ATMOSPHERIC SCIENCE LETTERS
Atmos. Sci. Let. (2015)
Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/asl2.558

Does convection vary in different cloud disturbances?
Yoshiaki Miyamoto,1* Ryuji Yoshida,1 Tsuyoshi Yamaura,1 Hisashi Yashiro,1 Hirofumi Tomita1,2 and Yoshiyuki
Kajikawa1
1 RIKEN Advanced Institute for Computational Science, Kobe, Japan
2 Seamless Environmental Prediction Research, Japan Agency for Marine-Earth Science and Technology, Yokohama-city, Kanagawa, Japan*Correspondence to:Y. Miyamoto, RIKEN Advanced
Institute for Computational
Science, 7-1-26,
Minatojima-minami-machi,
Chuo-ku, Kobe, Hyogo
650-0047, Japan.
E-mail: ymiyamoto@riken.jp
Received: 18 June 2014
Revised: 14 December 2014
Accepted: 17 December 2014

Abstract
Deep moist convection is an element of cloud disturbances such as Madden-Julian oscillation(MJO), tropical cyclones (TCs), mid-latitudinal low depressions (MDL), and fronts (FRT).
However, differences in convection characteristics in disturbances remain unclear. We firstclarified the statistical features of the convection (structure, intensity, and environmental
parameters) in various disturbances simulated by a global simulation with a sub-kilometre
grid spacing. The convection in MJO (TC) was tall with a strong (weak) upward motion, andwas driven by large convective available potential energy (strong low-level convergence). The
convection in MDL and FRT was shorter and characterized by a strong vertical wind shear.
Keywords:

global simulation; moist convection; cloudy disturbance1. Introduction
Cloud systems on Earth are globally distributed, varying from individual cloud cells (100 km) to large-scale
cloud disturbances (104 km). The cloud disturbances
play important roles in modifying the global-scaleenergy budget. Additionally, the disturbances often
accompany heavy rainfall and hence cause severe disasters around the world. Consequently, researchers have
paid special attention to understanding their physicalprocesses. It is widely recognized that these disturbances are driven by interaction with deep moist convection; a number of convection cells are produced
under strong forcing of the disturbances. In turn, theconvection itself affects its background (i.e. the disturbances) by transporting mass, momentum, and thermal energy. For instance, the intensity and structure of
TCs are significantly sensitive to convective activities
in the core region (e.g. Smith, 2000). Hence, for a better understanding of disturbances, their interaction withconvection needs to be examined.
There is great difficulty in studying the interaction
between the convection and disturbances due to thelarge-scale gap in both temporal and spatial dimensions. Convection has temporal and spatial scales from30 min to 1 h and from less than 1–10 km (Houze,1994), whereas disturbance timescales are 1 week to
1 month and 102 –104 km (Madden and Julian, 1994).
Detailed datasets ranging from individual convective
cells to entire disturbances are hard to obtain, especiallyfrom observations and even from numerical simulations
due to computer resource limitations. Hence, previous
studies have numerically examined the interaction byparameterizing the convection and/or limiting the target
field covering a particular disturbance. Because convection parameterizations can represent the effects of© 2015 Royal Meteorological Society

convection on the disturbances, only responses of the
disturbances to energy transport by the convection havebeen understood previously.
However, the understanding of convection structuresand intensities in the disturbances has remained limited. Convection features such as the maximum vertical velocity appear to be different from one disturbanceto another. Convection largely depends on its environment, and the structures and/or driving mechanisms ofthe disturbances mentioned above are different in each
disturbance. For a better understanding of the disturbances, an understanding of differences in convection
features is highly desirable.
Here, we elucidate differences in convection structures and intensity in cloud disturbances based on the
results of a sub-kilometre global simulation. Section 2
describes the simulation data and the methodology todetect convection and disturbances. Results and discussion are presented in Section 3. Section 4 provides theconcluding remarks.

