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1 APRIL 2015
LIU ET AL.
2531
Preceding Factors of Summer Asian–Pacific Oscillation and the
Physical Mechanism for Their Potential Influences
GE LIU, PING ZHAO, AND JUNMING CHEN
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China
SONG YANG
Department of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
(Manuscript received 2 May 2014, in final form 23 November 2014)
ABSTRACT
The authors explore the preceding factors of summertime Asian–Pacific Oscillation (APO) using observations and output from the NCEP Climate Forecast System version 2 (CFSv2). Results show that the winter
and spring sea surface temperatures (SSTs) in the tropical central-eastern Pacific (TCEP) and the spring sea
level pressure (SLP) over the north Indian Ocean (NIO) are significantly correlated with summer APO. The
preceding TCEP SST anomaly tends to exert a delayed impact on summer APO through the following
process. The previous winter TCEP SST anomaly persists until spring and results in SLP anomaly over the
NIO in spring. The latter induces a vertical motion anomaly over the western Tibetan Plateau, which alters
spring rainfall and underlying soil moisture in situ, further modulating local surface air temperature during the
following summer and hence the summer APO. The CFSv2 has high skills in predicting the winter and spring
TCEP SST and the spring NIO SLP and successfully captures the observed relationships of TCEP SST and
NIO SLP with summer APO. This result explains why the CFSv2 is capable of predicting the summer APO
teleconnection by several months in advance.
1. Introduction
Atmospheric teleconnections reflect the intrinsic
variations of atmospheric circulations and link climate
phenomena between different regions. Various teleconnection patterns have been identified over the
Asian–Pacific region during summer. For example,
Kutzbach (1970) found a zonal teleconnection pattern in
July sea level pressure between Asia and the North Pacific. Nitta (1987) showed a summer meridional Pacific–
Japan teleconnection pattern that links convective activity
from East Asia to North America via Japan. Lau (1992)
and Lau and Weng (2002) revealed an East Asian–North
American teleconnection based on a relationship of
Denotes Open Access content.
Corresponding author address: Dr. Ping Zhao, State Key Laboratory of Severe Weather, Chinese Academy of Meteorological
Sciences, 46 Zhong-Guan-Cun South Avenue, Beijing 100081,
China.
E-mail: [email protected]
DOI: 10.1175/JCLI-D-14-00327.1
Ó 2015 American Meteorological Society
rainfall between East Asia and North America. Wang
et al. (2001) found that a teleconnection between Indian
and East Asian summer monsoons might be part of
a global-scale wave train linking Asia and North America.
Moreover, variations of the South Asian high are associated with those of the western North Pacific subtropical
high, which suggests an atmospheric teleconnection over
the Asian–Pacific region (Zhang et al. 2005). Recently,
an extratropical large-scale teleconnection pattern, referred to as Asian–Pacific Oscillation (APO), has been
identified in the upper-tropospheric temperature over the
Northern Hemisphere in summer (Zhao et al. 2007b).
The interannual variability of APO is closely linked to the
precipitation over the Northern Hemisphere land in
summer (Zhao et al. 2012). Therefore, understanding the
mechanisms for summer APO and predicting its variability are helpful for forecasting climate anomalies in the
Northern Hemisphere.
In summer, atmosphere–ocean–land interactions modulate the variations of Asian monsoon climate (Meehl
1994; Yang and Lau 1998) through SST and land surface
processes such as El Niño–Southern Oscillation (ENSO)
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(e.g., Alexander et al. 2002; Lau et al. 2004) and
Eurasian snow cover and soil moisture (e.g., Webster
1983; Yasunari et al. 1991; Douville and Royer 1996;
Douville 2003). The variability of APO is also possibly
modulated by large-scale atmosphere–ocean–land interactions, and its formation is related to an extratropical
zonal vertical circulation with upward motion from the
Tibetan Plateau (TP) to the western North Pacific and
downward motion over the eastern North Pacific and the
North Atlantic (Zhao et al. 2010). Through this zonal
vertical circulation, the summertime APO and associated
atmospheric circulations are regulated by the Asian land
elevated heating, instead of the SSTs over the extratropical
North Pacific and the tropical central-eastern Pacific
(Zhao et al. 2011). Furthermore, the springtime Tibetan
heating anomaly may also modulate the zonal winds over
the tropical Pacific and hence the development of ENSO
events (Ose 1996; Nan et al. 2009), in which the APO
teleconnection acts as an important ‘‘bridge.’’
