Clim Dyn
DOI 10.1007/s00382-013-1724-x
Seasonal scale variability of the East Asian winter monsoon
and the development of a two-dimensional monsoon index
Yoojin Kim • Kwang-Yul Kim • Sunyoung Park
Received: 17 December 2012 / Accepted: 5 March 2013
Springer-Verlag Berlin Heidelberg 2013
Abstract This study investigates the seasonal scale variability of the East Asian winter monsoon (EAWM), which
is distinguished from the seasonal cycle with temporal
variation throughout winter. Winters lasting 120 days
(Nov. 17–Mar. 16) for a period of 64 years from the NCEP
daily reanalysis data set are used to study the seasonal
scale variability of the EAWM. Cyclostationary empirical
orthogonal function (CSEOF) analysis is adopted to
decompose the variability of the EAWM. The second
CSEOF mode of 850-hPa temperature exhibits a seasonal
scale variation, the physical mechanism of which is
explained in terms of physically consistent variations of
temperature, geopotential height, sea level pressure, wind,
and surface heat fluxes. The seasonal-scale EAWM
exhibits a weak subseasonal and a strong interannual variability and has gradually weakened during the 64 years. In
a weak EAWM phase, the land-sea contrast of sea level
pressure declines in East Asia. Consistent with this change,
low-level winds decrease and warm thermal advection
increases over the eastern part of mid-latitude East Asia.
Latent and sensible heat fluxes are reduced significantly
over the marginal seas in East Asia. However, during a
strong EAWM phase, the physical conditions in East Asia
reverse. A large fraction of the variability of the EAWM is
explained by the seasonal cycle and the seasonal scale
variation. A two-dimensional EAWM index was developed
to explain these two distinct components of the EAWM
variability. The new index appears to be suitable for
Y. Kim K.-Y. Kim (&) S. Park
School of Earth and Environmental Sciences, Seoul National
University, 1 Gwanangno, Gwanak-gu, Seoul 151-747,
Republic of Korea
e-mail: [email protected]
measuring both the subseasonal and the interannual variability of the EAWM.
1 Introduction
The East Asian winter monsoon (EAWM) is characterized
by cold surface air temperatures and strong low-level
northwesterlies over the northeastern part of East Asia,
(northeastern China, Korea, and Japan), and is driven by
differential heating between the continent and the ocean.
The strength of the EAWM and its variability is typically
measured by temperature or circulation variables (Wang
and Chen 2010; Wang et al. 2010a; Wu et al. 2006; Xu
et al. 2006; Jhun and Lee 2004). A strong EAWM primarily indicates cold temperatures and an increased surface
wind over the northeastern region of East Asia.
The EAWM system is suspected to weaken in response
to global warming (Wang et al. 2009). Since the late 1960s,
a steady decline in wind speed has been observed across
China (Xu et al. 2006), and model experiments suggest that
global warming weakens the EAWM (Hori and Ueda
2006). This decline of surface wind seems to be associated
with a stronger warming of the high-latitude continental
region than of the low-latitude ocean.
In earlier studies the strength of the EAWM was calculated based on monthly or seasonal mean variables (Wu
et al. 2006; Jhun and Lee 2004; Yang et al. 2002; Zhang
et al. 1997; Wang and Chen 2010), which proved useful in
the inspection of the long-term (interannual or interdecadal) variability of the EAWM. However, by solely using
monthly or seasonal variables there is an inevitable lack, or
under-representation, of the subseasonal variability of the
EAWM. Kim et al. (2012b) investigated the subseasonal
evolution of the EAWM using daily datasets. The strong
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Y. Kim et al.
amplitude modulation of the seasonal cycle (repeating
signal throughout the winter season) shows that the evolution of daily temperature during winter varies strongly
from one winter to another, while the temporal evolution of
winter temperature follows a typical pattern; a gradual
cooling in early winter, a gradual warming in late winter,
with the magnitude of the cooling and warming differing
dramatically on a yearly basis (Kim et al. 2012b).
In addition to the subseasonal (time scale less than one
season) evolution of temperature, seasonal scale variability
of the EAWM (which is conventionally viewed as being
the winter-mean stationary patterns) investigated in earlier
studies, is undoubtedly an important component of the
EAWM variability. However, the subseasonal evolution
should also be considered, since the time scale and the
magnitude of response of the continent and ocean to winter-mean forcing differ significantly, due to their vastly
different heating capacities. In particular, the response time
of air temperature over the continent is significantly shorter
than that of air over the ocean in East Asia (Kim et al.
2012b), but the magnitude of the response over the continent is much bigger than that over the ocean. These
important differences are crucial factors in the evolution of
the EAWM circulation (Zhang et al. 1997). Net surface
fluxes, in turn, are strongly affected by low-level atmospheric circulation and temperature. Thus, a sequence of
physical reactions determines the wintertime evolution of
key variables in response to altered insolation forcing.
Therefore, the seasonal-scale variability needs to be distinguished from the seasonal cycle and its physical mechanism should be clearly understood in a daily dataset.
