INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. (2011)
Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/joc.2293

Spatiotemporal structures of the intraseasonal oscillations ofprecipitation over northern Eurasia during summer
Yoshiki Fukutomi,a* Kooiti Masudaa and Tetsuzo Yasunaria,ba Research Institute for Global Change, JAMSTEC, Yokohama, Japanb Hydrospheric Atmsopheric Research Center, Nagoya University, Nagoya, Japan

ABSTRACT: This study identifies intraseasonal oscillations in summer precipitation over northern Eurasia using multichannel singular spectrum analysis (MSSA) and a gridded daily precipitation data set for northern Eurasia. The analysis
period is June–August of 1979–2002. Empirical orthogonal function analysis was first performed on 8-day low-passfiltered precipitation anomalies at 2.5° grid resolution for northern Eurasia; MSSA was then carried out on the ten leading
principal components (PCs). Three quasi-periodic oscillations with timescales of 45, 15, and 9 days were identified. The
spatiotemporal structures of precipitation oscillations were defined by composite analysis based on the reconstructed timeseries of the spatiotemporal PCs obtained from the MSSA. The composite life cycle of each mode was classified into eightphase categories. The 45-day oscillation is characterised by a slow eastward progression of a broad east/west contrastive
pattern which is associated with a replacement of elongated dry and wet zones across northern Eurasia. The 15-day
oscillation shows a regular eastward phase propagation of precipitation anomalies with a tripole structure across the domain.
The eastward displacement of dry and wet zones occurs in association with this oscillation. Spatiotemporal behaviours of
the precipitation anomalies associated with the 9-day oscillation are similar to those of the 15-day oscillation, but the spatial
scale of this oscillation is somewhat smaller. To explore the connections between the oscillations of precipitation and largescale atmospheric circulation patterns, a similar composite method was applied to 300-hPa geopotential height anomalies
over the Northern Hemisphere. The circulation patterns responsible for the 45-day oscillation are characterised by a wave
train extending across the northeastern Atlantic/northern Eurasian sector. The wave train, with zonal wavenumbers of 3–4,
indicates slow eastward moving and quasi-stationary features. The circulation patterns linked to the 15-day oscillation
exhibit an eastward propagating wave train extending from northeastern Europe into the North Pacific/North American
sector. In spatial scale, this wave train has zonal wavenumbers of 5–6. The wave trains associated with the 9- and 15-day
oscillations have similar behaviour patterns over northern Eurasian. However, the 9-day wave train has a comparatively
smaller spatial scale with zonal wavenumbers of 6–7. Therefore, the intraseasonal oscillations of precipitation are connectedto the well-organised wave trains that extend from the Euro-Atlantic region to northern Eurasia, suggesting that theoscillation on each intraseasonal time scale must be produced by propagation of the corresponding wave train. Copyright 2011 Royal Meteorological Society
KEY WORDS

northern Eurasia; precipitaion; intraseasonal oscillation; multi-singular spectrum analysis

Received 10 February 2010; Revised 30 November 2010; Accepted 2 December 2010

1.

Introduction

Hydrological processes in northern Eurasia play
important roles in controlling changes in Arctic climate
and hydrological cycles (Peng and Mysak, 1993; Wang
and Cho, 1997; Peterson et al., 2002; Serreze et al.,
2003; McClelland et al., 2004; Pavelsky and Smith, 2006;
Rawlins et al., 2006; MacDonald et al., 2007; Adam andLettenmaier, 2008). In particular, summer precipitationaccounts for 30–40% of the annual total precipitation in
the three major Siberian river basins (Fukutomi et al.,2003). The interannual variability of northern Eurasiansummer precipitation also significantly affects that of the* Correspondence to: Yoshiki Fukutomi, Research Institute for Global
Change, Yokohama Institute for Earth Sciences, Japan Agency for
Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showamachi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan.
E-mail: fukutomi@jamstec.go.jp
Copyright  2011 Royal Meteorological Society

annual total precipitation and the resulting river discharge
into the Arctic Ocean (Semiletov et al., 2000; Fukutomi et al., 2003). Our previous studies revealed important characteristics in the interannual variability of summer precipitation over northern Eurasia (Fukutomi et al.,2003, 2004, 2007). Precipitation variability was detected
on time scales of about 6–8 years in eastern and western
Siberia, and the spatial precipitation anomalies associated with this variability exhibited an east-west dipole
pattern across northern Eurasia. Over recent decades,
good progress has been made in understanding the longterm changes and variations of precipitation in northern Eurasia. The spatiotemporal characteristics of interannual variability of precipitation are understood well,along with the associated hydroclimates. However, fewer
attempts have been made to investigate the intraseasonalvariability of precipitation in this region.

Y. FUKUTOMI et al.

Intraseasonal atmospheric circulation and precipitation
can be important factors in determining the seasonal characteristics of precipitation in northern Eurasia. Several
recent studies have investigated the role of intraseasonal
systems on summer precipitation in the lower latitudes
of northern Eurasia. For instance, Iwasaki and Nii (2006)
examined the Mongolian rainy season and found a breakperiod that generally persisted for 10–20 days in July.
They concluded that mid-latitude Rossby wave propagation on intraseasonal time scales caused this breakperiod. Iwao and Takahashi (2008) discussed the rolesof intraseasonal waves across northern Eurasia in producing extreme phases in a north–south seesaw patternof summer precipitation over Northeast Asia. However,limited efforts have been made to identify the spatiotemporal structures of intraseasonal variability in the broad
northern Eurasian region, including all of Siberia. As
shown by Serreze and Etringer (2003), daily precipitation variability is highest during summer in the major
Siberian basins. Summer is also well-known to be themost active season for the hydrological cycle in Siberia.
Therefore, it is reasonable to focus on intraseasonal variability of precipitation and associated circulation in the
broad northern Eurasian region during summer. One of
the next steps towards understanding northern Eurasianhydroclimate is thus to explore the spatiotemporal characteristics of intraseasonal variability of precipitation.
This study examines the fundamental structures of
intraseasonal oscillations of precipitation at different spatial and temporal scales in summer in northern Eurasia.
A number of previous studies have attempted to detect
intraseasonal oscillations of precipitation in various continental regions. By using multi-singular spectrum analysis (MSSA: Plaut and Vautard, 1994), several studies(Wang et al., 1996b; Ye and Cho, 2001; Krishnamurthyand Shukla, 2007) successfully identified and categorised
spatiotemporal structures of the intraseasonal oscillationsof precipitation in China, North America, and India.
However, these structures were not detailed for northern Eurasia. Consequently, the purpose of this study is to
identify spatiotemporal structures of intraseasonal oscillations of precipitation and the associated atmosphericcirculation patterns over northern Eurasia during summer.
To do so, we analyse a new precipitation data set of gridded daily precipitation. We first detect oscillatory modes
by MSSA of daily precipitation anomalies over northernEurasia. Then we explore the spatiotemporal structures of
the isolated oscillatory modes by performing composite
analysis on the precipitation fields based on the MSSA
results. To reveal the evolution of large-scale atmosphericcirculation patterns linked to each oscillatory mode, we
also examine composites of geopotential height anomalies over the Northern Hemisphere. This article is organised as follows: Section 2 briefly describes the data anddata-processing methods used. Section 3 presents the climatological features of the summer precipitation. Section
4 explains the MSSA procedure employed and presentscharacteristics of the oscillatory modes identified by
MSSA. Section 5 describes the spatiotemporal structures
Copyright  2011 Royal Meteorological Societyof the oscillations. Section 6 examines the large-scale
circulation patterns associated with the oscillations.
Finally, Section 7 presents a summary and discussion.

2.

Data sources and processing

To determine primary modes of intraseasonal precipitation variability over northern Eurasia, we used gridded daily precipitation data obtained from the ResearchInstitute for Humanity and Nature (RIHN) in Japan(http://www.chikyu.ac.jp/precip/index.html). This precipitation data set was developed as part of the Asian Precipitation–Highly Resolved Observational Data Integration Towards the Evaluation (APHRODITE) of the water
resources project (Yatagai et al., 2008, 2009; Takashima
et al., 2009). The precipitation data set covers a broad
portion of continental Eurasia including East Asia, Southeast Asia, South Asia, North Asia, the Middle East, andnorthern Eurasia. We used the northern Eurasian subset
which is available for the period 1979–2002. The original
subset contains daily gridded precipitation with a horizontal resolution of 0.5° . We re-gridded the original precipitation fields into 2.5° by a simple area averaging method.
We also used an atmospheric reanalysis data set to
explore large-scale atmospheric circulation patterns associated with precipitation oscillatory modes. The reanalysis data set, the Japanese 25-year reanalysis (JRA25), wasrecently produced by the Japan Meteorological Agency(JMA) Numerical Assimilation and Forecast System andprovides many assimilated and forecasted variables available at a 6-h temporal resolution and various grid types(Onogi et al., 2005, 2007). We used the 300-hPa geopotential height and zonal and meridional winds on horizontal 2.5° grids from the “anl p25” subset of JRA25(Onogi et al., 2007). The data were daily-averaged before
analysis.
The data analysis was carried out for a 24-year period
from 1979 to 2002 when both the northern Eurasian precipitation analysis and JRA25 were commonly available.
The anomaly time series of all variables were computedby subtracting the first three harmonics from the original 365-day time series to remove the seasonal cycle.
The precipitation anomaly time series were then filtered
to extract intraseasonal components by using the lowpass filter of Kaylor (1977), which removes periods of<8 days corresponding to synoptic time scale fluctuations.

