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An MJO Simulated by the NICAM at 14- and 7-km Resolutions
PING LIU,* MASAKI SATOH,1 BIN WANG,* HIRONORI FUDEYASU,* TOMOE NASUNO,1
TIM LI,* HIROAKI MIURA,1,# HIROSHI TANIGUCHI,1 HIROHIKO MASUNAGA,@XIOUHUA FU,* AND H. ANNAMALAI** International Pacific Research Center, SOEST, University of Hawaii at Manoa, Honolulu, Hawaii
1 Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology,
Kanagawa, Japan# Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado@ Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan(Manuscript received 4 February 2009, in final form 28 March 2009)
ABSTRACTThis study discloses detailed Madden–Julian oscillation (MJO) characteristics in the two 30-day integrationsof the global cloud-system-resolving Nonhydrostatic Icosahedral Atmospheric Model (NICAM) using the allseason real-time multivariate MJO index of Wheeler and Hendon. The model anomaly is derived by excluding
the observed climatology because the simulation is sufficiently realistic. Results show that the MJO has a realistic
evolution in amplitude pattern, geographical locations, eastward propagation, and baroclinic- and westwardtilted structures. In the central Indian Ocean, convection develops with the low-level easterly wind anomaly thenmatures where the low-level easterly and westerly anomalies meet. Anomalous moisture tilts slightly withheight. In contrast, over the western Pacific, the convection grows with a low-level westerly anomaly. Moisturefluctuations, leading convection in eastward propagation, tilt clearly westward with height. The frictional
moisture convergence mechanism operates to maintain the MJO. Such success can be attributed to the explicit
representation of the interactions between convection and large-scale circulations. The simulated event, however, grows faster in phases 2 and 3, and peaks with 30% higher amplitude than that observed, although the 7-km
version shows slight improvement. The fast-growth phases are induced by the fast-growing low-level convergence in the Indian Ocean and the strongly biased ITCZ in the west Pacific when the model undergoes a spinup.
The simulated OLR has a substantial bias in the tropics. Possible solutions to the deficiencies are discussed.

1. Introduction
The Madden–Julian oscillation (MJO; Madden andJulian 1971, 1972, 1994) dominates tropospheric variability in the tropics on time scales of 30–60 days. The
oscillation influences weather and climate variability
substantially in both the tropics (e.g., Yasunari 1979;
Hendon and Liebmann 1990; Lawrence and Webster
2002) and extratropics (e.g., Weickmann 1983; Ferranti
et al. 1990; Lau and Chang 1992; Liebmann et al. 2004).
An improved prediction of a prevalent MJO event can
extend the atmospheric predictability by up to 20 days
(Ferranti et al. 1990; Waliser et al. 1999). Accurately
simulating and predicting MJO, however, has been a

Corresponding author address: Dr. Ping Liu, IPRC/SOEST, Post
409J, 1680 East–West Rd., Honolulu, HI 96822.
E-mail: pliu@hawaii.edu
DOI: 10.1175/2009MWR2965.1
Ó 2009 American Meteorological Society

challenge to the modeling community (Zhang 2005; Linet al. 2006).
The MJO is powered by the latent heat released fromcumulus and super cloud clusters that are modulated bylarge-scale circulations (Nakazawa 1988; Lau et al. 1989;
Chao and Lin 1994). As a response to the convective
anomaly in the west, near-boundary layer convergenceleads the MJO in eastward propagation and builds up
convective instability fostering the deep convection(Wang 1988; Wang and Rui 1990; Wang and Li 1994;
Hendon and Salby 1994; Maloney and Hartmann 1998).
Moisture anomalies thus tend to tilt westward with altitude as the MJO evolves in the eastern Indian Ocean–western Pacific (e.g., Sperber 2003; Kiladis et al. 2005).
Proper mean states in atmospheric winds and sea surface
temperature provide a background for the convection
development so that they are disclosed as a necessary
condition for AGCMs (e.g., Slingo et al. 1996) and coupled GCMs (e.g., Inness and Slingo 2003) to simulate aOCTOBER 2009LIU ET AL.

