Journal
August 2015of the Meteorological Society of Japan, Vol.
C.93,
KODAMA
No. 4, pp.et393−424,al.
2015
DOI:10.2151/jmsj.2015-024

A 20-Year Climatology of a NICAM AMIP-Type Simulation
Chihiro KODAMA, Yohei YAMADA, Akira T. NODA
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Kazuyoshi KIKUCHI
International Pacific Research Center, University of Hawaii, Hawaii, USA

Yoshiyuki KAJIKAWA
RIKEN Advanced Institute for Computational Science, Kobe, Japan

Tomoe NASUNO
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Tomohiko TOMITA
Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan

Tsuyoshi YAMAURA
RIKEN Advanced Institute for Computational Science, Kobe, Japan

Hiroshi G. TAKAHASHITokyo Metropolitan University, Hachioji, Japan
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Masayuki HARA
Center for Environmental Science in Saitama, Kazo, Japan

Yoshio KAWATANIJapan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Masaki SATOH
Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japanand
Masato SUGI
Meteorological Research Institute, Tsukuba, Japan(Manuscript received 6 November 2014, in final form 17 March 2015)
Corresponding author: Chihiro Kodama, Department
of Seamless Environmental Prediction Research, Japan
Agency for Marine-Earth Science and Technology, 3173-25
Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan
E-mail: kodamac@jamstec.go.jp©2015, Meteorological Society of Japan

393

394

Journal of the Meteorological Society of Japan

Vol. 93, No. 4

Abstract
A 20-year integration by the nonhydrostatic icosahedral atmospheric model (NICAM) with a 14 km mesh was
conducted for the first time to obtain a climatological mean and diurnal to interannual variability of a simulated
atmosphere. Clouds were explicitly calculated using a cloud microphysics scheme without cumulus convection
scheme. The simulation was performed under the atmospheric model intercomparison project-type conditions,
except that sea surface temperature was nudged toward observed historical values using the slab ocean model.
The results are analyzed with a focus on tropical disturbances, including tropical cyclones (TCs) and the Madden–
Julian oscillation (MJO).
NICAM simulates many aspects of atmospheric climatological mean state and variability. The geographical distributions of precipitation, including interannual, seasonal, and diurnal variations, are well reproduced. Zonal mean
basic states, clouds, and top-of-atmosphere radiation are qualitatively simulated, though some severe biases such as
underestimated low clouds, shortwave reflection, warmer surface, and tropical upper troposphere exist.
TCs and MJO are the main focus of the simulation. In the simulation, TCs are detected with the objective thresholds of maximum wind speed due to the realistic intensity of simulated TCs. The seasonal march of TC genesis ineach ocean basin is well simulated. The statistical property of the MJO and tropical waves is well reproduced in thespace–time power spectra, consistent with previous NICAM studies. This implies that the stratospheric variability isalso reproduced, as partially revealed in this study. Asian monsoon analysis shows that climatological western North
Pacific monsoon onset occurs near the observed onset, and that the Baiu front is reproduced to some extent. Somesignificant model biases still exist, which indicates a need for further model improvements. The results indicatethat a high-resolution global nonhydrostatic model has the potential to reveal multiscale phenomena in the climate
system.
Keywords 	
global non-hydrostatic model; climate simulation; moist process; tropical cyclone; Madden-Julian
oscillation and tropical wave; Asian monsoon

1.

Introduction

Moist processes play a major role in maintaining
the Earth’s climate system. They couple with largescale circulation by redistributing energy, angularmomentum, and water in the atmosphere. For
example, radiation budgets at the top-of-atmosphere(TOA) strongly depend on cloud properties associatedwith moist processes. Moist convection triggers
various types of atmospheric waves, which modify thebasic state of the troposphere and middle atmospherethrough wave-mean flow interaction. Most extreme
disaster events, such as tropical cyclones (TCs) andintense rainfall, are closely associated with moist
convection.
Despite the scientific and social importance ofmoist processes, they are parameterized in mostclimate models, which causes ambiguity and restricts
our ability to adequately simulate the climate system(Stevens and Bony 2013). Most current climate
models are performed with a horizontal resolution ofO (100 km), with cumulus convection parameterized
as a sub-grid scale phenomenon. Cumulus convection
scheme may delay the improvement and reliabilityof present and future climate simulations. This is of
specific importance in TC simulations because theirstatistics strongly depend on the cumulus convec-

