Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

REVIEW

Open Access

The Non-hydrostatic Icosahedral Atmospheric
Model: description and development
Masaki Satoh1,2*, Hirofumi Tomita3,2, Hisashi Yashiro3, Hiroaki Miura4,2,3, Chihiro Kodama2, Tatsuya Seiki2,
Akira T Noda2, Yohei Yamada2,1, Daisuke Goto5, Masahiro Sawada1, Takemasa Miyoshi3, Yosuke Niwa6,
Masayuki Hara2, Tomoki Ohno1, Shin-ichi Iga3, Takashi Arakawa7,2, Takahiro Inoue7,2 and Hiroyasu Kubokawa1

Abstract
This article reviews the development of a global non-hydrostatic model, focusing on the pioneering research of the
Non-hydrostatic Icosahedral Atmospheric Model (NICAM). Very high resolution global atmospheric circulation simulations
with horizontal mesh spacing of approximately O (km) were conducted using recently developed supercomputers.
These types of simulations were conducted with a specifically designed atmospheric global model based on aquasi-uniform grid mesh structure and a non-hydrostatic equation system. This review describes the developmentof each dynamical and physical component of NICAM, the assimilation strategy and its related models, andprovides a scientific overview of NICAM studies conducted to date.
Keywords: Global non-hydrostatic model; Icosahedral grid; Global cloud-resolving simulations

Review
Introduction

Diabatic heating due to the release of latent heat in deep
convection is the primary heat source in the atmosphere,
and it is interacted with the atmospheric general circulation,
especially the tropical large-scale overturning circulations
such as the Hadley and Walker circulations. Individual deep
convective cells are associated with meso-scale circulations
that have a horizontal scale of O (10 km), and an upward
convective core, along with a horizontal scale of O (km).
Until recently, since the horizontal resolution of the global
climate models that have been used for future climatechange projections has been O (100 km), such models
require the use of cumulus parameterizations in order toincorporate the effects of deep convection instead of by
explicitly resolving deep convective circulations. However, it
is known that cumulus parameterizations significantly affectthe results of climate model simulations and that they
are the most ambiguous factor used in climate models(Randall et al. 2003).
* Correspondence: satoh@aori.u-tokyo.ac.jp
1
Atmosphere and Ocean Research Institute, The University of Tokyo,
5-1-5 Kashiwanoha, Kashiwa, Chiba 277-85648, Japan
2
Japan Agency for Marine-Earth Science and Technology, 3173-15,
Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan
Full list of author information is available at the end of the article

To overcome the above-mentioned cumulus parameterization issue, global non-hydrostatic models that utilize ahorizontal mesh interval of O (km) for global atmosphericcirculation simulations have been developed. Such models
explicitly calculate deep convective circulations over the
global domain without using cumulus parameterizations.
At the grid-resolvable scale, water vapor is saturated into
the liquid or ice phase of water in the upward flow field inorder to form clouds and is eventually converted to rain
and snow through cloud microphysics processes. In
global non-hydrostatic models, clouds are spontaneously
organized and the multi-scale structures of convective
systems are reproduced over the global domain.
The Non-hydrostatic Icosahedral Atmospheric Model(NICAM) (Tomita and Satoh 2004; Satoh et al. 2008;
Satoh 2013) was first designed to be run with a horizontal
mesh size approximately 3.5 km over the global domain
by using the Earth Simulator (http://www.jamstec.go.jp/
es/en/) which was launched by the Japan Agency for
Marine-Earth Science and Technology (JAMSTEC) in
2002. NICAM uses an icosahedral grid, as shown in
Figure 1. Higher resolution grids are recursively subdivided
from a coarser resolution grid. Hereinafter, we will refer tothe grid division level as the g-level. The number of points,
arcs, and triangles of the icosahedral grids with g-level l are
given as follows:© 2014 Satoh et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work is properly credited.

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 2 of 32

Figure 1 Icosahedral grids. Icosahedral grids for grid division levels of 0, 1, and 2 (top, from left to right), and 3, 4, and 5 (bottom, from left to right).

N P ¼ 10n2 þ 2 ¼ 10  22l þ 2;
N A ¼ 30n2 ¼ 30  22l ;N T ¼ 20n2 ¼ 20  22l ;where n = 2l. We define the average area of the triangles A
 as follows:
and the average grid interval Δ22
 ¼ 4πR ¼ πR ;ANT
5  22l
rﬃﬃﬃﬃﬃﬃ
pﬃﬃﬃﬃﬃﬃ
2π R

;
Δ ¼ 2A ¼
5 2l

where an Earth radius of R = 6,371.22 km is used. The
values for each g-level are listed in Table 1.
NICAM has been shown to reproduce a realistic
multi-scale cloud structure from a meso-scale to aplanetary-scale cloud organization that is associated with
the Madden-Julian Oscillation (MJO) (Madden and = 14 km)
Julian 1971, 1972) at a g-level between 9 ( Δ = 3.5 km) (Tomita et al. 2005; Miura et al.
and 11 ( Δ
2007b). By using the K computer, which is installed at
the RIKEN Advanced Institute for Computational
Science (AICS) in Kobe, Japan (http://www.nsc.riken.
jp/index-eng.html), the resolution of NICAM has been
recently increased to the subkilometer level, and it was
shown that the deep convective core is more realistically = 870 m) meshresolved by using a g-level 13 ( Δsimulation (Miyamoto et al. 2013; Figure 2). In thecurrent study, various experiments including decadalcontinuous experiments and case sweep experiments(Miyakawa et al. 2014) were also conducted at g-levels
between 9 and 11. Future projection studies such as
those investigating changes in clouds and tropical cyclone activities are also investigated. The results areinterpreted based on the more physically based cloud
microphysics processes without the ambiguities of cumulus parameterizations.
This article describes the current development status,
design, and concepts behind the individual components
of NICAM. First, the background and an overview ofstudies related to the global non-hydrostatic model are
reviewed. Then, the history of NICAM development andthe scientific outcomes are summarized in the ‘Scientific
overview of NICAM’ section, while the NICAM computational design is described in the ‘Design, structure,development, and timeline’ section. The two sections that
follow describe the dynamical and physical components
of NICAM, and their respective subsections describe
each component of the physical processes. Next, the assimilation strategy is described. Finally, various NICAM
usages that have been developed by modifying the original NICAM geometry are presented.
Review of global non-hydrostatic models

Thanks to the significant advances in high-performancecomputers over the last decade, global atmosphericsimulations with a horizontal resolution of O (10 km)
can be achieved (Ohfuchi et al. 2004; Mizuta et al. 2005;
Kinter et al. 2013; Wedi 2014). At enhanced horizontal
resolution of less than 10 km, traditional atmosphericSatoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 3 of 32

Table 1 Number of points, arcs, triangles, and average grid interval of the icosahedral grids
PointsArcs

Triangles

g-level

NP

NA

NT

Average area (km2)
A

Average interval (km)
Δ

0

12

32

20

5,100,996.991

7,142.126

1

42

122

80

1,275,249.248

3,571.063

2

162

482

320

318,812.312

1,785.532

3

642

1,922

1,280

79,703.078

892.766

4

2,562

7,682

5,120

19,925.769

446.3835

10,24230,722

20,4804,981.442

223.191

6

40,962

122,882

81,9201,245.361

111.596

7

163,842

491,522327,680

311.340

55.7988655,362

1,966,082

1,310,720

77.83527.89992,621,442

7,864,322

5,242,880

19.45913.94910

10,485,762

31,457,282

20,971,520

4.865

6.975

11

41,943,042

125,829,122

83,886,080

1.216

3.487

12

167,772,162

503,316,482

335,544,320

0.304

1.744

13

671,088,642

2,013,265,922

1,342,177,280

0.076

0.872

14

2,684,354,562

8,053,063,682

5,368,709,120

0.019

0.436

The g-level is the grid division level. Here, R = 6371.22 km.

general circulation models (AGCMs) encounter fundamental
difficulties in their dynamic framework formulation as
well as in their computational efficiency. As theirresolutions increase, AGCMs can capture flow featureswith comparable scales of motion in the horizontal andvertical directions (such as deep convection and fine-scale
gravity waves) that can invalidate the hydrostatic approximation. Although deep convective systems in the tropics
play key roles in global atmospheric circulations, they havenot been directly resolved by AGCMs, and their effects
have only been considered in a parameterized form

(Arakawa 2004). The effects of fine-scale gravity waves
that are parameterized as gravity wave drag in AGCMs
can be captured as the resolution increases, but the
propagation of such gravity waves will be incorrectly
calculated unless the non-hydrostatic effect is taken
into consideration (Iwasaki et al. 1989).
Regarding the numerical algorithm, many of theexisting AGCMs employ the spectral transform method
to represent spherical fields, and it has been pointed outthat spectral transforms become increasingly inefficient for
high-performance computing as the horizontal resolution

Figure 2 Cloud distribution simulated by the NICAM 870 m grid spacing experiment for 6:00 UTC 25 August 2012 (Miyamoto et al. 2013).

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

increases (e.g., Stuhne and Peltier 1996; Taylor et al. 1997;
Randall et al. 2000; Satoh et al. 2005; Cheong 2006;
Tomita et al. 2008; Wedi 2014). Another problem that occurs during computation on a massively parallel computeris that the spectral transform method requires extensive
data movement between computer nodes. Although the
double Fourier transformation method has been proposed
as an alternative (e.g., Cheong 2006), this method still requires global communication between computer nodes.
To increase the horizontal resolution beyond O(10 km) in a global atmospheric model, the governing
equations and numerical algorithms must be reconsidered. More specifically, the governing equations must be
non-hydrostatic, and the grid point method replaces the
spectral method. As for the familiar latitude-longitude
grid (lat-lon grid), however, the grid spacing near the poles
becomes drastically reduced as the horizontal resolution isincreased, which means that reduced grids are generally
required to avoid severe time interval restrictions for the
Courant-Friedrichs-Lewy (CFL) condition. In principle,
the semi-Lagrangian, semi-implicit (SLSI) approach (cf.
Laprise 2008; Staniforth and Wood 2008) could be
employed to overcome the requirement of the CFL condition. Several authors (Semazzi et al. 1995; Cullen et al.
1997; Qian et al. 1998; Côté et al. 1998; Yeh et al. 2002;
Davies et al. 2005; Wedi and Smolarkiewicz 2009; Woodet al. 2013) have used the lat-lon grid to solve a set of
non-hydrostatic equations using the SLSI approach inorder to acquire a larger time interval for integration.
However, it is unclear how effective the elliptic solvers,
which were developed for SLSI schemes, would be forultra-high-resolution calculations, even though it has been
recently proven that a multi-grid approach is an ideal
solver for massively parallel computers (Heikes et al. 2013).
The pole problem can be overcome by using grid systems with quasi-homogeneous grids over the sphere.
One such grid is the icosahedral grid, which is currently
one of the major grid systems used for high-resolution
global atmospheric modeling. Primitive (hydrostatic)
equation global models using icosahedral grids havebeen developed at Colorado State University for climate
modeling (CSU AGCM) (Ringler et al. 2000), Deutscher
Wetterdienst for numerical prediction modeling (GME)(Majewski et al. 2002), the National Oceanic and Atmospheric Administration (NOAA) for the Flow-following
finite-volume Icosahedral Model (FIM) (http://fim.noaa.
gov/), and the Laboratoire de Météorologie Dynamique(DYNAMICO) (http://www.lmd.polytechnique.fr/~dubos/
DYNAMICO/). Global non-hydrostatic models using
icosahedral grids are also being developed by several
international groups including the geodesic grid model atColorado State University (UZIM; http://www.cmmap.org/
research/models.html), the Icosahedral Non-hydrostatic
model (ICON) at the Deutscher Wetterdienst and the Max

Page 4 of 32

Planck Institute for Meteorology (http://www.mpimet.mpg.
de/en/science/models/icon.html; Zängl et al, 2014), theModel for Prediction Across Scales (MPAS) at the National Center for Atmospheric Research (Skamarocket al. 2012), and the Non-hydrostatic Icosahedral
Model (NIM) at NOAA (http://www.esrl.noaa.gov/gsd/ab/
ac/GPU_Parallelization_NIM.html). Entries for all thesemodels can also be found at https://www.earthsystemcog.
org/projects/dcmip-2012/. As for other types of gridmodels, cubic grids are also candidates for high-resolution
atmospheric models (McGregor 1996; Lin 2004; Putman
and Suarez 2011).
In Japan, the icosahedral atmospheric model has beencreated by using a non-hydrostatic system, i.e., NICAM(Tomita and Satoh 2004; Satoh et al. 2008; Satoh 2013;
http://nicam.jp/). Development of NICAM began around2000. Since high-resolution modeling has now entered
the mainstream worldwide, NICAM has joined a number of international high-resolution numerical modelingprojects, such as the Athena Project (Kinter et al. 2013)
with the Integrated Forecast System (IFS) (Jung et al. 2012)
and the Icosahedral-grid Models for Exascale Earth systemsimulations (ICOMEX) (Zängl et al. 2011) with ICON,
MPAS, and DYNAMICO.
Scientific overview of NICAM

While more than a decade has passed since the development of NICAM began, the first milestone was reached in
2004 when Tomita and Satoh (2004) began finalizing the
dynamical core of the model. Two unique characteristicsof the NICAM dynamical core are the use of thespring dynamics to construct a modified icosahedral
grid (Tomita et al. 2001, 2002) and the use of a flux form
non-hydrostatic system which guarantees conservation ofmass and energy (Satoh 2002, 2003). Tomita et al. (2001,
2002) constructed NICAM's icosahedral grids by improving
the numerical accuracy of differential operators and using
spring dynamics to improve the homogeneity of the grid
system. An improved version of the spring dynamics
method, which is more homogeneous and applicable evenfor higher resolution, is also now available (Iga andTomita 2014). NICAM uses a fully compressible (elastic)
non-hydrostatic system to obtain statistically equilibrium
states by performing long-timescale simulations. For thispurpose, we devised a non-hydrostatic numerical schemethat guarantees the conservation of mass and energy(Satoh 2002, 2003). Tomita and Satoh (2004) implemented
this non-hydrostatic scheme in their global model using an
icosahedral grid configuration, with which they developed adynamical core for NICAM. Since the split-explicit time
integration scheme (Klemp and Wilhemson 1978) is used
for the horizontal propagation of fast waves and for the implicit treatment of the vertical propagation of fast waves,multi-dimensional elliptic solvers are not required. To

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

extend the original non-hydrostatic scheme to the global
domain, the set of equations provided in Satoh (2002,
2003) is reformulated for spherical geometry and modified
in order to make the equations suitable for an icosahedral
grid configuration. The finite volume method is used fornumerical discretization, so that the total mass and energy
over the domain are conserved. The resulting model issuitable for long-term climate simulations.
NICAM is used for ‘cloud-resolving simulations’ by
explicitly resolving convective circulation. This is accomplished by drastically increasing the horizontal resolution
using high-power computing systems, such as Earth Simulator and the K computer. After finalizing the developmentof the dynamical core (Tomita and Satoh 2004), thefirst 3.5 km mesh global NICAM simulation was performedon Earth Simulator by Tomita et al. (2005) using theaqua planet configuration (Neale and Hoskins 2001).
The results of this simulation clearly show multi-scaleconvective systems propagating near the equator (Satohet al. 2005), which is similar to the propagation of the observed cloud clusters and super cloud cluster structure(Hayashi and Sumi 1986; Nakazawa 1988; Takayabu et al.
1999).
Tomita et al. (2005) conducted aqua planet experiments at three horizontal resolutions, g-levels 9, 10, and = 14, 7, 3.5 km), using the same physical schemes
11 ( Δ
without cumulus parameterization. In their results, themulti-scale convective structure along the equatorial zone
and the diurnal cycle of convective precipitation werereproduced similarly in all simulations, although the propagation speed along the equator and the phase lag of thediurnal cycle depends on the resolution. This result motivated us to use a combination of different horizontal resolution experiments with the same physical schemes whenstudying convective properties simulated by NICAM.
The results of the NICAM aqua planet experimentwere analyzed intensively by Nasuno et al. (2007, 2008),
Nasuno (2008), and Nasuno and Satoh (2011a, b), whichshowed the roles of the equatorial convective systems
embedded in the multi-scale structure. Mapes et al.
(2008) also investigated the persistent structure in thetropics in terms of the predictability of disturbances.
The diurnal cycle of tropical convective precipitation
was clearly simulated with the dependency of the phaselag on the horizontal resolution (Tomita et al. 2005).
The semi-diurnal cycle was also captured. This was further investigated by Yasunaga et al. (2013) by using the
NICAM aqua planet experiment. The detailed structuresof convective systems near the tropopause were analyzed
by Kubokawa et al. (2010).
The aqua planet configuration is used to test cloudchanges that are related to increased surface temperatures in order to imitate global warming conditions.
Miura et al. (2005) compared the cloud cover responses

Page 5 of 32

between NICAM and the Model for Interdisciplinary
Research on Climate (MIROC) (Hasumi and Emori2004), which is an ordinary resolution climate model(denoted as CCSR/NIES/ FRCGC in Miura et al. 2005),
under the aqua planet condition. The comparison results
show that the cloud cover simulated by NICAM increases
in high-latitude regions, while that by MIROC decreases.
Such a contrasting response to cloud cover is very interesting and should be analyzed in more detail, as will be described later in this section. The change in the meridional
distribution of relative humidity is shown and comparedto the other model results by Sherwood et al. (2010).
The aqua planet experiments of NICAM were comparedto the other model results in Blackburn et al. (2013), andadditional aqua planet experiments were conducted by
Yoshizaki et al. (2012a, b) in order to investigate themultiple-scale convective structure along the equator.
A NICAM simulation that includes a realistic configuration with a land and sea contrast was performed by
Miura et al. (2007a). The results of this simulation show
not only realistically simulated tropical cyclogenesis but
also a clear dependency of convective organization on aplanetary boundary layer (PBL) scheme, specifically, water
vapor transport. Using the simulated dataset, the diurnal
variation of the convective systems over the Tibetan
Plateau was analyzed by Sato et al. (2007, 2008).
A successful simulation of a realistic MJO event waspresented by Miura et al. (2007b), in which a 1 monthintegration with a 7 km grid spacing and a 1 week integration with a 3.5 km grid spacing were performed. Alarge-scale cloud organization, its eastward propagation,and the multiple-scale structure of convective systems
were realistically reproduced (Nasuno et al. 2009). The
similarity of the simulation to the observed MJO wasfurther analyzed by Liu et al. (2009). In addition, multiple
tropical cyclones were realistically generated from theactive cloud areas of the MJO. Particularly, Fudeyasu et al.
(2008, 2010a, 2010b) analyzed the evolution of a tropicalstorm in detail, which was generated from the MJO 2
weeks after the initial condition.
Thanks to the high-resolution numerical simulations
performed by Miura et al. (2007b), simulation results
can be analyzed or used in various ways. The dataset
consists of 7 km mesh and 14 km mesh grid data for 1
month from 15 December 2006 and 3.5 km mesh grid
data for 1 week from 25 December 2006. The diurnal
cycle of tropical convective systems was intensively
analyzed by Sato et al. (2009). Convective momentum
transports of the MJO were analyzed by Miyakawa
et al. (2012). The dataset was compared with the satellite
observations of the Tropical Rainfall Measuring Mission(TRMM) and CloudSat (Masunaga et al. 2008), thegeostationary satellite, the Multi-functional Transport
Satellite (MTSAT-1R) (Inoue et al. 2008), and CloudSat

