Post on 23-Oct-2021
AMIP GCM Simulations of Precipitation Variability over the Yangtze River Valley
CHENGHAI WANG
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University, Lanzhou, China
XIN-ZHONG LIANG
Department of Atmospheric Sciences, and Illinois State Water Survey, University of Illinois at Urbana—Champaign, Urbana, Illinois
ARTHUR N. SAMEL
Department of Geography, Bowling Green State University, Bowling Green, Ohio
(Manuscript received 22 January 2010, in final form 10 January 2011)
ABSTRACT
Analysis of 26 simulations from 11 general circulation models (GCMs) of the Atmospheric Model In-
tercomparison Project (AMIP) II reveals a basic inability to simultaneously predict the Yangtze River Valley
(YRV) precipitation (PrYRV) annual cycle and summer interannual variability in response to observed global
SST distributions. Only the Community Climate System Model (CCSM) and L’Institut Pierre-Simon Laplace
(IPSL) models reproduce the observed annual cycle, but both fail to capture the interannual variability.
Conversely, only Max Planck Institute (MPI) simulates interannual variability reasonably well, but its annual
cycle leads observations by 2 months.
The interannual variability of PrYRV reveals two distinct signals in observations, which are identified with
opposite subtropical Pacific SST anomalies in the east (SSTe) and west (SSTw). First, negative SSTe anomalies
are associated with equatorward displacement of the upper-level East Asian jet (ULJ) over China. The re-
sulting transverse circulation enhances low-level southerly flow over the South China Sea and south China
while convergent flow and upward motion increase over the YRV. Second, positive SSTw anomalies are
linked with westward movement of the subtropical high over the west-central Pacific. This strengthens the
low-level jet (LLJ) to the south of the YRV. These two signals act together to enhance PrYRV. The AMIP II
suite, however, generally fails to reproduce these features. Only the MPI.3 realization is able to simulate both
signals and, consequently, realistic PrYRV interannual variations. It appears that PrYRV is governed primarily
by coherent ULJ and LLJ variations that act as the atmospheric bridges to remote SSTe and SSTw forcings,
respectively. The PrYRV response to global SST anomalies may then be realistically depicted only when both
bridges are correctly simulated. The above hypothesis does not exclude other signals that may play important
roles linking PrYRV with remote SST forcings through certain atmospheric bridges, which deserve further
investigation.
1. Introduction
Summer rainfall over the Yangtze River Valley (YRV),
as bounded by 258–358N and 1108–1208E, is extremely
important because this region is vital to commerce, agri-
cultural activity, transportation, and energy production in
China. Excessive rainfall during the summer monsoon
causes severe flooding and the dislocation of millions of
people (e.g., 1998 and 2008) while a failure of the mon-
soon can have catastrophic economic consequences (e.g.,
1985 and 2006).
The capability of numerical models to reproduce ob-
served relationships between summer monsoon precipi-
tation over east China (including the YRV) and the
physical mechanisms that explain rainfall variability varies
widely. Kang et al. (2002) found that general circulation
models (GCMs) have difficulty reproducing the observed
location and variations in the rainbands associated with
the East Asian monsoon, where the coarse resolution of
the models was attributed to substantial precipitation
Corresponding author address: Dr. Xin-Zhong Liang, Dept. of
Atmospheric and Oceanic Science and Earth System Science In-
terdisciplinary Center, University of Maryland, College Park, 5825
University Research Court, Suite 4001, College Park, MD 20740-3823.
E-mail: xliang@umd.edu.
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DOI: 10.1175/2011JCLI3631.1
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biases (Yu et al. 2000; Zhou and Li 2002). Although the
occurrence of local precipitation variations over short time
scales may not be resolvable on coarse grids (e.g., Lau and
Ploshay 2009), GCMs are better suited to simulate inter-
annual variability of regional precipitation and its tele-
connections with larger-scale circulations (e.g., Liang et al.
2001, 2002, 2008). These resolvable features will be the
focus of this study.
The identification of realistic model teleconnections be-
tween east China monsoon precipitation and the large-scale
circulation is complicated by the fact that many GCMs have
substantial regional precipitation biases. Liang et al. (2001)
analyzed the East Asian monsoon simulated by the GCMs
of the Atmospheric Model Intercomparison Project I
(AMIP I; Gates et al. 1999). A comparison with obser-
vations led to the identification of model biases in both
regional precipitation and the larger-scale circulation that
are physically linked during the summer months. In par-
ticular, the poleward (equatorward) displacement of the
East Asian jet was associated with negative (positive)
rainfall biases over the YRV. The results indicate that the
dominant atmospheric processes governing YRV rainfall
variations are essentially captured by current GCMs.
The relationship between regional precipitation and the
large-scale circulation is, however, time-scale dependent.
Liang et al. (2002) analyzed 30 AMIP I simulations for the
period 1979–88 and found that no correspondence exists
between model ability to predict the annual precipitation
cycle and the interannual variability of observed summer
rainfall over east China. Thus, the large-scale circulation
mechanisms that explain summer rainfall interannual
variability may differ from those that are linked with the
annual rainfall cycle. We will therefore examine both the
annual cycle and summer interannual variability of YRV
precipitation in this study. Data from 26 AMIP II simu-
lations for the period 1979–2000 will be analyzed. In ad-
dition to a doubling of the integration period length, the
AMIP II contains advanced models with updated physics
representations and resolution refinements. The results
will demonstrate that the new model suite is still unable to
simultaneously simulate both features.
This inability may arise from 1) local effects that de-
termine YRV precipitation and cannot be resolved by the
GCMs and/or 2) model deficiencies in simulating observed
YRV rainfall teleconnections with the large-scale circula-
tion. However, we will identify some AMIP II simulations
that are able to reproduce observed YRV precipitation
teleconnections that are forced by SST anomalies through
the atmospheric bridge. This suggests that the GCMs have
the potential to capture the circulation features that gov-
ern observed YRV precipitation variability. As such, YRV
precipitation may be predicted using indirect measures
such as circulation indices established from observations
(Webster and Yang 1992; Liang and Wang 1998; Wang
et al. 2001; Zhou et al. 2009a).