tion scheme (Murakami et al. 2012) as well as horizontal resolution (Camargo 2013). Zhao et al. (2012)
demonstrated the sensitivity of global TC frequency
to parameters in convection scheme and dynamics
formulation. Most climate models do not capture theeastward propagation and amplitude of the Madden–
Julian oscillation (MJO) (Hung et al. 2013) — adominant intraseasonal variability in the tropics, andmany authors suggested the relationship between
the cumulus convection scheme and the simulated
MJO (see references in Hung et al. 2013). In addition, uncertainty in climate sensitivity among climate
models is chiefly attributable to the different responseof the clouds to global warming (Cess et al. 1989;
Soden and Held, 2006; Vial et al. 2013; Zhao, 2014).
Increasing spatial resolution is a natural approachto improving climate simulations (Shukla et al. 2009).
Following the atmospheric model intercomparison project (AMIP) (Gates 1992) protocol, Mizuta
et al. (2006, 2012) and Kinter et al. (2013) conductedclimate simulations with a horizontal resolutionof 16–20 km using a cumulus convection scheme.
Another strategy used to treat moist processes in
global climate model is the multiscale modelingframework (MMF) (Grabowski 2001; Khairoutdinov
and Randall 2001), in which two-dimensional (2D)
cloud resolving model is locally operated in place ofAugust 2015

C. KODAMA et al.

the cumulus convection scheme. Khairoutdinov et al.
(2008) conducted an AMIP-style simulation using
MMF and demonstrated its benefits for simulating
the MJO and tropical waves. Such a new type modelis expected to simulate atmospheric phenomena suchas tropical disturbances even more realistically if its
simulated mean state is improved with the consideration of interaction mechanisms between mean stateand phenomena (Inness et al. 2003; Sperber andAnnamalai 2008; Kim et al. 2009).
We conduct the first-ever climate simulation using
a 14 km mesh nonhydrostatic icosahedral atmosphericmodel (NICAM) (Tomtia and Satoh 2004; Satohet al. 2008, 2014) with an explicit cloud microphysics
scheme. Such explicit treatment of moist processes
without a cumulus convection scheme might significantly improve present climate simulation performance and the reliability of future climate projections.
In particular, we investigate the climatological meanstate and diurnal, seasonal, and interannual variability
in a 20-year NICAM simulation to evaluate the simulation performance in reproducing the present climate.
A much finer resolution of NICAM better resolves
individual convection (Miyamoto et al. 2013).
However, according to previous studies using output
from seasonal-scale experiments (Noda et al. 2010,2012; Kodama et al. 2012, and the references therein),
the gross features of the convection and clouds arepractically simulated by the 14 km mesh model.
Holloway et al. (2013) showed that a 12 km meshmodel with an explicit convection scheme reproduced
the MJO well. Pauluis and Garner (2006) investigated
the relationship between horizontal resolution andconvection statistics under the radiative–convective
equilibrium. They suggested that the statistical behaviors of the deep convection and cirrus cloud remain
almost unaffected as the horizontal mesh increases
from 16 to 2 km, though they showed a strong relation between horizontal resolution and shallow
convection. As a milestone toward a true “convection
resolving” climate simulation, it is of great interest toevaluate “convection permitting” climate simulationin an attempt to develop and improve a global nonhydrostatic model with an explicit cloud microphysics
scheme.
Previous seasonal-scale experiments have shownthat NICAM has a significant advantage for simulating TCs and the MJO. NICAM simulates the TC
intensity (Yamada and Satoh 2013) and enables us
to comprehensively discuss the TC structure (Miyamoto et al. 2014). Miura et al. (2007) and Miyakawa
et al. (2014) showed that NICAM sufficiently simu-