Circulation and the ensuing distributions of physical
variables in East Asia are affected by atmospheric teleconnection patterns (Barnston and Livezey 1987), such as the
Arctic Oscillation (AO) and the East Atlantic/West Russia
(EA/WR) patterns, and many previous studies have examined the impact of teleconnection patterns on the variability
of the EAWM (Wang et al. 2011; D’Arrigo et al. 2005; Jhun
and Lee 2004; Wu and Wang 2002; Gong et al. 2001). The
Siberian High is a dominant surface pressure system in East
Asia, which exerts a direct influence on temperature and
wind over the adjacent areas, and the Aleutian Low is a tall
pressure system in the northern Pacific, which drives
cyclonic circulation over the ocean. Both the pressure systems are considered to directly and indirectly control the
atmospheric conditions over East Asia (Park et al. 2011;
Jhun and Lee 2004; Ding and Krishnamurti 1987). It is
therefore important to understand how the variability of both
the pressure systems and the atmospheric teleconnection
systems in Northern Hemisphere affect the subseasonal and
the seasonal variability of the EAWM.
Wang et al. (2010a) pointed out that northern (30–
60N, 100–140E) and southern (0–30N, 100–140E)
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parts of East Asia exhibit distinct circulation patterns. This
study examines the seasonal scale variability of the EAWM
in northern East Asia and its detailed physical mechanism.
Cyclostationary empirical orthogonal function (CSEOF)
analysis, followed by regression analysis in CSEOF space,
is employed to decompose the EAWM variability into
distinct physical modes and extract physically consistent
evolutions of key variables (Kim et al. 1996; Kim and
North 1997). A brief explanation of the methods employed
is provided in Sect. 2. The physical mechanisms of the
seasonal scale variability of the EAWM are described in
Sect. 3. Impacts of the teleconnection patterns on the
EAWM variability are addressed in Sect. 4, by calculating
correlations between the PC time series of the two CSEOF
modes (the seasonal cycle and the seasonal scale variability
of the EAWM) and the climate indices representing AO,
EA/WR, Siberian High, and Aleutian Low. A detailed
discussion on the different physical inferences between the
existing EAWM index and the two CSEOF PC time series
is also made. The concept of a two-dimensional EAWM
index is then introduced in order to measure both the
subseasonal and the seasonal variability of the EAWM.
Section 5 contains a summary and concluding remarks.
2 Data and method
Daily reanalysis data from the National Center for Environmental Prediction/National Center for Atmospheric
Research (NCEP/NCAR; Kalnay et al. 1996) are utilized
in this study. Daily sea surface temperature used in the
present study is the National Oceanic and Atmospheric
Administration Optimal Interpolation (NOAA OI) sea
surface temperature V2 (Reynolds et al. 2007) from
November 17, 1981 to March 16, 2010. Analysis is carried
out on 64-year (1948/1949–2011/2012) winter seasons
(except for the sea surface temperature); each winter season consists of 120 days (November 17–March 16).
The methodologies employed in this study are identical
with those in Kim et al. (2012b). CSEOF analysis divides
the space (r)–time (t) data into cyclostationary loading
vectors (CSLVs) and principal component (PC) time series
for each mode n as:
X
Dataðr; tÞ ¼
CSLVn ðr; tÞPCn ðtÞ:
ð1Þ
n
CSLVs are periodic time dependent spatial patterns with
a nested period of 120 days; each CSLV depicts the
temporal variation of a variable during the winter season.
The temporal evolution described in each CSLV is
modulated on a longer temporal scale according to the
respective PC time series for the interval of the given data
(120 9 64 = 7,680 days).
The development of a two-dimensional monsoon index
The target variable for CSEOF analysis is the 850-hPa
temperature over the domain [100–150E 9 25–50N];
this domain represents the most sensitive region of the
EAWM. The seasonal cycle represents the largest variability, and is captured as the first CSEOF mode. A detailed
explanation of the physical mechanism of the seasonal
cycle is described in Kim et al. (2012b). The second largest
variability is derived from seasonal scale variations. These
two CSEOF modes are not sensitive to the data domain or
the dataset used for analysis. A detailed explanation of the
physical mechanism of this second CSEOF mode is one of
the main objectives of this study.
The evolution of various atmospheric and surface variables such as sea level pressure, geopotential height, wind,
temperature, and surface fluxes are extracted to be physically consistent with the seasonal scale variation of 850hPa temperature. Physical consistency among the evolution
of different variables is enforced via a multiple regression
method in CSEOF space (Seo and Kim 2003; Kim et al.
2012b). Upon regression analysis in CSEOF space, CSLVs
of different variables share the same PC time series for
each mode as follows:
X
Dataðr; tÞ ¼
fHn ðr; tÞ; Vn ðr; tÞ; . . .; Qn ðr; tÞgPCn ðtÞ;
n
ð2Þ
where {Hn (r, t), Vn (r, t),…, Qn (r, t)} are loading vectors
of physical variables.
3 Seasonal scale variability of the EAWM
3.1 Low-level air temperature anomaly
As mentioned in Sect. 2, the second CSEOF mode of
850-hPa air temperature displays the seasonal scale variation of the EAWM (Fig. 1). This mode explains 5.5 % of
the total variability of 850-hPa air temperature and represents 7.0 % of the variability aside from that of the seasonal cycle. The loading patterns of daily air temperature
exhibit, in general, positive values throughout the winter
season (Fig. 1). Thus, this mode can be interpreted as a
season-wide warming/cooling over the target domain; the
upper left panel shows the unfiltered loading vectors and
the upper right panel shows the low-pass filtered loading
vectors with a cutoff frequency of 10 days. The corresponding PC time series shows a substantial interannual
modulation of the loading vector (Wang et al. 2009). The
sign of the loading vector is almost invariant during winter
except for small negative values displayed on several days.
