Satoh et al. Progress in Earth and Planetary Science (2017) 4:13
DOI 10.1186/s40645-017-0127-8

Progress in Earth andPlanetary Science

REVIEW

Open AccessOutcomes and challenges of global highresolution non-hydrostatic atmosphericsimulations using the K computer
Masaki Satoh1* , Hirofumi Tomita2, Hisashi Yashiro2, Yoshiyuki Kajikawa2,3, Yoshiaki Miyamoto4,2, Tsuyoshi Yamaura2,
Tomoki Miyakawa1, Masuo Nakano5, Chihiro Kodama5, Akira T. Noda5, Tomoe Nasuno5, Yohei Yamada5
and Yoshiki Fukutomi6

Abstract
This article reviews the major outcomes of a 5-year (2011–2016) project using the K computer to perform
global numerical atmospheric simulations based on the non-hydrostatic icosahedral atmospheric model(NICAM). The K computer was made available to the public in September 2012 and was used as a primaryresource for Japan’s Strategic Programs for Innovative Research (SPIRE), an initiative to investigate five strategic
research areas; the NICAM project fell under the research area of climate and weather simulation sciences.
Combining NICAM with high-performance computing has created new opportunities in three areas of research:(1) higher resolution global simulations that produce more realistic representations of convective systems, (2)multi-member ensemble simulations that are able to perform extended-range forecasts 10–30 days in advance,and (3) multi-decadal simulations for climatology and variability. Before the K computer era, NICAM was used
to demonstrate realistic simulations of intra-seasonal oscillations including the Madden-Julian oscillation (MJO),
merely as a case study approach. Thanks to the big leap in computational performance of the K computer, we
could greatly increase the number of cases of MJO events for numerical simulations, in addition to integratingtime and horizontal resolution. We conclude that the high-resolution global
non-hydrostatic model, as used in this five-year project, improves the ability to forecast intra-seasonal
oscillations and associated tropical cyclogenesis compared with that of the relatively coarser operationalmodels currently in use. The impacts of the sub-kilometer resolution simulation and the multi-decadalsimulations using NICAM are also reviewed.
Keywords: K computer, NICAM, Intra-seasonal oscillations, Madden-Julian oscillation, Tropical cyclone, Global
non-hydrostatic model

Introduction
The K computer began operation in September 2012 at
RIKEN Advanced Institute for Computational Science,
Japan. The Strategic Programs for Innovative Research(SPIRE), an initiative launched in October 2010 in Japan,
aims to leverage the unparalleled power of the K computer to produce the world’s most cutting-edge simulation studies in five key strategic fields. SPIRE also aims
more generally to boost the creation of promotionalframeworks for computer science and technology and to* Correspondence: satoh@aori.u-tokyo.ac.jp
1
The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan
Full list of author information is available at the end of the article

yield significant social breakthroughs. Field 3 of SPIRE,“Advanced prediction research for natural disaster prevention and reduction,” consists of weather and climate
simulations, including earthquake and tsunami simulations (Oishi et al. 2015; Tsuboi et al. 2016; Ando et al.
2016; Hyodo et al. 2016). Studies using meso-scale atmospheric modeling (Saito et al. 2013; Kunii 2014a,
2014b; Chen et al. 2015a, 2015b; Duc et al. 2015; Ito etal. 2015; Kobayashi et al. 2015a; Seko et al. 2015;
Yokota et al. 2016) and global modeling are also included under weather simulations. Here, we review
the outcome of the global high-resolution atmosphericmodeling studies conducted from 2012 to 2016 under© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

Field 3 of SPIRE and preview the challenges for the next K
computer project, called the FLAGSHIP2020 project (Future LAtency core-based General-purpose Supercomputerwith HIgh Productivity). Running from 2014 to 2019,FLAGSHIP2020 is in preparation for the Post-K supercomputer, which was originally planned to start operation in
2020. (Recently, it was announced that the operation of the
Post-K supercomputer has been delayed by at least 2 years.)
For global atmospheric modeling using the K computer, new research using the non-hydrostatic icosahedral atmospheric model (NICAM; Tomita and Satoh2004; Satoh et al. 2008, 2014) has been pursued by enhancing horizontal resolution, ensemble sizes, or duration of integration time. In particular, we establishedthe following research targets at the beginning of the
project: (1) enable extended-range forecasts for a timeframe of 10–30 days, for better simulations of intraseasonal oscillations (ISO) and (2) enable projection oftropical cyclones. Before the K computer became available, NICAM was known to more accurately reproduce
individual ISOs in case studies at a horizontal grid spacing between 3.5 and 14 km (Miura et al. 2007a; Oouchi
et al. 2009a, 2009b; Yanase et al. 2010). At that time,
simulations such as these were performed mainly on the
Earth Simulator at the Japan Agency for Marine-Earth
Science and Technology (JAMSTEC). Multi-year
simulations were also conducted with the 7 km grid spacing NICAM using the Athena computer (operated by the
University of Tennessee’s National Institute for Computational Science and hosted by Oak Ridge National Laboratory in the USA) and showed better simulations of boreal
summer ISO (Satoh et al. 2012b; Kinter et al. 2013). The
development of the K computer offered the opportunity
to obtain more robust performance from NICAM by drastically increasing ensemble size and integration time together with resolution.

Review
This article reviews the outcomes of the NICAM
studies using the K computer under Field 3 of SPIRE
and previews challenges for the forthcoming Post-K
supercomputer under the FLAGSHIP2020 project. In
the “Computational aspects” section, we describe how
NICAM was adapted for use on the K computer.
Following this, we list major outcomes using NICAM
with the K computer in the sections “Sub-kilometer
global simulation,” “Asian summer monsoon,” “Madden-Julian oscillation,” “Boreal summer intra-seasonal
oscillation and tropical cyclogenesis,” “Atmospheric
Model Intercomparison Project-type experiments,”
and “Projection of tropical cyclones.” The challenges
to overcome in the near future are reviewed in “Future perspectives,” and we conclude our review in the
final section, “Conclusions.”

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Computational aspects
Background

The K computer was the first 10 petaflop supercomputer(1015 floating-point operations per second; Yokokawa et
al. 2011). This massively parallel scalar machine wastwice ranked as the world’s top supercomputer in the
TOP500 List in 2011 (Top 500 2011a, 2011b). Furthermore, it showed the top performance in new benchmarks such as the high-performance conjugate gradient(Kumahata et al. 2016) and Graph500 (Graph 500 2014).
Two recipients of the Gordon Bell Prize used the K
computer to perform their award-winning work (Hasegawa et al. 2011; Ishiyama et al. 2012). In the researcharea of earth science, which Field 3 of SPIRE also covers,
an urban-scale earthquake simulation study using the K
computer was selected as a finalist for the Gordon Bell
Prize (Ichimura et al. 2014, 2015). These results reflect
the K computer’s high performance not only in benchmarks, but also in applications.
The K computer system possesses characteristics advantageous for weather and climate research, which
require data-intensive simulation models. The performance of these models is limited not only by the
number of calculations processed in the computer’s
central processing unit but also by data transfer processes, such as the memory-cache transfer, inter-nodecommunication, and file input/output (I/O). The K
computer has relatively high memory throughput(0.5 byte per floating-point operation) compared tothe today’s massively parallel scalar supercomputers.
The asynchronous, distributed file I/O system of the
K computer minimizes waiting time for frequent output of the atmospheric variables during the simulation. The K computer is also highly resilient and has
a lower machine failure rate (Yamamoto et al. 2014)
compared to other peta-scale supercomputers, which
is very important for long-term climate simulations
with a large number of computational nodes.
At an early stage of the development of the K computer, NICAM was selected as one of the target applications and was used in many of the computer’s
performance optimizations. In experiments with a small
number of nodes, we achieved about 10% of the performance efficiency (Terai et al. 2014; Yashiro et al. 2016b),
which is almost competitive with that of the state-of-theart weather and climate models. NICAM also shows goodscalability, up to almost the full-node of the K computerin the weak scaling test.
Single-node performance

For general model applications, a small number of major
calculation parts occupy most of the floating-point operation and elapsed time. These parts are called hot spots,
and we usually focus on these hot spots for theSatoh et al. Progress in Earth and Planetary Science (2017) 4:13

optimization of computational performance. However,most weather and climate models do not have a clear
hot spot. Every part of the source code contains a small
computation that consumes a little time, a characteristic
called a flat profile. Weather and climate models generally have a flat profile, where every part of the sourcecode has the potential to deteriorate the total performance. The effort to optimize NICAM on the K computer
aimed to overcome the flat profile (Yashiro et al. 2016b).
Optimizations were applied mainly to the calculation
parts (kernels) such as divergence, gradient, and Laplacian operations. These kernels are a type of stencil calculation, which updates the grid point value using thevalue of surrounding grids. Because the finite-volume
method is used for numerical discretization in NICAM,
these kernels are the most important part for solving thefluid dynamics equations of the atmosphere. After
optimization, each kernel showed improved performance(Terai et al. 2014). However, we achieved only a 1% gainin sustained performance over the total simulation, because the ratio of elapsed time of the kernels to total
time decreased. This result was related to the effect ofAmdahl’s law (Amdahl 1967). From this experience, we
realized that we needed to find the time-consuming, lesscomputationally intensive parts of the source code andremove them. As a result of these efforts, we achieved
about 10% of the sustained performance, which was appropriate to the memory performance of the K computer. We also succeeded in reducing the model
execution time by 30%.
Scalability