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observations (CALIPSO) (Inoue et al. 2010; Satoh et al.
2010; Ham et al. 2013), and with the estimation of cloudradiative forcing (Sohn et al. 2010). The dataset wasalso used to develop a cloud parameterization forcoarser resolution AGCMs (Watanabe et al. 2009). The
convective structure near the tropopause was also analyzed by Kubokawa et al. (2012). The energy spectrum
and similar frequency spectra of the high-resolution data
were analyzed by Terasaki et al. (2009) and Tsuchiya et al.
(2011).
Following Tomita et al. (2005) and Miura et al. (2007b),
studies of intra-seasonal variability (ISV) including MJOs
and boreal summer intra-seasonal oscillations (BSISO)(Fu and Wang 2004) are unique areas of NICAM studies.
Various aspects of the MJO and tropical disturbances simulated by Tomita et al. (2005) and Miura et al. (2007b) arebeing analyzed as mentioned above. NICAM studies arealso collaborated with observational field campaigns of
MJOs. Miura et al. (2009) examined an onset of MJO
observed at the Mirai Indian Ocean cruise for the Studyof the MJO-convection Onset (MISMO) field campaign(Yoneyama et al. 2008), suggesting a role of the sea surface
temperature (SST) gradient in the western Indian ocean.
Also, for the Cooperative Indian Ocean Experiment on
Intraseasonal Variability in the Year 2011/Dynamics of the
MJO (CINDY2011/DYNAMO) campaign (Yoneyama et al.
2013), Nasuno (2013) summarized a result from quasireal-time weather forecasting experiments that showed
simulated characteristics of MJOs during the CINDY2011/
DYNAMO period in the boreal winter between 2011 and2012. The quasi-real forecasting system using the stretch
NICAM (Tomita 2008a; see ‘Models related to NICAM’
section) was developed particularly for use in collaboration
with field experiments, as introduced by Oouchi et al.
(2012). More statistical results for MJO simulations areshown by Miyakawa et al. (2014), who reported a good
performance of MJO predictability by the hind cast approach with NICAM. BSISO simulations were analyzed
by Oouchi et al. (2009a, 2014), Taniguchi et al. (2010),
Yanase et al. (2010b), and Satoh et al. (2012a). In particular, they focused on relations between ISV andtropical cyclones, as described below, since ISV modulates large-scale vorticity distributions that generally leadto preferable conditions for cyclogenesis.
Tropical cyclones can also be adequately researched byusing NICAM. Tropical cyclogensis and its evolution
from the MJO were analyzed for boreal winter cases by
Fudeyasu et al. (2008, 2010a, 2010b). For boreal summer,
tropical cyclogenesis was found to be frequently related
to ISV activities (Kikuchi and Wang 2010). The cyclone
Nargis, which caused severe damage to Myanmar in
May 2008, was successfully simulated and its relation to
the northward propagation of BSISO in the Bay of Bengal

Page 6 of 32

was analyzed by Taniguchi et al. (2010) and Yanase et al.
(2010a, 2012a). Oouchi et al. (2009a) simulated tropical
cyclogenesis in the northwestern Pacific and showed its
relation to the MJO. The effects of an equatorial Kelvinwave on an abrupt development of a tropical cyclone wereclearly shown by Yanase et al. (2010b). Yanase et al.
(2012b) also analyzed some statistical behaviors of tropical
cyclones simulated by NICAM especially over the eastern
Pacific. The impacts of a tropical cyclone on the Baiu rainfall were analyzed by Yamaura et al. (2013). The preferable
conditions of tropical cyclogenesis were analyzed over the
Atlantic domain (Satoh et al. 2013).
Tropical cyclone climatology and possible changes in
the future projection of tropical cyclone activities areactively being researched using NICAM. For example,
Satoh et al. (2012a) analyzed 8 years of boreal summer
experiments that were conducted in the Athena Project(Kinter et al. 2013) in order to show the simulated climatology of tropical cyclones and produced results thatshow good relations between tropical cyclone activities
and MJO activities. Future changes in the tropical cyclone
structure were analyzed by Yamada et al. (2010, 2012),
Yamada and Satoh (2013), and Emanuel et al. (2010).
NICAM simulations also contribute to the studies on
Asian monsoon and tropical convective systems. For
example, Oouchi et al. (2009b) provided a realistic distribution of precipitation during a boreal summer andshowed its variation for diurnal and intra-seasonal
timescales. Additionally, the resolution dependency ofthe diurnal cycle of convective systems was analyzed
by Sato et al. (2009), and their results were found to begenerally consistent with those of the aqua planet experiment (Tomita et al. 2005); as resolution becomes coarser,
the timing of precipitation peak becomes increasingly delayed. Sato et al. (2009) also analyzed the diurnal evolutionof tropical convective cold pools, while Fujita et al. (2011)
analyzed the behavior of the diurnal convection over maritime continental regions. Dirmeyer et al. (2012) and Noda
et al. (2012) also analyzed the diurnal cycle of precipitation
over the global domain. A mechanism for the diurnal
variation of summer precipitation over southern China
was investigated by Satoh and Kitao (2013) using thestretched version of NICAM (see ‘Models related to
NICAM’ section).
The use of a cloud microphysics scheme in NICAM
experiments without cumulus parameterization leads torealistic behavior of cloud distribution in the horizontal
and vertical directions. Furthermore, as seen in the paragraph below, it might even produce a different behaviorof cloud feedback from that obtained by conventional
climate models that use cumulus parameterization. Iga
et al. (2007a) analyzed the climatological distributionof cloud cover and compared it with the data of theInternational Satellite Cloud Climatology Project (ISCCP)

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18(Rossow and Schiffe 1999), while in a later study, Iga et al.
(2011) analyzed upper tropospheric ice clouds and their
sensitivity using various model parameters. Noda et al.
(2010) investigated the importance of the subgrid turbulent process on cloud distributions. Kodama et al. (2012)
showed the sensitivities of upper clouds to the choiceof various cloud microphysics parameters. Satoh andMatsuda (2009) investigated how cloud microphysics
affects upper clouds under an idealized radiative convective equilibrium condition.
As for the future simulations of cloud change by
NICAM, Miura et al. (2005) showed the model's different
behavior when compared to a coarse resolution AGCM
(MIROC). In addition, Collins and Satoh (2009) showedthat as a response that was different from the existingmodels, the upper cloud cover might increase, whereas
the ice water path decreases under future warming conditions. Satoh et al. (2012b) analyzed their results and speculated that a future reduction in convective circulation
could lead to a reduction in the ice water path under thewarming condition. Tsushima et al. (2014) further investigated longwave cloud feedback and showed the possibilityof a stronger positive feedback than other coarse resolution AGCMs. Yamada and Satoh (2013) analyzed therelation between cloud changes over the global domain
and tropical cyclone changes. Kodama et al. (2014a) also
analyzed the cloud changes associated with storm tracks
simulated by the NICAM aqua planet experiments.
Recently, use of the K computer has allowed the horizontal resolution of NICAM to be increased. Miyamoto
et al. (2013) performed (for the first time) a subkilometer
mesh global domain simulation with grid spacing of
870 m (g-level 13) and 96 vertical layers (Figure 2). They
showed that at a resolution of less than 1.7 km, convective
cores could be resolved by multiple grid cells. The ability
to use the K computer introduces a new area of study for
NICAM. Numerous MJO case studies are currently being
conducted in order to evaluate their statistical predictability (Miyakawa et al. 2014). More than 10 years ofcontinuous simulations have and are being conductedunder present and future conditions in order to investigate future changes to tropical cyclones and other extreme
events. The subsequent sections describe the improvements in the simulated climatology that have been madepossible by improving the physical processes.
As described above, NICAM is usually run without
using cumulus parameterization. In general, the horizontal scale of meso-scale phenomena ranges from a few toseveral hundred kilometers. It is not readily understood
why a model with horizontal mesh spacing of O (10 km)
reproduces such meso-scale phenomena without subgrid
convective parameterization. It is speculated that for
relatively large-scale meso-scale convective systems, thestatistical effects of circulation resolved by grids behave

Page 7 of 32

similarly to nature. Figure 3 shows resolution depend = 28,
ency of upper cloud distributions in the range of Δ
14, 7, 3.5, 1.7, 0.87 km for the NICAM simulations conducted by Miyamoto et al. (2013). Although in thatstudy, it was analyzed that the convective core becomes = 1.7 km. Figure 3 shows that the large-scale
resolved at Δ
structure of upper clouds is nearly unchanged for these resolutions. This result implies that the usefulness of a model
without cumulus parameterization depends on researchtargets of meteorological phenomena. Large-scale organized convective structures such as MJOs are, in general,
well reproduced without using cumulus parameterization,even at the coarser horizontal resolutions around O(10 km) as also indicated by Holloway et al. (2013),
while the precise simulation of details of meso-scaleconvective structure needs much finer resolutions (Bryan
et al. 2003).
Design, structure, development, and timeline

A timeline of the development of NICAM is summarizedin Table 2. As was previously mentioned, the developmentof NICAM started in 2000 at JAMSTEC. Through theconstruction of icosahedral grid systems (Tomita et al.
2001, 2002), a shallow water model (SWM) (Tomita et al.
2001, 2002) was initially tested. At the same time, newnon-hydrostatic numerical schemes were being developed(Satoh 2002, 2003). By combining the two approaches, athree-dimensional (3D) dynamical core was eventually developed (Tomita and Satoh 2004). The resulting 3.5 km
mesh high-resolution dynamical core experiments were
examined by Iga et al. (2007b). Then, full-physics experiments have been underway since 2004. Furthermore, thedynamical schemes, computational stability, and a tracer
advection scheme (Miura 2007; Niwa et al. 2011a) have
improved. For physical processes, we have actively introduced more sophisticated schemes, especially for cloud
microphysics (Grabowski 1998; Tomita 2008b; Seiki and
Nakajima 2014) and PBL schemes (Nakanishi andNiino 2006, 2009; Noda et al. 2010). An accurate semiLagrangian scheme for sedimentation processes wasdeveloped for cloud microphysics schemes (Xiao et al.
2003). Aerosol processes (Suzuki et al. 2008) and atmospheric chemistry processes have also been introduced.
Each component of the abovementioned physical schemes
will be described in later sections of this review. Several
NICAM-based systems are also currently under development, including atmosphere-ocean coupling andensemble-based data assimilation systems, and downscaling methods for regional scale experiments havebeen developed for more advanced uses.
Since high-resolution simulations require large computational resources, the efficiency of state-of-the art
supercomputers needs to be maximized. In general,
weather/climate models use a large number of physicalSatoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 8 of 32

Figure 3 Resolution dependency of cloud structure simulated by NICAM between 28 km and 870 m grid spacing experiments. Cloud
distributions simulated by NICAM between 28 km and 870 m grid spacing experiments for 6:00 UTC 25 August 2012, shown by the outgoing
longwave radiation at the top of the atmosphere. The unit is W m−2.

variables during computation, so the size of 3D arrays
significantly increases with increases in spatial resolution.
Large-sized, various, frequent data output is also needed
for simulations. Taken together, these factors make it clearthat weather/climate models require faster data throughput in all layers of a computer system. NICAM has beentactically designed to achieve efficient throughput at the1) memory layer, 2) network layer, and 3) file input/output(I/O) layer.
Since numerous simple arithmetic operations are used
for large arrays in NICAM algorithms, memory throughput
is the most important aspect governing total computational
efficiency. For example, when using Earth Simulator, the ratio of memory bandwidth to the floating-point performance(Byte/Flops ratio; hereinafter B/F ratio) is 4, which makes it
easy to execute NICAM with a high degree of efficiency.
However, in the past decade, the B/F ratio has decreased
during the recent evolution of supercomputers. As a result,when NICAM was implemented on the K computer at
B/F = 0.5, numerous optimizations had to be applied to
improve the efficiency of computer performance. Saving
memory transfers is one effective optimization tactic.
To accomplish this, we suppressed the memory copies,
reduced the intermediate arrays, and avoided unnecessary zero-filling. These changes had to be performed
manually and required labor-intensive efforts. However,as a result of these optimizations, we succeeded in reducing the model execution time by 30%.
To improve network throughput, the finite volume
method with an explicit scheme was adopted to minimize
the global communications, as described in the section‘Dynamics’. When 640 nodes on the K computer are used,

the network communication of NICAM accounts forabout 10 % of the total elapsed time at the case of g-level = 14 km). This percentage increases as the number of
9 (Δnodes increases. Kodama et al. (2014b) developed analgorithm for optimally assigning of nodes in a network
topology such as a torus/mesh.
For the file I/O throughput, NICAM adopts a distributed file I/O. If the output data are aggregated into asingle file at the time of output, it would be easy to handle the file. However, NICAM cannot maximize the I/O
throughput using such a single output file. The data ofeach region on the icosahedral grid of NICAM are output
to a file at the node, and the icosahedral grid is converted
into a lat-lon grid during the post-process. Generally, we
use a simple bilinear interpolation for the post-process,although we have an option of another interpolation tokeep conservation of area integration. A recently developed post-process program can run in parallel with themain model using a coupler (see ‘Coupler’ section).
However, an increase in file size due to the higher resolution is a current issue, and we are planning to change
the file type to a compressible standard format, such as
Network Common Data Form version 4 (NetCDF4)(http://www.unidata.ucar.edu/software/netcdf/). Recently,
a benchmark run on the K computer has achieved 10% of
the peak performance using five nodes (40 cores), andusage has been verified with 81,920 nodes (655,360
cores). Furthermore, a subkilometer mesh global domain simulation (Miyamoto et al. 2013) was executedwith a performance of 230 Tflops in double precision
for 68 billion grid cells while using 20,480 nodes of
the K computer.

2000

2001

2002

2003

2004

Grid system→Modified grid using the spring dynamics

Dynamics→SWM

Basic scheme

2005

2006

2007

2008

2009

→Grid stretching method

2010

2011

2012

2013

→XTMS

→Diamond NICAM, plane NICAM→Tiedke→Chikira2014→3D dynamical core→Non-hydrostatic scheme→Miura scheme

Advection scheme→CWC consideration

Physics
Cumulus parameterizations model
Cloud microphysics model→Grabowski

Land surface model→BucketOcean surface model→Fixed SST

PBL model→MY2.5

Radiation model→MSTRN-X→NSW6→Slab Ocean

Milestone experiments→Coupled Ocean→MYNN→CHASER→NICAM-LETKF

Frameworks
Supercomputers→NDW6→MATSIRO→SPRINTARS

Aerosol/chemistry

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Table 2 Timeline of the development of NICAM→Coupler (Jcup)→NICAM-TM→ES→ES2→Aqua-Planet Experiment→MJO Experiment→K computer→Athena Project→Subkilometer Experiment

See the ‘Abbreviations’ section for the expanded forms of the abbreviations used in this table.

Page 9 of 32

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Further information on the NICAM development teamand the initial developments can be found at http://nicam.
jp. NICAM team members consist of researchers belonging to several research institutions in Japan. The sourcecode is primarily written in Fortran 95 and follows theMeteorological Research Institute/Japan Meteorological
Agency (MRI/JMA) standard coding rule (Muroi et al.
2002). The program is parallelized using Message Passing
Interface (MPI) process parallelization via automatic compiler thread parallelization. OpenMP is not actively usedas the current program. Each component is managed as
one or more modules. In order to maintain readability ofthe source code, C preprocessor macros are not used. The
source code has been managed at repositories using theConcurrent Versions System (CVS), and the NICAM
development team is currently moving to multi-branchdevelopment using the Git distributed revision control
and source code management system. Due to its flexible
portability, NICAM can run on various computer platforms, such as Earth Simulator, the K computer, IBM
AIX, Linux, and Mac OSX. We have also tested several
combinations of MPI libraries and Fortran compilers.
NICAM is also used as a benchmark program or a test
platform for new architectures. For this purpose, wehave released the dynamical core of NICAM as open
source software (NICAM-DC), which is distributed
under the terms of the Berkeley Software Development(BSD) 2-Clause License and is available for download(http://scale.aics.riken.jp/nicamdc/). The NICAM development team also participates in a working group forthe common base library environment of weather andclimate models and in cooperative development efforts
with other modeling groups in Japan. In the field ofcomputational science, NICAM has made significant
contributions as a benchmark program and is one of themain application programs used in Exascale computing
research.
Dynamics
Grid configuration and advection scheme

As reviewed in the ‘Introduction’ section, the use of gridmodels with finite-difference and finite-volume methods
for weather and climate models was reconsidered as massively parallel computer architectures entered widespreaduse. An icosahedral grid is a globally quasi-uniform and ispossibly the most suitable base for the development of an
atmospheric model that can be run on a supercomputer.
Additionally, the adoption of an icosahedral grid for thehorizontal grid of NICAM was partially motivated by the
results of Heikes and Randall (1995), in which a twodimensional (2D) shallow water system model was developed using the Z-grid arrangement of the prognostic variables (Randall 1994; Figure 4d), which followed Masuda
and Ohnishi (1986). The Z-grid model, however, had a

Page 10 of 32

disadvantage in that the model needed to diagnose thevelocity fields from the vorticity and divergence fields bysolving two Poisson equations in each time step. Furthermore, it was not obvious that an efficient Poissonsolver would become available for use on the massively
parallel computers, at least when the NICAM development began.
Tomita et al. (2001) did not follow any previous study(such as Heikes and Randall 1995; Masuda and Ohnishi1986) and instead examined a new method by using the
Arakawa A-grid (Arakawa and Lamb 1977), which located prognostic variables (fluid depth and velocity) at
the same nodes (Figure 4a) and thus did not require
Poisson solvers. The accuracy and stability of the shallow water model were improved after applying a grid
optimization that is now commonly known as ‘spring
dynamics’ (Tomita et al. 2002), and the physical performance of the model was evaluated using the standard
tests provided in Williamson et al. (1992). The methods
used to discretize the horizontal gradient, divergence,rotation, and Laplacian operators in NICAM are similar
to those described in Tomita et al. (2001) and Tomita
and Satoh (2004), except for some minor updates in thepassive tracer transport (Miura 2007; Niwa et al. 2011a)
and damping operators.
When the model was first developed by Tomita et al.
(2005), NICAM was capable of producing a global simulation with horizontal grid spacing of 3.5 km. It was also
the first model to produce a global simulation with horizontal grid spacing of less than 1 km (Miyamoto et al.
2013). These characteristics indicate that the A-gridarrangement is a good choice due to its simplicity andthe relative ease with which it attains high computational
performance on Earth Simulator and the K computer. It
should be noted that Tomita et al. (2001) provided thefirst example of the shallow water model on an icosahedral grid, which uses the finite-volume method and adopts
velocity components as prognostic variables. However,
it is well known that the A-grid model is unsuitable forsimulations of inertia-gravity waves and geostrophic
adjustments and that the model suffers from grid-scale
noise (Randall 1994). Therefore, other options should
be considered for the horizontal variable arrangement,such as those described below.
The disadvantages of the B/ZM-grid (Figure 4b) andC-grid (Figure 4c) arrangements, which were pointed
out by Ničkovic et al. (2002), on the hexagonal gridwere partially solved by Ringler and Randall (2002) andThuburn et al. (2009), respectively. Although problems
with the computational modes remain due to the mismatch of the degrees of freedom between the fluid
depth/pressure and horizontal velocity, the B/ZM-gridand C-grid not only allow a more realistic representationof inertia-gravity waves and geostrophic adjustments thanSatoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 11 of 32

Figure 4 Arrangements of variables on the hexagonal grid. Arrangements of prognostic variables on the hexagonal grid for the(a) A-grid, (b) B/ZM-grid, (c) C-grid, and (d) Z-grid staggering. The symbols ρ, T, u, v, v⊥, δ, and ζ refer to density, temperature,zonal components, and meridional components of the flow velocity, respectively, that is defined (a) at the center or (b) at thevertex of the hexagon, normal component of the flow velocity on each edge, divergence of the horizontal flow, and verticalcomponent of vorticity.

the A-grid but also are free from the checkerboard pattern. Currently, we are investigating the B/ZM-gridas a candidate to replace the A-grid, and the difficulty in managing the computational mode has been
largely eliminated by revising the operators of Ringler and Randall (2002). This activity is ongoing, anddetails of the improvements will be reported inforthcoming papers. The update of the dynamicalcore will also include the newly developed transport
algorithms of Miura and Skamarock (2013) and Miura(2013).
Vertical resolution issues

NICAM adopts the Lorenz grid and z* (terrain-following) coordinates (Gal-Chen and Somerville 1975) as avertical coordinate system (Figure 3 of Satoh et al. 2008).
Density, horizontal velocity, and internal energy are defined at the full levels, and vertical velocity is defined at
the half levels. In NICAM, z* is generally specified so as

to linearly depend on the geometric height z between
the model surface and top (the linear-z* coordinate).
Using the standard vertical levels Zk , geometric heightzX,k is formulated asZk
Z X;sfc ;zX;k ¼ Z k þ 1−
H top
where k is an index for the vertical level, X represents
the location of the horizontal grid, and Htop and ZX,sfc
are the heights of the model top and bottom, respectively. In general, Htop = 40 km is used with 40 verticallevels. For standard cases, the lowermost full level islocated at 81 m. The vertical interval of the half-levelΔZk increases with height, from ΔZk = 170 m at thebottom, ΔZk < 1 km below 10 km, and ΔZk ~ 3 km near
the model top.
In some situations, the hybrid-z* coordinate (Simmonsand Burridge 1981) is employed to reduce the amount ofSatoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

pressure gradient force numerical errors that arise from
topography. It is formulated as−Z !

