AMIP GCM Simulations of Precipitation Variability over the

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: [email protected].

2116 J O U R N A L O F C L I M A T E VOLUME 24

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.



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

15 APRIL 2011 W A N G E T A L . 2119


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.



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.



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.

15 APRIL 2011 W A N G E T A L . 2123


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.



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.

15 APRIL 2011 W A N G E T A L . 2125


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



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.

15 APRIL 2011 W A N G E T A L . 2127


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.



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.

15 APRIL 2011 W A N G E T A L . 2129


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.



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.

15 APRIL 2011 W A N G E T A L . 2131


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