Terai et al. (2014) performed a weak scaling test using
NICAM on the K computer and showed good scalability

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of elapsed time and sustained performance of the
NICAM (Fig. 1). The test was conducted by changing
the number of nodes and the horizontal grid spacing.
Horizontal grid spacing was decreased from 56 km to
440 m, which corresponded to an increase in the number of nodes from 5 to 81,920. The total time wasdivided into three parts: (1) the dynamical step, which
covered the time taken to solve the fluid dynamics; (2)the physical step, which covered the physics componentsof the calculations, such as cloud microphysics, atmospheric radiation, and boundary layer turbulence; and (3)the communication step, which comprised the internode data transfer using message passing interface(MPI) (referred to as comm_data_transfer in Fig. 1). The
computations and communications were completely
separated using barrier statements of MPI (MPI_barrier),in which all tasks must synchronize. The result shows
good scalability of total elapsed time. The timing of the
dynamical step does not change as the number of nodes
is increased, because the number of operations remains
almost the same. The increase in the communication
time as the number of nodes increases is not significant
because most of the communications are conducted
node-to-node using halo exchanges. The increase in the
total elapsed time is mainly caused by increases in thetime of the physical step due to the load imbalance
between the processes. Terai et al. (2014) found that theimbalances occurred mainly in the cloud microphysics
scheme. They found that the global map of the numberof floating-point operations in the cloud microphysics
showed inhomogeneous distribution, similar to that ofice hydrometeors. Overall, we achieved a maximum
performance of 0.87 petaflops with 81,920 nodes(655,360 cores), as shown by Terai et al. (2014).

Fig. 1 Results of the weak scaling test on the K computer. Bars show the elapsed time for the dynamical step, the physical step, and thecommunication (data transfer) step. Lines indicate the sustained performance. After Fig. 2 of Terai et al. (2014)

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

The results of the strong scaling test are different fromthose of the weak scaling test (Fig. 2). In the strong scaling test, we used NICAM with horizontal grid spacing of
14 km and 38 vertical layers. The number of grid points
in each process decreased from 33,800 to 100 as thenumber of nodes increased from 80 to 40,960. The
model showed good scalability up to 2560 nodes. The
number of simulation days per wall-clock day increased
in proportion to the number of nodes. However, theperformance saturated beyond 2560 nodes, due to therelative increase in communication time; at 40,960
nodes, the communication time occupied over 50% of
total elapsed time. This result led to production runs
being conducted with 640 nodes in the studies describedin the following sections. For long-term simulations infuture studies, strong scalability will need to be improved. Reducing the number of subroutine calls within
the MPI communication is one possible solution.
Pre- and post-processing

Owing to the comprehensive optimizations of NICAM,
we achieved high efficiency and good scalability. However, the elapsed pre- and post-processing times wererelatively increased in the total simulation time for thefollowing reasons. The remapping process has one of theheaviest workloads in post-processing. To use common
analysis and visualization tools, we usually remap themeteorological variables from an icosahedral grid to ageodesic (latitude-longitude) grid. For example, the data
size of the global 0.87 km horizontal grid spacing simulations was 6 TB for one snapshot of all 3-D and 2-D
variables (Miyamoto et al. 2013, 2015; Kajikawa et al.
2016). The required time for remapping and analyzing
all data was estimated to be more than 2 months using
typical computer clusters, which is much longer than
the time required for simulation on the K computer. To
solve this problem, we developed programs for parallel
analysis on the K computer without remapping, thereby
reducing the post-processing time from several months
to less than 1 h. This improvement was due to massively
parallel I/O rather than parallel computation. In the

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future, simulation sizes are expected to increase evenmore drastically. The most important consideration is
the total elapsed time of the workflow in simulation
studies, which includes pre- and post-processing, analysis, and visualization. All computational work will need
to be carried out on the supercomputer to keep thistime to a minimum.
Future directions

NICAM has been selected as the target application forthe development of Japan’s next-generation flagshipcomputer (the Post-K supercomputer), together with thelocal ensemble transform Kalman filter (LETKF).
NICAM and LETKF play an important role in theprocess of application-architecture co-design. Generally,
computations of weather and climate models are not expected to be carried out faster than improvement in microprocessors, because the rate of improvement inmemory throughput is slower than the floating-point operation speed. Thus, the computational performance of
NICAM will be improved by modifications of the algorithm and its optimization (e.g., changes of loop order,
data layout, and call structure).
Active use of mixed-precision floating-point operationis one possible approach to further enhance computational performance. The bit length of floating-pointvalue affects the data size of memory transfer and thecompression ratio of the output data. The effect of lessprecise values on the simulation results should be investigated. In recent years, the development of components
and schemes such as the atmosphere-ocean couplingsystem with coupling library Jcup (Arakawa et al. 2014)
and the ensemble-based data assimilation system(Terasaki et al. 2015) have expanded the capabilities ofthe simulations using NICAM. While taking these developments into account, we must increase the speed of
total workflow. Maximizing the power of future supercomputers will enable simulations with even more sophisticated physics and/or higher resolution and will
ensure that it becomes a powerful tool for further understanding weather and climate phenomena.

Fig. 2 Results of the strong scaling test on the K computer for the NICAM simulations. Vertical axis is simulation time per real time [day day−1].
Horizontal axis is the number of cores

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

Sub-kilometer global simulation
Background

One of the greatest challenges in climate modeling isachieving more accurate simulations of clouds (Stevens
and Bony 2013; Palmer 2014). Among various types ofnaturally occurring clouds, deep moist convective clouds
have important roles in determining the structure of atmospheric circulation through the release of latent heat.
This deep convection is also part of the cloudy atmospheric disturbances that sometimes cause natural disasters. Since it has been impractical to simultaneously
simulate all-scale phenomena in global atmosphericmodels, from deep convection on a scale of O(1) km to
global circulation on a scale of O(104) km, deep convection has been expressed as a parameterization in conventional general circulation models (GCMs) (e.g.,
Arakawa and Schubert 1974; Kain and Fritsch 1990).
NICAM was developed to simulate the global atmospheric circulation without cumulus parameterization(Satoh et al. 2008, 2014). Previous studies using NICAM
have shown advanced performance in simulating largescale convective systems and disturbances mainly using
the Earth Simulator (e.g., Miura et al. 2007a; Sato et al.
2009; Nasuno and Satoh 2011; and references in Satohet al. 2014). However, their grid spacing (several kilometers) was coarser than or similar to that of the observed
convection scale. The highest resolution simulations
using the Earth Simulator were the 3.5 km horizontal
grid spacing simulations by Tomita et al. (2005) and
Miura et al. (2007a, 2007b). Meanwhile, studies using
cloud-resolving models in a confined area, such as large
eddy simulation (LES) models, have indicated a change
in deep convection characteristics at grid spacing lessthan 1 km (Petch et al. 2002; Bryan et al. 2003). Under
Field 3 of SPIRE, we successfully conducted the firstever global atmospheric simulation with sub-kilometer
grid spacing using NICAM, which opened the door for
simulations resolving deep convective cores in global atmospheric models. This work also allowed us to highlight the resolution dependency of simulated convectionin the global model as well as its statistical features.
Results

Miyamoto et al. (2013) conducted a set of grid-refinement
experiments with grid spacing that varied from 14 to
0.87 km. Further analysis of these experiments is shownby Miyamoto et al. (2015), Kajikawa et al. (2016), andYashiro et al. (2016a), and the details of the experimental
settings are described in Miyamoto et al. (2013) and Kajikawa et al. (2016). Although the large-scale characteristics
of global cloud distributions are almost unchanged between the simulations with 14, 3.5, and 0.87 km grid spacing, the simulation with sub-kilometer resolution
provided a more detailed description of the smaller scale

