Clim Dyn (2013) 41:1213–1228
DOI 10.1007/s00382-012-1491-0
Seasonal evolution mechanism of the East Asian winter monsoon
and its interannual variability
Yoojin Kim • Kwang-Yul Kim • Jong-Gap Jhun
Received: 22 May 2012 / Accepted: 10 August 2012 / Published online: 21 August 2012
Springer-Verlag 2012
Abstract This study investigates the space–time evolution of the East Asian winter monsoon (EAWM) and its
relationship with other climate subsystems. Cyclostationary Empirical Orthogonal Function (CSEOF) analysis and
the multiple regression method are used to delineate the
detailed evolution of various atmospheric and surface
variables in connection with the EAWM. The 120 days of
winter (November 17–March 16) per year over 62 years
(1948–2010) are analyzed using the NCEP daily reanalysis
dataset. The first CSEOF mode of 850-hPa temperatures
depicts the seasonal evolution of the EAWM. The contrast
in heat capacity between the continent and the northwestern Pacific results in a differential heating in the lower
troposphere. Its temporal evolution drives the strengthening and weakening of the Siberian High and the Aleutian
Low. The anomalous sea level pressure pattern dictates
anomalous circulation, in compliance with the geostrophic
relationship. Thermal advection, in addition to net surface
radiation, partly contributes to temperature variations in
winter. Latent and sensible heat fluxes (thermal forcing
from the ocean to the atmosphere) increase with decreased
thermal advection. Anomalous upper-level circulation is
closely linked to the low-level temperature anomaly in
terms of the thermal wind equation. The interannual variability of the seasonal cycle of the EAWM is strongly
controlled by the relative strength of the Siberian High to
Y. Kim K.-Y. Kim (&) J.-G. Jhun
School of Earth and Environmental Sciences,
Seoul National University, 1 Gwanangno, Gwanak-gu,
Seoul 151-747, Republic of Korea
e-mail: [email protected]
J.-G. Jhun
Research Institute of Oceanography, Seoul National University,
1 Gwanangno Gwanak-gu, Seoul 151-747, Republic of Korea
the Aleutian Low. A stronger than normal gradient between
the two pressure systems amplifies the seasonal cycle of the
EAWM. The EAWM seasonal cycle in the mid-latitude
region exhibits a weak negative correlation with the Arctic
Oscillation and the East Atlantic/West Russia indices.
1 Introduction
East Asia frequently experiences strong winter monsoons
characterized by cold temperatures and strong northwesterly winds along the continental boundary. Many studies of
the East Asian Winter Monsoon (EAWM) have aimed to
understand its mean state and interannual variability (Jhun
and Lee 2004; Wang et al. 2010a; Wu and Wang 2002;
Zhang et al. 1997). Zhang et al. (1997) argued that the main
forcing of the EAWM is the available potential energy
generated by the differential heating between land and sea.
The Siberian High and the Aleutian Low are the key features of the winter mean of the EAWM and are related to
the surface air temperature distribution with a land-sea
contrast. A strong upper-level jet stream in the East Asian
region is one of the primary characteristics of the EAWM.
Jhun and Lee (2004) studied the interannual variability of
the EAWM using an index, which was defined as the
meridional gradient of upper-level zonal wind. The magnitude of this meridional gradient of upper-level zonal
wind measures the strength of the EAWM.
The large-scale winter monsoon system has been
assumed in previous studies to be quasi-stationary during
winter, and the interannual variability of the EAWM is
defined by its anomalies from the winter mean value. The
actual winter monsoon system, however, evolves over time
and undergoes stages of genesis, development, and decay.
This process repeats every year, defining the seasonal cycle
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of the EAWM. The seasonal cycle of the monsoon system
has been examined in other regions and seasons—e.g., the
summer monsoons in Australia (Kullgren and Kim 2006)
and Asia (Lim et al. 2002)—although the detailed temporal
evolution of the winter monsoon in East Asia has not yet
been investigated. Kim and Roh (2010) studied the seasonal evolution of the wintertime temperature variability in
Seoul, Korea in conjunction with the East Asian winter
climate.
The mid-latitude atmospheric circulation leads the oceanic changes by several months (Lau 1997). The latent and
sensible heat exchange is related to the surface wind and
the air-sea gradients of humidity and temperature at the airsea interface (Beljaars 1995). Interactions between the
atmosphere and the ocean have been examined in several
monsoon studies. The role of air-sea interaction has been
studied in the summer monsoon over the South China Sea
(Lau and Nath 2009) and in the variations of the Asian–
Australian monsoon (Wang et al. 2003). The air-sea
interaction in the EAWM, however, has rarely been studied. The temperature over the ocean evolves more slowly
than that over the continent. Thus, the air-sea interaction
may play a certain role in the evolution of the EAWM.
In recent years, the connection between the Arctic
Oscillation (AO) and the EAWM has attracted much attention (D’Arrigo et al. 2005; Gong et al. 2001; Jhun and Lee
2004; Wu and Wang 2002). The frequency of cold surges in
East Asia is also affected by the phase of the AO (Jeong and
Ho 2005; Park et al. 2011). According to Gong et al. (2001),
the negative phase of the AO is associated with a higher sea
level pressure in the polar region, resulting in a stronger
Siberian High and affecting the circulation in East Asia.
Likewise, East Atlantic/West Russia (EA/WR) pattern
(Barnston and Livezey 1987) is known to exert substantial
influences on the pressure anomaly over Siberia and the
Asian winter monsoon (Wang et al. 2011).
The role of the Siberian High, a subsystem embedded in
the EAWM, has been examined in previous studies (Ding
and Krishnamurti 1987; Gong et al. 2001; Jhun and Lee
2004; Park et al. 2011; Wu and Wang 2002). The Siberian
High sustains its influence through strong radiative cooling
and a large-scale downward motion (Ding and Krishnamurti 1987). This high-pressure system develops along the
east coast of the Asian continent strong northwesterly
wind, which eventually merges with the wind from the
Aleutian Low (Zhang et al. 1997). The Aleutian Low is
known to exert partial control over the intensity of the
EAWM. The development of the Aleutian Low is tied with
a trough at the 500-hPa level over the eastern boundary of
the Asian continent and the tropospheric circulation (Jhun
and Lee 2004).
