JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, D02109, doi:10.1029/2003JD003912, 2004

Role of storm track activity in the interannual seesawof summer precipitation over northern Eurasia
Yoshiki Fukutomi and Kooiti Masuda
Frontier Research System for Global Change, JAMSTEC Yokohama Institute for Earth Sciences, Yokohama, Japan

Tetsuzo Yasunari
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan
Frontier Research System for Global Change, JAMSTEC Yokohama Institute for Earth Sciences, Yokohama, Japan
Received 25 June 2003; revised 7 October 2003; accepted 10 October 2003; published 30 January 2004.

[1] Oscillation between relatively dry and wet extremes is a noteworthy characteristic of

the interannual variability in summer precipitation over Siberia. Recently, we identified an
out-of-phase relationship in the temporal signature of basin-scale precipitation between
eastern and western Siberia (ES and WS). This east-west seesaw has been especiallypronounced during the past 30 years. The spatial structure of large-scale precipitation andcirculation anomalies associated with this east-west seesaw is characterized by an eastwest dipole across northern Eurasia. We extended our work to investigate the important
atmospheric processes involved in the dipole patterns of precipitation and large-scale
circulation anomalies for the northern summers (June–August) of 1972–2001. In
particular, this work focuses on the role of storm track activities associated with synopticscale (<10 days) eddies in producing the stationary waves that correspond to theprecipitation anomaly dipoles. Linear regression and composite analyses of various eddy
statistics are performed for two contrasting extremes in the Siberian summer precipitation
seesaw. These two extreme phases are denoted ES-wet-WS-dry and WS-wet-ES-dry.
The summer mean atmospheric anomalies related to the precipitation seesaw show
stationary wavetrain patterns in conjunction with the precipitation dipole anomalies. The
cold trough/warm ridge dipole corresponds to the relative wet and dry region. Analysis ofstorm track activity reveals similar east-west contrasting structures, meaning that thedisplacements associated with the anomalous storm tracks are accompanied by a phasereversal between the two extreme phases. An anomalous stationary trough (ridge)
corresponds to the anomalous high-(low-)synoptic-scale-eddy-activity region in both ES
and WS. The forcing by synoptic-scale eddies is examined to confirm the storm track
feedback on the Eurasian stationary waves. The results show that barotropic feedback
induced by synoptic-scale eddy vorticity fluxes is in phase with the Eurasian wave
field. Cyclonic (anticyclonic) circulation forcing is collocated with the anomalous
stationary trough (ridge). These spatially coherent relationships suggest that storm track
feedback due to eddy vorticity fluxes helps to reinforce and maintain the Eurasian wave
structure coupled with the precipitation dipole pattern of each phase. Consequently,
the interannual variability of the Eurasian storm track intensity and location is a crucial
factor in forcing the mean precipitation and circulation patterns between the two extreme
INDEX TERMS: 1610 Global Change: Atmosphere (0315, 0325); 1620 Global Change:
phases.
Climate dynamics (3309); 1655 Global Change: Water cycles (1836); 3319 Meteorology and Atmospheric
Dynamics: General circulation; 3354 Meteorology and Atmospheric Dynamics: Precipitation (1854);
KEYWORDS: storm track, interannual variability, precipitation
Citation: Fukutomi, Y., K. Masuda, and T. Yasunari (2004), Role of storm track activity in the interannual seesaw of summer
precipitation over northern Eurasia, J. Geophys. Res., 109, D02109, doi:10.1029/2003JD003912.

1. Introduction[2] Large-scale hydrological processes over northernEurasia help to govern climate systems through atmoCopyright 2004 by the American Geophysical Union.
0148-0227/04/2003JD003912$09.00

sphere-ocean-land interactions and feedback (Walsh[2000], Yang et al. [2002], Serreze et al. [2003], Fukutomi
et al. [2003], and others). For example, long-term variability in the summer hydrological cycle in northern Eurasia
significantly affects the fresh water input into the Arctic
Ocean, which in turn affects climate in adjacent lower