HzX;k ¼ Z k þsinh Htopefold kHtopsinh H efold

Z X;sfc ;where Hefold is a height scale parameter (e.g., Hefold =10 km). In the hybrid-z* coordinate, intervals of the vertical level approach those of the standard vertical level
for Zk ≫ Hefold, whereas in the linear-z* coordinate, it isconstant with height even near the model top. Thishybrid-z* coordinate advantage is obtained at the expenseof packing more vertical levels just above the higher
mountain, which may cause the vertical CFL condition
to become a serious issue for the hybrid-z* coordinate.
Klemp (2011) also proposed a hybrid approach, inwhich the coordinate surfaces are smoothed with heightin order to remove smaller scale terrain structures fromthe surfaces.
Recently, we constructed a new configuration for thevertical levels of NICAM in order to perform simulations
with finer vertical resolutions in the troposphere, stratosphere, and mesosphere levels. The development of this‘middle atmosphere NICAM’ is motivated by the resultsof recent climate studies in which the tropospheric circulations were found to be affected by the stratosphere.
It should be noted that the inclusion of the middle atmosphere into a climate model is a worldwide trend in
the climate model community, since it improves the
reproducibility of the large-scale tropospheric circulation (e.g., Shindell et al. 1999). Currently, the middle
atmosphere NICAM employs the hybrid-z* coordinate,
and its model top height is set to 80 km. The verticalintervals in the lower atmosphere are the same as thestandard vertical coordinate. As the vertical interval
increases with height and reaches a specific criteria(Δzmax), the vertical level is set with the uniform interval of Δzmax (except near the model top) in order to appropriately simulate the propagation of atmosphericgravity waves. We have tested Δzmax = 2, 1, and 0.5 km
 = 120 km), aswith a horizontal resolution of g-level 7 ( Δshown in Figure 5. As the vertical resolution increases, thereproducibility of the zonal mean temperature and zonal
wind tends to improve in the upper troposphere and middle atmosphere. Interestingly, westerly winds around theequatorial lower troposphere in June 2004 area only wellsimulated when the vertical resolution is set to 1 km or
less. NICAM has an option that allows the deep atmosphere configuration (Tomita and Satoh 2004) to be
switched. For the middle atmosphere simulations, thechoice of the deep atmosphere is considered appropriate.
Determining the proper vertical coordinates is an ongoing issue for high-resolution non-hydrostatic models.

Page 12 of 32

For global non-hydrostatic models in particular, thenumerical instability of the terrain-following coordinates
becomes more severe because the steep topography over
the Andes or the Antarctica is within the simulation
domain. For the present simulations, relatively coarser
topographic data were used by applying a smoother for
the NICAM simulations. For example, for the 3.5 km
mesh simulations, 14 km mesh coarsened topographic data
were generally used. In addition, a minimum threshold valueof the Brunt-Väisälä frequency was specified for the calculations in the PBL schemes, that is and the Mellor-Yamada
Nakanishi-Niino (MYNN) scheme, in order to suppress anunusual increase in vertical shears near the surface.
To overcome the problem of terrain-following coordinates, different candidates have been proposed. For
example, the possibility of a vertical coordinate that isbased on geopotential height, i.e., the z-coordinate, hasbeen investigated. Although this approach is common
for ocean models (Adcroft et al. 1997), it must be
understood that the implementation of the z-coordinate
to non-hydrostatic atmospheric models is not straightforward (Gallus and Klemp 2000). The immersed boundary
method is well known for computational fluid dynamics(Mittal and Iaccarino 2003). Various approaches to overcome the problems of the z-coordinate were taken into
consideration, such as the cut-cell method or the thin-wall
approximation (e.g., Bonaventura 2000; Steppeler et al.
2002, 2006; Yamazaki and Satomura 2008, 2010), havebeen taken into consideration. We plan to introduce suchan option for the z-coordinate of NICAM in the near
future.
Physics
Cloud microphysics schemes

Cloud microphysics can be used to solve the growthequations of a single hydrometeor particle. The related
processes are mainly categorized into four components: nucleation processes, phase change, collisional
processes, and gravitational sedimentation. These processes have not been calculated explicitly and havebeen parameterized in conventional AGCMs since thecharacteristic timescale of these processes and spatial
variability of cloud ensembles cannot be resolved using
the time-step and grid resolution of GCMs. Global
non-hydrostatic models operate by calculating theseprocesses via cloud microphysics schemes in order toexplicitly evaluate time evolution of cloud growth overthe global domain. The implemented schemes are
generally developed for regional non-hydrostatic models.
This section introduces cloud microphysics models
that are applicable to global non-hydrostatic models.
For a more fundamental theoretical basis of cloud
microphysics processes, refer to Pruppacher and Klett(1997).

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 13 of 32

Figure 5 Dependency of the simulated NICAM climatology on the vertical resolution. Dependency on the vertical resolution of the (upper)
zonal mean temperature (K), and (lower) zonal mean zonal wind (m s-1). From left to right: JRA25 reanalysis (Onogi et al. 2007), CIRA86 (http://badc.
nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__dataent_CIRA), NICAM (Δzmax = 2 km), NICAM (Δzmax = 1 km), and NICAM (Δzmax = 0.5 km). For CIRA86, theclimatology for June is shown. All the other panels are the averages for June 2004. Shading denotes deviations from the CIRA86 climatology.

Multi-moment bulk method Hydrometeor particles
typically have a radius r from 10 μm to 1 mm and populations from 102 to 109 m−3. To manage numerous particles efficiently (in terms of computational cost), cloud
microphysics modeling generally assumes that particles
with the same radius are homogeneously distributed
within a grid cell of the model and develop in the samemanner while under the same environmental conditions.
Growth equations of hydrometeor particles with similar
radii are solved together and the particle size distribution (PSD) f (r), which is the number density sorted by
the radius of the hydrometeors, is then predicted. Numerous observational studies have shown that liquidhydrometeor particles have three major modes for the
PSD (Pruppacher and Klett 1997): particles in the condensational growth mode (which have a mode radiusaround 10 μm), particles in the collisional growth mode
(which have a mode radius larger than 100 μm), and thedrizzling mode (which is a transitional mode between
the two aforementioned modes).
There are two main methods that can be used to predict the time evolution of the PSD. One is the multimoment bulk method, which predicts the integrand ofeach mode by approximating the modes via the gamma
distribution (e.g., Milbrandt and Yau 2005b; Seifert and
Beheng 2006):
f ðr Þ ¼ K r v expð−λr μ Þ;

where κ, λ, μ, and ν are the diagnosed parameters. The
k-th moment of the PSD, M(k), is defined as
M ðk Þ ≡Z ∞f ðr Þr k dr;

0

and the time evolution of the prognostic moments isevaluated by integrating the growth rates of a singlehydrometeor particle over the entire range of the PSD(e.g., Seifert and Beheng 2006). The other is the spectralbin method, which solves growth equations by discretizing
the PSD with tens of bins covering these modes (Khainet al. 2000, 2008). Because of their computational efficiency, the multi-moment bulk methods are widely usedfor 3D non-hydrostatic simulations.
The multi-moment bulk method is categorized by the
number of its prognostic moments: the single-momentbulk method predicts the mass concentration of hydrometeors (Kessler 1969; Lin et al. 1983; Rutledge andHobbs 1983; Walko et al. 1995; Grabowski 1998; Honget al. 2004; Thompson et al. 2008; Tomita 2008b), andthe double-moment bulk method predicts the number
concentration in addition to the mass concentration(Meyers et al. 1997; Feingold et al. 1998; Morrison et al.
2005; Seifert and Beheng 2006; Phillips et al. 2007;
Morrison and Gettelman 2008; Lim and Hong 2010).
The four parameters used to represent the PSD (κ, λ, μ,and ν) are diagnosed by prognostic moments. Hence, as
the number of the prognostic moments is increased, aSatoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

variety of PSD shapes can be represented. For example, increases in the mode radius and skewness of the PSD are
observed in collisional growth (Berry and Reinhardt 1974).
These distortions of the PSD are important for initiationof precipitation (Seifert and Beheng 2001). Furthermore,
once precipitation occurs, the gravitational sedimentationof the hydrometeor particles induces further distortionof the PSD due to the gravitational size sorting mechanism and the time evolution of the PSD affects thesubsequent chain of particle growth (Wacker andSeifert 2001; Milbrandt and Yau 2005a; Milbrandt and
McTaggart-Cowan 2010). In addition, higher ordermoments are required for the closure of the governing
equations in the collisional processes (e.g., Seifert and
Beheng 2001). Thus, an increase in the number ofprognostic moments is linked to improvements in thetime evolution of the PSD and complexity of the cloud
microphysics modeling. Additionally, complexity andcalculation costs increase as the number of prognostic
moments increases.
For climate studies, collisional processes are particularly
important to the radiative budget because such processes
dominate the persistence of warm-phase clouds due tothe rapid growth rate of cloud droplets during the precipitating process, along with the strong cloud albedo perturbations caused by the spatial and temporal variabilities of
their growth rates (Albrecht 1989; Lohmann and Feichter2005). The double-moment bulk method enables us to
explicitly evaluate the dependence of the collisional
growth rate on the mode radii (Berry and Reinhardt 1974;
Feingold et al. 1998; Khairoutdinov and Kogan 2000;
Seifert and Beheng 2001). In addition, cloud radiative
forcing can be more accurately estimated because thecloud optical properties are calculated using the massconcentration and number concentration (Hansen andTravis 1974). In contrast, the single-moment bulk method
assumes that the radii of the hydrometeors are empiricalvalues. Empirical values do not reproduce the temporaland spatial variability of atmospheric states, and theirvalidity has not been confirmed for various types ofclimate change, such as global warming conditions.
The triple-moment bulk method, which was recently
developed and used (Milbrandt and Yau 2005a, b; Seifert
2008; Shipway and Hill 2012), predicts or diagnoses theradar reflectivity factor in addition to the aforementioned
two moments and is applied to the regional simulations ofsevere storms (Milbrandt and Yau 2005a, 2006). Of
course, the higher order moment bulk methods also contain empirical formulations and parameters that can beused to calculate cloud growth and optical properties.
However, previous process studies have shown that thedifferences of simulated results between double-moment
and triple-moment bulk methods are smaller than thosebetween single-moment and double-moment bulk methods

Page 14 of 32(e.g., Milbrandt and Yau 2005a; Shipway and Hill 2012).
In addition, it has also been demonstrated that a welloptimized double-moment method can capture thecharacteristics of cloud growth simulated by a spectralbin method (Seifert and Beheng 2001; Seifert et al. 2006;
Seifert 2008; Milbrandt and Yau 2005a, 2006). These
facts indicate that the double-moment bulk method ispractical and applicable in terms of cost performance ofnumerical simulations.
Cloud microphysics schemes in NICAM NICAM initially adopted a single-moment bulk cloud microphysics scheme that was proposed by Grabowski (1998) inorder to explore the multi-scale interaction of moist
convective systems because the organizations of deep
convective systems do not depend on the details ofcloud microphysics schemes. Instead, meso-scale circulations are reproduced if the latent heat release in updrafts
and the evaporation cooling caused by rain drops are considered by predicting the mass concentration of hydrometeor particles, as introduced in the single-moment bulk
scheme. In the latest version, the schemes proposed by
Kessler (1969), Lin et al. (1983), Hong et al. (2004), andTomita (2008b) are implemented. In particular, NICAM
single-moment scheme with six water categories proposedby Tomita (2008b) (hereinafter, referred to as NSW6) has
been well evaluated by comparisons with satellite observations (Satoh et al. 2010; Kodama et al. 2012; Hashino et al.
2013; Roh and Satoh 2014) and, hence, is used by default.
Because of the relatively low calculation cost, the use of
single-moment schemes enables us to conduct very highresolution global non-hydrostatic simulations at g-level 13
 = 870 m; Miyamoto et al. 2013), or for longer integra(Δtions up to several decades.
Recently, Seiki and Nakajima (2014) implemented adouble-moment bulk cloud microphysics scheme (NICAM
double-moment scheme with six water categories; hereinafter, referred to as NDW6) into NICAM. Seiki et al. (2014)
demonstrated that the NDW6 scheme well reproduces
cloud radiative forcing by calculating cloud ice number
concentration with using accurate databases of the cloudoptical properties. Figure 6 shows the simulated cloud optical thickness (COT) and outgoing shortwave radiativeflux at the top of the atmosphere that were obtained using
the NDW6 scheme, along with the observed outgoingshortwave flux at the top of the atmosphere that was
obtained from the satellite product of CERES SYN1deg_Ed3A (Wielicki et al. 1996; Kato et al. 2011). The
simulation showed that convective clouds with a high
COT were scattered over the tropics and formed regional
cloud clusters (e.g., over the central Pacific). For example,
Typhoon Fengshen remained over the Philippines andhigh COT clouds were distributed along its vorticalstructure. Furthermore, synoptic scale disturbances were

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 15 of 32

Figure 6 Simulated results for the 14 km mesh NICAM with the double-moment cloud microphysics scheme. Simulated results for the
double-moment cloud microphysics scheme for the 14 km mesh NICAM on 20 June 2004: (a) simulated cloud optical thickness at a wavelengthof 550 nm at 00:00 UTC, (b) daily averaged outgoing shortwave flux (W m−2) at the top of the atmosphere, and (c) the daily averaged outgoingshortwave flux (W m−2) at the top of atmosphere, as produced by the CERES SYN1deg_Ed3A product.

organized over the mid-latitude regions and optically thickregions were located along the front. A shortwave flux
corresponding to these high COT regions was largely
reflected by clouds. These features agree well with thesatellite observation. As shown by the NDW6 scheme,
the impact of the cloud radiative forcing, which resultsin improvements to the climatological biases in
NICAM, should be evaluated in future studies.
Subgrid-scale turbulence of the planetary boundary layer

Boundary layer turbulence plays an important role indriving large-scale circulations of the atmosphere as it
transports heat upward from the Earth's surface in theform of sensible and latent heats, which are converted
from incoming solar energy at the surface (Trenberth
et al. 2009). To simulate atmospheric circulations, aparameterization scheme that computes the effects ofsuch turbulent transport in the subgrid scale of a numerical model needs to be incorporated, unless the resolution
is high enough to resolve turbulence.
NICAM adopts the MYNN scheme, which is a modified version of the original Mellor-Yamada (MY) model(Mellor and Yamada 1982; Nakanishi and Niino 2006,2009), for such turbulent transport models (Noda et al.
2010). There are three major advantages to employing
the MY-type model: 1) the MY-type model can consistently and simultaneously compute subgrid-scale condensation and resultant turbulent fluxes by assuming
the probability density functions regarding the inhomogeneity of temperature and water on a subgrid scale, 2)
the model can consistently evaluate the impacts of thedifferences of turbulent moments, and 3) the model canrun with low computational cost because it omits minor
terms in the turbulent transport equation system. Basedon the degree of approximation from the original equations, level 1 to level 4 can be included in the MY-typemodel. The level-2, level-2.5, and level-3 MYNN version
models are installed in NICAM, and the level-2 modelis used as a standard PBL scheme.

Figure 7 compares the impacts of these different level
models on the spatial distributions of simulated clouds.
As can be seen in the figure, all turbulent models properly simulate the spatial characteristics of, for example,
high and mid-level clouds, even though the magnitudeof the high clouds tends to be larger than those seen insatellite observations. For low clouds, the level-2 model
properly represents the characteristics of the regional
contrast between a clear and cloudy sky, such as thoseseen over the eastern and western regions of the Pacific
Ocean. Conversely, the low clouds that systematicallydevelop along and off the Peruvian coast are not simulated
well. Compared with the results of the level-2, level-2.5
and level-3 models produce a much smaller fraction of
low clouds. It is speculated that these two models calculate more vertical turbulent heat transports of heat fromthe boundary layer to the free atmosphere, which results
in a less humid boundary-layer state.
Due to the remarkable progress of computational
technology, the model resolution of NICAM is steadily
being increased, with the highest resolution to date having
been achieved when a horizontal mesh size of 870 m wasused (Miyamoto et al. 2013). For a global non-hydrostatic
model, boundary-layer scale turbulence becomes explicitly
and partially calculatable around this resolution, but will
not be sufficiently resolved until the mesh size is less than
100 m (Khairoutdinov et al. 2009; Moeng et al. 2009). It isa challenging issue to parameterize turbulent transport
processes in such an intermediate resolution (Wyngaard2004), and continuous efforts are needed to improve thesubgrid-scale turbulence parameterization schemes alongwith shallow cumulus convection.
Radiation

Atmospheric radiative transfer models calculate heating
rates in the atmosphere and radiative flux at the Earth's
surface using the 3D distribution of clouds/gases/aerosols
and land/ocean surface properties with the atmosphericstate. NICAM adopts the broadband radiative transfer

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 16 of 32

Figure 7 Comparison of the cloud amounts simulated by using different turbulent models to the satellite observation. The comparisonsof the cloud amounts from the ISCCP satellite data, level-2, level-2.5, and level-3 MYNN turbulent schemes were obtained by using the 14 km
mesh NICAM. The NICAM data shown are averages during 29 July 1982 and 15 August 1982, and that of the satellite observation are the climatology
for July and August.

model ‘MSTRN’, which is based on the discrete ordinate
method with a delta two-stream approximation and thecorrelated k-distribution method. The MSTRN model calculates gas absorption (H2O, CO2, O3, N2O, CH4, and O2)
and the scattering/absorption of hydrometeors and aerosols (dusts, sea salts, carbonaceous, and sulfates) over anarbitrary time interval and then outputs short- and longwave radiation fluxes. A basic description of this model iswritten in Nakajima et al. (2000). In the latest package,‘MSTRN-X’ (Sekiguchi and Nakaima 2008), the gas absorption processes are improved and successfully used toreduce the error of the heating rate. The scheme was introduced into NICAM through the AGCM version implemented for MIROC from the original one-dimensional(1D) model. The calculation of solar insolation (Berger1978) was added to the AGCM version. The other cloud
overlapping method (maximum-random overlapping) was
incorporated since this version of the radiation scheme
was included in MIROC3.2 (Hasumi and Emori 2004).
The calculation cost of the radiation scheme is very
high in the AGCM, because both computing speed andprecision are required for the scheme. The latest MSTRNX scheme improved the optimization method in order toreduce the integration points in the correlated k-distribution
approximation without decreasing accuracy. In the NICAM
default setting, the spectrum between 0.2 and 200 μm is
divided into 29 spectral bands with 111 integration points.
Cloud-radiation feedback is one of the most important issues in the study of climate change. The high-resolution
experiments performed by NICAM have an advantage
in that they reduce the uncertainties of the horizontal/
vertical cloud distributions. In the recent studies describedabove, both the cloud scheme and radiation scheme are
improved by considering the detailed size distribution of
the hydrometeors and non-sphericity of the ice particles.
This cooperative development of the cloud and radiation
schemes will facilitate improvements in the future development of aerosol and chemistry modules (see ‘Aerosol andchemistry modules’ section).
NICAM also introduces the ISCCP simulator (Klein
and Jakob 1999; Webb et al. 2001), with which the distribution of every cloud category can be calculated online.

