; Ohta
et al., 2014), consistent with the features of the PC2time series (Figure 7(e)). Successive wet years from 2005
to 2008 damaged the taiga forest due to water logging
and changed the characteristics of the hydroclimate. Soil
moisture, soil temperature, and annual maximum thawing
depth increased after the wet year of 2005 (Hiyama et al.,2013; Ohta et al., 2014). The taiga–permafrost ecosystem is unique to eastern Siberia and is maintained underInt. J. Climatol. 36: 355–368 (2016)

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H. FUJINAMI et al.

a low-precipitation climate. The 2005–2008 wet summers
around Yakutsk were induced by the wet phase of EOF2
that fluctuates on a 6–8-year period. The weak increasing
trend may also affect the middle reaches of the Lena river
basin at longitudes of 110∘ –130∘ E (Figure 4). Thus, from2005 to 2007 the Lena river basin as a whole received highrainfall, probably due to a combination of the long-term
trend and the EOF2 mode. Because precipitation is an
important parameter maintaining the taiga–permafrost
ecosystem, increased precipitation may change the features of the hydroclimate and the ecosystem in the future.
Thus, monitoring the hydroclimate over eastern Siberia,
and determining how the ecosystem reacts to increasing
precipitation, is important for the understanding of waterand carbon cycles in this region.

5.

Conclusions

The forest–permafrost coupled ecosystem of eastern
Siberia is maintained under conditions of low precipitation and is sensitive to changes in precipitation. Using
daily gridded precipitation datasets for the period from1979 to 2007, we investigated the long-term trend andleading modes of interannual variation in summer precipitation over eastern Siberia in recent decades. Atmosphericcirculation patterns related to the trend and the first threeleading modes were also examined using an atmosphericreanalysis dataset. The results are summarised as follows.
1. We examined the long-term trend in summer (JJA) precipitation over eastern Siberia on a seasonal total basis.
A significant increasing trend in summer precipitation
is observed over eastern Siberia (e.g. the Yenisei andLena river basins). The time series of area-averaged
summer total precipitation for 55∘ –70∘ N, 90∘ –130∘ E
shows a linear trend with a 13.9-mm increase per
decade over 1979–2007, which is significant at
the 99% level (Mann–Kendall test). Summer mean
850-hPa geopotential height decreases from Siberia
to the marginal seas north of Siberia, with the largest
decreases occurring over the northern part of eastern
Siberia, while significant increases are found around
Mongolia. The trend in geopotential height induces asignificant increase in westerly moisture flux vectors
and their convergence over eastern Siberia, probably resulting in the rainfall increase over eastern
Siberia.
2. To examine the dominant spatiotemporal patternsof summer precipitation variability, EOF analysis
was applied to normalised and detrended JJA precipitation over the east Siberian domain (50∘ –70∘ N,
90∘ –140∘ E) for the period 1979–2007. The first leading mode (EOF1) represents interannual variation witha 3–5-year period centred over the Central Siberian
Plateau, including the middle reaches of the Yenisei
river basin and a large area of the Lena river basin,from the upper to lower reaches. This mode accounts
for 20.2% of the total variance. The spatial pattern© 2015 Royal Meteorological Societyof EOF1 is similar to the trend pattern. The second
leading mode (EOF2; 17.5%) shows a dipole pattern
between the middle reaches of the Lena river basin
centred around 130∘ E, 60∘ N and the western part ofthe central Siberian Plateau, with a large interannual
variation with a period of 6–8 years. In the third mode
(EOF3; 11.9%), the pattern represents the variation
between the northeastern mountainous region (theVerkhoyansk Mountains), the region around the border with Mongolia, and the latitudes from 55∘ to 65∘ N.
However, the values of EOF3 are small over the entire
domain compared with those seen in EOF1 and EOF2.
3. Robust large-scale atmospheric circulation signals are
discernible in relation to the first two leading modes,
while relatively local and weak circulation signals areassociated with the third mode. Regression analysis of
the geopotential height at 850 hPa based on PC1 showsthat high summer precipitation is related to a cyclonic
circulation anomaly centred at around 65∘ N, 100∘ E
that extends over an area from eastern Siberia to the
Arctic seas around Siberia. The regression pattern issimilar to the spatial pattern of the long-term trend ingeopotential height. A deeper quasi-stationary troughappears with increasing frequency in recent decades,
superimposed on the long-term trend. The regression
pattern for EOF2 shows a dipole pattern between the
Obi and Lena river basins. The high summer precipitation around the upper and middle reaches of the Lenariver basin is related to the low-level cyclonic circulation anomaly centred around 60∘ N, 130∘ E. Weak andrelatively local cyclonic anomalies surround the positive values seen in EOF3.
4. The circulation patterns of the first two leading modes
are related to changes in the stationary trough andridge systems along the Arctic polar jet. The composite of geopotential height at 850 hPa in the wetsummers identified using PC1 shows that a large-scalestationary trough appears with its axis along 80∘ E
over Siberia, and this trough influences eastern Siberia.
Southwesterly/westerly moisture flux vectors are inturn enhanced over the whole of eastern Siberia. The
areas of moisture convergence then expand over eastern Siberia. The EOF2 composite of wet summers
shows that a stationary trough is observed around120∘ –130∘ E over the upper/middle reaches of the
Lena river basin. The westerly/southwesterly moisture
fluxes are enhanced and converge east of the trough,producing high rainfall.
5. The regression pattern in geopotential height basedon PC1 shows a wave train structure from the exit
region of the North Atlantic jet over Western Europe,
across Siberia to the east of the Verkhoyansk Mountains where the exit region of the Arctic polar jet is
located. The cyclonic circulation related to wet summers of the EOF1 mode in eastern Siberia is part of thiswave train. Another wave pattern is also found along
the Asian subtropical jet south of the northern wave
train. In EOF2, the wave pattern tends to be confinedto the exit region of the Atlantic Ocean and the Arctic
Int. J. Climatol. 36: 355–368 (2016)

TREND AND INTERANNUAL VARIATION IN SUMMER HYDROCLIMATE IN SIBERIA

polar jet. There are significant geopotential anomalies
over the jet exit region in the Northern Atlantic andthe jet entrance region of the polar jet to the north of
the Scandinavian Peninsula, indicating a dynamic linkin the fluctuation between the east–west dipole over
Siberia and the two upstream anomalies.

Acknowledgements
We thank Prof. Tetsuya Hiyama of Nagoya University
for his many valuable comments. We also thank Dr Rikie
Suzuki of the Department of Environmental Geochemical
Cycle Research at JAMSTEC for providing the NDVI
datasets, Dr Kanamori for helpful comments and support
with data processing, and Dr Yatagai of Nagoya University
and Dr Arakawa of Tsukuba University for making valuable comments on the details of the APHRODITE dataset.
The APHRODITE-RU V1101 dataset was obtained fromthe project web site (http://www.chikyu.ac.jp/precip/).
This study was partly supported by Research Project No.
C-07 of the Research Institute for Humanity and Nature(RIHN), entitled ‘Global Warming and the Human-Nature
Dimension in Siberia: Social Adaptation to the Changesof the Terrestrial Ecosystem, with an Emphasis on Water
Environments’(PI: T. Hiyama). Portions of this study wereconducted as part of a collaborative research program of
the Hydrospheric Atmospheric Research Center, Nagoya
University.

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