395

lates the MJO, which is disreputable for its elusiveness in conventional GCMs. Miyakawa et al. (2014)
conducted a series of NICAM experiments with 54
ensemble members and confirmed the NICAM’s
ability to simulate the evolution of the MJO overapproximately 30 days, whereas most conventional
GCMs cannot provide a useful forecast skill beyond
two weeks (Matsueda and Endo 2011). An adequate
representation of the MJO leads to a better representation of TC genesis that occurs with the MJO, whichis also reproduced in NICAM (Fudeyasu et al. 2008;
Oouchi et al. 2009; Taniguchi et al. 2010). High horizontal resolution simulations also produce better
representation of diurnal precipitation cycles (Satoet al. 2009; Dirmeyer et al. 2011; Noda et al. 2012).
We also expect that NICAM sufficiently simulates
many other phenomena in the climate system. In thisstudy, we subjectively choose several important atmospheric phenomena in addition to TC and the MJO
to evaluate NICAM’s capability to simulate overall
climate over a 20-year period.
The remainder of this paper is as follows. In
Section 2, we describe the experimental design andobservational datasets to be compared with the model
output. In Section 3, the reproducibility of global
basic fields such as precipitation (3.1), radiation andclouds (3.2), zonal mean state (3.3), and surface airtemperature (3.4) are presented. In Section 4, theindividual phenomena results are described, especially TCs (4.1), the MJO and tropical waves (4.2),
the Asian monsoon (4.3), the Baiu front (4.4), diurnal
precipitation cycles (4.5), and lower stratosphericvariability (4.6). Discussion—including future model
improvements—is presented in Section 5. Finally,
conclusions are provided in Section 6. Further analysis of individual topics will be discussed elsewhere.
2.

Method

2.1 Observational datasets
Table 1 shows a list of observational datasets used
for simulation and evaluation. The spatiotemporal
resolutions are consistent between the observed andsimulated data for each figure.
2.2 Experimental design
NICAM (Tomita and Satoh 2004; Satoh et al.
2008, 2014) is a nonhydrostatic atmospheric model
developed for ultra-high resolution simulations. In
this study, the 2012 version (hereafter, NICAM.12)
was used. Table 2 shows the descriptions of thephysics schemes implemented in NICAM.12. Water
vapor, liquid cloud, ice cloud, rain, snow, and graupel

396

Journal of the Meteorological Society of Japan

Vol. 93, No. 4

Table 1. List of observational dataset
Short name

Full nameResolution

CERES

Clouds and Earth’s Radiant Energy System(CERES) Energy Balanced and Filled(EBAF) TOA Edition 2.8 (Ed2.8)

1.0° × 1.0°, monthly-mean

Loeb et al. (2009)

CLAUS

Brightness temperature data from the cloudarchive user service (CLAUS)

0.5° × 0.5°, 3-hourly

Hodges et al. (2000)

CMAP

Climate Prediction Center (CPC) Merged
Analysis of Precipitation

2.5° × 2.5°, monthly-mean

Xie and Arkin (1997)

ERA-40European Centre for Medium-Range
Weather Forecasts (ECMWF) Reanalysis-40

2.5° × 2.5°, 6-hourly

Uppala et al. (2005)

ERA-I

European Centre for Medium-Range
Weather Forecasts (ECMWF) Interim
Reanalysis

2.5° × 2.5°, 6-hourly

Dee et al. (2011)

GPCP

Global Precipitation Climatology Project(version 2.2)

2.5° × 2.5°, monthly-mean/pentadAdler et al. (2003)

HadISST

Hadley Centre Sea Ice and Sea Surface
Temperature dataset

1.0° × 1.0°, monthly-mean

Rayner et al. (2003)

IBTrACS

International Best Track Archive for Climate
Stewardship

ISCCP

International Satellite Cloud Climatology
Project

2.5° × 2.5°, monthly-mean

Rossow and Schiffer (1999)

JRA-25

Japanese 25-year reanalysis

1.25° × 1.25°, 6-hourly

Onogi et al. (2007)

JRA-55

Japanese 55-year reanalysis

1.25° × 1.25°, 6-hourly

Ebita et al. (2011)

NOAA

National Oceanic and Atmospheric Administration (NOAA) Interpolated outgoing
longwave radiation (OLR)

2.5° × 2.5°, daily

Liebmann and Smith (1996)

TRMM-PR

Tropical Rainfall Measuring(TRMM) 2A25 product, version 7

Converted to 0.1° × 0.1°,
monthly with diurnal variation

Iguchi et al. (2000)

