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GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L11815, doi:10.1029/2009GL038271, 2009

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ArticleAsian summer monsoon simulated by a global cloud-system-resolving
model: Diurnal to intra-seasonal variability
Kazuyoshi Oouchi,1 Akira T. Noda,1 Masaki Satoh,1,2 Bin Wang,3 Shang-Ping Xie,3Hiroshi G. Takahashi,1 and Tetsuzo Yasunari1,4Received 27 March 2009; revised 11 May 2009; accepted 20 May 2009; published 13 June 2009.

[1] Interaction of convection and circulation is key to the
Asian summer monsoon, but difficult to represent in global
models. Here we report results from simulations for thesummer of 2004 by a global cloud-system-resolving model,
NICAM. At both 14- and 7-km horizontal resolution, NICAM
simulates the observed monsoon circulation patterns, and thenorthward propagation of precipitation. The 7-km run
simulates summer-mean precipitation maxima in narrow
bands along the western Ghats, Himalayan foothills, theArakan Yoma highlands, and the Annamite range.
Precipitation 1) is modulated by orography, 2) is affected bysynoptic-scale systems, and 3) displays a pronounced diurnal
cycle, especially over Indo-China, with its strong/weaksignal propagating westward/eastward in the wet/dry phaseof the intraseasonal oscillation. This set of simulations captures
these intraseasonal changes of the Indian monsoon withhigh fidelity from June to early July. NICAM exhibits apositive bias in precipitation over the Indian Ocean, common
to atmospheric models with prescribed sea surface
temperature. This calls for the inclusion of ocean-atmosphere
coupling process to improve monsoon simulation skills.
Citation: Oouchi, K., A. T. Noda, M. Satoh, B. Wang, S.-P. Xie,
H. G. Takahashi, and T. Yasunari (2009), Asian summer monsoon
simulated by a global cloud-system-resolving model: Diurnal tointra-seasonal variability, Geophys. Res. Lett., 36, L11815,
doi:10.1029/2009GL038271.

1. Introduction[2] The Asian summer monsoon is among key targets forweather/climate research and prediction. Monsoon convection is highly organized in space and time, on diurnal,
intraseasonal and seasonal time scales [Krishnamurthy andShukla, 2007]. Its modeling remains a major challenge.
Here we take a new approach of using a global cloudsystem-resolving model (GCRM) that represents cloudclusters explicitly.
[3] The GCRM used is NICAM (Nonhydrostatic
ICosahedral Atmospheric Model) [Satoh et al., 2008;
Tomita and Satoh, 2004] that showed great promise in its
1
Frontier Research Center for Global Change, Japan Agency for
Marine-Earth, Science and Technology, Yokohama, Japan.
2Center for Climate System Research, University of Tokyo, Kashiwa,
Japan.
3
International Pacific Research Center, School of Ocean and Earth
Science and Technology, University of Hawaii at Manoa, Honolulu,
Hawaii, USA.
4
Hydrospheric Atmospheric Research Center, Nagoya University,
Nagoya, Japan.

Copyright 2009 by the American Geophysical Union.
0094-8276/09/2009GL038271$05.00

successful simulation of a boreal winter Madden-Julian
Oscillation (MJO) event [Miura et al., 2007] in terms of its
eastward propagation and associated cloud features, compared quite consistently with observations [Masunaga et al.,2008; Inoue et al., 2008; Nasuno et al., 2009; H. Taniguchi etal., Diagnostic evaluation of a global cloud-resolving model
simulation of a Madden-Julian Oscillation event, submitted
to Monthly Weather Review, 2009]. Although the time
integration was limited to one month due to highly demanding computational resource, NICAM is successful in reproducing the diurnal cycle of precipitation [Sato et al., 2008]
and tropical extreme events including tropical cyclones[Fudeyasu et al., 2008]. We have since completed a seasonal-long, boreal summer (June – August, 2004) simulations
using NICAM with 14- and 7-km grid spacing. This letter
presents highlights of some results from these first seasonallong NICAM simulations, focusing on precipitation features
spanning various time scales.
[4] Boreal summer is a challenging season for climate
and weather forecasts. The insight obtained from the present
study will help improve the simulation and prediction oftropical phenomena, and explore GCRM’s potential forclimate studies as high-end computing continues to improve
performance [Satoh et al., 2008; Heffernan, 2008].