However, these negative values do not have any serious
effect on the winter-mean intensity of the EAWM. Thus
the PC time series can primarily be interpreted as an
amplitude of the seasonal mean temperature of the EAWM
and a positive PC value during 1 year means that the
EAWM is weaker in that particular year and vice versa. On
close examination, however, it is revealed that the sign of
the loading vector occasionally switches during winter in
some years. Nonetheless, the high-frequency component of
the loading vector does not seriously affect the seasonalmean temperature, as is shown in the comparison between
the unfiltered and filtered loading patterns. Thus, our discussion mainly focuses on the low-frequency components,
or the winter mean values, of loading vectors.
In Fig. 1c, the five-year central running mean of the PC
time series is plotted as a blue curve. The smoothed time
series fluctuates on decadal time scales and depicts a conspicuous warming trend (red curve). An increasing trend of
low-level temperature is obvious over the record period; the
slope of the linear trend is 0.021 per year. The amplitude has
increased by *1.3 during 64 years. As a result, the 850-hPa
temperature averaged over the domain has increased by
*0.86 C. Thus, winter warming/cooling occurs naturally
on interannual time scales on top of a steady warming;
according to CSEOF analysis, the natural component and
the apparent anthropogenic component of seasonal scale
variability have identical seasonal evolution patterns.
3.2 Physical mechanism in the atmosphere
Figure 2 plots spatial patterns of the climatology of temperature and atmospheric variables (and of anomalies),
which share a common PC time series with the second
CSEOF mode of the 850-hPa temperature. The domain of
predictor variables [80–200E 9 20–70N] is wider than
that of the target variable. Regressed loading vectors are
averaged over the 120-day winter period in order to present
the winter-mean spatial patterns when the PC is positive;
bear in mind that the loading vectors have, in general, one
sign of anomalies during winter at a given location.
Climatology of the 850-hPa temperature during winter
has a nearly zonally uniform structure and decreases
northward as is expected (Fig. 2a). The climatological
mean temperature is coldest over the northeastern part of
the Eurasian continent due to both the continentality with a
lower heat capacity and the prominent westerly in midlatitude region (Fig. 2d). An anomalous 850-hPa temperature warming is strongest over the eastern part of the
continent (see also Wang et al. 2009). The latitude-elevation distribution of the temperature climatology over the
[100–140E] longitudinal band shows that temperature
decreases as latitude and height increase, as is expected
(Fig. 2b). The magnitude of the anomalous warming is
largest near the surface in the mid-latitude region. Atmospheric warming is seen throughout the troposphere, but
atmospheric cooling is seen in the lower stratosphere.
123
Y. Kim et al.
(a) Unfiltered T850 mode2
120
(b) Filtered T850 mode2
0
2 2
120
0
1Mar
1Mar
2
2
90
90
0
Day
Fig. 1 Longitude-time section
of a unfiltered and b low-pass
filtered daily loading vectors of
850-hPa temperature anomalies
(C) of the second CSEOF
mode averaged over the latitude
band of 25–50N. The lowpass filter uses a cutoff period of
10 days. The target domain is
[100–150E 9 25–50N] and
the time interval is November
17–March 16. c Corresponding
PC time series for 64 years as a
black curve with 5-year running
mean as a blue curve. An
increasing linear trend is
exhibited as a red line. The time
series are broken at the
boundary of each winter
(120 days). This mode
represents seasonal scale
variability of the EAWM
0
0
0
60
1Feb
0
0
60
0
0
1Jan
30
1Feb
1Jan
30
0
1Dec
0
1Dec
0
1
100
0
110
120
130
140
0
1
100
150
110
Longitude
-4
-2
0
2
120
130
140
150
Longitude
4
-4
-2
0
2
4
(c) PC time series of CSEOF mode 2
3
2
1
0
-1
-2
-3
1950
1960
1970
1980
1990
2000
2010
Year
Climatological mean sea level pressure is characterized by the Siberian High and the Aleutian Low
(Fig. 2c). A notable decrease in sea level pressure is
located over the northern part of East Asia, particularly
to the north of the Siberian High. This signal appears to
be associated with warming over the continent; warming
in the troposphere over the continent reduces the air
column mass, thereby decreasing sea level pressure.
However, sea level pressure over the northwestern
Pacific is slightly increased, particularly to the south of
the Aleutian Low. The configuration of the anomalous
sea level pressure therefore indicates that the wintertime
pressure contrast between the continent and the ocean is
reduced, thereby weakening the EAWM.
The sea level pressure pattern matches the low-level
wind pattern in the context of their geostrophic relationship; the geostrophic relationship is reasonably satisfied in
both the climatology and the anomaly fields (Fig. 2c, d).
The climatological wind pattern at 850 hPa shows a strong
northwesterly along the continental boundary in the midlatitude region (Fig. 2d). The anomalous wind shows that
123
the northwesterly weakens along the continental boundary.
This weakening is related to the decreased sea level pressure contrast between the continent and the northwestern
Pacific. An anomalous anticyclonic flow then develops
over the northwestern Pacific as a consequence of the
increased sea level pressure.