realistic MJO. However, the interactions between thesubgrid-scale cumulus convection and grid-scale circulation in GCMs generate and maintain the MJO fundamentally (e.g., Grabowski 2003).
Because the interactions are represented roughly byparameterizations, the simulated MJO has smaller variance, a shorter period, and less-prevalent eastward propagation than that observed in early atmospheric generalcirculation models (AGCM; Slingo et al. 1996) and recent
climate system models (Lin et al. 2006) at 100–500-km
resolutions. The MJO is still less realistic in an AGCM
that has a 20-km resolution (Liu et al. 2009), indicatingthat the resolution is not a determining factor in conventional GCMs. Using an alternate scheme or partialchange of a parameterization may improve the simulated
MJO (e.g., Maloney and Hartmann 2001; Liu et al. 2005).
However, the uncertainties and large sensitivities to theschemes make it difficult to conclude which aspect of aparameterization is universally essential to the simulated
MJO. The uncertainties can be fundamentally reduced byusing cloud-system-resolving models to explicitly resolve
the convective process. When a two-dimensional cloudresolving module at a 2-km resolution is embedded in an
AGCM at a 300-km resolution over the MJO active areas
so that the local interactions are represented explicitly,
the simulated MJO is improved substantially in amplitude and eastward propagation (Khairoutdinov et al. 2005;
Ziemiański et al. 2005). The improvement using the cloudresolving modules as a superparameterization (Grabowski
2004) encourages treating the cumulus–circulation interactions explicitly in three-dimensional AGCMs for the
MJO simulation.
A global cloud-system-resolving model integrated
with realistic geophysical processes, the Nonhydrostatic
Icosahedral Atmospheric Model (NICAM; Satoh et al.
2008), was recently developed. It successfully simulated
the MJO event occurring in December 2006 and January
2007 using the 7-km resolution version (Miura et al.
2007). Snapshots of winds, a Hovmöller diagram of precipitation along the equatorial area, and cloud images
resemble those observed in eastward propagation. The
event demonstrates a clear multiscale structure comparedto observed radar images (Masunaga et al. 2008). In thesimulation, convectively coupled tropical waves are
mixed with an MJO-like envelope propagating eastward(Nasuno et al. 2009). The two tropical cyclones with thisMJO event are simulated realistically as well (Fudeyasu
et al. 2008). However, the MJO in these studies is demonstrated with the climatic background and variations on
shorter time scales. Whether the evolution of MJO threedimensional structure agrees with that observed is notdisclosed in detail. Nor is how the convection interacts
with large-scale circulations so that the simulated MJO is3255

realistic, which can shed light on improving conventional
GCMs. Because the model started with the output fromthe operational analysis system of the National Centers
for Environmental Prediction (NCEP) at a 100-km resolution, how the interpolated initial condition affects thesimulated MJO can be of interest. By contrasting the
MJO in the previous 7- and recent 14-km runs, the sensitivity of simulated MJO to resolution in a global cloudsystem-resolving model will be intriguing as well.
This study addresses these issues using the all-season
real-time multivariate MJO (RMM) index of Wheeler
and Hendon (2004, hereinafter WH04). The RMM framework can disclose the MJO features using daily data
without any time filtering, which is appropriate for thepresent short-term integrations. Results show that thesimulated moisture, wind, and convection anomalies associated with the MJO event have a realistic evolution inthree-dimensional structures. The frictional moisture
convergence mechanism (e.g., Wang 1988; Maloney andHartmann 1998; Sperber 2003) operates to maintain theeastward propagation. Section 2 introduces the model,
data, and methodology. Section 3 presents results. Section 4 includes the discussion and conclusions.

2. Model, data, and methodology
The NICAM is a global nonhydrostatic model designed
for 1-km-order fine-mesh simulations. It uses an icosahedral grid system suitable for parallel computation (Tomita
and Satoh 2004). The grid size is reduced gradually using arecursion method starting from level 0 with 20 triangles on
the sphere (Satoh et al. 2008, their Fig. 2). Each triangle isdivided into four small triangles at every recursion level.
At level 9 of the recursion, the structure produces a horizontal resolution at about 14 km; and at level 10 the resolution is about 7 km. The grid structure is then smoothedby the spring dynamics. Both the 14- and 7-km resolutions
use a step size of 30 s to satisfy the Courant–Friedrichs–
Lewy condition. Because of the same step size and physics
configurations, the sensitivity in the two simulations canbe attributed to the change of horizontal resolutions.
The moist processes are represented explicitly. For example, cloud physics are calculated prognostically by asimplified bulk cloud microphysical scheme (Grabowski
1998). Subgrid-scale turbulence is represented by the
Nakanishi and Niino (2006) scheme that is an improved
version of Mellor and Yamada (1974). Surface fluxes ofheat and momentum are modeled using Louis’s (1979)
bulk formula. The radiation scheme is a revised version
of Nakajima et al. (2000). Details of the model are referred to Satoh et al. (2008).
Using the observed weekly SST as external forcing and
the NCEP operational analysis product in pressure-sigma

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FIG. 1. The 200-hPa velocity potential (0.5 3 107 m2 s22) along the equator averaged in 158S–158N during the MJO event for (a) the
NCEP–NCAR reanalysis, (b) the 14-km NICAM, and (c) the 7-km NICAM. Values less than and equal to 0 are shaded.