2. Experimental Design[5] The NICAM experiments are performed using thegrid spacing at 7- and 14-km, which resolve gross featuresof mesoscale convection, and cloud clusters. The experimental setup is explained by Oouchi et al. [2009], including
the use of an improved turbulent boundary layer scheme[Noda et al., 2009] of the Mellor-Yamada scheme modified
by [Nakanishi and Niino, 2004].
[6] The time integration is performed from 1 June to
31 August/10 November 2004 for 7-/14-km mesh run, andrepresents the mature phase of monsoon. The initialatmospheric conditions are interpolated from the National
Centers for Environmental Prediction (NCEP) Global
Tropospheric Analyses at 00 UTC on 1 June, 2004. The
experiment is conducted using the sea surface temperature(SST) dataset from a weekly interpolated National Oceanicand Atmospheric Administration (NOAA)-Optimum Interpolated (OI) SST dataset. No nudging is applied; clouds are
fully interactive with atmospheric disturbances.
[7] Ocean-atmosphere coupling processes are importantfor oceanic precipitation [Wang et al., 2004] and predictionof monsoon intraseasonal oscillation [Fu et al., 2007]. The
NICAM experiments with the prescribed SST can provide
insights into the problem, and help clarify the relative
importance between ocean-atmosphere coupling process

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L11815OOUCHI ET AL.: MONSOON IN GLOBAL-CLOUD-RESOLVING MODEL

Figure 1. (a and c) Observed and (b and d) simulated
precipitation rate over the Indo-China monsoon region(Figures 1a and 1b) and global tropics (Figures 1c and 1d)
as June – July –August average (in units of mm day 1). The
observed precipitation is from TRMM_3B42, and thesimulation is for 7km-mesh run.

and cloud-resolving framework for modeling monsoon
precipitation. The seasonal integrations include a few
MJO cycles and associated tropical cyclogeneses [Oouchi
et al., 2009], and demonstrate an improvement in low-level/
boundary layer clouds [Noda et al., 2009]. This paper
presents a case study based on one single realization with
NICAM at 7 or 14 km resolution. Ensemble experiments areunderway for a more rigorous assessment of NICAM’sskills in simulating monsoon variability.

L11815tours (the westerly exceeding 6 m s 1). The time average ofprecipitation is displayed in Figure 2 (bottom), along withthe orography. Strikingly, precipitation displays conspicuous diurnal cycle both in TRMM observations and NICAM
simulation. The diurnal cycle is the most evident over theland area of the Indo-China peninsula (98° – 108°E), and toa lesser degree, in regions west of the coastal mountains(70°– 75°E and 95°– 100°E). The observed increase inwesterly winds over the Bay of Bengal (Figure 2) persistsfrom 16 June to 5 July, but rainfall is concentrated on threesynoptic ‘‘events’’ around 16 June, 26 June, and 2 July,
indicative of the importance of synoptic-scale systems.
Orographic lift can be a factor enhancing the synoptic
system-induced rainfall. The simulation also captures such
synoptic-scale precipitation systems. Sensitivity of synoptic
systems and intraseasonal behavior to initial conditions is an
important area for future work [Goswami, 1998; Krishnan
et al., 2009]. On the windward side of narrow mountain
ranges, the impinging westerlies intensify precipitation byorographic lift, as suggested in an observational study [Xieet al., 2006]. The simulation overestimates the precipitation
maxima by a factor of 1.5 – 2 times compared to theobservations, as revealed in a precipitation analysis of
the boreal-winter NICAM simulation [Sato et al., 2009].
The result nonetheless suggests the importance of theinteraction among the synoptic-scale systems, mesoscale
orograpy and diurnal precipitation for the monsoon season.
[10] Figure 2 also demonstrates that the intraseasonal
oscillation (ISO) modulates the diurnal cycle over the
Indo-china region; it controls not only its amplitude but
its propagation direction. The observed diurnal cycle isstrong, and propagates westward during the first part of

