Characteristics of cloud size of deep convection
simulated by a global cloud resolving modelover the western tropical PacificT. Inoue1,

M. Satoh1, 2, H. Miura2, B. Mapes3

1) Center for Climate System Research, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi,
Chiba, 277-8568, Japan
2) Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science andTechnology, 3173-25 Showa-machi,Kanazawa-ku,Yokohama-shi, Kanagawa,236-0001,Japan
3) Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, FL, USA

4 Oct. 2007, submitted to J. Meteor. Soc. Japan, Special Issue
29 Dec 2007, revise(accepted for publication)

Corresponding author address:
Toshiro Inoue, toshiro@ccsr.u-tokyo.ac.jp
Center for Climate System Research, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba,277-8568, Japan-1-

Abstract

Cloud horizontal size distributions of cloud clusters were analyzed for global cloud resolving
simulations with the global nonhydrostatic model NICAM whose mesh interval is about 3.5km and
7km. The 3.5km-mesh simulation was performed for 7 days starting at 00 UTC 25 Dec 2006 by
giving an initial condition of reanalysis data, while the 7km-mesh simulation was run for 32 days
from 00 UTC 15 Dec 2006. We used outgoing long-wave radiation (OLR) simulated by NICAM to
calculate size distributions of deep convection, and compared them with those analyzed using
equivalent blackbody temperature (TBB) of the infrared channel of 11 μm of the Japanesegeostationary meteorological satellite (MTSAT-1R). We selected two threshold temperatures, 208 K
and 253 K, to identify deep convective areas including anvil cloud. Specifically, we call clouds
defined by the 208 K-threshold “deeper” convective clouds.

Over the tropical region covering themaritime continent and the western tropical Pacific (10S-10N, 90E-160W), we examined the sizeof cloud areas defined by the two TBB threshold values and corresponding threshold values of OLR
of 90 W m-2 and 210 W m-2, which were chosen by comparing cumulative histograms of TBB and
OLR in this region.
Resolution dependency by NICAM shows that the overall cloud size distribution of the 3.5
km-mesh simulation is much closer to that of the MTSAT-1R observation than that of the 7-2-

km-mesh simulation. Size distributions of deep convection in both simulations indicate nearly
lognormal as is seen in the MTSAT-1R observations. The 3.5 km-mesh simulation shows slightly
less frequency than the MTSAT-1R observation for smaller size of deeper convection, and it does
not reproduce very large clouds. When comparing cloud characteristics over land and ocean,simulated cloud size statistics are closer to the MTSAT-1R observation in the maritime continent
region (westward of 160E) than in the open ocean region (eastward of 160E).

A comparison of

temporal variation of cloud area shows that the 3.5 km-mesh simulation captures clear signals of
diurnal cycles over both the maritime continent and the open ocean regions, together withamplification associated with the MJO event. Morning and afternoon difference of convective
activity over a large island within the maritime continent is also simulated by 3.5km-meshsimulation.
When one uses a global cloud resolving model for climate studies, the analysis of cloud size
distributions gives another dimension to improve the cloud properties of simulations. It is not only
relevant to realistic representations of deep convection, but is also useful for improving theradiation budget of global cloud resolving simulations.

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1. Introduction
Recently, simulations of a global cloud resolving model are becoming available using
enormous computer power (Tomita et al, 2005; Satoh et al, 2005). The Nonhydrostatic ICosahedral
Atmospheric Model (NICAM) has been developed (Tomita and Satoh, 2004; Satoh et al., 2007a)and is used for global cloud resolving simulations. A global cloud resolving model (GCRM), which
is defined here as a global nonhydrostatic model with a mesh size of a few kilometers and uses an
explicit cloud process without cumulus parameterizations, simultaneously calculate meso-scale
circulations associated with deep convection and large-scale circulations driven by organized
convection. As an organized structure of meso-scale circulations, cloud clusters are naturally
resolved in these simulations with NICAM (Nasuno et al., 2007), while they are not simulated wellby traditional general circulation models (GCMs) with cumulus parameterizations. Simulations
with GCRM enable us to discuss realisms of cloud characteristics, particularly those of cloudclusters whose scale is a few hundreds of kilometers. At this new stage of high-resolution global
atmospheric modeling, we can analyze numerically simulated data with GCRM using the methods
used for geostationary meteorological satellite (GMS) observations, whose resolution iscomparable to the mesh size of GCRM.
Analysis of cloud clusters using the GMS observations has a long history (e.g., Nakazawa 1988;
Mapes and House 1993; Sui et al., 1997). To identify deep clouds, appropriate values of equivalent-4-