hybrid coordinate at 18 3 18 resolution as initial conditions, runs at 7- and 14-km resolutions were carried out
for the MJO event from 15 December 2006 to 13 January 2007. The outputs are interpolated horizontally from
7 and 14 km to 2.58 3 2.58 (about 275 km 3 275 km)
using a box-mean method and vertically from altitude topressure levels at 1000, 925, 850, 700, 600, 500, 400, 300,
200, and 100 hPa using a mass-weighted algorithm. Daily
mean is then derived from the spatially interpolated
6- or 1.5-hourly data for analysis.
The OLR observed by the National Oceanic and Atmospheric Administration (NOAA) satellites, the precipitation rate from the Tropical Rainfall Measuring
Mission (TRMM), version 6_3B42 (Huffman et al. 2007),the surface latent heat flux, winds, and specific humidityof the NCEP–National Center for Atmospheric Research(NCAR) reanalysis (Kalnay et al. 1996) are used as
observational references. All referenced variables areinterpolated to daily interval, horizontal 2.58 3 2.58 andvertical pressure levels. Daily anomalies of the model
variables are derived by excluding the climatology ofobservations or the reanalysis from 1979 to 2007 (1998–
2008 for the TRMM precipitation) because the two runs
are too short in time to provide their own, because theobserved climatology carries nearly null information of
MJO, and—more importantly—because the simulated

30-day averages of precipitation and winds are very
close to the references in the tropics, as shown in section 3(Fig. 1). We define these fields as ‘‘simple anomalies’’ todifferentiate them from other two types of anomalies tobe described. The simulated specific humidity, however,is systematically different from that in the reanalysis in
the entire tropics so that the zonal means for all variables at each pressure level are excluded from the simple
anomalies for the composites (section 3b). We refer tothese fields as anomalies; they are equivalent to eddytransients.
We use the RMM index of WH04 to disclose the evolution of amplitude, phase, and vertical structure, andpossible mechanism for the MJO event. The index isdefined by the first pair of the principal components(PCs) from a combined empirical orthogonal function(CEOF) analysis using anomalous OLR, 850-, and 200hPa zonal winds from 1 January 1979 to 31 December
2001. The anomalous fields are derived by excluding thedaily climatology, the first three harmonics of the seasonal cycle, and the interannual variability associatedwith ENSO. They are normalized with their global
standard deviation so that each field contributes to the
CEOF comparably. The first two CEOFs represent the
MJO salient structure (e.g., the baroclinity and coupling
between convection and circulation; Fig. 1 in WH04).

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FIG. 2. Difference of the 30-day mean specific humidity from the reanalysis along the equator(averaged in 158S–158N) for (a) the 14- and (b) 7-km NICAM. Values less than and equal to 0
are shaded. The contour interval is 1 g kg21.

The detailed method is referred to as WH04. The RMM
can demonstrate the MJO amplitude and geographicalevolution in the model compared with observations. We
will use the CEOFs and variances of each variable
provided by WH04 to derive the index described below.
The standardized simple anomalies from the NICAM
and observations are projected onto the two CEOFs tocalculate the corresponding RMMs, which differentiateslightly from those derived using the anomalous fields ofWH04 (section 3). MJO phases 2–6, which are defined in
the RMM diagram (WH04), are composited based on
the dates for each phase. The average of several days ineach phase can eliminate synoptic perturbations effectively so that the spatial filtering is not used (e.g., Hendon
and Salby 1994). Inspection of the Hovmöller diagram forthe convective centers to pass specific longitudes along anarrow equatorial band [e.g., 608, 908, 1208, and 1508E as
used by Rui and Wang (1990)]; dates can be observed forcomposite as well. The derived days agree with thoseobserved from the RMM diagram so that the composite
structures are similar. It is noteworthy that model OLR is
used for deriving the RMMs but not for composites because it has a reasonable pattern but much larger value inthe tropics (see Fig. 12 in the discussion). The precipitation represents convection well as a direct product and it
is used in the composites.

3. Resultsa. The MJO event
The simulated MJO event resembles that observed, as
illustrated roughly by a Hovmöller diagram of precipitation in time–longitude space (Fig. 4b in Miura et al. 2007).
Here we show the evolution of 200-hPa velocity potential
in the equatorial area averaged between 158S and 158N
during the 30-day integrations (Fig. 1). The velocity potential in the reanalysis (Fig. 1a) is about 21 3 107 m2 s22
near 608E around 15 December 2006. It is strengthened to22.5 3 107 m2 s22 near 708E on 23 December. This centermoves to the date line around 13 January 2007. An eastward propagation is discernible, for example, from thenegative values (shaded). The strengthening center andeastward-propagation patterns are simulated well by the
14- and 7-km NICAM except that the central values areslightly higher than those in the reanalysis (Figs. 1b,c). The
simulated winds have a similar pattern and values to thereanalysis in the tropical troposphere (not shown; Fig. 2 inMiura et al. 2007). Because of such similarities, the model
anomalies for winds, OLR, precipitation, and surface latent heat fluxes are derived by excluding the climatologyof observations or the reanalysis. However, the simulated
specific humidity has a systematic difference from thereanalysis as shown in Fig. 2. In both model versions, a