A strong upper tropospheric zonal jet over the southern
part of Korea and Japan is a prominent feature in winter
(Fig. 2e). A strong zonal jet is established as a result of a
strong meridional temperature gradient in the lower troposphere; this connection is well explained in terms of
the thermal wind relationship (figure not shown). An
anomalous easterly weakens the zonal jet along its central
axis, but the zonal wind speed slightly increases in the
northern part of East Asia (Fig. 2e). The latitude–altitude
plot of the climatological zonal wind shows a strong jet in
the upper troposphere (Fig. 2f). An anomalous zonal
wind is also strong in the upper troposphere and its patterns can be interpreted as either a mid-latitude anticyclonic circulation or a northward shift of the jet (Wang
et al. 2009). The vertical structure of temperature in
The development of a two-dimensional monsoon index
(a)
(b)
(c)
(d)
(e)
(f)
Fig. 2 The patterns of the climatology (120 days 9 64 years) of
atmospheric variables (black contours and vectors) and regressed
loading vectors (shades and streamlines) on the second CSEOF mode.
The regressed loading vectors are averaged over the 120 days of
winter. The atmospheric variables are a 850-hPa temperature (C),
b latitude-pressure section of temperature, averaged over the
longitude band [100–140E], c sea level pressure (hPa), d 850-hPa
wind (m s-1), where the red streamlines denote anomalous southerly
and the blue streamlines denote anomalous northerly, e 300-hPa zonal
wind (m s-1), and f latitude-pressure cross section, averaged over the
longitude band [120–160E]
Fig. 2b shows that the meridional temperature gradient
decreases in the lower latitude (*30 to 40N) and
increases in the higher latitude (*50 to 60N), where a
northward shift of the jet develops, or an anomalous
anticyclonic circulation, according to the thermal wind
relationship. Similar physical interpretations can be made
based on the 1979–2012 ERA-interim reanalysis data
(Dee et al. 2011).
of anomalous energy (Wang et al. 2010b; Kim et al. 2012b).
The rate of local temperature change is expressed as:
þ V0 Þ rðT þ T 0 Þ þ Q þ Q0 ;
oðT þ T 0 Þ=ot ¼ ðV
ð3Þ
3.3 Thermal transport and air-sea interaction
where the primed variables denote anomalies from the
climatology (barred variables). Evolution of anomalous
variables is derived from CSEOF analysis, followed by
regression analysis in CSEOF space. Averaged over a long
period, Eq. (3) is modified as:
rT V0 rT 0 þ Q:
oT=ot
¼ V
ð4Þ
Subtracting Eq. (4) from Eq. (3), we have:
Due to the altered circulation, the pattern of thermal
advection also changes. An anomalous form of thermal
advection can be used to examine the source and transport
0
rT 0 V0 rT 0 þ V0 rT 0 þ Q0 ;
oT =ot ¼ V rT V
ð5Þ
0
123
Y. Kim et al.
which describes the rate of anomalous temperature change
in terms of the anomalous thermal advection and heating.
Note that V0 rT 0 and V0 rT 0 are very small compared
0
rT 0 .
with V rT or V
Anomalous thermal advection in the lower troposphere
(1,000–850 hPa) associated with the second mode is
illustrated in Fig. 3; the 120-day averaged patterns of the
two major terms are shown together with that of total
thermal advection. Mean temperature advection by the
0
indicates warm advection to
anomalous wind (V rT)
the south of the Korean Peninsula and Japan, and cold
advection over the Sea of Okhotsk (Fig. 3a). The warm
advection is due to the anomalous southerly and the
cold advection is due to the decreased mean wind speed by
the anomalous westerly from the continent. Anomalous
rT 0 ) exhibits
temperature advection by mean wind (V
warm advection along the mid-latitude continental
boundary and the extratropical northwestern Pacific
(Fig. 3b), which is related with the greater atmospheric
warming over the continent than over the ocean. Total
advection, the sum of the four advection terms in Eq. (5),
shows substantial warm advection over the coastal seas to
the east of China and Korea (Fig. 3c); total thermal
advection derives mainly from the first two terms of
Eq. (5). The temporal evolution of thermal advection
shows the patterns to be fairly noisy, although stronger
anomalies on the western side of the northwestern Pacific
tend to have the same sign during winter (Fig. 4). Positive
anomalies are prominent over the mid-latitude marginal
seas, although their magnitude tends to increase in the late
winter period (Fig. 4c).
Surface heat fluxes consist of shortwave and longwave
radiation and latent and sensible heat fluxes, and account
for interactions between the surface and the atmosphere.
The climatological flux fields are plotted in the left column
(a)
(b)
Fig. 3 The patterns of 120-day averaged thermal advection terms
(C day-1) related to the second CSEOF mode of 850 hPa temperature. The pattern represents an average of three levels—1,000, 925,
and 850 hPa. a Mean temperature advection by anomalous wind
(shades) with mean temperature (contour) and anomalous wind
123
of Fig. 5. The climatological net shortwave radiation is
negative (downward) and longwave radiation is positive
(upward). The climatological turbulent heat flux is positive
(upward) over the ocean because the ocean tends to be
warmer than the surface air. In the climatology field,
upward latent heat flux is strongest along the path of the
Kuroshio Current, while upward sensible heat flux is strong
over the marginal seas due mainly to the large temperature
differences between the continental air mass and the surface of the ocean (Peixoto and Oort 1992), and partly due
to the stronger wind along the coasts (Marshall and Plumb
2008).
The anomalous fluxes associated with the second
CSEOF mode are averaged over the 120 winter days and
are depicted in the right column of Fig. 5. In the anomaly
field, downward radiation flux is reduced over the Tibetan
Plateau. Longwave radiation increases over much of the
mid-latitude continental region because of the relatively
strong near-surface warming (Fig. 2b). Radiation changes
tend to be smaller than anomalous heat fluxes, particularly
over the ocean. Turbulent heat fluxes represent an important source of energy for the lower atmosphere and are
strongly associated with thermal advection (Kim et al.