3.
3.1.

Climatological features
Summer mean precipitation

We first present summer precipitation climatology innorthern Eurasia. The mean precipitation for June–
August (JJA) 1979–2002 is shown in Figure 1. The
features in this climatology are basically similar to
those demonstrated by Fukutomi et al. (2003, 2004). The
summer Siberian precipitation zone is evidently seen.
Int. J. Climatol. (2011)

INTRASEASONAL OSCILLATIONS OF PRECIPITATION OVER NORTHERN EURASIA
Precipitation

JJA 1979-2002
4.0

70°N

3.5

65°N

3.0

60°N

2.5

55°N

2.0

50°N
40°E

1.5
50°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

150°E

Figure 1. Twenty-four-year (1979–2002) climatology of the June–August (JJA) mean precipitation over northern Eurasia. Contour interval is
0.5 mm day−1 . Regions with values >1.5 mm day−1 are shaded. The eastern and western Siberian domains for the spectral analysis are indicatedby black boxes.

3.2. Spectral properties
Prior to performing MSSA, we computed power spectra for precipitation for JJA 1979–2002 to briefly detect
dominant intraseasonal time scales in northern Eurasia.
A fast Fourier transform (FFT) technique was employed
for calculating these spectra based on the procedure of
Fukutomi and Yasunari (2005). The power spectra foreach JJA period were averaged over 24 years to obtain
ensemble averaged spectra. Precipitation indices for the
spectral computation were defined as precipitation timeseries averaged over the two domains where the mean
precipitation was comparatively larger in the Siberian
region (Figure 1). One was the eastern Siberian domainbounded by 50° N to 55° N, 120 ° E to 130 ° E, and anotherwas the western Siberian domain enclosed by 60° N to
65° N, 75 ° E to 85 ° E. The resulting power spectra are
given in Figure 2. The spectrum for the eastern Siberianprecipitation index (Figure 2(a)) shows two peaks significant at the 95% confidence level within the typicalsubmonthly (6–30 days) range. A maximum peak and asecondary maximum peak are observed at 11 and 9 days,
respectively. However, pronounced spectra are found inboth the lower-frequency intraseasonal (>30 days) andsubmonthly range for the western Siberian precipitationindex (Figure 2(b)). The spectra at 43 and 16 days are
actually significant at the 95% confidence level. These
spectra suggest that the submonthly variability is prominent in broad Siberia and the low-frequency intraseasonalvariability is dominant in western Siberia. These features
Copyright  2011 Royal Meteorological Society

Power(mm2day-2)(a) Precip
80
70
60
50
40
30
20
10
0

101
Period(days)(b) Precip
80
70
60
50
40
30
20
10
0

JJA 1979-2002

95% Confidence levelES
50°-55°N:120°-130°E

102Power(mm2day-2)

A prominent band of precipitation exceeding 2 mm day−1
extends from the east of Lake Baikal to the Far East
Asian coast along 50° N to 60° N. In particular, local precipitation maxima >3 mm day−1 are concentrated in thisband. These enhanced precipitation signals might reflect
effects of the mountain orography in the southern part ofcentral-eastern Siberia. Another band of enhanced precipitation extends from European Russia to western Siberia
along 55° N to 65° N. The Siberian precipitation zone corresponds closely to the area of maximum extratropical
cyclone and frontal activity over northern Eurasia (Serreze et al., 2001; Serreze and Etringer, 2003; Yoon andChen, 2006; Fukutomi et al., 2007). These previous studies discussed the maintenance of the Siberian precipitation zone.

JJA 1979-2002

95% Confidence levelWS
60°-65°N:75°-85°E

102

101
Period(days)

Figure 2. (a) Twenty-four-summer (JJA 1979–2002) ensemble power
spectrum of precipitation averaged over the eastern Siberian domain(50° N to 55° N, 120 ° E to 130 ° E). The thin solid curve is the 95%
confidence level. Dashed curve is the red noise spectrum. (b) Same as(a) except for the western Siberian domain (60° N to 65° N, 75 ° E to
85 ° E).

are confirmed by the MSSA results presented in later
sections.
4. Application of MSSA to the precipitation
anomalies
4.1. Overview
We applied multi-channel singular spectrum analysis(MSSA) to the precipitation data to identify modes of
intraseasonal variability of precipitation over northernEurasia. Many studies have reported MSSA to be aneffective tool for isolating quasi-periodical oscillating
modes of atmospheric and oceanic variability on
Int. J. Climatol. (2011)

Y. FUKUTOMI et al.

intraseasonal to interannual time scales (Plaut andVautard, 1994; Dettinger et al., 1995; Jiang et al., 1995;
Wang et al., 1996a, 1996b; Moron, 1997; Wang and Cho,
1997; Zhang et al., 1997, 1998; Moron et al., 1998; Qianet al., 2000; Simonnet and Plaut, 2001; Ye and Cho,2001; Krishnamurthy and Shukla, 2007, 2008). A detailed
description of MSSA procedure was first provided by
Plaut and Vautard (1994). They performed MSSA on 700hPa geopotential heights to identify recurrent intraseasonal circulation patterns in the wintertime Northern
Hemisphere extratropics. Subsequently, several studies
successfully detected regional oscillations of precipitation
using MSSA (Wang et al., 1996b; Wang and Cho, 1997;
Qian et al., 2000; Ye and Cho, 2001; Krishnamurthy andShukla, 2007). Other studies have applied MSSA to sea
level pressure, surface air temperature, and sea surface
temperature (Dettinger et al., 1995; Jiang et al., 1995;
Moron, 1997; Moron et al., 1998; Zhang et al., 1998;
Simonnet and Plaut, 2001). Thus, MSSA has been widelyused to capture space–time patterns of climatic oscillations in various fields. As explained by the above papers,
MSSA is an extension of empirical orthogonal function(EOF) analysis and equivalent to an extended EOF analysis. Basically, MSSA consists of two steps. The first is aconventional EOF analysis of a particular data field (timeseries at grid points) to obtain its principal components(PCs). The second is to solve an eigenvalue problem
of a lag cross-covariance matrix constructed from theleading PCs which are viewed as multi-channel series.
Generally, eigenvectors and PCs resulting from the second step are referred to as space–time EOFs (ST-EOFs)
and space–time PCs (ST-PCs), respectively (Plaut andVautard, 1994). These elements consolidate information
on both the spatial and temporal variability of eigenmodes
derived from MSSA.
In this study, MSSA was applied to daily precipitation
anomalies in northern Eurasia basically following theprocedure of Wang et al. (1996a) and Krishnamurthyand Shukla (2007, 2008). In particular, we referred to
Krishnamurthy and Shukla (2007, 2008) for carryingout MSSA using a multi-year set of daily data for aparticular season. To investigate space–time structuresof intraseasonal oscillations of precipitation, we analysed
the 92-day period from 1 June to 31 August (JJA)
of each year from 1979 to 2002. We prepared a 24year set of precipitation data from mid-April to midOctober to incorporate a lag window length for MSSA.
In the following sections, we briefly describe the MSSA
procedure and initial results. We do not present themathematical formulation of MSSA because that has been
given in previous works (Plaut and Vautard, 1994; Jiang
et al., 1995; Wang et al., 1996a; Wang and Cho, 1997;
Qian et al., 2000), which can also be referred to forgeneral technical details of MSSA.
4.2.