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FIG. 3. Evolution of the MJO event in amplitude represented by (RMM12 1 RMM22)1/2.
The black, solid, plus curve is derived using the anomalous fields described in WH04; others
use simple anomalies by excluding the observed climatology for observations (black, dashed,open), the 14-km NICAM (gray, solid, closed), and the 7-km NICAM (gray, dashed, square).

much drier boundary layer between the surface and
900 hPa is produced along the equator (averaged in 158S–
158N). The difference is as much as 24 g kg21. In the layer
between 900 and 600 hPa, it is much wetter in the
NICAM, particularly in the 7-km version. The specifichumidity decreases again systematically in the layers above
600 hPa. Such systematic difference is likely induced by
the boundary layer scheme incorporating with the explicit microphysics. Because of the systematic difference
and our focus on the MJO fluctuations, the zonal means
for all variables at each pressure level are excluded fromthe simple anomalies for the composites in section 3b.
After the simple anomalies of OLR, 850-, and 200-hPa
zonal winds are derived, they are standardized withthe global standard deviation and then projected onto
the first pair of COEFs provided by WH04 to derive thecorresponding principal components of RMM1 andRMM2. The time series of (RMM12 1 RMM22)1/2 candisclose the MJO evolution in amplitude. WH04 provides a 91-day running mean of the series. In an extended version (not shown), the current MJO has anamplitude only about 1/3 of other 10 strong events occurring in the past 20 years. Moreover, the 91-day filtering is not suitable for the 30-day simulations. Figure 3
shows the unfiltered series of RMM amplitude for theobservations using anomalous fields and for both observations and the NICAM using simple anomalies. The
two curves for observations (black) coincide with eachother from 1 to 31 December 2006 and have a slightdifference thereafter, indicating that the simple anomalies (black-dashed-open curve in Fig. 3) can disclose the
MJO event fairly well. The MJO event becomes detectable with an amplitude larger than 1 near 8 December 2006, which agrees well with the Hovemöller
diagram of precipitation (Fig. 4b in Miura et al. 2007).
The event peaks with an RMM amplitude of 3.3 near
27 December, then decays back to less than 1 near
16 January 2007. The detectable cycle lasts about 40 days.
The RMM amplitude evolution in the two NICAM
versions generally agrees well with that observed (graycurves in Fig. 3). The simulated event starts on 15 December 2006 with slightly higher amplitude. This small
difference can originate from the spinup of interpolated
initial conditions from the NCEP operational analysis in
a vertical sigma-pressure coordinate and at horizontal
18 3 18 resolution. The difference remains small until
21 December 2006 in the 14-km version and until 23 December in the 7-km version, indicating an improvement
due to the higher resolution. The simulated event by
14-km NICAM peaks at the same day as that observed,
but the RMM amplitude reaches nearly 4.2, about 30%
larger than that observed. One day later, the MJO in the
7-km version peaks with the RMM amplitude of 3.8,
which is improved but still high. The MJO then decays
with a higher amplitude. A small turnaround occurs near
the RMM amplitude of 1 on 9 January 2007 in observations and rebounds to near 1.4 on 15 January. However, the turnaround occurs on 6 January in the 14-km

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LIU ET AL.

FIG. 4. RMM diagram for the MJO event in observations (black, solid, plus), the 14-km
NICAM (gray, solid, open), and the 7-km NICAM (gray, dashed, closed).

version and regains the amplitude up to 3.2 on
11 January. The 7-km version improves the simulation by
a turnaround occurring on the same day as that observed,
but the amplitude is still much higher and reaches nearly
3 on 13 January, indicating that the simulated event
might last longer if the integration continues. Similar
agreement occurs in the geographical evolution of the
MJO event as shown in the RMM diagram.
Eight phases of an MJO event are defined in the RMM
diagram (WH04) with each in the 1/ 8 portion as shown in
Fig. 4. Phases 2 (marked as PH2) and 3 (PH3) correspond to an enhanced convection in the Indian Ocean
while 4 (PH4) and 5 (PH5) in the Maritime Continent
and 6 (PH6) in the central-west Pacific. The truncatedevent in observations (black) starts from 15 December
over the western Indian Ocean then evolves throughphase 6 in the western Pacific. The simulated event (graycurves) agrees well with that observed in phase 2, earlyphase 3, and early phase 5. It has a similar evolution
but higher RMM values in remaining phase 3, most ofphase 4, and phase 6. A close inspection of the diagram
indicates that the simulated event grows faster than thatobserved in phases 2 and 3. Dates for other phases aredifferent as well, which is summarized in Table 1.
The striking difference in Table 1 occurs in phases 2
and 3. Phase 2 ends on 26 December 2006 in observations, while on 21 December in the two model versions,indicating that a faster growth rate is simulated in the
Indian Ocean. This fast growth is caused by the low-level
convergence, which will be shown in the composite
structures below. During the 12 days of phase 2, twowestward-moving synoptic waves west of 908E can beobserved in the Hovmöller diagram of precipitation(e.g., Fig. 4b of Miura et al. 2007). Both waves are simulated fairly well in the two NICAM versions (notTABLE 1. Dates for each phase from the RMM diagram.

