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Global Monsoon, El Niño, and Their Interannual Linkage Simulated by
MIROC5 and the CMIP3 CGCMs
HYUNG-JIN KIM AND KUMIKO TAKATA
Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan

BIN WANGInternational Pacific Research Center, and Department of Meteorology, School of Ocean and Earth Science andTechnology, University of Hawaii at Manoa, Honolulu, Hawaii

MASAHIRO WATANABE AND MASAHIDE KIMOTO
Atmospheric and Ocean Research Institute, University of Tokyo, Chiba, JapanTOKUTA YOKOHATA
Center for Global Environmental Research, National Institute for Environmental Studies, Ibaraki, JapanTETSUZO YASUNARI
Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Kanagawa, andHydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan(Manuscript received 13 October 2010, in final form 23 February 2011)
ABSTRACTThis study evaluates the capability of coupled global climate models (CGCMs) in simulating the prime examples ofthe forced response (global monsoon) and internal feedback process (El Niño). Emphases are also placed on the
fidelity of the year-to-year variability of global monsoon precipitation that is coordinated by the interannual sea
surface temperature (SST) fluctuation over the tropics. The latest version of the Model for Interdisciplinary Research on Climate 5 (MIROC5) with advanced physical schemes is compared with the two previous versions(MIROC3.2, high- and medium-resolution versions) and with the 20 CGCMs participating in the third phase of the
Coupled Model Intercomparison Project (CMIP3). The climatological annual mean and cycles of precipitation and
850-hPa winds, the key components to demarcate the global monsoon domain, are reproduced better in MIROC5
than in MIROC3 versions. As a consequence, the former considerably outperforms the latter and is generally superior to the CMIP3 CGCMs in replicating the intensity and domain of global monsoon precipitation and circulations. These results highlight the importance of the improved physical parameterization in a model. Analyses of themonthly Niño-3 index suggest that the amplitude and periodicity of El Niño are simulated better in MIROC5 than in
the MIROC3 versions. Yet the reality of nonlinear ENSO dynamics measured indirectly by the SST asymmetricity
over the equatorial Pacific is unsatisfactory in the MIROC family as well as in the majority of the CMIP3 models. The
maximum covariance analysis shows that a significant fraction of the interannual global monsoon rainfall variability is in concert with El Niño. The multimodel results reveal that such coupling is robust across the current
CGCMs. More importantly, the fidelity of the global monsoon precipitation significantly relies on the realism of
tropical SST. Comparison among the MIROC models suggests that improved El Niño is likely attributable to themore realistic Bjerknes feedback loop, which results from the intensified convective activity over the equatorial
central Pacific Ocean.

1. Introduction
Corresponding author address: Hyung-Jin Kim, Research Institute for Global Change, JAMSTEC, 3173-25 Showa-machi,
Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan.
E-mail: hyungjin@jamstec.go.jp
DOI: 10.1175/2011JCLI4132.1
Ó 2011 American Meteorological Society

Verifying and tracing the performance of a coupled
global climate model (CGCM) is indispensable for its
continuous improvement. These efforts can also provide

1 NOVEMBER 2011

KIM ET AL.

valuable information from which the scientific community gains confidence in utilizing a CGCM for various
purposes. The evaluation more often focuses on thefundamental processes of the climate system. The processes involved in the evaluation may be reasonably
categorized into two groups, depending upon the type of
impetus: forced responses to external forcings and selfrecurrent phenomena due to internal feedbacks.
Solar radiation change on daily-to-orbital time scales
is a typical example of the external forcings. It induces
distinctive meteorological phenomena that are in phasewith the temporal variation of the solar forcing. Amongthe diverse periodicities of the forced responses, however,emphasis could be placed on the annual cycle of precipitation and circulations since it manifests the globalscale monsoon, one of the profound meteorologicalevents on the planet (Trenberth et al. 2000; Wang andDing 2008). Internal feedback processes are complementary to the forced responses as they represent thenatural fluctuations within the atmosphere or the coupled climate system. The El Niño–Southern Oscillation(ENSO) is often described as the most salient illustrationof the internally organized modes of variability. Therefore, the fidelity of global monsoon and ENSO reproduced in a CGCM primarily reflects its skill in replicating
forced responses and internal feedback processes.
El Niño is also known to exert enormous impacts onregional monsoon systems by modulating the onset, intensity, and retreat of monsoon precipitation (Tanaka
1997; Wu and Wang 2000; Annamalai et al. 2007; Vecchi
and Wittenberg 2010). Traditionally, the effect of ENSO
on interannual monsoon variability has been studied
from a regional point of view. However, a concept ofglobal monsoon has been recently proposed, by which theregional monsoons around the globe can be viewed as anintegrated system (Wang and Ding 2006, 2008). Wanget al. (2011b, manuscript submitted to Nat. Geosci.) further demonstrated that the global monsoon precipitation,
the rainfall amount falling into the global monsoon domain, displays a coherent year-to-year variability that iscoordinated by ENSO. Though the internationally organized CGCM experiments and the accessible archiveof the model outcomes have led to numerous researchstudies to assess the characteristics, physical processes,
and feedbacks of ENSO in CGCMs (Neelin et al. 1992;
Mechoso et al. 1995; Latif et al. 2001; AchutaRao andSperber 2006; Joseph and Nigam 2006; Jin et al. 2008;
Guilyardi et al. 2009), the extent to which a CGCM simulates the interannual relationship between global monsoon precipitation and ENSO is not investigated yet.
The present study aims to address the overall aspectsof global monsoon climatology, El Niño properties and,in particular, year-to-year El Niño–global monsoon