Recent studies reveal that the winter monsoon circulation over the northern part of East Asia is distinct from that
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Y. Kim et al.
in the southern part (Jhun and Lee 2004; Wang et al.
2010a). Unlike the tropical EAWM, the winter monsoon in
mid-latitude East Asia shows a weak coupling with the
near-equatorial convection. Previous studies have given
little attention to the EAWM in the northern part of East
Asia. This motivated the present study on the EAWM in
the extratropical region of East Asia, including northeastern China, Korea, and Japan.
This study examines the seasonal cycle of the EAWM in
terms of the intraseasonal evolution and its interannual variability. The detailed physical evolution of the seasonal cycle
of the EAWM is investigated using various atmospheric and
surface variables. Cyclostationary Empirical Orthogonal
Function (CSEOF) analysis is used to extract the seasonal
cycle from 850-hPa air temperatures, and multiple regression in CSEOF space is conducted to derive a physically
consistent evolution from other atmospheric and surface
variables. The CSEOF analysis and multiple regression
method are described in Sect. 2. Circulation patterns and the
atmospheric and surface conditions associated with the
seasonal cycle of the EAWM are examined in detail in Sects.
3 and 4. Section 5 discusses the subseasonal variation of the
air-sea interaction during the EAWM period. The interannual variability of the seasonal cycle of the EAWM is
addressed in conjunction with the EAWM index defined by
Jhun and Lee (2004) and key climate indices in Sect. 6. The
summary and conclusion follow in Sect. 7.
2 Data and methods
Based on data from the National Center for Environmental
Prediction/National Center for Atmospheric Research
(NCEP/NCAR) daily reanalysis dataset (Kalnay et al.
1996), the 62-year (1948/1949–2009/2010) winter seasons
are examined in this study. Wintertime variability is
investigated by selecting 120 days (November 17–March
16) from each year. To extract the seasonal cycle of the
EAWM, CSEOF analysis (Kim et al., 1996; Kim and
North, 1997) is employed. In the CSEOF analysis,
space(r)–time(t) data, Var(r, t), are divided into spatial
evolutions and their long-term modulations as
X
Var ðr; tÞ ¼
Bn ðr; tÞTn ðtÞ;
ð1Þ
n
where Bn(r, t) are cyclostationary loading vectors (CSLVs)
and Tn(t) are their corresponding principal component (PC)
time series. CSLVs are time-dependent and periodic with
the nested period of 120 days:
Bn ðr; tÞ ¼ Bn ðr; t þ dÞ; d ¼ 120 days:
ð2Þ
This is a crucial difference from the EOF analysis, whose
loading vectors are simply spatial patterns. The time
Seasonal evolution mechanism of the East Asian winter monsoon
dependence of CSLVs makes it profitable to describe a
temporally varying process, such as the EAWM. Each PC
time series, Tn(t), describes temporal variation of the
amplitudes of a CSLV and is as long as the given data.
To investigate the physical mechanisms of the EAWM,
we have analyzed geopotential height, wind, and temperature at standard levels from 1,000 to 200 hPa. Sea level
pressure and net surface heat fluxes, including shortwave
and longwave radiation, are also analyzed. The CSEOF
analysis is conducted on each of these variables (predictors), yielding
X
Pðr; tÞ ¼
Cn ðr; tÞPn ðtÞ;
ð3Þ
n
where Cn(r, t) and Pn(t) are, respectively, the CSLVs and
PCs of a predictor variable P(r, t). The PCs of a predictor
variable are then regressed on the PCs of the target variable
(850-hPa temperature; see Sect. 3 for details):
Tn ðtÞ ¼
M
X
ðnÞ
aðnÞ
m Pm ðt Þ þ e ðtÞ;
n ¼ 1; 2; . . .;
ð4Þ
m¼1
(n)
where a(n)
m are the regression coefficients and e (t) is the
regression error. Twenty predictor PC time series are used
(M = 20) in this study. The regression coefficients are
determined so that the regression error variance is
minimized. The new loading vectors of a predictor
variable is then obtained by
CnðregÞ ðr; tÞ ¼
M
X
aðnÞ
m Cm ðr; tÞ:
ð5Þ
m¼1
The predictor variable is then rewritten as follows:
X
Pðr; tÞ ¼
CnðregÞ ðr; tÞTn ðtÞ:
ð6Þ
n
The procedures described in (4)–(6) are referred to as
the regression in CSEOF space. As a result of the
regression in CSEOF space, the entire data set can be
expressed as follows:
Dataðr; tÞ
X
¼
fBn ðr; tÞ; Hn ðr; tÞ; Vn ðr; tÞ; . . .; Qn ðr; tÞgTn ðtÞ;
ð7Þ
n
where {Bn(r, t), Hn(r, t),Vn(r, t), …, Qn(r, t)} are the
loading vectors of the target variable and the predictor
variables. These space–time evolution patterns for each
mode n share the identical PC time series; in particular,
n = 1 describes the physical evolution of the EAWM.
More discussions of the regression method in CSEOF
space and the applications of the CSEOF technique can be
found in Seo and Kim (2003), Kullgren and Kim (2006),
Kim and Roh (2010), Kim et al. (2012a, b).
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3 Seasonal cycle of the EAWM
Figure 1 presents the winter mean atmospheric conditions.
The temperature at 850 hPa depicts a typical meridional
distribution of temperature with a maximum gradient along
the eastern coast of the Asian continent (Fig. 1a). The sea
level pressure is high over the East Asian continent and
relatively low over the northern part of the Pacific Ocean
(Fig. 1b). This sea level pressure pattern represents the
Siberian High and the Aleutian Low and influences
the lower-level tropospheric circulation and temperature.