The major characteristics of summer APO and associated climate anomalies can be successfully simulated
by ocean–atmosphere–land coupled models (Zhao et al.
2010; Man and Zhou 2011; X. Chen et al. 2013). The
interannual variability of summer APO can be well predicted one month in advance by the European Centre for
Medium-Range Weather Forecasts (ECMWF), the Centre National de Recherches Météorologiques (CNRM),
and the Met Ofﬁce (UKMO) general circulation models
from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction
(DEMETER) project (Huang et al. 2013, 2014). The
National Centers for Environmental Prediction (NCEP)
Climate Forecast System version 2 (CFSv2) can even
successfully predict the interannual variability of summer
APO and associated anomalies in atmospheric circulation, precipitation, surface air temperature (SAT), and
SST by up to 5 months in advance (J. Chen et al. 2013).
However, in spite of the progress in studies of APO,
the reasons for successful predictions of the summer
APO remain unclear. For example, are there any preceding factors responsible for summer APO variations?
Can these factors be captured by a model forecast system? In the present study, we address the above questions by examining the summer APO and associated
preceding signals from both observations and dynamic
prediction results by the CFSv2.
The rest of this paper is organized as follows: The
CFSv2, data, and analysis methods are described in
section 2. Based on observations, the preceding factors of
summer APO and the associated physical processes are
examined in section 3. Hindcast results by the CFSv2 are
analyzed in section 4. Finally, a summary of the results
obtained and a further discussion are presented in section 5.
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2. Data, model, and methods
We analyze monthly-mean horizontal winds, vertical
p velocity (v), geopotential height, and temperature
from the NCEP–U.S. Department of Energy (DOE)
Reanalysis-2 (Kanamitsu et al. 2002), the Global Precipitation Climatology Project (GPCP) precipitation
(Adler et al. 2003), the Climate Prediction Center (CPC)
Merged Analysis of Precipitation (CMAP; Xie and Arkin
1997), and the National Oceanic and Atmospheric
Administration (NOAA) extended reconstructed version 3b SST (Smith et al. 2008). We also use soil moisture at four layers (7, 21, 82, and 189 cm) in thickness
from ERA-Interim (a new version of the ERA-40) (Dee
et al. 2011), since the data are close to the observations
of Chinese meteorological stations (Li et al. 2005). The
above datasets are all available from January 1979 to
August 2009.
The NCEP CFS is a state-of-the-art climate forecast
system. It is a fully coupled atmosphere–ocean–land
model used in seasonal prediction operations. Its atmospheric component is the NCEP Global Forecast
System model (Moorthi et al. 2001), and the oceanic
component is the Modular Ocean Model (MOM) version 4.0 of the NOAA/Geophysical Fluid Dynamics
Laboratory (GFDL) (Griffies et al. 2004). The land
surface model is the four-layer land model from the
NCEP, the Oregon State University (OSU), the U.S.
Air Force, and the Hydrologic Research Laboratory
(Noah), which was used in the Global Land Data Assimilation System (Koren et al. 1999; Chen and Dudhia
2001; Ek et al. 2003). Additionally, a three-layer interactive global sea ice model was also introduced into
the second version of the CFS (CFSv2), whose details
have been given by Saha et al. (2014). The earlier version of the CFS (CFSv1) has a high skill in simulating
and predicting ENSO (Wang et al. 2005), the tropical
Atlantic SST (Hu and Huang 2007; Misra et al. 2009),
the Northern Hemisphere upper-tropospheric circulation (Lee et al. 2011), the Asian summer monsoon
(Yang et al. 2008a,b; Achuthavarier and Krishnamurthy
2010), and precipitation over many regions of the world
(Wang et al. 2010; Goddard et al. 2006; Higgins et al.
2008; Yoon et al. 2012; Liang et al. 2009). The CFSv2 has
generally higher skills than CFSv1 in predicting the
world’s climate (Saha et al. 2014) and demonstrates
good skills in predicting several regional monsoons
(Jiang et al. 2013b,c; Liu et al. 2013; Zuo et al. 2013),
SSTs over the tropical Pacific and North Atlantic (Xue
et al. 2013; Hu et al. 2013), and the dominant modes of
Indo-Pacific SST (Jiang et al. 2013a; Yang and Jiang
2014). Moreover, the CFSv2 reasonably predicts the
variability of summer APO and associated anomalies of
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LIU ET AL.