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fluctuation simulated by the latest version of the Modelfor Interdisciplinary Research on Climate, MIROC5,
in comparison with its predecessors, MIROC3(hires) and
MIROC3(medres), and the Coupled Model Intercomparison Project phase 3 (CMIP3) CGCMs. MIROC5 wasdeveloped jointly at the Center for Climate System Research at the University of Tokyo, National Institute forEnvironmental Studies, and the Japan Agency for MarineEarth Science and Technology. To date, one realization
for the Twentieth-Century Climate in Coupled Model(20C3M) simulations has been conducted by using T85
atmospheric and approximately 18 ocean models. The
atmospheric resolution of MIROC5 is between the two
versions of MIROC3 [MIROC3(medres) and MIROC3(hires) with T42 and T106 atmospheric components,
respectively]. But its oceanic component has the same resolution with MIROC3(medres). In general, the MIROC5
grid system is close to that of MIROC3(medres). Most of
the physical parameterizations in MIROC5 are upgraded or
replaced with new schemes, whereas the atmospheric dynamical core remains nearly intact. Readers may refer to
Watanabe et al. (2010) for a comprehensive description of
MIROC5.
The MIROC versions have been widely used to
study decadal-to-centennial changes in the coupledatmosphere–ocean system (van Oldenborgh et al. 2005;
Lin 2007; Wang et al. 2009) and over the ocean surface(Polito et al. 2008), extreme climates (Shiogama et al.
2008; Sugiyama et al. 2010), and climate change andcarbon cycle (Yokohata et al. 2007; Yoshikawa et al.
2008). Although various atmospheric and oceanic aspects of mean states, time-dependent variability, andclimate sensitivity reproduced by the MIROCs havebeen previously explored, the realism in simulating global
monsoon and its linkage with El Niño has not been addressed yet. In particular, a brief analysis on El Niñoproperty carried out by Watanabe et al. (2010) showedmuch more realistic El Niño amplitude in MIROC5 thanin MIROC3. Considering the instrumental role of El Niñoon regional monsoons as stated above, this suggests thatthe impacts of El Niño on global monsoon precipitation
which is, in essence, the integral of all of the subregionaland regional monsoons, may differ between the old andnew models and thus deserve further investigation.
We first describe the data and models used in thisstudy in section 2. Section 3 validates the fidelity of thesimulated global monsoon with the aid of the diagnostic
metrics designed and proposed by Wang et al. (2011a).
Section 4 examines model ability in representing theessential characteristics of El Niño, such as amplitude,
asymmetricity, and periodicity. In section 5, the year-toyear coupling between global monsoon precipitationand El Niño is extracted from the model outputs by using

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TABLE 1. A list of the 23 CMIP3 CGCMs used in this study.
Resolution
a

Model

Atmosphere (8)Oceanb

1. CGCM3.1-T47 (Canada)
2. CGCM3.1-T63 (Canada)
3. CNRM-CM3 (France)
4. CSIRO Mk3.0 (Australia)
5. CSIRO Mk3.5 (Australia)
6. GFDL CM2.0 (United States)
7. GFDL CM2.1 (United States)
8. GISS-AOM (United States)
9. GISS-EH (United States)
10. GISS-ER (United States)
11. IAP FGOALS-g1.0 (China)
12. INM-CM3.0 (Russia)
13. IPSL CM4 (France)
14. MIUBECHOG (Germany/Korea)
15. MPI ECHAM5 (Germany)
16. MRI CGCM2.3.2a (Japan)
17. NCAR CCSM3 (United States)
18. NCAR PCM (United States)
19. UKMO HadCM3 (United Kingdom)
20. UKMO HadGEM1 (United Kingdom)
21. MIROC3.2(hires)c (Japan)
22. MIROC3.2(medres)d (Japan)
23. MIROC5 (Japan)

3.75 3 ;3.75
2.8125 3 ;2.8125
2.8125 3 ;2.8125
1.875 3 ;1.875
1.875 3 ;1.875
2.5 3 2.0
2.5 3 ;2
433
534
534
2.8125 3 ;3534
3.75 3 ;2.5
3.75 3 ;3.75
1.875 3 ;1.875
2.8125 3 2.8125
1.40625 3 ;1.40625
2.8125 3 ;2.8125
3.75 3 2.5
1.875 3 ;1.25
1.125 3 ;1.125
2.8125 3 ;2.8125
1.40625 3 ;1.40625

192 3 96256 3 192
180 3 170
192 3 189
192 3 189
360 3 200360 3 200
90 3 60
72 3 46
72 3 46360 3 170
144 3 84
180 3 170
128 3 117360 3 180
144 3 111
320 3 395
360 3 180
288 3 144360 3 216
320 3 320
256 3 192
360 3 180aLongitude 3 latitude.
Number of grids in longitude and latitude.
c
MIROC3(hires) in this study.
d
MIROC3(medres) in this study.
b

the maximum covariance analysis (MCA) (Bretherton
et al. 1992) and compared with the observed—with particular emphasis on the MIROC5 integration. In addition, the mechanism that is possibly responsible for thediffering El Niño simulations among the MIROC versions is discussed. Concluding remarks are presented in
the last section.