2. Simulation data and definition of cloud
disturbances
We used simulation results with 0.87-km grid spacing(Miyamoto et al., 2013) without convection parameterization, which had been performed using nonhydrostatic
icosahedral atmospheric model (NICAM) (Tomita andSatoh, 2004; Satoh et al., 2008) on the K computer.
They showed that the simulation realistically captures
the global cloud field and convective cells. This dataset
enabled us to compare convection generated in disturbances under realistic conditions. To detect bothconvective cells and disturbances, simple methodologies developed previously (Miyamoto et al., 2013) wereY. Miyamoto et al.

applied to the simulated results. These were used uniformly across a global dataset.
The number of vertical model layers was 94, which
was vertically stretched. The height of the lowest levelwas 36 m, and the average grid spacing in the troposphere was about 250 m. Physical processes suchas solar radiation and boundary-layer turbulence weresolved using parameterizations (Louis, 1979; Nakanishiand Niino, 2004; Tomita, 2008; Sekiguchi and Nakajima, 2009; Noda et al., 2010). The initial conditions inthe simulation were constructed using the results of a
3-day integration at 3.5-km grid spacing in which theinitial conditions were obtained from linearly interpolated data from the final analysis of the National Centre
for Atmospheric Research (Kalnay et al., 1996). The
simulation period was 12 h.
The convection was detected from the simulated
results by the following two-step method (Miyamoto
et al., 2013). First, convective grids were defined by
the optical thickness (>35) and cloud-top pressure(<400 hPa) following the cloud separation scheme of
the International Satellite Cloud Climatology Project(ISCCP; Rossow and Schiffer, 1999). Then, the convection core was detected in the convective grids as the gridat which the vertically averaged vertical velocity in the
troposphere was greater than that in all neighbouring
grids.
As large-scale atmospheric cloud disturbances, four
disturbances were chosen in the present study: theMadden-Julian oscillations (MJO), tropical cyclones(TCs), mid-latitudinal low-pressure disturbances(MDL), and synoptic-scale fronts (FRT). To extract
the MJO signal in the outgoing longwave radiation(OLR) from the NOAA satellite (Liebmann and Smith,
1996), the boreal summer intraseasonal oscillationindex (Kikuchi et al., 2012) was applied. The MJO
region was defined as the grid where the filtered OLR
was less than −10 W m−2 . The TCs were extracted byMiyamoto et al.’s (2014) methodology. The TC region
was defined as that inside the 600-km radius fromthe centre. To detect the MDL centres, the simulated
data were coarsened to 2.5 × 2.5∘ grid spacing. Then,grids at which the sea-surface pressure (SLP) was
5 hPa less than the areal average in the 10∘ radius weredetected as low centres. The MDL region was assumed
to be 1000 km from the low-pressure minimum. The
frontal region was detected by the thermal frontal
locator (Renard and Clarke, 1965), which represented
a third-order differential of a variable in the horizontal direction with a direction modulation term. The
equivalent potential temperature at the 1500-m level
was employed as the variable in the thermal frontal
locator. The grids where the thermal frontal locator was>10−13 in the 2.5 × 2.5∘ data were first detected. Then,
if the girds had a continuous region whose maximum
distance was greater than 10∘ , the region was detected
as frontal grids. The frontal regions were defined as
those inside the 200-km distance from the frontal
grids.
© 2015 Royal Meteorological Society

3. Results and discussion
Figure 1(a) shows the simulated cloud field at
201208251200UTC in the sub-kilometre simulation with the detected convection centre grids anddisturbances. The simulation successfully reproduced
multi-scale cloudy convective phenomena ranging
from 10∘ to 104 km in a single simulation. The disturbance regions (MJO, red; TC, blue; MDL, green; andFRT, cyan) were reasonably detected. The MJO region
was detected at 90∘ –120∘ longitude and −15∘ to 15∘
latitude. Although re-analysis data were used to detect
the MJO, simulated organized convective systems wereseen in the MJO region. Two TCs were extracted at(lon, lat) = (123.5, 17.52) and (128.5, 25.90). The best
track data of the Joint Typhoon Warning Centre showedthat there were actually two TCs around these points
at this time. Further analysis showed that the TCs arein the quasi-steady stage. Especially the TC close to
Japan has developed well, and has a clear eye. MJO is alarge scale (a few thousand km) envelop of convection
with circulation, whereas TC is a relatively small scale(a few hundred km) convective phenomenon. Since
the TCs are distant from the MJO region, it is strongly
suggested that the TCs are not related to the MJO. In
the mid-latitudes, both MDL and FRT regions were
widely detected, especially in the regions of low OLR.
Thus, compared with the cloud field, the locations ofeach disturbance were reasonable. Convection cores
were also detected reasonably frequently in low-OLR
regions (e.g. low-latitude regions in the western Pacificor mid-latitude cyclonic regions), as found previously(Miyamoto et al., 2013). Figure 1(b) displays the latitudinal distributions and the number of convective
cells in the different disturbances. The convection
associated with the MJO exists around the equatorial
region, that associated with TCs is detected only inthe northern hemisphere, and those of MDL and FRT
are distributed mainly in latitudes ±30∘ to ±60∘ . The
spatial distributions of convection and disturbances arereasonable at all latitudes.
Figure 2 shows the radius-height cross-sections forthe vertical velocity w composed of all convection ineach disturbance, as well as the global average. The
convection structure and intensity are significantly different in each disturbance. The convection in the MJO
has the strongest w value among the four disturbancesand global average. It is also stronger than the averageat the same latitudes (not shown). The MJO convection accompanies the strongest precipitation, and thepeak is located at the core and decays with radius. The
strong precipitation is consistent with the strong upward
motion and with the large downdraft at the bottom of
atmosphere. The convection in the TC has a tall structure similar to that in the MJO, but w is weaker. It iseven weaker than the average at the corresponding latitudes. Unlike the MJO convection, w at the bottom is
not generally negative, but slightly positive. Consistent
with these parameters, the precipitation is smaller thanthat of the MJO. In the mid-latitudinal disturbances (i.e.
Atmos. Sci. Let. (2015)