Missionwere calculated using a single-moment bulk cloud
microphysics scheme (NSW6) (Tomita 2008), andthe cumulus convection scheme was not used. The
detailed descriptions of NICAM are reviewed by
Satoh et al. (2014).
The horizontal resolution of the model used wasapproximately 14 km (grid division level 9). Thirty-eight vertical levels were used, extending to aheight of 40 km above the sea level. The interval
between each vertical level was increased from 160
m to 1 km below 11 km (25 layers) and to 2 km
below 25 km (additional 9 layers). The sensitivityof a simulated climate such as cirrus and radiation
to the vertical resolution will be discussed in a separate paper (Seiki et al. 2015b). The integration timeinterval was set to 30 s over the first five years, andReference

Knapp et al. (2010)

then 60 s over the remaining term.
The simulation began in June 1, 1978 and was
performed for more than 20 years. In this study, thedataset beginning on January 1, 1979 (after 7-monthspin-up) and ending December 31, 1998 was used forthe analysis, unless otherwise stated. Atmosphericinitial conditions were taken from the ERA-40 (Table1). Monthly mean sea surface temperature (SST) andthe sea ice of the HadISST1 (Table 1) were interpolated to initial time for the slab ocean model. To
reduce land model initial shock, which could lastfor several years, land initial conditions were takenfrom the monthly mean five-year climatology ofthe 220-km mesh NICAM simulation, which was
conducted for 10 years including the five-year spinup.

August 2015

397

C. KODAMA et al.
Table 2. Physics schemes in NICAM. 12

Physics process

Description

Cloud microphysics

NICAM Single-moment Water 6 (NSW6) with top-ofatmosphere (TOA) radiation tuning

Cumulus convection

Not used

Radiation

mstrnX

Sekiguchi and Nakajima (2008)

Turbulence

Mellor-Yamada Nakanishi-Niino (MYNN2)

Nakanishi and Niino (2006);
Noda et al. (2010)

Gravity wave

Not used

Land surface

Minimal Advanced Treatments of Surface Interaction and
RunOff (MATSIRO)

Takata et al. (2003)

Surface flux (ocean)

Bulk surface flux.

Louis (1979); surface roughness
following Moon et al. (2007)
and Fairall et al. (2003)

Ocean model

Single layer slab-ocean

The simulation was performed under the AMIPtype conditions, except that SSTs were nudged toward
Taylor-corrected (Taylor et al. 2000) HadISST1
values using the slab ocean model. The nudging relaxation time was 7 days, and the depth of the slab ocean
model was set to 15 m. These “AMIP-like” configurations were used instead of those following the strict
AMIP protocol because of the better performance ofthe simulation in terms of the geographical distributions of the precipitation (Appendix A) and the
MJO (Grabowski 2006). Sea ice concentration wasconstrained to the HadISST1 values. The interannual
variation of the three-dimensional ozone distribution
was obtained from the Meteorological Research Institute chemical transport model (Shibata et al. 2004)
monthly mean output of the simulation performed
under CCMVal REF2 conditions (Eyring et al. 2008).
For each month, five-year running means wereapplied to obtain ozone data prescribed for the model.
The annual mean concentrations of CO2, N2O, CH4,
CFC11, and CFC12, including interannual variability(Hansen and Sato 2004), were prescribed uniformly
globally. Aerosols and 11-year solar cycles were notconsidered in the simulation.
The model was executed using 640 computing
nodes (i.e., 640 CPUs or 5,120 cores) of the K
computer (Yokokawa et al. 2011; Miyazaki et al.
2012), which comprised 7.7 % of the total system. In
this case, the total elapsed time (excluding queuingtime) is approximately 100 days to achieve the20-year simulation. The actual time for the 20-year

Reference
Tomita (2008)

simulation is approximately 14 months. We did notface any numerical instability throughout the simulation.
3.