The Siberian High is relatively a short air mass, while the
Aleutian Low is a tall one. In mid-troposphere, higher
pressure is situated in the equatorial region, while lower
pressure is positioned in the polar region; a trough is
developed over the marginal seas in East Asia, which is
linked to the surface Aleutian Low (figure not shown). The
850-hPa wind depicts a strong mid-latitude northwesterly
flow along the continental boundary, which is consistent
with the distribution of pressure. A strong upper-level jet
develops over the western North Pacific due to the strong
meridional gradient of temperature; this jet is clearly visible throughout the upper troposphere and is maximized at
the 200-hPa level (Fig. 1c and d).
The main forcing of the winter circulation in the East
Asian region is the available potential energy generated by
differential heating between the continent and the ocean.
The temporal evolution of the low-level temperature differs
significantly between the continent and the ocean; consequently, the circulation varies significantly throughout the
winter. Wang et al. (2010a) identified the low-level temperature as the key variable in the EAWM because it is a
good indicator of the severity of winter weather and the
low-level temperature has a stronger spatial homogeneity
than the circulation or pressure. For these reasons, the
850-hPa temperature has been investigated as the key
physical element of the EAWM in this study.
Figure 2 presents the spatial evolution of the temperature (Fig. 2a–d) and the corresponding PC (amplitude) time
series (Fig. 2e) for the first CSEOF mode. The spatial
domain is selected at 100–150E and 25–50N because
this area exhibits a significant contrast between the continent and the ocean in terms of heating and intraseasonal
variations of temperature during winter.
For presentation purposes, the daily evolutions of temperature are averaged every 30 days. For the sake of convenience, the temporal evolution of the EAWM is
classified into four stages (Fig. 2f) according to the intraseasonal evolution of 850-hPa air temperature and its
contrast between the continent and the ocean (Fig. 3). The
first stage (November 17–December 16) denotes the initiation period of the EAWM. The second (December
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Fig. 1 Winter mean
(November 17–March 16)
patterns of a 850-hPa
temperature (C), b sea level
pressure (hPa) in shading and
850-hPa wind (ms-1), c 300hPa zonal wind (ms-1), and
d latitude-pressure section of
zonal wind (ms-1) averaged
over the longitude range of
110–200E. Shading in red
represents positive values and
shading in blue negative values,
with the exception of b
Y. Kim et al.
(a)
(c)
17–January 15) and the third (January 16–February 14)
stages represent the developing and mature periods,
respectively. The fourth stage (Feb. 15–Mar. 16) denotes
the decaying period. A low-level temperature anomaly with
respect to the winter mean temperature is positive, particularly over the ocean in the initiation stage (Fig. 2a). In the
developing and mature stages, negative anomalies are
prominent over the continent (Fig. 2b, c). Positive temperature anomalies develop over the continent in the decay
stage (Fig. 2d). With the onset of winter, the air over the
continent with a much lower heat capacity cools down
earlier than that over the northwestern Pacific Ocean. In
turn, the air over the continent warms up earlier in the
spring season. The temperature evolution in Fig. 2 (see
also Fig. 3) is clearly linked to the differential heating over
the continent and the ocean.
The CSLVs in Fig. 2 delineate the evolution of the
seasonal cycle of the EAWM and explain *21 % of the
total variability. The corresponding PC time series demonstrates that the strength of the seasonal cycle varies
significantly by more than ±50 % of the mean strength
over the study period. The strength of the EAWM resembles the amplitudes of the seasonal cycle of the winter
temperature in Seoul, which was analyzed by Kim and Roh
(2010) using data collected from 1979 to 2008. They noted
that the seasonal cycle of winter temperature in Seoul has
decreased steadily from 1979 to 2008. The analysis of an
extended period of 62 years in the present study reveals
that the amplitude of the seasonal cycle has undergone
oscillations on a multi-decadal time scale, instead of a
steady decrease (Fig. 2e). It is obviously difficult to
123
(b)
(d)
confirm a linear trend or multi-decadal oscillations in the
presence of much stronger interannual variability. More
detailed discussion of the PC time series will be provided
in Sect. 6. The seasonal cycle of the EAWM is the main
focus of this study.
The longitude-time section of the seasonal cycle of
850-hPa temperature explicitly illustrates two characteristics of the intraseasonal variations of the EAWM (Fig. 3):
the sinusoidal temperature variation as manifested in the
direction of time and the differential heating as displayed
in the direction of longitude. The initial positive temperature anomaly pattern lasts until the middle of December
over the continent, while the cooling pattern begins over
the ocean in late December/early January. A warming
pattern returns in the middle of February over the continent
but not until the end of March over the ocean. Due to the
later onset of cooling and warming, temperature anomalies
appear to propagate from the continent toward the ocean.
Note that the temperature anomalies are extended over a
wider domain in Fig. 3 than in the target domain (Fig. 2)
by conducting a regression analysis in CSEOF space. The
regressed patterns of 850-hPa temperature are nearly
identical with those of the target variable over the overlapping region (figure not shown).
4 Characteristic dynamical features of the EAWM
The evolutions of the key atmospheric variables are obtained
to be physically consistent with the seasonal cycle of the
850-hPa temperature via multiple regression analysis in
Seasonal evolution mechanism of the East Asian winter monsoon
Fig. 2 The seasonal cycle of
the 850-hPa temperature:
a–d wintertime (November 17–
March 16) evolution of 850-hPa
temperature anomalies (C)
over East Asia, with each panel
representing a non-overlapping
30-day average; e the
corresponding PC time series,
presenting the strengths of the
seasonal cycle for 62 years; and
f the four stages of the winter
monsoon defined in the present
study and the corresponding
calendar days
1217
(a)
(b)
(d)
(c)
(e) PC time series of CSEOF mode 1
2.0
1.5
1.0
0.5
0.0
1950
1960
1970
1980
1990
2000
2010
Year
(f) Stage
CSLV Day
1
1
2
30
3
60
4
90
120
Calendar 17Nov 1Dec 16Dec 31Dec 15Jan 30Jan 14Feb 1Mar 16Mar
CSEOF space. Figure 4 depicts the regressed patterns of sea
level pressure and wind anomalies. Evolutions of the sea
level pressure and zonal and meridional wind anomalies in
the mid-latitude area are plotted in Fig. 5; note that 120E is
the approximate longitude of the continental boundary in the
mid-latitude region. Figures 4 and 5 depict the evolution of
the anomalous Siberian High and Aleutian Low, along with
the corresponding wind anomalies in the East Asian region.