FIG. 1. (a) EOF1 (30.01) of summer upper-tropospheric (300–
200 hPa) eddy temperature (T 0 ) anomalies during 1979–2009 in
which positive (negative) values larger (smaller) than 1 (21) are
shaded. The EOF1 accounts for 27% of total variance. (b) Time
series of the principal component of EOF1 (i.e., the APO index; red
solid line) and its linear trend (blue dashed line).
large-scale atmospheric circulation, precipitation, SAT,
sea level pressure (SLP), and SST by 5 months in advance (J. Chen et al. 2013).
To examine the relationships between two variables,
correlation and regression analyses are used. Since the
variation of one variable may sometimes be caused by
multiple factors, a method of partial correlation analysis
is performed to further reveal the relatively independent
effects of different factors. Here, the partial correlation
is defined as follows (Zar 1998; Wu and Kirtman 2007):
r12 2 r13 r23
,
r12,3 5 qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
2 )(1 2 r2 )
(1 2 r13
23
where rij refers to the correlation coefficient between i and
j; index 1 represents the target variable; indices 2 and 3
indicate the first and second factors correlating with the
target variable, respectively; and r12,3 indicates the partial
correlation coefficient between the target variable and the
first factor after removing the effect of the second factor.
In addition, an empirical orthogonal function (EOF)
analysis with a latitudinal weighting function and a composite analysis are used. The statistical significance of
a relationship is assessed using the Student’s t test at the
95% confidence level unless otherwise stated.
3. Preceding factors of summer APO
a. Spring SLP signal over the north Indian Ocean
To better display a teleconnection feature over the
Asian–Pacific sector, we perform an EOF analysis on the
anomalies of summer (June–August) upper-tropospheric
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FIG. 2. (a) Distribution of the coefficients of correlation between
the summer (June–August) APO index and the spring (April–
May) SLP field during 1979–2009. Shading is significant at the 95%
confidence level, and the red box is for the region defining the NIO
SLP index. (b) Normalized time series of the summer APO (red
solid line) and spring NIO SLP (blue dashed line) after removing
their linear trends.
(300–200 hPa) eddy temperature (T 0 ) over the Asian–
Pacific region (08–908N, 08–1208W) during 1979–2009, in
which T 0 is defined as the deviation of temperature from
the zonal mean. As shown in Fig. 1a, the leading EOF
mode (EOF1) accounts for 27% of the total variance and
displays an out-of-phase relationship between Asia and
the North Pacific, which is generally consistent with the
structure of APO (Zhao et al. 2007b; J. Chen et al. 2013).
The leading EOF mode over the entire Northern Hemisphere also displays an APO pattern (figure not shown)
that is generally consistent with that shown in Fig. 1a.
Meanwhile, the time series of EOF1 for the Asian–Pacific
region (Fig. 1b) is highly correlated with that for the
Northern Hemisphere, with a correlation coefficient of
0.99 for 1979–2009. Therefore, the principal component
of EOF1 can still be referred to as an APO index. Since
the APO index experiences a linear increase, we remove
the linear trend of the index because the present study is
focused on interannual variations. Hereafter, the linear
trends of all variables are also removed unless specifically
stated.
Figure 2a exhibits the coefficients of correlation between spring (April–May) SLP and summer APO index
for 1979–2009. Significantly negative correlations appear over a broad region from the north Indian Ocean
(NIO) via South Asia to the South China Sea, indicating
that the spring SLP anomalies over this region may be
considered as a preceding signal for the summer APO.
The spring SLP over the NIO and its adjacent regions
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FIG. 3. (a) Composite differences of spring CMAP precipitation
(mm day21) between the low and high NIO SLP years (low minus
high) in which the positive (negative) anomalies significant at the
90% confidence level are shaded with purple (yellow). The contour
interval is 1 (0.2) for positive (negative) anomalies and zero contours are omitted. The red dash contours measure the topography
of 1500 m. (b) As in (a), but for simultaneous meridional vertical
circulation along the longitude band 658–758E (unit for horizontal
wind: m s21; unit for vertical p velocity: 0.01 Pa s21). Significant
anomalous upward (downward) motion at the 95% confidence
level is shaded with yellow (purple).
(58–308N, 358–908E) is defined as a NIO SLP index.