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cloud structure (Fig. 3). The resolution dependency
showed that the properties of simulated convection werequalitatively changed between 3.5 and 1.7 km grid spacing.
We defined convective cores in the simulations and madea composite structure of convective cores averaged overthe globe. In simulations at finer resolution than the
3.5 km grid spacing, the convective core is expressed bymultiple grid points instead of a single grid point for the
3.5 km grid spacing; the dependency of the number ofconvective cores on resolution changes, with smaller dependency for finer resolutions (Fig. 4); and the minimum
distance between convective cores is larger than four grid
points for the finer resolutions than the 3.5 km grid spacing (Fig. 4). From these results, the horizontal grid spacing around 2 km can be viewed as the minimum
required resolution for a deep convective resolving modelover the entire Earth, although the required grid spacing
might depend on numerical discretization methods.
Miyamoto et al. (2015) conducted detailed analysis ofdeep convection in the simulation with the finest
0.87 km grid spacing. They defined cloud disturbancessuch as the Madden-Julian oscillation (MJO), tropical
cyclones (TCs), mid-latitude lows, and fronts and analyzed the statistical features of deep convection associated with these disturbances. The cloud-top height,
strength of upward motion, and the environmental fieldof the convection were different in each type of disturbance. For example, the MJO convection was generated
under high convective available potential energy (CAPE)
and relatively weak lower convergence, whereas the TC
convection was accompanied by low CAPE with strong
convergence.
Kajikawa et al. (2016) analyzed the grid-refinement
experiments to address the difference in the resolution
dependence of the simulated convection by location andenvironment over the globe. This study extends thework of Miyamoto et al. (2013), which mainly discussed
globally averaged convection properties. Kajikawa et al.
(2016) showed that convective clouds over the tropics
are more dependent on resolution than those in midlatitudes; on the other hand, no significant difference isfound in the resolution dependence of convection properties between land and ocean. Convection within cloud
disturbances such as the MJO and TCs also shows large
resolution dependency. Consequently, deep convectionthat is not categorized as cloud disturbances makes alarge contribution to the global mean convection properties in Miyamoto et al. (2013). It should be stressedthat most deep convection is expressed by multiple grid
points in the 0.87 km grid spacing simulation even intropical cloud disturbances.
Yashiro et al. (2016a) investigated the diurnal cycle ofprecipitation in this series of grid-refinement experiments and also found that the resolution dependency ofSatoh et al. Progress in Earth and Planetary Science (2017) 4:13

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Fig. 3 Resolution dependencies of simulated cloud distribution. Global view of simulations with a 14 km, b 3.5 km, and c 0.87 km grid spacing.
d–f Focus over the western North Pacific region of (a–c), respectively. After Fig. 3 of Kajikawa et al. (2016)

a

b
Δ
Δ
Δ
Δ
Δ

Δ

Fig. 4 Resolution dependencies of convective features. a Number of convective cores, and b grid distance to convective cores. The dashed line in(a) indicates a log Δ4 crossing at the point of simulation results with 14 km grid spacing as a reference. After Fig. 4 of Miyamoto et al. (2013)

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

its characteristics changed at a grid spacing around
2–3 km. The simulations using 1.7 and 0.87 km grid
spacing better reproduced the observed peak time ofthe diurnal cycle compared to the experiments withcoarser grid spacing. This could be a result of thebetter representation of deep convective cores, as
shown by Miyamoto et al. (2013) and Kajikawa et al.
(2016).
Future directions

We showed the resolution dependency of the simulated
convection properties and their differences in various
environments through a set of grid-refinement experiments. The simulation with sub-kilometer grid spacing
revealed new results that differed from previous studies;for example, deep convective cores were expressed bymultiple grid points. Higher spatial resolution and more
realistic convection simulations are required to better
understand cloud disturbances from the meso-scale tothe synoptic scale (Prein et al. 2015). Moreover, longer
integration at sub-kilometer grid spacing would enable
us to investigate the interaction between convection andlarger-scale cloud disturbances. Using the Post-K supercomputer, we estimate that we can perform experiments
at further finer resolution, such as 220 m, to resolve
more detailed structure of clouds.
Asian summer monsoon
Background

The Asian monsoon system is characterized by a seasonal cycle of convective activity and atmospheric circulations (e.g., Murakami and Matsumoto 1994). The
variability of the monsoon system’s effects on weather
and climate over Asia is largely achieved through local
convective activities. This variability is also affected bysynoptic scale tropical disturbances. For example, themonsoon onset is often triggered by tropical disturbances or ISO. Recently, global climate modeling has
made advances in many aspects of Asian summer monsoon simulations (Sperber et al. 2013; Ogata et al. 2014;
Lee and Wang 2014). However, numerical representations of the seasonal transition of the monsoon, such as
the monsoon onset, with scale interactions remain challenging. Meanwhile, NICAM has shown an advantage
over conventional GCMs in simulating tropical disturbances, such as TCs (Fudeyasu et al. 2008; Taniguchi etal. 2010; Nakano et al. 2015) and MJO (Miura et al.
2007a; Miyakawa et al. 2014). Under Field 3 of SPIRE,
we investigated the seasonal transition of the monsoon
and anticipated a potential extension of its predictability
using a large number of ensemble NICAM simulations,in contrast to previous studies that used simulations of
only a single or a few members.

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Experimental setup

Kajikawa et al. (2015) investigated the process of the Indian summer monsoon onset in 2012 based on ensemble
simulations using NICAM with a grid spacing of about
14 km and 38 vertical levels. Moist processes were calculated using the NICAM single-moment water 6-cloud
microphysics scheme (NSW6; Tomita, 2008) without any
convective parameterization. Sea surface temperature(SST) was forecasted using a slab ocean model to allow diurnal variation and nudged to the persistent SST anomaly
at the initial time with an e-folding time (that is, the timein which SST is nudged to specified values) of 7 days. The
ensemble simulations were started at 00 UTC each day
during the period from 10 May to 10 June 2012, and integrated for 30 days. Using the same set of NICAM ensemble simulations, Yamaura et al. (2013) clarified the role ofTCs on the migration of the Baiu front.
Results

The onset of the 2012 Indian summer monsoon wasannounced on 5 June that year by the Indian Institute of Tropical Meteorology. The NICAM simulations using initial conditions 2 weeks before theonset (15 May) reproduced the onset very well(Fig. 5). Both the observations and simulations included the abrupt onset with northward and eastward migrating tropical disturbances over the Bay of
Bengal and the Arabian Sea in late May. Based on acomparison with the Japan Meteorological Agency’s
operational Ensemble Prediction System, Kajikawa etal. (2015) pointed out that the better reproducibilityof the tropical disturbances seen in the NICAM results extended the predictability of the Asian summer monsoon transition phase.
Yamaura et al. (2013) specifically used the NICAM
simulations initialized at 29 and 30 May 2012 to clarify

Fig. 5 Time series of the Indian summer monsoon indices for theensemble simulations. The black curves are for each run, the blue curve isfor averaging five members with initial conditions covering May 15–19,
and the red curve is for the actual observations. The Indian summer
monsoon index is defined as the meridional shear of zonal winds at
850 hPa over the Indian continent (40°E–80°E, 5°N–15°N minus 70°E–90°
E, 20°N–30°N). After Fig. 1 of Kajikawa et al. (2015)

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

the role of a TC in the migration of the Baiu front. Observations revealed that the Baiu front shifted northwardwith the TC. A simulation integrated from 29 May
reproduced both the TC and the northward migration of
the Baiu front, whereas the Baiu front stayed stagnant inanother simulation integrated from 30 May. Since the
TC in the latter simulation developed farther eastward
than the observed TC, Yamaura et al. (2013) suggested
that the potential extension of the predictability of theBaiu front migration was due to the realistic reproducibility of TCs.
Seasonal simulations of boreal summer using the global 7 km grid spacing NICAM have captured the Asiansummer monsoon as a case study (Oouchi et al. 2009a,
2009b; Satoh et al. 2012b; Kinter et al. 2013). However,these simulations have large biases in precipitation andlower tropospheric wind fields, particularly over thewestern North Pacific. These biases were partly due toinsufficient spin-up of the land surface model, which
was upgraded in the experiments conducted during Field
3 of SPIRE.
Another achievement of this project was the first-ever
30-year simulations (see “Atmospheric Model Intercomparison Project-type experiments” section; Kodama et al.
2015). The 30-year simulation successfully simulated theclimatology of the annual cycle of the western North
Pacific monsoon and its inter-annual variability (seeFigures 14 and 15 of Kodama et al. 2015). In Murakami
and Matsumoto (1994), the climatological Asian monsoon was defined by differences between the annual
maximum and minimum outgoing longwave radiation(OLR) (Fig. 6). The NICAM simulation adequately represents each monsoon sub-system in the observation(e.g., northern Australia-Indonesia monsoon, Southeast
Asian monsoon, western North Pacific monsoon);
although, the magnitude is stronger by approximately
20–40 W m−2. The NICAM simulation also included anorthward extension of the Southeast Asian monsoon

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and the western North Pacific monsoon which are related to the northward displacement of a low-level
westerly and a subtropical high; this in turn led toearlier termination of the Baiu season and to a
weaker monsoon trough (Kodama et al. 2015).
Future directions