blackbody temperature (TBB) of infrared data are used. Convective clouds over the tropics have aspectrum in size ranging from small isolated cumulus to large convective clusters. These
convective clouds affect the vertical distribution of heating in the troposphere and radiation budget
as well. Size distributions and temporal evolutions of deep convection have been studied using
satellite observation data (e.g., Williams and Houze 1987; Mapes and Houze 1993; Machado et al1993). Methods to analyze spatial and temporal variations of deep convection defined by infrared
data can be used for simulated data with GCRM, because both data are provided by a few
kilometer-interval. Similar analysis methods could be used for data produced by traditional GCMs,
but direct comparison cannot be made because of the gap of the resolutions between GCMs andobservations.
Using GCRM data, we obtain statistical properties of cloud and precipitation systems similar to
those retrieved from satellite observations. Satoh et al. (2007b) showed the statistics of the
precipitation height for the aqua planet experiment with NICAM (Tomita et al. 2005; Satoh et al.
2005) and compared them with those of the Precipitation Radar (PR) on board Tropical Rainfall
Measurement Mission (TRMM) (Takayabu 2002). It was found that the frequency of the
precipitation top height of convective rains with GCRM has profiles similar to the satellite
observations at least qualitatively. However, since the simulation was an idealized one under theaqua planet condition, the comparison was limited to statistics over a wide domain of ocean in-5-general and it was difficult to identify the causes of discrepancies.
Recently, simulations with GCRM under more realistic conditions with land and sea distributions
have been performed (Miura et al., 2007a, 2007b). Giving initial fields interpolated using reanalysis
data, they integrated NICAM for one month with the mesh interval of about 7 km and for one weekwith the mesh interval of about 3.5 km at highest resolution. Miura et al. (2007b) captured therealistic behaviors of a Madden-Julian Oscillation event seen in the observation; slowly eastward
propagation of cloud systems was reproduced and fine scale structure within the MJO was resolved.
In this simulation, not only large-scale organized cloud systems associated with the MJO, but cloudclusters whose size was hundreds of kilometers were also simulated. Figure 1 compares the cloud
distribution of the Japanese geostationary meteorological satellite of the Multi-functional Transport
Satellite (MTSAT-1R) observation and that of the NICAM simulation at 00 UTC 26 Dec 2006 over
the western tropical Pacific covering the maritime continent: TBB colder than 253 K is shown for
the infrared image of MTSAT-1R, while the outgoing longwave radiation OLR lower than 210 W
m-2 is shown for the NICAM simulation of about 3.5 km-mesh size (DX3.5km). This shows thatsizes and locations of cloud clusters are realistically simulated, particularly over the maritime
continent and the ITCZ region over the ocean between 160E-160W.
In the present study, we analyzed deep convective clouds over the western tropical Pacific using
the simulated data by NICAM shown above (Miura et al., 2007b). Cloud size distribution by-6-

NICAM is compared with that analyzed by using TBB of MTSAT-1R. From this comparison, we
will argue quantitatively how cloud clusters of the model simulation are comparable to those of theobservations. We will investigate the dependency on the model resolution, and contrast between themaritime continent and the open ocean regions, together with temporal variations. Since OLR is
directly related to the energy transfer, this analysis is useful to investigate the radiation budget ofthe model. In section 2, we will describe the experimental setting, and the analysis method is
described in section 3. Results are shown in section 4, and summary and discussion are followed in
the final section.