The Siberian High strengthens between the initiation stage
and the mature stage with a northward shift in its central
location, while the Aleutian Low deepens from the developing stage to the mature stage. The commencement of the
Siberian High anomaly precedes that of the Aleutian Low
anomaly by approximately 1 month (Fig. 5a). Due to this
differential onset of the pressure anomalies, the evolution of
the pressure contrast between the continent and the ocean
follows neither the Siberian High nor the Aleutian Low.
The wind direction in Fig. 4 is consistent with the pressure
pattern with respect to the geostrophic equation, although the
wind level is different from the pressure level. The wind
anomalies are averaged within the latitude band of 30–40N
in Fig. 5, as the winter mean and anomalies of wind vectors
are strongest along this latitudinal band. As the winter mean
pattern of wind in Fig. 1b demonstrates, the westerly is
dominant over the continent, the northwesterly is strong
along the continental boundary, and the cyclonic flow is
prominent over the North Pacific. In the initiation stage, the
anti-cyclonic flow is strong over the North Pacific, and the
mid-latitude (20–40N) westerly remains weak (Figs. 4a, 5b
and c). The cyclonic flow over the North Pacific then begins to
develop, and the northerly wind along the coastline becomes
stronger in the developing stage (Figs. 4b and 5c). The
cyclonic flow over the North Pacific, the main characteristic
of the EAWM, is strongest in the mature stage, and the wind
along the coastline contains a strong northerly component in
the mature stage (Figs. 4c and 5c). Finally, the direction of the
anomalous wind in the decay stage is generally opposite to the
mean flow, weakening the winter mean flow (Figs. 4d, 5b, c).
The time rate of the change in local temperature consists
of a thermal advection and heating rate due to turbulent and
123
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Y. Kim et al.
radiation fluxes. Anomalous thermal advection is divided
into four terms (Wang et al. 2010b) so that
oT 0
rT 0 V0 rT 0 þ V0 rT 0 þ Q0 ;
¼ V0 rT V
ot
ð8Þ
Fig. 3 Longitude-time section of the first CSEOF mode (seasonal
cycle) of 850-hPa temperature (C) averaged over the latitude range of
30–50N. The contour interval is 1 C and the shading interval is 0.5 C
Fig. 4 Evolution of SLP (hPa)
and 850-hPa wind (ms-1)
anomalies regressed on the first
CSEOF mode of 850-hPa
temperature shown in Fig. 2.
Each panel represents 30-day
averaged patterns: a stage 1,
b stage 2, c stage 3, and d stage
4, as defined in Fig. 2f
123
where the first term on right-hand side represents the
advection of the mean temperature by anomalous wind, the
second term denotes the advection of an anomalous temperature by the mean wind, the third term is the nonlinear
eddy flux, and the fourth term is the climatological average
of the nonlinear eddy flux. The final term represents the
rate of anomalous heating due to radiational and turbulent
heat fluxes. The eddy flux and its climatological average
are much smaller than the first two terms on the right-hand
side of (8). The first two thermal advection terms and the
total thermal advection; i.e. the sum of the four thermal
advection terms are presented in Fig. 6.
In the initiation stage, warm advection is noticeable to the
south of 45N over the western North Pacific, while cold
advection manifests in the northern region along the coast of
the Asian continent and the northern part of the western
Pacific (Fig. 6c). A separate account for the thermal advection terms indicates that the warm advection is controlled
primarily by the mean temperature advection by anomalous
meridional wind ðv0 oT=oyÞ,
while the cold advection is
explained primarily by the mean temperature advection by
anomalous zonal wind ðu0 oT=oxÞ.
Anomalous temperature
advection by the mean wind is relatively small except along
the continental boundary and the central Pacific in this stage.
In the developing stage, cold advection to the south of 50N
is prominent (Fig. 6f). In northeastern China, Korea and
Japan, mean temperature advection by anomalous meridio
nal wind, ðv0 oT=oyÞ,
and anomalous temperature advection by mean zonal wind, ðuoT 0 =oxÞ, are important. These
two terms contribute comparably to the cold advection, and
the other terms are negligible. In the mature stage, the patterns are similar to those of the initiation stage, with the
(a)
(b)
(c)
(d)
Seasonal evolution mechanism of the East Asian winter monsoon
(a)
(b)
1219
(c)
Fig. 5 Longitude-time section of a SLP (hPa), b 850-hPa zonal wind
(ms-1), and c 850-hPa meridional wind (ms-1) anomalies in the midlatitude East Asia regressed on the seasonal cycle of 850-hPa air
temperature. The SLP pattern represents a 30–50N average and the
wind patterns represent 30–40N averages
exception of the opposite sign (Fig. 6i). The important terms
of the thermal advection are similar to those of the initiation
stage; mean temperature advection by anomalous zonal
wind, ðu0 oT=oxÞ,
and meridional wind, ðv0 oT=oyÞ,
consist of the warm advection and the cold advection, respectively. In the decay stage, warm advection is significant to the
south of 50N near the coastal region and the northwestern
Pacific (Fig. 6l). The thermal advection anomaly patterns
appear to be opposite to those of the developing stage. The
mean temperature advection by anomalous meridional wind,
ðv0 oT=oyÞ,
and anomalous temperature advection by mean
zonal wind, ð
uoT 0 =oxÞ, primarily explain the warm
advection over northeastern China, Korea and Japan.