Model Intercomparison Project phase 3 (CMIP3). Table 1
summarizes the models. Note that only the first realizationof each model was analyzed to preserve the interannual
signal. The model outputs were, then, remapped onto acommon grid system of 2.58 3 2.58 for fair intercomparisonby conducting bilinear interpolation.

3. Global monsoon climatology
2. Data and models

a. Annual mean and annual cycles

A 30-yr climatology from the Global Precipitation
Climatology Project (GPCP) (Adler et al. 2003) over
1979–2008 and National Centers for Environmental
Prediction–Department of Energy Reanalysis 2 (R2)
(Kanamitsu et al. 2002) over 1980–2009 was constructed
to verify climatological monsoon precipitation and circulations. For the validation of El Niño and its interannual
linkage with global monsoon precipitation, the GPCP and
the NOAA Extended Reconstruction Sea Surface Temperature (ERSST), version 3, (Smith et al. 2008) datasets
for the period of 1979–2008 were utilized.
To facilitate model-to-model comparison, the 20C3M
monthly rainfall, winds, and SST datasets for the period
from 1970 to 1999 were collected from the Coupled

Wang and Ding (2008) extracted the major modes ofseasonal variability in global precipitation and circulations through the application of a multivariate empiricalorthogonal function (MVEOF) to the climatological annual cycle of precipitation and 850-hPa winds. The results
showed that the first two leading modes together accountfor ;84% of the total annual variance of the climatologicalannual cycle of global precipitation and low-level winds
with the maximum and minimum of the first (second)mode occurring around local summer (spring) and winter(fall). They further showed that the spatial patterns of thefirst and second modes can be faithfully represented byJune–September minus December–March mean (AC1)and April–May minus October–November mean (AC2).

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FIG. 1. Evaluation of the CGCM performances on the annual climatology of (left) precipitation and (middle) zonal
and (right) meridional 850 hPa wind. The abscissa (ordinate) is the pattern correlation coefficient (PCC) squared(domain-averaged RMSE normalized by the observed spatial standard deviation); numbers in parenthesis indicate
linear regression coefficients. Domain used is 408S–458N, 08–3608E.

To assess the performance of MIROC5 in modeling climatological annual variation, we first examine the annual
mean (AM), AC1, and AC2 in comparison with the two
versions of MIROC3, the CMIP3 CGCMs, and the multimodel ensemble (MME) mean, derived from the simple
arithmetic average among the CMIP3 models.
Figure 1 compares multimodel performances on theAM, AC1, and AC2 of precipitation and 850-hPa zonal
and meridional winds in terms of the coefficient of determination (in other words, square of the Pearson pattern correlation coefficient g2 ) and domain-averaged rms
error (RMSE) normalized by the observed spatial standard deviation over the global tropics and subtropics(408S–458N, 08–3608E). First, it is noted that the g2 and
RMSE for the CMIP3 model outputs have a statisticallysignificant linear relationship with confidence level higher

than 95% (regression coefficient is given in parenthesis).
The PR AC2 (Fig. 1g) is only one outlier of such linearity.
Thus, a model with a higher g2 tends to have a smaller
RMSE. Second, the g2 (RMSE) is lowest (largest) in theAC2, suggesting that the current CGCMs have difficulty
in representing premonsoon and/or postmonsoon conditions. Third, for the square of the pattern correlation
coefficients (PCCs) of all annual components, MIROC5
unexceptionally outperforms MIROC3 and is generally
superior to the CMIP3 models. Besides, its biases are,when compared with MIROC3, either reduced in windfields or, at least, not degraded in precipitation.

b. Global monsoon precipitation and circulations
The annual evolution of the first two leading MVEOF
modes reflects the seasonal contrast between rainy summer

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FIG. 2. Global monsoon precipitation domain (solid contours) and MPI (color shading) defined using (a) the GPCP
data (1979–2008) and the model counterparts (1970–99) derived
from (b) 20C3M MME, (c) MIROC5, (d) MIROC3(hires),and (e) MIROC3(medres).

and dry winter as well as the seasonal reversal of low-levelwinds. Because the monsoon climate features these characteristics, Wang et al. (2011a) delineated the global monsoon precipitation domain as the annual range (AR) of
precipitation rate exceeding a threshold of 2.5 mm day21,
where
AR 5 MJJAS (NDJFM) minus NDJFM (MJJAS)
in NH (SH),where MJJAS (NDJFM) indicates the May–September(November–March) mean precipitation rate in the Northern (NH) and Southern (SH) Hemisphere. Note that the
AR is defined by the combination of the first two leading
MVEOF modes. The AR could be used to measure themonsoon precipitation intensity. However, because of its
latitudinal dependency, an alternative measure, monsoon
preicipitation index (MPI), is defined as
MPI [ AR/AM.
Similarly, the monsoon circulation domain can be demarcated as the AR of the westerly winds or poleward
winds at 850 hPa exceeds 2.5 m s21. This thresholdvalue is also chosen carefully to be consistent with thecriteria used in Wang and Ding (2008). The monsoon