Global basic fields

3.1 Precipitation
Figure 1 shows the seasonal climatological meansof the observed and simulated precipitation rates
during June–July–August (JJA), September–October–
November (SON), December–January–February (DJF),
and March–April–May (MAM). The global mean
precipitation rates are quantitatively well simulatedcompared with GPCP (Table 1) observation, but areslightly overestimated compared with CMAP (Table
1) observation. The distributions of zonal mean
precipitation rates and seasonal peak precipitation
migration are also captured. The model reproduces
greater than observed peak precipitation rates near
the intertropical convergence zone (ITCZ). The South
Pacific convergence zone is also captured, though
its south-eastward extension is underrepresented in
the model. A notable double-ITCZ bias exists during
SON. The model overestimates precipitation along
the coast in the Bay of Bengal and over the tropicalIndian Ocean during JJA and underestimates precipitation near the Philippines in both JJA and SON andin the northeastern parts of South America during all
seasons.
In seasonal-scale experiments using the previous
versions of NICAM, simulated global and low-latitude mean precipitation exceeded those produced by398

Journal of the Meteorological Society of Japan

Vol. 93, No. 4

Fig. 1. Seasonal mean climatological precipitation rates in mm day−1 during JJA, SON, DJF, and MAM analyzed
from March 1979 to February 1999 for GPCP (a), NICAM (b), and NICAM − GPCP (c) results. The zonal mean
(ZM) and global mean (GM) precipitation rates for GPCP, CMAP, and NICAM are shown in (d). The zonal
and global mean values were calculated using grid point values at which the monthly mean GPCP, CMAP, andNICAM data were available.

GPCP by 26 % and 27 %, respectively (Noda et al.
2012; Tables 1, 3)1. The reduced global mean precipitation bias in this study compared with Noda et al.
(2012) is consistent with a decrease in global meanTOA OLR (Fig. 2 in Noda et al. 2010; Fig. 3 in thisstudy). According to the seasonal-scale experiments
using NICAM.12 — the version used in this study —the simulated global mean precipitation is greaterthan what is analyzed in GPCP by approximately 10% (not shown), indicating that some of the precipitation bias in the previous study arises from modelspin-up and/or integration term. Another possible
factor that changes simulated global mean radia1Unlike this study, the global mean precipitation in Noda
et al. (2012) was derived without applying latitudinal areal
weight. The definition difference does not affect the conclusion that the bias in the global mean precipitation is reducedin this study compared with that in Noda et al. (2012)tion and precipitation is the model update including
the cloud microphysics scheme (Grabowski 1998/
NSW6), the land model (bucket model/MATSIRO),
and ocean model (fixed SST/slab ocean model). The
update of the ocean model configurations, which
improves geographical precipitation distribution bias,
is discussed in Appendix A.
In the tropics, the El Niño Southern Oscillation(ENSO) mostly governs the interannual variabilityof the precipitation distribution. Figure 2 shows
precipitation response to ENSO. In general, precipitation variation is reasonably captured in response
to ENSO-induced SST variation; both the observation and the model show more precipitation over thecentral to eastern Pacific and less precipitation over
the Maritime Continent during the El Niño phase. In
NICAM, positive precipitation anomalies are biased
eastward with greater amount, and negative anomalies
in the northern hemisphere (NH) subtropics are moreAugust 2015

C. KODAMA et al.

Fig. 2. Precipitation response to El Niño Southern Oscillation (ENSO) during 1979–1998 for GPCP (a) andNICAM (b) results. The monthly mean precipitation rate in mm day−1 was regressed on ENSO index, which isdefined as the departure of the NINO3 (150°W–90°W, 5°S–5°N; black box) mean SST in K from its monthly
mean climatological value, and the regression coefficient is shown.

Fig. 3. Annual mean TOA OLR and OSR and longwave and shortwave cloud radiative forcing (LWCRF and
SWCRF, respectively) in W m–2. Positive cloud radiative forcing denotes warming effect for the climate system.
CERES (Table 1) data averaged from 2001 to 2012 are shown in (a), and NICAM data averaged from 1979 to
1998 are shown in (b). ZM and GM values for each dataset are shown in (c).