There is an out-of-phase relationship between the spring
NIO SLP and summer APO indices (Fig. 2b), with a correlation coefficient of 20.48 (significant at the 99% confidence level). This high correlation further supports the
close relationship between summer APO and spring NIO
SLP. However, no significant correlation is detected between summer APO and the previous winter NIO SLP
(figure not shown), implying that the preceding signal in
NIO SLP can only be traced to the previous spring. In the
following analysis, we discuss the possible reason for the
link between spring NIO SLP and summer APO.
From the NIO SLP index, we select six high-value
years and five low-value years to perform a composite
analysis. The NIO SLP anomalies in these years are
beyond the 0.90 standard deviation. Figure 3a shows the
composite difference in spring CMAP precipitation
between the high and low years (low minus high).
Significantly positive anomalies appear over the NIO,
with two centers over a region from the Bay of Bengal to
the South China Sea and over the Arabian Sea around
108N, 708E, respectively, while significantly negative
anomalies appear over the western TP and its adjacent
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regions centered near 358N, 708E, indicating less local
precipitation in spring. Negative anomalies over the
western TP and its adjacent regions are also seen from the
GPCP precipitation data (figure not shown). These
negative precipitation anomalies may be related to an
anomalous meridional vertical circulation between the
Arabian Sea and the TP. Figure 3b shows the composite difference in the spring meridional vertical circulation along longitudes 658–758E. Corresponding to
low NIO SLP values, a vertical circulation appears
between 108 and 408N, with anomalous upward motion
around 108–158N. This anomalous upward motion may
be related to the low SLP over the NIO, which favors
the occurrence of local convection and precipitation
(Fig. 3a). The anomalous upward flow moves southward and northward at the upper troposphere (around
300–100 hPa) over the Arabian Sea. The northward
branch meets the anomalous southerlies from the
higher latitudes, which forms compensated anomalous
sinking motion around 308–408N over the western TP,
suppresses local convection, and leads to less precipitation in situ (Fig. 3a). These results suggest that
the NIO SLP anomalies trigger vertical motion
anomalies over the western TP and its adjacent regions
through modulating a meridional vertical circulation
between the Arabian Sea and the western TP.
Corresponding to less (more) spring precipitation,
local soil tends to be drier (wetter) over the western TP
and its adjacent areas. Accordingly, negative (positive)
anomalies of spring soil moisture appear (Fig. 4a) and
maintain to the subsequent summer in these areas
(Fig. 4b). Soil moisture modulates climate variability
through land–air interactions (Yang and Lau 1998;
Douville 2003; Koster et al. 2006; Seneviratne et al. 2006;
Kim and Hong 2007; Zhao et al. 2007a; Zhang and Zuo
2011). Drier (wetter) soil leads to an increase (a decrease) in SAT in summer (Fig. 5a). It is evident that the
variation of SAT over the western TP (308–388N, 658–
758E) is negatively correlated with the variation of
spring SLP over the NIO (Fig. 5b). This result further
supports the close link between spring NIO SLP and the
summer SAT over the western TP.
Figure 6a shows the longitude–height cross section
along 32.58N for the difference in composite summer T 0
between the high and low NIO SLP years. Positive
(negative) anomalies appear mainly between 800 and
200 hPa over Asia (the North Pacific), with maximum
values in the upper troposphere. This feature is consistent with the vertical structure of the difference in
composite summer T 0 between the years of high and
low APO indices (Fig. 6b), in which six high and five
low APO cases beyond the 0.90 standard deviation are
selected.
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LIU ET AL.
FIG. 4. As in Fig. 3a, but for the composite differences in (a) spring
and (b) summer soil moisture (%). The solid (dash) contours represent 1% (21%) soil moisture anomalies. The positive (negative)
anomalies significant at the 90% confidence level are shaded with
purple (yellow). The red dashed contours measure the topography
of 1500 m.
Based on the above analysis, we propose that the
negative anomalies of spring SLP over the NIO weaken
the sinking motion over the Arabian Sea and the rising
motion over the western TP and its adjacent regions
through stimulating an anomalous meridional vertical
circulation between the Arabian Sea and the western
TP. The weakened rising motion over the western TP
reduces precipitation and soil water content in situ. The
reduced soil moisture maintains to the subsequent
summer and causes an increase in the local SAT. Previous observations and numerical simulations have
demonstrated that a warm surface over the TP often
increases local tropospheric temperature (Ose 1996;
Zhang et al. 2004; Zhao et al. 2007a) and accordingly
affects the variation of summer APO (Nan et al. 2009;
Zhao et al. 2011). Here, we further reveal a cross-season
effect of the spring NIO SLP anomaly on the subsequent
summer APO, in which the land–air interaction over the
western TP may play an important role.
b. Earlier signal in the tropical central-eastern
Pacific SST
The variations of APO-related SSTs during the previous winter and spring, which may reflect the earlier
signals for summer APO, are investigated in this section.