Phase 2Phase 3Phase 4Phase 5Phase 6

Obs

14 km

7 km

15–26 Dec
27–30 Dec
31 Dec–3 Jan
4–8 Jan
9–13 Jan

15–21 Dec
22–26 Dec
27 Dec–2 Jan
3–10 Jan
11–13 Jan

15–21 Dec
22–27 Dec
28 Dec–5 Jan
6–13 Jan—

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FIG. 5. Composited anomalies for phase 2 of the MJO event in (a) observations and (b) the 7-km
NICAM. Contours represent the velocity potential (interval is 1 3 107 m2 s22 with thick, black as 0) at
925 hPa and vectors (m s21) for the divergence wind at this level. The shading represents the precipitation
rate (mm day21) from the (a) TRMM and (b) NICAM.

shown) except the model convection crosses 908E earlier. As indicated in the RMM diagram, the simulated
event thus starts earlier and ends earlier in phase 3; but
lasts longer in phases 4 and 5. The 14-km version simulates comparable dates for phase 6 but the 7 km ends theevent in phase 5. This early ending corresponds to amuch higher amplitude of RMM in Fig. 1. By assuming
the event has a similar structure during the days in eachphase, composites of consecutive phases will provide theevolution in vertical structures. Because the composited
difference is small in the two runs, only the 7-km case iscompared with observations below.

b. Phase composites
Previous studies (e.g., Hendon and Salby 1994; Maloneyand Hartmann 1998; Sperber 2003; WH04; a review by
Zhang 2005) have disclosed similarly salient structuresof an MJO during successive development stages in spite
different methods were used. In the western-central
Indian Ocean corresponding to phase 2 in the RMM
framework, convection is initiated over low-level easterly anomalies and then matures over the intersectionof low-level easterly and westerly anomalies. Moistureanomalies generally are in phase with the convection
without an apparently westward tilt with height. The
structures are different in the Maritime Continent andthe western Pacific. Boundary layer convergence forcedby the convection in the west builds up moist instability,
fostering deep convection during which low-level westerly
anomalies dominate. The moisture anomalies thus have aclearly westward tilt structure with height. These distinct
structures of MJO are summarized as the models I and II
in a schematic diagram by Zhang (2005, their Fig. 6). The
following composites for phases 2–6 of the MJO will
demonstrate that these structures occur in this event aswell and they are maintained by the frictional moisture
convergence mechanism.
Phase 2 is the average of 12 days in observations andof 7 days in the NICAM (cf. Table 1). During this phase,convection is enhanced near (138–18S, 608–708E) and(08–38N, 858–908E) with a precipitation anomaly of over
15 mm day21 (Fig. 5a). It is weaker to the west of 608E,
corresponding well to the westward-propagating waves
as observed in the Hovmöller diagram (not shown).
Shallow convection is discernible up to 1108E near theequator. Suppressed convection occurs in the Maritime

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FIG. 6. As in Fig. 5, but the contours represent the specific humidity (interval 0.5 g kg21) withshaded values greater than 0. The thick, black curve is for the precipitation rate (mm day21) in(a) TRMM and (b) NICAM. All values are along the equator (averaged between 158S and
158N).