The goal of this study is to determine the ability of the
AMIP II GCMs to reproduce observed relationships be-
tween YRV rainfall, global SST, and the large-scale circu-
lation. Section 2 describes the observed data and simulated
suite that are used in this study. Section 3 compares the
observed annual precipitation cycle over the YRV with
those generated by the GCMs and illustrates the general
failure of the models. Section 4 discusses the interannual
variability of observed YRV summer rainfall and focuses
on its teleconnections with Eurasian circulation and Pacific
SST anomalies. Section 5 compares observed and AMIP-
simulated YRV summer rainfall interannual variations,
with an emphasis on their teleconnection patterns. Sec-
tion 6 summarizes the results with discussion of the un-
derlying physical mechanisms for YRV summer rainfall
prediction.
2. Model simulations and observations
This study analyzes the climates simulated by 11 sep-
arate GCMs under the AMIP II protocol (Gates et al.
1999). Each model was run for the period 1979–2000,
where identical ‘‘perfect’’ ocean surface conditions, as
specified by the observed global distributions of monthly
mean SST and sea ice variations, were incorporated to drive
the atmosphere. Multiple realizations with the same oce-
anic forcing, but different initial conditions, were available
for five GCMs. This resulted in a total of 26 simulations.
The spread between multiple realizations of a single model
defines the uncertainty due to internal variability in iso-
lating externally forced signals. The basic model infor-
mation can be found online at http://www-pcmdi.llnl.gov/
projects/modeldoc/amip2/.
Observational data for the period 1979–2000 are de-
rived from several sources. Precipitation data over the
YRV are provided by daily rain gauge measurements
from 98 stations in the area of (258–358N, 1108–1208E), in-
cluding all standard monitoring sites from the China Me-
teorological Administration. The precipitation anomaly
time series for each month is constructed by calculating the
area-averaged value for that month and then computing
the difference between the yearly values and the 1979–2000
climatological mean. The YRV summer rainfall anomaly
time series is constructed in a similar manner but repre-
sents the average for the months of June, July, and August
(JJA). Over the global oceans, SST data come from the
18 analysis of Hurrell et al. (2008) for the observational
analysis and from each GCM archive at the corresponding
model resolution for the AMIP suite comparison. This is
to ensure a consistent comparison of the model responses
to the actual SST forcings that were incorporated into
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individual GCMs. For the circulation fields (wind, humid-
ity), we use the 2.58 National Centers for Environmental
Prediction (NCEP)–Department of Energy (DOE) AMIP
II reanalysis (Kanamitsu et al. 2002).
In this study, 1979–2000 is chosen as the reference pe-
riod to construct the annual cycle (i.e., average over the
AMIP II period) and interannual anomalies (i.e., de-
partures from the average). The same period applies for all
comparisons between model simulations and observa-
tions. Time series correlations will be determined through
the use of the Student’s t test with 20 degrees of freedom
(i.e., assuming independence between years), where sig-
nificance at the 0.05 level occurs when coefficient magni-
tude is greater than or equal to 0.42.
3. Annual cycle of YRV precipitation
The annual precipitation cycle over the YRV is domi-
nated by the East Asian monsoon circulation (Ding 1994;
Samel et al. 1999). Amounts during the fall, winter, and
early spring are light (,2 mm day21). Precipitation then
increases rapidly during the late spring and summer as the
primary monsoon rainband impacts the region. Climato-
logically, the monsoon rainband resides over the YRV be-
tween mid-June and mid-July. Rainband movement leads to
a distinct annual precipitation cycle over the region, where
the heaviest rainfall occurs during June (6.9 mm day21) and
approximately half of the mean annual total falls during
summer, which is defined to be the months of JJA. Given
the seasonality of YRV precipitation and the inability of
coarse-domain GCMs to resolve local precipitation var-
iations over small times scales (Yu et al. 2000; Zhou and
Li 2002; Kang et al. 2002; Liang et al. 2001, 2002, 2008),
JJA mean fields will be used to analyze observed and
simulated YRV rainfall teleconnections.
The capability of the AMIP II models to reproduce the
observed annual YRV precipitation cycle is assessed by
analyzing the phase of the simulated annual cycles as well
as the root-mean-square (RMS) errors of their forecasts.
For a given run, the size of the GCM phase shift is de-
termined by the number of months that the model pre-
cipitation climatology must be shifted to produce the
maximum correlation with observations. Figure 1 shows
that only one simulation, the single Community Climate
System Model (CCSM) run, has the same phase as ob-
servations. An analysis of the six L’Institut Pierre-Simon
Laplace (IPSL) realizations reveals that correlations with
the observed annual cycle are large when there is no phase
lag (10.89) as well as when the runs lead observations by
one month (10.92). Because the small correlation dif-
ference makes it difficult to distinguish whether the sim-
ulations lead observations by a month or have no phase
lag, we assign a 20.5 month phase lag for all IPSL runs. In
every other case, model precipitation leads observations
by 1–2 months with a clear correlation peak. In addition to
these negative phase lags, all realizations have substantial
RMS errors, between 3 and 5 mm day21, which are
comparable to the observed annual mean precipitation
rate of 3.38 mm day21.
When multiple realizations of a single GCM (e.g.,
IPSL.1–6) are compared in Fig. 1, we see that the phase
lags are identical, and the RMS errors are similar. This is
true for the Goddard Institute for Space Studies (GISS),
Flexible Global Ocean–Atmosphere–Land System Model
(FGOALS), Model for Interdisciplinary Research on Cli-
mate (MIROC), Max Planck Institute (MPI), and IPSL
FIG. 1. The phase lags (month, hollow bars) and rms errors (mm day21, solid bars) of all
model simulated from observed YRV precipitation annual cycles. Each simulation is labeled as
the modeling institution name, followed by an identification for a specific model (if any), and
then by a dot plus a number for every run. In particular, MIROCh and MIROCm denote the
high- and medium-resolution versions, respectively.
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GCMs. Thus, the YRV precipitation annual cycles pro-
duced by these models do not appear to be sensitive to
initial condition differences.
The AMIP II suite also includes two GCMs that have
high- and low-resolution realizations. Figure 1 shows that
the high-resolution simulation of the MIROC GCM
(MIROCh) produces the same phase lag (21 month) and
a larger RMS error than the three medium-resolution
(MIROCm) realizations. Thus, the increased resolution
does not lead to a more realistic YRV precipitation an-
nual cycle. On the other hand, the CCSM simulation is
a higher-resolution version of the Parallel Climate Model
(PCM) that also incorporates an enhanced physics pack-
age. In this case the CCSM produces no phase lag and has
the smallest RMS error among all AMIP II runs. This is a
substantial improvement over the PCM, which has a
phase lag of 22 months and a much larger RMS error.