Figure 7a displays the correlation between summer APO
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FIG. 5. (a) Composite difference of summer surface air temperature between the low and high NIO SLP years (low minus
high). The positive (negative) anomalous values significant at the
95% confidence level are shaded with yellow (purple). (b) Coefficients of correlation between the spring NIO SLP and the
time series of the summer surface air temperature averaged over
the western TP (308–388N, 658–758E). The positive (negative)
correlations significant at the 95% confidence level are shaded
with yellow (purple). The red dashed contours measure the topography of 1500 m.
index and the previous spring SST for 1979–2009. Significantly negative correlations appear in the tropical
central-eastern Pacific (TCEP), with a central value of
20.60. Negative correlations are also seen between summer APO index and the previous winter (February–March)
SST (Fig. 7b). Referring to the positions of negative
correlations in the TCEP shown in Fig. 7, we define the
mean SST over 58S–58N, 1708E–1508W (red boxes in
Fig. 7) as an index to represent the variation of TCEP
SST. The coefficient of correlation between the summer
APO index and the spring (winter) TCEP SST index for
1979–2009 is 20.60 (20.42), significant at the 99.9%
(98%) confidence level. Meanwhile, the TCEP SST index in winter is significantly and positively correlated
with that in the following spring, with a correlation coefficient of 0.88. In contrast, the SST index is negatively
correlated with that during the subsequent summer, with
a correlation coefficient of 20.57. This result implies the
large persistence of TCEP SST anomalies from winter to
spring but not to the following summer, consistent with
the result of previous studies (e.g., Fig. 2 of Torrence and
Webster 1998), which show that the persistence of
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FIG. 6. (a) Longitude–height cross section along 32.58N for
composite difference in summer T 0 (8C) between the low and high
NIO SLP years (low minus high) in which the positive (negative)
anomalies significant at the 95% confidence level are shaded with
yellow (purple). (b) As in (a), but for the composite difference
between the high and low APO index years (high minus low).
TCEP SST is large from winter to spring but declines
rapidly from spring to summer.
Figure 8a shows the longitude–height cross section
along 32.58N for the coefficients of correlation between
the previous spring TCEP SST index and the summer T 0 .
Significantly negative (positive) correlations appear over
Asia (the Pacific), similar to the vertical structure of
summer APO pattern (Fig. 6b). The correlation between
the previous winter TCEP SST index and the summer
T 0 also presents such a vertical structure (Fig. 8b). These
correlation patterns further imply that the earlier signal
of summer APO can be traced back to the TCEP SST in
the previous spring and winter.
Figure 9a shows the coefficients of correlation between spring TCEP SST index and the simultaneous
SLP. Significant positive correlations appear over the
NIO and the tropical western Pacific and significant
negative correlations are seen over the TCEP. In turn,
the spring NIO SLP index is significantly and positively
correlated with the simultaneous SST in the TCEP
(Fig. 9b). It is evident that the two preceding factors (i.e.,
NIO SLP and TCEP SST) are not independent from
each other.
Both observations and numerical simulations have
shown that corresponding to a winter El Niño (La Niña)
event, the SLP over the NIO and the western Paciﬁc
increases (decreases) during the following spring and
therefore modulates the variability of SST outside the
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FIG. 7. Distribution of correlation coefficients of the summer
APO index with (a) spring and (b) winter SSTs. The positive
(negative) values significant at the 95% confidence level are shaded
with yellow (purple). The contour interval is 0.2, and zero contours
are omitted. The red boxes denote the domain of the TCEP.
tropical Paciﬁc (Alexander et al. 2002), in which the
atmospheric process acts as an important atmospheric
bridge that may convey the ENSO signal from the
tropical Pacific to the globe (Lau and Nath 1994, 1996;
Alexander et al. 2002). In particular, an ENSO event
may induce anticyclonic fluctuations on both sides of the
FIG. 8. Longitude–height cross section along 32.58N for the
correlation of summer T 0 with the (a) spring and (b) winter TCEP
SST indices. The positive (negative) values significant at the 90%
confidence level are shaded with yellow (purple).
1 APRIL 2015
LIU ET AL.