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

The results from the simulator have been used in numerous studies (Iga et al. 2007a; Suzuki et al. 2008;
Collins and Satoh 2009; Noda et al. 2010; Sohn et al.
2010; Satoh et al. 2012b; Miyamoto et al. 2013; Tsushima
et al. 2014). In some studies, new satellite simulator packages have been applied offline to the NICAM dataset.
Kodama et al. (2012) used a series of satellite simulatorsthat were part of the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package(COSP) (Bodas-Salcedo et al. 2011), and Hashino et al.
(2013) used the Joint Simulator for Satellite Sensors(J-simulator). Therefore, the online use of these packages should also be taken into consideration.
Aerosol and chemistry modules

Currently, NICAM is implemented with a global 3D
aerosol transport-radiation model, the Spectral RadiationTransport Model for Aerosol Species (SPRINTARS)(described fully in Takemura et al. 2000, 2002, 2005,2009; Goto et al. 2011a). The SPRINTARS module predicts the mass mixing ratios of the main troposphericaerosol species, i.e., carbonaceous (black carbon andorganic matter), sulfate, soil dust, sea salt, and the precursor gases of sulfate (sulfur dioxide and dimethylsulfide (DMS)). The aerosol transport processes include
emission, advection, diffusion, sulfur chemistry, wet
deposition, dry deposition, and gravitational settling.
Emissions of soil dust, sea salt, and DMS are calculatedusing the internal state of the model, whereas the emissions for the other primary aerosols and the distributionsof oxidants (hydroxyl radical, ozone, and hydrogen peroxide) for the sulfur chemistry are prescribed by externallyassumed data. In the module itself, meteorological fields
such as clouds and precipitation, which are critical to determining aerosol lifetimes, are provided from the outermodule after the cloud microphysics calculations are computed at each time step. In contrast, the calculated mass
mixing ratios of the aerosols in each grid cell are calculated by the aerosol module SPRINTARS and thenreturned back to the outer module. In addition, aerosol
variables are forwarded to the radiative transfer module,
MSTRN-X (Sekiguchi and Nakaima 2008), in order torepresent the direct aerosol effects, and also to cloud
schemes such as NSW6, to represent the indirect aerosol
effects.
SPRINTARS, which was originally coupled to MIROC(Watanabe et al. 2010) with a low spatial resolution of
100 to 300 km, has compared well with various measurements (Goto et al. 2011a, b, 2012) and other global
aerosol models participating in the AeroCom international project (e.g., Kinne et al. 2006). The results of
the SPRINTARS module coupled to NICAM were also
shown to generally agree with the satellite observationsof aerosols and clouds for versions based on low spatial

Page 17 of 32

 = 220 km) (Dai et al. 2014a,
resolution of g-level 5 ( Δb; Goto 2014) and high spatial resolution of g-level 10
 = 7 km) (Suzuki et al. 2008). Using Earth Simulator,(Δ
Suzuki et al. (2008) conducted a global simulation
with the finest resolution to date and showed the global
aerosol and cloud distributions for July 2006 (Figure 8).
Most of the aerosol plumes were in generally good
agreement with the satellite observations, except for
North America, Australia, and most remote oceans.
Especially, over North America, the values of NICAMsimulated aerosol optical thickness were underestimated,possibly because of insufficient emissions from biomassburning and anthropogenic sources, or the lack of secondary organic aerosols formed from isoprene in forests(Henze and Seinfeld 2006). Through the aerosol-cloudinteraction, the cloud droplet effective radius generally
decreases, as the aerosol optical thickness increases,
which were generally captured by both NICAM and
MODIS. The cloud droplet effective radii simulated by
NICAM were smaller than those obtained from MODIS,
probably because those retrieved from 2.2 μm (shown in
Figure 8) correspond to those captured below the cloud
top heights where the cloud droplet effective radii are larger than those at cloud top height (Nakajima et al. 2010).
Very recently, regional aerosol simulations that used thestretched grid system with a high spatial resolution of
approximately 10 km were performed for the Kanto region of Japan under the MEXT/RECCA/SALSA project(SALSA 2014; Goto et al. 2014). These simulations
were carried out at relatively low computational cost byusing two supercomputers at the University of Tokyo(SR16000 and FX10).
The CHASER atmospheric chemistry module has also
been implemented into NICAM (SALSA 2014). This
module primary focuses on tropospheric chemistry whileconsidering the chemical cycle of Ox-NOx-HOx-CH4-CO
relation to the oxidation of volatile organic compounds(VOCs) and halogen chemistry in order to calculate theconcentrations of 92 chemical species with 262 chemical
reactions (58 photolytic, 183 kinetic, and 21 heterogeneous reactions) (Sudo et al. 2002a, b; Watanabe et al.
2011). Using the CHASER module, the mixing ratios ofatmospheric gases, which mainly include ozone, NOx, CO,SO2, VOCs, and sulfate aerosols, can be predicted. The
transport processes of the chemical tracers include
emission, advection, diffusion, chemical reaction, wet
deposition, and dry deposition. The emissions of lighting
NOx and DMS are calculated by using the internal parameters of the model, whereas the emissions of theother gases are prescribed. The photolysis rates are calculated online by using the temperature and radiation
fluxes computed in the radiative transfer module. Like
the aerosol module, the meteorological field information is input from the outer module after the cloudSatoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 18 of 32

Figure 8 Distributions of aerosol optical thickness and cloud droplet effective radius from the NICAM-SPRINTARS simulations. Global
geographical distributions of (a, c) aerosol optical thickness and (b, d) cloud droplet effective radius from (c, d) the NICAM-SPRINTARS simulations
in comparison to those obtained from (a, b) the MODIS satellite observations for 1 to 8 July 2006 (cited from Suzuki et al. 2008). The unit of cloud
droplet effective radius is micrometers.

microphysics calculations and to the chemistry module
at each time step. In contrast, the calculated mass mixing
ratios of the tracers in each grid cell within the chemistry
module are returned back to the outer module. In
addition, ozone, N2O, and CH4 are calculated online andused in the radiative transfer module to represent the atmospheric warming effects.
The distributions of the trace gases obtained by
CHASER, which is currently coupled to MIROC at alow spatial resolution of approximately 300 km, aregenerally compared to the observations (Sudo et al. 2002b;
Sudo and Akimoto 2007; Nagashima et al. 2010) and toother chemistry models under international model comparison projects such as the Atmospheric Chemistry andClimate Model Intercomparison Project (ACCMIP) (e.g.,
Lamarque et al. 2013). Under the MEXT/RECCA/SALSA
project, the results of CHASER, along with the globally
uniform and regionally stretched grid systems, werecompared with the observations (SALSA 2014). Furthermore, the chemical module has been currently expanded to a fully coupled aerosol-chemistry module bycoupling CHASER with SPRINTARS, as developed in
Watanabe et al. (2011) and used by MIROC.
The unified module, consisting of the fully coupledaerosol-chemistry, coupled to NICAM (hereinafter referred
to as NICAM-Chem) can explicitly consider various interactions between aerosols and gases, as shown in Figure 9.
For example, as an aspect of the aerosol simulation, theunified module can precisely calculate the sulfate formation
with online oxidants such as hydroxyl radicals and ozone,
whereas SPRINTARS uses assumed oxidants. In contrast,
as an aspect of short-lived gases, calculations of theheterogeneous reactions onto the aerosol surface are
expected to provide the greatest advantage when using
the unified model. In addition to the sophisticated interactions between aerosols and gases, the chemistry ofnitrate will be implemented into NICAM-Chem byusing a thermodynamic equilibrium condition andsecondary organic aerosols (SOAs). As a result, the
new unified chemistry module is expected to provide
better distributions of aerosols and gases, and, in thenear future, we expect to be able to provide such distributions globally with a high spatial resolution of lessthan 10 km.
Land model

We implemented two land surface schemes in NICAM
in order to calculate the water and energy balance overland. The first scheme is a simple bucket land surface
model (Kondo 1993), and the other involves the minimal advanced treatments of surface interaction andrunoff (MATSIRO) model (Takata et al. 2003). Whenused with NICAM, the simple bucket land surface
model, which was first used by Miura et al. (2007a), has
three vertical layers for predicting soil temperature andone layer for predicting the soil moisture.

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Figure 9 Relationship between the aerosol, gas, cloud,precipitation, and radiation fields for NICAM-Chem.
Relationship between the aerosol, gas, cloud, precipitation,and radiation fields for the unified aerosol-chemistry module(NICAM-Chem). The aerosol and gas modules correspond to
SPRINTARS and CHASER, respectively.

The MATSIRO scheme is the same as that originallyemployed in MIROC (Hasumi and Emori 2004). Kodama
et al. (2012) were the first to use MATSIRO with NICAM.
This scheme has five vertical layers for predicting soil
temperature and soil moisture, as well as a snow schemewith thee vertical layers.
Even in a several-kilometer grid simulation, land use
and land cover cannot be regarded as homogeneous in
one grid cell if it is to be used to evaluate the land surface
heterogeneities smaller than the horizontal grid scale.
Accordingly, we are currently implementing a mosaic
treatment of the land surface models in order to improve the accuracy of the water and energy fluxes atthe land surface. We will also attempt to incorporate
an urban canopy model in order to simulate the global
urban energy balance.
Assimilation
Local ensemble transform Kalman filter applied to NICAM:NICAM-LETKF

The introduction of a data assimilation system into
NICAM is a challenge because it might enable us to
produce a high-resolution dataset with O (km) meshsize that can be used for improved better numericalsimulations and research analysis. Data assimilation isan analysis technique that provides an accurate estimateof the atmospheric state by using observation data andmodel forecasts. It plays an important role in improving
numerical weather prediction by providing an accurate
initial condition. The ensemble Kalman filter (EnKF) isan advanced data assimilation method that uses flowdependent forecast error statistics that are represented
by the ensemble prediction (Evensen 1994). For a linear
system, the Kalman filter (KF) gives a minimum mean

Page 19 of 32

square error if the error is a Gaussian distribution (Kalman
1960). However, it is difficult to apply the KF because thecomputational cost for an atmospheric model with a highdegree of freedom (order of 107) is high. To resolve thisproblem, Evensen (1994) proposed the EnKF, which approximates the KF by using the forecast spread of theensemble prediction. Furthermore, the local ensemble
transform Kalman filter (LETKF) (Hunt et al. 2007) was
developed based on the ensemble transform Kalman filter (ETKF) (Bishop et al. 2001) and used an algorithm
that was designed for suitability with parallel computers
by taking advantage of the independent local analyses ofthe local ensemble Kalman filter (LEKF) (Ott et al. 2004).
For details on the mathematical formulation and implementation of these approaches, refer to each reference.
Previous studies have applied the LETKF to regionaland global atmospheric models and have shown promising
results (Miyoshi and Aranami 2006; Miyoshi and Yamane2007). Kondo and Tanaka (2009) applied the LETKF to
NICAM in order to develop NICAM-LETKF, which was
the first LETKF to be applied to a global non-hydrostatic
model. They then investigated the feasibility and stabilityof NICAM-LETKF using a perfect model and determinedthat the system works appropriately for a realistic nonhydrostatic model (NICAM). However, the horizontal
resolution used in their study was not very high (g-level 5
 = 220 km), where the model would behave hydrostator Δ
ically. Additionally, it is unclear whether NICAM-LETKF
will work stably when real observations are used. Therefore, a future task will be to investigate data assimilation
with a high-resolution global non-hydrostatic regime
using real observations. This would not only contribute to
an improvement in weather prediction but also to the research on multi-scale interaction, by using the product of
the assimilation. We are now developing a version of
NICAM-LETKF that will be based on the LETKF codethat was developed and continuously improved by
Miyoshi (e.g., Miyoshi and Kunii 2011) and are currently
testing assimilation of precipitation data observed by satellites in order to obtain a fine-mesh precipitation databaseusing the method proposed by Lien et al. (2013).
NICAM-based transport model: NICAM-TM

An accurate estimation of the past and current CO2
budget for the Earth's surface is needed for reliable predictions of carbon cycle changes under global warming
conditions. Therefore, an inversion method coupled witha tracer transport model was used to estimate the spatial
and temporal variations of CO2 sources and sinks basedon observations of CO2 concentrations (e.g., Tans et al.
1990). In an inversion analysis, we quantitatively estimated regional CO2 fluxes by considering the a priori
estimate and uncertainties of the CO2 fluxes, whichwere derived from our present understanding of CO2

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

source/sink mechanisms and uncertainties of observations(including model representative errors). This analysis isbased on Bayesian theory and is similar to data assimilations for meteorological forecasts (Tarantola 2005). However, in contrast to the observations of meteorologicalparameters, CO2 concentrations are not sufficiently observed around the globe. Therefore, estimates of CO2
sources and sinks are not well constrained by actual observations and are extremely sensitive to model transport
errors (e.g., Gurney et al. 2002).
In response to the need for an accurate transportmodel, Niwa (2010) developed the NICAM-based transport model (NICAM-TM). For transport simulations ofCO2, NICAM has an advantageous property: consistency
with continuity (CWC) (Jöckel et al. 2001; Gross et al.
2002). Under the CWC condition, the mass conservationand Lagrangian conservation of a tracer are simultaneously guaranteed; that is, the volume integral of mass isconserved and a constant specific mass of a tracer ismaintained along flow trajectories. The ability to achieve
the CWC property is attributed to the fact that NICAM
uses the finite volume method for its meteorologicalfield integration and performs tracer transport on acommon dynamical frame (Niwa et al. 2011a). The tracer
mass conservation is strictly required for analyses of theCO2 budget, which is the main target of inversion studies.
Furthermore, the Lagrangian conservation property is imperative for CO2 because it is an abundantly existing
tracer (the global average CO2 concentration was about
390 ppm for 2013) and the concentration changes that are
analyzed are very small (no more than a few tens of ppm).
NICAM-TM consists of online and offline transportmodels, as well as an adjoint model for the tracer transport. In the online model, tracer transport is calculatedconcomitantly but independently with the integration of
the meteorological field. More specifically, the tracers
other than those for water are passive. Compared to theonline model, the offline model is computationally inexpensive because it only calculates the tracer transport
by using meteorological data calculated by the online
NICAM-TM and stored in a disk beforehand. The adjoint model was developed based on the offline model,
which calculates the sensitivities to certain tracer variables backward in time. Because long-term and multiple
simulations are required for the inverse calculation,
NICAM-TM is currently run with a relatively lowresolution for CO2 transport simulations; for the mostpart, a resolution of g-level 5 or 6 is used. However,after conducting intercomparison experiments (Law et al.
2008; Patra et al. 2008; Niwa et al. 2011b), we found thatthe performances of CO2 transport obtained are comparable to those of other models. Generally, the modeledwinds are nudged towards the analyzed data (e.g., JMA
Climate Data Assimilation System (JCDAS); Onogi et al.

Page 20 of 322007) in order to match the simulated CO2 concentrations
to the observed concentrations.
Niwa et al. (2012) performed an inversion analysis byusing the synthesis inversion method (Enting 2002) with
NICAM-TM. This was the first inversion study in whichaircraft data were extensively used to constrain the global
and regional CO2 budgets. The aircraft data were obtained
from the Comprehensive Observation Network for Trace
Gases by Airliner project (CONTRAIL) (Machida et al.
2008; Matsueda et al. 2008; Sawa et al. 2012). By adding
the CONTRAIL data (Figure 10a) to the conventionallyused surface observation network, Niwa et al. (2012) successfully reduced uncertainties of the CO2 flux estimatesfor Asia. Moreover, this study revealed the existence ofa strong summer uptake by the biosphere in South
Asia (Figure 10b), where the CO2 observations from
CONTRAIL in the middle to upper troposphere have a
predominant constraint on the flux estimation. Because
biosphere models have not shown such a strong summer
uptake (Patra et al. 2013), Niwa et al. (2012) suggested theexistence of some unknown mechanisms of the CO2
sources and sinks in this region. To further improve
the CO2 inversion, a four-dimensional (4D) variational
method system is now being developed by using theadjoint model of NICAM-TM. Using this system, a large
amount of CO2 concentration data that have been recently available from regular aircraft observations andsatellite observations (e.g., Greenhouse Gases Observing
Satellite (GOSAT)) will be exploited to further constrain
the estimates of CO2 sources and sinks.
Models related to NICAM
Stretch NICAM and diamond NICAM

As described in ‘Grid configuration and advection
scheme’ section, NICAM was originally designed for use
with an icosahedral grid, which covers a sphere of the
Earth quasi-uniformly and has a grid system based on
10 large diamonds. As extensions to the original configuration, NICAM can also be applied to a non-uniform
resolution distribution grid or a regional grid by changing the positions of the grid points, the total number of
diamonds, or the topology of their combination.
NICAM can also be adapted to simulations that target
a particular area. Two approaches to performing a partially high-resolution simulation are typically used with
NICAM. One includes the stretched icosahedral grid
system developed by Tomita (2008a). In this approach,the icosahedral grid is stretched using the Schmidt transformation method and the grid size becomes graduallyfiner as grid points are concentrated onto the area ofinterest. In the stretch NICAM, the inhomogeneity of
the horizontal grid sizes requires resolution-dependent
parameters for physical schemes if the global domain isof interest. However, if a limited region is of interest,

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 21 of 32

Figure 10 Simulated CO2 concentration field and monthly variation of the CO2 flux by NICAM-TM. (a) Mean CO2 concentration field at
250 hPa for June 2007, as simulated by NICAM-TM, and the locations of the CONTRAIL measurements for 2007 (white dots). (b) Monthly variationof the CO2 flux averaged for 2006 to 2008, estimated by the inversion analysis of Niwa et al. (2012) (this figure is slightly modified from Figure 5din Niwa et al. 2012). The blue and red lines are the CO2 fluxes (positive values indicate sources and negative values indicate sinks) estimated byonly using surface data and by using both surface and CONTRAIL data, respectively, and the colored shades and error bars denote the range ofthe estimated flux uncertainty and standard deviation for 2006 to 2008, respectively.

such as within regions with finer mesh sizes, resolutiondependent parameters are not required. The stretch
NICAM is widely used for studies on tropical convective
systems (Satoh et al. 2010; Satoh and Kitao 2013; Roh and
Satoh 2014), tropical cyclones (Yanase et al. 2010a, b;
Satoh et al. 2013; Arakane et al. 2014), and MJOs (Nasuno2013).
The other approach is similar to the use of a regionalmodel. Only 1 of the 10 diamond regions that make up
the icosahedral grid is used for the simulation (hereinafter, the diamond NICAM). The simulation domain of
the diamond NICAM consists of 1 diamond (2 triangles
of an icosahedron), even though the original NICAM
global domain consists of 10 diamonds (20 triangles).
The diamond NICAM is used as a non-hydrostatic regional model. By using this approach, we can share thesame code for both the global domain and limited-areamodels. This is advantageous for the maintenance of thesource code because all components of NICAM are usedin common in both models.
To enable the limited-area simulations using the diamond NICAM, we developed a nudging scheme in which
the strength of the nudge depends on the distance from acertain point on the globe. Figure 11 shows the simulation
domain of a regional climate simulation targeted on Japan.
The lateral boundary of the simulation domain (outeredge of the gray circle in Figure 11) is forced by six-hourly
reanalysis data. Verification of the diamond NICAM isstill under investigation and the results of test simulations
performed using the diamond NICAM were comparedwith the results of the high-resolution experiments using
the stretch and global NICAM. The comparison results
showed good agreement with those produced by the original global NICAM simulation. The temporal evolutionof a convective system, such as the locations of triggering,development, and decay, was also well simulated during
the integration.
Plane NICAM

Although the stretch and diamond NICAM can beused for limited-area simulations, an f-plane system isuseful when conducting some types of numerical simulations, such as idealized simulations on an f-plane andFigure 11 Simulation domain of the diamond NICAM targetedon Japan. Light blue dots indicate the grid points. Reanalysis
nudging was applied to the outer side of the gray circle. This grid isbased on g-level 5. Stretching parameter (β in Equation 8, Tomita
et al. 2008) of grid transformation is 10. The minimum grid interval isabout 59 km.