Continent–west Pacific, forming a dipole structure atzonal wavenumber 1 with the enhanced convection in
the Indian Ocean (Fig. 5a). The convergence at 925 hPa,
shown by the positive anomalies of velocity potential(contours) and convergent wind vectors, occurs in thewhole Indian Ocean fostering the convection. It approaches the Maritime Continent, indicating a lead ineastward propagation. Weak divergence occurs in theeast Pacific and weak convergence in the rest of theWestern Hemisphere; the velocity potential has a zonal
wavenumber 1 structure as well. The convection andlow-level convergence develop a typical structure for an
MJO developing phase in the Indian Ocean (e.g., Hendon
and Salby 1994; Sperber 2003). The simulated convection
generally agrees with that observed in the Indian Ocean(Fig. 5b) except for relatively larger values in the eastern
Indian Ocean and southern Africa. Correspondingly, thesimulated convergence extends to 1608E near the equator(Fig. 5b); and the center is near 608E about twice stronger
than that observed. Both convection and convergence areabout 208–308 farther eastward than those observed,
showing a faster growth rate. It is noteworthy that the
ITCZ in the central Pacific north of the equator is muchstronger than that observed, which can be caused by theapparently stronger low-level convergence. This fast growthof low-level convergence is probably due to the spinup of
upscaled initial conditions from resources at a coarse resolution (see the discussion).
Figure 6 shows the vertical structure of specific humidity (contours with shading) with precipitation (thickblack curve) anomalies averaged over 158S–158N duringphase 2 along the equator. Corresponding to the low-level
convergence, the positive moisture anomalies (shaded)near 608E are in concert with the developing convection(thick black curve) in both observations and the NICAM.
They expand from the surface up to 450 hPa. Meanwhile, the humidity shows a developing west-tilt structure along the equatorial Eastern Hemisphere. Small
positive anomalies occur to the east of the convection inlower levels to the surface.
Model I of MJO in Zhang (2005) and the schematic
diagram in Sperber (2003) predict that the maturing
convection near 608E in phase 2 corresponds to the intersection of low-level westerly and easterly anomalies.
This structure is clearly shown in Fig. 7. Easterly anomalies (dashed contours) occur right under the convective

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FIG. 7. As in Fig. 6, but the contours represent the zonal wind (interval 0.5 m s21).

center (thick black curve) and extend from near 608E
to 1208W in the layer of 1000–600 hPa; while westerly
anomalies (solid contours with shading) dominate from
608E west to 508W near the surface to 400 hPa. In thelayers above, the phase is nearly reversed, indicating abaroclinic structure. The easterly anomalies appear as a
Kelvin wave response and the westerly anomalies appear
as a Rossby wave response. The NICAM has realistically
reproduced this complicated structure except that thewesterly wind anomalies are weaker in the 800–500-hPa
layer near 508–608E (Fig. 7b).
Observed convection anomalies in phase 3 (notshown) transits to negative in the central Indian Ocean
near 608E, turns to weak and shallow in the east Maritime Continent to the date line, and matures near 58S–08,
908–1008E. Corresponding to this transition, the 925-hPa
convergence (not shown) weakens in the central Indian
Ocean, shifts its center to the west Maritime Continent,
and edges near the date line, fostering convection in theeast Maritime Continent and West Pacific. The velocity
potential and convergent wind are still stronger than thatobserved. The simulated convection by two NICAM
versions has a similar pattern to that observed (notshown) except for its persistence in the west Indian
Ocean, and that it damps in the east Indian Ocean. The
ITCZ is still strong and extends westward to connect theconvection in the east Maritime Continent, producing afaster growth than that observed. The persistently strong
ITCZ indicates a bias in the mean state of the model.
The specific humidity anomalies along the equatorial
area have a vertical structure matching the surface convergence and convection anomalies in observations during phase 3 (not shown). It becomes negative from thesurface to 500 hPa near 608E. The positive anomalies
have a center at 700 hPa near 1208E and extend eastward
at lower levels, indicating that the frictional moisture
convergence is functioning. Very similar structures are
simulated by the two NICAM versions (not shown) except that the anomalies are somewhat weaker over the
Maritime Continent–west Pacific.
As a wave response to the convective heating centerednear 1208E along the equator, observed zonal winds (notshown) become westerly anomalies from 850 to 500 hPa
and easterly aloft from equatorial Africa to 1208E while
easterly from the surface to 400 hPa and westerly aloft in
the rest of the equatorial areas. The baroclinic structureof zonal winds has a zonal wavenumber 1 feature. This
structure is simulated well by the two NICAM versions(not shown).
During phase 4, observed convection anomalies (shadings in Fig. 8a) return back to near normal in the west
Indian Ocean, and become more suppressed near 908E

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FIG. 8. As in Fig. 5, but for phase 4.

north of the equator with a central precipitation anomalyof 212 mm day21. Enhanced convection prevails over the
Maritime Continent and west Pacific with precipitation
anomalies of over 15 mm day21 in 1158–1508E. The otherequatorial areas have weak convective anomalies. The
observed convergence at 925 hPa (contours and vectors
in Fig. 8a) is weakened in Africa–Indian Ocean, whilestrengthened in the Maritime Continent–west Pacific with
its center shifts to 1208–1508E near the equator. The
convergence at this level crosses the date line. The simulated convection anomalies (shadings in Fig. 8b) have asimilar pattern to that observed: suppressed convection in
the Indian Ocean and enhanced precipitation in the Maritime Continent and west Pacific. The convection over
ITCZ in the central and eastern Pacific is still muchstronger than that observed, which indicates the bias ofmean state. The simulated convergence at 925 hPa (contours and vectors in Fig. 8b) has a center near 1208–1508E
around the equator, very close to that observed. However,positive velocity potential and convergent vectors are
generally stronger in this area and much stronger over the
ITCZ than the observations. The stronger low-level winds
contribute to the much higher amplitude of RMM near
this phase (Fig. 3).