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FIG. 10. Longitude–height cross section along 32.58N for the
partial correlation between summer T 0 and the winter TCEP SST
index after removing the variability of spring NIO SLP index. The
positive (negative) values significant at the 90% confidence level
are shaded with yellow (purple).
FIG. 9. (a) Distribution of correlation coefficients between the
spring TCEP SST index and the simultaneous SLP field. (b) As in
(a), but between the NIO SLP index and the SST field. The contour
interval is 0.2 with zero contours omitted. (c) Longitude–height
cross section along 08–208N for the correlation between the spring
TCEP SST index and the simultaneous vertical p velocity. The
positive (negative) values significant at the 95% confidence level
are shaded with yellow (purple).
equator over the Indian Ocean and increases SLP in situ
(Huang and Shukla 2007). Meanwhile, warm SST in the
TCEP may stimulate strong rising motion over the
eastern Pacific and compensated sinking motion in other
longitudes (Watanabe and Jin 2003), which is also seen
in this study. Figure 9c shows the longitude–height cross
section of correlation coefficients between spring TCEP
SST index and vertical p velocity along latitudes 08–208N.
Significant negative correlations are found throughout
almost the entire troposphere mainly between 1508E
and 908W, while significant positive correlations appear
largely between 308 and 1208E. These anomalous features in Fig. 9c are generally consistent with the simulation by Watanabe and Jin (2003) (see their Fig. 11a).
This consistency implies that the anomalies of vertical
motion and SLP over the NIO and western Pacific
(shown in Fig. 9) can be attributed to the forcing of
TCEP SST anomalies.
To sum up, winter–spring TCEP SST and spring NIO
SLP are two preceding factors of the variability of
summer APO, although they are not independent from
each other. Winter TCEP SST anomalies may maintain
to the subsequent spring and modulate spring NIO SLP
through a tropical atmospheric bridge. The anomalous
NIO SLP in spring tends to induce an anomalous meridional vertical circulation between the Arabian Sea
and the western TP, and thus changes the simultaneous
vertical motion and precipitation over the western TP.
The anomalous spring precipitation leads to anomalous
signal in soil for the subsequent summer and affects the
summer APO through the land–air interactions over the
western TP. We also note that, after removing the variability of spring NIO SLP, there is no large-scale significant partial correlation between the winter TCEP
SST index and the summer T 0 over Asia and the Pacific
(Fig. 10). This result indicates the importance of spring
NIO SLP, which must play a bridge role in linking winter
TCEP SST and summer APO.
4. Preceding factors and related mechanisms
a. NIO SLP in CFSv2
In this section, we investigate whether the observed
processes discussed above can be predicted by analyzing
the output from the NCEP CFSv2 hindcast. We analyze
the hindcast data of February–August, which is predicted on the basis of real-time oceanic and atmospheric
initial conditions in the previous December. For each
year of 1983–2009, the model is run from 1 December of
the previous year with an interval of 5 days and repeated
4 times per day using initial data at 0000, 0600, 1200, and
1800 UTC. Thus, there are 24 ensemble members for
each month. Similarly, linear trends are removed from
the CFSv2 data as done for observations.
We perform an EOF analysis on the anomalies of summer 300–200-hPa mean T 0 for 1983–2009. Consistent
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VOLUME 28
FIG. 12. As in Fig. 2, but for the CFSv2 hindcast during
1983–2009.
FIG. 11. (a) Normalized time series of the summertime observed
(red solid line) and CFSv2 (blue dashed line) APO indices. (b)–(d)
As in (a), but for (b) the spring NIO SLP index, (c) the spring
TCEP SST index, and (d) the winter TCEP SST index.
with observations (Fig. 1a), the leading EOF mode in
CFSv2 also shows an APO pattern (figure not given).
The CFSv2 APO index (Fig. 11a) is highly correlated
with the observed, with a correlation coefficient of 0.58
(significant at the 99.9% confidence level), which shows
the ability of CFSv2 in predicting summer APO.
In the CFSv2, significantly negative correlations between the summer APO index and spring SLP appear
over a large region from Africa via the NIO to the
western North Pacific during 1983–2009 (Fig. 12a). The
area is larger than that in observation (see Fig. 2a), due
possibly to the overestimate of ENSO events in CFSv2.