Both NICAM ensemble simulations and climate simulation showed realistic behavior of the Asian summer
monsoon system including the relationship between
TCs and the Baiu front. However, similar to other climate models, NICAM suffers from model biases. In
particular, the Asian summer monsoon simulations
are severely affected by the warm temperature bias
over continents. In addition, the mid-latitude jets
were stronger in the simulation than in observations
partly because a gravity drag parameterization scheme
was turned off in the 14 km grid spacing NICAM
simulations. In future, simulations of Asian summer
monsoons will need to reduce such biases in order toimprove their performance; this is currently being
tested through development of the land surface modeland the introduction of the gravity wave drag scheme.
NICAM simulations with atmosphere and ocean
coupling are also being investigated, because realistic
responses of convection to basin-scale ocean surface
conditions are strongly related to ocean-atmosphere
coupling (Wang et al. 2005). In addition, processoriented diagnosis in comparison with other climate
models (Sperber et al. 2013; Ogata et al. 2014;
Kusunoki and Arakawa 2015; Zou and Zhou 2015) isplanned to understand the effects of basic-state biases
on monsoon systems and to make the necessary
improvements. The multi-scale processes of monsoons
and the Baiu front under global warming conditionsin cloud-system resolving simulations are anotherongoing research topic (e.g,. Krishnamurthy et al.
2014).

b

Fig. 6 Horizontal distribution of OLR comparing Asian monsoon between simulations and observation. a National Oceanic and Atmospheric
Administration (NOAA) OLR and b OLR of the NICAM AMIP-type simulation defined by the difference between the annual maximum andminimum OLR in the pentad mean climatology (1979–2008). The northern Australia-Indonesia monsoon (NAIM), Southeast Asian monsoon(SEAM), and western North Pacific monsoon (WNPM) are identified after Murakami and Matsumoto (1994)

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

Madden-Julian oscillation
Background

The MJO, which is often described as an eastward
propagating atmospheric pulse with a typical lifespan of
30–60 days, is one of the dominant disturbances in theequatorial atmosphere at the intra-seasonal timescale(Madden and Julian 1972). It is viewed as a key sourceof predictability for the extended-range forecasts thattarget the time range of 10–30 days. More than 40 years
since its foundation, the majority of GCMs still struggle
to produce a satisfactory MJO (Hung et al. 2013).
NICAM had enjoyed success in producing a particular
MJO event, maintaining a highly accurate eastward
propagation speed along with realistic precipitation andzonal wind structures (Miura et al. 2007a). In Field 3 of
SPIRE, we utilized the increased computational resourceof the K computer to conduct ensemble simulations of
MJOs using NICAM to statistically assess the model’sability to predict MJOs (Miyakawa et al. 2014). Applying
a widely used evaluation method proposed byGottschalck et al. (2010), NICAM was diagnosed to havea prediction limit of 26–28 days. The precipitation patterns associated with MJO phases also compared wellwith observations.
Experimental setup

The following procedure based on the real-time multivariate MJO (RMM) indices (Wheeler and Hendon
2004) was applied to the boreal winter (October–March)
MJOs during an evaluation period (2003–2012) to assign
initial dates for the experiment. We identified target
MJO cases when a succession of observational RMM
plots move counterclockwise through phase 2 to phase 5(corresponding to the MJO convective envelope traveling from the western Indian Ocean to the eastern Indonesian Archipelago) without retreating more than one
phase and had an average amplitude greater than 1 overthe span between phases 2 and 5. For each of the MJO
cases extracted, the first dates on which the RMM plot
falls in phases 8, 1, and 2 are assigned as initial dates.For
the 19 MJO cases identified, 54 initial dates were assigned.We used the European Centre for Medium-Range
Weather Forecasts interim reanalysis (ERA-Interim)
dataset (Dee et al. 2011) for initial conditions of the atmosphere and ocean. The resolution and the physicalschemes of NICAM were the same as those described in
the “Asian summer monsoon” section except for modified cloud microphysics parameters that control the formation and fall speeds of precipitation particles andminor updates of land-process/boundary layer scheme
parameters. A mixed-layer ocean model was applied,
and its SST was nudged to externally provided SST at an
e-folding time of 7 days. The external SST was definedas the sum of the time-varying mean annual cycle andPage 9 of 24

constant anomalous component. The difference fromthe mean annual cycle was averaged over the week before the initial date to derive the anomalous component.
Thus, no information observed after the initial date wasused in the simulations. Further details are found in
Miyakawa et al. (2014).
Results

NICAM maintained a valid (>0.6) MJO skill score for
26–28 days depending on the initial MJO phase, and for
27 days when all 54 cases were included. Composites ofobserved and simulated precipitation anomalies for MJO
phases 3, 5, and 7 are shown in Fig. 7. The horizontal
structure of the simulated precipitation anomalies
closely resembles the observations, even for phase 7, forwhich the average lead time was 28 days. However, precipitation anomalies were overestimated over the central
Pacific in phase 5. Such a bias is occasionally found in
14 km grid spacing NICAM, perhaps due to underresolved local vertical moisture transport, which could
be a common problem for models that explicitly calculate cloud systems without cumulus parameterization.
The simulation series includes an MJO case that
occurred in November–December 2011 during the Cooperative Indian Ocean Experiment on Intraseasonal
Variability in Year 2011 and Dynamics of the MJO field
campaign (CINDY2011/DYNAMO; Yoneyama et al.
2013). Figure 8 shows the observed and simulated convective signals that clearly propagate at a very similar
eastward speed over the tropical Indian to the western
Pacific Ocean (10°S–10°N, 40°E–160°W). Time-heightsections of observed and simulated zonal wind over Gan
Island (not shown) further reveal that the model captures the depth and timing of the intrusion of the westerly winds as well as the deepening of moisture withtime, both of which are key aspects of MJO propagation.
Despite a lead time of nearly 4 weeks, the model also appears to capture the occurrence of the next MJO-likesignal in mid-December. The successful prediction of
the event provides the opportunity to compare a global
non-hydrostatic model simulation with a major in situ
observation for the same MJO event for the first time.
Future directions

Although NICAM appears to be one of the uniquemodels that can produce MJO events with high accuracy, we believe that the model still does not live up to its
potential in terms of MJO prediction. The limit in available resources has prevented us from applying ensemble
simulations of significant size for each event. However, anext-generation supercomputer designed to be roughly
20–100 times more powerful than the K computer
should be able to accommodate such simulations. The
initialization process of this experiment was a simple

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

aPage 10 of 24b

Observation

50N

Simulation

Phase 3Phase 3Phase 5Phase 5Phase 7

Phase 7

Eq
50S
50N
Eq
50S
50N
Eq
50S

0

60E

120E

180

120W

60W

–6

–3

0

0
0

60E
3

120E

180

120W

60W

0

6

–1

(mm day )Fig. 7 Precipitation anomaly composites for different MJO phases. a Observed 1° daily product version 1.2 by the Global Precipitation Climatology
Project (Huffman et al. 2001), and b simulation using NICAM. The average number of days from the initial dates of the simulations are 16 days (phase 3),
25 days (phase 5), and 28 days (phase 7). Resolution of the simulation output is lowered to 1° mesh to equal the resolution of the observational data
set. Anomalies are calculated as deviations from 40-day mean values in each case. After Fig. 2 of Miyakawa et al. (2014)

interpolation from the ERA-Interim dataset, whichallows significant initial shock, but a new data assimilation system specifically designed for NICAM is being developed. The use of a simple mixed-layer ocean model
limits the model skill in situations in which dynamical
features of the ocean drastically affect the SST conditions. A fully coupled version of NICAM with a threedimensional dynamical ocean is now available and currently being tested (NICAM-COCO coupled model;
Satoh et al. 2014).
Boreal summer intra-seasonal oscillation and tropical
cyclogenesis
Background

Tropical cyclones cause huge socio-economic impacts,
so more accurate TC forecasts are demanded. Because
forecasting tropical cyclogenesis (TC genesis) is still achallenge in numerical weather forecasting, the predictability of TCs at sub-seasonal to seasonal time scales(2 weeks to 2 months) is promoted worldwide (Vitart etal. 2012, 2017). TC genesis is affected by large-scale environmental conditions, such as low-level cyclonic vorticity, small vertical wind shear, the convective

instability, and mid-level humidity (Gray 1975, 1979).
The boreal summer intra-seasonal oscillation (BSISO;
Wang and Rui 1990; Wang and Xie 1997) modulates
the large-scale environment and TC activity. For example, Nakazawa (2006) pointed out that the active
phase of BSISO in the 2004 summer season successively generated TCs, and persistent steering flow led toa record-breaking number of TC landfalls in Japan.
Therefore, the accurate prediction of BSISO and themodulation of large-scale environmental conditions associated with BSISO are believed to lead to accurate
forecasting of TC genesis.
Studies using NICAM show that the model is apromising tool for accurate BSISO and TC genesis
forecasting. For example, Oouchi et al. (2009a) demonstrated that NICAM successfully simulated modulations of BSISO and that TC genesis was captured
3 weeks in advance. However, the authors examined
only a limited number of TC genesis cases using a single run because of the limitations in computational
resources. The K computer enables us to examine thepredictability of the BSISO and TC genesis using theensemble approach. Here, we examine the predictability

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

aPage 11 of 24

Observed OLRbObserved precipitationd

Simulated precipitation

19 Nov

29 Nov

9 Dec

19 Decc

Simulated OLR

19 Nov

29 Nov

9 Dec

19 Dec
4 0E

80E
100

120E
150

200

160E
250

160W 40E

300 (W m–2)