circulation index (MCI), a measure for monsoon circulation intensity, is defined in the same way as for MPI
except using wind speed to utilize both zonal and meridional winds in an integrated manner. The use of windspeed, however, makes it difficult to distinguish thetropical MCI with positive zonal wind AR from theextratropical MCI with negative zonal wind AR. Thus,
the final MCI is derived by multiplying the sign of zonal
wind AR to the AR/AM ratio of wind speed.
Figure 2 shows the observed and simulated monsoon
precipitation domain (solid curves) and index (shadings). It is apparent from the observation that the use of
the simple criterion separates the monsoon precipitation
regime quite well from the dry, Mediterranean, and equatorial perennial rainfall regimes. The global monsoon precipitation domain consists of six major monsoon regions:Southeast Asia, Indonesia–Australia, North and South
Africa, and North and South America. All of these major
monsoon regions encompass adjacent marginal seas andoceanic regions, which signifies a land–sea thermal contrast.
Thus, the oceanic monsoon is an essential component ofa regional monsoon. In the subtropical mid South Pacific,
on the other hand, there is a pure oceanic region that does
not involve land–sea thermal contrast but follows the typicalseasonal distribution of monsoon precipitation. Therefore,
this region is ruled out from the monsoon precipitation

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FIG. 3. As in Fig. 2 but for the global monsoon circulation
domain (solid contours) and MCI (color shading) definedusing the R2 data (1980–2009) in (a).

domain. It should be also mentioned that there are someregions, for instance, tropical Southeast Asia, where both
local summer and winter monsoons appear to be equallyimportant (Chang et al. 2005). However, the suggested regions are narrowly confined to the north of the equator.
More notably, the seasonal rainfall contrast of the local
winter monsoon is far weaker than that of the local summer
monsoon: none of the local winter monsoon regions suggested in Chang et al. (2005) passes the threshold seasonality of 2.5 mm day21 (figure not shown). This indicates
that, even though the local summer and winter monsoons
coexist in the same hemisphere, the local summer monsoon
is the typical event after all.
Globally, the monsoon precipitation domains modeledby the MIROC versions are in general agreement with theobserved (Figs. 2c–e). Regional details, however, differ remarkably between MIROC5 and MIROC3. For the South
Asian monsoon, for example, MIROC5 compares quitefavorably with observations on the southeastern coast of
the Indian subcontinent. The North American monsoon
domain is also reproduced better in MIROC5 than in
MIROC3. Note that not only MIROC3(medres) but also
MIROC3(hires), which has the finest spatial resolution
among the MIROCs, failed to reproduce such regional
details in the aforementioned domains. Common errors
also exist among the MIROC versions as revealed by theabsence of monsoon domains over the western North
Pacific. Further investigation shows that the source oferrors is attributable to the underestimation of MJJAS
precipitation and overestimation of NDJFM precipitation. Some of the CMIP3 models seem to be able toreplicate the western North Pacific monsoon domains,
as inferred from Fig. 2b.
Figure 3 presents the global monsoon circulation domains and index in the same format with Fig. 2. The observed Eastern Hemispheric tropical monsoon circulation
domains that are symmetric and straddling the equator
are depicted by the seasonal reversal of zonal winds.
Meanwhile, the criterion of poleward winds allows us topick up the East Asian monsoon regions. The North
American monsoons are demarcated by zonal winds withthe threshold being relaxed to 1.5 m s21 due to the weak
amplitude of wind reversal (dashed curves). The SouthAmerican monsoon circulation domains are portrayed byboth zonal and poleward wind criteria. These monsoon
circulation domains are physically consistent with themonsoon precipitation domains as discussed in Wang
and Ding (2008). All of the MIROC simulations are able
to reproduce the monsoon circulation domains throughthe corresponding wind criteria. Yet differences are seen
over the tropical eastern Pacific. While MIROC5 canreproduce the monsoon domains north of the equator,

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those in MIROC3 seem to be shifted far southward andeventually merged with the South American branch.
Again, these errors appear to be model-dependent since
the CMIP3 multimodle ensemble bears close resemblance
to the observation over the tropical eastern Pacific.
The panels in Fig. 4 summarize the multimodel performance in simulating global monsoon intensity (Fig. 4a)and domain (Fig. 4b). Here, we take advantage of thethreat score (TS) (Wilks 1995) to assess model reproducibility on global monsoon domains. The TS is definedas the ratio of ‘‘hit’’ grids to the sum of hit, missed, andfalse-alarm grids. The hit grid indicates the grid at whichmodel and observation match each other, and the missed
grid means an observed grid being missed in the simulation, and the false-alarm grid is a grid that is recognizedsolely by the model. The TS ranges from 0 to 1 with
0 being the worst agreement between the observation andsimulation. The performance of the CMIP3 models showsa linear relationship between monsoon precipitation andcirculation with a regression coefficient of 0.53 and 0.80
for intensity and domain, respectively. The linearity of
global monsoon intensity becomes stronger (0.71) if thetwo exceptions located at middle left were excluded. Allof these relations are significant at a confidence level
higher than 99%. A comparison among MIROC simulations demonstrates the importance of the physical parameterization. Though the intensity and domain of
global monsoon precipitation are improved for a higher
resolution, those of global monsoon circulation are ameliorated little [MIROC3(medres) vs MIROC3(hires)]. In
contrast, the improved physical schemes lead to better
representation not only for the monsoon precipitation
but also for the monsoon circulation (two MIROC3s vs
MIROC5). It is recognized further that the low-levelwinds are reproduced better than precipitation both interms of intensity and domain, which is also generally
valid among the CMIP3 models. Such superiority of thelarge-scale dynamical fluctuation to the rainfall variation
has been reported on the interannual time scale as well
(Sperber and Palmer 1996; Sperber et al. 1999). One
possible explanation is that even though the modeled
precipitation occurs at slightly different places from theobserved, the response of the geostrophic winds would
not deviate substantially from the observations as longas the precipitation events lie in common within the
Rossby radius of deformation.