Spatial EOF analysis

An EOF analysis was performed on the 8-day lowpass-filtered precipitation anomalies from 17 April to
Copyright  2011 Royal Meteorological Society

15 October (182 days) from 1979 to 2002 (24 years)
in northern Eurasia (50° N to 70° N, 40 ° E to 140 ° E).
The total data length (182 days) for each year was thetargeted analysis period and a length associated with a lag
window for MSSA. The targeted analysis period was the
92 days from 1 June to 31 August (JJA). The remaining
90 (182 − 92) days was the period associated with thelag window length (M) defined by the subsequent MSSA.
Since we used a lag window length of 91 days (M = 91),
we had to add a 90-day-long (M − 1 = 90) time series
to the time series of the targeted analysis period. This
90-day period was divided into two 45-day periods, preand post-JJA: 17 April to 31 May and 1 September to
15 October, respectively. Note that the total length of
the time series to be analysed by the EOF analysis was
182 × 24 = 4368 days.
As a result of the EOF analysis, we obtained spatial
principal component (S-PC) time series and corresponding spatial EOF (S-EOF) patterns. We used the leading
ten S-PCs, which account for about 53% of the total
variance, as ten channels for the subsequent MSSA. The
leading ten S-EOF patterns are shown in Figure 3. Mostof the S-EOFs exhibit east-west or wave-like patterns
and several other modes show zonally elongated band
structures spanning most of the area studied. S-EOF
1 indicates the highest fluctuations in the southeasternpart of the study area; S-EOFs 2 and 3 represent northwest–southeast oriented wave-like patterns; S-EOFs 4, 5,
and 7 show zonally elongated band structures and S-EOF
6 is characterised by the largest anomalies in the southernpart of central Siberia. S-EOFs 8, 9, and 10 show comparatively smaller-scale wave-like patterns. Therefore, itwould appear that the S-EOFs of the precipitation anomalies in the northern Eurasian domain have well-organised
structures.
4.3.

Oscillatory modes identified by MSSA

The first ten S-PCs directly formed a multi-channel series
with ten channels (L = 10; L is the number of channels)
for MSSA. We computed a cross-covariance of the multichannel series at 91 lags (M = 91) and then produced alag cross-covariance matrix for the entire period (17 April
to 15 October of 1979–2002) with (L × M) × (L × M)
dimensions. The main process of the MSSA was to solve
an eigenvalue problem for this matrix. By doing this,
we finally derived space–time eigenvectors and corresponding space–time PCs (ST-PCs). The ST-PC is a timeseries of 92-day length for each year which represents
the temporal evolution of space–time eigenmodes. The
space–time eigenvector is referred to as ST-EOF and is
91 days in length. The ST-EOF represents a pattern of
the space–time eigenmodes in the time dimension.
As a result of the MSSA, we obtained L × M =910 space–time eigenmodes composed of ST-PC k andST-EOF k (k = 1, 2, . . . , 910, where k is the orderof the modes) and the corresponding eigenvalues. A
quasi-periodic oscillatory mode is basically defined by apair of successive eigenmodes in the order k and k + 1,
Int. J. Climatol. (2011)

INTRASEASONAL OSCILLATIONS OF PRECIPITATION OVER NORTHERN EURASIA
(a)
70°N

S-EOF 1S-EOFs, Precip. APHRO_RU, JJA 1979-2002

7.4%

(f)
S-EOF 6

3.4%

65°N

70°N
65°N

60°N

60°N

55°N

55°N

50°N
40°E

50°E

60°E

70°E

80°E

90°E

100°E 110°E 120°E 130°E 140°E

(b)
70°N

5.4%

S-EOF 2

40°E

50°E

60°E

70°E

80°E

90°E

50°N
100°E 110°E 120°E 130°E 140°E(g)
S-EOF 7

3.3%

70°N

65°N

65°N

60°N

60°N

55°N

55°N

50°N
40°E

50°E

60°E

70°E

80°E

90°E

100°E 110°E 120°E 130°E 140°E

(c)
70°N

4.5%

S-EOF 3

40°E

50°E

60°E

70°E

80°E

90°E

50°N
100°E 110°E 120°E 130°E 140°E(h)
S-EOF 8

3.1%

70°N

65°N

65°N

60°N

60°N

55°N

55°N

50°N
40°E

50°E

60°E

70°E

80°E

90°E

100°E 110°E 120°E 130°E 140°E

(d)
70°N

S-EOF 4

3.9%

40°E

50°E

60°E

70°E

80°E

90°E

50°N
100°E 110°E 120°E 130°E 140°E(i)
S-EOF 9

2.9%

65°N

70°N
65°N

60°N

60°N

55°N

55°N

50°N
40°E

50°E

60°E

70°E

80°E

90°E

100°E 110°E 120°E 130°E 140°E

(e)
70°N

S-EOF 5

3.7%

40°E

50°E

60°E

70°E

80°E

90°E

50°N
100°E 110°E 120°E 130°E 140°E(j)
S-EOF 10

2.7%

65°N

70°N
65°N

60°N

60°N

55°N

55°N

50°N
40°E

50°E

60°E

70°E

80°E

90°E

100°E 110°E 120°E 130°E 140°E

40°E

50°E

60°E

-0.7

-0.5

-0.3

70°E
-0.1

80°E
0.0

90°E
0.1

50°N
100°E 110°E 120°E 130°E 140°E

0.3

0.5

0.7

0.9

Figure 3. Spatial patterns of the first 10 S-EOFs of precipitation anomalies over northern Eurasia. Contour interval is 0.1 (unit of correlation
coefficient). Positive (negative) anomalies are drawn with solid (dashed) contours. Zero contours are omitted. Positive (negative) anomalies arelightly (darkly) shaded.

Copyright  2011 Royal Meteorological Society

50
Variance Fraction(%)

which have nearly equal eigenvalues (Plaut and Vautard,
1994; Wang et al., 1996a). That is, ST-PCs k and k + 1
reflect an oscillation on the same time scale and are inquadrature with each other. Here they are represented as
an oscillatory pair k –k + 1. To identify a dominant timescale of each oscillatory mode, power spectrum analysis
by the maximum entropy method (MEM) was carriedout on the ST-PCs following Wang et al. (1996a). In
this analysis, ST-PCs 1 and 2, 5 and 6, and 14 and15 had peaks in their maximum entropy (ME) power
spectra at 15, 45, and 9 days, respectively (not shown).
Therefore, these ST-PCs form three oscillating pairs:
1-2, 5-6, and 14-15 (hereafter denoted as ST-PCs 12, ST-PCs 5-6, and ST-PCs 14-15). The ME power
spectra of these three pairs are shown in Figure 4, plottedin the form of a variance fraction of the sum of thespectra of the paired modes. The variance fraction was
estimated as a percentage of the total variance definedby the sum of the spectra of all ST-PCs (Plaut andVautard, 1994; Wang et al., 1996a). It was confirmedthat the spectra of ST-PCs 1-2, ST-PCs 5-6, and ST-PCs
14-15 had peaks at 15, 45, and 9 days, respectively.

MSSA ST-PCs

JJA 1979-2002

40
14-15

30
20

5-6

1-2

10
0
0.00

0.05
0.10
0.15
Frequency(cycles/day)

0.20

Figure 4. ME power spectra of three ST-PC pairs (ST-PCs 1-2, ST-PCs
5-6, and ST-PCs 14-15) derived by MSSA in the form of variancefraction (%).

Overall, ST-PCs 1-2 corresponded to a 15-day oscillation
fluctuating within a range of 10–20 days. ST-PCs 5-6
exhibited a 45-day oscillation which included fluctuations
on time scales longer than about 30 days. ST-PCs 14-15
gave a 9-day oscillation, substantially a quasi-10-day
oscillation. Characteristics of the three oscillating pairs
are summarised in Table I. Here, one can notice that
Int. J. Climatol. (2011)

Y. FUKUTOMI et al.

Pair

Period(days)

Variance(%)

Peak variance
(%)

1–2
5–6
14–15

15
45
9

3.8
3.4
2.6

16.4
15.9
29.5

(a)

(mm day-1)

Table I. Characteristics of the oscillatory pairs (k, k + 1)
identified by the MSSA.

RCs 1-2 (15-day oscillation)
6
4
2
0
-2
-4
-6
1 Jun

(mm day-1)(b)

The first column gives the order number of the pair. The second
column contains the period of oscillation at the peak value of the
ME spectra of each pair (Figure 4). The third column shows the total
percentage variance explained by each pair which is estimated fromthe eigenvalues of the MSSA results. The fourth column gives thepercentage variance at the peak frequency of the ME spectra of eachpair (Figure 4).