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latitudes. However, relatively few studies have investigatedthe characteristics of large-scale atmospheric circulations
and the mechanisms forcing changes in the summer hydrological cycle in northern Eurasia.
[3] Our previous study [Fukutomi et al., 2003] investigated the water balances in Siberian river basins andassociated atmospheric fields and identified an east-west
seesaw of dry and wet conditions as a marked characteristicof interannual variability of summer (June –August: JJA)
precipitation in Siberia. They detected an out-of-phaserelationship in the temporal signature of basin-scale precipitation with a 6- to 8-year cycle between eastern and western
Siberia (hereafter ES and WS). This oscillatory tendency
has dominated the area over the past 30 years. The spatial
structure of large-scale precipitation, moisture transport, andcirculation anomalies are all characterized by an east-west
dipole pattern that spans northern Eurasia. This study posed
a question; what are the dominant processes that might
produce dipole variability in precipitation and concurrent
stationary wave patterns?
[4] Over northern Eurasia, the Arctic frontal zone is wellorganized during the summer and is characterized by increased cyclogenic and frontal activity over the continent[e.g., Serreze, 1995; Serreze et al., 2001]. The frontal
location corresponds to the Eurasian portion of the summertime storm track activity in eddy variance statistics [e.g.,
White, 1982; Blackmon and White, 1982]. Greater storm
track activity supports the formation of the summertime
precipitation belt over northern Eurasia [Serreze et al.,2003]. The interannual variability in Siberian summer precipitation may be related to interannual modulation in storm
tracks. Stronger and weaker storm activity will result inabove- and below-normal Siberian summer precipitation,
respectively. In other words, changes in the mean circulation
patterns associated with the east-west precipitation seesaw
over Siberia may be related to changes in storm track
activities over northern Eurasia. Past observational andmodeling studies have suggested that representative teleconnection patterns in monthly or seasonal mean flow fields
are strongly linked to storm track activities. High-frequency
transient eddies (at synoptic and submonthly scales) generally act to reinforce and maintain the large-scale stationary
flows of teleconnection patterns in regions of major storm
tracks [e.g., Lau, 1988; Lau and Nath, 1991; Ting and Lau,
1993; Hurrell, 1995; Sheng et al., 1998]. Therefore it seems
reasonable to suppose that storm track feedback can reinforce and maintain also summer stationary waves coupledwith the Siberian precipitation dipole patterns.
[5] This study sought to identify the atmospheric processes responsible for maintaining Eurasian waves associated with the Siberian precipitation dipole pattern. This
study extends previous work, and focuses on storm track
feedback on the seasonal mean circulations for extreme
phases in the precipitation seesaw. Assessing the impact ofsynoptic-scale eddy forcing associated with storm track
activities will help determine the physical mechanisms
controlling the summer hydrological cycle in northernEurasia. First, we document features in the spatial-temporal
structure of these phases and link them to observational
evidence given by Fukutomi et al. [2003]. Then, we
illustrate the corresponding mean circulation pattern andstorm tracks for Northern Hemisphere JJAs from 1972 –D021092001. Eddy variance statistics on synoptic-scale perturbation timescales (1 – 10 days) capture the summertime storm
track activity as it relates to the precipitation seesaw.
Finally, we examine the storm track feedback on thesummer stationary flow at the extreme phases of the seesaw.
To achieve this, we use diagnostic techniques suggested by
Lau and Holopainen [1984], Lau and Nath [1991], andothers (see section 5 for details) and calculate the eddy
vorticity and thermal forcing terms. These constitute thegeopotential height tendency and the thermodynamic equations, respectively. We then use linear regression and thecomposite method to compare anomalous spatial structuresin stationary flow patterns with the eddy statistic fields todetermine the influence of synoptic-scale eddies on theformation of stationary waves over northern Eurasia.
[6] This paper is organized as follows: Section 2 briefly
describes data sets and processing. Section 3 addresses
statistical aspects of the interannual variability of summer
precipitation in Siberia. Section 4 presents the large-scaleatmospheric patterns and storm track activities. Section 5
discusses the storm track feedback based on the dynamicalframework. Section 6 includes a summary and discussion.

2. Data Sources and Processing[7] The global land precipitation data set produced by the
National Oceanic and Atmospheric Administration (NOAA)
Climate Prediction Center (CPC) is used to describe thelarge-scale precipitation variability in Siberia. The data set iscalled PREC/L: the gauge-based analysis of monthly precipitation over the global land areas [Chen et al., 2002]. This
precipitation analysis has been constructed on a 2.5 latitude/longitude grid using the optimal interpolation techniqueapplied to gauge observations at over 15000 stations.
The analysis has been updated for an extended period longer
than 50 years from 1948 to the present at the NOAA/CPC.
This study uses a record for the 54-yr period from 1948 to2001.
[8] The National Centers for Environmental Prediction(NCEP)-National Center for Atmospheric Research(NCAR) reanalysis data set [Kalnay et al., 1996; Kistler
et al., 2001], obtained from the NOAA Climate Diagnostics
Center (CDC), is used to examine large-scale atmosphericpatterns and storm track signatures associated with theSiberian precipitation variability. This data set includes
pressure level analyses on a global 2.5 latitude/longitudegrid. Data used in this study are daily and monthly geopotential height (z), temperature (T), zonal and meridional
wind (u, v), and sea level pressure (SLP) fields for the 30-yr
period from 1972– 2001.
[9] This study focused on summertime storm track activity over northern Eurasia. To diagnose storm track activity,
daily variables that retain synoptic-scale fluctuations withtimescales shorter than 10 days are analyzed using a highpass Butterworth filter developed by Kaylor [1977]. Then,various eddy statistics are calculated from time-filtered eddy
components as described in the following sections. Daily
perturbations of the variables are computed by subtracting
the climatological seasonal cycle from the raw 365-day timeseries for each year before filtering. The climatologicalseasonal cycle is defined as the sum of the annual mean
and the first three harmonics of the daily climatological-

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mean values (30-yr average of each respective calendar day)at each grid point. This study only incorporates data for JJA.
Final products are computed as 10-day high-pass filteredanomalies after removing the JJA mean values in individualyears.
[10] A number of past studies that examined synopticscale baroclinic disturbances in the midlatitudes and highlatitudes have used band-pass filters that retain timescales of
2.5 – 6 days [Blackmon, 1976] or 2 – 8 days [Trenberth,1991]. Our study applies a high-pass filter with a cutoff
period of 10 days to the time series of each perturbation
quantity. This broader filter is more appropriate for capturing long-lived, slowly moving synoptic systems. Summertime synoptic-scale disturbances in northern Eurasia haveperiodicities of 8 to 10 days, which is a longer timescalethan for synoptic-scale disturbances in the North Pacific andNorth Atlantic storm tracks, as noted by Chang and Yu[1999].