Does convection vary in different cloud disturbances?

Figure 1. (a) Horizontal distribution of the extracted convection core in Δ0.87 (yellow) superimposed on the OLR (shaded) at
201208251200UTC. The regions enclosed by the red, blue, green, and magenta contours indicate regions detected as the MJO,
TC, MDL, and FRT disturbances, respectively. (b) Latitudinal profile of the number of extracted convective cells in the four classesof disturbances in a 1∘ latitude bin.

(a)

(b)

(c)

Globe [#66985](d)

MJO [#4222](e)

TC [#4691]

MDL [#12046]

16

16

16

16

16

14

14

14

14

14

12

12

12

12

12

10

10

10

10

10

FRT [#9655] w (m s−1)
18

z (km)

8z (km)

8z (km)z (km)z (km)

0.8

8

0.6

8

0.4

4

4

4

4

4

2

2

2

2

2

0
−6−4−2 0 2 4 610

0−6−4−2 0 2 4 6
10
x (km)

0−6−4−2 0 2 4 6
10
x (km)

0−6−4−2 0 2 4 6
10
x (km)

0−6−4−2 0 2 4 6
10
x (km)

50x (km)

50x (km)

50x (km)

50x (km)(mm h−1)

6(mm h−1)

6(mm h−1)

6(mm h−1)

6(mm h−1)

6

0.2
01 (m s−1)

50

x (km)

Figure 2. (Top) Radius-height cross-sections for composites of vertical velocity w (shaded) and velocity vector of radial velocity
and w (arrows) for the simulated convection detected in (a) the globe, (b) MJO, (c) TC, (d) MDL, and (e) FRT. The contours at
w = 0.3, 0.6, and 0.9 m s−1 are highlighted by the white lines. The number of convection cells is shown in the figure title. (Bottom)
Radial distributions of precipitation averaged from all detected convection.

MDL and FRT), the convection is shorter than those in
the two tropical disturbances; the peak height of w is
located around 5–6 km. The convection in the MDL has
stronger w than that in the FRT, or even in the TC. The
precipitation is accordingly stronger in the MDL thanin the FRT. However, compared with the tropical disturbances, the amount of precipitation and radial gradientin the MDL and FRT convection are small. In short,the convection structure largely depends on the disturbances: the convection in tropical disturbances has
stronger maximum w at a higher level compared withthat in the mid-latitudinal disturbances, and the amountof precipitation is correspondingly large in the tropicaldisturbances.
To explore the differences in convection environments, Figure 3 shows the convection distance andenvironmental parameters (i.e. low-level mass convergence, CAPE, and vertical shear of horizontal winds),© 2015 Royal Meteorological Society

which are closely related to the physical processes of
convection. The environmental parameters are areally
averaged inside the 30-km radius from the convection
centre. The differences in the medians of all parameterssatisfy the 99% level of statistical significance. The distance to the nearest convection centre is shortest in the
TC of the four disturbances, with a median of 4.35 km(=5 model grids), and 50% of the convection in the TC
has a nearest convection centre at 2.61–6.02 km (3–7
grids) distance. In the other disturbances, the median is
5.22 km (6 grids). This indicates that the number density of the convection is large in the TC compared withthose in the other disturbances and the global average.
Figure 3(b) shows that the TC convection has thestrongest low-level mass convergence, calculated in
the lowest 500 m. The convergence averaged acrossthe entire disturbance is slightly positive in all disturbances. The convergence below the MJO convection is
Atmos. Sci. Let. (2015)