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

simulations with a narrow region in which the curvatureof the Earth is not important. For this reason, an f-planemodel (hereinafter, the plane NICAM) whose dynamicalcore is the same as the original NICAM was developed.
To save maintenance costs, the plane NICAM used thesame code as the global NICAM, which also makes
knowledge gained by the plane NICAM immediately
and directly applicable to the global NICAM.
In the plane NICAM, the shape of the calculation domain is a diamond composed of two regular triangles witha double periodic lateral boundary condition. Figure 12
shows the configuration of the calculation domain for theplane NICAM, as indicated by black bold lines. The outward fluxes across side AB are equivalent to the inward
fluxes across side DC. The red, green, blue, and yellow
areas inside the diamond area surrounded by the blackbold lines correspond to the hatched areas of the same respective color. Therefore, if the double periodic boundary
condition is applied to the diamond-shaped domain withside length L, the calculation domain is equivalent to theregular hexagonal domain with side length l = L/√3, asrepresented by the hatched area in Figure 12. Regularhexagonal geometry is better suited than square or regulartriangle geometry in terms of isotropy.
As an example of the use of the plane NICAM, webriefly show a result of an idealized tropical cyclone
simulation on an f-plane (Ohno and Satoh 2014). The
model was initialized with an axisymmetric cyclonicvortex that had a maximum azimuthal wind of 12 m s−1
as formulated by Rotunno and Emanuel (1987). The
initial thermodynamic structure of the unperturbed model
atmosphere is defined by moist adiabatic lapse rate up to
17 km and a constant temperature above this height.

Page 22 of 32

The length of the sides of the numerical domain is L =3,000 km, and a horizontal resolution of 2.7 km is used
for the entire domain. We used 50 vertical levels up to
45 km where the intervals of the vertical levels become
smaller in the boundary layer. The SST is fixed at 31°C andan f-plane with a constant Coriolis parameter at 18 N is
assumed. After an initial shock, the minimum sea levelpressure continued to drop for 140 h and a quasi-steadystage was achieved. The cyclone reached a minimum central surface pressure of 931 hPa at about 160 h. A plan viewof the distribution of the hourly precipitation amounts after
128 h of the time integration is shown in Figure 13, whichindicates that the tropical cyclone has an eye, an eyewall,inner core rainbands, and distant rainbands. The array ofrainbands and the eyewall are typical features of a tropical
cyclone (Houze 2010).
Further modified grids

In addition to the original icosahedral grid, alternative
spherical grids denoted by extended triangular meshes(XTMS) (Iga 2014a) are also implemented on NICAM.
The mesh topologies of these grids are different fromthe topology of an icosahedral grid and are generated bythree processes: grid relaxation via spring dynamics,
transformation via an analytical function, and theSchmidt transformation. By changing mesh topologies
and transformation functions, it is possible to obtain agrid with non-uniform distributions of resolution on asphere, which is applicable to various situations. For example, the resolution can be increased at a particular region or along a tropical zone as shown by Figure 14a,b.
The grid structure shown by Figure 14a looks similar tothat of the stretch NICAM, but the variability of the horizontal mesh spacing is smaller and the variability of theresolution is smoother than that of the stretch NICAM.
The resolution is approximately proportional to the combination of map factors of two polar stereographic projections. As for the grid structure shown by Figure 14b,
resolution is enhanced along the equatorial belt and isapproximately proportional to the combination of map
factors of two Lambert conformal conic projections andone Mercator projection (hereinafter, LML grid). The LML
grid is more useful for studying the multi-scale structure ofconvective systems in the tropics. The performance of the
LML grid was examined by aqua planet experiments(Iga 2014b).
Coupler

Figure 12 A schematic image of the calculation domain for theplane NICAM. A schematic image showing the relationship between
the calculation domain and a unit cell for the plane NICAM. The
diamond-shaped area (ABCD) surrounded by black bold lines indicatesthe calculation domain, and the hatched regular hexagonal area
indicates the unit cell.

Currently, high-resolution NICAM is mainly used forshort-term simulations such as for tropical cyclones andintra-seasonal variability. Since deep sea circulations are
not important for these simulations, a simple mixedlayer ocean model is implemented in NICAM. However,in order to cover wider areas of research, such as

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 23 of 32

Figure 13 A horizontal distribution of precipitation for a tropical cyclone simulation by the plane NICAM. A horizontal distribution ofhourly precipitation amount by the plane NICAM after a time integration of 128 h for a 2.7 km mesh tropical cyclone simulation.

climate projection, coupling with an ocean model is necessary when NICAM is used as an atmospheric component of an Earth system model. To couple an atmospheremodel and an ocean model, we used a coupler called Jcup
(Figure 15). Because Jcup connects models with different
grid systems, it can be used as a grid transformation tool.
Thus, in addition to ocean coupling, Jcup is used as an
I/O tool of NICAM by converting the output data fromthe icosahedral grid to the lat-lon grid which is generally
used for analysis. In this section, the Jcup coupling librarythat was used as a core of the coupled model system will
be introduced first, after which the ocean and I/O
coupling will be described.
The Jcup coupling library is a collaborative work of
JMA/MRI, JAMSTEC, and the Research Organization for
Information Science and Technology (RIST) (Arakawa

and Yoshimura 2009; Arakawa et al. 2011), that isfreely available as an open source program at https://sites.google.com/a/rist.jp/jcup/. Jcup inherited the development experience and design concept of Scup,
which was developed by Yoshimura and Yukimoto (2008).
The most remarkable feature of Jcup is its wide applicability. Generally speaking, the applicability range
of a coupler is restricted by the specific grid structuresand interpolation schemes it supports. For example,
OASIS version 4 supported logically rectangular (i.e.,
2D structured) or reduced Gaussian grids, along withbilinear, trilinear, bicubic, nearest-neighbor, and 2D
conservative interpolations (Redler et al. 2009). This
indicates that the models that utilize other grid systems,
such as non-structured or substructured grids, cannot be
coupled.

Figure 14 Grid configurations of the regionally enhanced grid and the tropically enhanced grid. Grid configurations of (a) the regionallyenhanced grid and (b) the tropically enhanced grid (after Iga 2014a).

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 24 of 32

Figure 15 A schematic of the coupling system.

The spectral method has been widely used for global
atmospheric models. However, this method is not suitable
for massively parallel processor (MPP) supercomputers
because all-to-all communication is required. This is one
reason why substructured grid models such as NICAM
were developed energetically by the research organizationsof numerous countries. Therefore, it is also necessary todevelop a coupler that can handle such models. Jcup is
specifically designed to couple different grid systems, sousers can (and must) implement their own interpolation
code and prepare a ‘mapping table’ in advance. See
Arakawa et al. (2011) for details.
Next, we will describe NICAM and ocean model coupling. Currently, the Center for Climate System Research(CCSR) Ocean Component Model (COCO) (Hasumi
2000, 2006) is used for the ocean model coupled with
NICAM. In the coupler of the NICAM-COCO coupledmodel, Jcup is used as a core library and is implemented
via a package called Joint-shell, which contains definitionof the grid structures, exchange data, and the interpolation
code (Figure 15). The interpolation code is based on thefirst-order conservative remapping scheme in Jones(1999). The ‘mapping table’ that must be set up as external
files for beforehand defines grid-to-grid relations andJcup's weight for grid systems of coupled models.

Furthermore, we also implement a utility program to calculate such table files as a part of the Joint-shell. Note that
COCO adopts a tri-polar grid, in which the northern polar
region grid points do not follow latitude-longitude lines.
As a result, coupling NICAM and COCO requires coupling two substructured grid models, which is why Jcup is
used as a core of the coupler.
To couple NICAM, a new module has been implemented to replace the mixed-layer ocean module that has
already implemented. Most of the other parts, such as the
dynamical core, cloud microphysics, and land models, areuntouched. The physical quantities exchanged between
NICAM and COCO are listed in Table 3. The most significant issue was the inconsistency of ocean definition
points between the two models. More specifically, somepoints that are recognized as ocean points by NICAM arerecognized as land points by COCO. At such grid points,
no data are provided to NICAM. Inconsistencies such as
this occur if the land-ocean distribution has been set independently between the two models. In the future, we plan
to improve pre-process utilities in order to create a
NICAM land-ocean map that will maintain consistency
with the COCO map. However, to avoid this problem in
the interim, we applied the mixed-layer ocean module,
which is pre-implemented in NICAM to such grid points.

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Table 3 Physical quantities exchanged between NICAM
and COCO through the coupler
NICAM to COCO

COCO to NICAM

Wind stress to ocean (eastward)

Sea surface temperatureWind stress to ocean (northward)

Sea ice thickness

Wind stress to sea ice (eastward)

Ice covered ratio

Wind stress to sea ice (northward)

Snow amount on sea ice

Energy flux from atmosphere to ocean

Temperature of sea ice

Energy flux from atmosphere to sea ice
Energy flux from sea ice to ocean
Radiation flux (short wave)
Sublimation from sea ice
Evaporation
Precipitation
Snowfall
Runoff

The icosahedral grid employed by NICAM cannot be
used to analyze the results. For example, the calculationof the zonal mean values is not straightforward and thevisualization tools assume a lat-lon grid in many cases.
Because of this, we generally use a conversion tool called‘ico2ll’ to move data from the icosahedral grid to thelat-lon grid for post-processing of simulations. However, in many cases, running ico2ll as a post-process results in a bottle neck because it requires numerous I/Otransfers and cannot parallelize itself since it is necessary
to create a single file for the lat-lon grid of each variable.
Jcup allows NICAM-I/O coupling, which leads to an efficient I/O conversion, to be successfully achieved. Morespecifically, the I/O module converts the icosahedral gridto the lat-lon grid and is executed in parallel with
NICAM. The implemented conversion schemes include abi-linear interpolation, a control volume weighted average,
and the nearest-neighbor method. A bi-linear interpolationis originally implemented method in the stand-alone version of ico2ll’ , and this method has first-order accuracy.
The control volume weighted average scheme is the samewith the one used in NICAM-COCO coupling and also has
first-order accuracy, but the global averaged fluxes areconserved. The nearest-neighbor method has less accuracy
and conservation properties than the other two methods.
This method was implemented due to the demands fromNICAM users and is used to provide a quick look at the
raw values by using the general latitude-longitude graphic
tools, such as the Grid Analysis and Display System(GrADS), without an averaging process. These schemes
can be set for each output data type and can be switchedby altering the configuration file.
Figure 15 is a schematic of the coupling system describedabove. The coupling system is designed so that NICAM

Page 25 of 32

automatically detects the coupling pattern at runtime
without taking any other configuration. For example,
when COCO is executed in parallel with NICAM, subroutines for NICAM-COCO coupling are used, and if
not, subroutines of the mixed-layer ocean model arecalled. I/O is also the same as the case of COCO, inwhich NICAM automatically sends output data to I/O
only when the I/O component is executed.

Conclusions
This paper reviewed recent activities of the global nonhydrostatic model, NICAM, and described its development
and the details of each component of the dynamics andphysics, the strategy of assimilation, and the related models.
In the ‘Introduction’ section, a review of other global nonhydrostatic models, scientific overviews of NICAM, andthe current design/structure and a future timeframe of
NICAM were described. In the ‘Dynamics’ section, horizontal grid issues and vertical resolution issues were discussed. In the ‘Physics’ section, components of cloud
microphysics, subgrid-scale turbulence, radiation, aerosoland chemistry, and land surface were described. As for assimilation, NICAM-LETKF was presented, and NICAMTM was introduced to describe the assimilation of thetransport model. The ‘Models related to NICAM’ section
showed a variety of ways that NICAM can be used, specifically, stretch NICAM, diamond NICAM, plane NICAM,
the tropically enhanced grid model, and coupling with theocean model or the I/O module.
A number of comprehensive NICAM-related review
articles have been published (Satoh et al. 2008; Satoh 2013)
since the 3.5 km mesh global non-hydrostatic experiments
were conducted by Tomita et al. (2005) and Miura et al.
(2007b). However, these were primarily limited to descriptions of the governing equations, and because many of themodel components continue to evolve and develop as theentire package expands, we were requested to provide acomprehensive overview of NICAM activities and future
projections. NICAM has been a pioneering global nonhydrostatic model for almost a decade, and now that otherglobal non-hydrostatic models are emerging, we believethat our experiences and considerations will be useful forother high-resolution modeling groups. Mutually beneficial
information exchanges between different modeling groups
will be required for future fruitful studies during the forthcoming era of global non-hydrostatic modeling.
Abbreviations
AICS: RIKEN Advanced Institute for Computational Science;
AORI: Atmosphere and Ocean Research Institute, The University of Tokyo;
AGCM: atmospheric general circulation model; AMIP: Atmospheric Model
Intercomparison Project; CALIPSO: Cloud-Aerosol Lidar andInfrared Pathfinder Satellite Observations; CCSR: Center for Climate System
Research, The University of Tokyo; CFL condition: Courant-Friedrichs-Lewy
condition; CFMIP: Cloud Feedback Model Intercomparison Project;
CHASER: Chemical Atmospheric General Circulation Model for Study ofSatoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Atmospheric Environment and Radiative Forcing; CINDY2011: Cooperative
Indian Ocean Experiment on Intraseasonal Variability in the Year 2011;
CONTRAIL: Comprehensive Observation Network for Trace Gases by Airliner
Project; COCO: CCSR Ocean Component Model; COSP: CFMIP Observation
Simulator Package; CWC: consistency with continuity; DYNAMO: Dynamics ofthe MJO; EnKF: ensemble Kalman filter; ETKF: ensemble transform Kalmanfilter; ES: Earth Simulator; ES2: Earth Simulator 2; GrADS: Grid Analysis andDisplay System; GOSAT: Greenhouse Gases Observing Satellite; g-level: grid
division level; ISCCP: International Satellite Cloud Climatology Project;
JAMSTEC: Japan Agency for Marine-Earth Science and Technology;
JCDAS: JMA Climate Data Assimilation System; JMA: Japan Meteorological
Agency; J-simulator: Joint Simulator for Satellite Sensors; LETKF: local
ensemble transform Kalman filter; MATSIRO: Minimal Advanced Treatmentsof Surface Interaction and Runoff; MEXT/RECCA/SALSA: Development of
Seamless Chemical Assimilation System and its Application for Atmospheric
Environmental Material (SALSA) Project of the Research Program on Climate
Change Adaptation (RECCA) in Ministry of Education, Culture, Sports, Scienceand Technology, Japan (MEXT); MIROC: Model for Interdisciplinary Researchon Climate; MIROC ESM: MIROC Earth System Model; MISMO: Mirai Indian
Ocean cruise for the study of the MJO-convection onset; MRI: MeteorologicalResearch Institute; MSTRN-X: the latest version of the broadband radiativetransfer model MSTRN based on the discrete ordinate method with a delta
two-stream approximation; MTSAT-1R: Multi-functional Transport Satellite;
MYNN: Mellor-Yamada Nakanishi-Niino scheme; MY: Mellor-Yamada Scheme;
NDW6: NICAM Double Moment Scheme with six water categories;
NSW6: NICAM Single Moment Scheme with six water categories;
NICAM: non-hydrostatic icosahedral atmospheric model;
NICAM-Chem: Unified Aerosol-Chemistry Model coupled to NICAM;NIES: National Institute for Environmental Studies; RIST: Research
Organization for Information Science and Technology; SPRINTARS: Spectral
Radiation-Transport Model for Aerosol Species; SWM: Shallow Water Model;
TRMM: Tropical Rainfall Measuring Mission; VOC: volatile organic compound;
XTMS: extended triangular meshes.
Competing interests
The authors declare that they have no competing interests. For more
information, visit the website, ‘http://nicam.jp/’.
Authors’ contributions
MS and HT coordinated the structure of the paper and wrote the‘Introduction’ and ‘Conclusions’ sections. HY wrote the ‘Design, structure,development, and timeline’ and ‘Radiation’ sections. In the ‘Dynamics’
section, HM wrote the ‘Grid configuration and advection scheme’ section,
while CK and MS wrote the ‘Vertical resolution issues’ section. In the ‘Physics’
section, TS wrote the ‘Cloud microphysics schemes’ section, while AN wrote
the ‘Subgrid-scale turbulence of the planetary boundary layer’ section using
experimental data simulated by YY. DG wrote the ‘Aerosol and chemistry
modules’ section, and MH wrote the ‘Land model’ section. In the‘Assimilation’ section, MSw and TM wrote the ‘Local ensemble transform
Kalman filter applied to NICAM: NICAM-LETKF’ section, while YN wrote the‘NICAM-based transport model: NICAM-TM’ section. In the ‘Models related to
NICAM’ section, MH wrote the ‘Stretch NICAM and diamond NICAM’ section,
while TO wrote the ‘Plane NICAM’ section. The ‘Further modified grids’
section was written by SI, and the ‘Coupler’ section was written by TA, TI,
and HK. All authors read and approved the final manuscript.
Acknowledgements
This research used the computational resources of the High Performance
Computing Infrastructure (HPCI) system provided by the Information
Technology Center of The University of Tokyo, through the HPCI System
Research Project (Project ID:hp120190). The K computer at AICS was used
under the supported by Strategic Programs for Innovative Research (SPIRE)
Field 3 (Projection of Planet Earth Variations for Mitigating Natural Disasters),
which is funded by MEXT (ID: hp120279 and hp130010). Earth Simulator at
JAMSTEC and FX10 at The University of Tokyo were also used. The
development of coupler is supported by Japan Science and Technology
Agency/Core Research for Evolutional Science and Technology (JST/CREST),‘ppOpen-HPC: Open Source Infrastructure for Development and Executionof Large-Scale Scientific Applications on Post-Peta-Scale Supercomputerswith Automatic Tuning (AT)’. The development of the diamond NICAM
and NICAM-Chem is supported by MEXT/RECCA/SALSA.