80E
0

120E
16

32

160E
48

160W

(mm day –1)

Fig. 8 Precipitation anomaly composites for different Madden-Julian oscillation (MJO) phases. a Interpolated OLR by NOAA polar-orbiting series ofsatellites (NOAA-OLR; Liebmann and Smith, 1996) and c OLR by a single simulation initialized at 00 UTC 17 November 2011. b Precipitation by the 3B42
product of the Tropical Rainfall Measuring Mission (TRMM) satellite (Huffman et al. 2007) and d precipitation by the simulation. The figures consist of slicesthat show horizontal snapshots of the tropical Indian Ocean to the western Pacific Ocean (10°S–10°N, 40°E–160°W). The resolution of the simulated OLR
is lowered to 2.5° mesh to equal the resolution of the NOAA-OLR data set. The resolutions of the TRMM 3B42 data set and the simulated precipitation
are lowered to 1° mesh. After Fig. 3 of Miyakawa et al. (2014)of eight cases of TC genesis that occurred successively
in August 2004.
Experimental setup

Nakano et al. (2015) performed 30-day simulations foreach day of August 2004; that is, a total of 31 ensemble
30-day simulations were performed. The initial conditions of the atmosphere and SST were derived from the
ERA-Interim dataset (Dee et al. 2011). The resolution
and the physical schemes of NICAM were the same as
those described in the “Asian summer monsoon”
section. This setting is “forecast-mode” because themodel never knows the observed SST during the time
integration.
A candidate vortex that corresponds to an observed
TC should pass within 10° of the location of the observed TC genesis within 1 day of the observed TC

genesis time. The candidate vortex should meet TC criteria (e.g., surface wind speed >17.5 m s−1) within 5 daysof the observed TC genesis time. Detailed descriptionsof the experimental setup and method of TC detection
are provided in Nakano et al. (2015).
Results

Table 1 summarizes the results of the TC genesis forecast. The model captured six out of the eight cases ofTC genesis 1 week in advance with a low missing rate(all were above 70%). Moreover, four cases of TC genesisin late August were captured 2 weeks in advance withhigh probability (all were above 40% and 3 of 4 wereabove 50%). The genesis of TC Songda was well captured 2 weeks in advance, though not 3 weeks in advance. Note that two TC genesis events (Malou andMalakas) were not captured, probably because they were

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

Page 12 of 24

Table 1 Simulation performance for forecasting TC genesis
Malou

MerantiRananimMalakas

Megi

Chaba

Aere

SongdaCentral pressure (hPa)

996

960

950

990

970

910

955

925

Lifetime (days)

0.875

4.75

4.5

2.75

4.125

11.75

6.25

11

Week 1

0

100

75

43

86

71

71

86

Week 2

N/A

N/A

N/A

N/A

80

43

57

57

Week 3

N/A

N/A

N/A

N/A

N/A

N/A

N/A

29

Each of the eight TCs occurring in August 2004 is listed with their observed central pressure and lifetime, and the simulation’s hitting ratio (%) in weeks 1, 2, and
3. Adapted from Table 1 of Nakano et al. (2015).

very weak (minimum central pressure >990 hPa) andhad short lifespan (less than 3 days).
Figure 9 shows the zonal wind at 850 hPa and the
OLR, which is related to convective activities. In the analyzed field, the shear line between easterly wind in thetropics and monsoonal westerly wind extended eastward,
accompanied by active convection in mid-August. This
eastward extension was maintained throughout late August. TC Songda was generated on the shear line. The
experiments initialized between August 5 and 11(3 weeks before Songda’s genesis) captured the eastward
extension of the shear line in mid-August but it
retreated in late August. The experiments initialized between August 12 and 18 (2 weeks before Songda’s

genesis) captured the eastward extension of the shear
line in late August. Therefore, we conclude that the eastward extension of the shear line is closely related toSongda’s genesis, and if models reproduce the longitudinal evolution of the shear line, the associated TC
genesis will be simulated effectively.
Future directions

Once we have achieved more reliable predictability ofTC genesis with a longer lead time, our next targets are
the probabilities of TC tracks, intensities, and landfall.
Such information could be calculated from the large
samples of ensemble simulations. Since the NICAMLETKF system (Terasaki et al. 2015) is an ensemble-

a

b

d

c

e

f

Fig. 9 Comparison of 10-day mean OLR and zonal wind at 850 hPa between observation and simulation. Observed and ensemble mean of 10-day
mean OLR (green contour) and zonal wind at 850 hPa (color shading) averaged during a 1–10 August, b, d 11–20 August, and c, e, f 21–30 August 2004.
d, e show NICAM simulation initialized from 5 to 11 August; f shows 12–18 August. a Black crosses indicate the locations of tropical cyclogenesis. OLRvalues less than 200 W m−2 are shown with contour intervals of 20 W m−2. Broken lines depict the shear line. After Fig. 1 of Nakano et al. (2015)

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

based data assimilation system, it can be used to makethe large number of initial atmospheric fields fit to themodel’s dynamics and physics. It is expected that theprobability forecast of TC tracks and landfall probability
would be affected by the mid-latitude atmospheric conditions including the structure and evolution of the jetstream and the subtropical high. Given that the midlatitude jet stream and the subtropical high are affectedby the weather systems in the tropics such as MJO(Kawamura et al. 1996; Moon et al. 2013; Molinari andVollaro 2012), we should further examine the relationship between mid-latitude model biases and those in thetropics to improve probability forecasting of TC tracks.
For forecasts of TC intensity, current atmosphericmodels have typical biases: they are not able to reproduce rapid intensification, and TCs in the models continuously intensify even in the mid-latitude, where TCs
begin to weaken, then resulting in overintensification. A
higher resolution model is needed to improve the representation of the structure of the TC’s inner core, which
results in accurate simulation of rapid intensification(Rogers et al. 2013; Wang and Wang, 2014). Interestingly, a recent study shows that high-resolution global
models reduce forecast error in TC intensity and tracking forecasts (Nakano et al. 2017). The atmosphereocean interacting processes should also be incorporated
in the numerical model to avoid overintensification(Wada et al. 2014; Zarzycki 2016). Simulations of TC intensities are sensitive to details of physical schemes suchas the boundary layer scheme and the cloud microphysics scheme (Kanada et al. 2012). More improvement anddevelopment of NICAM together with evaluations using
available observational data will be necessary to pursue
the aforementioned future directions in TC predictability
studies.
Atmospheric Model Intercomparison Project-type
experiments

Atmospheric Model Intercomparison Project (AMIP) isa standard experimental protocol for atmospheric GCMs
to test models’ climatology with multi-decadal simulations. Such climate simulations are now achievable using
a high-resolution global non-hydrostatic model, and a
new era of climate-system research has begun. For example, the simulated intensities of TCs strongly dependon horizontal resolution (Camargo 2013) and convection
scheme (Murakami et al. 2012). Thus, by taking advantage of high-resolution global non-hydrostatic simulations without deep convection schemes, the climatology
of TCs is expected to be simulated more informatively,
thereby facilitating further investigations of the probability and geographical distributions of TC intensities andstructures and their projected changes under a warmer
climate (Oouchi et al. 2006; Murakami et al. 2012;

Page 13 of 24

Knutson et al. 2015). The global non-hydrostatic climate
model is also a powerful tool for simultaneously investigating phenomena at various spatiotemporal scales of atmospheric disturbances, such as planetary-scaleatmospheric general circulations, MJO, tropical waves,
diurnal meso-scale convective events, severe rainfall, andatmospheric gravity waves and their interactions (Holt etal. 2016). In addition, better representation of clouds,
convection, and circulations is key to reducing uncertainties of climate sensitivity (Bony et al. 2015; Stevens
and Bony 2013).
Motivated by the aforementioned demands for AMIPtype experiments with high-resolution global models, we
performed present (Kodama et al. 2015) and future(Satoh et al. 2015) climate simulations using the 14 km
grid spacing NICAM. The model settings were the sameas those described in the “Asian summer monsoon” section. Specifically, we should note that instead of following the strict AMIP protocol, the slab ocean model andnudging technique were employed instead of imposing afixed SST condition to obtain better performance of the
distribution of tropical precipitation, as shown in
Kodama et al. (2015). The simulations were performed
to reproduce the climates of 1979–2008 (Kodama et al.
2015) and to project the climates of 2074–2100 (Satohet al. 2015).
Kodama et al. (2015) reported the simulated climatology of basic state, TCs, MJO, Asian monsoon, diurnal
precipitation cycle, and quasi-biennial oscillation (QBO).
In-depth analysis of the MJO and mean and intense precipitation around Japan were presented in Kikuchi et al.
(2016) and Fujita et al. (2017, personal communication),
respectively. Fukutomi et al. (2015) evaluated the activity
and structure of the simulated tropical synoptic scale
waves over the western Pacific.
Here, we extend the analysis of Fukutomi et al. (2015)
to the various types of convectively coupled equatorial
waves (Kiladis et al. 2009), which govern the tropical
day-to-day weather. Spatiotemporal spectral and filter
analyses were applied to OLR, precipitation, and windfields to diagnose OLR variances contributed from equatorial Kelvin waves, equatorial Rossby waves, mixed
Rossby gravity waves, and tropical depression waves.
The National Oceanic and Atmospheric Administration(NOAA) OLR (Liebmann and Smith 1996) and the Japanese 55-year Reanalysis (JRA-55; Kobayashi et al. 2015b)
datasets were used to evaluate the results. Here, theequatorial wave-filtered OLR variances are plotted(Fig. 10). Geographical distributions of tropical waves
over the Indian and Pacific Oceans were qualitatively
simulated, though their overall activities tended to be
weaker than the observed ones (Fig. 10). Significant
biases were found over Central and South America, theAtlantic, and Africa in all the wave components. Kelvin