399

400

Journal of the Meteorological Society of Japan

Vol. 93, No. 4

Fig. 4. Annual mean ISCCP daytime cloud amount (%) for high, middle, and low clouds averaged from 1984 to
1998. The satellite observations are shown in (a), and NICAM with ISCCP simulator results are shown in (b).
ZM and GM values are shown in (c), which were obtained using grid point values at which all the monthly meansatellite data were available.

prominent. The mean state biases (Fig. 1) seem to berelated to some of the variability biases such as low
variability around the Philippines and high variability
along the coast in the Bay of Bengal and over tropicalIndian Ocean.
3.2 Radiation and clouds
Figures 3a and 3b show the annual mean TOA
outgoing longwave radiation (OLR) and outgoingshortwave radiation (OSR). Overall, the distributionsof the 2D and zonal mean OLR are well simulatedby the model, capturing the position of OLR maxima
in the subtropics and OLR minima in the tropics.
The simulated OLR is in good agreement with theobserved values in the extratropics but lower thanobservation in the tropics. This leads to the lower
global mean OLR bias of 3.47 W m−2. OSR is simulated in the tropics but underestimated in the extratropics. Consequently, the simulated global mean
OSR is lower than observed values by 7.92 W m−2.

Such OLR and OSR biases at the TOA are mostly
contributed by longwave cloud radiative forcing(LWCRF) and shortwave cloud radiative forcing(SWCRF), as shown in Figs. 3c and 3d. The global
mean LWCRF and SWCRF simulated by the model
exceed observations by 1.97 and 7.41 W m−2, respectively, and these biases are mainly contributed bylower latitude LWCRF and higher latitude SWCRF.
Figure 4 shows the annual mean climatology of
ISCCP (Table 1) cloud amount for high, middle, andlow clouds. The ISCCP simulator (Klein and Jakob
1999; Webb et al. 2001) was used to estimate the
ISCCP cloud amount in the model. The model qualitatively reproduces the geographical distributions ofglobal clouds. High clouds are frequently observed in
the tropics and midlatitudes, and the model captures
this to some extent. The model quantitatively overestimates high cloud amount at most latitudes, whichleads to the LWCRF bias in the tropics (Fig. 3). In theextratropics, the magnitude of the positive LWCRF

August 2015

C. KODAMA et al.

401

Fig. 5. Cloud amount response to ENSO during 1984–1998 for satellite (left) and NICAM (right) results. The
monthly mean high (top) and low (bottom) cloud amounts in % were regressed on ENSO index, respectively, andregression coefficients are shown.

bias is small despite the significant overestimationof high cloud amount. This may be attributable to anunderestimation of middle cloud (as described next)
as well as lower sensitivity of the LWCRF on highcloud amount in the extratropics compared with thatin the tropics. Note that observed ISCCP polar cloudis highly uncertain (Rossow and Schiffer 1999), so
the model bias is unclear there. Low and middle cloudamounts simulated by the model are significantly
underestimated compared with observations, though
their geographical distributions are qualitatively simulated. The underestimation is most prominent overthe continent, near the west coast, and in storm track
regions where the simulated negative SWCRF tends
to be lower than observations (Fig. 3).
Compared with typical CMIP5 models (Wangand Su 2013), global mean OSR bias simulated by
NICAM is more significant in association with thedeficit in low and middle cloud amounts. In contrast
to this study, Noda et al. (2010) showed that the low
cloud amount was overly simulated in the extratropics
using the previous version of NICAM. The sensitivityof cloud amount and radiation to the model configurations such as cloud microphysics scheme, horizontal
and vertical resolutions, and several tuning parameters are being investigated intensively to improve themodel’s climate.
Figure 5 shows low and high cloud amount
responses to ENSO. The interannual variability ofhigh cloud amount due to ENSO resembles that of
precipitation (Fig. 2). Both the observation and themodel show more high cloud amount over the trop-

ical central to eastern Pacific and less over the tropical western Pacific during the El Niño phase. The
model simulates weaker negative anomaly of highcloud amount around the equatorial western Pacificcompared with the observation. The basic characteristics of the ENSO-induced low cloud variability
are also captured by the model; low cloud amountis decreased (increased) over the tropical central toeastern (western) Pacific due to ENSO. The simulated ENSO-induced low cloud variability is weakerin amplitude than the observation over the tropicalwestern Pacific.
3.3 Zonal mean basic states
Figures 6 and 7 show the zonal mean temperatureand zonal wind during JJA and DJF. Temperature andzonal wind structures, including seasonal and hemispheric differences, are simulated. Warm bias is foundin the tropical upper troposphere compared to JRA-55
reanalysis (Table 1), i.e., simulated tropical static
stability is greater. It is related to the excess of highcloud (Fig. 4, bottom) due to the treatment of cloud
ice removal processes in cloud microphysics scheme(Seiki et al. 2015a). During JJA, warm bias is also
found in the NH extratropical troposphere. The simulated tropospheric westerly jets (Fig. 7) are stronger
and/or located more poleward relative to reanalysis
results, part of which is associated with the lack oforographic gravity wave drag scheme in the simulation (McFarlane 1987). In the stratosphere, the simulated polar night jets are located more equatorward,
and the easterly jets in the summer hemisphere are402