Y. Miyamoto et al.

(a)Convection distance

8

−5

(b)

x 10

convergence

4
2

(s−1)

(km)

6
4

0

2
−2
0

Globe MJO TC MDL FRT

(c)

CAPE

1000

Globe MJO TC MDL FRT(d)Vertical wind shear

30

600

(m s−1)(J kg−1)

800

400

20

10

200
0

Globe MJO TC MDL FRT

0

Globe MJO TC MDL FRT

Figure 3. (a) Distance to the nearest convection cell, (b) low-level mass convergence, (c) convection available potential energy(CAPE), and (d) vertical shear of horizontal wind between the 2.5-km height and 2 km below the tropopause. The colours are
the same as those in Figure 1. The square and circle indicate the median of the detected convection and the average of all grids
diagnosed as disturbances, respectively. The vertical line ranges between the top and bottom quantiles.

© 2015 Royal Meteorological Society−4

1
Divergence (s−1)

negative. This is consistent with the composite presented in Figure 2, which indicates the presence ofstrong rainfall and horizontal divergence flow. However, the CAPE value is largest in the MJO convection(Figure 3(c)). The averaged CAPE in the entire disturbance and the difference from the disturbance average
are most significant in the MJO. This implies that convection in the MJO is driven by consumption of the
background CAPE because the convergence in the MJO
convection is not strong (Figure 3(b)). The CAPE difference between the convection and disturbance is also
large in the TC convection, whereas the difference isquite small in the mid-latitudinal convection. In both
the MDL and FRT, the convection is characterized bystrong vertical wind shear (that is, the absolute valueof the wind-vector difference between the 2.5-km leveland 2 km below the tropopause; Figure 3(d)). Specifically, the vertical wind shear is stronger in the FRT
convection than in the MDL convection, whereas theconvergence is weaker in the MDL.
For more comprehensive understanding of the
MJO and TC convection differences based on the
different environments, we show the histogramof convection of the globe, and MJO and TC in a
CAPE-convergence space (Figure 4). The histogramsof MJO and TC convection are the differences fromthe global convection. It is clearly shown that the MJO(TC) convection tends to have high (low) CAPE andweak (strong) convergence environments compared× 10 Number density of convection
0.1

0
MJO
TC
Globe
−1
0

1000

2000

3000

CAPE (J kg−1)

Figure 4. Number distributions of convection in a CAPE-lowlevel mass convergence space. The numbers are normalized
by total number, and are binned into 300 J kg−1 and 0.2 × 10−4intervals. Shaded area shows the global data. The red andblue contours respectively show the differences of convection
number in MJO and TC from the global one. Positive values are
only plotted. The contour interval is 0.01.

with the global one. There is another peak for the
TC convection in high CAPE and weak convergence
range. This is simply because the histogram of global
convection tends to have lower CAPE than that oftropical convection, owing to taking account of the
mid-latitudinal region whose CAPE is much lower thantropics.
The result indicates that the MJO convection is characterized by strong w and precipitation, high CAPE, andAtmos. Sci. Let. (2015)

Does convection vary in different cloud disturbances?

weak convergence suggests that it is generated by strong
thermodynamic forcing. However, the strong convergence, low CAPE, and weak w of the TC convection
imply that it is driven by strong background dynamical
forcing convergence in relatively low-CAPE environments. The relationship between the maximum verticalvelocity and CAPE is consistent with the potentiallyattainable velocity, wmax ∼ (2CAPE)1/2 . The stronger
convergence and relatively weaker vertical wind shear
in the MDL convection compared with the FRT are consistent with stronger w in the MDL convection. The
convection in the tropical disturbances (MJO and TC)
has a tall structure, whereas that in the mid-latitudinal
disturbances (MDL and FRT) is short, indicating thatconvection height is determined by the depth of thetroposphere.

4. Concluding remarks
This study elucidated the convection differences invarious atmospheric disturbances (MJO, TC, MDL,
and FRT). The cloud-top height is clearly different
between the tropical disturbances (MJO and TC)
and mid-latitudinal ones (MDL and FRT). The MJO
convection has a strong upward motion and precipitation, whereas the TC convection has a weak updraft.
The MDL convection is characterized by relatively
strong convergence and weak vertical wind shear
compared with the FRT convection. The differences
may be attributable to the environmental fields ofthe convection (i.e. the disturbances themselves):
the MJO (TC) convection is generated under a high(low) CAPE and relatively weak (strong) convergenceregion. Therefore, the feedback process of convection
within the disturbance appears to be significantly
different in each disturbance. The present study analysed a snapshot of global simulation result with ahigh spatial resolution. We emphasize the importanceof examining the interactions between convection
and disturbances based on datasets covering longer
periods with a spatial resolution high enough toresolve it.