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

a OLR variance

KelvinNOAAPage 14 of 24

1979-2008
160

20˚N
10˚N

140

0˚
10˚S

120

20˚S

b OLR variance

Kelvin

NICAM

100

1979-2008

20˚N
80

10˚N
0˚

60

10˚S
20˚S

40

c OLR variance

ER

NOAA

1979-2008

20˚N

270

10˚N

240

0˚

210

10˚S
20˚S

180

d OLR variance

ER

NICAM

1979-2008

20˚N

150

10˚N

120

0˚

90

10˚S
20˚S

60

e OLR variance

MRG

NOAA

1979-2008

20˚N

80

10˚N
70

0˚
10˚S

60

20˚S

f OLR variance

MRG

NICAM

50

1979-2008

20˚N
40

10˚N
0˚

30

10˚S
20˚S

20

g OLR variance

TD

NOAA

1979-2008

20˚N

140

10˚N
0˚

120

10˚S
20˚S

100

h OLR variance

TD

NICAM

1979-2008

20˚N

80

10˚N
0˚

60

10˚S
20˚S

40
0˚

20˚E

40˚E

60˚E

80˚E 100˚E 120˚E 140˚E 160˚E 180˚ 160˚W 140˚W 120˚W 100˚W 80˚W 60˚W 40˚W 20˚W

0˚

Fig. 10 Comparison of climatology of the OLR variances of equatorial waves between observation and simulation. Annual mean climatology (1979–2008)of the OLR variances in (W m−2)2 for Kelvin wave (Kelvin) (a, b), equatorial Rossby waves (ER) (c, d), mixed Rossby gravity waves (MRG) (e, f), and tropicaldepression waves (TD). The observed NOAA OLR variances are shown by (a, c, e, g), whereas the NICAM simulated OLR variances are shown by (b, d, f, h)Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

waves were very weakly simulated, particularly over the
Atlantic, whereas simulated equatorial Rossby waves
were stronger over the Amazonian region and weakerover the Atlantic than those of the observation. Distribution of the simulated mixed Rossby gravity waves wasbiased over Central and South America and the Atlantic.
Tropical depression waves were weaker over Central andSouth America and the western Atlantic, whereas theAfrican easterly waves in the simulation were more active over the eastern Atlantic compared with those in
the observation. The reasoning behind the model biases
in convectively coupled equatorial waves will be further
investigated from the perspective of reproducibility ofthe seasonal scale background fields.
Future projected climate simulations were analyzed by
Satoh et al. (2015), in which the response of TCs to global warming was investigated using present and futureclimate simulation data. Yamada et al. (2017, personalcommunication) further investigated structural changes
in TCs. In addition to TCs, how the precipitation around
Japan (Fujita et al., 2017, personal communication) andthe precipitation associated with extratropical cyclones
respond to global warming (Kodama et al. 2017, personal communication) has been investigated. Before the
K computer was available, projection studies using
NICAM were limited to short time-slice simulations
such as one year or season (Yamada et al. 2010; Satoh etal. 2012a; Tsushima et al. 2014; Noda et al. 2014, 2015),
and the insufficient integration period made it difficult
to achieve statistical significance of the global warming
response. It is now possible to confirm the results derived from shorter-integration NICAM data. For example, Chen et al. (2016) performed seasonal scalesensitivity experiments and showed a large longwave
cloud radiative feedback, irrespective of the cloud microphysics schemes that were employed. This result was
further supported by an additional analysis of the interannual variability found in the NICAM AMIP-type data.
Figure 11 shows another example depicting the distribution of high-cloud size that was evaluated using themethod described by Noda et al. (2014). Qualitatively,

Page 15 of 24

the size distributions under the present and the future
conditions show a result similar to those evaluated in a
1-year NICAM simulation data in Noda et al. (2014).
Responding to global warming, the number of smaller
clouds (<40 km radius) notably increases, while changes
to the number of larger clouds (≥40 km) are muchsmaller and not significant. We found that the differencein the smaller clouds is statistically significant. The result
implies less organization of tropical cloud systems in thewarmer world. As analyzed by Chen et al. (2016),
NICAM shows larger positive high cloud feedback thanthe CMIP5 models due to the increase of high cloud
cover and more positive longwave feedback.
The NICAM AMIP-type simulations described above
are the first step toward global cloud-resolving climate
simulation. Our next plan, the High Resolution Model
Intercomparison Project (HighResMIP), is briefly described in the “Future perspectives” section.
Projection of tropical cyclones
BackgroundA primary energy source of TCs is latent heat supplied
from the warm tropical ocean (Emanuel 2003). Thechange in TC activities in a warmer climate is one of theimportant issues of climate change (Walsh et al. 2016).
Broccoli and Manabe (1990) used a GCM to project the
responses of TCs to global warming for the first time.
They used a horizontal grid spacing of a few hundred kilometers, which was too coarse to resolve TC structures
such as a warm core. However, their simulation showed
the global distribution of simulated TCs in generalagreement with the observation. They indicated that climate models have the potential to predict future changes
in TCs and advocated reducing the horizontal grid interval in order to realistically reproduce TC structures.
Many GCMs have used finer resolutions than that of
Broccoli and Manabe (1990) to assess the response of
TCs to global warming, indicating some consensus in
the change of TC statistics in a warmer world: decreasein TC genesis frequency, increase in intense TCs, mean
lifetime maximum intensity, and rainfall around TCs

Fig. 11 Size distribution of high clouds showing its changes between present and future climate simulations. Size distribution of high clouds inpresent (dark blue) and future (light blue) climate simulations using the NICAM AMIP-type simulation are analyzed by following the method
described by Noda et al. (2014). Standard variations are designated at each bin

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

(Knutson et al. 2010; Christensen et al. 2013; Walsh etal. 2016). However, Emanuel (2013) showed an increasein global annual frequency of TCs under warmer climate
conditions using a downscaling method in which incipient vortices are embedded into large-scale climate conditions projected by CMIP5 models. To achieve a better
understanding of the relationships between TC genesis
and climate change, the Hurricane Working Group(HWG) established by the US Climate and Ocean: Variability, Predictability and Change (CLIVAR) group coordinated common experiments (Walsh et al. 2015).
Although the downscaling methodology of Emanuel(2013) projected an increase in TC genesis under thewarmer climate condition projected by the HWG
models, there were no models that generated a substantial increase in global TC frequency. The reduction of
global TC numbers is associated with a decrease in upward mass flux (Sugi et al. 2002, 2012), and an increasein saturation deficit of the mid-troposphere (Emanuel etal. 2008). The mechanism underpinning the decrease in
TC numbers, however, remains controversial.
Due to recent advances in supercomputing and
GCMs, fine-resolution models with grid spacing lessthan 25 km have been used to investigate future change
in TC activities (Murakami et al. 2012; Manganello et al.
2014; Knutson et al. 2015; Roberts et al. 2015; Wehneret al. 2015). These models reproduced TC structure
more realistically. The advent of the K computer enabledresearchers to perform long-term simulations using ahigh-resolution global non-hydrostatic model (Kodama
et al. 2015; Satoh et al. 2015).
In this section, future changes in TC activities are investigated using the outputs of the aforementioned
NICAM AMIP-type simulation (referred to as thepresent day (PD) simulation; Kodama et al. 2015) and awarmer climate simulation (referred to as the global
warming (GW) simulation; Satoh et al. 2015). Kodama
et al. (2015) documented the model settings and experimental design of the PD simulation. The model settingsof the GW simulation were identical to those of the PD
simulation, and the experimental design is described in
Satoh et al. (2015).
Results

Figure 12 shows geographical distributions of TC genesis
density for the PD and GW simulations and the futurechange (GW  PD). The PD simulation reproduces theobserved distribution of TC genesis fairly well; although,the model has clear bias particularly over the north Atlantic for less TC genesis (See Fig. 10 of Kodama et al.
2015). The GW simulation showed a salient decrease in
TC genesis over the eastern North Pacific (Fig. 12c).
Satoh et al. (2015) documented the annual global TC
number of 104.0 for the PD simulation and 81.8 for the