4. El Niño properties: Amplitude, asymmetricity,and periodicity
As mentioned earlier, MIROC3 is unable to reproduce
sufficiently the amplitude of El Niño although the zonal
gradient of the mean thermocline is realistic (Guilyardi

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FIG. 4. Evaluation of the CGCM performance on (a) the climatological global monsoon index and (b) domain. In (a) the abscissa(ordinate) is the PCC squared of the global MPI (global MCI),
and in (b) the threat score is plotted for the global monsoon
precipitation (GMP) domain (abscissa) vs global monsoon circulation (GMC) domain (ordinate). The regression coefficient is shownin lower-right corner of each panel. Domain used is 408S–458N,
08–3608E.

et al. 2009). This is evident in Fig. 5a, which compares
the observed and modeled El Niño amplitude measured
by the standard deviation (SD) of the monthly Niño-3index, that is, area-averaged (58S–58N, 908–1508W)
monthly SST anomaly calculated by removing the climatological monthly mean. The El Niño amplitude in1 NOVEMBER 2011

KIM ET AL.

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FIG. 5. (a) Standard deviation and (b) asymmetricity of the monthly Niño-3 index: the dashedline denotes the 620% limits of the observed value.

MIROC3 is underestimated by about 35%. In contrast,
the SD of MIROC5 is nearly the same as the observed.
The amplitude of CMIP3 CGCMs varies widely fromone model to another. Nevertheless, about one-third of
the models are able to replicate a reasonably realistic
El Niño amplitude.
Another useful measure for El Niño reproducibility isskewness. Several previous studies (Jin et al. 2003; An
and Jin 2004; An et al. 2005) have measured the observed El Niño nonlinearity over the equatorial Pacificthrough a statistical method (skweness). They pointed
out that El Niño has strong nonlinearity that causes theasymmetry of El Niño, with El Niño being stronger than
La Niña. The skewness by definition is a measure of theasymmetry of a probability distribution function and iszero for a normal distribution (White 1980). It is definedas the normalized third statistical moment [m3/(m2)3/2],
where mk is the kth moment,N

mk 5

å (xi 2 X)k /N,

i51

in which xi is the ith observation, X is the mean, and N
is the number of observations. The original definition,
however, can cause a large skewness when the standard
deviation is less than a unit. Thus, to avoid such difficulty, the asymmetricity, defined as m3/(m2)1/2 (An et al.
2005), is used in the present study. The statistical significance of the asymmetricity can be estimated from thestandard error of asymmetricity (White 1980). In nature,
the asymmetricity is significantly positive near the east
coast of the South American continent and tends todecrease to the west (e.g., An et al. 2005). As a result, theasymmetricity of the monthly Niño-3 index that is anarea average over the equatorial eastern Pacific seldom
passes the significant test. Thus, asymmetricity greaterthan observed will be subjectively considered significant. The asymmetricity of the monthly Niño-3 index
obtained from the observation and model simulations
is presented in Fig. 5b. The majority of the CMIP3
CGCMs, including the two versions of MIROC3, havea very small asymmetricity, hence similar to the normal
distribution. Several models are negatively skewed, inferring that cold events are unrealistically stronger thanwarm events. Only three models have a positive asymmetricity as large as that observed. For the MIROC5
simulation, the asymmetricity is positive but the amplitude is about half of the observed. Interestingly, themodels with a large positive skewness have intensified
amplitude without exception (Figs. 5a and 5b). Therefore, it is argued that nonlinearity likely necessitates
sufficient amplitude.
The dominant periods of the monthly Niño-3 index
that pass the red noise test with 95% confidence level areplotted in Fig. 6. The observation exhibits a clear separation between high and low frequency variability. They
represent the annual-to-quasi-biennial mode (1 # period , 2.5 yr) and the El Niño mode (3 # period , 5 yr).
Only one CMIP3 CGCM can capture such separation of

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FIG. 6. Dominant periods of the monthly Niño-3 index: periods thatpass the red noise test at the 95% confidence level.

periodicity. Several models, including MIROC3(hires)are not able to reproduce both modes. The rest of themodels, including MIROC3(medres), are only partiallysuccessful at capturing the observed variability. Recently, Lin (2007) examined the interdecadal variability of ENSO in the CMIP3 CGCMs and argued that
MIROC3(medres) has substantial variance distributed
at a period longer than 6 yr without conducting a significant test. Our analysis also shows a similar periodicity in the MIROC3(medres) outputs. However, thepower spectrum for the low frequency variability does
not pass the 95% confidence level (figure not shown).
This low frequency variability becomes significant in
MIROC5 in which the high frequency variability is also
reproduced to some degree.