Mean temperature advection by anomalous meridional
wind, ðv0 oT=oyÞ,
is the largest term over the southwestern part (120–180E, 25–45N) of the North Pacific. This
component is large near the eastern boundary of the continent, where the meridional gradient of the winter-mean
temperature is large. The sign of the thermal advection is
controlled primarily by the direction of the anomalous
meridional wind (see Fig. 5c). The mean temperature
advection by anomalous zonal wind, ðu0 oT=oxÞ,
grows
large over the Sea of Okhotsk in the initiation and mature
stages. The anomalous zonal wind undergoes a large
change in this area, where the zonal gradient of winter-
mean temperature is large. Anomalous temperature
advection by the mean zonal wind, ðuoT 0 =oxÞ, is
important over northeastern China, Korea, and Japan in the
developing and decay stages. The winter-mean zonal wind
is large in this area, while the meridional gradient of
anomalous temperature varies significantly.
A strong jet in the upper troposphere is one of the main
characteristics of the EAWM. In the mean field, the uppertropospheric jet is located in 120–160E and 30–40N
(Fig. 1c). Figure 7 presents the vertical distribution of
anomalous zonal wind regressed on the seasonal cycle of the
850-hPa temperature. The anomalous zonal wind illustrates
the location of the jet during winter. In the initiation stage,
the maximum zonal wind is shifted to the north of the wintermean position, as inferred from a positive zonal wind
anomaly to the north of *40N and a negative anomaly to
the south of *40N. It then migrates to the south of the
winter-mean position in the developing stage. The zonal
wind in the northern part of the domain becomes weaker in
this stage. In the mature stage, the zonal wind anomaly
exhibits a positive maximum at approximately 30N, indicating its southernmost location during winter. In the final
stage, the zonal wind anomaly becomes weak again.
The evolution of the upper-tropospheric jet can be
explained in terms of temperature distribution using the
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Y. Kim et al.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
Fig. 6 Evolution of 850-hPa thermal advection anomalies (C day-1)
regressed on the seasonal cycle of EAWM: advection of mean
temperature by anomalous wind (left), advection of anomalous
temperature by mean wind (middle), total thermal advection (right).
The contour lines denote the winter-mean temperature in the left
panels and winter-mean wind in the middle panels, respectively. The
four rows represent the four stages of EAWM
thermal wind relationship. The vertical distribution of the
anomalous zonal wind calculated from the vertical distribution of anomalous temperatures appears as contour lines in
Fig. 7; the thermal wind equation is integrated with respect
to pressure from the surface, where the magnitude of zonal
wind is assumed negligible. The anomalous zonal wind
derived from the thermal wind equation is fairly consistent
with the regressed zonal wind anomaly, implying that the
zonal wind anomaly is physically consistent with the temperature anomaly in the seasonal cycle of the EAWM.
radiation flux (Qlw), latent heat flux (Qlh), and sensible heat
flux (Qsh). The winter mean heat fluxes are plotted in Fig. 8.
The minus sign of the shortwave flux indicates the incoming
(downward) solar radiation at the surface (Fig. 8a). The
positive sign of the longwave flux implies the outgoing
(upward) radiation from the surface (Fig. 8b). The latent heat
flux generally transports heat from the surface to the atmosphere by evaporating water from the sea surface (Fig. 8c). A
higher wind speed and drier air above the sea surface lead to a
greater latent heat flux, and the temperature of air just above
the sea surface essentially determines the saturation vapor
pressure. The sensible heat flux is generated by the turbulent
energy exchange through the sea surface (Fig. 8d) and is
linked with the wind speed and the surface-air temperature
difference (Marshall and Plumb 2008).
The shortwave radiation exhibits a downward flux, while
the other fluxes move in the upward direction, indicating an
5 Air-surface interaction in the EAWM
Interaction between the atmosphere and the surface is presented in terms of the net surface heat flux (Qnet), which
consists of the shortwave radiation flux (Qsw), longwave
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Seasonal evolution mechanism of the East Asian winter monsoon
Fig. 7 Latitude-height section
of zonal wind anomalies (ms-1)
regressed on the seasonal cycle
of EAWM (shading) and
reconstructed zonal wind
(ms-1) by the thermal wind
relationship (contour). The
intervals of contours and
shadings are 1 ms-1. The
patterns represent 130–190E
averages. The four panels
denote the four stages of
EAWM
Fig. 8 Winter (November 17–
March 16) mean vertical heat
fluxes at the surface: a net
shortwave radiation flux
(Wm-2), b net longwave
radiation flux (Wm-2), c latent
heat flux (Wm-2), and
d sensible heat flux (Wm-2)
1221
(a)
(b)
(c)
(d)
(a)
(b)
(c)
(d)
energy transfer from the surface to the atmosphere, with
the exception of the shortwave flux. The magnitude of the
incoming flux (shortwave radiation) is smaller than the
outgoing flux (sum of the longwave radiation, latent and
sensible heat fluxes); thus, the net heat flux is upward,
implying that the surface loses heat during winter. As a
consequence, the temperature will decrease unless there is a
net positive lateral heat transport into the region, compensating for the heat loss through vertical heat fluxes.
The shortwave and longwave heat fluxes exhibit zonally
symmetric distributions, while the latent and the sensible
heat fluxes are strongly dependent on the land-sea configuration. The latent heat flux is large over the Kuroshio Current,
which transports warm tropical water poleward. The sensible
heat flux is large over the coastal regions, due to a large
temperature difference between the sea surface and the
overlying air; regions of strong sensible heat flux appear to
the north of the regions of strong latent heat flux in the
123
1222
Y. Kim et al.
northwestern Pacific Ocean. Turbulent heat flux, the sum of
the latent and sensible heat fluxes, is mainly released over the
western North Pacific, as shown in Fig. 8c and d.
The evolution of an anomalous latent, sensible, and
turbulent heat fluxes are illustrated in Fig. 9 for each stage.