Page 16 of 24

GW simulation. The decrement in the TC number due
to global warming is 21.3%, which is within the range ofprevious studies (Knutson et al. 2010; Christensen et al.
2013). Figure 12c shows that TCs are mainly decreased
over the north eastern Pacific.
As the mechanism of the decrement was not completely understood, Satoh et al. (2015) used outputs of
the PD and GW simulations to propose a new concept
based on the relationship between convective mass flux
and TCs. They extracted a convective mass flux associated with TCs and defined its contribution rate toconvective mass flux in the tropics (R) as follows:
R¼

MT;
T int M

where MT is convective mass flux associated with TCs(defined as integration over the area with a radius
smaller than 500 km from the center of TCs), Tint is
the integration period, and M is the total convective
mass flux over the tropical domain (30°N–30°S). Since
MT = NTmT, where N is the number of global TCs, T
is the average lifetime of TCs, and mT is the average
convective mass flux per single TC, we can rewrite
this as (Eq. 15 of Satoh et al. 2015)

N ¼R

T int M:
TmT

Satoh et al. (2015) found that the contribution of TC(R) in GW is almost unchanged or less than that in PD.
The convective mass flux in the tropics (M) generally
decreases in GW (Vecchi and Soden 2007), and our results confirm this change. In the analysis of the TC convective mass flux, Satoh et al. (2015) also found that theconvective mass flux associated with TCs and its fractional area become greater as TCs become more intense(Fig. 13a and b, respectively). They also found that the
TC convective mass flux and its frictional area in GW
are greater than those in PD for the same intensity ofTC (Fig. 13a and b, respectively). These results are contrasted by Fig. 13c, where the area-averaged convective
mass flux, or vertical velocity, does not show a robustchange with warming. Intense TCs increase under awarmer climate, leading to greater convective mass flux
associated with TCs. In these simulations, the incidence
of intense TCs that develop to less than 945 hPa increases by 38.6% due to global warming (Fig. 14). Thus,
Satoh et al. (2015) showed that mT increases as warming.
Assuming that R is unchanged between PD and GW,
this constraint implies that TC number decreases inorder to intensify TCs under a warmer climate.

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

Page 17 of 24

Fig. 12 Geographical distributions of annual TC genesis density. a for the PD simulation for 1979–1998; b for the GW simulation of 2074–2093; c shows
the difference between the GW and PD simulations. TC genesis density is defined as the number of TCs generated in each 2.5° × 2.5° grid box per year

Future directions

Walsh et al. (2016) highlighted the importance of thefollowing topics for future research on TCs and climatechange: detection methods as a source of uncertainty,lack of climate theory of TC formation, confidence indetection and attribution of observed changes in TCs toSatoh et al. Progress in Earth and Planetary Science (2017) 4:13

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Fig. 13 Scatter plots of the TC properties versus the minimum sea-level pressure. Scatter plot represents a the relationship between the minimum sealevel pressure (Min. SLP) and the convective mass flux over the TC domain [A], b the relationship between the min. SLP and the fractional area of thestrong updrafts [B], and (c) the relationship between the min. SLP and the area-averaged convective mass flux [A/B] for all the simulated TCs. The PD
simulation is shown by blue dots and the GW simulation by red dots; blue and red lines show regressions for the PD and GW simulations, respectively. The
convective mass flux and the fractional area of the strong updrafts are accumulated for the lifetime of each TC, so that these have units of kg m-2 and s,
respectively. Only the vertical velocities exceeding the threshold value of 0.5 m s-1 were used. The values at an altitude of 4 km are shown. After Fig. 5 ofSatoh et al. (2015)

date, impact of uncertainty in projections of large-scale
climate fields, and correct simulations of TC frequency
response in high-resolution climate models. It is expected that high-resolution models will be useful tools
for investigating these topics. Thanks to the K computer,we completed the first-ever climate simulation with a
non-hydrostatic GCM using 14 km grid spacing

Fig. 14 Normalized frequencies (%) for each TC intensity from the
PD and GW simulations. The two boxes represent the number of TCs
categorized into six intensities using the lifetime minimum central
sea-level pressure (Pc). The number in each colored box indicatesthe number of TCs categorized in each intensity level(Kodama et al. 2015; Satoh et al. 2015). The outputs
from this work will contribute important new findings
on TC climatology.
Higher-resolution models have simulated more realistic TC climatology (Walsh et al. 2016). However, TC distributions simulated in high-resolution models still show
inconsistency with observed TC activities such as fewer
TCs over the north Atlantic (Kodama et al. 2015;
Murakami et al. 2015; Roberts et al. 2015), more TCs
over the western North Pacific (Manganello et al. 2012;
Murakami et al. 2015) and the eastern North Pacific(Kodama et al. 2015; Roberts et al. 2015), weak maximum wind speed compared with low central sea-levelpressure (Roberts et al. 2015), and an overestimation ofmean TC intensities at higher latitudes (Murakami et al.
2012; Kodama et al. 2015). Reducing these biases will
enhance confidence in GCM simulations of futurechange. For example, Lloyd and Vecchi (2011) investigated the observation that the passage of TCs causes
SST cooling, indicating that this oceanic feedback
suppresses intensification of TCs. Atmosphere-ocean
coupled models will improve the overestimation of TC
intensities at higher latitudes.
In addition, high-resolution models simulate TC structures more realistically. An increase in the convective
mass flux associated with TCs due to global warming is
not caused by an enhancement of updraft velocities(Fig. 13c), but by an increase in the area of the updraft
region around TCs (Fig. 13b). This suggests that global
warming influences the dynamical structure of TCs. The

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

scale of damage caused by a disaster associated with TCs
is measured by TC gale force wind radii as well as its
frequency, intensity, and track. Recent studies have focused on future changes in the size of TCs (Kanada etal. 2013; Manganello et al. 2014; Knutson et al. 2015).
High-resolution global non-hydrostatic models should
also make it possible to statistically evaluate futurechanges of TC structure.
Ohno and Satoh (2015) revealed that the flow fromthe lower stratosphere near the TC eyewall plays a keyrole in constituting the upper-level warm core which develops TC intensity prior to reaching the mature stage.
To increase confidence in this theory, it must be compared with an authentic observation with high temporaland spatial resolution. Today, geostationary satellites canobserve atmospheric wind vectors associated with TCs
in the upper-troposphere with high temporal and spatial
resolution (Bessho et al. 2016). Outputs of highresolution models should be evaluated with such newsatellite data, promoting our understanding of TCs.
Future perspectives
Post-K supercomputer project

Among the outcomes of Field 3 of SPIRE summarized inthis review, the series of studies using the sub-kilometer
global atmospheric simulation (Miyamoto et al. 2013,2015; Kajikawa et al. 2016) are typical examples of “capability computing” using the K computer; that is, a single
complex problem solved in the shortest time possibleusing the maximum computing power available. Although this kind of simulation does not always lead tothe maximum output or outcome, the knowledge gained
has great potential to bolster next-generation research.
In much larger computer system such as the Post-K
supercomputer, problems of this size will be easily handled with various configurations. We can expect to getmuch more information from these supercomputers interms of “capacity computing,” or the solution of smalleror less complex problems using more efficient computing power.
A research strategy similar to that mentioned above
has already been adopted. In the era of the Earth Simulator, Miura et al. (2007a) take a capability computing
approach to demonstrate the ability to represent the
MJO with 3.5 and 7 km grid spacing using NICAM.
Using a large number of ensemble simulations, Miyakawa et al. (2014) statistically confirmed the MJO predictability suggested by Miura et al. (2007a) andsuggested that high-resolution global non-hydrostatic
models like NICAM generally have better skill in MJO
predictability than GCMs. Thus, combining the two
strategies of capability computing and capacity computing may be an effective approach for future researchusing the Post-K supercomputer.

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The project to develop the next-generation flagshipsupercomputer is now underway. The Post-K supercomputer is designed by using homogeneous CPU architecture. The CPU will be a many-core processor, andaccelerators are not included in the system. The system
will consist of a six-dimensional mesh/torus interconnected network (Tofu; Ajima et al. 2009) and a threelevel hierarchical storage system. The co-design is basedon collaboration between computational scientists andcomputer scientists, because a system constructed using
commodity computers along current computer trends
cannot satisfy the socio-scientific demands of all research areas, including earth sciences. NICAM has been
selected as a co-design application from the viewpoint ofa structured stencil calculation. In addition, LETKF has
also been selected in this co-design project as an application requiring the dense-matrix calculation. NICAMLETKF (Terasaki et al. 2015) is one of the most important target applications for the development of the newcomputer system. The current target problem for the
NICAM-LETKF is as follows: in the data assimilation
cycle, 1000 ensembles by NICAM with 3.5 km grid spacing and 100 vertical levels are conducted every 6 h. The
prediction using 440 m horizontal grid spacing is integrated for several months. In terms of the computational
performance, the workflow between the simulation codeand the data assimilation system, including I/O andcommunication in a massively parallel supercomputer
becomes a serious problem. A framework proposed by
Yashiro et al. (2016c), which was designed to reduce data
movement and to utilize parallel I/O, showed a reduction in the workflow’s total elapse time. Based on thescientific outcomes summarized in the present review,
the big data assimilation by NICAM-LETKF using
tremendous volumes of observational data, such as satellite data, is expected to further contribute to the improvement of mid-range predictions around the globe.
Post-K supercomputer studies with NICAM