5. Interannual variability of ENSO–global
monsoon precipitationa. Observation and multimodel simulations
Conventional efforts devoted to the study of interannual monsoon variability have usually shed light on
the regional aspects of each monsoon system due to its
indigenous characteristics. Wang et al. (2011b, manuscript submitted to Nat. Geosci.), however, recently
demonstrated that, by means of the global monsoon
concept used in the present study, all regional monsoons
can be viewed as an integrated system. In this section, we
adopt their methodology to evaluate the realism of thesimulated interannual global monsoon variability with

FIG. 7. (a) Climatological annual cycle of precipitation averaged
over the observed global monsoon domain. Scatter diagrams forthe month of the (b) minimum and (c) maximum precipitation. The
domain used is 408S–458N, 08–3608E.

particular focus on the coherent patterns with low- andmidlatitude SST.
Investigation of year-to-year variability is usually basedon the calendar-year average. This approach is, however,
inappropriate at least in terms of global monsoon precipitation (Wang et al. 2011b, manuscript submitted to Nat.
Geosci.). Figure 7a illustrates the climatological annual
cycle of precipitation over the global monsoon domain.
The observation has a primary peak in July–August anda secondary peak in January–February owing to the NH
and SH monsoons. Also a prominent minimum takes
place in April. These seasonal variations are robust inother observation datasets too [e.g. the Climate Prediction
Center (CPC) Merged Analysis of Precipitation (CMAP)(Xie and Arkin 1997) and Precipitation Reconstruction
over Land (PREC/L) (Chen et al. 2002)]. As such, the‘‘monsoon year,’’ spanning from May to the subsequent
April, is used in the present study to investigate the interannual variability of global monsoon precipitation.
Figures 7b and 7c summarize the month of primary valley
and peak obtained from the model simulations. Here, the
GPCP global monsoon domain is used in common for allmodels. Most of the CMIP3 models are able to pick up the1 NOVEMBER 2011

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FIG. 8. The leading mode of monsoon precipitation and SST obtained from the MCA and EOF analyses: the spatial patterns of(a) global monsoon precipitation and (b) SST obtained from the MCA, (c) global monsoon precipitation obtained from the EOF, and(d) corresponding time expansion coefficients. For comparison, the Niño-3 SST anomaly averaged over the monsoon year is shown in (d).
Data period is 1979–2008.

primary peak in July–August. Meanwhile, the April minimum is missed in half of the CGCMs. The two versionsof MIROC3 reproduce the April valley with a realistic
magnitude. But MIROC3(hires) tends to overpredict
monsoon precipitation from September to December(also see Fig. 7a) and, as a result, the major peak occurs
in October. In MIROC5, this caveat is remedied. In addition, the seasonal evolution of the annual cycle is akinto the observed. However, the magnitude of the annual
cycle is systematically overestimated by about 25% yearround. The cumulus parameterization in MIROC5 was
newly introduced by Chikira and Sugiyama (2010). The
replacement led to a very realistic representation of
precipitation patterns over the tropics and subtropics(Chikira 2010). Nonetheless, shortcomings appeared tobe heavy rainfall over South Africa, the Indian subcontinent, southern China, the Maritime Continent, andBrazil (Watanabe et al. 2010), all of which fall into theglobal monsoon precipitation domains, thereby contributing to overprediction.
Figure 8 shows the first leading MCA mode between
rainfall within the global monsoon precipitation domain[precipitation rate (PR) MCA1 (Fig. 8a)] and SST overthe global tropics and extratropics (SST MCA1; Fig. 8b)
and corresponding time expansion coefficients (Fig. 8d)

obtained from the observations. The MCA1 accounts
for about two-thirds of the total covariance, and thetemporal correlation between the PR MCA1 and SST
MCA1 is 0.94. Of note is that the spatial patterns of the
SST MCA1 exhibit a warm phase of ENSO over thetropical Pacific and its time coefficient is nearly identicalto the time series of the monsoon-year mean Niño-3 SST
anomaly. The MCA1, therefore, illustrates the interannual rainfall variability over the global monsoon domain that is coupled with El Niño variability. The
footprints of El Niño in rainfall distribution appeared as
the negative anomalies over the majority of the monsoon domain and the scattered positive anomalies in theSouthern Hemisphere monsoon domain. The increased(decreased) rainfall result from El Niño (La Niña). It has
been known that during the development (boreal summer) and mature phases (austral summer) of El Niñoevents, the warmer SST in the eastern central Pacificshifts the equatorial Pacific rainbands (i.e., the intertropical convergence zone and the South Pacific convergence zone) equatorward, thereby directly suppressing
Asian–Australian and North American monsoon rainfall.
The mature phase of El Niño also diminishes rainfall overcontinental South Africa and northern South America
through atmospheric teleconnections.

5614

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FIG. 9. As in Fig. 8 but for the MIROC5 simulations over 1970–98.