2. Experimental design
We used the NICAM for global cloud resolving simulations (Tomita and Satoh 2004; Satoh etal.2007a). The horizontal mesh size are about 7 km and 3.5 km, which correspond to the recursive
grid-division level 10 and 11, respectively (Tomita et al., 2001; 2002). The 3.5km-mesh simulation
was started at 00 UTC 25 Dec 2006 and integrated for 7 days, while the 7km-mesh simulation wasstarted at 00 UTC 15 Dec 2006 and integrated for 32days to study the whole cycle of the MJO
event (Miura et al., 2007b). These simulations will be denoted by DX3.5km and DX7km. The
computation was performed using the Earth Simulator. All the physical schemes are the same
regardless of the spatial resolutions and time steps. No cumulus parameterization was used, and the-7-explicit cloud physics scheme of Grabowski (1998) is used. The boundary layer scheme based on
Mellor and Yamada level 2 with moist process is implemented (Yamada and Mellor, 1979;
Nakanishi and Niino 2004). The radiation scheme MSTRNX (Nakajima et al. 2000; Sekiguchi ,2004) is called every 5 minutes. We use 40 levels in the vertical with thinner depths near thesurface using the terrain following coordinate. The time step interval is 20 sec for DX7km, and 15
sec for DX3.5km.
The output data were stored at 90 min intervals. The original icosahedral grid data wereconverted to the longitude-latitude grid by bi-linear interpolation. The numbers of grid points along
the equator are 10,240 for DX3.5km and 5,120 for DX7km, so that the typical grid intervals are 3.9
km and 7.8 km. Note that the resolution of the infrared data of MTSAT-1R is provided by 0.05 deg
interval, which is almost comparable to the grid interval of DX3.5km.

3. Data and analysis
In this study, we focus on evaluations of deep convection simulated by NICAM over the western
tropical Pacific. Simulated upwelling longwave radiation (OLR) by NICAM is compared with TBB
observed by MTSAT-1R. The target area of our study is confined over the maritime continent
region of 10S-10N and 90E-160E and the open ocean region of 10S-10N and 160E-160W. The
brightness temperatures of 208 K and 253 K are used to define deep convective areas associated-8-with anvil cloud. We specifically call clouds defined by the 208 K threshold “deeper” convective
clouds. The 208 K TBB threshold value is selected considering the study by Mapes and Houze(1993). Machado et al. (1993) used the 253 K threshold as the largest TBB to define deep convective
clouds. The temperature 253 K corresponds to about 7.9 km height (400 hPa) in the US tropicalstandard atmosphere. Although OLR and TBB are different physical parameters, we can see thesimilarity of distribution patterns associated with deep convection. Inoue and Ackerman (2002)
studied the OLR in terms of cloud types. Inoue et al. (2006) showed that the conventional OLR
threshold to define deep convection corresponds to the optically thicker cirrus type cloud rather
than cumulonimbus type cloud. Cumulative distributions of TBB and OLR are used to bridge thetwo parameters. The corresponding OLR values for the TBB threshold values are determined bycomparing the cumulative distributions of TBB and OLR.
Figure 2 shows cumulative distributions of TBB observed by MTSAT-1R (a) and OLR simulated
by NICAM of DX3.5km and DX7km (b) over the area of 10S-10N and 90E-160W during 26-31
Dec 2006. The arrows show the cumulative percentage corresponding to 208 K and 253 K TBB in
Fig. 2(a) and corresponding OLR values in Fig. 2(b). The cumulative percentages of TBB
corresponding to the 208 K and 253 K thresholds are 3 % and 32 % in Fig. 2(a). The equivalent
OLR to the TBB threshold is determined from the cumulative distributions of OLR over the same
area and period. The corresponding OLR values to 208 K and 253 K in DX3.5km are 90 W m-2 and-9-210 W m-2, respectively. For DX7km, we used the same OLR threshold values 90 W m-2 and 210
W m-2 in spite that the cumulative distribution is slightly different from that of DX3.5km. Using theabove thresholds, we studied the characteristics of deep and deeper convective clouds simulated by
NICAM and compared them with the MTSAT-1R observations over the tropical western Pacific of
10S-10N and 90E-160W.