Page 26 of 32

Author details
Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5
Kashiwanoha, Kashiwa, Chiba 277-85648, Japan. 2Japan Agency for
Marine-Earth Science and Technology, 3173-15, Showa-machi, Kanazawa-ku,
Yokohama, Kanagawa 236-0001, Japan. 3RIKEN Advanced Institute for
Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe,
Hyogo 650-0047, Japan. 4Department of Earth and Planetary Science, The
University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
5
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki
305-8568, Japan. 6Meteorological Research Institute, 1-1 Nagamine, Tsukuba,
Ibaraki 305-0052, Japan. 7Research Organization for Information Science andTechnology, 2-32-3, Kitashinagawa, Shinagawa-ku, Tokyo 140-0001, Japan.
1

Received: 13 March 2014 Accepted: 21 August 2014

References
Adcroft A, Hill C, Marshall J (1997) Representation of topography by shaved cells
in a height coordinate ocean model. Mon Wea Rev 125:2293–2315
Albrecht BA (1989) Aerosols, cloud microphysics, and fractional cloudiness.
Science 245:1227–1230
Arakane T, Satoh M, Yanase W (2014) The excitation of the deep convection to
the north of tropical storm Bebinca (2006). J Meteor Soc Japan 92:141–161
Arakawa A (2004) The cumulus parameterization problem: past, present, andfuture. J Clim 17:2493–2525
Arakawa A, Lamb VR (1977) Computational design of the basic dynamical
processes of the UCLA general circulation model. Methods Comput Phys
17:173–265
Arakawa T, Yoshimura H (2009) Performance evaluation of a coupling software
for climate modeling (in Japanese). J Inform Process Japan 2:95–110
Arakawa T, Yoshimra H, Saito F, Ogochi K (2011) Data exchange algorithm andsoftware design of KAKUSHIN coupler Jcup. Procedia Comp Sci 4:1516–1525
Berger AL (1978) Long-term variations of daily insolation and quaternary climaticchanges. J Atmos Sci 35:2362–2367
Berry EX, Reinhardt RL (1974) An analysis of cloud drop growth by collection:
part II. Single initial distribution. J Atmos Sci 31:1825–1831
Bishop CH, Etherton B, Majumdar SJ (2001) Adaptive sampling with theensemble transform Kalman filter. Part I: theoretical aspects. Mon Wea Rev
129:420–436
Blackburn M, Williamson DL, Nakajima K, Ohfuchi W, Takahashi YO, Hayashi Y-Y,
Nakamura H, Ishiwatari M, McGregor J, Borth H, Wirth V, Frank H, Bechtold P,
Wedi NP, Tomita H, Satoh M, Zhao M, Held IM, Suarez MJ, Lee M-I, Watanabe
M, Kimoto M, Liu Y, Wang Z, Molod A, Rajendran K, Kitoh A, Stratton R (2013)
The Aqua-Planet Experiment (APE): Control SST simulation. J Meteor Soc
Japan 91A:17–56
Bodas-Salcedo A, Webb MJ, Bony S, Chepfer H, Dufresne JL, Klein SA, MarchandR, Haynes JN, Pincus R, John VO (2011) COSP: satellite simulation software formodel assessment. Bull Am Meteorol Soc 91:1023–1043
Bonaventura L (2000) A semi-implicit semi-Lagrangian scheme using the height
coordinate for a nonhydrostatic and fully elastic model of atmospheric flows.
J Comp Phys 158:186–213
Bryan GH, Wyngaard JC, Fritsch JM (2003) Resolution requirements for thesimulation of deep moist convection. Mon Wea Rev 131:2394–2416
Cheong H-B (2006) A dynamical core with double Fourier series: comparison withthe spherical harmonics method. Mon Wea Rev 134:1299–1315
Collins WD, Satoh M (2009) Simulating global clouds, past, present, and future. In:
Heintzenberg J, Charlson RJ (eds) Clouds in the perturbed climate system:
their relationship to energy balance, atmospheric dynamics, andprecipitation, Struengmann Forum Report, vol. 2. The MIT Press, Cambridge,
pp 469–486
Côté J, Gravel S, Méthot A, Patoine A (1998) The operational CMC-MRB global
environmental multiscale (GEM) model. Part I: design considerations andformulation. Mon Wea Rev 126:1373–1395
Cullen MJP, Davies T, Mawson MH, James JA, Coutler SC, Malcolm A (1997) An
Overview of Numerical Methods for the Next Generation U. K. NWP and
Climate Model. In: Lin CA et al (eds) Numerical Methods in Atmospheric andOceanic Modelling. The Andrew J. Robert Memorial Volume, NRC Research
Press, Ottawa, pp 425–444
Dai T, Goto D, Schutgens NAJ, Dong X, Shi G, Nakajima T (2014a) Simulated
aerosol key optical properties over global scale using an aerosol transport
coupled with a new type of dynamic core. Atmos Environ 82:71–82

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Dai T, Schutgens NAJ, Goto D, Shi G, Nakajima T (2014b) Improvement of aerosol
optical properties modeling over Eastern Asia with MODIS AOD assimilation
in a global non-hydrostatic icosahedral aerosol transport model. Environ
Pollut doi:10.1016/j.envpol.2014.06.021
Davies T, Cullen MJP, Malcolm AJ, Mawson MH, Staniforth A, White AA, Wood N(2005) A new dynamical core for the Met Office's global and regionalmodelling of the atmosphere. Quart J Roy Meteor Soc 131:1759–1782
Dirmeyer PA, Cash BA, Kinter JL III, Jung T, Marx L, Satoh M, Stan C, Tomita H,
Towers P, Wedi N, Achuthavarier D, Adams JM, Altshuler EL, Huang B, Jin EK,
Manganello J (2012) Simulating the diurnal cycle of rainfall in global climate
models: resolution versus parameterization. Clim Dyn 39:399–418
Emanuel K, Oouchi K, Satoh M, Tomita H, Yamada Y (2010) Comparison of
explicitly simulated and downscaled tropical cyclone activity in ahigh-resolution global climate model. J Adv Model Earth Syst 2:9,
doi:10.3894/JAMES.2010.2.9
Enting IG (2002) Inverse problems in atmospheric constituent transport.
Cambridge University Press, Cambridge, p 392, doi:10.1017/
CBO9780511535741
Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte-Carlo methods to forecast error statistics. J Geophys Res
99:10143–10162
Feingold G, Walko RL, Stevens B, Cotton WR (1998) Simulations of marine
stratocumulus using a new microphysical parameterization scheme. Atmos
Res 47–48:505–528
Fu X, Wang B (2004) Differences of boreal summer intraseasonal oscillations
simulated in an atmosphere–ocean coupled model and an atmosphere-onlymodel. J Clim 17:1263–1271
Fudeyasu H, Wang Y, Satoh M, Nasuno T, Miura H, Yanase W (2008) The global
cloud-system-resolving model NICAM successfully simulated the lifecycles of
two real tropical cyclones. Geophys Res Lett 35:L22808, doi:10.1029/
2008GL0360033
Fudeyasu H, Wang Y, Satoh M, Nasuno T, Miura H, Yanase W (2010a) Multiscaleinteractions in the lifecycle of a tropical cyclone simulated in a global
cloud-system-resolving model. Part I: large scale aspects. Mon Wea Rev
138:4285–4304
Fudeyasu H, Wang Y, Satoh M, Nasuno T, Miura H, Yanase W (2010b) Multiscaleinteractions in the lifecycle of a tropical cyclone simulated in a global cloudsystem-resolving model. Part II: mesoscale and storm-scale processes. Mon
Wea Rev 137:3254–3268
Fujita M, Yoneyama K, Mori S, Nasuno T, Satoh M (2011) Diurnal convection
peaks over the eastern Indian Ocean off Sumatra during different MJO
phases. J Meteor Soc Japan 89A:317–330
Gal-Chen T, Somerville CJ (1975) On the use of a coordinate transformation forthe solution of the Navier-Stokes equations. J Comp Phys 17:209–228
Gallus WA Jr, Klemp JB (2000) Behaviour of flow over step orography. Mon Wea
Rev 128:1153–1164
Goto D (2014) Modeling of black carbon in Asia using a global-to-regional seamlessaerosol-transport model. Environ Pollut doi:10.1016/j.envpol.2014.06.006
Goto D, Nakajima T, Takemura T, Sudo K (2011a) A study of uncertainties in thesulfate distribution and its radiative forcing associated with sulfur chemistry
in a global aerosol model. Atmos Chem Phys 11:10889–10910
Goto D, Takemura T, Nakajima T, Badarinath KVS (2011b) Global aerosol model-derived
black carbon concentration and single scattering albedo over Indian region andits comparison with ground observations. Atmos Environ 45:3277–3285
Goto D, Kanazawa S, Nakajima T, Takemura T (2012) Evaluation of a relationshipbetween aerosols and surface downward shortwave flux through an
integrative analysis of modeling and observation. Atmos Environ 49:294–301
Goto D, Dai T, Satoh M, Tomita H, Uchida J, Misawa S, Inoue T, Tsuruta H, Ueda K,
Ng CFS, Takami A, Sugimoto N, Shimizu A, Ohara T, Nakajima T (2014)
Application of a global nonhydrostatic model with a stretched-grid system
to regional aerosol simulations around Japan. Geosci Model Dev Discuss
7:131–179
Grabowski WW (1998) Toward cloud resolving modeling of large-scale tropicalcirculations: a simple cloud microphysics parameterization. J Atmos Sci
55:3283–3298
Gross ES, Bonaventura L, Rosatti G (2002) Consistency with continuity inconservative advection schemes for free-surface models. Int J Numer Meth
Fluids 38:307–327
Gurney KR, Law RM, Denning AS, Rayner PJ, Baker D, Bousquet P, Bruhwiler L,
Chen YH, Ciais P, Fan S, Fung IY, Gloor M, Heimann M, Higuchi K, John J,
Maki T, Maksyutov S, Masarie L, Peylin P, Prather M, Pak BC, Randerson J,

Page 27 of 32

Sarmiento J, Taguchi S, Takahashi T, Yuen CW (2002) Towards robust
estimates of CO2 sources and sinks using atmospheric transport models.
Nature 415:626–630
Ham S-H, Sohn B-J, Kato S, Satoh M (2013) Vertical inhomogeneity of ice cloudlayers from CloudSat and CALIPSO measurements and comparison to NICAM
simulations. J Geophys Res 118:9930–9947
Hansen JE, Travis LD (1974) Light scattering in planetary atmosphere. Space Sci
Rev 16:527–610
Hashino T, Satoh M, Hagihara Y, Kubota T, Matsui T, Nasuno T, Okamoto H (2013)
Evaluating global cloud distribution and microphysics from the NICAM
against CloudSat and CALIPSO. J Geophys Res 118:7273–7292
Hasumi H (2000) CCSR Ocean Component Model (COCO). CCSR Rep 13. The
University of Tokyo, Chiba, Japan
Hasumi H (2006) CCSR Ocean Component Model (COCO) version 4.0. CCSR Rep
25. The University of Tokyo, Chiba, Japan
Hasumi H, Emori S (2004) K-1 coupled model (MIROC) description. K-1 Tech Rep.
p 34, http://ccsr.aori.u-tokyo.ac.jp/~hasumi/miroc_description.pdf. Accessedat 29 Sep 2014
Hayashi YY, Sumi A (1986) The 30–40 day oscillations simulated in an aqua-planet
model. J Meteorol Soc Japan 64:451–467
Heikes R, Randall DA (1995) Numerical integration of the shallow-water equations
on a twisted icosahedral grid. Part I: basic design and results of tests. Mon
Wea Rev 123:1862–1880
Heikes R, Randall DA, Konor CS (2013) Optimized icosahedral grids: performanceof finite-difference operators and multigrid solver. Mon Wea Rev
141:445–4469
Henze DK, Seinfeld JH (2006) Global secondary organic aerosol from isopreneoxidation. Geophys Res Lett 33:L09812, doi:10.1029/2006GL025976
Holloway CE, Woolnough SJ, Lister GMS (2013) The effects of explicit versus
parameterized convection on the MJO in a large-domain high-resolution
tropical case study. Part I: characterization of large-scale organization andpropagation. J Atmos Sci 70:1342–1369
Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice microphysicalprocesses for the bulk parameterization of clouds and precipitation. Mon
Wea Rev 132:103–120
Houze RA (2010) Clouds in tropical cyclones. Mon Wea Rev 138:293–344
Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for
spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D
230:112–126
Iga S (2014a) Smooth, seamless and structured grid generation with flexibility inresolution distribution on a sphere based on conformal mapping and spring
dynamics method. J Compt Phys submitted
Iga S (2014b) Aqua-planet experiment on an AGCM with tropics-enhanced grid.
SOLA, submitted
Iga S, Tomita H (2014) Improved smoothness and homogeneity of icosahedral
grids using the spring dynamics method. J Comp Phys 258:208–226
Iga S, Tomita H, Tsushima Y, Satoh M (2007a) Climatology of a nonhydrostatic
global model with explicit cloud processes. Geophys Res Lett 34:L22814,
doi:10.1029/2007GL031048
Iga S, Tomita H, Satoh M, Goto K (2007b) Mountain-wave-like spurious waves due
to inconsistency of horizontal and vertical resolution associated with cold
fronts. Mon Wea Rev 135:2629–2641
Iga S, Tomita H, Tsushima Y, Satoh M (2011) Sensitivity of upper tropospheric ice
clouds and their impacts on the Hadley circulation using a global
cloud-system resolving model. J Climate 24:2666–2679
Inoue T, Satoh M, Miura H, Mapes B (2008) Characteristics of cloud size of deep
convection simulated by a global cloud resolving model. J Meteor Soc Japan
86A:1–15
Inoue T, Satoh M, Hagihara Y, Miura H, Schmetz J (2010) Comparison of high-levelclouds represented in a global cloud system-resolving model with CALIPSO/
CloudSat and geostationary satellite observations. J Geophys Res 115:D00H22,
doi:10.1029/2009JD012371
Iwasaki T, Yamada S, Tada K (1989) A parameterization scheme of orographic
gravity wave drag with two different vertical partitionings. Part I: impacts on
medium-range forecasts. J Meteor Soc Japan 67:11–27
Jöckel P, von Kuhlmann R, Lawrence M, Steil B, Brenninkmeijer C, Crutzen P, Rasch P,
Eaton B (2001) On a fundamental problem in implementing flux-form advection
schemes for tracer transport in 3-dimensional general circulation and chemistrytransport models. Quart J Roy Meteor Soc 127:1035–1052
Jones PW (1999) First- and second-order conservative remapping schemes forgrids in spherical coordinates. Mon Wea Rev 127:2204–2210

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Jung T, Miller MJ, Palmer TN, Towers P, Wedi N, Achuthavarier D, Adams JM,
Altshuler EL, Cash BA, Kinter JL III (2012) High-resolution global climate
simulations with the ECMWF model in Project Athena: experimental design,model climate, and seasonal forecast skill. J Climate 25:3155–3172
Kalman RE (1960) A new approach to linear filtering and prediction problems. J
Basic Eng Trans ASME 82:35–45
Kato S, Rose FG, SunMack S, Miller WF, Chen Y, Rutan DA, Stephens GL, Loeb NG,
Minnis P, Wielicki BA, Winker DA, Charlock TP, Stackhouse PW Jr, Xu KM,
Collins WD (2011) Improvements of top-of-atmosphere and surface irradiancecomputations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol
properties. J Geophys Res 116:D19209, doi:10.1029/2011JD016050
Kessler E (1969) On the distribution and continuity of water substance inatmospheric circulations. Meteorologial Monograph. Amer Meteor Soc
32:1–84
Khain A, Ovtchinnikov M, Pinsky M, Pokrovsky A, Krugliak H (2000) Notes on thestate-of-the-art numerical modeling of cloud microphysics. Atmos Res
55:159–224
Khain A, BenMoshe N, Pokrovsky A (2008) Factors determining the impact ofaerosols on surface precipitation from clouds: an attempt at classification.
J Atmos Sci 65:1721–1748
Khairoutdinov M, Kogan Y (2000) A new cloud physics parameterization in alarge-eddy simulation model of marine stratocumulus. Mon Wea Rev
128:229–243
Khairoutdinov M, Krueger SK, Moeng CH, Bogenschutz PA, Randall DA (2009)
Large-eddy simulation of maritime deep tropical convection. J Adv Model
Earth Systems 1:15, doi:10.3894/JAMES.2009.1.15
Kikuchi K, Wang B (2010) Formation of tropical cyclones in the northern Indian
Ocean associated with two types of tropical intraseasonal oscillation modes.
J Meteor Soc Japan 88:475–496
Kinne S, Schulz M, Textor C, Guibert S, Balkanski Y, Bauer SE, Berntsen T, Berglen
TF, Boucher O, Chin M, Collins W, Dentener F, Diehl T, Easter R, Feichter J,
Fillmore D, Ghan S, Ginoux P, Gong S, Grini A, Hendricks J, Herzog M,
Horowitz L, Isaksen I, Iversen T, Kirkevag A, Kloster S, Koch D, Kristjansson JE,
Krol M et al (2006) An AeroCom initial assessment - optical properties inaerosol component modules of global models. Atmos Chem Phys
6:1815–1834
Kinter JL III, Cash B, Achuthavarier D, Adams J, Altshuler E, Dirmeyer P, Doty B, Huang
B, Marx L, Manganello J, Stan C, Wakefield T, Jin E, Palmer T, Hamrud M, Jung T,
Miller M, Towers P, Wedi N, Satoh M, Tomita H, Kodama C, Nasuno T, Oouchi K,
Yamada Y, Taniguchi H, Andrews P, Baer T, Ezell M, Halloy C et al (2013)
Revolutionizing climate modeling - Project Athena: a multi-institutional,
international collaboration. Bull Am Meteor Soc 94:231–245
Klein SA, Jakob C (1999) Validation and sensitivities of frontal clouds simulated by
the ECMWF model. Mon Wea Rev 127:2514–2531
Klemp JB (2011) A terrain-following coordinate with smoothed coordinate surfaces.
Mon Wea Rev 139:2163–2169
Klemp JB, Wilhemson RB (1978) The simulation of three-dimensional convective
storm dynamics. J Atmos Sci 35:1070–1096
Kodama C, Noda AT, Satoh M (2012) An assessment of the cloud signals
simulated by NICAM using ISCCP, CALIPSO, and CloudSat satellite simulators.
J Geophys Res 117:D12210, doi:10.1029/2011JD017317
Kodama C, Iga S, Satoh M (2014a) Impact of the sea surface temperature rise on
storm-track clouds in global non-hydrostatic aqua-planet simulations.
Geophys Res Lett 41:3545–3552, doi:10.1002/2014GL059972
Kodama C, Terai M, Noda AT, Yamada Y, Satoh M, Seiki T, Iga S, Yashiro H, Tomita
H, Minami K (2014b) Scalable rank-mapping algorithm for an icosahedral grid
system on the massive parallel computer with a 3-D torus network. Parallel
Comput 40:362–373
Kondo J (1993) A new bucket model for predicting water content in the surface
model. J Japan Soc Hydrol and Water Resour 6:344–349 (in Japanese withEnglish abstract)
Kondo K, Tanaka HL (2009) Applying the local ensemble transform Kalman filter to
the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). SOLA 5:121–124
Kubokawa H, Fujiwara M, Nasuno T, Satoh M (2010) Analysis of the tropical
tropopause layer using the Nonhydrostatic Icosahedral Atmospheric Model(NICAM): aqua planet experiments. J Geophys Res 115:D08102, doi:10.1029/
2009JD012686
Kubokawa H, Fujiwara M, Nasuno T, Miura H, Yamamoto M, Satoh M (2012) Analysisof the tropical tropopause layer using the Nonhydrostatic Icosahedral
Atmospheric Model (NICAM): 2. An experiment under the atmosphericconditions of December 2006 to January 2007. J Geophys Res 117:D17114