Now a question arises as to whether these precipitation patterns that are concatenated with El Niño arealso visible in the year-to-year global monsoon precipitation variability. To answer this question, we applied
the EOF analysis to the global monsoon precipitation
itself: the first leading EOF mode (PR EOF1) is presented in Figs. 8c and 8d. Interestingly, the spatiotemporal patterns of the PR EOF1, which explain about
24% of the total variance, agree extremely well with the
PR MCA1 with pattern and temporal correlation coefficients of 0.96 and 0.99, respectively. Thus, it can besuggested that El Niño plays a pivotal role in determining
the interannual variability of the global monsoon precipitation.
How well does the year-to-year coupling work in the
CGCMs? It is found in the MIROC5 simulation that
El Niño also greatly affects global monsoon precipitation (Fig. 9). The MCA1 explains about 81% of thetotal covariance, and the temporal correlation between
rainfall and SST is 0.90. The SST MCA1 exhibits the
El Niño patterns seen in the observation, and the timecoefficients are in tandem with the time series of theNiño-3 index. The PR EOF1 accounts for about 21% of
the total variance, and its spatiotemporal patterns are
virtually identical to the PR MCA1 with correlation
coefficients greater than 0.98 for both time and space.
The impacts of El Niño are also of vital importanceacross the CMIP3 models: 19 (12) out of the 22 models
that have El Niño amplitude greater than 50% (80%) of
the observed show a PCC between PR EOF1 and PR
MCA1 higher than 0.66 (0.80) (figure not shown).
The aforementioned results suggest a clear link between SST anomalies in the equatorial Pacific and rainfall
anomalies over the global monsoon domain. Thus, one
may plausibly assume that improved El Niño simulationentails more realistic monsoon precipitation in a model.
To test this hypothesis, a scatter diagram of spatial similarity between the observation and model simulations
is presented (Fig. 10). The skill of global monsoon variability (PCC for PR EOF1, ordinate) is linearly related
to that of tropical SST variability (PCC for SST MCA1,
abscissa) with a regression coefficient of 0.51 (0.69 without one outlier at middle left) that is statistically significant at the 99% confidence level. Thus, the fidelity of
the global monsoon rainfall indeed depends upon thereality of the tropical SST. For the MIROC simulations,for instance, the PCCs between the simulated and observed SST MCA1 are increased from MIROC3 [0.68for MIROC3(hires) and 0.70 for MIROC3(medres)] to
MIROC5 (0.84). As a consequence, the PR EOF1 simulation of MIROC5 is improved remarkably to a PCC
of 0.56 compared with that of the two MIROC3 versions, of which PCCs are below 0.25. Note also that
applying an enhanced resolution does not result in an1 NOVEMBER 2011

KIM ET AL.

FIG. 10. Evaluation of CGCM performance on the spatial similarity of tropical SST and global monsoon precipitation: abscissa(ordinate) is the PCC of SST MCA1 (PR EOF1). The regression
coefficient is shown in the lower-right corner.

appreciable difference between MIROC3(medres) and
MIROC3(hires). These results once again reflect the importance of the advanced physical parameterization in amodel.

b. Possible mechanism for the improved simulationof ENSO in MIROC5
On an interannual time scale, the classical feedback
described by Bjerknes (1969) is generally believed toregulate the state of the tropical Pacific and amplify incipient El Niño events (e.g., Collins et al. 2010). Thus, insearch of the underlying mechanism that is possibly responsible for the MIROC5 improvement in El Niñosimulation, further analyses are made with particularfocus on the Walker circulation in the Pacific as thiszonally aligned overturning circulation is a key player
not only for the initiation but also for the intensification of the positive feedback loop. Climatologically,
the upward motion of the Walker circulation along theequatorial plane is placed over the western Pacific andMaritime Continents, with a maximum over Indonesia(1158–1208E) (Lau and Yang 2003). However, during
El Niño, strong updraft prevails over the equatorial
central Pacific, which induces a less-vigorous Walker
circulation and hence intensifies the Bjerknes feedback. Here, we use precipitation averaged over 58S–58N,
1158–1208E (1608–2008E) as a proxy for the rising limb of5615

the Pacific Walker circulation (ascending motion due to
El Niño) as this overturning circulation is basically driven
by the diabatic heating processes (via the release oflatent heat in the atmosphere) in connection with thedivergent component of the tropical winds.
Figure 11a shows lead–lag correlation coefficients ofthe monthly precipitation anomalies averaged over the
maximum upward branch of the climatological Walker
circulation with respect to the monthly Niño-3 index.
Note that the 3-month running average was applied prior
to calculation. The decorrelation time for such smoothed
time series is 9–10 months (figure not shown). Thus, theeffective degree of freedom is estimated as 360/9.5 anda correlation of 60.31 reaches the 95% significance levelbased on a t test. In observation, the rising motion of
the Walker circulation is generally negatively correlated with the Niño-3 index over a wide range of lead–lag times, with maximum correlation when the former
leads by 1–2 months. The observed convective activity
in response to anomalous SST warming is displayed inFig. 11b. Large positive rainfall anomalies are accompanied mostly by positive SST anomalies, indicatingthat the warmth of the SST is of importance for deep
convection to occur. The implication of these results
is that El Niño events are often triggered by slowing
down the Walker circulation at a rising branch and,once established, the convective activity over the central Pacific is a key to the amplification of the Bjerknes
feedback. Obviously, the two versions of MIROC3 areless skillful mimicking the initiation and intensificationof the feedback loop; the correlation coefficients are
insignificant at all lead–lag ranges (Fig. 11a), and there
is no sign of strong convection, albeit the SST anomalies
often reach up to 28C (Figs. 11d and 11e). In MIROC5,
in comparison, the Bjerknes feedback appears to playa nontrivial role. The suppressed Walker circulation inassociation with the reduction of precipitation seems tobe able to excite El Niño events with a reasonable realistic lead time (Fig. 11a). Furthermore, enhanced deep
convection tend to occur more frequently in proximity of
the 28C warming of SST (Fig. 11c).