Moreover, the CFSv2 also well predicts the variability of
NIO SLP, with a significant correlation coefficient of
0.68 between the observed and CFSv2 spring NIO SLP
indices (Fig. 11b). An out-of-phase relationship between
the spring NIO SLP and summer APO indices is successfully captured by the CFSv2, with a correlation coefficient of 20.89 for 1983–2009 (Fig. 12b), indicating
that spring NIO SLP is also a good preceding factor of
the summer APO in CFSv2. It is also noted that the
negative correlation in CFSv2 appears over a larger area
compared to the observed (Fig. 2a).
Figure 13a shows the composite difference in the
spring precipitation of CFSv2, in which eight high and
six low NIO SLP years beyond the 0.90 standard deviation are selected. Significantly negative anomalies
appear over the western TP and its adjacent regions,
while significantly positive anomalies occur over the
Arabian Sea, the Bay of Bengal, and the South China
Sea. Figure 13b shows the composite difference in spring
meridional vertical circulation along longitudes 658–
758E. Anomalous rising motion is seen at 108–158N over
the Arabian Sea and compensated anomalous sinking
motion is found at 308–408N over the western TP. The
compensated sinking motion effectively suppresses
convection and leads to less precipitation over the
western TP (Fig. 13a). Meanwhile, soil moisture in the
CFSv2 (figure not shown) exhibits significantly negative
anomalies in the western TP and its adjacent regions in
spring and the subsequent summer. These features in
CFSv2 are similar to those in observations (shown
in Figs. 3b and 4).
Figure 14a further displays the longitude–height cross
section along 32.58N for the composite difference in
summer T 0 between high and low NIO SLP years in the
CFSv2. Significantly positive (negative) anomalies appear over Asia (the Pacific), which resemble those associated with the CFSv2 summer APO (Fig. 14b). In
Figs. 14a,b, significant positive anomalies over a large
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LIU ET AL.
FIG. 14. As in Fig. 6, but for the CFSv2 hindcast.
FIG. 13. As in Fig. 3, but for the CFSv2 hindcast.
area of Asia at the upper troposphere seem to originate
from the surface of the TP. The feature is generally
similar to that in Figs. 6a,b, further supporting that the
close relationship between spring NIO SLP and summer
APO is likely attributed to the effect of Tibetan surface
heating.
b. TCEP SST in CFSv2
The significant correlation between the observed and
CFSv2 TCEP SST indices, with correlation coefficients
of 0.84 and 0.95 for spring and winter, respectively
(Figs. 11c,d), reveals a high skill of the CFSv2 in predicting the variability of TCEP SST. In the CFSv2, the
summer APO index is negatively correlated with the
spring and winter TCEP SSTs (Fig. 15), with a correlation coefficient of 20.82 (20.73) between the spring
(winter) TCEP SST and the summer APO for 1983–2009.
Meanwhile, the spring TCEP SST is significantly correlated to the winter TCEP SST (R 5 0.85), indicating
a large persistence of SST from winter to spring. The
coefficients of correlation between spring NIO SLP and
spring (winter) TCEP SST are also up to 0.85 (0.79). All
these features are consistent with those observed and
support the possible role of spring NIO SLP in linking
summer APO and the previous TCEP SST.
Figure 16a shows the longitude–height cross section of
correlation between winter TCEP SST and summer T 0
in the CFSv2. Significantly negative correlations appear
over Asia, and significantly positive correlations occur
over the North Pacific. Compared to observation
(Fig. 8b), the center of significant correlations in CFSv2
over Asia is more extensive and shifts more eastward
over the Pacific (Fig. 16a). Nevertheless, the major
features of the CFSv2 APO are similar to those observed. The partial correlation between summer T 0 and
the winter TCEP SST index excluding the variability of
spring NIO SLP (Fig. 16b) is evidently weaker over Asia
compared to Fig. 16a, and it does not present an APOlike pattern. The result is in good agreement with that
shown in Fig. 10.
FIG. 15. As in Fig. 7, but for the CFSv2 hindcast. The contour
interval is 0.4 and zero contours are omitted.
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well predict the interannual variations of previous winter and spring TCEP SSTs and spring NIO SLP. Second,
the CFSv2 successfully captures the possible influence of
previous TCEP SST on summer APO, in which the
spring NIO SLP may act as a bridge linking the preceding TCEP SST to the subsequent APO.
5. Summary and discussion
FIG. 16. (a) Longitude–height cross section along 32.58N for
correlation coefficient of summer T 0 with the winter TCEP SST
index in the CFSv2. (b) As in (a), but for the partial correlation with
the winter TCEP SST index after removing the variability of the
spring NIO SLP index. The positive (negative) values significant at
the 95% confidence level are shaded with yellow (purple).