4. Resultsa) Cloud size of deep convection
Cloud size of deep convection is defined as contiguous regions of TBB colder than the thresholdvalues for infrared image of the MTSAT-1R observations and those of OLR smaller than thecorresponding threshold values for the NICAM simulations. Figure 3 is a symbolic representationof the horizontal size of deep convection shown by circle for 00 UTC 26 Dec 2006. Deep
convective areas with TBB colder than 253 K by the MTSAT-1R observations and those with OLR
less than 210 W m-2 by the NICAM DX3.5km and DX7km simulations are shown in (a), (b), and(c), respectively. The size of a circle represents the horizontal size of a contiguous region of clouds
and the center of the circle is located at the areal center of the contiguous cloud region. Note thatcircles are depicted only for deep convective areas whose centers are located within the analysis
domain (10S-10N, 90E-160W). The same scale is used for these three figures. The area coverage-10-

over 10S-10N and 90E-160W in this case is 5.8×106 , 9.1×106, and 8.4×106 km2 in total and 2.8
×103, 5.1×103, and 8.7×103 km2 as average, respectively. The maximum sizes are 5.8×105,
4.2×105 and 3.2×105 km2 and the number of deep convective clouds are 2107, 1790 and 973,
respectively. As seen from Fig. 3, the largest size of cloud clusters appeared in the MTSAT-1R
infrared images, while there are more numbers of cloud that has a relatively large area appeared inboth NICAM DX3.5km and DX7km simulations at this time. The mean size of deep convection islarger in DX7km than in DX3.5km, while numbers of deeper and deep convection are smaller in
DX7km than in DX3.5km.
Similar analysis for deeper convective areas defined by the 208 K threshold (not shown)indicated that the area coverage over 10S-10N and 90E-160W is 6.3×105, 7.3×105 and 3.7×105 km2
in total and 1.8×103, 1.5×103, and 4.5×103 km2 as average for MTSAT-1R, DX3.5km and DX7km.
The maximum size is 8.4×104, 4.1×104 and 6.0×104 km2 and the number of cloud clusters are 357,
485, and 85, respectively. Again, the largest size of deep convection is observed in the MTSAT-1R
analysis. The mean size of deeper convection is larger in DX7km than in DX3.5km, while numbersof deeper convection are smaller in DX7km than in DX3.5km , as in the case with deep convection
shown in Fig. 3.
Figure 4 shows histograms of the size dimension (square root of area) in km for deeper and deep
convection.defined by TBB = 208 K and OLR = 90 W m-2 (top) and by TBB = 253 K and OLR = 210-11-

W m-2 (bottom). This shows that the DX7km simulation represents a relatively larger size of deep
and deeper convection compared with the MTSAT-1R observation and DX3.5km simulation. We
also notice that the frequency of the smaller size of deep and deeper convection is larger in the
MTSAT-1R observation compared with the DX3.5km simulation.
Figure 5 depicts the cumulative distribution functions with log scale for cloud area of deeper
convection.

Each set of data appears as a straight line in Fig.5; therefore, cloud size is

log-normally distributed for both simulations and the MTSAT-1R observation. The size of deeper
convection of DX3.5km is much closer to that of MTSAT-1R than that of DX7km. This figure also
indicates that the size of deeper convection of DX7km is larger than that of DX3.5km and
MTSAT-1R. The size of deeper convection of MTSAT-1R becomes less frequent for the size larger
than 5,000 km2 compared with that of DX3.5km. On the other hand, smaller size of deeper
convection is more frequently observed by MTSAT-1R than DX3.5km. This implies that thesmaller clouds marginally resolved by MTSAT-1R cannot be simulated well by DX3.5km as seen
in relative size distribution.
The contrasting feature of cloud clusters between the maritime continent and the open ocean was
examined using the size distributions of deep/deeper convection. Figure 6 compares size
distributions over the maritime continent and over the open ocean for the 208 K threshold (a) and
253 K threshold (b). The MTSAT-1R observation indicates that the smaller clouds (cloud size