As a result, the improved physics in the CCSM leads to a
much more realistic annual precipitation cycle over the
YRV. This result is supported by Chen et al. (2010), who
found that the mean state and seasonal cycle of East
Asian summer monsoon elements simulated by a single
GCM was highly sensitive to modifications in the physics
package.
Figure 2 compares climatological monthly mean pre-
cipitation variations between observations and six distinct
groups of model simulations to highlight the substantial
AMIP II precipitation RMS errors and annual cycle phase
shifts. Clearly, the CCSM produces a more realistic pre-
diction than the PCM (Fig. 2a) as a result of its increased
resolution and improved physics. The annual cycles of
these two runs also show a common bias among the AMIP
II simulations, where amounts tend to be overestimated in
the winter and spring but underestimated during the
summer and fall. The annual cycles of the GISS and MRI
lead observations by two months (Fig. 2b). These runs also
underestimate peak precipitation. The four GISS reali-
zations are very similar and illustrate the small RMS error
differences indicated in Fig. 1. On the other hand, the
GISS annual cycles differ substantially from that of MRI,
especially between July and December. This suggests that
the annual cycles simulated by different GCMs vary more
than those among multiple realizations of the same model.
The annual cycles of FGOALS and Centre National de
Recherches Meteorologiques (CNRM) lead observations
by one month (Fig. 2c). The three FGOALS runs are
virtually identical and very similar to the CNRM. While
the phase shifts are not as large as those shown in Fig. 2b,
the wet season in each of the FGOALS and CNRM sim-
ulations occurs over a longer period, and the rainfall peaks
are smaller than observations. The six IPSL realizations
are very similar and consistently underestimate YRV
summer rainfall (Fig. 2d). The annual cycles of the MIROC
differ little between its single high-resolution and three
medium-resolution realizations (Fig. 2e). The remaining
MPI (three realizations), Institute of Numerical Math-
ematics (INM), and Met Office (UKMO) runs all yield
substantial overprediction between December and May
and underprediction between July and October (Fig. 2f).
This reflects the very large RMS errors shown in Fig. 1.
4. Observed YRV summer precipitationinterannual teleconnections
Figures 1 and 2 clearly illustrate the failure of the AMIP
II suite to simulate the observed annual precipitation cycle
over the YRV. Liang et al. (2002), however, found that no
linkage exists between the ability of a model to simulate
the annual precipitation cycle and its skill in reproducing
the interannual variability of seasonal rainfall. Thus, the
purpose of the following sections will be to determine the
capability of the AMIP II GCMs to simulate observed
YRV summer rainfall (PrYRV) interannual variability
and its linkages with the atmospheric circulation and its
teleconnections. Observed relationships will be described
in this section while the ability of the AMIP II suite
to reproduce these associations will be determined in
section 5.
Observed PrYRV is governed by the movement and in-
tensity of both large- and regional-scale circulation fea-
tures, including the upper-tropospheric East Asian polar
jet over northern Eurasia (Liang and Wang 1998) and the
midtropospheric subtropical high over the west-central
Pacific Ocean (Chang et al. 2000). Relationships between
observed PrYRV and the atmospheric circulation during
JJA are identified in Fig. 3, which shows correlations of
PrYRV with the 200-hPa zonal wind (U200), 500-hPa me-
ridional wind (V500), 850-hPa meridional wind (V850), and
850-hPa relative humidity (RH850). Significant positive
(negative) correlations with U200 along and north of the
YRV (over extreme north China) indicate that increased
PrYRV is accompanied by an equatorward shift in the lo-
cation of the upper-level jet. This result is consistent with
Liang and Wang (1998) and Zhou and Yu (2005), who
found that displacement of the jet to the south of its mean
position during the summer months causes both the as-
cending branch of the jet indirect transverse circulation
and precipitation to intensify along the YRV. In addition,
significant negative correlations over extreme southern
China and adjacent areas of the South China Sea and
Pacific Ocean suggest that enhanced PrYRV occurs in
conjunction with a weakening of the Hadley circulation.
Significant positive correlations of PrYRV with both V500
and V850 (Figs. 3b,c) are located over the South China Sea
and south China. Thus, increased PrYRV is accompanied
by stronger southerly flow to the south of the YRV. Given
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the broad area of negative correlations located over the
west-central Pacific at both levels, this increased southerly
flow as well as enhanced PrYRV are likely explained by a
westward shift in the position of the subtropical high. The
link between PrYRV interannual variability and subtrop-
ical high movement has been established in numerous
studies (e.g., Chang et al. 2000; Lu 2001; Samel and Liang
2003; Huang et al. 2004; Zhou and Yu 2005).
A second area of negative correlations at both 500 and
850 hPa (Figs. 3b,c) is located immediately to the north of
the YRV. Thus, in addition to enhanced southerly flow
south of the YRV, an increase in PrYRV is accompanied by
stronger lower- and middle-tropospheric northerly flow to
the north of the YRV. This suggests that greater PrYRV
coincides with intensified lower- and middle-tropospheric
convergence and vertical ascent along the YRV. A similar
FIG. 2. The YRV precipitation (mm day21) annual cycle observed and modeled by six distinct groups. The legend
lists OBS (thick solid) for observations and model names (thick dashed or thin curves with various patterns). Multiple
realizations, if any, are depicted by a same curve pattern. The UKMO peaks at 8.7 mm day21 in April, not shown to
accommodate clearer contrast for other models.
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result was found by Liang and Wang (1998). The increase
in upward motion along the YRV inferred by Figs. 3b and
3c explains the occurrence of significant positive correla-
tions between PrYRV and local RH850 (Fig. 3d). This occurs
in conjunction with large negative correlations with RH850
over the South China Sea and subtropical west Pacific and
suggests that the Hadley circulation is suppressed. This is
confirmed by the existence of strong positive correlations
(0.45–0.65) of PrYRV with 500-hPa geopotential height (not
shown) over a broad region (108–288N, 908–1508E), in-
dicating persistent local anticyclonic circulation anomalies
due to the westward extension or intensification of the
North Pacific subtropical high. A GCM sensitivity study
by Shen et al. (2001) suggested that the anticyclonic
anomalies in the subtropical western Pacific were re-
sponsible for the 1998 YRV record flood.