The vertical structures in phase 4 repeat those illustrated by Sperber (2003) and Zhang (2005). Observed
anomalies of specific humidity (contours in Fig. 9a)
become weakly positive from the surface to 500 hPa in
408–708E averaged between 158S and 158N, and strongly
negative from the surface to 500 hPa in 708–1008E. The
moisture anomalies correspond to the suppressed convection as shown by the averaged TRMM precipitation (thickblack curve) and shadings in Fig. 8a. In relation to thestrong convection anomalies in the Maritime Continent–
west Pacific (thick black curve in Fig. 9a and shadings in
Fig. 8a), strong positive anomalies of moisture extend
from the surface to 400 hPa near 1208E and to 600 hPa
near 1608E, demonstrating an apparent westward tilt
with height. Weak positive anomalies are building up in
the boundary layer from near 1708E–1508W, indicatingthat the frictional moisture convergence mechanism isfunctioning. The simulated vertical structure has a very
similar pattern and a close relation between moistureand convection anomalies (Fig. 9b) compared to thatobserved. Weak/negative moisture anomalies correspondto a suppressed convection in the Indian Ocean; while thereversed anomalies prevail over the Maritime Continent–
west Pacific. Moisture anomalies in the boundary layer

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FIG. 9. As in Fig. 6, but for phase 4.

are clearly built up, indicating that the frictional moisture convergence mechanism operates (cf. Fig. 8b). It isnoteworthy that the specific humidity has relatively
smaller anomalies in the NICAM than that in the reanalysis. This is likely due to the systematic difference asshown in Fig. 1.
Zonal wind anomalies in phase 4 have the typicalvertical structure disclosed in Sperber (2003) and summarized in Zhang (2005) as well. In observations (contours and shadings in Fig. 10a), westerly anomalies prevailfrom the surface to nearly 400 hPa in the Eastern
Hemisphere up to 1308E except that easterly anomalies
occur below 850 hPa and above 600 hPa in 608–908E.
The convection anomaly maximum (thick black curve in
Fig. 10a) occurs near 1208E, very close to the westerly
anomaly maxima from the surface to 500 hPa. At thesame time, easterly anomalies dominate from the surface to near 400 hPa in the rest of the equatorial Pacificwith a center at 700 hPa around the date line. Convection is weakening with strengthening easterly anomalies.
The patterns are reproduced realistically in the NICAM.
Westerly anomalies occur in the deep layer to the westof the enhanced convection and easterly anomalies occur to the east. However, the low-level zonal wind
anomalies in the NICAM (Fig. 10b) are weaker partly
because of the excluded zonal means. The simulated

wind anomalies in the upper levels extend farther
downward so that the baroclinic structure is more pronounced than in observations in particular from 1508E to
1308W. Such small differences do not negate that thesalient vertical structures of the MJO are reproduced
realistically by the NICAM. Phases 5 and 6 are thedamping stage for the current event. The structures inobservations (not shown) are generally similar to thoseof phase 4 with centers shifting more eastward. The
simulated structure resembles that observed as well (notshown). The 14-km NICAM performed generally thesame as the 7-km version although small differences areproduced.

4. Conclusions and discussion
The MJO event occurring from December 2006
to January 2007 is simulated realistically in amplitude
evolution, eastward propagation, and three-dimensional
structures by the NICAM at 14- and 7-km resolutions as
disclosed using the index of WH04. The frictional moisture
convergence mechanism operates to maintain the simulated MJO event so that the vertical structures of moisture
and wind anomalies are realistic. Such success can be attributed to the explicit representation of the interactions
between cumulus clouds and large-scale circulations. The

OCTOBER 2009LIU ET AL.

3265

FIG. 10. As in Fig. 7, but for phase 4.

promising simulation of this MJO event and perturbations
on smaller scales with the NICAM suggests that explicitly
resolved microphysics can take the place of parameterizations for the future dynamical subseasonal range fore-

FIG. 11. Evolution of anomalies for precipitation rate (blacksolid), surface latent heat flux (black dashed), and wind speed at
10 m (gray) over central Indian Ocean (108S–108N, 708–908E mean)
during the MJO event for (a) observations and the reanalysis and(b) the 7-km NICAM. Values are standardized for comparison.

cast. Successive studies will disclose the interactions onvery short time scales to specifically guide improving theparameterization schemes for climate models. In spite of
the overall success, small deficiencies occur in higher peak
amplitudes and faster-evolving phases 2 and 3 than observations, which are improved slightly in the 7-km simulation. Other possible mechanism for the MJO andcauses to the fast growth of phases 2 and 3 are discussed
below, followed by possible corrections.
In the central Indian Ocean, because the climatology of
low-level wind can be easterly and convection development can occur over easterly anomalies (phase 2 in Fig. 7),
the wind-induced surface heat exchange (WISHE; Neelin
et al. 1987; Emanuel 1987) mechanism may operate for

FIG. 12. The 30-day mean of OLR (W m22) along the equator(108S–108N mean) from NOAA satellite observations (black), the14- (gray solid), and 7-km (gray dashed) NICAM.