2012a). Turbulent heat fluxes are reduced over the marginal seas consistent with warm advection over much of the
coastal area; the Sea of Okhotsk is an exception. Both
latent and sensible heat fluxes are reduced significantly
over the Sea of Okhotsk; the anomalous sensible heat flux
is approximately 5 times larger than the anomalous latent
heat flux. This reduction does not seem to be the result of
warm advection, and the wind speed is, in fact, reduced
significantly over the Sea of Okhotsk (Fig. 2d) resulting in
a reduction in a turbulent heat flux. The temperature and
circulation changes associated with the second CSEOF
mode and the subsequent anomalous thermal advection,
(c)
(vector), b anomalous temperature advection by mean wind (shaded)
with anomalous temperature (contour) and mean wind (vector), and
c total advection. Climatological mean fields represent averages over
64 winters
The development of a two-dimensional monsoon index
Fig. 4 Longitude-time plot of
thermal advection (C day-1)
related to the second CSEOF
mode of 850-hPa temperature
averaged over the latitude band
of 30–45N. The plot
represents the average over the
three levels (1,000, 925, and
850 hPa) and exhibits the lowpass filtered result with a cutoff
period of 10 days. a Mean
temperature advection by
anomalous wind, b anomalous
temperature advection by mean
wind, and c total advection
(a)
undoubtedly result in significant changes in latent and
sensible heat fluxes, particularly over the marginal seas.
The temporal evolution of latent and sensible heat fluxes
are presented in Fig. 6. The signs of latent and sensible
heat flux over the marginal seas along the continental
boundary (130–140E) are almost invariant throughout
the winter, with the exception of the early winter period
when thermal advection is not fully established and displays much weaker values along the continental boundary
(as can be seen in Fig. 4); the positive heat flux anomaly
persists until early December. Figure 6c, together with the
PC time series in Fig. 1c, indicates that sea surface temperature is undergoing strong interannual fluctuations while
gradually increasing. While the reduction of heat fluxes can
in theory induce sea surface temperature warming, the
change may be too small to detect due to the large heat
capacity of sea water and the increased depth of the mixed
layer in winter. It should be noted, however, that the
decreased turbulent heat flux over a long period of time
restricts the ocean from releasing its increasing energy into
the atmosphere, potentially resulting in a more rapid
warming of the ocean interior.
(b)
(c)
4 Comparison with the EAWM index and climate
indices
4.1 Comparison with the EAWM index
A large fraction of the EAWM variability in this study is
decomposed into the seasonal cycle and the seasonal scale
variation. A time series of daily 850-hPa temperature
anomalies from the climatology during winter is plotted in
Fig. 7. The resulting plot exhibits a strong variability
ranging from -9.1 to 10.7 C, with a standard deviation of
3.21 C. The winter-mean values, however, show a much
smaller variability compared to the daily time series; the
winter-mean temperature fluctuates between -2.0 and
1.8 C and its standard deviation is 0.85 C. Many of
earlier studies considered seasonal mean values for the
purpose of defining the strength of the EAWM, thus
neglecting the subseasonal variability of the EAWM.
Temperature anomalies reconstructed from the first
CSEOF mode (the seasonal cycle) are plotted in Fig. 8a,
with the daily anomalies of the NCEP/NCAR reanalysis
product. A large fraction (*21 %) of the daily anomalies
123
Y. Kim et al.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Fig. 5 Left column depicts climatological net surface heat fluxes
(W m-2) and right column denotes anomalous net surface heat fluxes
regressed on the second CSEOF mode of 850 hPa temperature.
Anomalies are averages for 120 winter days. From top to down:
shortwave radiation, longwave radiation, latent heat flux, and sensible
heat flux. Positive values denote upward fluxes and negative values
downward fluxes
is explained by the seasonal cycle. Anomalies not
explained by the seasonal cycle are plotted as the orange
curve in Fig. 8b; high-frequency fluctuations as well as
low-frequency undulations are seen in the remaining variability. The seasonal scale variability is then reconstructed
from the second CSEOF mode as the black curve in
123
The development of a two-dimensional monsoon index
Fig. 6 Longitude-time section
of anomalies of a latent heat
flux, b sensible heat flux and
c sea surface temperature; all of
which are regressed on the
second CSEOF mode of
850-hPa temperature. Low-pass
filtered results with a cutoff
period of 10 days are depicted
after averaged over the latitude
band of 40–50N. Note that the
longitudinal extent of fluxes and
that of sea surface temperature
are different
(a)
(b)
(c)
Mode 2
[40N-50N]
(a) T850 mode 1
T 850
Reconstructed
10
10
5
0
5
-5
0
-10
1950
-5
1960
1970
1980
1990
(b) T850 mode 2
-10
1950
1960
1970
1980
1990
2000
2010
2000
2010
Reconstructed
10
5
Fig. 7 Time series of domain averaged 850 hPa air temperature
anomalies (orange) for 7,680 days from 1948 to 2012. Anomalies are
departures from the winter climatology for 64 years. The domain is
[100–150E 9 25–50N]. Each black bar is the winter mean for
each year. Dotted horizontal lines represent one standard deviation of
daily anomalies (red) and one standard deviation of winter-mean
anomalies (black)
Fig. 8b. It appears that the reconstructed time series only
captures the seasonal scale variability, leaving high-frequency components grossly unexplained. Thus it can be
anticipated that the variability explained by the second
CSEOF mode is similar to the winter-mean variability of
the EAWM, as indexed by Jhun and Lee (2004) based on a
totally different approach.