3. An Interannual East-West Seesaw in Summer
Precipitation Over Siberia[11] Our previous paper discussed the basic statisticalfeatures of the interannual variability of summer precipitation over Siberia. In this section, we reconfirm the principal
mode of precipitation variability, and show results fromstatistical analyses to define an objective index that will be
used for linear regression and composite analysis in thesubsequent sections.
[12] Figure 1a is a time series of the interannual anomalyof the summer precipitation index for the East and West
Siberian domains (shown in Figure 2) which is the sameplot as Figure 9b of Fukutomi et al. [2003]. The ES (WS)
precipitation index is the JJA precipitation anomaly averaged over the box bounded by 95– 135E, 50– 70N
(55– 95E, 50 – 70N). These time series appear to be
out-of-phase with each other after the 1970s, which suggests an east-west seesaw in relative dry and wet regions
over Siberia. To test this out-of-phase relationship, we
calculated 21-year sliding correlation between ES versus
WS precipitation index of Figure 1a. The result (Figure 1c)shows a strong negative correlation above 95% significant
level during 1969 – 1989.
[13] For the regression and composite analysis, we construct a unified precipitation index that incorporates the outof-phase nature of the ES and WS precipitation variability(Figure 1b). This index, the Siberian Precipitation Seesaw
Index (hereafter SPSI), is the first principal component(PC1) obtained from the empirical orthogonal function(EOF) analysis of the JJA precipitation anomalies for1948 – 2001 over the Siberian domain (see the green rectangle in Figure 2). The SPSI is derived as follows: Anomalous precipitation time series are meridionally averaged
over latitudes 50– 70N, for which the zonal distribution
spans from 50 – 140E at a 2.5 grid interval. Then, an EOF
analysis is performed on these longitudinally distributed
anomalies. As a result, the leading EOF mode (EOF1)
reflects an east-west seesaw pattern (not shown). As shownin Figures 1a and 1b, the SPSI increases during ES-wetWS-dry phases and decreases during WS-wet-ES-dry
phases. It is noted here that the EOF1 mode explains
29.6% of the total variance.

D02109[14] Linear regression and composite methods are used in
the following sections to identify the spatial pattern ofelements associated with the wet and dry phases identified
by the SPSI. Following our previous study [Fukutomi et al.,2003], we employ the 30-yr period from 1971– 2001 for thecurrent analysis. Despite the decrease of absolute value ofthe sliding correlation from the late 1980s through the early
1990s (Figure 1c), we include the period after early 1990s in
the analysis period in order to choose sufficient number ofprecipitation seesaw events for the composite analysis.
[15] The five most extreme positive (negative) events for
the ES-wet-WS-dry (WS-wet-ES-dry) phase based on the
SPSI in combination with the ES and WS precipitationindex are used to construct the composite. The five ES-wetWS-dry summers are 1974, 1981, 1982, 1988, and 1989;
the five WS-wet-ES-dry summers are 1979, 1986, 1992,
1996, and 2001) (Figure 1b). These are the major precipitation phases of high SPSI and low SPSI, respectively.
[16] Figure 2 shows the spatial pattern of precipitation
anomalies regressed from a 30-year time series of the SPSI
from 1972– 2002. The figure captures the east-west dipole
distribution during a high-SPSI (ES-wet-WS-dry) condition.
The dipole pattern shows many similarities to that produced
by a regression that used a basin-scale precipitation index
for the Lena River basin [Fukutomi et al., 2003]. The
statistical significance of this pattern is confirmed at the
95% level using a standard t-test (not shown). Anomalous
dry and wet regions in Siberia switch locations as the lowSPSI phase event develops. It should be noted here that the
regression analysis result based on the 22-yr period when
the sliding correlation is high (1969 – 1989) reproduce thesimilar pattern (not shown). Ye [2002, Figure 7a] also showsa similar precipitation dipole pattern over Eurasia. That
pattern is a summer leading mode derived from a rotated
EOF analysis. However, there is little discussion on thatmode in the paper.
[17] Now, let us consider the composite patterns ofprecipitation based on the SPSI. Figure 3 displays composite fields for the two extreme phases and 30-yr summer
mean climatology of precipitation. The climatological JJA
mean precipitation field (Figure 3a) shows basically thesimilar distribution with the 17-yr (1979 – 1995) climatology
presented by Fukutomi et al. [2003]. A band of precipitation
exceeding 60 mm month1 extends from Europe into
central Siberia along 55 – 65N and another band withhigher amount lies from the east of Lake Baikal to northeast
Asia and the south of eastern Siberia. The composite for thehigh-SPSI (ES-wet-WS-dry) summers (Figure 3b) reveals
an increase of precipitation to the above-normal values incentral-eastern Siberia to the north of 60N and a decreaseof it to the below-normal values in western Siberia. In
contrast, the composite for the low-SPSI (WS-wet-ES-dry)summers shows a striking increase over the entire western
Siberia and a decrease in eastern Siberia. The differencein absolute precipitation between high- and low-SPSI composites (Figure 3d) well captures the Siberian dipole patternwhich is very similar to that in the regressed precipitation
field (Figure 2). In relation to Figure 3d, composite percentage difference to the normal (climatological mean)
precipitation value between the two phases is computedfor the ES and WS domains. The resulting percentage is
27% for the ES domain and is 31% for the WS domain.