3. Results[8] The Asian monsoon affects local weather in the
Indian-subcontinents, and Southeast Asian regions. The
GCRM allows investigation into complex regional featuresby representing explicitly convection systems and theirinteractions with synoptic- and meso-scale disturbances,and topography. Such a potential of GCRM is partly confirmed in Figure 1, which compares the precipitation rate
averaged for June– August period between TRMM observations (Figure 1a) and the 7-km grid run (Figure 1b). The
7-km model simulates successfully most of the local maxima
of summer precipitation organized in narrow strips on thewest slope of the western Ghats range, Himalayan foothills,
the Arakan Yoma highlands, and the Annamite range. These
orography-induced precipitation features significantly regulate the wet and dry phases of local monsoon [Takahashi andYasunari, 2006; Xie et al., 2006]—features poorly reproduced in large-scale atmospheric models [Xie et al., 2006].
The result is encouraging, and suggests a high potential of
GCRM in process and regional climate studies.
[9] To understand association of these local precipitation
maxima with the orography, and temporal variations embodied in them, Hovmoller diagrams of the precipitation areplotted for the latitudinal band of 14°– 16°N in Figure 2(top), superimposed on zonal velocity at 850 hPa in con-Figure 2. (top) Hovmoller representation of precipitation
rate (mm day 1, shaded) and zonal velocity (contoured for
6, 9 and 12 ms 1) for the average over 14° –16°N, for
TRMM_3B42 (precipitation) and NCEP/NCAR Reanalysis(zonal velocity), and 7km-mesh run. The plot period spansfrom 1 June to 11 July. (bottom) Precipitation amountaveraged over the period (blue lines), and the orography(elevation  50 m; grey) that is constructed by the spatialsmoothing of the global digital elevation model GTOPO30
from the U.S. Geological Survey.

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L11815[12] Monsoon is also characterized by the northwardmigration of precipitation ISO [Fu and Wang, 2004;
Rajendran and Kitoh, 2006]. Figure 4 (top) illustrates thisphenomenon in time-meridional sections of precipitation
and 850 hPa velocity (vector) and its zonal component(shade). The 7-km mesh run predicts the strength of the
Indian monsoon trough quite well up to 40 days (1 June to
10 July), although more ensemble members are necessary
to conclude robustness of the result. The observed northward migration of the precipitation area is simulated inearly June to mid-July, but becomes unclear thereafter inthe simulations.
[13] To investigate the monsoon evolution, the time seriesof two monsoon circulation indices (Indian Monsoon Index;
IMI and Western North Pacific Monsoon Index; WNPMI)
as defined by Wang et al. [2004] are compared withobservations in Figure 4 (bottom). The indices provide
measures to track dynamical features of the regional monsoon subsystems. Remarkably, IMI is high (enhanced monsoon trough) during the first part of June and low
Figure 3. (a) 14 km run, (b) 7 km run and (c) observed
850-hPa horizontal wind (arrows), its zonal component(shade) and precipitation region (purple, exceeding 14 mm
day 1). The observed wind and precipitation is from
NCEP_NCAR Reanalysis dataset and TRMM_3B42,
respectively. (d) precipitation rate in meridional average(10 – 30°N) for both runs, and observation.
June (wet phase of ISO), but it weakens and propagateseastward during the second part of June (dry phase of ISO).
The westward/eastward propagation is apparently initiatedfrom Annam Cordillera/Bilaukraung. The model simulates
these two features of ISO modulation quite well. The
simulated westward/eastward propagations seem to beembedded in the low-level (700 hPa) easterly/westerly
regions (not shown). The relationship between monsoon
flows and precipitation systems is discussed by H. G.
Takahashi et al. (High-resolution modelling of the potential
impact of land-surface conditions on regional climate over
Indochina associated with the diurnal precipitation cycle,
submitted to International Journal of Climatology, 2009).
The interpretation of these findings is an interesting future
research topic.
[11] The relationship between precipitation and velocity
fields are shown in Figure 3 displaying the June– Augustmean 850 hPa zonal velocity (vector), its zonal component(color shade) and precipitation (purple) for the NICAM runs
and observation. The simulated precipitation patterns are
generally in consistent with observations, except for theexcessive precipitation in the Indian Ocean and east of
the maritime continent; the bias is somewhat improved inthe 7-km mesh run in the 60°– 70°E area (Figure 3d). The
simulated 850 hPa zonal velocity captures general featuresof observations, including the Somali jet. Following biases
are identified: the simulated winds over the Indian longitudes are too strong over the equator while they are too
weak over the Indian landmass compared to observation.
The simulated westerlies north of the equator do not extend
east of 115°E whereas they extend up to 150°E. Likewise
the easterly trades over the tropical Pacific are too strong in
the model.