The above analysis indicates that the CFSv2 reasonably captures the relationships among winter TCEP
SST, spring NIO SLP, and summer APO, and the model
also reproduces the associated physical processes. The
high skill of CFSv2 in predicting summer APO may be
due to the following two reasons: First, the CFSv2 can
Using observations and the CFSv2 retrospective ensemble hindcast dataset, we explore the preceding factors of summertime APO. The spring (April–May) SLP
over the north Indian Ocean (NIO) and the winter
(February–March) and spring SSTs over the tropical
central-eastern Pacific (TCEP) are significantly correlated with summer APO, and they can thus be considered as two predictors for summer APO. However,
these two predictors are not independent from each
other. The preceding TCEP SST may exert a delayed
impact on summer APO via the spring NIO SLP, and
this possible impact becomes weaker when the effect of
NIO SLP is removed.
The processes linking the winter TCEP SST to the subsequent summer APO via NIO SLP can be summarized
in a schematic diagram (Fig. 17). The TCEP SST anomalies maintain from winter to spring and may trigger
spring NIO SLP anomalies. Through a meridional vertical circulation between the NIO and the western Tibetan
Plateau, the spring NIO SLP anomalies cause the simultaneous anomalies of vertical motion and precipitation
FIG. 17. Schematic diagram summarizing the processes linking the preceding SST in the tropical
central-eastern Pacific with summer APO.
1 APRIL 2015
2541
LIU ET AL.
over the western TP. The signal of precipitation anomalies resides in the underlying soil wetness, so it can
maintain to the subsequent summer and lead to anomalies of summer SAT in the western TP. The summer APO
is accordingly modulated through the land–air interaction
processes over the western TP.
The CFSv2 can capture the relationships between
summer APO and the preceding NIO SLP and TCEP
SSTs, demonstrating a high skill in predicting these
anomalous signals. Meanwhile, the CFSv2 successfully
reproduces the major processes linking the preceding
TCEP SST signal to the subsequent summer APO via
spring NIO SLP, which implies that the relationships
among summer APO, NIO SLP, and TCEP SST can be
explained both statistically and dynamically. Indeed, the
CFSv2 is capable of predicting the summertime APO
teleconnection by several months in advance.
The TP is one of the most important heat sources
during boreal summer because of its high topography,
and it affects the monsoon circulation and precipitation
over Asia (Duan and Wu 2005; Wang et al. 2008; Zhao
et al. 2007a) and even the larger-scale atmospheric circulations over the Northern Hemisphere (Ose 1996;
Zhao and Chen 2001; Nan et al. 2009; Zhou et al. 2009).
Several studies based on observations and model sensitivity experiments have demonstrated that TCEP SST
anomalies can modulate the variations of atmospheric
circulation over the TP and its adjacent areas during
winter (Zhao et al. 2009, 2011). Summer Tibetan heating
anomalies stimulate the extratropical large-scale teleconnection (e.g., APO) over the Northern Hemisphere
through adjusting the extratropical zonal circulation
(Zhou et al. 2009). Therefore, it is claimed that the
preceding winter SST anomaly affects the land surface
conditions in the TP and further modulates the variability of summer APO.
This study also suggests the important role of Tibetan
land–air interactions in relaying the effect of TCEP SST
on Asian climate from winter to summer. In addition to
the capacitor effect of SST anomalies in the tropical
Indian Ocean (Xie et al. 2009), an alternative mechanism is provided here for explaining the lingering
ENSO-related climate anomalies after the SST anomalies in the tropical Pacific become weaker. Nevertheless,
more numerical simulations with different ocean and
land forcing conditions are needed to further verify this
mechanism proposed. Because the summer APO is also
related to the previous Atlantic SST (Fig. 7a) and the
Asian climate is related to snow cover and sea ice
(Douville and Royer 1996; Wu and Kirtman 2007; Zhao
et al. 2007a, 2004; Wu et al. 2009), the potential effects of
the Atlantic SST, sea ice, and snow cover on summer
APO should also be addressed in the future.
Acknowledgments. This work was sponsored by the
support of the National Key Research Program of China
(Grant 2014CB953904), the National Science Foundation of China (Grants 41375090 and 41221064), the
special project of China Meteorological Administration
(Grant GYHY201406001), and the Basic Research
Fund of Chinese Academy of Meteorological Sciences
(Grant 2013Z002).
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