6. Concluding remarks
By using process- and feedback-based metrics, thepresent study casts light on the fidelity of the prime examples of the forced response (global monsoon), internal feedback process (El Niño), and their year-to-year
coupling simulated by the new model version, MIROC5,
in comparison with its predecessor having coarser andfiner resolutions than MIROC5 [MIROC3(medres) and
MIROC3(hires), respectively] and the coupled global
climate models participating in the CMIP3. The effect5616

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FIG. 11. (a) Lead–lag correlation coefficients of the monthly precipitation anomalies averaged over the maximum upward branch of theclimatological Walker circulation (58S–58N, 1158–1208E) with respect to the monthly Niño-3 index obtained from the observations (scaleon the left) and MIROC simulations (scale on the right). Note that a 3-month running average is applied prior to calculation. Horizontal
gray line denotes the 95% confidence level with scale on the right. Scatterplots of monthly SST and precipitation averaged over theequatorial central Pacific (58S–58N, 1608–2008E) for (b) observation, (c) MIROC5, (d) MIROC3(hires), and (e) MIROC3(medres).
Numbers in parenthesis indicate climatological mean SST averaged over the analysis domain.

of El Niño on regional monsoons has long been a topic ofinterest. In contrast, its influence on the global monsoon asa whole has received little attention and, hence, was investigated in the present study. Thirty years of observational data and the Twentieth-Century Climate in the
Coupled Model simulations are analyzed using a set of
diagnostic metrics and the maximum covariance analysis to assess the reality of monsoon climatology and theinterannual El Niño–monsoon relationship, respectively.
MIROC3(hires), the model with the highest resolution,
reproduces better the domain and intensity of global monsoon precipitation when compared with MIROC3(medres),the model with the lowest resolution and sharing thesame physical schemes with MIROC3(hires). On theother hand, MIROC5, the model with moderate resolution but improved parameterizations, considerably
outperforms the two versions of MIROC3 and is generally superior to the CMIP3 models in simulating global
monsoon precipitation as well as the circulation. This is
the result of overall improvement in the climatology of
the annual mean and the two components of the annual
cycle. Furthermore, the simulation of the year-to-year variability of global monsoon precipitation is improved substantially from MIROC3 to MIROC5. These are suggestiveof the importance of continuously developing physicalprocesses in a model.
Unlike observations, most CMIP3 CGCMs exhibit normally distributed El Niño, with SST asymmetricity nearzero in the tropical Pacific. The reality of the El Niñononlinearity also remains unsatisfactory from MIROC3
to MIROC5. In the latter, however, the amplitude andperiodicity of El Niño are improved to some degree.
As recently pointed out by Wang et al. (2011b, manuscript submitted to Nat. Geosci.), the MCA shows observational evidence of the salient year-to-year interplay
between El Niño and global monsoon precipitation.
Multimodel results indicate that such linkage is robust
across the contemporary CGCMs and the reality of thespatial patterns of global monsoon rainfall significantly
depends upon the reproducibility of the tropical SST
distributions. In particular, MIROC5 simulates the spatial patterns of the interannual tropical SST swing quite
realistically as compared with the old versions, which
entails an improved simulation of interannual global
monsoon precipitation. Better representation of El Niñoand its teleconnection with global monsoon precipitation is likely ascribed to the newly incorporated cumulus convection scheme, which enables MIROC5

1 NOVEMBER 2011

KIM ET AL.

to mimic the Bjerknes feedback loop to some extent
through strengthening of the convective activity over theequatorial central Pacific. The intensified convection isable to improve further the positive feedback process:
Watanabe et al. (2011) showed that the Bjerknes feedback in MIROC5 tends to be more effective if the deep
convection along the eastern ITCZ is enhanced throughreduction of the entrainment rate.
The better reproducibility of MIROC5 bodes well for
its use in future climate projection. Though CGCMs havebeen extensively used to predict future climate changes,
assessing the accuracy of the model projection is hampered owing to limited knowledge of the highly complex
climate system. Thus, the fidelity of a CGCM’s performance for current climate variations is alternatively considered a measure of its capability to project the future(Kim et al. 2008). In this context, near-term to centurylong predictions of MIROC5, which are currently being undertaken, would be more reliable. Specifically,
the forthcoming international collaboration, such as
CMIP5, calls for intergraded efforts from around theworld to meet the needs of both the scientific community
and the public. The results suggest that MIROC5 will beable to contribute to such efforts as a credible member.
Acknowledgments. We thank the anonymous reviewers
for valuable suggestions. We also acknowledge the modeling groups for making their simulations available foranalysis, the Program for Climate Model Diagnosis andIntercomparison (PCMDI) for collecting and archiving
the CMIP3 model output, and the WCRP’s Working
Group on Coupled Modelling (WGCM) for organizing
the model data analysis activity. The WCRP CMIP3
multimodel dataset is supported by the Office of Science,
U.S. Department of Energy. This work was performed
under the auspices of the Ministry of the Environment,
Japan, under the Environment Research & Technology
Development Fund (Grant A0902). MW, MK, and TY
were supported by the Innovative Program of Climate
Change Projection for the 21st Century (Kakushin Program) of the Ministry of Education, Culture, Sports,
Science and Technology, Japan. BW was supported by
APEC Climate Center. The computation was carried out
on the Earth Simulator.

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