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D02109Figure 1. (a) Area-averaged precipitation index for JJA 1948– 2001 (mm month1). Solid (dashed) line
indicates the time series for the eastern (western) Siberian domain. The eastern (western) Siberian domain
is enclosed by the grid box of 95– 135E, 50– 70N (55 – 95E, 50– 70N). (b) First principal
component (PC1) of precipitation anomalies in the broad Siberian domain (50 – 140E, 50– 70N).
(c) 21-year sliding cross correlation between the East and West Siberian precipitation index. The dashedlines indicate the 95% and 99% significance levels.

These facts also confirm the occurrence of the east-west
oscillation of above-normal and below-normal precipitation
condition in Siberia.

4. Large-Scale Atmospheric Patterns and Storm
Track Activity Associated With the Precipitation
Seesaw
4.1. Stationary Wave Patterns[18] Basic atmospheric patterns related to the SPSI mustbe identified before discussing the role of storm track
activity over northern Eurasia. Figure 4a shows the JJA
mean 500-hPa geopotential height field regressed onto the

SPSI. The polarity indicates a positive SPSI phase. A clear
wave-train pattern with a zonal-wave number of about four
extends from western Eurasia to the north Pacific. The eastwest oriented dipole circulation structure across northernEurasia is the upstream portion of this wavetrain. The
wavetrain includes an anticyclonic center over the Ural
Mountains-West Siberian Plain and a cyclonic center over
the Central Siberian Plateau. A wave anomaly pattern of
opposite sign occurs during the negative SPSI phase (notshown). These features are similar to those observed in amap of the leading rotated EOF mode of the NH 500-hPa z
and maps of the regressed 500-hPa z based on the basin-scaleprecipitation index for the Lena and Ob rivers [cf. Fukutomi

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D02109Figure 2. Spatial pattern of the JJA mean precipitation anomalies related to PC1 (mm month1). The
eastern (blue), western (red) Siberian domain, and the broad Siberian domain for estimating PC1 (green)
are shown.

et al., 2003]. Serreze et al. [2003] reported similar atmospheric patterns relating the interannual variability of precipitation minus evaporation for the Lena and Ob Riverbasins. Regression maps of 300- and 850-hPa z associatedwith the SPSI also show good agreement with features in
Figure 4a, suggesting a strong equivalent barotropic structure through the troposphere for this wave pattern.
[19] The 850-hPa temperature pattern (Figure 4b) shows
troughs and ridges co-located with the 500-hPa z pattern.
An anomalous cold (warm) core structure accompanies thetrough (ridge) of the 500-hPa flow fields. Comparison ofthese patterns with Figure 2 reveals cyclonic (anticyclonic)
and cold (warm) anomalies coupled with relative wet (dry)regions in northern Eurasia. This trough-ridge couplet
is slightly upstream (west) from the precipitation dipole(Figure 2). Regression maps show that these wave anomalies over northern Eurasia and the north Pacific do not havea strong simultaneous connection to the Atlantic region
signals.
4.2. Changes in the Storm Track Signature[20] Transient eddy statistics were calculated using thirty
92-day summers to determine the spatial relationshipbetween the storm track activity due to synoptic-scale eddies
and the Siberian precipitation dipole pattern. The following
variance statistics were used as measures of synoptic-scale
eddy activity: mean standard deviation (SD) of 10-day highpass filtered daily 500-hPa geopotential height (z0), sealevel pressure (SLP0), 300-hPa meridional wind (v0), andthe 850-hPa poleward heat flux due to synoptic-scale eddies(T 0 v 0 ). The prime denotes the 1– 10-day perturbations and theoverbar represents the seasonal average. Height anomalies z0
are normalized using the factor sin 45/sin f, which isinversely proportional to the sine of latitude f, before
computing the SD. Such normalization makes z behave more
like a geostrophic stream function. Statistics based on thenormalized z0 become more comparable with those based on
the wind fields, as emphasized by Wallace et al. [1988].
[21] The 30-yr climatology of summer (JJA) storm tracks
in northern Eurasia is shown in Figure 5. The distribution of
the 500-hPa z0 SD (Figure 5a) shows a region of highamplitude (enclosed by the 32-m contour) localized overwestern Siberia and adjacent Arctic coastal regions. The
positions of these climatological maxima resemble those
identified by eddy activity on subseasonal timescales[White, 1982] and 2.5 – 6 days [Blackmon and White,1982] for the summer season. In contrast, a band of
maximum transient variance at 300 hPa v0 (Figure 5b)
exceeding 8m s1 covers a broad region from the northAtlantic to the Lena River basin (around 120E) along 50–
65N. The 850-hPa T 0 v0 (Figure 5c) clearly shows low-level
synoptic-scale eddy activity across northern Eurasia. A
zonally stretched belt with maxima exceeding 3K m s1prevails over northern Eurasia. This agrees with the 11-yr
July climatology of 2– 8-day T 0 v0 at 850 hPa plotted byRaphael [1997]. This marked band is distinct from the bandover the north Atlantic or the north Pacific. The location of
the well-organized Siberian storm track inferred from thestatistics is coincident with the Arctic frontal zone [cf.
Serreze et al., 2001] and extra-tropical cyclone tracks [cf.
Whittaker and Horn, 1984; Chen et al., 1991] over northernEurasia during summer.
[22] To determine how changes in storm track activity areassociated with the interannual precipitation seesaw, it isnecessary to regress the JJA mean SD of 500-hPa z0 and
SLP0 against the SPSI for the 30 years from 1972 – 2001.
The results are displayed in Figures 6a and 6b, respectively.
The anomaly signs in these maps correspond to a positive
SPSI phase. The regression reveals a dipole-like structure in
the zonal direction over northern Eurasia, although thepositive anomaly centers over central-east Siberia for z0 SD
at 500-hPa are shifted westward about 15 relative to thosein the SLP0 SD. In addition, these patterns are out-of-phasewith the stationary waves (Figure 4) but are in phase with
precipitation anomalies (Figure 2). Storm track activity
increases over east-central Siberia and decreases over west
Siberia in the high-SPSI phase, and vice versa in thenegative SPSI phase (not shown). Positive (negative)
anomalies identified by both SD maps almost lie over the
positive (negative) precipitation anomalies in each phase.
[23] Composites of the storm track field defined by the
500-hPa z0 SD anomalies (Figure 7) further highlight thedifference in the structure of synoptic-eddy activity between
the two precipitation extreme phases. As noted earlier, the