5. Space–time structures of the oscillationsof precipitation
5.1. Composite analysis
Three distinct oscillations of precipitation were identified
by the MSSA. We conducted composite analysis to examine the space–time evolution of these oscillations, using
Copyright  2011 Royal Meteorological Society

1 Jul

15 JulRCs 5-6 (45-day oscillation)(c)(mm day-1)

15 Jun1 Aug 15 Aug
JJA 2000

6
4
2
0
-2
-4
-6
1 Jun

these time scales identified by MSSA are similar to
those detected by the FFT-based spectrum analysis in
the previous section. It should also be noted that thesethree oscillations were reproducible by MSSA with otherlag window sizes. We also examined M = 61 and M =51 cases, and the resulting spectral characteristics weresimilar to those for the present (M = 91) case. Thisimplies certain robustness in these oscillations.
A space–time reconstructed component (RC) of each
MSSA eigenmode was computed for the 182-day periodof each year following the mathematical formula of Plautand Vautard (1994). In their formula, the RC for eachchannel is constructed from the corresponding ST-PC
and ST-EOF. The resulting RC is a time series which
represents the time evolution of each channel series on
the time scale of the corresponding MSSA eigenmode. In
other words, the RC is equivalent to a filtered componentof the original S-PC.
The time series of paired RCs of an oscillation is thesum of the RCs of the oscillatory pair (k, k + 1) (Plautand Vautard, 1994; Wang et al., 1996a; Krishnamurthyand Shukla, 2007, 2008). The time series of the paired
RCs of the 15-, 45-, and 9-day oscillations are denoted as
RCs 1-2, RCs 5-6, and RCs 14-15, respectively. Example
time series of the paired RCs are displayed in Figure 5.
In each panel, the quasi-periodical oscillating nature ofeach mode is obvious. Channel 1 was chosen to plot
RCs 1-2 and RCs 14-15 (the 15- and 9-day oscillations)and channel 3 was chosen for RCs 5-6 (the 45-day
oscillation). The RC for the chosen channel has maximal
variance among all the RCs for ten channels of each
MSSA eigenmode. Also, the time series of the paired
RCs for the chosen channel was used as an index forcomposite analysis, described in the next section.

JJA 1989

15 Jun

1 Jul

15 JulRCs 14-15 (9-day oscillation)

1 Aug 15 Aug
JJA 1992

6
4
2
0
-2
-4
-6
1 Jun

15 Jun

1 Jul

15 Jul

1 Aug 15 Aug

Figure 5. Time series of RCs corresponding to ST-PCs. (a) RCs 1-2(15-day oscillation) in channel 1 for JJA 1989. (b) RCs 5-6 (45-day
oscillation) in channel 3 for JJA 2000. (c) RCs 14-15 (9-day oscillation)in channel 1 for JJA 1992.

the time series of paired RCs as an index to constructcomposites. The phase compositing technique based on
the RC has been described in detail in several papers(Plaut and Vautard, 1994; Wang et al., 1996a; Krishnamurthy and Shukla, 2007, 2008). We basically followed
the technique and determined instantaneous phases of theoscillation. A cycle of oscillation was divided into eightphase categories and the space–time composited fields
were keyed to these eight phases, denoted as phase 1 tophase 8. The time intervals between consecutive phases
were supposed to be almost equal. A positive (negative)
peak of the oscillation was assigned as phase 3 (phase7). The point at which an increasing (decreasing) index
curve crossed zero was assigned as phase 1 (phase 5). The
remaining phases 2, 4, 6, and 8 were intermediate phases.
The composite structures of phases 1–4 were consideredout of phase with those of phases 5–8. Cycles of oscillation for the composite analysis were chosen using theRC-based index of each mode. We employed 1.5 standard deviation of the index for the entire period as acriterion to identify the peak phases of the oscillation(phases 1 and 8). We eventually chose cycles of oscillation which included the peak phases that satisfied thiscriterion. Thus, space–time structures of the three distinct
oscillatory modes were obtained by composite analysis ofprecipitation anomalies during JJA based on the index of
the three oscillatory modes. Space–time fields of precipitation anomalies used for the composite analysis wereInt. J. Climatol. (2011)

INTRASEASONAL OSCILLATIONS OF PRECIPITATION OVER NORTHERN EURASIA(a)

Phase 115-day Mode (ST-PCs 1 & 2), PR RCs(10 S-EOFs), JJA 1979-2002

70°N
65°N
60°N
55°N
50°N
40°E

50°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

(b)
Phase 2
70°N
65°N
60°N
55°N
50°N
40°E

50°E

(c)
Phase 3
70°N
65°N
60°N
55°N
50°N
40°E
(d)
70°N

50°E

Phase 4

65°N
60°N
55°N
50°N
40°E

50°E

(e)
Phase 5
70°N
65°N
60°N
55°N
50°N
40°E
(f)
70°N

50°E

Phase 6

65°N
60°N
55°N
50°N
40°E
(g)
70°N

50°E

Phase 7

65°N
60°N
55°N
50°N
40°E

50°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

(h)
Phase 8
70°N
65°N
60°N
55°N
50°N
40°E

50°E

1.3
1.1
0.9
0.7
0.5
0.3
0.1
0.0
-0.1
-0.3
-0.5
-0.7
-0.9
-1.1

Figure 6. Spatial–temporal structures of the 15-day oscillation of precipitation over northern Eurasia, represented by composites of reconstructedprecipitation anomalies (mm day−1 ) keyed to phase categories 1–8. Contour interval is 0.1. Positive (negative) anomalies are drawn with solid(dashed) contours. Zero contours are omitted. Positive (negative) anomalies are lightly (darkly) shaded.

reconstructed by projecting the RCs for all channels of
the oscillatory mode onto their respective S-EOFs. The
space–time fields were reproduced separately for each
oscillatory mode.
5.2. The 15-day oscillation
The 15-day mode is the strongest intraseasonal oscillationof precipitation over northern Eurasia indicated by the
Copyright  2011 Royal Meteorological Society

MSSA eigenmode pairs (ST-PCs) 1 and 2. Figure 6
displays composite space–time structures of the 15-day
oscillation. A total of 57 cycles of the oscillation wereused to construct the composites. Precipitation anomalies
exhibit regular eastward phase propagation of a zonallyoriented tripole pattern with positive (anomalous wetzone) and negative anomalies (anomalous dry zone). In
phase 1, the precipitation anomalies are characterised by
Int. J. Climatol. (2011)

Y. FUKUTOMI et al.

an east-west dipole pattern between western and eastern
Siberia. Comparatively weaker positive anomalies coverwestern Siberia, whereas stronger negative anomalies
cover eastern Siberia. An anomalous wet zone moves
eastward to central Siberia by phase 3 and then arrives
at eastern Siberia by phase 5. In phase 5, an anomalous
dry zone over eastern Siberia in phase 1 is completely
replaced with a strong wet zone from the west. Anotherdry zone prevailing over European Russia in phase 1
moves towards western Siberia by phase 5. The whole
pattern in phase 5 becomes almost out of phase with thatin phase 1. After phase 5, the wet anomalies strengthen
in the southern part of eastern Siberia. This wet zone
continues travelling eastward and disappears towards theNorth Pacific by phase 8. Meanwhile, a new dry zone
arrives over eastern Siberia. We notice that another wet
zone is established in European Russia from phase 3
through phase 5. This wet zone moves to western Siberia
by the next phase 1. Thus, the composite sequence of
this mode shows a clear recurrent cycle. Overall, the 15day mode produces an apparent eastward displacementof the anomalous dry and wet zones across northernEurasia associated with the eastward phase propagation.
Both the positive and negative precipitation anomalies
become stronger as they move eastward in the Siberian
region. In particular, the southern part of eastern Siberia
corresponding to the upper Lena River basin is covered
with the strongest anomalies.
5.3.

The 45-day oscillation

The 45-day mode is characterised by a broad east-west
contrastive pattern in the entire area of northern Eurasia.
This mode is represented by ST-PCs 5 and 6. Figure 7
presents a composite evolution of the 45-day oscillation.
A total of 17 cycles of the oscillation were selected for thecompositing. A zonally elongated anomalous wet zone
appears over European Russia in phase 2 and a zonallyelongated anomalous dry zone appears over westerncentral Siberia at the same time. Another distinct dry
zone develops on the southeastern edge of the study
area. The distinct dry zone eventually combines withthe zonally elongated dry zone from the west. The wetzone progresses slowly eastward into central Siberia byphase 5. At the same time, wet (positive) anomalies are
dominant in western Siberia and dry (negative) anomalies
dominated in eastern Siberia. After phase 6, as thewestern Siberian wet zone continues to extend towards
eastern Siberia, a new dry zone extends eastward from
European Russia into western-central Siberia. The wetzone is then replaced by the dry zone in this region
between phases 6 and 8. In addition, the distinct dry
zone is replaced with a new distinct wet zone by phase
6, which is then merged with the wet zone movingfrom the west. Compared with the 15-day mode, the45-day mode indicates zonally elongated band structuresof the dry and wet zones throughout its life cycle.
The zonal extent of individual dry and wet zones islarger than that of the 15-day mode. In addition, theCopyright  2011 Royal Meteorological Society

45-day mode indicates stronger fluctuations in European
Russia through western and central Siberia. There arecharacteristic east-west dipole patterns between European
Russia and western and central-eastern Siberia aroundphases 3 and 7 in particular. The eastward progression of
the dry and wet zones may reflect a replacement of an
above (below) normal precipitation episode by a below(above) one in European Russia and western Siberia(central-eastern Siberia) and vice versa. Each regional
precipitation episode might last for about 2 weeks. For
example, the dry episode persists for at least three phasesfrom phases 1 to 3, while the wet episode persists fromphases 5 to 7 over central Siberia.
5.4.