Following the SPIRE project, the FLAGSHIP2020 project
began in 2014 to examine possible maximum use of the
Post-K supercomputer. Among the research subjects defined for the weather and climate simulation studies,seamless simulations using NICAM have been proposed.
Seamless means beyond a week to seasonal prediction,
especially for the predictability of TC statistics such as
TC genesis, tracks, and intensities. Furthermore, seamless studies cover a year to decadal scales, and even to
the centennial scale, as aimed for by the HighResMIP
study, which is introduced below. Successful extendedrange forecasts of MJO and BSISO (Miyakawa et al.
2014; Nakano et al. 2015) suggest greater predictabilityof TCs beyond a week to a few weeks. Through ensemble simulations, many aspects of TCs such as TC

Satoh et al. Progress in Earth and Planetary Science (2017) 4:13

genesis, tracks, and intensities will be obtained. In
addition, together with the development of the highresolution atmosphere and ocean coupled model(NICAM-COCO), the seasonal scale predictability of
ISO and associated TC characteristics will be explored.
For further multi-year simulation, we will examine thepotential predictability of the near future from a year toa decadal scale, particularly focusing on TC statistics (asdescribed in the “Projection of tropical cyclones”
section). These longer time-scale simulations have also
been examined in the “Atmospheric Model Intercomparison Project-type experiments” section and are further
pursued in the next subsection, “High Resolution Model
Intercomparison Project.”High Resolution Model Intercomparison ProjectAs a part of Coupled Model Intercomparison Project
phase 6 (CMIP6), the High Resolution Model Intercomparison Project (HighResMIP; Haarsma et al. 2016) is
now under way. In this project, climate modeling with ahorizontal resolution of 25–50 km will be performed formore than half a century. The aim is that such fineresolution climate model data will be available for everyone within a few years as CMIP6 continues. Following
the HighResMIP protocol, we plan to perform 56 and
28 km grid spacing NICAM for 1950–2014 and 14 km
grid spacing NICAM for ten years. Currently, a series ofsensitivity experiments are being performed to improveclimatology in the simulation. Our aim in participatingin the HighResMIP is to investigate multi-scale phenomena including tropical and extratropical cyclones, MJO,
tropical waves, convection, and clouds in a seamlessmanner (see Section 7.2 of Haarsma et al. 2016).
Process-based assessment using satellite datasets will
also be key to evaluating and constraining the highresolution climate model.

Conclusions
NICAM-related studies using the K computer under
Field 3 of SPIRE have produced a wealth of valuable
weather and climate modeling research and present clear
challenges that may be solved with the Post-K supercomputer. Key achievements of these studies include
high-resolution sub-kilometer global simulations, ensemble simulations for MJO and BSISO, and simulationsof TC genesis. The longer multi-decadal simulation by
NICAM was conducted for the first time with the global
non-hydrostatic model. The climatology of the 14 km
grid spacing NICAM shows comparable results withother climate models; although, biases similar to thoseof other models exist. The results suggest that, eventhough it has been arguable whether deep convective
parameterization is required for the 14 km grid spacing
model, the simulation without the parameterization

Page 20 of 24

reproduces climatology consistent with observation.
Thanks to the K computer, we obtained considerable evidence of NICAM’s usability for weather and climate simulations. Further research using the Post-K supercomputerin the next decade is expected to contribute even morecrucial knowledge to this exciting research field.
Abbreviations
BSISO: Boreal summer intra-seasonal oscillation; CMIP6: Coupled Model
Intercomparison Project phase 6; ERA-Interim: European Centre for MediumRange Weather Forecasts interim reanalysis; FLAGSHIP2020: Future LAtency
core-based General-purpose Supercomputer with HIgh Productivity;
GCM: General circulation model; GW: The global warming simulation;
HighResMIP: High Resolution Model Intercomparison Project; I/O: Input/output;
ISO: Intra-seasonal oscillations; JAMSTEC: Japan Agency for Marine-Earth Scienceand Technology; LETKF: Local ensemble transform Kalman filter; MJO: MaddenJulian oscillation; NAIM: Northern Australia-Indonesia monsoon; NICAM: Nonhydrostatic icosahedral amospheric model; NOAA: National Oceanic andAtmospheric Administration; NSW6: NICAM single-moment water 6-cloud
microphysics scheme; OLR: Outgoing longwave radiation; PD: The present day
simulation; QBO: Quasi-biennial oscillation; RMM: Real-time multivariate MJO;
SEAM: Southeast Asian monsoon; SPIRE: Strategic Programs for Innovative
Research; SST: Sea surface temperature; TC: Tropical cyclone; TCgenesis: Tropical cyclogenesis; TRMM: Tropical Rainfall Measuring Mission;
WNPM: Western North Pacific monsoon
Acknowledgements
All the simulations analyzed in this study were performed on the K
computer at the RIKEN Advanced Institute for Computational Science(Proposal number hp120279, hp120313, hp130010, hp140219, andhp150213). This study was supported by Strategic Programs for Innovative
Research (SPIRE) Field 3 (Projection of Planet Earth Variations for Mitigating
Natural Disasters), which is promoted by the Ministry of Education, Culture,
Sports, Science and Technology (MEXT), Japan. All the authors acknowledge
Ms. Hisae Tamura for formatting the manuscript and INLEXIO for the English
editing.
Funding
This study was supported by the Strategic Programs for Innovative Research(SPIRE) Field 3 (Projection of Planet Earth Variations for Mitigating Natural
Disasters) and Future LAtency core-based General-purpose Supercomputerwith HIgh Productivity (FLAGSHIP2020 project), which are promoted by the
Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.
Availability of data and materials
The NICAM data simulated by the K computer will be available. The
following three papers are to be submitted or in review process and referred
to as personal communication in the text: Fujita et al. (2017), Kodama et al.
(2017), and Yamada et al. (2017). Please contact the authors for these data
and unpublished manuscripts:
Fujita M, Kodama C, Yamada Y, Noda AT, Nakano M, Nasuno T, Satoh M(2017) Change in precipitation around Japan induced by tropical cyclones as
projected by the NICAM AMIP-type 25-year simulation.
Kodama C, Stevens B, Mauritsen T, Seiki T, Satoh M (2017) Evidence forlarger-than average increases in rainfall from intense cyclones with warming.
Yamada Y, Satoh M, Sugi M, Kodama C, Noda AT, Nakano M, Nasuno T(2017) Response of tropical cyclone activity and structure to a global
warming using a high-resolution global non-hydrostatic model.
Authors’ contributions
MS led the studies using NICAM in SPIRE and FLAGSHIP2020 andcoordinated this review. HY wrote the “Computational aspects” section. YKand YM wrote the “Sub-kilometer global simulation” section, and theexperiments described in this section were carried out by the team at theAdvanced Institute for Computational Science: YM, YK, HY, TY, and HT. YK
and HY wrote the “Asian summer monsoon” section, and HY conducted theexperiments described in this section. TM wrote the “Madden-Julian
oscillation” section and conducted the experiments described in thissection. MN wrote the “Boreal summer intra-seasonal oscillation and tropical
cyclogenesis” section and conducted the experiments described in thisSatoh et al. Progress in Earth and Planetary Science (2017) 4:13

section. CK, ATN, TN, YY, and YF wrote the “AMIP-type experiments” section,
and YY and MS wrote the “Future projection of tropical cyclones” section. CK,
ATN, and YY conducted the experiments described in these two sections.
HT, MS, and CK wrote the “Future perspectives” section. MS is responsible forthe whole manuscript and wrote the “Introduction” and “Conclusions”
sections. All authors read and approved the final manuscript.
Authors’ information
MS and HT started the development of NICAM around the year 2000, andthe NICAM studies have been conducted mainly in these three institutes
since then: Atmosphere and Ocean Research Institute of the University ofTokyo, JAMSTEC, and the Advanced Institute for Computational Science.
Competing interests
The authors declare that they have no competing interests. For more information,
visit the websites, http://nicam.jp/ and http://www.jamstec.go.jp/hpci-sp/en/.
Consent for publication
All the figures in this paper are permitted to be used by the copyrightholders of the original manuscripts.
Ethics approval and consent to participate
Not applicable.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan.
2
RIKEN Advanced Institute for Computational Science, 7-1-26,
Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan. 3ResearchCenter for Urban Safety and Security, Kobe University, 1-1, Rokko-dai,Nada-ku, Kobe 657-8501, Japan. 4Rosenstiel School of Marine andAtmospheric Science, University of Miami, 4600 Rickenbacker Causeway,
Miami, FL 33149, USA. 5Japan Agency for Marine-Earth Science andTechnology, 3173-15, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa
236-0001, Japan. 6Institute for Space-Earth Environmental Research, Nagoya
University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.
Received: 26 September 2016 Accepted: 2 April 2017

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