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Figure 3. (a) Thirty-year (1972– 2001) climatology of the JJA mean precipitation. Contour interval is
20 mm month1. Regions with values greater than 40 mm month1 are shaded. (b) Composite of the JJA
mean precipitation defined for the high-SPSI (ES-wet and WS-dry) phases (1974, 1981, 1982, 1988, and1989). The definition of the contour interval and the shadings are same as those for Figure 3a. (c) Sameas Figure 3b except for the low-SPSI (WS-wet and ES-dry) phases (1979, 1986, 1992, 1996, and 2001).
(d) Composite difference between the two phases as defined by the high-SPSI composite minus the lowSPSI composite. Solid (dashed) contours indicate positive (negative) anomalies. Contour interval is
5 mm month1. Zero contours are omitted. Positive (negative) anomalies statistically significant at 90%
level are lightly (darkly) shaded.
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D02109Figure 4. (a) Regression coefficient of the JJA-mean 500-hPa height anomalies with PC1 for JJA
1972 –2001. Shaded regions indicate significance on the 95% level according to t-test. Solid (dashed)
contours show the positive (negative) values. Zero contours are omitted. Contour interval is 3 m. (b) Sameas Figure 4a except for the 850-hPa temperature anomalies. Contour interval is 0.2 K.

composites use two precipitation extreme phases: the highest-SPSI (ES-wet-WS-dry) summers (1974, 1981, 1982,
1988, and 1989) and the lowest-SPSI (WS-wet-ES-dry)summers (1979, 1986, 1992, 1996, and 2001). These
composites are defined as departures from the climatological mean SD field (as in Figure 5a).
[24] In the high-SPSI phase (Figure 7a), maximum positive values with the 90% statistically significance leveloccur along the Arctic coast of central-eastern Siberia to thenorth of about 60N, and extend southward over the western
half of the Lena River basin. On the other hand, negativevalues spread over western Siberia although the coverageof the local statistically significant area is smaller. The
composite in the low-SPSI phase (Figure 7b) reveals anearly out-of-phase pattern over northern Eurasia to thatin Figure 7a. The maxima with high positive values extend
from northwest European Russia to the south of western
Siberian Plain. Besides a band of significant negative values
replace the positive one over the central-eastern Siberia in
the opposite phase (Figure 7a). The difference field of thetwo composites (high-SPSI minus low-SPSI, Figure 7c) is
spatially similar to the regressed pattern in Figure 6a. The
east-west dipole pattern is also present. Composite maps of
the SLP0 SD field were produced as in Figure 7 (not shown);
the resulting patterns show many similarities with thoseof the 500-hPa z0 SD field. These features confirm thedisplacement of the relative high and low storm track
activity associated with the east-west alternation of relative
dry and wet regions.

[25] In summary, there is a strong east-west contrast in
anomalous activities of synoptic systems in two extreme
phases of the precipitation seesaw. Linear regression andcomposite patterns show that positive (negative) SD anomalies are roughly collocated with positive (negative) anomalies of the precipitation dipole. This spatial relationshipsuggests that longitudinal displacement of relatively enhanced (reduced) storm track activity leads to high (low)precipitation anomalies in each phase. Systematic changes
in storm track activity reflect a geographical change in the
primary path of synoptic-scale precipitating systems overnorthern Eurasia. The stationary wave trough (ridge) overEurasia enhances (reduces) storm track activity. Therefore
the redistributed synoptic-eddy activity associated with thestorm track shift may play an important role in creating thesummer stationary wave pattern accompanying the eastwest precipitation dipole.