The 9-day oscillation

The 9-day mode corresponds to ST-PCs 14 and 15, which
has a comparatively smaller spatial scale than the othertwo modes. Strong precipitation fluctuations associatedwith the 9-day oscillation are confined to the southeastern part of northern Eurasia. Figure 8 gives space–timestructures of the 9-day oscillation. A total of 54 cyclesof the oscillation were chosen to create the composite
fields. Eastward and southeastward propagation of precipitation anomalies is evident across the domain, similar to
the 15-day oscillation. This mode contributes less to precipitation fluctuations over European Russia and western
Siberia. As the anomalies increase eastward, their amplitude becomes larger and reaches maxima in the southernpart of central-eastern Siberia. In particular, both positive
and negative anomalies develop over the upper Yenisey
River basin/Lake Baikal area and the southern upper Lena
River basin. From phase 2 to phase 3, an east-west dipole
pattern with eastern positive and western negative anomalies is evident. An almost opposite pattern appears duringphases 6 and 7. A localised displacement of drier andwetter zones occurs in these regions throughout the life
cycle of this oscillation.
6. Large-scale circulation patterns associated withthe oscillations of Precipitation
The previous section identified the space–time structuresof the three oscillatory modes of intraseasonal precipitation over northern Eurasia. Our next concern is the
structure and evolution of a large-scale atmospheric circulation pattern that produces each precipitation oscillatory mode. The space–time alternation of anomalous
dry and wet zones over northern Eurasia presumably
results from specific atmospheric circulation systems. To
establish the relationships between the precipitation oscillatory modes and atmospheric circulation patterns, weapplied a similar composite method to 300-hPa geopotential height anomaly fields in the Northern Hemisphere.
The wave activity flux vector derived by Takaya andNakamura (1997, 2001) was used to diagnose the propagating Rossby wave nature of the composited circulation
pattern. We employed the two-dimensional formulationof the stationary wave activity flux used by Jiang and Lau
Int. J. Climatol. (2011)

INTRASEASONAL OSCILLATIONS OF PRECIPITATION OVER NORTHERN EURASIA(a)

Phase 145-day Mode (ST-PCs 5 & 6), PR RCs(10 S-EOFs), JJA 1979-2002

70°N
65°N
60°N
55°N
50°N
40°E
(b)
70°N

50°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

Phase 2

65°N
60°N
55°N
50°N
40°E
(c)
70°N

50°E

Phase 3

65°N
60°N
55°N
50°N
40°E
(d)
70°N

50°E

Phase 4

65°N
60°N
55°N
50°N
40°E
(e)
70°N

50°E

Phase 5

65°N
60°N
55°N
50°N
40°E
(f)
70°N

50°E

Phase 6

65°N
60°N
55°N
50°N
40°E
(g)
70°N

50°E

Phase 7

65°N
60°N
55°N
50°N
40°E
(h)
70°N

50°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

Phase 8

65°N
60°N
55°N
50°N
40°E

50°E

1.3
1.1
0.9
0.7
0.5
0.3
0.1
0.0
-0.1
-0.3
-0.5
-0.7
-0.9
-1.1

Figure 7. Same as Figure 6 except for the 45-day oscillation.

(2008). Perturbation geostrophic wind and geostrophic
stream function were estimated from the composited
geopotential height anomalies at 300 hPa to compute thewave activity flux. The summer (JJA) averaged zonal andmeridional winds at 300 hPa were also used.
Figure 9 shows a composite evolution of 300-hPa
geopotential height anomalies together with horizontal wave activity flux vectors over the mid- andCopyright  2011 Royal Meteorological Society

high-latitude Northern Hemisphere associated with the
15-day oscillation of precipitation. The geopotential
height anomalies were 8- to 25-day band-pass filtered
before compositing to gain a clearer picture of theatmospheric circulation pattern, which is responsible forthe submonthly scale oscillation. A circulation pattern
exhibits an eastward-travelling wave train originating
in northeastern Europe and extending into the North
Int. J. Climatol. (2011)

Y. FUKUTOMI et al.
(a) Phase 1
70°N

9-day Mode (ST-PCs 14 & 15), PR RCs(10 S-EOFs), JJA 1979-2002

65°N
60°N
55°N
50°N
40°E

50°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

(b)
Phase 2
70°N
65°N
60°N
55°N
50°N
40°E
(c)
70°N

50°E

Phase 3

65°N
60°N
55°N
50°N
40°E
(d)
70°N

50°E

Phase 4

65°N
60°N
55°N
50°N
40°E
(e)
70°N

50°E

Phase 5

65°N
60°N
55°N
50°N
40°E

50°E

(f)
Phase 6
70°N
65°N
60°N
55°N
50°N
40°E

50°E

(g) Phase 7
70°N
65°N
60°N
55°N
50°N
40°E

50°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

(h) Phase 8
70°N
65°N
60°N
55°N
50°N
40°E

50°E

1.3
1.1
0.9
0.7
0.5
0.3
0.1
0.0
-0.1
-0.3
-0.5
-0.7
-0.9
-1.1
-1.3

Figure 8. Same as Figure 6 except for the 9-day oscillation.

Pacific/North American sector throughout the life cycle.
The wave train consists of meridionally elongated anomalous troughs and ridges with a spatial scale of zonal
wavenumbers 5–6. The waves propagate eastward from
northeastern Europe into central Siberia and then southeastward from central Siberia into Far East Asia and thenorthwestern Pacific. As the waves propagate eastward,
Copyright  2011 Royal Meteorological Society

the anomalous troughs and ridges are strengthened in theeastern part of northern Eurasia. Comparing the precipitation composite (Figure 6), we notice that locations of theeastward moving troughs (ridges) almost corresponded
to those of the anomalous wet (dry) zones at each phase,suggesting that this wave train dominates the 15-day precipitation oscillatory mode. The eastward–southeastward
Int. J. Climatol. (2011)

INTRASEASONAL OSCILLATIONS OF PRECIPITATION OVER NORTHERN EURASIA
Composite 300hPa Z Anom. (BPF 8-25 days), 15-day mode (PR ST-PCs 1 & 2)(e) Phase5

12

90°E

15
0°E
0°E

0°E
15

15

30
°W

30
°W

180°

12

°W

0°W

0°E

30

0°E

180°

0°

180°

°E

°E

15

0°W

0°W

30
12

30

°W

12

15

15

30

90°W

°W
60

0°E

90°E

0°W

°E
30

Phase8

(h)
0°W

°E

0°E

90°E

0°
60

0°W

60
°E

12

12

12

0°

180°

90°W
°W

12

15

0°W

60

90°W

°W
60
15

°E
30

Phase4Phase7

(g)

12

0°E

90°E
(d)

30
°W

15
0°E

90°E

0°W

°E

0°E

60
°E

12

0°
60

180°

90°W
°W

0°W

°E
30

60

12

0°

180°

Phase3

12

0°W
15

0°W
15

°E
30
0°E

°E

90°W

°W
60

0°W

90°E
(c)

Phase6

(f)

12

0°
60

15
0 °E

15
0 °E

90°W
°W

0°E

60
°E

12

30
°W

60

180°

Phase2

(b)

°E
30

90°E

0°W

0°

180°

°E
30
0°E

°E

2 m2s-2

12

0 °W
15

0 °W
15

0°
60

90°W

°W
60

0°W

15

90°W
°W

30
°W

60

30
°W

Phase1

(a)

0°E

60
°E

90°E

12

Figure 9. Composites of 300-hPa geopotential height anomalies (8- to 25-day band-pass filtered) over the Northern Hemisphere keyed to phasecategories 1–8 of the 15-day oscillation and corresponding wave activity flux vectors (m2 s−2 ). Contour interval is 10 m. Positive (negative)
anomalies are drawn with solid (dashed) contours. Zero contours are omitted. Positive (negative) anomalies with statistically significant at 95%
level are lightly (darkly) shaded.

phase propagation and downstream amplification featuresof the northern Eurasian wave packet are consistent withthose of the precipitation anomalies of the 15-day mode
(Figure 6). The downstream amplification of the waves
could have been due to Rossby wave energy propagation,
as represented by the wave activity flux vectors in the
Copyright  2011 Royal Meteorological Society

latitudes between 45° N and 70° N over northern Eurasia.
These emanate from the initial development region of the
wave train over northeastern Europe close to Scandinavia
and European Russia. The most prominent signature in
the wave activity flux vector field is that it has stronger
eastward and southeastward flux across northern Eurasia,
Int. J. Climatol. (2011)