Figure 4 shows the observed correlation pattern between
JJA mean SST in the North Pacific and PrYRV. A broad
region of significant positive correlations is located over
the South China Sea and subtropical west Pacific while a
region of significant negative values is found over the east
Pacific. Isolated regions of marginally significant positive
correlations appear in the Bay of Bengal and tropical Indian
Ocean (TIO). Numerous studies have highlighted the
important effects of TIO SST on climate anomalies over
East Asia and the northwest Pacific during the summer
following an El Nino event (Shen et al. 2001; Yang et al.
2007; Chowdary et al. 2009; Xie et al. 2009, 2010). The SST
signals identified with summer PrYRV interannual vari-
ations, however, are much smaller in the TIO than those
in the North Pacific, and hence the latter will be the main
focus in the present study.
To examine the relationship between Pacific SST and
PrYRV more closely, area-averaged JJA SST time series
were constructed for the domains with the largest positive
and negative correlations. The region in the west Pacific
with the largest positive correlations (118–228N, 1128–
1288E) will be referred to as SSTw, and the area in the east
Pacific with the largest negative correlations (258–388N,
1278–1448W) will be called SSTe. The observed PrYRV,
SSTw, and SSTe time series are shown in Fig. 5. The PrYRV
relationships with the SST anomalies are highly significant,
where the correlations are negative for SSTee (20.66) and
positive for SSTw (10.58).
FIG. 3. Geographic distributions of the summer interannual correlations (310) of the YRV precipitation with
pointwise (a) 200-hPa zonal wind, (b) 500- and (c) 850-hPa meridional wind, and (d) 850-hPa relative humidity
observed during 1979–2000. Outlined in each plot is the YRV region that defines the PrYRV index with which the
correlations are calculated. Contour intervals are 2 units. Shading areas denote where correlations are statistically
significant at the 95% confidence level.
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Note that two major YRV floods (1983 and 1998) oc-
curred in the summer following the mature phase of strong
El Nino events (1982/83 and 1997/98). Numerous studies
(e.g., Wu et al. 2003; Wang et al. 2009; Wu et al. 2009a,b)
have found that precipitation anomalies are more pre-
dictable during the summer of a decaying El Nino. This
seems to support the concept of TIO SST warming that
persists through the summer that follows El Nino spring
dissipation and acts as a capacitor that anchors atmo-
spheric anomalies over the Indo–western Pacific Oceans
(Xie et al. 2009; Chowdary et al. 2009). As a result, summer
rainfall decreases over the subtropical Northwest Pacific
but increases over the East Asian monsoon (Mei-yu or
Baiu) region (Wang et al. 2000, 2003; Yang et al. 2007; Xie
et al. 2010; Chowdary et al. 2010). There are, however, a
larger number of cases that lack this correspondence. YRV
rainfall was normal in the summer of 1987, 1992, and 1995,
prior to which occurred modest, strong, and modest El Nino
events (1986/87, 1991/92, and 1994/95), respectively. In ad-
dition, the record flood in 1980 and heavy precipitation
during 1996 were led by El Nino signatures that were quite
weak. Figure 4 clearly shows that YRV summer rainfall
anomalies exhibit a much closer correspondence with
SSTe and SSTw. This does not exclude the TIO forcing
mechanism, which may be linked with the anomalies in
the west Pacific. As demonstrated by Wu et al. (2010),
from June to August, the SST forcing gradually weakens
in the west Pacific but is enhanced in the TIO. This
linkage can be realized, for example, as an integral part of
the uniform tropospheric warming that dominates the
tropics in both observations and GCM simulations in re-
sponse to the El Nino forcing (Liang et al. 1997; Xie et al.
2009).
Figure 6 shows geographic correlation distributions
between the SSTe anomaly time series and Eurasian cir-
culation variables. A broad band of negative correlations
with U200 (Fig. 6a) occurs along an east–west axis that is
positioned just to the north of the YRV. Within this band,
significant values are located over east-central and west-
central China. On the other hand, bands of positive cor-
relations are found both to the north and south, where the
values within the broad southern band are significant. This
pattern reveals that the East Asian jet advances toward
the YRV when SSTe anomalies are negative. Meanwhile,
tropical easterlies migrate toward the equator, the Hadley
circulation weakens and, consequently, convection over
the warm pool is suppressed, and the Walker circulation
diminishes. This causes increased easterly flow over the
Indian and Pacific Oceans, which corresponds to the area
of significant positive U200 correlations.
Correlations with both V500 and V850 (Figs. 6b,c), while
generally small, reveal two distinct atmospheric responses.
First, negative correlations are located to the south of the
YRV, while positive values are found to the north. Al-
though the V850 correlations are larger, the overall pattern
indicates that negative SSTe anomalies occur in conjunction
with increased southerly flow to the south, stronger north-
erly flow to the north, and greater lower-tropospheric con-
vergence and increased relative humidity (Fig. 6d) along
the YRV. These results are consistent with those of Liang
and Wang (1998), who found that the indirect transverse
circulation caused by the equatorward displacement of
the East Asian jet strengthens lower-tropospheric con-
vergence and vertical ascent along the YRV. The second
is located at 500 hPa over the subtropical west Pacific,
where a small region of significant positive correlations is
FIG. 4. Geographic distributions of the summer interannual correlations (310) of the YRV
precipitation with pointwise SST observed during 1979–2000. Outlined are the two key sub-
tropical Pacific centers with high correlations, where the SSTw and SSTe indices are calculated.
Contour intervals are 2 units. Shading areas denote where correlations are statistically signif-
icant at the 95% confidence level.
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centered at (208N, 1358E) while an area of negative values
is centered at (138N, 1278E). A similar response occurs at
850 hPa, although the positive correlation center is not
significant. The overall pattern reveals that, when SSTe is
negative, anticyclonic flow increases over the west Pacific.
The enhanced subtropical high over the west Pacific fa-
vors increased clear conditions and greater solar radia-
tion reaching the surface. This, in turn, warms the local
SST.
Figure 6 indicates that SSTe teleconnects strongly with
the East Asian jet over China. Negative SSTe anomalies are
identified with an equatorward shift of the jet toward the
YRV, where the jet transverse circulation causes southerly
(northerly) winds to strengthen south (north) of the YRV.
This leads to increased lower-tropospheric convergence,
vertical ascent, and relative humidity along the YRV. These
teleconnections between SSTe and the Eurasian circulation
are very similar to the relationships described between
FIG. 5. Interannual variations during 1979–2000 of summer anomalies for the YRV
precipitation (mm day21), and subtropical west and east Pacific SST (8C).