Journal of the Meteorological Society of Japan

Vol. 93, No. 4

Fig. 6. Seasonal climatology of the zonal mean temperature in K for JRA-55 (a) and NICAM (b) results calculated during JJA (1979–1998) and DJF (1979/80–1998/99), respectively. The vertical axis shows pressure in hPa.
Colored regions in (b) show anomalies from JRA-55. Anomaly exceeding 8 K is denoted as purple contour withan interval of 2 K.

weaker than those in JRA-55.
Figure 8 shows Lagrangian-mean meridional circulations derived using mass-weighted isentropic zonalmeans (Iwasaki 1989). There are two major direct
circulations: the Hadley circulation in the tropics andthe extratropical circulation associated with baroclinic
instability in the winter hemisphere. The simulated
strength and width of the Hadley and extratropicalcirculations are comparable to JRA-55 results. During
DJF (Fig. 8, bottom panels), the upwelling branch of
the simulated Hadley circulation is located slightly
southward, consistent with the zonal mean precipitation (Fig. 1). The Hadley circulation mass stream
function has double extrema in JRA-55 results but asingle extremum in NICAM results. Such differences
seem to be within the range of uncertainties among
reanalysis datasets. The strength and shape of the
Hadley circulation strongly depend on reanalysis datasets (Nguyen et al. 2013; Iwasaki et al. 2009).

3.4 Surface air temperature
Figure 9 shows the surface air temperatures during
JJA and DJF. The simulated global mean surface airtemperature is higher than that in JRA-55 by 0.72 K
in JJA and 0.49 K in DJF. Such a warm bias mainly
occurs over the continents, and is usually greater insummer than in winter. Significant warm bias is foundover the Eurasian and North American continents insummer. The warm bias persists over the South American and South African continents, and is consistent
with the under-simulated continental low and middle
cloud amounts (Fig. 4) and the associated deficit innegative SWCRF (Fig. 3). With the exception ofGreenland, the model simulates warmer arctic wintersurface air temperatures (Fig. 9, bottom panels). SST
drift from prescribed boundary conditions (AppendixA) causes ocean surface air temperature bias but by asmaller amount relative to the continental temperature
bias.

August 2015

C. KODAMA et al.

403

Fig. 7. Seasonal climatology of the zonal mean zonal wind in m s−1 for JRA-55 (a) and NICAM (b) results calculated during JJA (1979–1998) and DJF (1979/80–1998/99), respectively. The vertical axis shows pressure in hPa.
Colored regions in (b) show anomalies from JRA-55.

4.

Individual phenomena

4.1 Climatological tropical cyclone activity
It is not easy for most climate models to adequately
simulate the TC intensity and structure (Emanuel2013). Though a climate model with a horizontal resolution of O (100 km) frequently reproduces geographical distributions and seasonal march of TCs, it tends
to simulate much weaker than observed intensity
and larger than observed size (Camargo 2013). Even
25–50 km mesh AGCMs cannot simulate intense(category 3–5) TCs despite the successful simulations of TC frequency (Zhao et al. 2009; Robert et al.
2015). In such cases, statistical downscaling approach(Zhao et al. 2010) may be useful to investigate thehistorical and future TC intensity. Recent studies haveshown that AGCMs with a finer horizontal resolution,such as 10–20 km, reproduce more realistic TC intensity histograms (Murakami and Sugi 2010; Manganello et al. 2012; Manganello et al. 2014). However,the simulated TC intensity also strongly depends onthe cumulus convection scheme (Murakami et al.