-12-

smaller than 104 km2 for deeper convection and 5X104 km2 for deep convection) appeared over the
open ocean region more frequently than the maritime continent region. The larger clouds (cloudsize between 2X104 km2 and 105 km2) frequently appeared over the maritime continent region fordeeper convection. While clouds of about 105 km2 appeared more frequently over the open ocean
region, considering the slope between the two. This tendency is not clear for the 253 K threshold.
Figure 7 shows the contrast of size distributions over the maritime continent and the open ocean
regions for the 90 W m-2 threshold (a) and the 210 W m-2 threshold (b) in the NICAM DX3.5km
simulation. The cloud size is larger over the maritime continent region than over the open ocean
region. This tendency is consistent with the MTSAT-1R observation, although the difference isslightly enhanced in DX3.5km. However, contrary to the MTSAT-1R observation, the larger clouds
are simulated more over the maritime continent region for the 210 W m-2 threshold.
Comparisons between the NICAM simulation and the MTSAT-1R observation over both regions
are shown in Figs. 8 and 9, which can be reproduced from Figs. 6 and 7. The deeper clouds(defined by the 208 K and 90 W m-2 thresholds) are compared in Fig. 8 over the maritime continent(a) and over the open ocean (b). The size distribution is generally similar for both regions. The
number of clouds smaller than 7×103 km2 is less frequent in DX3.5km over both the maritime
continent and the open ocean regions. The difference for larger clouds is more clearly seen over the
open ocean region, although the number of such large clouds is small. This suggests that the-13-

NICAM DX3.5km simulation has deficiency in depicting larger clouds of deeper convection overboth the maritime continent and the open ocean regions. The deeper convection is related to coresof deep cumulonimbus. It is difficult to underpin the reasons for this discrepancy only from thepresent analysis alone. We speculate that the detrainment process near the tropopause might not be
represented well by the simulation due to insufficient vertical resolution near the tropopausetransition layer. We note that cloud amount in the core of deep convection is also related to cloud
microphysics schemes through precipitation efficiency (Sui et al. 2005).
Figure 9 is the comparison of deep convection defined by the 253 K and 210 W m-2 thresholds.
The cloud size of DX3.5km is larger than that of MTSAT-1R over both regions. This suggests thatthe cloud areas defined by OLR = 210 W m-2 (TBB = 253 K) is overestimated by DX3.5km. Figure
10 shows the cloud type map constructed from the split window method (Inoue, 1987) at 00 UTC
26 Dec 2006. The brightness temperature difference between the split window, BTD=TBB(11μm) TBB(12μm), indicates optical thickness. BTD is larger for optically thinner clouds, while BTD
becomes zero for optically thick clouds that can be assumed to be black-body clouds. In Fig.10, weselect the 0.5 K BTD threshold to classify optically thick or thinner clouds. Clouds shown in the
figure are all colder than 253 K. Clouds colored in purple, blue, and green correspond to clouds
colder than 208 K, optically thick clouds, and optically thinner clouds,. As seen in the figure, larger
clouds whose TBB is between 208 K and 253 K are mostly optically thinner clouds (green in the-14-

figure), while smaller clouds are mostly optically thick clouds (blue in the figure). Further, most ofthese optically thinner clouds are surrounding the clouds colder than 208 K (purple in the Fig. 10)
over the 10S-10N area. Therefore, this larger size of deep convective cloud areas corresponds to
optically thinner clouds. The size of clouds defined by the 253 K threshold is generally smaller thanthose of the DX3.5km simulation (Figs 3 and 9); this implies that the NICAM DX3.5km simulation
overestimates optically thinner clouds such as cirrus anvil or underestimates optically thick clouds
that have a smaller than 3.5km grid size. The representation of cirrus anvil is sensitive to details ofcloud microphysics (Iga et al. 2007).

b) Temporal variations of cloud size
Figure 11 shows temporal variations of the total area of deeper convection defined by the 208 K, 90
W m-2 thresholds over the maritime continent region (a) and the open ocean region (b) during 00
UTC 26 Dec 2006 and 00 UTC 1 Jan 2007. In this figure, cloud areas whose centers are locatedwithin the respective regions are all counted. The total sizes of deep convection simulated by
DX3.5km and DX7km are generally smaller than the MTSAT-1R observations. However, the phaseof the temporal variation observed by satellite is well depicted by DX3.5km except for the first few
days of the simulation. The DX7km simulation cannot capture the characteristics of this temporalvariation and the amplitude is generally smaller than that of DX3.5km.