Page 28 of 32

Lamarque JF, Shindell DT, Josse B, Young PJ, Cionni I, Eyring V, Bergmann D,
Cameron-Smith P, Collins WJ, Doherty R, Dalsoren S, Faluvegi G, Folberth G,
Ghan SJ, Horowitz LW, Lee YH, MacKenzie IA, Nagashima T, Naik V, Plummer
D, Righi M, Rumbold ST, Schulz M, Skeie RB, Stevenson DS, Strode S, Sudo K,
Szopa S, Voulgarakis A, Zeng G (2013) The Atmospheric Chemistry and Climate
Model Intercomparison Project (ACCMIP): overview and description of models,simulations and climate diagnostics. Geosci Model Dev 6:179–206
Laprise R (2008) Regional climate modelling. J Comp Phys 227:641–3666
Law RM, Peters W, Rödenbeck C, Aulagnier C, Baker I, Bergmann DJ, Bousquet P,
Brandt J, Bruhwiler L, Cameron-Smith PJ, Christensen JH, Delage F, Denning
AS, Fan S, Geels C, Houweling S, Imasu R, Karstens U, Kawa SR, Kleist J, Krol
MC, Lin SJ, Lokupitiya R, Maki T, Maksyutov S, Niwa Y, Onishi R, Parazoo N,
Patra PK, Pieterse G et al (2008) TransCom model simulations of hourly
atmospheric CO2: experimental overview and diurnal cycle results for 2002.
Global Biogeochem Cycles 22:GB3009, doi:10.1029/2007GB003050
Lien G-Y, Kalnay E, Miyoshi T (2013) Effective assimilation of global precipitation:
simulation experiments. Tellus A 65:19915, http://dx.doi.org/10.3402/tellusa.
v65i0.19915
Lim K-SS, Hong S-Y (2010) Development of an effective double-moment cloud
microphysics scheme with prognostic cloud condensation nuclei (CCN) for
weather and climate models. Mon Wea Rev 138:1587–1612
Lin SJ (2004) A “vertically Lagrangian” finite-volume dynamical core for global
models. Mon Wea Rev 132:2293–2307
Lin Y-L, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in acloud model. J Climate Appl Meteor 22:1065–1092
Liu P, Satoh M, Wang B, Fudeyasu H, Nasuno T, Li T, Miura H, Taniguchi H,
Masunaga H, Fu X, Annamalai H (2009) A MJO simulated by the NICAM at
14-km and 7-km resolutions. Mon Wea Rev 137:3254–3268
Lohmann U, Feichter J (2005) Global indirect aerosol effects: a review. Atmos
Chem Phys 5:715–737
Machida T, Matsueda H, Sawa Y, Nakagawa Y, Hirotani K, Kondo N, Goto K,
Nakazawa T, Ishikawa K, Ogawa T (2008) Worldwide measurements ofatmospheric CO2 and other trace gas species using commercial airlines.
J Atmos Oceanic Technol 25:1744–1754
Madden RA, Julian PR (1971) Description of a 40–50 day oscillation in the tropics.
J Atmos Sci 28:702–708
Madden RA, Julian PR (1972) Description of global-scale circulation cells in thetropics with a 40–50 day period. J Atmos Sci 29:1109–1123
Majewski D, Liermann D, Prohl P, Ritter B, Buchhold M, Hanisch T, Paul G, Wergen
W (2002) The operational global icosahedral-hexagonal gridpoint model
GME: description and high-resolution tests. Mon Wea Rev 130:319–338
Mapes B, Tulich S, Nasuno T, Satoh M (2008) Predictability aspects of globalaqua-planet simulations with explicit convection. J Meteor Soc Japan
86A:175–185
Masuda Y, Ohnishi H (1986) An integration scheme of the primitive equation
model with an icosahedral-hexagonal grid system and its application to theshallow water equations. Short- and Medium-Range Numerical Weather
Prediction, Collection of Papers Presented at the WMO/IUGG NWP
Symposium. Japan Meteorological Society, Tokyo, pp 317–326
Masunaga H, Satoh M, Miura H (2008) A joint satellite and global cloud-resolving
model analysis of a Madden-Julian Oscillation event: model diagnosis.
J Geophys Res 113:D17210, doi:10.1029/2008JD009986
Matsueda H, Machida T, Sawa Y, Nakagawa Y, Hirotani K, Ikeda H, Kondo N, Goto
K (2008) Evaluation of atmospheric CO2 measurements from new flask air
sampling of JAL airliner observations. Pap Meteorol Geophys 59:1–17
McGregor JL (1996) Semi-Lagrangian advection on conformal-cubic grids. Mon
Wea Rev 124:1311–1322
Mellor GL, Yamada T (1982) Development of a turbulence closure model for
geophysical fluid problems. Rev Geophys Space Phys 20:851–875
Meyers MP, Walko RL, Harrington JY, Cotton WR (1997) New RAMS cloud microphysics
parameterization. Part II: the two-moment scheme. Atmos Res 45:3–39
Milbrandt JA, McTaggart-Cowan M (2010) Sedimentation-induced errors in bulk
microphysics schemes. J Atmos Sci 67:3931–3948
Milbrandt JA, Yau MK (2005a) A multimoment bulk microphysics
parameterization. Part I: analysis of the role of the spectral shape parameter.
J Atmos Sci 62:3051–3064
Milbrandt JA, Yau MK (2005b) A multimoment bulk microphysics
parameterization. Part II: a proposed three-moment closure and scheme
description. J Atmos Sci 62:3065–3081
Milbrandt JA, Yau MK (2006) A multimoment bulk microphysics parameterization.
Part IV: sensitivity experiments. J Atmos Sci 63:3137–3159

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Mittal R, Iaccarino G (2003) Immersed boundary methods. Annu Rev Fluid Mech
37:239–261
Miura H (2007) An upwind-biased conservative advection scheme for spherical
hexagonal-pentagonal grids. Mon Wea Rev 135:4038–4044
Miura H (2013) An upwind-biased conservative transport scheme for multi-stage
temporal integrations on spherical icosahedral grids. Mon Wea Rev
141:4049–4068
Miura H, Skamarock WC (2013) An upwind-biased transport scheme using aquadratic reconstruction on spherical icosahedral grids. Mon Wea Rev
141:832–847
Miura H, Tomita H, Nasuno T, Iga S, Satoh M, Matsuno T (2005) A climate
sensitivity test using a global cloud resolving model under an aqua planet
condition. Geophys Res Lett 32:L19717, doi:1029/2005GL023672
Miura H, Satoh M, Tomita H, Nasuno T, Iga S, Noda AT (2007a) A short-duration
global cloud-resolving simulation with a realistic land and sea distribution.
Geophys Res Lett 34:L02804, doi:10.1029/2006GL027448
Miura H, Satoh M, Nasuno T, Noda AT, Oouchi K (2007b) A Madden-Julian
Oscillation event realistically simulated by a global cloud-resolving model.
Science 318:1763–1765
Miura H, Satoh M, Katsumata M (2009) Spontaneous onset of a Madden-Julian
oscillation event in a cloud-system-resolving simulation. Geophys Res Lett
36:L13802, doi:10.1029/2009GL039056
Miyakawa T, Takayabu YN, Nasuno T, Miura H, Satoh M, Moncrieff MW (2012)
Convective momentum transport by rainbands within a Madden-Julian
oscillation in a global nonhydrostatic model with explicit deep convective
processes. Part I: methodology and general results. J Atmos Sci 69:1317–1338
Miyakawa T, Satoh M, Miura H, Tomita H, Yashiro H, Noda AT, Yamada Y, Kodama
C, Kimoto M, Yoneyama K (2014) Madden-Julian oscillation prediction skill ofa new-generation global model demonstrated using a supercomputer. Nat
Commun 5:3769, doi:10.1038/ncomms4769
Miyamoto Y, Kajikawa Y, Yoshida R, Yamaura T, Yashiro H, Tomita H (2013) Deepmoist atmospheric convection in a sub-kilometer global simulation. Geophys
Res Lett 40:4922–4926
Miyoshi T, Aranami K (2006) Applying a four-dimensional local ensemble
transform Kalman filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM).
SOLA 2:128–131
Miyoshi T, Kunii M (2011) The local ensemble transform Kalman filter with theweather research and forecasting model: experiments with real observations.
Pure Appl Geophys 169:321–333
Miyoshi T, Yamane S (2007) Local ensemble transform Kalman filtering with an
AGCM at a T159/L48 resolution. Mon Wea Rev 135:3841–3861
Mizuta R, Oouchi K, Yoshimura H, Noda A, Katayama K, Yukimoto S, Hosaka M,
Kusunoki S, Kawai H, Nakagawa M (2005) 20-km-mesh global climate
simulations using JMA-GSM model - mean climate states. J Meteor Soc
Japan 84:165–185
Moeng CH, LeMone MA, Khairoutdinov M, Krueger SK, Bogenschutz PA, Randall
DA (2009) The tropical marine boundary layer under a deep convection
system: a large-eddy simulation study. J Model Earth Systems 1:16,
doi:10.3894/JAMES.2009.1.16
Morrison H, Gettelman A (2008) A new two-moment bulk stratiform cloud
microphysics scheme in the Community Atmospheric Model, version 3(CAM3). Part I: description and numerical tests. J Climate 21:3642–3659
Morrison H, Curry JA, Khvorostyanov VI (2005) A new double-moment
microphysics parameterization for application in cloud and climate models.
Part I: description. J Atmos Sci 62:1665–1677
Muroi C, Toyoda E, Yoshimura H, Hosaka M, Sugi M (2002) Standard coding rule.
Tenki 49:91–95 (in Japanese)
Nagashima T, Ohara T, Sudo K, Akimoto H (2010) The relative importance ofvarious source regions on East Asian surface ozone. Atmos Chem Phys
10:11305–11322
Nakajima T, Tsukamoto M, Tsushima Y, Numaguti A, Kimura T (2000) Modeling of
the radiative process in an atmospheric general circulation model. Appl Opt
39:4869–4878
Nakajima TY, Suzuki K, Stephens GL (2010) Droplet growth in warm water clouds
observed by the A-Tran Part 1: sensitivity analysis of the MODIS-derived
cloud droplet sizes. J Atmos Sci 67:1884–1896
Nakanishi M, Niino H (2006) An improved Mellor-Yamada level-3 model: its
numerical stability and application to a regional prediction of advection fog.
Boundary-Layer Meteor 119:397–407
Nakanishi M, Niino H (2009) Development of an improved turbulence closure
model for the atmospheric boundary layer. J Meteor Soc Japan 87:895–912

Page 29 of 32

Nakazawa T (1988) Tropical super clusters within intraseasonal variations over thewestern Pacific. J Meteor Soc Japan 66:823–839
Nasuno T (2008) Equatorial mean zonal wind in a global nonhydrostatic
aquaplanet experiment. J Meteor Soc Japan 86A:219–236
Nasuno T (2013) Forecast skill of Madden-Julian Oscillation events in a global
nonhydrostatic model during the CINDY2011/DYNAMO observation period.
SOLA 9:69–73
Nasuno T, Satoh M (2011a) Properties of precipitation and in-cloud verticalmotion in a global nonhydrostatic aquaplanet experiment. J Meteor Soc
Japan 89:413–439
Nasuno T, Satoh M (2011b) Statistical relationship between maximum verticalvelocity and surface precipitation of Tropical convective clouds in global
nonhydrostatic aquaplanet experiment. J Meteor Soc Japan 89:553–561
Nasuno T, Tomita H, Iga S, Miura H, Satoh M (2007) Multi-scale organization of
convection simulated with explicit cloud processes on an aqua planet.
J Atmos Sci 64:1902–1921
Nasuno T, Tomita H, Iga S, Miura H, Satoh M (2008) Convectively coupledequatorial waves simulated by a global nonhydrostatic experiment on an
aqua planet. J Atmos Sci 65:1246–1265
Nasuno T, Miura H, Satoh M, Noda AT, Oouchi K (2009) Multi-scaleorganization of convection in a global numerical simulation of the
December 2006 MJO event using explicit moist processes. J Meteor Soc
Japan 87:335–345
Neale RB, Hoskins BJ (2001) A standard test for AGCMs including their physicalparameterizations: I: the proposal. Atmos Sci Lett 1:153–155, doi:10.1006/
asle.2000.0019
Ničkovic S, Gavrilov MB, Tosic IA (2002) Geostrophic adjustment on hexagonal
grids. Mon Wea Rev 130:668–683
Niwa Y (2010) Numerical Study on Atmospheric Transport and Surface Source/
Sink of Carbon Dioxide, Dissertation. The University of Tokyo, Tokyo
Niwa Y, Tomita H, Satoh M, Imasu R (2011a) A three-dimensional icosahedral gridadvection scheme preserving monotonicity and consistency with continuity
for atmospheric tracer transport. J Meteorol Soc Jpn 89:255–268
Niwa Y, Patra PK, Sawa Y, Machida T, Matsueda H, Belikov D, Maki T, Ikegami M,
Imasu R, Maksyutov S, Oda T, Satoh M, Takigawa M (2011b) Three-dimensionalvariations of atmospheric CO2: aircraft measurements and multi-transport model
simulations. Atmos Chem Phys 11:3359–13375
Niwa Y, Machida T, Sawa Y, Matsueda H, Schuck TJ, Brenninkmeijer CAM, Imasu
R, Satoh M (2012) Imposing strong constraints on tropical terrestrial CO2
fluxes using passenger aircraft based measurements. J Geophys Res
117:D11303, doi:10.1029/2012JD017474
Noda AT, Oouchi K, Satoh M, Tomita H, Iga S, Tsushima Y (2010) Importance ofthe subgrid-scale turbulent moist process: Cloud distribution in global
cloud-resolving simulatioins. Atmos Res 96:208–217
Noda AT, Oouchi K, Satoh M, Tomita H (2012) Quantitative assessment of diurnal
variation of tropical convection simulated by a global nonhydrostatic model
without cumulus parameterization. J Climate 25:5119–5134
Ohfuchi W, Nakamura H, Yoshioka MK, Enomoto T, Takaya K, Peng X, Yamane S,
Nishimura T, Kurihara Y, Ninomiya K (2004) 10-km mesh meso-scale resolving
simulations of the global atmosphere on the Earth Simulator: Preliminary
outcomes of AFES (AGCM for the Earth Simulator). J Earth Simulator 1:8–34
Ohno T, Satoh M (2014) On the warm core of the tropical cyclone formed near
the tropopause. J Atmos Sci in press
Onogi K, Tsutsui H, Koide H, Sakamoto M, Kobayashi S, Hatsushika H, Matsumoto
T, Yamazaki N, Kamahori H, Takahashi K, Kadokura S, Wada K, Kato K, Oyama
R, Ose T, Mannoji N, Taira T (2007) The JRA-25 reanalysis. J Meteorol Soc Jpn
85:369–432
Oouchi K, Noda AT, Satoh M, Miura H, Tomita H, Nasuno T, Iga S (2009a) A
simulated preconditioning of typhoon genesis controlled by a boreal
summer Madden-Julian Oscillation event in a global cloud-system-resolving
model. SOLA 5:65–68
Oouchi K, Noda AT, Satoh M, Wang B, Xie S-P, Takahashi HG, Yasunari T (2009b)
Asian summer monsoon simulated by a global cloud-system-resolving
model: Diurnal to intra-seasonal variability. Geophys Res Lett 36:L11815,
doi:10.1029/2009GL038271
Oouchi K, Taniguchi H, Nasuno T, Satoh M, Tomita H, Yamada Y, Ikeda M,
Shirooka R, Yamada H, Yoneyama K (2012) A Prototype Quasi Real-Time
Intra-Seasonal Forecasting of Tropical Convection Over the Warm Pool
Region: A new Challenge of Global Cloud-System-Resolving Model for a
Field Campaign. In: Oouchi K, Fudeyasu H (eds) Cyclones: Formation, Triggers
and Control. Nova Science Publishers Inc, pp 233–248

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Oouchi K, Satoh M, Yamada Y, Tomita H, Sugi M (2014) A hypothesis and a casestudy projection of an influence of MJO modulation on boreal-summer
tropical cyclogenesis in a warmer climate with a global non-hydrostatic
model: a transition toward the central Pacific? Front Earth Sci 2:1,
doi:10.3389/feart.2014.00001 (accepted)
Ott E, Hunt BR, Szunyogh I, Zimin AV, Kostelich EJ, Corazza M, Kalnay E, Patil DJ,
Yorke JA (2004) A local ensemble Kalman filter for atmospheric data
assimilation. Tellus 56A:415–428
Patra PK, Law RM, Peters W, Rödenbeck C, Takigawa M, Aulagnier C, Baker I,
Bergmann DJ, Bousquet P, Brandt J, Bruhwiler L, Cameron-Smith PJ, Christensen
JH, Delage F, Denning AS, Fan S, Geels C, Houweling S, Imasu R, Karstens U,
Kawa SR, Kleist J, Krol MC, Lin SJ, Lokupitiya R, Maki T, Maksyutov S, Niwa Y,
Onishi R, Parazoo N et al (2008) TransCom model simulations of hourly
atmospheric CO2: analysis of synoptic scale variations for the period 2002–2003.
Global Biogeochem Cycles 22:GB4013, doi:10.1029/2007GB003081
Patra PK, Canadell JG, Houghton RA, Piao SL, Oh N-H, Ciais P, Manjunath KR,
Chhabra A, Wang T, Bhattacharya T, Bousquet P, Hartman J, Ito A, Mayorga E,
Niwa Y, Raymond P, Sarma VVSS, Lasco R (2013) The carbon budget of South
Asia. Biogeoscience 10:513–527
Phillips VTJ, Donner LJ, Garner ST (2007) Nucleation processes in deep
convection simulated by a cloud-system-resolving model with doublemoment bulk cloud microphysics. J Atmos Sci 64:738–761
Pruppacher HR, Klett JD (1997) Microphysics of Clouds and Precipitation. Kluwer
Academic Publisher, Heidelberg, p 954
Putman WM, Suarez M (2011) Cloud-system resolving simulations with the NASA
Goddard Earth Observing System global atmospheric model (GEOS‐5).
Geophys Res Lett 38:L16809, doi:10.1029/2011GL048438
Qian J-H, Semazzi FHM, Scroggs JS (1998) A global nonhydrostatic semi-Lagrangian
atmospheric model with orography. Mon Wea Rev 126:747–771
Randall DA (1994) Geostrophic adjustment and the finite-difference shallowwater equations. Mon Wea Rev 122:1371–1377
Randall DA, Heikes R, Ringler T (2000) Global Atmospheric Modeling Using a
Geodesic Grid With an Isentropic Vertical Coordinate. In: General Circulation
Model Development, Chapter 17. Academic Press, California London, pp 509–538
Randall DA, Khairoutdinov M, Arakawa A, Grabowski WW (2003) Breaking thecloud-parameterization deadlock. Bull Amer Meteor Soc 84:1547–1564
Redler R, Valcke S, Ritzdorf H (2009) OASIS-4 – a coupling software for next
generation earth system modeling. Geoscientific Model Development
Discussions 2:797–843
Ringler TD, Randall DA (2002) A potential enstrophy and energy conserving
numerical scheme for solution of the shallow-water equations on a geodesic
grid. Mon Wea Rev 130:1397–1410
Ringler TD, Heikes RH, Randall DA (2000) Modeling the atmospheric generalcirculation using a spherical geodesic grid: a new class of dynamical cores.
Mon Wea Rev 128:2471–2490
Roh W, Satoh M (2014) Evaluation of precipitating hydrometeor
parameterizations in a single-moment bulk microphysics scheme for deep
convective systems over the tropical open ocean. J Atmos Sci 71:2654–2673
Rossow WB, Schiffe RA (1999) Advances in understanding clouds from ISCCP. Bull
Am Meteorol Soc 80:2261–2287
Rotunno R, Emanuel KA (1987) An air-sea interaction theory for tropical cyclones.
Part II: evolutionary study using a nonhydrostatic axisymmetric numericalmodel. J Atmos Sci 44:542–561
Rutledge SA, Hobbs P (1983) The mesoscale and microscale structure andorganization of clouds and precipitation in midlatitude cyclones. VIII: a model forthe “seeder-feeder” process in warm-frontal rainbands. J Atmos Sci 40:1185–1206
SALSA (2014) Annual report of Development of Seamless Chemical AssimiLation
System and its Application for Atmospheric Environmental Materials (SALSA).
In: Project of the Research Program on Climate Change Adaptation (RECCA)
in Ministry of Education and Sports in Japan (MEXT). Aavailable at http://
157.82.240.167/~salsa/Program/SALSA_Annual_Report_FY2013.pdf. Accessedat 29 Sep 2014
Sato T, Miura H, Satoh M (2007) Spring diurnal cycle of clouds over Tibetan
Plateau: global cloud-resolving simulations and satellite observations.
Geophys Res Lett 34:L18816, doi:10.1029/2007GL030782
Sato T, Yoshikane T, Satoh M, Miura H, Fujinami H (2008) Resolution dependencyof the diurnal cycle of convective clouds over the Tibetan Plateau in amesoscale model. J Meteor Soc Japan 86A:17–31
Sato T, Miura H, Satoh M, Takayabu YN, Wang Y (2009) Diurnal cycle ofprecipitation in the tropics simulated in a global cloud-resolving model.
J Climate 22:4809–4826