Figure 4. (top) Time-meridional plots of (left) precipitation rate in mm day 1 and (right) 850-hPa horizontal wind(arrows) and its zonal component in m s 1 (shade), as
average over the Indo-monsoon region (60 – 90°E) during
June, July and August 2004. (bottom) Time series (1 June to
10 July) of monsoon circulation indices for Indian Monsoon
and Western North Pacific sectors defined by Wang et al.
[2004]. They are smoothed with the 2-day running mean.
The numbers in the bottom panels denote the correlation
coefficients between the simulation (14 km-mesh, black;
7 km-mesh, blue) and NCEP_NCAP Reanalysis.

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L11815OOUCHI ET AL.: MONSOON IN GLOBAL-CLOUD-RESOLVING MODEL(weakened monsoon trough) in the second half of June. The
model thus captures both the wet and dry spells well,although its sensitivity to initial conditions remains tobe seen.

4. Concluding Remarks[14] The advantage of NICAM over conventional GCMs
in simulating tropical clouds and convection was demonstrated by Miura et al. [2007] for a boreal winter MJO
event. This paper discusses the results from the first boreal
summer (2004) seasonal-long GCRM simulations, in an
attempt to simulate complex multi-scale interaction among
the monsoon-related precipitation and circulation. Our study
identifies a number of strengths and weaknesses of NICAM.
[15] NICAM exhibits an encouraging simulation resultsof intraseasonal variability of the Indian monsoon up to
40-days (June to early July), and the representation of local
precipitation features, especially those anchored by mountain ranges over the Indian Subcontinents, and Indo-China
Peninsula—features poorly reproduced in conventional
GCMs [Xie et al., 2006]. Some precipitation features tend
to be organized into synoptic systems as speculated by anobservational study [Yokoi and Satomura, 2008], and modulated by orographic effects in the West Ghats, and at thefoothills of Himalayas; both features are clearly simulated inthe 7-km. Precipitation there involves a unique diurnal
cycle, especially over Indo-China. These relationships meritfurther investigation, along with the attracting multiscalefeatures of oceanic convection [Zuidema, 2003]. The good
performance of NICAM in simulating monsoon convection
should come from improved representation of interaction
among moisture, circulation and topography in GCRM.
[16] A weakness of the model is the over-prediction of
precipitation over the Indian Ocean, a common problem forstate-of-the-art conventional atmospheric GCMs [Wang etal., 2004]. The problem can have the same root as inconventional atmospheric GCMs from the prescription of
SST as the bottom boundary condition. By design, theprescribed SST method does not capture the observed
negative correlation between SST and rainfall over warm
oceans with weak SST gradients [Wang et al., 2004;
Krishna Kumar et al., 2005]. The lack of oceanic feedback
may be a source of precipitation bias, which in turn affects
ISO and seasonal variations of monsoon [Goswami, 1998;
Krishnan et al., 2006]. We are working to include oceanatmosphere interaction in NICAM.
[17] Acknowledgments. The simulations were performed on the
Earth Simulator/JAMSTEC, under the framework of the Innovative Program of Climate Change Projection for the 21st century (KAKUSHIN)
project funded by Ministry of Education, Culture, Sports, Science andTechnology. The authors thank T. Matsuno, J.-X. Fu, Y. Wang, Y. Kajikawa
for discussions, and the reviewers for constructive comments that improved
the presentation.

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A. T. Noda, K. Oouchi, and H. G. Takahashi, Frontier Research Center for
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M. Satoh, Center for Climate System Research, University of Tokyo, 5-1-5
Kashiwanoha, Kashiwa-Shi, Chiba 277-8568, Japan.
B. Wang and S.-.P. Xie, International Pacific Research Center, School of
Ocean and Earth Science and Technology, University of Hawaii at Manoa,
1680 East West Road, Honolulu, HI 96822, USA.

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T. Yasunari, Hydrospheric Atmospheric Research Center, Nagoya
University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.

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