Note that these anomalies are departures from the mean
values in Fig. 8. Thus, the positive sign indicates an
increased upward flux and so forth. In the initiation stage,
the turbulent heat fluxes over the coastal seas and the
central North Pacific exhibit positive values (Fig. 9c). The
negative value along the Kuroshio Current means that
the turbulent heat flux has not yet reached the winter-mean
value. One primary reason for this is that the sensible heat
flux is smaller than the winter-mean value; the air temperature above the warm current is still relatively warm,
which reduces the amount of the sensible heat flux. In the
developing stage, the turbulent heat flux increases significantly over the coastal seas and the western Pacific Ocean,
particularly along the Kuroshio Current and its extension
(Fig. 9f). The increased turbulent flux over the coastal seas
comes mainly from the increased sensible heat flux. Over
the western Pacific Ocean, particularly along the Kuroshio
Current and its extension, the increased turbulent heat flux
results primarily from the increased latent heat flux. The
turbulent heat flux then gradually decreases to a slight
positive value in the mature stage over the western North
Pacific and a strong negative value in the decay stage
(Fig. 9i and l). The pattern of turbulent heat flux in the
decay stage resembles that of the developing stage with the
opposite sign, suggesting that the same physical mechanism with the opposite sign is responsible for the decreased
heat flux over the western North Pacific (Fig. 9l).
Anomalous thermal advection and the presence of a turbulent heat flux anomaly appear to be strongly connected;
this connection, as explained below, is satisfied in a rough
manner, as can be inferred from the patterns of anomalous
thermal advection and turbulent heat flux. Cold advection
brings cold (and potentially dry) air over the warmer ocean
surface; the upward flux commences to alleviate the temperature (and moisture) difference(s) between the ocean
surface and the air above it. In the initiation stage, cold
advection north of *45N increases the upward heat flux
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
Fig. 9 Evolution of latent (left), sensible (middle) and turbulent (right) heat flux anomalies (Wm-2), regressed on the seasonal cycle of EAWM.
Positive values denote upward flux from the surface to the atmosphere. The four rows represent the four stages of EAWM
123
Seasonal evolution mechanism of the East Asian winter monsoon
(a)
(b)
1223
(c)
(d)
Fig. 10 Longitude-time section of a shortwave radiation flux
(Wm-2), b longwave radiation flux (Wm-2), c latent heat flux
(Wm-2), and d sensible heat flux (Wm-2) anomalies. The shaded
patterns represent averages over 30–40N and the contours over
40–50N. The shading interval is 5 Wm-2 and the contour interval
is 10 Wm-2
over the coastal seas. Warm advection south of *45N over
the ocean decreases turbulent heat flux, although the magnitude of the decrease is not substantial. In the developing
stage, cold advection to the south of *45N increases the
upward turbulent heat fluxes over the ocean and the northern
coastal seas. In the mature and decay stages, the connection
between thermal advection and turbulent flux can be
explained in a similar manner, but their signs are reversed.
The longitude-time sections of the seasonal cycle of heat
fluxes are shown in Fig. 10. The heat fluxes are averaged
over 30–40N (shades) and 40–50N (contours) to
express the characteristic mid-latitude patterns. Positive
values indicate reduced downward shortwave radiation and
increased upward fluxes of longwave radiation and latent
and sensible heat. Downward shortwave radiation is at a
minimum in late December (winter solstice) and steadily
increases with the positive anomalies (above the winter
mean value), starting in early February.
Longwave radiation exhibits a relatively small variability
because the surface temperature variation continues to be
small throughout the winter. The magnitude of the anomalous longwave radiation flux is smaller than that of the other
fluxes, indicating that the subseasonal variation of the
longwave flux is small. The 120E meridian is a rough
continental boundary in the mid-latitude region; the anomalous longwave flux exhibits opposite signs across the continental boundary. The longwave radiation flux is affected by
the land-sea configuration, although the magnitude of the
anomalous longwave radiation flux is relatively small, and
the winter-mean pattern is insensitive to the land-sea configuration. From December to the middle of February, the
longwave flux is negative over the continent and positive
over the ocean, implying that the ocean surface is warmer
and the land surface colder than the air above.
Latent heat flux anomalies are positive over the ocean
until early February. On the other hand, the evolution of
sensible heat flux anomalies are nearly in phase with the
circulation anomalies (Figs. 4 and 5) and lags that of latent
heat flux anomalies by approximately 15–30 days. While
both fluxes are affected by wind speed and air temperature,
exhibiting a significant covariability with the previously
described thermal advection, the latent heat flux is also
strongly affected by the relative humidity of the air above
the sea surface. In fact, the evolution of latent heat flux
anomalies is consistent with that of the relative humidity
(figure not shown), confirming their close relationship.
6 Interannual variability of the EAWM
The PC time series in Fig. 2 depicts the strength of the
EAWM seasonal cycle, as described in Sect. 3. The best
autoregressive (AR-52) spectrum of the PC time series
identifies the maximum spectral peak at the period of
123
1224
Y. Kim et al.
Best AR(52) Spectrum
4
Max (0.31, 3.5)
Power (log)
2
0
-2
-4
-6
-8
0.0
0.5
1.0
1.5
2.0
Frequency (per year)
Fig. 11 The AR(52) spectrum of the first PC time series of EAWM.
The maximum peak is at the period of 3.2 years
3.2 years (Fig. 11). Most of the powers reside in time
scales longer than 3.2 years, and the spectral power
diminishes rapidly when the time scale decreases to less
than 3.2 years. Thus, the interannual and longer-term
modulation of the EAWM is most prominent in the first PC
time series.