The winter-mean values of the reconstructed time series
for each mode are plotted as black bars in Fig. 9. The
0
-5
-10
1950
1960
1970
1980
1990
2000
2010
Fig. 8 a Time series of domain averaged 850-hPa air temperature as
in Fig. 7 (orange curve) in comparison with the reconstruction based
on the first CSEOF mode (black curve). b Time series of domain
averaged 850-hPa air temperature after removing the seasonal cycle
in a (orange curve) in comparison with the reconstruction based on
the second CSEOF mode (black curve). Averages are over the domain
[100–150E 9 25–50N]. The black curve denotes the seasonal
cycle in a and the seasonal scale variation in b. Dotted horizontal
lines represent one standard deviation of the reconstructed time series
winter-mean time series of the first CSEOF mode are close
to zero because the seasonal cycle is nearly sinusoidal
within the winter season. However, the winter-mean time
123
Y. Kim et al.
series of the second mode exhibits a much larger variation
and explains a significant fraction of the total seasonalmean variability for many years (orange bars with a red
border).
The EAWM index defined by Jhun and Lee (2004),
which measures the seasonal-mean meridional gradient of
upper tropospheric zonal wind, is compared with the second CSEOF mode; there are many different EAWM indices but they are similar in describing the changes of the
EAWM system (Wang and Chen 2010). The PC time series
of the second CSEOF mode is averaged over 90 days
(December, January, February), which is the same averaging period for the EAWM index by Jhun and Lee (2004).
The correlation coefficient is -0.51, which is significant at
a 95 % level (Table 1). A positive amplitude of the second
CSEOF mode means a weaker EAWM than normal; thus
the correlation has a minus sign. The second CSEOF mode
is more strongly correlated with the EAWM index by Jhun
and Lee (2004) than is the first CSEOF mode (Kim et al.
2012b); correlation between the first PC time series and the
EAWM index is 0.44.
The first and second modes together better explain the
variability of the EAWM index; the correlation between
the optimally combined PC time series and the EAWM
index is 0.63, which is slightly larger than the correlation
with the second PC time series. This suggests that the
EAWM index by Jhun and Lee (2004) also includes the
winter-mean contribution by the seasonal cycle.
As mentioned in Kim et al. (2012b), the subseasonal
evolution of lower tropospheric temperature is an important characteristic of the EAWM and should be considered
together with the seasonal scale variability of the EAWM.
(a) T850 mode 1
Reconstructed
2
1
0
-1
-2
1950
1960
1970
1980
1990
(b) T850 mode 2
2000
2010
Reconstructed
2
1
0
-1
-2
1950
1960
1970
1980
1990
2000
2010
Fig. 9 Seasonal mean values of domain-averaged 850-hPa air
temperature anomalies as in Fig. 7 (orange bars with red border) in
comparison with the seasonal mean values of reconstruction based on
a the first CSEOF mode and b the second CSEOF mode. The black
bars are the annual mean values of the black lines in Figs. 8a and 8b.
The domain is [100–150E 9 25–50N]. Dotted horizontal lines
denote one standard deviation of the black bars
123
Table 1 Correlation of PC1, PC2, and the combined PCs with the
conventional EAWM index and relevant climate indices
Climate
Indices
EAWMI
AO
EA/WR
SH
SH ? AL
PC1
0.44
-0.33
-0.35
0.45
0.50
PC2
-0.51
0.39
0.41
-0.53
0.56
PC1–a 9 PC2
(a)
0.63
0.47
0.50
0.65
(0.459)
(0.439)
(0.445)
(0.437)
These values are significant at a 95 % level. The numbers in parenthesis
are the mixing ratios of PC2 with respect to PC1
The PC time series in Fig. 10a illustrates years when the
amplitude is larger than one sigma for each mode. In years
that are colored, the amplitude of each CSEOF mode
exceeds the one-sigma level. Years with an extreme seasonal cycle do not seem to have any preferential period,
although the occurrence of stronger seasonal cycles has
been rare since 1986. However, extreme negative amplitudes of seasonal scale variability occurred more frequently
in the earlier record whereas extreme positive amplitudes
occurred in the latter part of the record. There does not
seem any substantial correlation between the years with
extreme seasonal cycles and the years with extreme seasonal scale variability. In fact, the lagged correlation of the
two PC time series is fairly low; maximum correlation is
less than 0.3 in magnitude.
The scatter plot in Fig. 10b depicts the amplitudes of the
two CSEOF modes, (time is color coded). This plot facilitates an examination of the evolution of the prominent
modal characteristics on a decadal time scale. Extreme
amplitudes are placed outside the black box. A year is
declared to be extreme when the amplitude of a PC time
series is larger than one sigma level on more than 30 days
in that year. Years printed at the four corners of the plot
represent extreme years in terms of the amplitudes of both
the first and the second CSEOF modes. Figure 11a–d show
the winter temperatures averaged over the region [100–
130E, 40–60N] (see Fig. 2a) for the years in each
quadrant of Fig. 10b. Figure 11e–h show the cumulative
temperatures for the years in Fig. 10b; the cumulative
temperature at a given day is defined to be an accumulation
of winter temperatures from the beginning of each winter
(November 17) up to the specified day.