FIG. 6. As in Fig. 3, but correlations are calculated with SST anomalies over the subtropical east Pacific. (a) Outlined
are the two dipole centers with high correlations, where the ULJ index is calculated.
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PrYRV and the Eurasian circulation in Fig. 3, where the
equatorward movement of the East Asian jet is linked
with greater PrYRV.
Spatial correlations between SSTw anomalies and the
Eurasian circulation are shown in Fig. 7. The U200 pattern
(Fig. 7a), while almost opposite in phase with that shown
in Fig. 6a, has significant correlations which are more lo-
calized. This suggests that SSTw anomalies have a smaller
impact on East Asian jet location and intensity than do
SSTe variations. In contrast, a large region of significant
positive correlations with V500 (Fig. 7b) extends from the
South China Sea to south China. The V850 pattern (Fig. 7c)
has a similar phase, but significant correlations are larger
and extend further north, to the YRV. Both the V500 and
V850 patterns include smaller negative correlations over
the west-central Pacific. This dipole structure indicates
that the subtropical high moves toward the west when
SSTw anomalies are positive. This signal corresponds with
the findings of Zhou et al. (2009c). Westward movement
of the subtropical high strengthens the southerly flow in
summer over southeastern China (Lu 2001; Zhao et al.
2007). In spite of the positive relationship with the me-
ridional wind south of the YRV, there exists no significant
correlation with RH850 over this region (Fig. 7d). On the
other hand, an extensive band of significant negative RH850
correlations is located south of 208N. This is associated with
increased descent that results from westward movement of
the subtropical high.
Figure 7 shows that SSTw anomalies teleconnect more
strongly with the regional circulation than they do with
the large-scale East Asian jet. Positive SSTw anomalies
are associated with westward movement of the subtrop-
ical high and intensification of the LLJ to the south of
the YRV. The Eurasian circulation teleconnections with
SSTw are very similar to those with PrYRV identified in
Fig. 3.
The patterns shown in Figs. 3, 6, and 7 reveal that
PrYRV anomalies are most likely to be positive when both
SSTe is negative and SSTw is positive. The resulting tele-
connections with the Eurasian circulation cause the East
Asian jet to shift toward the YRV while the subtropical
high moves to the west. The YRV is located downstream
of the jet core, where the composite analysis shows that
the upper-level jet exit region both intensifies and moves
south toward the YRV during summers with heavy
PrYRV. The resulting indirect transverse circulation causes
southerly flow to increase south of the YRV (Liang and
Wang 1998). The composite analysis also shows that
200-hPa easterlies increase along the south China coast
and adjacent areas of the Pacific Ocean. This occurs in
FIG. 7. As in Fig. 3, but correlations are calculated with SST anomalies over the subtropical west Pacific. (c) Outlined
is the center with high correlations, where the LLJ index is calculated.
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response to the westward movement of the subtropical
high, which further contributes to enhanced southerly
flow. Both changes cause convergence, vertical ascent,
and precipitation increase along the YRV. Previous
studies (Wu et al. 2003; Wu et al. 2009a) have found this
atmospheric response to SST to occur during the summer
that follows the decay of an El Nino event. While this is
true for a majority of El Nino events, there are still many
cases, as discussed earlier (see Fig. 5), where the corre-
spondence was not observed.
5. AMIP II model SST-forced interannualteleconnections
The purpose of this section is to ascertain the ability of
the AMIP II models to reproduce observed teleconnections
between Pacific SST anomalies and the Eurasian circu-
lation features that explain PrYRV interannual variability.
Figure 8 is a bar plot that, for each AMIP II simulation,
shows the correlation between observed and model
PrYRV (black) anomalies. Observed and model correla-
tions of PrYRV with SSTe (hatched) and SSTw (white) are
also shown. Correlations between PrYRV anomaly time
series reveal that only the MPI.3 realization produces a
significant positive relationship (10.44). The correlation
is also large for IPSL.1 (10.36), but not significant. The
remaining models have much smaller values. This indi-
cates that the AMIP II models generally cannot be used
for direct comparisons with observed PrYRV and agrees
with Zhou et al. (2008), who found that AMIP-type models
have very little skill in reproducing observed precipitation
over East Asia, including China. However, the possibility
exists that the GCMs are able to simulate observed tele-
connections between Pacific Ocean SST anomalies and
the Eurasian circulation features that explain PrYRV in-
terannual variability.
To determine this possibility, observed and GCM cor-
relations between PrYRV and Pacific SST anomalies are
compared. Observations show significant relationships
with both SSTe (20.62) and SSTw (10.58). However, few
of the AMIP simulations produce large correlations. Only
the MPI.3 realization generates both significant re-
lationships, negative with SSTe (20.42) and positive with
SSTw (10.67). Although significant positive correlations
with SSTw are also produced in the MRI and UKMO
runs, neither realization simulates observed PrYRV vari-
ations and the relationship with SSTe. On the other hand,
while the IPSL.1 realization has a significant negative
relationship with SSTe (20.61), the correlations with
SSTw (10.36) and observed PrYRV (10.36) are not sig-
nificant. Thus, MPI.3 is the only realization that repro-
duces observed PrYRV interannual variability and is able
to simulate the significant observed correlations with both
SSTe and SSTw. Note that both the CCSM and IPSL,
which are the only two GCMs to forecast the observed
PrYRV annual cycle, are unable to replicate observed
PrYRV interannual variability and the relationships with
both SSTe and SSTw. These results reinforce the finding
of Liang et al. (2002) that no correspondence exists be-
tween model ability to predict the observed annual
precipitation cycle and interannual variability of summer
rainfall over east China.
The general failure of the AMIP II models to simulate
PrYRV interannual variability may result from the inability
of model SST variations to adequately force the atmo-
spheric features that teleconnect with PrYRV. Li et al.
(2010) found that GCMs forced with historical SST fields
are able to simulate observed variations in the East
FIG. 8. Interannual correlations between observed and model PrYRV (black) anomalies, as
well as observed (OBS) and AMIP simulated correlations of PrYRV with SSTe (hatched) and
SSTw (white). The simulation labels follow the convention of Fig. 1.
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Asian summer monsoon circulation but essentially fail
to reproduce smaller-scale precipitation variations over
the monsoon region. This indicates the possibility that the
models are better able to capture relationships between
PrYRV and specific circulation features. Thus, AMIP II
teleconnections between Pacific Ocean SST and PrYRV
will be assessed using a pair of circulation indices that are
established from observations.