Acknowledgements
The authors would like to thank Drs M. Satoh, M. Sugi, T.
Miyakawa, S. Nishizawa, S. Iga, and H. Miura, T. Nasuno andour colleagues for fruitful discussions. The simulation was performed using the K computer at the RIKEN Advanced Institutefor Computer Science.

© 2015 Royal Meteorological Society

References
Houze RA Jr. 1994. Cloud Dynamics. Academic Press: San Diego, CA;
573 pp.
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L,
Iredell M, Saha S. 1996. The NCEP/NCAR 40-year reanalysis project.
Bulletin of the American Meteorological Society 77: 437–471, doi:
10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Kikuchi K, Wang B, Kajikawa Y. 2012. Bimodal representation of thetropical intraseasonal oscillation. Climate Dynamics 38: 1989–2000,
doi: 10.1007/s00382-011-1159-1.
Liebmann B, Smith CA. 1996. Description of a complete (interpolated)outgoing longwave radiation dataset. Bulletin of the American Meteorological Society 77: 1275–1277.
Louis JF. 1979. A parametric model of vertical eddy fluxes in theatmosphere. Boundary-Layer Meteorology 17: 187–202, doi:
10.1007/BF00117978.
Madden RA, Julian PR. 1994. Observations of the 40-50-day tropicaloscillation-a review. Monthly Weather Review 122: 814–837, doi:
10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2.
Miyamoto Y, Kajikawa Y, Yoshida R, Yamaura T, Yashiro H, Tomita
H. 2013. Deep moist atmospheric convection in a subkilometer
global simulation. Geophysical Research Letters 40: 4922–4926, doi:
10.1002/grl.50944.
Miyamoto Y, Satoh M, Tomita H, Oouchi K, Yamada Y, Kodama
C, Kinter J III. 2014. Gradient wind balance in tropical cyclones
in high–grid spacing–global experiments. Monthly Weather Review,
142: 1908–1926, doi: 10.1175/MWR-D-13-00115.1.
Nakanishi M, Niino H. 2004. An improved Mellor–Yamada Level-3
Model with condensation physics: its design and verification.
Boundary-Layer Meteorology 112: 1–31, doi: 10.1023/B:BOUN.
0000020164.04146.98.
Noda AT, Oouchi K, Satoh M, Tomita H, Iga S. 2010. Importance ofthe subgrid-scale turbulent moist process: Cloud distribution in global
cloud-resolving simulations. Atmospheric Research 86: 208–217,
doi: 10.1016/j.atmosres.2009.05.007.
Renard RJ, Clarke LC. 1965. Experiments in numerical objective
frontal analysis 1. Monthly Weather Review 93: 547–556, doi:
10.1175/1520-0493(1965)093<0547:EINOFA>2.3.CO;2.
Rossow WB, Schiffer RA. 1999. Advances in understanding clouds
from ISCCP. Bulletin of the American Meteorological Society 80: 2261–2287, doi: 10.1175/1520-0477(1999)080<2261:
AIUCFI>2.0.CO;2.
Satoh M, Matsuno T, Tomita H, Miura H, Nasuno T, Iga S. 2008.
Nonhydrostatic icosahedral atmospheric model (NICAM) for global
cloud resolving simulations. Journal of Computational Physics 227:
3486–3514, doi: 10.1016/j.jcp.2007.02.006.
Sekiguchi M, Nakajima T. 2009. The improvement of the absorption
process using a computational optimization in an atmospheric generalcirculation model, vol. 89, International Radiation Symposium, 1100.
Smith RK. 2000. The role of cumulus convection in hurricanes andits representation in hurricane models. Reviews of Geophysics 38:
465–489.
Tomita H. 2008. New microphysical schemes with five and six categories by diagnostic generation of cloud ice. Journal of the Meteorological Society of Japan Ser II 86A: 121–142, doi: 10.2151/jmsj.
86A.121.
Tomita H, Satoh M. 2004. A new dynamical framework of nonhydrostatic global model using the icosahedral grid. Fluid Dynamics
Research 34: 357–400, doi: 10.1016/j.fluiddyn.2004.03.003.

Atmos. Sci. Let. (2015)