5. Synoptic-Scale Eddy Forcing in the Summer
Stationary Waves Associated With thePrecipitation Seesaw[26] Evidence from the regression and the composite
maps of variance statistics suggests a spatially coherent
relationship between the summer stationary waves andstorm tracks in two contrasting precipitation phases. The
next section considers dynamic forcing due to a synopticscale eddy flux to evaluate whether the systematic change insynoptic-scale eddy activity that occurs within a storm track

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D02109Figure 5. (a) Mean standard deviation (std dev) of 10-day high-pass filtered 500-hPa height for JJA
1972 –2001. Contour interval is 2 m. Regions with std dev greater than 30 m are shaded. (b) Same as
Figure 5a but for the 300-hPa meridional wind. Contour interval is 1 m s1. Regions with std dev greaterthan 7 m s1 are shaded. (c) Synoptic-scale transient eddy heat flux at 850 hPa averaged over the same
period. Contour interval is 1 K m s1. Regions with values greater than 3 K m s1 are shaded.
helps to reinforce and maintain the anomalous stationary
flow for each phase.
5.1. Geopotential Height and Temperature Tendencies
Induced by Transient Eddies[27] The effects of vorticity and thermal forcing bysynoptic-scale eddies on the mean flow are calculated using
methods introduced by Lau and Holopainen [1984] and Lau
and Nath [1991]. They defined barotropic and baroclinic
eddy feedbacks to the mean flow that are represented bygeopotential height and temperature tendencies in terms ofthe convergence of eddy vorticity and heat fluxes. The(barotropic) eddy vorticity forcing expressed as a heighttendency is
h

i
@z
f
¼  r2 r  z0 V0 ;
@t
g

ð1Þ

[28] Where, z is the relative vorticity; V is the horizontal
wind vector; f is the latitudinally dependent Coriolis
parameter; and g is the gravitational acceleration. The othernotation is the same as that used in the variance statistics.
The (baroclinic) eddy thermal forcing expressed as a temperature tendency is@T¼ r  T 0 V0 :@tð2Þ[29] Using these equations, Lau [1988] and Lau and Nath[1991] demonstrated that synoptic-scale eddy activity associated with the large-scale changes in storm tracks reinforces anomalous monthly flow patterns over the North Pacificand the North Atlantic during winter. Many subsequent
observational studies applied the same or similar formulas
to investigate interactions between transient eddies and NH
winter teleconnection patterns, like the Pacific-North American (PNA) and the North Atlantic Oscillation (NAO)
pattern (Klasa et al. [1992], Trenberth and Hurrell[1994], Sheng et al. [1998], Hurrell [1995], Hurrell andvan Loon [1997], and others).
[30] These formulas can be used as a diagnostic of storm
track feedback to explain possible causes of the stationary

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D02109Figure 6. (a) Regression coefficient of the std dev of 10-day high-pass filtered 500-hPa height with PC1for JJA 1972 – 2001. Solid (dashed) contours show the positive (negative) values. Zero contours areomitted. Contour interval is 1 m. Shaded regions show statistically significant at the 95% level. (b) Sameas Figure 6a except for the sea level pressure with 0.05 hPa interval.
wave structure for each precipitation extreme. Geopotential
height tendency fields for the equations were computed at
300 hPa because past studies have shown that eddy vorticity
fluxes generally have stronger amplitudes in the upper
troposphere. By contrast, temperature tendencies were estimated at 850 hPa because maximum eddy heat fluxes most
likely occur at this level over land. The equations were solved
globally using the spherical harmonics transform method(SPHEREPACK 3.0: Adams and Swartztrauber [1999]).
[31] Regression patterns of the geopotential height andtemperature tendency associated with 10-day high-passtransients projected onto the SPSI are shown in Figure 8.
As with the other regression maps, the fields presented
are for the positive SPSI phase case. Note that negative(positive) geopotential height tendency in general corresponds to cyclonic (anticyclonic) circulation forcing. The
overall pattern of the geopotential height tendencies(Figure 8a) shows a mostly in-phase relationship with thesummer stationary waves (Figure 4a) over northern Eurasia.
This suggests that barotropic feedback of synoptic-scaleeddies onto the mean flow is mostly positive. Cyclonic(anticyclonic) forcing is amplified in regions of enhanced(reduced) eddy activity (Figure 6). Consequently, anomalous cyclonic (anticyclonic) forcing due to eddy vorticity
flux convergence reinforces the anomalous cyclonic (anticyclonic) mean flow over central-east (west) Siberia. The
regression map of the geopotential height tendency at
700 hPa (not shown) is similar to that at 300 hPa, whichindicates that low-level vorticity forcing is in the same
sense as that at upper-levels. However, the contribution at
700 hPa is minor; the magnitudes are one-fifth to one-eighth
those at 300 hPa.
[32] The temperature tendency pattern (Figure 8b) ismostly out-of-phase with the stationary temperature pattern(Figure 4b). Positive (negative) anomalous eddy heat flux
convergence occurs within the negative ( positive) stationary
temperature anomalies, implying that baroclinic feedback
induced by synoptic-scale eddy heat fluxes tends to destroy
the anomalous mean thermal structure in the lower troposphere. In other words, the synoptic-scale eddy heat fluxes
act to dissipate the baroclinic component of the mean flow.
However, the Siberian portion of the anomalous temperature tendency pattern is less organized than the heighttendency pattern. Besides areas with 95% significancelevels are comparatively small in the temperature tendency
pattern.
5.2. Composite Structures in the Two Contrasting
Precipitation Extremes[33] Composite maps showing the spatial structures of
the transient eddy forcing terms and stationary waves foreach of the two extreme phases were constructed todemonstrate the robust behavior of the synoptic-scale eddy
forcing. Figures 9 and 10 contain anomaly composites of the
300-hPa height, 850-hPa temperature, and their tendencies
for the high-SPSI and the low-SPSI summers, respectively.
An anomaly is defined as a deviation from the 30-yr JJA
climatology.
[34] Height anomalies at 300-hPa (Figures 9a and 10a)
depict a wavetrain over northern Eurasia, which is a patternsimilar to the regressed pattern in Figure 4a. In the highSPSI phase, the cyclonic (anticyclonic) circulation forcing
over central-east (west) Siberia (Figure 9b) is nearly collocated with the anomalous summer mean cyclonic (anticyclonic) circulation (Figure 9a). In the low-SPSI phase, theheight tendency anomalies (Figure 10b) and the stationary
wave anomalies (Figure 10a) are nearly mirror images of
those in the high-SPSI phase. The polarity of anomalous