-15-The simulated temporal variation by DX3.5km becomes more evident when the thresholds arechanged to 253 K and 210 W m-2 as depicted in Fig. 12. The correlation coefficients increase from
0.62 to 0.81 for the maritime continent region and from 0.13 to 0.54 for the open ocean region.
While, the DX7km simulation has systematic time lag in phase over the maritime continent region,
although it captures some signal of the temporal variation. In summary, the temporal variation ofdeep convective cloud area is reasonably represented by the DX3.5km simulation over both themaritime continent and the open ocean regions, but the DX7km simulation has biases in amplitude
and phase of the temporal variation. However, further studies will be required to explain the reasonof remaining differences between MTSAT-1R and NICAM, considering the initial conditions,spatial resolution, and so on.
Diurnal variation of deep convection has been studied over this area using the infrared data of
GMS and the PR data of TRMM (e.g. Murakami 1983; Takayabu, 2002; Hirose and Nakamura,2005). It has been shown that the maximum activity of deep convection appears late afternoon overthe land area, and early morning over the ocean area. In the present study, we define the two areasof the maritime continent region and the open ocean region with relatively larger longitudinal area,
so that there is some time lag in identifying local time. Further, the MJO was able to modulate thediurnal variation of convective activity (Tian et al, 2006; Ichikawa and Yasunari 2007). However,as is seen in Fig.11(a), the MTSAT-1R observation suggests the early morning maximum (21 UTC;-16-

5 LT) in convective activity over the maritime continent region. This phase is not consistent withprevious studies. We can see clear diurnal signal during this specific period and relatively larger
cloud area in the MTSAT-1R observation (red curve in Fig. 11(a)). The NICAM DX3.5km
simulation depicts this diurnal variation, although the peak of convective activity appears about 3
hours later (except 9 hours on 27 Dec), while the maximum convective activity over the open ocean
region is seen during 18-21 UTC (5-8 LT) in the MTSAT-1R observation (not clear on 29 Dec). The
time of the peak of deep convection activity during this period is slightly later than previous studies.
The temporal variation of deep convection defined by the 208 K threshold is generally represented
by DX3.5km including the diurnal variation. The correlation coefficient between DX3.5km and
MTSAT-1R observation is 0.62 for Fig. 11(a), although 0.13 for Fig. 11(b). However, the DX7km
simulation cannot represent the temporal variation appearing in the MTSAT-1R observation.
As is seen in Fig.12, the diurnal variation of deep convection defined by 210 W m-2 over the
maritime continent region corresponds well to that of the MTSAT-1R observation, considering thetime lag. The peak time of cloud area appears at 9 UTC (17 LT) over the maritime continent region,
while that over the open ocean region appears at about 6 UTC (17 LT). The correlation coefficient
between DX3.5km and the MTSAT-1R observation is 0.82 for Fig. 12(a) and 0.54 for Fig. 12(b).
The time lag between the peak of deeper convective cloud area colder than 208 K and that of cloudarea colder than 253 K is about 12 hours over both the maritime continent and the open ocean-17-

regions. A similar time lag is reported in Machado et al. (1993). They studied 15 large cloudclusters and found that the time lag between the peak of cloud cluster size defined by 207 K
threshold and 253 K threshold was 12 hours. They conclude that this time lag is a reflection of thedevelopment of cloud cluster and clusters merging into larger clusters.
Mapes and Houze (1993) discussed the morning and afternoon difference of deep convection
using GMS data. They reported a clear difference of convective activity between morning andafternoon over large islands in the maritime continent for cloud clusters of 1,000-3,000 km2. in size
although they reported no difference for cloud clusters larger than 30,000 km2. Figure 13 shows thegeographical distribution of deeper convection defined by the 208 K threshold at 00 UTC and 09
UTC composite of 6 days during 26 Dec and 31 Dec 2006. Over the large islands of the maritime
continent (New-Guinea, Borneo, and Sumatra), there are almost no deep clouds at 00 UTC (about 8LT), while deep convection becomes active at 09 UTC (about 17 LT). Over the open ocean region,on the other hand, deep convection is more active at 00 UTC (about 11 LT) than at 09 UTC (about
20 LT). This feature is consistent with the previous studies. Figure 14 depicts the geographicaldistribution of deeper convection defined by the 90 W m-2 threshold in the NICAM DX3.5km
simulation for 00 UTC and 09 UTC. The DX3.5km simulation represents similar characteristics ofdeeper convective activity. The deeper convection over the large islands of the maritime continentis less active at 00 UTC (about 8 LT) and enhanced at 09 UTC (17 LT). The deeper convection over-18-

the open ocean region is more active at 00 UTC (about 11 LT) than 09 UTC (about 20 LT).