The strength of the EAWM seasonal cycle in this study
is compared with one of the EAWM indices. Jhun and Lee
(2004) defined an EAWM index based on the meridional
gradient of upper-level zonal wind, as presented in
Fig. 12a. To compare this index with the daily PC time
series, the latter is averaged over 90 days (Dec. 1–Feb. 28)
for each winter; the resulting PC time series for 62 years is
plotted in Fig. 12b. The two time series are correlated at
0.44, which is significant at the 95 % level. Figure 12c
displays sliding correlation coefficients with a window
width of ± 10 years. The correlation coefficients indicate a
significant relationship during 1967–1987 but a weak
relationship elsewhere, particularly in the 1990s; the weak
relationship in the latter period is due to the decadal change
in circulation (Sun-Seon Lee and Kyung-Ja Ha, personal
communication, July 9, 2012). While the two time series
exhibit a reasonable correlation, the two measures of the
EAWM are substantially different. The EAWM index
presented by Jhun and Lee (2004) measures the annual
mean strength of the 300-hPa cyclonic circulation in the
region north of the jet core, which is an essential feature of
the EAWM. On the other hand, the PC time series in this
study measures the strength of the seasonal cycle of the
EAWM. Therefore, there is a significant difference in what
the two indices measure. As implied in Figs. 4 and 7, the
evolutions of both the lower- and the upper-tropospheric
circulations are nearly sinusoidal in time; therefore, annual
averaging will offset the positive and negative phases of
the EAWM, leaving only a small residual. Annual
123
averaging will not reflect the strength of the seasonal cycle.
Rather, the EAWM index by Jhun and Lee (2004) reflects
the winter-mean strength of the monsoon circulation,
which is not closely linked to the amplitude of the seasonal
cycle. On the other hand, the amplitude of the seasonal
cycle in this study does not aptly describe the interannual
variation of the winter-mean monsoon circulation. In this
respect, the two indices complement each other.
The interannual variability of the EAWM seasonal cycle
has also been studied in the context of its relationship with
such climate indices as AO and ENSO (Gong et al. 2001;
Lau and Nath 2006; Jhun and Lee 2004; Wang et al. 2010a;
Wu and Wang 2002; Zhang et al. 1997). For this comparison, the AO and SO (Southern Oscillation) indices
from 1951 to 2010 have been acquired from the Climate
Prediction Center (CPC). The AO is a teleconnection pattern in the winter Northern Hemisphere that primarily
measures the atmospheric pressure differences between the
middle and high latitudes. In a negative phase of the AO,
the pressure over the polar region is higher than normal,
and cold air protrudes from the north toward the mid-latitude region. Wu and Wang (2002) investigated a connection between the AO and the EAWM, revealing that the
AO exerts a partial influence on the EAWM. Jhun and Lee
(2004) found that the AO exerts a small influence on the
interannual variability of the EAWM. This study finds that
the interannual variability of the seasonal cycle of the
EAWM is correlated with the AO at -0.33, which is not
particularly high but is significant at the 95 % level
(Fig. 13).
The SO is the sea level pressure oscillation in the
tropical Pacific, which reflects the El Niño (negative phase
of SO) and La Niña (positive phase of SO) events. Lau and
Nath (2006) noted that the EAWM weakens during El Niño
events, which is associated with the establishment of
anomalous anticyclones over the Philippine Sea. Wang
et al. (2010a) noted that the connection between the ENSO
and the EAWM is strong over the southern part of East
Asia (0–30N, 100–140E), which is not of primary
interest in this study. The correlation between the seasonal
cycle of the EAWM and the SO index is only -0.15
(Fig. 13).
East Atlantic/West Russia (EA/WR) pattern (Barnston
and Livezey 1987) is an important planetary-scale circulation pattern that originates in the East Atlantic. When
North Atlantic SST is anomalously cold, the wave train
forced by SST over the East Atlantic penetrates the European high latitudes including western Russia and continental mid-latitude Asia. This teleconnection influences the
pressure anomaly over Siberia and the Asian winter monsoon (Wang et al. 2011). The interannual variability of the
seasonal cycle is correlated with the EA/WR at -0.35
(Fig. 13).
Seasonal evolution mechanism of the East Asian winter monsoon
Fig. 12 A comparison of a the
East Asian winter monsoon
index defined by Jhun and Lee
(2004) and b the PC time series
of the first CSEOF mode of
850 hPa temperature in this
study; the two time series are
correlated at 0.44. c Sliding
correlation between the two
time series with a 21-year
window. Dashed line in
c denotes a 95 % significance
level based on Student’s t test
1225
(a) EAWM index
60
50
40
30
20
1950
1960
1970
1980
1990
2000
2010
1970
1980
1990
2000
2010
1970
1980
1990
2000
2010
(b) PC of CSEOF mode 1
2.0
1.5
1.0
0.5
0.0
1950
1960
(c) Correlation
0.6
0.4
0.2
0.0
1950
Fig. 13 The amplitude time
series of the East Asian winter
monsoon (EAWM) and the
normalized climate indices of
Arctic Oscillation (AO), East
Atlantic/West Russia (EA/WR),
Southern Oscillation (SO),
Siberian High (SH) and
Aleutian Low (AL) and the
difference between the Siberian
High and Aleutian Low for
59 years, from 1951/1952
through 2009/2010. The signs of
the AO, EA/WR, SO and AL
index time series are reversed
by multiplying -1. The
correlation coefficients between
the PC time series of the first
CSEOF mode and the climate
indices appear on the right-hand
side. The grey lines denote one
standard deviation of each time
series
1960
T 850 CSEOF PC & climate indices
1.6
PC1
PC
1.0
0.4
2
AO*(-1)
0.33
0
-2
2
EA/WR*(-1)
0.35
0
-2
2
SO(-1)
0.15
0
-2
2
SH*
0.45
0
-2
2
AL(-1)
0.24
0
-2
2
SH-AL*
0.50
0
-2
1950
1960
1970
1980
1990
2000
2010
Year
123
1226
The Siberian High is known to directly and significantly
influence the EAWM (Wu and Wang 2002). The amplification of the Siberian High is considered a major factor for
decreases in temperature in East Asia (Park et al. 2011).
The sea level pressure patterns of the EAWM described in
Fig. 4 suggest that the Siberian High over the continent is
explicitly linked with the strength of the EAWM. The
interannual variability of the Siberian High is obtained by
averaging the sea level pressure over (40–60N 9 80–
120E). The normalized yearly anomalies constitute the
index time series of the Siberian High. The correlation
between the EAWM and the Siberian High is 0.45
(Fig. 13).