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MONTHLY WEATHER REVIEWVOLUME 137

FIG. 13. The first day mean anomalies for velocity potential (contours; interval is 1 3 106 m2 s22 and thick black is 0),
divergent wind vector (m s21) at 925 hPa. Shaded are precipitation rate (mm day21) for (a) TRMM, (b) 14-km
NICAM, and (c) 7-km NICAM.

the local MJO development, which was shown in GCM
studies (e.g., Maloney and Sobel 2004; Liu et al. 2005).
Nevertheless, WISHE does not have a favorable condition in this MJO event as shown in Fig. 11. The curve of
surface latent heat flux (black and dashed) follows closelythat of the wind speed at 10 m (gray) in both the reanalysis
and NICAM. There is no clear lead in time of the negative

anomalies for surface latent heat flux and positive sea
surface temperature (not shown) to the enhanced convection (black and solid). Instead, the enhanced convection leads the negative flux during 17–22 December in thereanalysis; and it is in phase with the positive flux during
the first several days in the model. This is not a favorable
structure for WISHE, which requires that negative surface

OCTOBER 2009

3267

LIU ET AL.

latent heat flux leads convection in the east by 1–2 pentads(e.g., Maloney and Sobel 2004).
It is noteworthy that the enhanced convection, represented by positive precipitation anomalies (black solid
curve in Figs. 11b), persists up to 25 December 2006,
longer than the OLR anomalies shown in the RMM
diagram (Fig. 4). This is because the simulated OLR is
not connected as closely with precipitation as in observations. Although Inoue et al. (2008, their Fig. 15) showedthat the latitudinal bias of OLR is small in the tropics
relative to observations when averaged in 908E–1608W,
the bias is prominent in the MJO active areas. Figure 12
shows the 30-day mean total OLR along the equator(108S–108N) in observations and the two NICAM versions. The simulated OLR has a similar pattern to thatobserved so that the standardized series are used to derive
the RMM index (Figs. 3 and 4). However, a large bias
occurs from 508 to 908E and from 708W to the Greenwich
meridian in both model versions. The 7-km version improves the simulation but the bias is still large; thus, theOLR was not used for composites for the MJO structures.
The large bias is likely induced by the separation of the
explicit moist process and the radiation transfer scheme.
The boundary layer process can contribute to the bias aswell considering the systematic difference of the simulated
moisture compared with that in the reanalysis (Fig. 2). A
comprehensive microphysics package and an improved
boundary layer scheme will be incorporated with the next
version of NICAM so as to reduce the bias.
The strikingly fast growth of phases 2 and 3 canoriginate from the low-level convergence and biased
ITCZ in the initialization. Figure 13 shows the first-day
average of simulated convection, 925-hPa velocity potential, and divergent vector compared with observations. Small difference of winds in the initial condition
produced by interpolation becomes large after the 1-day
spinup. For example, although enhanced convection
occurs in a slope area of 708–958E and 138S–58N in bothobservation and two NICAM versions (shaded in red),the simulated convection has already extended to the
Maritime Continent. Both the velocity potential andwind vectors show that the corresponding convergencehas developed to be much stronger than that observed in
the Indian Ocean–Maritime Continent. More importantly, the ITCZ in the central and eastern Pacific aredeveloping dramatically. Rotational wind vectors at
lower levels (not shown) grow fast in the Indian Ocean–west Pacific as well. Such faster growth demands aneffective method for the initialization, for example, a fourdimensional variational data analysis (4DVAR) or a
Karman filter as widely used in the operational global
systems for medium-range weather forecast. Moreover,the stronger ITCZ is persistent in all the phases 2–5 as abias in the two NICAM versions. The 7-km version has aslight improvement. Considering the other improvements in the 7-km version (e.g., Fig. 1), the NICAM at
higher resolutions can be tested to reduce this bias besides the improved microphysical package.
Acknowledgments. This study is supported by theJapan Agency of Marine Science and Technology(JAMESTEC) and NASA Grant NNX07AG53G
through their sponsorship of the International Pacific Research Center (IPRC). We thank Dr. David A. Randall
and Ms. Diane Henderson for helpful comments. The
CEOFs and variance for anomalous OLR, 850-, and200-hPa zonal winds are provided by the Bureau of
Meteorology Research Centre, Australia, through their
Web site http://www.bom.gov.au/bmrc/clfor/cfstaff/matw/
maproom/RMM/.
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