5. Summary and discussion
We have compared the size distributions of deep convection between the global cloud resolving
simulations by NICAM and the satellite observations by MTSAT-1R. The numerically simulated
data by NICAM with the horizontal mesh-size of 3.5 km (DX3.5km) and 7 km (DX7km) from 00
UTC 26 Dec 2006 to 00 UTC 1 Jan 2007 were used for the analysis. To define deep convection, we
first determine threshold values of the outgoing longwave radiation (OLR) corresponding to theequivalent blackbody temperature TBB of the infrared channel of the MTSAT-1R observations using
the cumulative distribution functions of TBB and OLR. The contiguous cloud areas are calculated byusing these thresholds both for the NICAM simulations and the MTSAT-1R observations.
The size distribution of deep convective clouds simulated by both DX3.5km and DX7km
indicates log-normal distribution as is seen in the MTSAT-1R observation. In general, the size
distribution of DX3.5km is much closer to that of the MTSAT-1R observation than that of DX7km.
In detail, DX3.5km has less frequency in clouds smaller than 5×103 km than the MTSAT-1R
observation. This might be due to the fact that DX3.5km cannot resolve very small clouds
comparable to the grid size, 3.5km. From the split window analysis based on Inoue (1987), it was
found that these small clouds are mostly optically thick and could be categorized as cumulus-19-congestus clouds. Size distributions over the maritime continent and the open ocean are basicallysimilar, but the larger clouds in the maritime continent are represented better than in the open ocean.
This is because the convection in the maritime continent is stronger than the open ocean due to thediurnal cycle caused by the land surface heating.
To examine the diurnal cycle of deep convection, the temporal variations of the total cloud area
were analyzed over the maritime continent and the open ocean regions. DX3.5km captured thediurnal variations of the total cloud area very well, particularly over the maritime continent region.
Although a slight time delay exists in the simulation, the lag becomes smaller as the model
resolution becomes finer. The peak times of the cloud size are different between the cases when the
208 K and 235 K thresholds are used. The time lag is about 12 hours both for MTSAT-1R andDX3.5km; this suggests that the spread of anvil cloud from the core of deep convection or merging
of cloud clusters is well represented by DX3.5km.
The size distribution analysis of cloud clusters presented in this study has the following
importance. First, we can evaluate quantitatively the difference and realism of clouds by the global
cloud resolving simulations. We compared the size and temporal variation of deep convective
clouds to understand the behaviors of the model simulations. From these results, we have obtaine
guidance in improving model simulations. For example, the present analysis showed that NICAM
has less frequency in grid-scale small clouds. These clouds are categorized as cumulus congestus as-20-

detected by the split window analysis. This result suggests that the temporal variation of individual
cumulus convection is not well represented by the model. Therefore, it will be a future task to
examine how the evolution of each cumulus affects simulations of successive cloud clusters. We
examined cloud characteristics over the western tropical Pacific covering the maritime continent.
Recently, it has been realized that a more realistic representation of convection over the maritime
continent is one of the major challenges to global atmospheric modeling because of its complex
system of islands and oceans (e.g. Neale and Slingo, 2003). Here, we examine overall behaviors ofdeep convection simulated by a global cloud resolving model in a statistical sense using cloud size
distributions analysis. We will further proceed to detailed comparisons of individual convective
events.
Second, the energy budget of global cloud resolving simulations are directly affected by the sizeof deep convection, i.e., the coverage of anvil clouds. It is also very sensitive to the cloud
microphysical processes used in the model. We can evaluate the cloud microphysical processes interms of the radiation budget through the size of upper clouds. Figure 15 shows the latitudinal
distributions of OLR for the NICAM simulations between 90E-160W and compares them with theobserved OLR by the interpolated OLR data of the National Oceanic and Atmospheric
Administration (NOAA). For the NICAM simulations, we compared three different resolutions:DX3.5km, DX7km, and DX14km, which is the same simulation as DX7km but for the grid interval-21-