The years in the first quarter experienced higher-thannormal winter-mean temperatures and stronger-than-normal seasonal cycles; winter temperature, on average, was
milder, but subseasonal temperature fluctuations were
stronger (Fig. 11a, e). The years in the second quarter had
lower-than-normal winter-mean temperatures and strongerthan-normal seasonal cycles. Thus, the winter-mean temperatures were lower than normal and the temperature
range during winter was larger than normal, probably
The development of a two-dimensional monsoon index
(a) PC time series
1st PC
2.0
1.5
1.0
0.5
2nd PC
0.0
1950
1960
1970
1980
1990
2000
2010
1950
1960
1970
1980
1990
2000
2010
3
2
1
0
-1
-2
-3
(b) Daily PC 1st vs 2nd
2.0
1st PC
1.5
52/53
68/69
04/05
89/90
07/08
2010
2000
1990
1980
1.0
48/49
78/79
88/89
98/99
06/07
0.5 49/50
56/57
69/70
threshold: 30 days
0.0
-3
-2
-1
0
1
2
1970
1960
1950
3
2nd PC
Fig. 10 a PC time series of the first and the second PC time series.
Dotted horizontal lines show the one standard deviation of each PC
time series. Extreme days with amplitudes exceeding one sigma level
are colored in red (positive) and blue (negative). b Scatter plot of
daily PC time series of the first CSEOF mode versus the second mode.
The black box exhibits one sigma level for each mode. Years printed
at four corners denote years with the amplitudes of the two PC time
series exceeding one sigma level for more than 30 days
resulting in some very cold days (Fig. 11b, f). Years in the
third quarters had lower-than-normal winter-mean temperatures and weaker-than-normal seasonal cycles; temperatures were generally lower than normal throughout the
winter (Fig. 11c, g). Finally, years in the fourth quarter had
weaker-than-normal seasonal cycles and higher-than-normal winter-mean temperatures. Thus, winter was fairly
mild with a relatively small temperature range (Fig. 11d,
h). As demonstrated in Fig. 11, the two-dimensional index
yields a better description of the evolution of winter temperatures. The mean and amplitude of winter temperatures
for the years in Fig. 10b are given in Table 2 for two different domains; Fig. 11 is consistent with Table 2.
4.2 Comparison with climate indices
Correlation of the first two PC time series with relevant
climate indices measuring the strength of the teleconnection
patterns that affect the EAWM temperature and circulation,
are presented in Table 1. The PC time series are averaged
over 90 days (December, January, February) to construct
winter-mean indices from 1951 to 2010. Monthly climate
indices are also averaged in the same manner. The AO and
the EA/WR indices are obtained from the Climate Prediction Center (CPC). The Siberian High is defined as the areaaveraged sea level pressure over [40–60N 9 80–120E].
Anomalies from the climatology are normalized by the
respective standard deviation to construct the Siberian High
index. The Aleutian Low index is obtained in a similar
manner over the area [40–60N 9 160–200E].
The AO measures the pressure difference between the
middle and the high latitudes in the Northern Hemisphere
winter. During a negative phase of the AO, air temperature
tends to be colder than normal in mid-latitude regions. The
AO time series and the second PC time series are correlated
at 0.39, which is significant at a 95 % level. The EA/WR
pattern, which originates from the East Atlantic sea surface
temperature (Wang et al. 2011), exerts influence on the
pressure anomaly over Siberia and the EAWM. The second
PC time series is correlated with the EA/WR index at 0.41.
The Siberian High is a major factor in determining the
temperature distribution of East Asia (Wu and Wang
2002). Its correlation with the second PC time series is
-0.53. The Siberian High and the Aleutian Low together
explain the second PC time series better than a single
index; correlation with the second PC time series is 0.56.
The seasonal scale variability of the 850-hPa temperature is more strongly correlated with the AO, the EA/WR,
and the Siberian High than with the seasonal cycle; correlation improves slightly with that of the seasonal cycle
(Table 1). However, the two PC time series combined are
more strongly correlated with the indices discussed above;
correlations are 0.47, 0.50 and 0.65 with the AO, the EA/
WR, and the Siberian High, respectively. It is apparent that
both the seasonal cycle and the seasonal scale variability of
the EAWM are significantly correlated with these climate
indices. Nonetheless, these climate indices lend no clue as
to the subseasonal evolution of the EAWM.
5 Summary and concluding remarks
The seasonal scale variability of 850-hPa air temperature
was investigated to explain the variability of the EAWM
from 1948/1949 to 2011/2012. The seasonal scale variability was obtained as the second CSEOF mode of the
850-hPa-temperature. The seasonal cycle (first CSEOF
mode) and the seasonal scale mode together explain
*27 % of the total variability. These two components
seem to adequately explain the daily variation in lower
tropospheric temperature during winter. However, the
conventional approach based on monthly mean or seasonal
mean values of physical variables is not able to explain the
123
Y. Kim et al.
e
0
Cumulative Temp.
Temperature
a
-10
-20
-30
0
20
40
60
80
100
0
-500
-1000
-1500
-2000
-2500
120
0
20
40
DAYS
f
0
Cumulative Temp.
Temperature
b
-10
-20
-30
0
20
40
60
80
100
Cumulative Temp.
Temperature
-20
-30
60
80
100
20
40
Cumulative Temp.