An index time series of the Aleutian Low is similarly
obtained by averaging the sea level pressure over (40–
60N 9 160–200E). The correlation between the interannual variability of the EAWM and that of the Aleutian
Low is only -0.24, although Fig. 4 clearly depicts the
importance of the Aleutian Low in the intraseasonal evolution of the seasonal cycle of the EAWM. This implies
that the variation of the Aleutian Low has minimal control
over the interannual variability of the EAWM seasonal
cycle, although the former is one of the most crucial elements of the seasonal variation of the EAWM. Together,
the Siberian High and the Aleutian Low explain the
interannual variability of the EAWM slightly better than
individual index time series; the correlation with the
amplitude of the EAWM seasonal cycle is 0.50 (Fig. 13).
7 Discussion and concluding remarks
The seasonal cycle of the EAWM in East Asia has been
extracted and analyzed via CSEOF analysis. The first mode
of the 850-hPa temperature defines the seasonal cycle of
the EAWM. This new definition of the EAWM is useful in
describing both the seasonal evolution of the EAWM and
its interannual variability. This CSEOF representation is
beneficial to a description of the space–time evolution and
the physical mechanism of the EAWM variation. The intraseasonal evolution of the EAWM depicts a sinusoidal
change of temperature with a significant land-sea contrast.
Although intraseasonal evolution is a crucial feature of the
EAWM, it has not been investigated in detail in previous
studies. Instead, the interannual variability of the wintermean condition has been studied extensively, assuming that
the monsoon system evolves only weakly during winter.
On the other hand, the winter-mean condition is only one
aspect of the EAWM; the seasonality of the EAWM is
strong, and the intraseasonal evolution of the EAWM
should be dealt with explicitly. In fact, the winter-mean
condition of the monsoon system could be normal while
123
Y. Kim et al.
the seasonality of the EAWM is significantly amplified and
vice versa.
To delineate the seasonal cycle of the EAWM and
understand its physical mechanism, physically consistent
evolutions of temperature, circulation, pressure and net
surface heat fluxes have been derived for the four stages of
the EAWM (Fig. 14). In the initiation stage, early winter,
low-level temperature anomalies are positive in East Asia.
The Siberian High becomes stronger, but the Aleutian Low
is not yet sufficiently deep. The corresponding anomalous
circulation is strong, but the direction is opposite to that of
the mean circulation; therefore, the monsoon circulation is
relatively weak. In the developing stage, low-level temperature anomalies over the continent are negative. Cold
advection is strong over the western North Pacific, and the
surface heat flux increases, releasing heat from the surface
of the ocean into the atmosphere. Negative temperature
anomalies spread across East Asia in the mature stage. The
Siberian High migrates slightly northward, and the Aleutian Low is deepest. Anomalous circulation is strongest in
the same direction as the mean circulation over the western
North Pacific. Thus, the monsoon circulation is strongest in
the mature stage. The upper-level jet is also strongest at
this stage. Thermal advection is relatively weak, and the
surface heat flux is not significant. In the final stage, lowlevel temperature anomalies are positive over the continent, but the air over the ocean remains cold. The Siberian
High and the Aleutian Low are weakened. The warm
advection over the western North Pacific is strong, leading
to a decreased heat flux over the ocean.
The interannual variability of the seasonal cycle of the
EAWM is explained to some degree by the AO and the EA/
WR. The negative phases of the AO and the EA/WR tend
to exhibit a stronger seasonal cycle of the EAWM and vice
versa. The Siberian High is a significant continental feature
associated with the seasonal cycle of the EAWM. Thus, a
relatively high correlation is expected between the interannual variation of the Siberian High and that of the
EAWM seasonal cycle. The Aleutian Low is another
conspicuous oceanic feature associated with the seasonal
evolution of the EAWM. However, the correlation between
the interannual variation of the Aleutian Low and that of
the EAWM seasonal cycle is not significant; a relatively
low correlation implies that the Aleutian Low is an
essential element of the intraseasonal variation of the
EAWM, but its winter-mean condition bears no significant
connection with the interannual variability of the EAWM.
While the EAWM index by Jhun and Lee (2004) measures
the winter-mean strength of the monsoon circulation, the
PC time series of the seasonal cycle measures the strength
of the intraseasonal evolution of the monsoon circulation.
The seasonal cycle averaged over the winter period is not
Seasonal evolution mechanism of the East Asian winter monsoon
Fig. 14 Temperature (shade),
geopotential height (contour),
and wind anomalies at 200 and
850 hPa and vertical heat flux
(shade) and thermal advection
(contour) at the surface for the
a initiation, b developing,
c mature, and d decay stages of
the EAWM
1227
(b) Stage 2
(a) Stage 1
200 hPa
850 hPa
surface
(c) Stage 3
(d) Stage 4
200 hPa
850 hPa
surface
exactly zero; the residual temperature is weakly correlated
with the EAWM index.
While the temperature in the lower troposphere is primarily determined by the amount of shortwave radiation
during winter and the thermal response time (lag) of the
continent and ocean, the detailed energy budget is fairly
complicated, involving the advection of energy, the air-sea
interaction via surface heat fluxes and the adjustment of
longwave radiation according to the temperatures of the
surface and the air above. The latter physical processes are
interrelated and depend strongly on the circulation pattern
of the EAWM seasonal cycle. Anomalous monsoon circulation, in turn, depends critically on the local energy
budget and the subsequent temperature anomalies. The
fundamental interactions of the different physical components of the EAWM investigated in the present study
should facilitate the description of the physical mechanism
of the EAWM variation, particularly in conjunction with its
seasonal evolution. Future studies should focus on the
higher modes to understand the variability of the EAWM
beyond that of the seasonal cycle.
Acknowledgments This research was supported by the project
entitled ‘‘Ocean Climate Change: Analyses, Projections, Adaptation
(OCCAPA)’’ funded by the Ministry of Land, Transport, and Maritime Affairs, Korea. This work was also supported by the National
Research Foundation of Korea (NRF) grant funded by the Korea
government (MEST 2011-0021927, GRL). KYK and YK acknowledge the support by Brain Korea 21 (BK 21) program.
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