The first index is a measure of East Asian westerly jet
intensity and will be called the upper-level jet (ULJ).
Figure 3a clearly shows that PrYRV increases when the
East Asian jet is displaced equatorward toward the YRV.
This jet movement occurs in concert with a weakening of
the Walker circulation, which causes increased upper-level
easterly flow over the South China Sea and subtropical
west Pacific. Figure 6a reveals that the teleconnections
over both regions are forced by negative anomalies in the
SSTe domain. Thus, the ULJ index is constructed such that
positive (negative) values occur in conjunction with posi-
tive (negative) anomalies over the northern region and
negative (positive) anomalies in the southern region. Based
on these criteria, the ULJ is defined to be the time series of
area averaged U200 anomalies in the region bounded by
(328–368N, 1108–1308E) minus those in the region bounded
by (188–228N, 1108–1308E).
The second index is an indicator of lower-tropospheric
southerly flow along and south of the YRV and will be
called the low-level jet (LLJ). Figure 3c shows that PrYRV
increases when the subtropical high moves to the west of
its mean position and causes V850 to strengthen along and
south of the YRV. In addition, Fig. 7c indicates that this
teleconnection is forced by positive anomalies in the
SSTw domain. Thus, the LLJ index is constructed such
that positive (negative) values occur in conjunction with
positive (negative) V850 anomalies along and south of the
YRV. Given this, the LLJ is defined to be the time series
of area averaged V850 anomalies in the region bounded by
(158–258N, 1058–1158E).
Figure 9a is a bar plot that shows observed and model
ULJ correlations with PrYRV (black), SSTe (hatched), and
SSTw (white) anomalies. The observed significant positive
correlation with PrYRV (10.58) indicates that summer
rainfall increases when both the East Asian jet migrates
toward the YRV and the Hadley circulation weakens
(Fig. 3a). The significant negative correlation with SSTe
(20.52) reveals that these circulation features occur in
response to cold SST anomalies over the subtropical east
Pacific (Fig. 6a). The positive correlation between ULJ
and SSTw (10.33), however, is not significant. Thus, SST
anomalies in the subtropical west Pacific have a less
meaningful impact on the East Asian jet.
The model correlations in Fig. 9a show that several of
the simulations are able to capture one or more of the
observed relationships between PrYRV, ULJ, and SSTe.
In particular, the GISS.1, FGOALS.1, FGOALS.2, MPI.2,
and MPI.3 realizations produce significant positive corre-
lations between PrYRV and ULJ. However, among these
runs, only the FGOALS.1 and MPI.3 generate the ob-
served negative correspondence between ULJ and SSTe.
Thus, while several simulations indicate that PrYRV in-
creases when the East Asian jet migrates toward the YRV
and the Hadley circulation weakens, only two realiza-
tions link these circulation changes with negative SSTe
anomalies.
Figure 9b shows observed and model LLJ correlations
with PrYRV, SSTe, and SSTw anomalies. The observed sig-
nificant positive correlation with PrYRV (10.69) indicates
that summer rainfall increases when southerly flow along
and south of the YRV intensifies (Fig. 3c). In addition, the
very large positive correlation with SSTw (10.86) means
that this circulation feature occurs in response to warm
SST anomalies over the subtropical west Pacific (Fig.
7c). On the other hand, the negative correlation between
LLJ and SSTe (20.41) is substantial but not significant.
Thus, SSTe anomalies do not have nearly as important
an impact on low-level jet intensity as SSTw does.
The model correlations shown in Fig. 9b reveal that
several simulations capture one or more of the observed
relationships between LLJ, PrYRV, and SSTw. In particular,
the CNRM, FGOALS.1, IPSL.2, MIROCh, MPI.1, MPI.3,
MRI, and UKMO realizations reproduce the observed
positive relationship between LLJ and PrYRV. Among
these runs, the MIROCh, MPI.1, MPI.3, and UKMO show
the observed positive correspondence between LLJ and
SST. This indicates that the AMIP II models possess some
skill in simulating both the observed relationship between
increased PrYRV and enhanced V850 along and south of the
YRV as well as the SSTw anomalies that force this circu-
lation response.
The above comparisons clearly demonstrate that, among
the 26 AMIP II realizations, only MPI.3 is able to re-
produce observed PrYRV interannual variations that occur
in response to specified global SST forcings. This success
results from the ability of the model to simulate observed
relationships with prominent Eurasian circulation features,
including the ULJ and LLJ, as well as their teleconnections
with Pacific Ocean SST anomalies. It appears that PrYRV is
governed primarily by coherent ULJ and LLJ variations,
which act as the atmospheric bridges to remote SSTe and
SSTw forcings, respectively. The PrYRV response to global
SST anomalies may then be realistically depicted only
when both bridges are correctly simulated. Our finding is
supported by Sampe and Xie (2010), who identified both
the upper westerly jet and the low-level southerlies as the
essential environmental forcing mechanisms for the Mei-
yu-Baiu rainband. This result also concurs with several
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studies (e.g., Wu et al. 2003; Wu et al. 2009a) that docu-
ment the existence of a signal between SST forcing and
PrYRV during the summer that follows the decay of an
El Nino event.
Figure 10 shows that MPI.3 has skill in predicting PrYRV
responses to specified global SST forcings. The PrYRV tele-
connection pattern with SST anomalies (Fig. 10a) closely
resembles observations (Fig. 4), with significant positive
(negative) correlations over broad regions of the subtrop-
ical west (east) Pacific Ocean. Meanwhile, MPI.3 realis-
tically depicts the overall temporal evolution of PrYRV
during 1979–2000 (Fig. 10b). In particular, the model
accurately predicts the major YRV summer floods in 1983
and 1998 as well as the severe drought in 1985. However,
MPI.3 overestimates precipitation in 1993 and reverses the
anomaly signs in 1994 and during the 1980 major flooding
event. We speculate that the MPI.3 failure in 1980 may
result from model initialization errors or the weak SSTw
forcing in observations (Fig. 5).
6. Discussion and conclusions
Our analysis shows that the AMIP II models generally
fail to simultaneously predict PrYRV annual cycle and
summer interannual variability in response to observed
global SST forcings. Only two models (CCSM and IPSL)
reproduce the observed annual cycle, but both are unable
to capture the interannual variability. On the other hand,
among the 26 AMIP II realizations, only MPI.3 correctly
simulates the interannual variability. Yet, its annual cycle
leads observations by 2 months. This result reinforces the
finding of Liang et al. (2002) that no correspondence exists
between model ability to predict the observed annual
precipitation cycle and interannual variability of summer
rainfall over east China.