of 14 km. Here, we concentrate on the equatorial region between 10S-10N. This shows that thethree simulated values of OLR do not have a systematic bias over the equatorial region, and thethree profiles are around the curve of the NOAA interpolated OLR. It should be noted that the
NOAA interpolated OLR data is only provided as a daily value, and cannot be regarded as a truth
but only as a reference (Inoue and Ackerman 2002). The important point is that, for the equatorial
cloud region, it is hard to evaluate the simulated results only from the averaged OLR as in Fig. 15.
By further analyzing the size of deep convective clouds as shown in the present study, we haveclarified the resolution dependency. If we change parameters in physical processes such as cloud
microphysics and boundary schemes, the zonal mean OLR changes (Iga et al. 2007). The size ofclouds will provide another dimension to improve the performance of global high-resolution
models through the radiation budget process.

Acknowledgment
Numerical experiments of NICAM were carried out using the Earth Simulator. This research was
supported by Core Research for Evolutional Science and Technology, Japan Science andTechnology Agency (CREST,/JST). We also thank members of the NICAM team: Hirofumi Tomita,
Tomoe Nasuno, Shin-ichi Iga, Akira Noda, Wataru Yanase, and Kazuyoshi Oouchi for theirvaluable work.

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Figure captions

Figure 1
Distributions of TBB by MTSAT-1R (top) and OLR by DX3.5km of the NICAM simulation(bottom) at 00 UTC 26 Dec 2006. Only the areas with TBB colder than 253 K and OLR lower than
210 W m-2 are shown for deep convection.

Figure 2
Cumulative distributions of TBB observed by MTSAT-1R (a) and OLR simulated by NICAM (b)over the area of 10S-10N and 90E-160W during 26-29 Dec 2006.

Figure 3
Deep convection regions defined by 253K (210 W m-2) are represented by circle for the MTSAT-1R
observation (top), DX3.5km of the NICAM simulation (middle) and DX7km of the NICAM
simulation (bottom) at 00 UTC 26 Dec 2006.

Figure 4
Histograms of cloud size of TBB and OLR for deeper convection defined by the 208 K and 90 W

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m-2 thresholds (top) and deep convection by the 253 K and 210 W m-2 thresholds (bottom) . The
size in abscissa is defined as square of area (km2).

Figure 5Size distributions of TBB and OLR for deeper convection defined by 208 K and 90 W m-2. Statistics
are analyzed for data between 26 and 29 Dec 2006.

Figure 6Size distributions of TBB for deeper convection defined by 208 K (a) and 253 K (b) for the
MTSAT-1R observation over the maritime continent and the open ocean regions.

Figure 7
Size distributions of OLR for deep and deeper convection defined by 90 W m-2 (a) and 210 W m-2(b) for the NICAM DX3.5km simulation.

Figure 8Size distributions of TBB and OLR for deeper convection defined by 208 K and 90 W m-2 over the
maritime continent (a) and the open ocean (b).

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Figure 9
Same as Fig. 8 except for TBB and OLR for thresholds of 253 K and 210 W m-2.

Figure 10
Cloud type map constructed from the split window method at 00 UTC 26 Dec 2006. Clouds
colored in purple, blue, and green correspond to clouds colder than 208 K, optically thick clouds,
and optically thin clouds colder than 253 K, respectively.

Figure 11
Temporal variations of the total area of deeper convection defined by the 208 K and 90 W m-2
thresholds over the maritime continent (a) and the open ocean (b) during 00 UTC 26 and 21 UTC
31 Dec 2006.

Figure 12
Same as Fig.11 except for the 253 K and 210 W m-2 thresholds.

Figure 13-30-

Geographical distribution of deeper convection defined by 208K-threshold and size shown in color
for MTSAT-1R observation at 00 UTC and 09 UTC during 26-31 Dec 2006.

Figure 14
Same as Fig. 13 except for the deeper convection defined by 90 W m-2 thresholds.

Figure 15
Latitudinal distributions of OLR between 90E-160W for one-day average of 26 Dec 2006. Red
curve is for DX3.5km, blue curve is for DX7km, green curve if for DX14km, and black curve is for
the NOAA-interpolated OLR.

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