Temperature
-20
-30
60
80
100
120
80
100
120
-500
-1000
-1500
-2000
-2500
20
40
60
DAYS
-10
40
60
0
0
h
20
120
-2500
120
0
0
100
-2000
DAYS
d
80
-1500
DAYS
-10
40
120
-1000
0
g
20
100
0
120
0
0
80
-500
DAYS
c
60
DAYS
80
100
120
DAYS
0
-500
-1000
-1500
-2000
-2500
0
20
40
60
DAYS
Fig. 11 Winter temperatures (blue) in years in a the first quadrant,
b the second quadrant, c the third quadrant, and d the fourth quadrant
in Fig. 10b and their mean (red) in comparison with the climatological mean (black curve) and 1r range (black dots). Cumulative
temperatures (blue) in years in e the first quadrant, f the second
quadrant, g the third quadrant, and h the fourth quadrant in Fig. 10b
and their mean (red) in comparison with the climatological mean
(black curve) and 1r range (black dots). The temperatures are
averages over the domain [100–130E, 40–60N]. See the text for
details
Table 2 The winter mean temperature and the amplitude (highest
20 days–lowest 20 days) averaged for the years in each quadrant of
Fig. 10b for two different domains
subseasonal evolution of daily temperature explained by
the seasonal cycle; which motivated the present study.
The CSLV of the second CSEOF mode appears to
describe overall winter warming/cooling. Thus, this particular mode represents seasonal scale (winter-mean) variability of 850-hPa temperature. Indeed, the corresponding
PC time series is strongly correlated with the conventional
EAWM index based on the winter-mean variables.
Spatial patterns of key variables are examined to
describe the physical mechanism of the seasonal scale
[100–130E, 40–60N]
[100–150E, 25–50N]
Mean
Mean
Amp.
9.40
Amp.
I
-14.46
15.76
I
-3.63
II
-18.55
12.16
II
-4.61
9.35
III
-18.60
8.59
III
-5.55
5.20
IV
-14.72
6.30
IV
-3.02
5.68
123
The development of a two-dimensional monsoon index
variability of the EAWM. A positive phase of the second
CSEOF mode represents a significant warming over much
of the domain, particularly the eastern part of China,
Korea, and Japan; therefore, a positive phase of the second
CSEOF mode denotes a weaker-than-normal EAWM. The
vertical structure of air temperature confirms that mid-latitude warming is most significant near the surface, gradually decreasing to zero near the tropopause. The sign of
temperature anomaly then reverses. Sea level pressure
contrast between the continent and the ocean is an important ingredient for the monsoonal flow and the seasonal
scale variability of the EAWM. During a weaker EAWM
phase, the sea level pressure contrast weakens. In particular, there is a significant weakening of sea level pressure
over the northern part of the Siberian High. The related
northerly decreases along the continental boundary in midlatitude East Asia. The upper tropospheric jet shifts
northward due to the altered temperature gradient.
Thermal advection in the lower troposphere increases
over the continental boundary regions, particularly to the
east of eastern China and Korea. The increased thermal
advection leads to a reduced turbulent heat flux over the midlatitude marginal seas. However, over the high-latitude
marginal seas turbulent heat flux decreases primarily
because of the reduced wind speed above the sea surface.
Although the reduction of turbulent heat flux is not a direct
cause of the increased heat content in the ocean particularly
in the coastal seas (Na et al. 2012), the ocean cannot effectively ventilate the increased energy into the atmosphere. As
a result, the rate of ocean warming accelerates.
In the PC time series, a linear trend is conspicuous in the
midst of much stronger interannual variability; the trend is
0.021 per year based on the 64-year record. This linear trend
implies that the effect of greenhouse warming is similar to
that of naturally occurring seasonal scale warming/cooling.
The 850-hPa temperature in the target domain rose by about
0.86 C during the data period. Thus, the effect of greenhouse warming is a gradual weakening of the EAWM.
Both the seasonal cycle and seasonal scale variability of
the EAWM are fairly correlated with the relevant climate
indices. The highest correlation is observed with the
Siberian High index, in which 28 % of the seasonal scale
variability is explained. The Siberian High index combined
with the Aleutian Low index explains more variance of the
seasonal scale variability although the Aleutian Low by
itself is not significantly related to the seasonal scale variability of the EAWM. The seasonal scale variability is also
correlated with the AO and the EA/WR indices although
correlation is not high. It appears that the teleconnection
patterns associated with these indices affect both the seasonal cycle and the seasonal scale variability.
It is emphasized that the seasonal evolution, as represented by the seasonal cycle, is hardly projected on the
conventional EAWM index since averaging over the winter
period nearly annihilates the effect of the seasonal cycle,
which is virtually sinusoidal. The conventional measure of
the strength of the EAWM is reasonably similar to the PC
time series of the seasonal scale variability (second CSEOF
mode). However, the strength of the seasonal evolution is
an important aspect of the EAWM; for example, during a
strong seasonal cycle the subseasonal temperature variation
is more pronounced. Thus, it is useful to measure both the
winter-mean temperature and the subseasonal evolution of
temperature. For this purpose, a two-dimensional EAWM
index was explored. A new EAWM index was constructed
in the two-dimensional space, spanned by the first two PC
time series of 850-hPa air temperature. The resulting index
appeared to convey more useful information about the
EAWM variability, particularly the temperature variability
associated with the EAWM. The utility of the twodimensional index is yet to be demonstrated in future
studies.
Acknowledgments This work was funded by the Korea Meteorological Administration Research and Development Program under
Grant CATER 2012-3010.
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