Y. FUKUTOMI et al.

especially during phase 3 to phase 4 and phase 7 to phase
8. These are quite consistent with the downstream wave
development over the eastern part of northern Eurasia.
Another interesting feature is that waves develop
farther downstream into the North Pacific and North
America. In phase 3, an isolated northern Eurasian
wave packet ends off the east coast of Far East Asia.
In the following phases, new anomalous troughs andridges grow downstream and accompany eastward wave
activity flux vectors along 40° N to 65° N over the North
Pacific. The wave packet subsequently traverses theNorth Pacific and reaches North America in phase 4.
A consecutive wave train, extending from northeastern
Europe to the east coast of North America, is thencreated by phase 5. In phase 6, the wave train divides
into two parts: the northern Eurasian part disperses
into the northwest Pacific and the North American partprogresses towards the northwestern Atlantic. In phase
7, the North American wave packet entirely dissipates,
while the northern Eurasian wave packet remains andcontinues to propagate eastward. New anomalous troughs
and ridges are continuously formed downstream over the
North Pacific and North America from phase 8 to thenext phase 1. Consequently, a consecutive wave train
with an opposite sign to that in phase 5 is createdin phase 1. However, the extent of the wave train
associated with the 15-day oscillation is not completely
circumglobal; rather, the wave train traverses about
three-quarters of the mid- and high-latitude circle. The
structure and propagation characteristics of this wave
train are comparable to those observed by Kanaya (1986)
and Jiang and Lau (2008), who reported summertime
intraseasonal wave propagation in the mid- and highlatitudes. The nearly circumglobal wave propagation
detected by Kanaya (1986) is similar to the wave train
structure in phases 1 and 5 (Figure 9). The trans-Pacificwave train on the submonthly time scale defined by
Jiang and Lau (2008) is also similar to the wave train
extending from the North Pacific to North America
that appears in phases 1, 4, 5, and 8 (Figure 9). It
should also be noted that there is no organised wave
propagation over the North Atlantic. The North American
wave packet ends off the northwestern Atlantic and does
not propagate farther downstream towards northwestern
Europe. Successive wave propagation does not occur over
the North Atlantic. Thus, the northern Eurasian wave train
does not seem to be excited in the North Atlantic region.
Figure 10 displays large-scale circulation patterns governing the 45-day oscillation of precipitation. A geopotential height anomaly and the wave activity flux composites were constructed in a similar manner as in
Figure 9, except that 25-day low-pass-filtered anomalies were used to capture lower-frequency intraseasonal
waves relevant to the 45-day oscillation. The resulting
circulation pattern is mainly characterised by a wave
train that apparently emanates from the northeastern
Atlantic and extends across northern Eurasia. The wave
train has zonal wavenumbers of 3–4 over the northeastern Atlantic/northern Eurasian sector. Throughout the
Copyright  2011 Royal Meteorological Societylife cycle, the waves appear to have mixed standing
and propagating characteristics. In phase 2, the wave
train forms a clear tripole pattern with a ridge over thenortheastern Atlantic, a trough over northeastern Europe,and a ridge over western-central Siberia. As part ofthis wave train, a trough–ridge couplet which consistsof the northeastern European trough and the Siberianridge prevails over the western part of northern Eurasia. Eastward–southeastward pointing wave activity flux
vectors are aligned along the trough–ridge couplet. The
trough–ridge couplet persists while moving slowly eastward and southeastward into Siberia from phase 2 tophase 3, although the northeastern Atlantic ridge rapidly
decays. Similar quasi-stationary features are observed ina trough–ridge pattern with an opposite sign over thenortheastern Europe–Siberian sector, between phases 6
and 8. The trough initially located over northeastern
Europe travels eastward and southeastward into central
Siberia from phase 2 to phase 8. The ridge also travels across almost the same path from phase 6 to phase
4 in the next cycle. The trough (ridge) weakens oncein the downstream movement around phase 5 (phase 1),
but remains and develops again from phase 6 (phase 2).
This redevelopment may be due to Rossby wave energy
propagation from upstream, as evidenced by the wave
activity flux aligned along the wave train. Obviously,
troughs and ridges, as parts of the wave train, correspondto the anomalous wet and dry zones, respectively, as seenin Figure 7. Throughout all these phases, the centre of thezonally elongated anomalous wet (dry) zone (Figure 7) islocated to the east ahead of the trough (ridge). The eastward and southeastward phase propagation of the wet(dry) zone from European Russia into eastern Siberia(Figure 7) is accompanied by the movement of the trough(ridge) from Scandinavia into central-eastern Siberia fromphase 2 to phase 8 (phase 6 to phase 4 in the next
cycle). Thus, the wave train which spans the northeastern Atlantic–northern Eurasian sector produces the slow
alternation of the wet and dry zones over northern Eurasia.
Overall, the well-organised wave activity associatedwith the 45-day oscillation is mostly over the northeastern Atlantic–northern Eurasian sector. There is little organised wave signal over the North Pacific, North
America, and northwestern Atlantic. The waves do notexhibit successive downstream development towards theNorth Pacific–North American sector. The waves do not
propagate deep into the North Pacific–North American
sector. Amplification and decay of the waves occurs more
locally in the northeastern Atlantic–northern Eurasian
sector. These features are the largest differences from thewave train connected with the 15-day oscillation. The
wave structure somewhat resembles that found by Iwao
and Takahashi (2008). They detected the zonally oriented
wave train across northern Eurasia associated with summertime Mongolian precipitation on intraseasonal timescales. However, the wave propagation path in our resultsappears to be rather northwest–southeast oriented compared to their results.
Int. J. Climatol. (2011)

INTRASEASONAL OSCILLATIONS OF PRECIPITATION OVER NORTHERN EURASIA

Composite 300hPa Z Anom. (LPF >25 days), 45-day mode (PR ST-PCs 5 & 6)
(e)

30
°W

15
0°E

90°E
(f)

30
°W

30
°W

15
0°E

(g)

0 °E

0 °E

30
°W

30
°W

180°

Phase8
°W
60

15

°W

0 °W

0°E

30
°E

°E

15

180°

0°

180°

30
12

30

°W

12

0 °W

0 °W

30

90°W

12

15

15

0°
90°E

0 °W

°E
30

(h)

0 °E

°E

0 °E

90°E

0 °W

60

0 °W

60
°E

12

12

12

0°

180°

90°W
°W

60

90°W

15

0 °W

15

°E
30

Phase4Phase7
°W
60

12

15

12

0°
90°E
(d)

180°

90°E

0 °E

°E

0 °E

60
°E

12

0 °W

60

0 °W

°E
30

90°W
°W

60

12

0°

180°

°E
30

90°E

90°W

0°W
15

0°W
15

0°
0 °E

°E

Phase3Phase6
°W
60

12

15
0°E

12

0 °W

60

0 °E

60
°E

12

0°E

90°W
°W

60

(c)

180°

Phase2

°E
30

90°E

3 m2s-2
0 °W

12

0°

180°

°E
30
°E

90°W

0°W
15

0°W
15

0°
0 °E

60

(b)

Phase5
°W
60

0 °W

15
0°E

12

15

90°W
°W

60

30
°W

Phase1

(a)

0 °E

60
°E

90°E

12

Figure 10. As in Figure 9 except for the 300-hPa geopotential height anomalies (25-day low-pass filtered) keyed to phase categories 1–8 of the
45-day oscillation. Contour interval is 20 m.

Finally, composite large-scale circulation patterns andcorresponding wave activity flux vectors associatedwith the 9-day oscillation precipitation are presented
in Figure 11. To represent the circulation patterns relevant to another submonthly scale mode, 6- to 12day filtered 300-hPa geopotential height anomalies wereused for compositing. The composite circulation pattern in each phase shows a wave train extending
Copyright  2011 Royal Meteorological Society

across northern Eurasia, consisting of a sequence of
meridionally elongated anomalous troughs and ridges anddominated by zonal wavenumbers 6–7. Throughout thelife cycle, the waves propagate eastward from Europe
into Northeast Asia and the northwest Pacific. As thetroughs and ridges travel eastward into Northeast Asia,
they are amplified with their northeast to southwest elongation. The travelling troughs and ridges accompany theInt. J. Climatol. (2011)

Y. FUKUTOMI et al.
Composite 300hPa Z Anom. (BPF 6-12 days), 9-day mode (PR ST-PCs 14 & 15)(e) Phase5
0°W(f) Phase6
0°W
°W
30