Results also indicate that AMIP II model spread is
substantial for both the PrYRV annual cycle and in-
terannual variability, while initial condition differences
critically impact only the latter. The sensitivity of simulated
FIG. 9. Observed (OBS) and AMIP simulated interannual correlations of (a) ULJ and (b)
LLJ indices with PrYRV (black), SSTe (hatched), and SSTw (white) anomalies. The simulation
labels follow the convention of Fig. 1.
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PrYRV interannual variations and teleconnection patterns
to initial conditions makes it unlikely that a direct com-
parison of a single-model realization or ensemble mean
with observations can determine GCM predictive skill.
Thus, the subsequent focus is to determine the underlying
physical mechanisms that explain why the MPI.3 is the
only realization to successfully predict PrYRV interannual
variability. This is facilitated by correlation analyses to first
identify observed teleconnection patterns with regional
circulation features and global SST anomalies. Obser-
vations reveal two distinct signals: 1) the exit region of
the ULJ advances toward the YRV and intensifies when
SSTe anomalies are negative, where the associated in-
direct jet transverse circulation causes convergent flow
along the YRV; and 2) the subtropical high moves to-
ward the west when SSTw anomalies are positive, which
leads to LLJ intensification south of the YRV. Therefore,
PrYRV is most likely to increase when subtropical Pacific
SST anomalies are both negative in the east and posi-
tive in the west. The resulting movements of the ULJ
and subtropical high (associated with LLJ intensification)
enhance mass convergence and vertical ascent along
the YRV.
Teleconnections between the AMIP II simulations and
PrYRV are then assessed using the ULJ and LLJ regional
circulation indices that are established from observations.
Many simulations capture one or more of the observed
relationships between PrYRV, ULJ or LLJ, and SSTe or
SSTw. However, only MPI.3 is consistently able to repro-
duce the observed relationships between PrYRV, ULJ and
SSTe, as well as LLJ and SSTw. The MPI.3 realization also
realistically simulates overall PrYRV temporal evolution
during 1979–2000, including the 1983 and 1998 floods and
the 1985 drought. It appears that PrYRV is governed pri-
marily by coherent ULJ and LLJ variations, which act as
atmospheric bridges to remote SSTe and SSTw forcings,
respectively. The PrYRV response to global SST anom-
alies may then be replicated only when both bridges are
correctly simulated, as in the single MPI.3 realization.
The general failure of the remaining AMIP II suite to
simulate PrYRV interannual variability in response to global
SST forcings may result from model inability to adequately
FIG. 10. As in Fig. 4, but for the (a) MPI.3 realization; (b) interannual variations during 1979–
2000 of summer YRV precipitation anomalies (mm day21) as observed (OBS) and simulated
by the MPI.3.
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represent the atmospheric bridges that teleconnect
with PrYRV.
Note that PrYRV is more closely linked with SSTe and
SSTw than with TIO SST anomalies. Although several
studies have suggested possible TIO forcing of climate
anomalies over East Asia and the northwest Pacific during
the summer following an El Nino event (Shen et al. 2001;
Yang et al. 2007; Chowdary et al. 2009; Xie et al. 2009,
2010), we found only small areas of marginally signifi-
cant positive PrYRV correlations with TIO SST variations.
Figure 11 depicts the lagged relationships between PrYRV,
SSTe, SSTw, and the Nino-3.4 index (58N–58S, 1708–
1208W). Clearly, PrYRV does not have a direct link with
Nino-3.4, where all correlation magnitudes are less than
0.25 regardless of the lag period. Thus, PrYRV predict-
ability from Nino-3.4 is low. The correlations with SSTe
and SSTw are also small for the preceding seasons, while
they are significant during subsequent seasons; SSTe
correlations are 20.64 (JAS) and 20.49 (ASO) and SSTw
values are 0.49 (JAS), 0.49 (ASO), and 0.50 (SON). Re-
garding persistence, the lagged signal is stronger for SSTe
than SSTw back to the preceding February–April (FMA)
while the relative strength is reversed in subsequent sea-
sons through September–November (SON). The Nino-3.4
persistence is highly skewed toward the seasons that fol-
low JJA. In addition, SSTw correlations with Nino-3.4 from
the preceding seasons are significant, ranging from 0.58
[November–January (NDJ)] to 0.45 [March–May (MAM)],
FIG. 11. Observed lag correlations of interannual variations during 1979–2000 with 3-month
running means during the seasons preceding and following the summer (JJA) of the central
variable: (a) JJA PrYRV with SSTw, SSTe, and Nino-3.4 at various lags; (b) autocorrelations of
SSTw, SSTe, and Nino-3.4; and (c) JJA SSTw and SSTe with Nino-3.4 at various lags.
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while the SSTe values are small for all lag periods. These
results indicate that both PrYRV and Nino-3.4 lead SSTw.
Liang and Wang (1998) demonstrated that the ULJ
fluctuations governing PrYRV are strongly coupled with
Southern Oscillation variations and that their interactions
tend to precede (follow) El Nino phenomena during
October through May (summer). This teleconnection may
work with the TIO forcing mechanism to bridge the de-
layed influence of the PrYRV anomalies and El Nino on
SSTw. The actual physical links between these compo-
nents (monsoon, SSTe, SSTw, TIO and El Nino) are
complex and deserve further investigation.
The physical mechanisms that explain the occurrence of
the two distinct SST signals identified in the Fig. 4 corre-
lation analysis are difficult to discern without conducting
comprehensive model sensitivity experiments. However,
to derive a plausible interpretation, we performed a com-
posite analysis of 200 and 850 hPa winds and SST during
years when PrYRV anomalies were significantly positive
(negative) and corresponded with positive (negative)
SSTw and negative (positive) SSTe. Based on these criteria
(i.e., Fig. 5), the years used for the positive (negative)
PrYRV composite were 1983, 1993, and 1998 (1985, 1994,
and 1997). Figure 12 illustrates the geographic distribu-
tions of the differences between the positive and negative
composites as observed and simulated by MPI.3. The SST
composite difference pattern closely resembles the corre-
lation map with PrYRV shown in Fig. 4, especially over the
SSTw and SSTe regions. This indicates that the composite
analysis essentially captures the two signals.