Page 30 of 32

Satoh M (2002) Conservative scheme for the compressible non-hydrostatic
models with the horizontally explicit and vertically implicit time integration
scheme. Mon Wea Rev 130:1227–1245
Satoh M (2003) Conservative scheme for a compressible nonhydrostatic modelwith moist processes. Mon Wea Rev 131:1033–1050
Satoh M (2013) Atmospheric Circulation Dynamics and General Circulation
Models, 2nd edn. Springer-PRAXIS, Heidelberg, p 730
Satoh M, Kitao Y (2013) Numerical examination of the diurnal variation ofsummer precipitation over southern China. SOLA 9:129–133
Satoh M, Matsuda Y (2009) Statistics of high-cloud areas and its sensitivity to
cloud microphysics with single cloud experiments. J Atmos Sci 66:2659–2677
Satoh M, Tomita H, Miura H, Iga S, Nasuno T (2005) Development of a global
cloud resolving model - a multi-scale structure of tropical convections.
J Earth Simulator 3:11–19
Satoh M, Matsuno T, Tomita H, Miura H, Nasuno T, Iga S (2008) Nonhydrostatic
icosahedral atmospheric model (NICAM) for global cloud resolving
simulations. J Comput Phys 227:3486–3514
Satoh M, Inoue T, Miura H (2010) Evaluations of cloud properties of global andlocal cloud system resolving models using CALIPSO and CloudSat simulators.
J Geophys Res 115:D00H14, doi:10.1029/2009JD012247
Satoh M, Oouchi K, Nasuno T, Taniguchi H, Yamada Y, Tomita H, Kodama C,
Kinter J III, Achuthavarier D, Manganello J, Cash B, Jung T, Palmer T, Wedi N(2012a) The Intra-Seasonal Oscillation and its control of tropical cyclones
simulated by high-resolution global atmospheric models. Clim Dyn
39:2185–2206
Satoh M, Iga S, Tomita H, Tsushima Y, Noda AT (2012b) Response of upper
clouds due to global warming tested by a global atmospheric model with
explicit cloud processes. J Climate 25:2178–2191
Satoh M, Nihonmatsu R, Kubokawa H (2013) Environmental conditions fortropical cyclogenesis associated with African easterly waves. SOLA 9:120–124
Sawa Y, Machida T, Matsueda H (2012) Aircraft observation of the seasonalvariation in the transport of CO2 in the upper atmosphere. J Geophys Res
117:D05305, doi:10.1029/2011JD016933
Seifert A (2008) On the parameterization of evaporation of raindrops as simulated
by a one-dimensional rainshaft model. J Atmos Sci 65:3608–3619
Seifert A, Beheng KD (2001) A double-moment parameterization for simulating
autoconversion, accretion and selfcollection. Atmos Res 59–60:265–281
Seifert A, Beheng KD (2006) A two-moment cloud microphysics parameterization
for mixed-phase clouds. Part I: model description. Meteorol Atmos Phys
92:45–66
Seifert A, Khain A, Pokrovsky A, Beheng KD (2006) A comparison of spectral bin
and two-moment bulk mixed-phase cloud microphysics. Atmos Res 80:46–66
Seiki T, Nakajima T (2014) Aerosol effects of the condensation process on aconvective cloud simulation. J Atmos Sci 71:833–853
Seiki T, Satoh M, Tomita H, Nakajima T (2014) Simultaneous evaluation of ice
cloud microphysics and non-sphericity of the cloud optical properties usinghydrometeor video sonde and radiometer sonde in-situ observations.
J Geophys Res 119:6681–6701, doi:10.1002/2013JD021086
Sekiguchi M, Nakaima T (2008) A k-distribution-based radiation code and its
computational optimization for an atmospheric general circulation model.
J Quant Spectrosc Radiat Transfer 109:2779–2793
Semazzi FHM, Qian J-H, Scroggs JS (1995) A global nonhydrostatic semi-Lagrangian
atmospheric model without orography. Mon Wea Rev 123:2534–2550
Sherwood SC, Ingram W, Tsushima Y, Satoh M, Roberts M (2010) Relative
humidity changes in a warmer climate. J Geophys Res 115:D09104,
doi:10.1029/2009JD012585
Shindell DT, Miller RL, Schmidt GA, Pandolfo L (1999) Simulation of recent
northern winter climate trends by greenhouse-gas forcing. Nature
399:452–455
Shipway BJ, Hill AA (2012) Diagnosis of systematic differences between multiple
parametrizations of warm rain microphysics using a kinematic framework.
Quart J Roy Meteor Soc 138:2196–2211
Simmons AJ, Burridge DM (1981) An energy and angular-momentum conserving
vertical finite-difference scheme and hybrid vertical-coordinates. Mon Wea
Rev 109:758–766
Skamarock WC, Klemp JB, Duda MG, Fowler LD, Park S-H (2012) A multi-scale
nonhydrostatic atmospheric model using centroid Vornoi tesselations andC-grid staggering. Mon Wea Rev 140:3090–3105
Sohn BJ, Nakajima T, Satoh M, Jang H-S (2010) Impact of different definitions ofclear-sky flux on the determination of longwave cloud radiative forcing:
NICAM simulation results. Atmos Chem Phys 10:11641–11646

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Staniforth A, Wood N (2008) Aspects of the dynamical core of a nonhydrostatic,
deep-atmosphere, unified weather and climate-prediction model. J Comput
Phys 227:3445–3464
Steppeler J, Bitzer HW, Minotte M, Bonaventura L (2002) Nonhydrostatic
atmospheric modeling using a z-coordinate representation. Mon Wea Rev
130:2143–2149
Steppeler J, Bitzer HW, Janjic Z, Schättler U, Prohl P, Gjertsen U, Torrisi L,
Parfinievicz J, Avgoustoglou E, Damrath U (2006) Prediction of clouds
and rain using a z-coordinate nonhydrostatic model. Mon Wea Rev
134:3625–3643
Stuhne GR, Peltier WR (1996) Vortex erosion and amalgamation in a new modelof large scale flow on the sphere. J Comput Phys 128:58–81
Sudo K, Akimoto H (2007) Global source attribution of tropospheric ozone:
long-range transport from various source regions. J Geophys Res 112:D12302,
doi:10.1029/2006JD007992
Sudo K, Takahashi M, Kurokawa J, Akimoto H (2002a) CHASER: A global chemical
model of the troposphere: 1. Model description. J Geophy Res 107:4339,
doi:10.1029/2001JD001113
Sudo K, Takahashi M, Akimoto H (2002b) CHASER: a global chemical model ofthe troposphere 2. Model results and evaluation. J Geophys Res 107:4586
Suzuki K, Nakajima T, Satoh M, Tomita H, Takemura T, Nakajima TY, Stephens GL(2008) Global cloud-system-resolving simulation of aerosol effect on warm
clouds. Geophys Res Lett 35:L19817, doi:10.1029/2008GL035449
Takata K, Emori S, Watanabe T (2003) Development of the minimal advanced
treatments of surface interaction and runoff. Global and Planetary Change
38:209–222
Takayabu YN, Iguchi T, Kachi M, Shibata A, Kanzawa H (1999) Abrupt terminationof the 1997–98 El Nino in response to a Madden-Julian oscillation. Nature
402:279–282
Takemura T, Okamoto H, Maruyama Y, Numaguti A, Higurashi A, Nakajima T(2000) Global three-dimensional simulation of aerosol optical thickness
distribution of various origins. J Geophys Res 105:17853–17873
Takemura T, Nakajima T, Dubovik O, Holben BN, Kinne S (2002) Single scattering
albedo and radiative forcing of various aerosol species with a global
three-dimensional model. J Climate 15:333–352
Takemura T, Nozawa T, Emori S, Nakajima TY, Nakajima T (2005) Simulation ofclimate response to aerosol direct and indirect effects with aerosol transportradiation model. J Geophys Res 110:D02202, doi:10.1029/2004JD005029
Takemura T, Egashira M, Matsuzawa L, Ichijo H, O’ishi R, Abe-Ouchi A (2009) A
simulation of the global distribution and radiative forcing of soil dust
aerosols at the Last Glacial Maximum. Atmos Chem Phys 9:3061–3073
Taniguchi H, Yanase W, Satoh M (2010) Ensemble simulation of cyclone Nargis
by a global cloud-system-resolving model–modulation of cyclogenesis by
the Madden-Julian Oscillation. J Meteor Soc Japan 88:571–591
Tans PP, Fung IY, Takahashi T (1990) Observational constrains on the global
atmospheric CO2 budget. Science 274:1431–1438
Tarantola A (2005) Inverse problem theory and methods for model parameter
estimation. Soc Ind Appl Math, Philadelphia, p 342, doi:10.1137/
1.9780898717921
Taylor M, Tribbia J, Iskandarani M (1997) The spectral element method for theshallow water equations on the sphere. J Comp Phys 130:92–108
Terasaki K, Tanaka HL, Satoh M (2009) Characteristics of the kinetic energy
spectrum of NICAM. SOLA 5:180–183
Thompson G, Field PR, Rasmussen RM, Hall WD (2008) Explicit forecasts of winterprecipitation using an improved bulk microphysics scheme. Part II:
implementation of a new snow parameterization. Mon Wea Rev
136:5095–5115
Thuburn J, Ringler T, Skamarock WC, Klemp JB (2009) Numerical representation ofgeostrophic modes on arbitrarily structured C-grids. J Comput Phys
228:8321–8335
Tomita H (2008a) A stretched icosahedral grid by a new grid transformation.
J Meteor Soc Japan 86A:107–119
Tomita H (2008b) New microphysical schemes with five and six categories bydiagnostic generation of cloud ice. J Meteor Soc Japan 86:121–142
Tomita H, Satoh M (2004) A new dynamical framework of nonhydrostatic global
model using the icosahedral grid. Fluid Dyn Res 34:357–400
Tomita H, Tsugawa M, Satoh M, Goto K (2001) Shallow water model on amodified icosahedral geodesic grid by using spring dynamics. J Comp Phys
174:579–613
Tomita H, Satoh M, Goto K (2002) An optimization of icosahedral grid modified
by spring dynamics. J Comp Phys 183:307–331

Page 31 of 32

Tomita H, Miura H, Iga S, Nasuno T, Satoh M (2005) A global cloud-resolving
simulation: preliminary results from an aqua planet experiment. Geophys Res
Lett 32:L08805, doi:10.1029/2005GL022459
Tomita H, Goto K, Satoh M (2008) A new approach of atmospheric generalcirculation model: Global cloud resolving model NICAM and its
computational performance. SIAM J Sci Comp 30:2755–2776
Trenberth K, Fasullo FT, Kiehl J (2009) Earth’s global energy budget. Bull Amer
Meteor Soc 90:311–323
Tsuchiya C, Sato K, Nasuno T, Noda AT, Satoh M (2011) Universal frequency
spectra of surface meteorological fluctuations. J Climate 24:4718–4732
Tsushima Y, Iga S, Tomita H, Satoh M, Noda AT, Webb M (2014) High cloudincrease in a perturbed SST experiment with a global nonhydrostatic model
including explicit convective processes. J Adv Model Earth Syst 06:,
doi:10.1002/2013MS000301
Wacker U, Seifert A (2001) Evolution of rain water profiles resulting from puresedimentation: spectral vs. parameterized description. Atmos Res 58:19–39
Walko RL, Cotton WR, Meyers MP, Harrington JY (1995) New RAMS cloud
microphysics parameterization. Part I: the single-moment scheme. Atmos Res
38:29–62
Watanabe M, Emori S, Satoh M, Miura H (2009) A PDF-based hybrid prognostic
cloud scheme for general circulation models. Clim Dyn 33:795–816
Watanabe M, Suzuki T, O’ishi R, Komuro Y, Watanabe S, Emori S, Takemura T, Chikira
M, Ogura T, Sekiguchi M, Takata K, Yamazaki D, Yokohata T, Nozawa T, Hasumi
H, Tatebe H, Kimoto M (2010) Improved climate simulation by MIROC 5: meanstates, variability, and climate sensitivity. J Climate 23:6312–6335
Watanabe S, Hajima T, Sudo K, Nagashima T, Takemura T, Okajima H, Nozawa T,
Kawase H, Abe M, Yokohata T, Ise T, Sato H, Kato E, Takata K, Emori S,
Kawamiya M (2011) MIROC-ESM 2010: model description and basic results of
CMIP5-20c3m experiments. Geosci Model Dev 4:845–872
Webb M, Senior C, Bony S, Morcrette JJ (2001) Combining ERBE and ISCCP datato assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate
models. Clim Dyn 17:905–922
Wedi NP (2014) Increasing horizontal resolution in numerical weather prediction
and climate simulations: illusion or panacea? Philos Trans R A 372:20130289
Wedi NP, Smolarkiewicz PK (2009) A framework for testing global non-hydrostatic
models. Q J R Meteorol Soc 135:469–484
Wielicki BA, Barkstrom BR, Harrison EF, Lee RB, Smith GL, Cooper JE (1996) Clouds
and the Earth’s Radiant Energy System (CERES): an earth observing system
experiment. Bull Amer Meteor Soc 77:853–868
Williamson DL, Drake JB, Hack JJ, Jakob R, Swarztrauber PN (1992) A standard test
set for numerical approximations to the shallow water equations in sphericalgeometry. J Comp Phys 102:211–224
Wood N, Staniforth A, White A, Allen T, Diamantakis M, Gross M, Melvin T, Smith
C, Vosper S, Zerroukat M, Thuburn J (2013) An inherently mass-conserving
semi-implicit semi-Lagrangian discretization of the deep-atmosphere global
non-hydrostatic equations. Q J R Meteorol Soc 140:1505–1520, doi:10.1002/
qj.2235
Wyngaard JC (2004) Toward numerical modeling in the “Terra Incognita”.
J Atmos Sci 61:1816–1826
Xiao F, Okazaki T, Satoh M (2003) An accurate semi-Lagrangian scheme for rain
drop sedimentation. Mon Wea Rev 131:974–983
Yamada Y, Satoh M (2013) Response of ice and liquid water paths of tropical
cyclones to global warming simulated by a global nonhydrostatic modelwith explicit cloud microphysics. J Climate 26:9931–9945
Yamada Y, Oouchi K, Satoh M, Tomita H, Yanase W (2010) Projection of changes
in tropical cyclone activity and cloud height due to greenhouse warming:
global cloud-system-resolving approach. Geophys Res Lett 37:L07709,
doi:10.1029/2010GL042518
Yamada Y, Oouchi K, Satoh M, Noda AT, Tomita H (2012) Sensitivity of Tropical
Cyclones to Large-Scale Environment in a Global non-Hydrostatic Model
With Explicit Cloud Microphysics. In: Oouchi K, Fudeyasu H (eds) Cyclones:
formation, Triggers and Control. Nova Science, pp 145–159
Yamaura T, Kajikawa Y, Tomita H, Satoh M (2013) Possible impact of a tropical
cyclone on the northward migration of the Baiu frontal zone. SOLA 9:89–93
Yamazaki H, Satomura T (2008) Vertically combined shaved cell method in
z-coordinate for non-hydrostatic atmospheric model. Atmos Sci Lett 9:171–175
Yamazaki H, Satomura T (2010) Nonhydrostatic atmospheric modeling using a
combined cartesian grid. Mon Wea Rev 138:3932–3945
Yanase W, Taniguchi H, Satoh M (2010a) Environmental modulation andnumerical predictability associated with the genesis of tropical cyclone
Nargis (2008). J Meteor Soc Japan 88:497–519

Satoh et al. Progress in Earth and Planetary Science 2014, 1:18
http://www.progearthplanetsci.com/content/1/1/18

Page 32 of 32

Yanase W, Satoh M, Yamada H, Yasunaga K, Moteki Q (2010b) Continual influencesof tropical waves on the genesis and rapid intensification of typhoon Durian(2006). Geophys Res Lett 37:L08809, doi:10.1029/2010GL042516
Yanase W, Satoh M, Taniguchi H, Fujinami H (2012a) Seasonal and intraseasonal
modulation of tropical cyclogenesis environment over the Bay of Bengal
during the extended summer monsoon. J Clim 25:2914–2930
Yanase W, Satoh M, Iga S, Chan JCL, Fudeyasu H, Wang Y, Oouchi K (2012b)
Multi-Scale Dynamics of Tropical Cyclone Formations in an Equilibrium
Simulation Using a Global Cloud-System Resolving Model. In: Oouchi K,
Fudeyasu H (eds) Cyclones: formation, triggers and control. Nova Science,
pp 221–231
Yasunaga K, Nasuno T, Miura H, Takayabu YN, Yoshizaki M (2013) Afternoon
precipitation peak simulated in an aqua-planet global non-hydrostatic model(aqua-planet-NICAM). J Meteor Soc Japan 91A:217–229
Yeh K-S, Côté J, Gravel S, Méthot A, Patoine A, Roch M, Staniforth A (2002) The
CMC-MRB global environmental multiscale (GEM) model. Part III:nonhydrostatic formulation. Mon Wea Rev 120:329–356
Yoneyama K, Katsumata M, Mizuno K, Yoshizaki M, Shirooka R, Yasunaga K,
Yamada H, Sato N, Ushiyama T, Moteki Q, Seiki A, Fujita M, Ando K, Hase H,
Ueki I, Horii T, Masumoto Y, Kuroda Y, Takayabu YN, Shareef A, Fujiyoshi Y,
McPhaden MJ, Murty VSN, Yokoyama C, Miyakawa T (2008) MISMO field
experiment in the equatorial Indian Ocean. Bull Am Meteorol Soc 89:1889–1903
Yoneyama K, Zhang C, Long CN (2013) Tracking pulses of the Madden-Julian
Oscillation. Bull Amer Meteor Soc 94:1871–1891
Yoshimura H, Yukimoto S (2008) Development of a simple coupler (Scup) forearth system modeling. Pap Met Geophys 59:19–29
Yoshizaki M, Iga S, Satoh M (2012a) Eastward propagating property of large-scaleprecipitation systems simulated in the coarse-resolution NICAM and an
explanation of its formation. SOLA 8:21–24
Yoshizaki M, Yasunaga K, Iga S, Satoh M, Nasuno T, Noda AT, Tomita H (2012b)
Why do super clusters and Madden Julian Oscillation exist over theequatorial region? SOLA 8:33–36
Zängl G, Tomita H, Satoh M, Ludwig T, Linardakis L, Thuburn J, Dubos T (2011)
ICOMEX: ICOsahedral-grid Models for EXascale Earth system simulations.
IS-ENES Workshop, Lecce
Zängl G, Reinert D, Rípodas P, Baldauf M (2014) The ICON (ICOsahedral Non-hydrostatic)modelling framework of DWD and MPI-M: Description of the non-hydrostatic
dynamical core. Quart J Roy Meteor Soc, doi:10.1002/qj.2378
doi:10.1186/s40645-014-0018-1
Cite this article as: Satoh et al.: The Non-hydrostatic Icosahedral
Atmospheric Model: description and development. Progress in Earth andPlanetary Science 2014 1:18.

Submit your manuscript to ajournal and beneﬁt from:
7 Convenient online submission
7 Rigorous peer review
7 Immediate publication on acceptance
7 Open access: articles freely available online
7 High visibility within the ﬁeld
7 Retaining the copyright to your article

Submit your next manuscript at 7 springeropen.com