0°E

0°E

°W

0°E

0°E
15

15

°W
30

30

°E

(h) Phase8

0°W

°W

12

0°E

30

0°E

0°E

30
°E

15

180°

0°

180°

0°E

0°W

30

°W

12

15

0°W

12

90°W

60
15

°W

0°W

°E

90°E

180°

60

90°E

30
°E

0°W

°E

0°E

0°
60

0°W

30

12

12

0°E

0°

180°

°W

°W

12

15

0°W

90°W

60

12

90°W

60
15

(d) Phase4°E(g) Phase7
0°W

°E

90°E

15

30

15

60

90°E

30
°E

180°

0°E

0°
60

0°W

°E

12

0°W

30

°W

12

0°E

0°

180°

90°W

60

°W

12

15

0°W

(c) Phase3

12

90°W

60
15

°E

90°E

°E

90°E

30
°E

0°E

0°E
15

60

0°
60

15

30

12

0°E

°W

°W

°E

90°W

60

180°

30

(b) Phase2

122 m2s-2
0°W

0°

180°

°E

90°E

12

0°W

0°W

30
°E

°W

15

15

0°
60

90°W

60

15

12

°W

°W

°W

90°W

60

30

(a) Phase1

60

°E

90°E

12

0°E

Figure 11. As in Figure 9 except for the 300-hPa geopotential height anomalies (6- to 12-day band-pass filtered) keyed to phase categories 1–8of the 9-day oscillation.

eastward displacement of the anomalous wet and dry
zones (Figure 8). The amplification of the troughs andridges over Northeast Asia corresponds to the stronger
precipitation fluctuations in the southern part of centraleastern Siberia (Figure 8). Interestingly, the amplified
trough (ridge) over Northeast Asia, especially in phases
3 and 4 (phases 7–8), emanates enhanced wave activity
fluxes towards the downstream (ridge) trough over the
Copyright  2011 Royal Meteorological Society

northwest Pacific. This suggests that the trough (ridge)
over Northeast Asia is an important local wave energy
source.
The overall wave train pattern displays similar structures in some respects to that associated with the 15-day
mode (Figure 9) over northern Eurasia. The propagation
properties of the 9-day wave are basically similar to thoseof the 15-day wave. However, the 9-day mode wave
Int. J. Climatol. (2011)

INTRASEASONAL OSCILLATIONS OF PRECIPITATION OVER NORTHERN EURASIA

train has a comparatively smaller spatial scale and its
propagation path is located in the lower latitudes between
approximately 35° N and 60° N. In addition, organised
wave propagation is confined to northern Eurasia. The
troughs and ridges which move into the northwest Pacific
eventually dissipate over the northwest Pacific off the east
coast of Northeast Asia and do not progress eastward
into the North Pacific. Similarly, successive downstream
development of the waves due to Rossby wave energy
propagation does not occur in the North Pacific–North
American sector.

7.

Summary and discussion

This study analysed the intraseasonal variability of precipitation over northern Eurasia during the summers of
1979–2002. Dominant modes of intraseasonal oscillations of precipitation on different time scales were identified by applying MSSA to the gridded daily precipitation data. Three distinct oscillations were found in the
MSSA spectral peaks centered at 15, 45, and 9 days.
The spatiotemporal structures of these oscillations wererevealed by constructing composite maps of the precipitation anomaly fields. The 15-day oscillation is thestrongest oscillation and falls within the range of the socalled submonthly variability. The composite life cycleof the 15-day oscillation shows regular eastward phasepropagation of precipitation anomalies, with a tripole
structure across northern Eurasia. The 45-day oscillation lies within the range of low-frequency intraseasonalvariability. The composite evolution of the 45-day oscillation exhibits a slowly varying signature with zonallyelongated precipitation anomalies which are characterisedby a broad east-west contrastive pattern across northern Eurasia. The 9-day oscillation falls within the higher
frequency range of submonthly variability. While thespatiotemporal behaviours of the precipitation anomalies
associated with this oscillation are similar to those of the
15-day oscillation, the spatial scale of this oscillation issomewhat smaller.
The composite evolution of 300-hPa geopotential
height anomalies was also examined to highlight thelarge-scale circulation patterns connected to the life
cycle of each oscillation of precipitation. The circulation anomalies linked to the 15-day oscillation exhibit aneastward propagating wave train extending from northeastern Europe into the North Pacific–North American
sector. One of the interesting features of the wave-train
behaviour is the nearly circumglobal propagation. The
circulation patterns responsible for the 45-day oscillation are characterised by the wave train extending acrossthe northeastern Atlantic–northern Eurasian sector. The
wave train indicates the slowly eastward moving andquasi-stationary features. The well-organised wave train
appears to be only over this sector and does not develop
downstream towards the North Pacific–North American
sector. The behaviour of the wave train pattern associatedwith the 9-day oscillation resembles that of the 15-day
Copyright  2011 Royal Meteorological Society

oscillation in the northern Eurasian sector. However, thewave train has a comparatively smaller spatial scale andwave propagation is confined to northern Eurasia.
The intraseasonal oscillations of precipitation foundin this work are connected to the well-organised wave
trains that extend from the Euro-Atlantic region to northern Eurasia. In general, the main precipitation producing system over northern Eurasia is an extratropical
cyclone which accompanies precipitating fronts (Serreze
et al., 2001; Serreze and Etringer, 2003; Fukutomi et al.,2007). The wave trains identified here could affect thestrength of the cyclones and fronts and modulate subseasonal cyclogenic and frontal activities. The troughs(ridges) shown in the composite maps are coupled withanomalous wet (dry) zones. These zones must correspond to enhanced (reduced) precipitation associated withstrengthened (weakened) frontal activities forced by thetroughs (ridges) of the wave trains. Thus, the oscillationof precipitation on each intraseasonal time scale must be
produced by the propagation of the corresponding wave
train.
One can notice that the spectral characteristics in
the MSSA-MEM results (Figure 4) and those in the
FFT results for the selected domains (Figure 2) are
not completely consistent with each other. The three
distinct modes detected by the MSSA are not necessarily
observed in the FFT results. For instance, peaks at nearly
15 and 45 days are not observed in the ensemble averaged
spectra for the eastern domain (Figure 2(a)) and any
significant peak at nearly 9 days is not found in thosefor the western domain (Figure 2(b)). One of the reasonsfor these inconsistencies could be due to a year-to-year
variability of the spectral characteristics. By checking
the spectra for individual years, we found out spectral
peaks at nearly 15 and 45 days in several cases forthe eastern domain (not shown). However, the ensemble
averaging for the all cases could smooth the resulting
spectra and diminish these peaks. The same reason might
be applicable to the disappearance of a peak at 9 daysin the western domain. The MSSA 9-day mode tends
to have stronger signals in the southeastern Siberian
domain and weaker signals in the western Siberian
domain (Figure 8). This could also be a cause of thedisappearance of a 9-day peak in the western domain.
The 11-day signal in the FFT spectra for the easterndomain (Figure 2(a)) can be included in a category of
the MSSA 15-day mode. The 15-day mode (ST-PCs 12) actually spans somewhat broader period range fromabout 11 days to 15 days (Figure 4). The similarities anddifferences between the MSSA and FFT results appear to
be still open to discussion. We have tried to give possible
explanations for those by considering the present results.
It is interesting to note the relationships between
the intraseasonal oscillations and interannual variability of precipitation over northern Eurasia. Future study
should examine subseasonal precipitation events associated with intraseasonal oscillations and interannual precipitation extremes. To ascertain the roles of the intraseasonal oscillations in producing interannual extremes,
Int. J. Climatol. (2011)