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D02109Figure 7. (a) Composite of the std dev. anomalies of 10-day high-pass filtered 500-hPa height definedfor the high-SPSI (ES-wet and WS-dry) phases (1974, 1981, 1982, 1988, and 1989). Contour interval is
2 m. Zero contours are omitted. Positive (negative) anomalies statistically significant at 90% level are
lightly (darkly) shaded. (b) Same as Figure 7a except for the low-SPSI (WS-wet and ES-dry) phases(1979, 1986, 1992, 1996, and 2001). (c) Composite difference between the two phases. Contour interval
is 4 m.
forcing and stationary waves is almost the same. The
cyclonic and anticyclonic circulation forcings over Eurasia
are collocated with the cyclonic and anticyclonic circulations. Vorticity forcing due to synoptic-scale eddies helps
maintain the anomalous stationary wave pattern in bothphases.
[35 ] Temperature anomalies at 850 hPa (Figures 9c
and 10c) also show good agreement with the regressed
pattern (Figure 4b). Strong anomalous eddy heat fluxes
overlie anomalous troughs and ridges. The thermal forcing
patterns associated with these heat fluxes (Figures 9d and10d) are rather noisy, although they account for basic featuresof the regressed temperature tendency pattern (Figure 8b). In
the ES-wet-WS-dry case (Figures 9c and 9d), coolingtendency anomalies well collocate with the summer mean
warm anomalies for the WS domain. On the other hand,
warming tendency anomalies are not distributed homogeneously in the summer mean cold anomalies for the ES
domain. Cooling and warming tendency anomalies in themean warm and cold anomalies have complex structures also

in the WS-wet – ES-dry case (Figures 10c and 10d).
Maximum and minimum centers of the tendency anomalies
do not coincide with those of the mean temperature anomalies for both the ES and WS domain. Overall, the synopticscale baroclinic damping effect on the summer mean
temperature anomalies associated with the precipitation
dipole seems to be more complicated and less straightforward. Case-to-case variation presumably produces the noisy
composite structures.

6. Summary and Discussion[36] In this study, we identified changes in storm track
activity and how the changes affect the seasonal mean
flow over northern Eurasia that is associated with aninterannual seesaw in summer precipitation between east
and west Siberia. In the two extreme phases of this seesaw,
an east-west dipole pattern of precipitation anomalies
prevails over much of Siberia (Figure 2). We focused on
the 30 years from 1972– 2001 when such spatial-temporal

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D02109Figure 8. (a) Regression coefficient of the 300-hPa height tendencies due to the synoptic-scale eddy
vorticity forcing with PC1 for JJA 1972– 2001. Solid (dashed) contours show the positive (negative)
values. Zero contours are omitted. Contour interval is 1.0  105 m s1. Zero contours are omitted.
Shaded regions show statistically significant at the 95% level. (b) Same as Figure 8a except for the
850-hPa temperature tendencies. Contour interval is 0.5  106 K s1.

characteristics are clear and consistent (Figure 1c), as
noted by Fukutomi et al. [2003]. The two precipitation
extreme phases that are manifest as out-of-phase dipole
patterns are represented as the ES-wet-WS-dry phase andthe WS-wet-ES-dry phase. We documented large-scaleatmospheric patterns and storm track activities associatedwith these contrasting phases using linear regression andcompositing, and highlighted the synoptic-scale eddy
forcing on the mean circulations.
[37] Atmospheric circulation and temperature anomalies
related to the precipitation seesaw (Figures 3a, 3b, 9a, 9c,10a, and 10c) show a clear stationary wavetrain extending
from western Eurasia to the North Pacific. The Eurasian
dipole pattern at the upstream portion of the wavetrain
occurs in conjunction with the precipitation dipole. A cold
trough and warm ridge correspond to the relatively wet anddry regions. Regression and composite analyses for thetransient eddy statistics (Figures 6 and 7) indicate thatanomalous storm track displacement is accompanied byphase reversals between two phases. The ES-wet-WS-dry(WS-wet-ES-dry) phase is associated with intensification ofstorm track activity over central-east (west) Siberia, and aweakening of storm track activity over west (east) Siberia.
The pair of enhanced and reduced storm track activity is
collocated with the Eurasian wave pattern during summer.
The anomalous stationary trough (ridge) corresponds to ahigh-(low-)eddy-activity region in each of two phases. In
addition, the high (low) eddy activity roughly coincides
with the cyclonic (anticyclonic) circulation forcing(Figures 8, 9b, and 10b). These spatially coherent relationships suggest that storm track feedback reinforces andmaintains the Eurasian wave structure coupled with the