As the ULJ shifts toward the YRV, observations reveal
a distinct dipole circulation pattern over an extensive
area of the East Asia-west Pacific sector, with a cyclonic
anomaly centered in Northeast China and an anticyclonic
anomaly center over Southeast China and the subtropical
west Pacific (Figs. 12a,b). The anticyclonic center pro-
duces increased clear conditions and greater incident solar
radiation which, in turn, warms surface waters over the
SSTw region. Meanwhile, as the ULJ migrates toward the
YRV, the observed midlatitude long wave pattern is al-
tered, causing an anomalous anticyclonic (cyclonic) circu-
lation to develop over the northeast Pacific (western North
America). This indicates that the North American upper-
level jet stream shifts toward the west. The enhanced an-
ticyclonic circulation over the northeast Pacific produces
anomalous north to northeast flow along the southeast
FIG. 12. Composite differences in summer wind circulations at (a),(c) 200- and (b),(d) 850-hPa between the PrYRV
positive (1983, 1993, 1998) and negative (1985, 1994, 1997) extremes as (a),(b) observed and (c),(d) simulated by
MPI.3. The wind anomalies are drawn by vectors that are scaled to 4 m s21. Overlaid are the corresponding SST
composite differences, normalized by grid-point standard deviations during 1979–2000, and shaded with the gray-
scale as shown. Small discrepancies between the observed and simulated SST patterns are due to resolution dif-
ferences.
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flank of the circulation, which strengthens the California
Current. As a result, cold water is advected along the south
and southeast flanks of the circulation to produce the ob-
served negative anomaly over the SSTe region.
We speculate that the SSTe signal is an oceanic response
to atmospheric forcing that is bridged by the ULJ, which
explains anomalous PrYRV and accompanies changes in
the midlatitude long wave circulation pattern. This in-
terpretation is consistent with the lagged SSTe correlation
with PrYRV that is shown in Fig. 11a. On the other hand, as
indicated above, the large positive SSTw anomaly appears
to be a local response to clear conditions beneath the in-
tensified subtropical high. Positive SSTw may, in turn,
cause the subtropical high to intensify. Thus, the lagged
correlation between PrYRV and SSTw shown in Fig. 11a is
consistent with Liang and Wang (1998), who demonstrated
that the upper level jet fluctuations governing PrYRV are
strongly coupled with Southern Oscillation variations and
that their interactions tend to precede (follow) El Nino
phenomena during October–May (summer).
There are three additional SST anomaly centers in the
composite difference map that do not appear in the Fig. 4
correlations maps. The first is a negative region located in
the central Pacific along 158N. This center is associated
with 200-hPa southwesterly and 850-hPa northeasterly
anomalies, typical of tropical convection responses to lo-
cal SST forcing. The SST anomaly is clearly shown in the
positive composite while very weak in the negative com-
posite. A second negative SST anomaly center is located
in the vicinity of Korea and Japan. This center occurs only
in the negative composite map. The third SST anomaly is
positive and located in the TIO region, where the aerial
coverage of the anomaly pattern is much smaller in the
negative composite. The lack of opposite SST anomalies
with comparable magnitudes in the positive and negative
composites may explain why the teleconnection with PrYRV
in each of these three regions is absent in the Fig. 4 corre-
lation map.
The MPI.3 composites (Figs. 12c,d) capture the major
circulation anomalies over the East Asia–west Pacific
sector. This includes the cyclonic anomaly in northeast
China and the anticyclonic response in the subtropical west
Pacific. It, however, simulates a cyclonic anomaly, opposite
to observations, over the northeast Pacific, while producing
a realistic response over western North America. For the 6
extreme event years used in the composite analysis, MPI.3
captured PrYRV anomalies during all but 1993 and 1994,
when the model substantially overpredicted precipitation
(see Fig. 10b). These failures may indicate some model
deficiency in capturing air–sea interactions over the mid-
latitude region. As discussed earlier, the SSTe anomaly
may likely be the mixed layer ocean response to the mid-
latitude circulation pattern forcing induced by the ULJ
movement that governs PrYRV variations. The lack of two-
way interaction in the AMIP type experiment (see below)
may explain why the MPI.3 fails to simulate the response
in the North Pacific Ocean. The realistic simulation over
western North America suggests that the interactive land
surface generates a correct response to the atmospheric
forcing.
Our conclusion may be affected by the AMIP experi-
mental design, where observed SST variations are pre-
scribed globally to force the atmospheric responses. This
prescription excludes feedback mechanisms that con-
tribute to SST regional variability (i.e., atmosphere forces
oceans) and, consequently, Asian monsoon evolution
(Meehl and Arblaster 1998; Kitoh and Arakawa 1999;
Zhou et al. 2009b). Lau et al. (1996) showed that most
AMIP I GCMs are able to predict observed tropical
rainfall responses to ENSO-related SST forcing but have
very limited skill in the extratropics. Liang et al. (2001,
2002) found that the prescribed SST field limits model
ability to simulate realistic teleconnections of east China
monsoon precipitation with the large-scale circulation.
Wang et al. (2004) also attributed this prescription to the
common AMIP failure in reproducing the observed in-
verse relationship between summer local rainfall and SST
anomalies over the Philippine Sea, the South China Sea,
and the Bay of Bengal. Fu et al. (2002) and Wu et al.
(2006) demonstrated the need to incorporate air–sea in-
teractions for realistic simulation of summer monsoon
and rainfall variations in tropical Indo–western Pacific
Ocean regions and the midlatitudes. Over these areas,
where the atmospheric effect (primarily from negative
convection–SST feedback) is significant, the AMIP-type
simulations produce excessive SST forcing. Thus, the im-
pact that the prescribed AMIP SST pattern has on the
general circulation plays a major role in determining the
extent to which the models are able to simulate observed
teleconnections with summer PrYRV. We plan to use
available fully coupled atmosphere–ocean GCM simu-
lations, following Liang et al. (2008), to revisit the issue
and focus on how air–sea interactions actually affect our
findings.
Acknowledgments. We thank Jinhong Zhu and Tiejun
Ling for help on data processing. We acknowledge LLNL/
PCMDI and the modeling groups for making available the
AMIP II simulations, and NCSA/UIUC for the comput-
ing support. The research was partially supported by the
National Natural Science foundation of China Award No.
40875050 to Wang and the National Aeronautics and
Space Administration Award NNX08AL94G to Liang.
The views expressed are those of the authors and do not
necessarily reflect those of the sponsoring agencies or the
Illinois State Water Survey.
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