Y. FUKUTOMI et al.

classification of precipitation regimes is needed alongwith the associated circulation patterns on intraseasonal
time scales. Cluster analysis would be a useful tool forthis task. Investigation into the characteristics and occurrence frequency of intraseasonal precipitation regimes inextreme years should also be undertaken.
Acknowledgements
We appreciate the comments of anonymous reviewers,
which helped to improve the article. Special thanks go
to Akiyo Yatagai and Hisahiro Takashima for providing
information on the data processing for APHRODITE’s
daily precipitation analysis (http://www.chikyu.ac.jp/
precip/index.html).
References
Adam JC, Lettenmaier DP 2008. Application of new precipitation andreconstructed stream flow products to streamflow trend attribution innorthern Eurasia. Journal of Climate 21: 1807–1828.
Dettinger MD, Ghill M, Keppenne CL 1995. Interannual and interdecadal variability in United States surface-air temperatures,
1910–87. Climatic Change 31: 35–66.
Fukutomi Y, Igarashi H, Masuda K, Yasunari T 2003. Interannual
variability of summer water balance components in three majorriver Basins of northern Eurasia. Journal of Hydrometeorology 4:
283–296.
Fukutomi Y, Masuda K, Yasunari T 2004. The role of storm track
activity in the interannual seesaw of summer precipitation overnorthern Eurasia. Journal of Geophysical Research 109: D02109,
DOI: 10.1029/2003JD003912.
Fukutomi Y, Masuda K, Yasunari T 2007. Cyclone activity associatedwith the interannual seesaw oscillation of summer precipitation overnorthern Eurasia. Global and Planetary Change 56: 387–398.
Fukutomi Y, Yasunari T 2005. Southerly surges on submonthly timescales over the eastern Indian Ocean during the Southern Hemisphere
winter. Monthly Weather Review 133: 1637–1654.
Iwao K, Takahashi M 2008. A precipitation seesaw mode between
Northeast Asia and Siberia in summer caused by Rossby waves overthe Eurasian continent. Journal of Climate 21: 2401–2419.
Iwasaki H, Nii T 2006. The break in the Mongolian rainy season and its
relation to the stationary Rossby wave along the Asian Jet. Journalof Climate 19: 3394–3405.
Jiang N, Neelin D, Ghil M 1995. Quasi-quadrennial and quasi-biennial
variability in the equatorial Pacific. Climate Dynamics 12: 101–112.
Jiang X, Lau N-C 2008. Intraseasonal teleconnection between North
America and western North Pacific monsoons with 20-day time scale.
Journal of Climate 2: 2664–2679.
Kanaya T 1986. An observational analysis of wave activities indifferent general circulation regimes for 1982 summer. Journal of
the Meteorological Society of Japan 64: 599–604.
Kaylor RE 1977. Filtering and decimation of digital time series. Tech.
Note BN 850. Institute of Physical Science Technology, Universityof Maryland, College Park; 42.
Krishnamurthy V, Shukla J 2007. Intraseasonal and seasonallypersisting patterns of Indian monsoon rainfall. Journal of Climate
20: 3–20.
Krishnamurthy V, Shukla J 2008. Seasonal persistence and propagationof intraseasonal patterns over the Indian monsoon region. Climate
Dynamics 30: 353–369.
MacDonald GM, Kremenetski KV, Smith LC, Hidalgo HG 2007.
Recent Eurasian river discharge to the Arctic Ocean in the context
of long-term dendrohydrological records. Journal of GeophysicalResearch 112: G04S50, DOI: 10.1029/2006JG000333.
McClelland JW, Holmes RM, Peterson BJ, Stielitz M 2004. Increasing
river discharge in the Eurasian Arctic: consideration of dams,
permafrost, and fires as potential agents of change. Journal ofGeophysical Research 109: D18102, DOI: 10.1029/2004JD004583.
Moron V 1997. Trend, decadal and interannual variability in annual
rainfall of subequatorial and tropical North Africa. International
Journal of Climatology 17: 785–805.
Copyright  2011 Royal Meteorological Society

Moron V, Vautard VR, Ghil M 1998. Trends, interdecadal andinterannual oscillations in global sea-surface temperatures. Climate
Dynamics 14: 545–569.
Onogi K, Koide H, Sakamoto M, Kobayashi S, Tsutsui J, Hatsushika H, Matsumoto T, Yamazaki N, Kamahori H, Takahashi K,
Kato K, Oyama R, Ose T, Kadokura S, Wada K. 2005. JRA-25:
Japanese 25-year reanalysis–progress and status. Quarterly Journalof the Royal Meteorological Society 131: 3259–3268.
Onogi K, Tsutsui J, Koide H, Sakamoto M, Kobayashi S, Hatsushika H, Matsumoto T, Yamazaki N, Kamahori H, Takahashi K,
Kadokura S, Wada K, Kato K, Oyama R, Ose T, Mannouji N,
Taira R. 2007. The JRA-25 analysis. Journal of the MeteorologicalSociety of Japan 85: 369–432.
Pavelsky TM, Smith LC 2006. Intercomparison of four global
precipitation data sets and their correlation with increased Eurasianriver discharge to the Arctic Ocean. Journal of Geophysical Research
111: D21112, DOI: 10.1029/2006JD007230.
Peng S, Mysak LA 1993. A teleconnection study of interannual sea
surface temperature fluctuations in the northern North Atlantic andprecipitation and runoff over western Siberia. Journal of Climate 6:
876–885.
Peterson BJ, Holmes RM, McClelland JW, Vorosmarty CJ, Lammers RB, Shiklomanov AI, Shiklomanov IA, Rahmstorf S 2002.
Increasing river discharge into the Arctic Ocean. Science 298:
2171–2173.
Plaut G, Vautard R 1994. Spells of low-frequency oscillations andweather regimes in the Northern Hemisphere. Journal of Atmospheric
Science 51: 210–236.
Qian B, Xu H, Corte-Real J 2000. Spatial-temporal structures of quasiperiodic oscillations in precipitation over Europe. International
Journal of Climatology 20: 1583–1598.
Rawlins MA, Willmott CJ, Shiklomanov A, Linder E, Frolking S,
Lammers RB, Vorosmarty CJ 2006. Evaluation of trends in derived
snowfall and rainfall across Eurasia and linkages with discharge to
the Arctic Ocean. Geophysical Research Letters 33: L07403, DOI:
10.1029/2006GL025231.
Semiletov IP, Savelieva NI, Weller GE, Pipko II, Pugach SP, Gukov
AY, Vasilevskaya LN 2000. The dispersion of Siberian river flows
into coastal waters: Meteorological, hydrological and hydrochemical
aspects. In The Freshwater Budget of the Arctic Ocean, NATO
Science Series 2: Environmental Security 47, Lewis EL, Jones EP,
Lemke P, Prowse TD, Wadhams P (eds). Kluwer Academic
Publishers: Dordrecht, The Netherlands; 323–366.
Serreze MC, Bromwich DH, Clark MP, Etringer AJ, Zhang T, Lammers R 2003. The large-scale hydro-climatology of the terrestrial
Arctic drainage system. Journal of Geophysical Research 108:
ALT 1-1–ALT 1-28, DOI: 10.1029/2001JD000919.
Serreze MC, Etringer AJ 2003. Precipitation characteristics ofthe Eurasian Arctic drainage system. International Journal of
Climatology 23: 1267–1291.
Serreze MC, Lynch AH, Clark MP 2001. The Arctic frontal zone
as seen in the NCEP–NCAR reanalysis. Journal of Climate 14:
1550–1567.
Simonnet E, Plaut G 2001. Space–time analysis of geopotential heightand SLP, intraseasonal oscillations, weather regimes, and local
climates over the North Atlantic and Europe. Climate Research 17:
325–342.
Takashima H, Yatagai A, Kawamoto H, Arakawa O, Kamiguchi K
2009. Hydrological balance over northern Eurasia from gauge-based
high-resolution daily precipitation data. In From Headwaters to the
Ocean: Hydrological Change and Water Management, Taniguchi M(ed.). Taylor & Francis: London; 37–42.
Takaya M, Nakamura H 1997. A formulation of a wave-activity flux
for stationary Rossby waves on a zonally varying basic flow.
Geophysical Research Letters 24: 2985–2988.
Takaya M, Nakamura H 2001. A formulation of a phase-independentwave-activity flux for stationary and migratory quasigeostrophic
eddies on a zonally varying basic flow. Journal of Atmospheric
Sciences 58: 608–627.
Wang XL, Cho H-R 1997. Spatial-temporal structures of trend andoscillatory variability of precipitation over northern Eurasia. Journalof Climate 10: 2285–2298.
Wang XL, Corte-Real J, Zhang X 1996a. Low-frequency oscillations
and associated wave motions over Eurasia. Tellus 48A: 238–253.
Wang XL, Corte-Real J, Zhang X 1996b. Intraseasonal oscillations andassociated spatial-temporal structures of precipitation over China.
Journal of Geophysical Research 101: 19035–19042.
Yatagai A, Arakawa O, Kamiguchi K, Kawamoto H, Nodzu M,
Hamada A 2009. A 44-year daily gridded precipitation dataset forInt. J. Climatol. (2011)

INTRASEASONAL OSCILLATIONS OF PRECIPITATION OVER NORTHERN EURASIA
Asia based on a dense network of rain gauges. SOLA 5: 137–140,
DOI: 10.2151/sola.2009-035.
Yatagai A, Xie P, Alpert P 2008. Development of a daily gridded
precipitation data set for the Middle East. Advances in Geosciences
12: 165–170.
Ye H, Cho H-R 2001. Spatial and temporal characteristics ofintraseasonal oscillations of precipitation over the United States.
Theoretical and Applied Climatology 68: 51–66.

Copyright  2011 Royal Meteorological Society

Yoon J-H, Chen T-C 2006. Maintenance of the boreal forest rainbelts
during northern summer. Journal of Climate 19: 1437–1449.
Zhang X, Corte-Real J, Wang XL 1997. Low-frequency oscillations in
the Northern Hemisphere. Theoretical and Applied Climatology 57:
125–133.
Zhang X, Sheng J, Shabbar A 1998. Modes of interannual andinterdecadal variability of Pacific SST. Journal of Climate 11:2556–2569.

Int. J. Climatol. (2011)