dipole precipitation pattern of each phase. Consequently,
the interannual variability of the Eurasian storm track
intensity and location is a crucial factor controlling thealternation in mean precipitation and circulation patterns
between the two phases.
[38] We have stressed that the east-west displacement ofstorm track activity can reinforce and maintain the Eurasianstationary waves associated with the two contrasting summer precipitation extreme phases. For both the two phases,
barotropic and baroclinic eddy feedback on the stationary
waves over northern Eurasia is observed. The barotropic
feedback due to eddy vorticity fluxes reinforces the mean
circulation structures. This eddy-mean phase relation is
analogous to those occurring over the oceanic storm track
region, as derived in several works [e.g., Lau and Nath,1991; Trenberth and Hurrell, 1994; Hurrell and van Loon,
1997; Sheng et al., 1998]. The baroclinic feedback due toeddy heat fluxes seems to dampen the mean thermal
structures (baroclinicity) of the lower troposphere. Nevertheless, it is not easy to give a straightforward interpretation as done by these previous studies for oceanic storm
tracks because of the complicated distribution of thesynoptic-scale eddy heat flux convergence in the present
results.
[39] The stationary wavetrains do not originate upstream.
Basin-scale precipitation in Siberia does not exhibit any
dominant covariance with the Atlantic signals. This suggests much weaker relationships between Atlantic variability and the northern Eurasian summer mean flow linked tothis dipole precipitation variability. This conclusion isfurther supported by poor coherence between the northAtlantic sea surface temperature and northern Eurasian

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D02109Figure 9. Composites for the high-SPSI (ES-wet and WS-dry) phases. Regions with positive (negative)
values are lightly (darkly) shaded. Zero contours are omitted. (a) The 300-hPa height anomalies. Contour
interval is 5 m. (b) The 300-hPa height tendencies due to the synoptic-scale eddy vorticity forcing.
Contour interval is 5.0  105 m s1. (c) The 850-hPa temperature anomalies and the anomalous vectorsof synoptic-scale eddy heat flux. Contour interval is 1 K. (d) The 850-hPa temperature tendencies due tosynoptic-scale eddy thermal forcing. Contour interval is 0.5  106 K s1.
atmospheric circulations for JJA, as shown by Lin and
Derome [2003, Figure 7]. Accordingly, anomalous wave
patterns can be interpreted in an average sense as primarilya response to storm track feedback involving possibly
diabatic heating effects induced by synoptic-scale activity,
rather than geographically-fixed external forcing in the
upstream. However, this interpretation does not preclude
the coexistence of other forcing mechanisms accounting forwave patterns in individual cases. Further investigation of

the respective elements that support Eurasian stationary
waves during summer as proposed by Park and Schubert[1997] is needed.
[40] The causes of the interannual variability in the storm
track activity that determines the Siberian precipitation
dipole are still unresolved. Northern Eurasian storm tracks
lie in the Arctic frontal zone where there are sharp meridional gradients in the lower tropospheric temperature[Serreze et al., 2001]. Serreze et al. [2001] suggested that

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D02109Figure 10. Same as Figure 9 except for the low-SPSI (WS-wet and ES-dry) phases.
the Arctic frontal zone in a climatological sense is supported
by differential surface heating between land covered byboreal forest/tundra and the Arctic Ocean. Recent studies
suggest that air-land energy exchange and topography
maintain the baroclinicity over Alaska [e.g., Pielke andVidale, 1995; Lynch et al., 2001]. However, how suchfactors vary from summer to summer and how they affectthe atmosphere-land interaction in northern Eurasia remains
unclear.
[41] Extensive work is required to document all thefactors that modulate summer storm track activity and theresulting dynamics over northern Eurasia. One simple
approach is to identify regional modes of storm track
variability [e.g., Lau, 1988; Rogers, 1997; Chang and Fu,

2002]. Then, the underlying dynamic and thermodynamic
processes that locally shift or intensify storm track activities
can be examined.
[ 42 ] Acknowledgments. We wish to thank three anonymous
reviewers for constructive comments on an earlier version of this manuscript. Helpful discussions with Hiromichi Igarashi (FRSGC/JAMSTEC)
are also appreciated. The PREC/L data set was obtained from the NOAA/CPC (ftp://ftp.ncep.noaa.gov). The NCEP-NCAR reanalysis data set was
taken from the NOAA/CDC (ftp://ftp.cdc.noaa.gov). The Generic Mapping
Tools (GMT) graphics package outlined by Wessel and Smith [1995] wasused for drawing all the figures.

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Y. Fukutomi, K. Masuda, and T. Yasunari, JAMSTEC Yokohama Institutefor Earth Sciences, 3173-25 Showamachi Kanazawa-ku, Yokohama,
Kanagawa, 236-0001, Japan. (fukutomi@jamstec.go.jp)

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