2012). As mentioned previously, no cumulus convection scheme was used in this study.
Here, TCs simulated by NICAM were analyzed
as follows. First, TCs were directly detected using
six-hourly mean-sea-level pressure data. They werethen tracked following the method detailed by Sugi
et al. (2002) and Oouchi et al. (2006) with a duration
criterion of 36 h. The maximum wind speed thresholdof 17.5 m s−1 was objectively determined by thehorizontal grid interval (Walsh et al. 2007). The TC
detection and tracking algorithms and criteria do notdepend on the ocean basin. We employed best trackdatasets produced by the National Hurricane Center(North Atlantic and eastern North Pacific; Jarvinen
et al. 1984) and the Joint Typhoon Warning Center(western North Pacific, North Indian Ocean, andsouthern Hemisphere; Chu et al. 2002), and thesedatasets were unified in the IBTrACS (Table 1).
Figures 10a and 10b show the geographical distributions of TC genesis. The TCs detected in the
IBTrACS and NICAM per one year are approximately
82.4 and 81.8, respectively. Observed and simulated

404

Journal of the Meteorological Society of Japan

Vol. 93, No. 4

Fig. 8. Seasonal climatology of the zonal mean mass stream function in 1010 kg s−1 for JRA-55 (a) and NICAM(b) results calculated during JJA (1979–1998) and DJF (1979/80–1998/99), respectively. The vertical axis showspressure in hPa. Positive values indicate clockwise circulation.

Fig. 9. Seasonal mean climatology of 2 m temperature in °C for JRA-55 (a) and NICAM − JRA-55 (b) calculated
during JJA (1979–1998) and DJF (1979/80–1998/99), respectively.

August 2015

C. KODAMA et al.

Fig. 10. Annual climatology of TC genesis density, TC track density, and mean TC intensity during 1979–1998.
Observed values are shown in (a), (c), and (e), and NICAM simulations are shown in (b), (d), and (f). Dashedlines denote ocean basins. TC genesis and track density represent the number of TCs generated in and passing
through each 2.5° × 2.5° grid box per month. Mean TC intensity is defined as the maximum wind speed in m s−1
averaged over all the TCs passing through each 2.5° × 2.5° grid box.

405

406

Journal of the Meteorological Society of JapanTC genesis distributions are usually similar. The
number of TCs in both the IBTrACS and NICAM isin good agreement over the western North Pacific,
Indian, and South Pacific oceans. However, NICAM
produces less TCs over the North Atlantic Ocean andmore from the eastern to central North Pacific Ocean.
In addition, Figures 10a and 10b show that the equatorward edge of the TC genesis region in the simulation is closer to the tropics than observation by a few
degrees in latitude.
Figures 10c and 10d show the TC track density
distribution. Over the Indian and South Pacificoceans, the TC track density is fairly well simulated.
Over the western North Pacific Ocean, though the
distributions of the TC track density are reasonably
captured in the simulation, more TCs over the western
North Pacific Ocean tend to move toward East Asia
(from Taiwan to Japan) compared with the observations. The greater numbers of simulated TCs move
westward across the date line in the North Pacific,
consistent with high TC genesis in the central/eastern
North Pacific region. Focusing on the North Indian
Ocean, TC occurrences are less than observation
over the Bay of Bengal and more over the ArabianSea, which is similar to that in Zhao et al. (2009) and
Murakami et al. (2012) but dissimilar to Manganello
et al. (2012) and Roberts et al. (2015).
Figures 10e and 10f show the mean TC intensity.
The simulated TC intensity, on average, is comparable to observations. As with genesis and track densities, the mean TC intensity over the South Pacificand South Indian oceans are relatively well simulated. Overall, the simulated mean TC intensity tends
to be higher than the observations, especially in thehigher latitudes. However, an exception is found in
the central/eastern North Pacific Ocean, where, on
average, a greater number of the simulated TCs tends
to move westward.
Figure 11 shows the seasonal march of TC genesis
in the six typical ocean basins. The number of simulated monthly TC genesis closely resembles observations in most ocean basins. Both the mean andmagnitude of the interannual variations in monthly
TC genesis are well reproduced in the simulation.
Over the North Indian Ocean, the two observed local
TC genesis maxima are captured by the model, but thetiming of the simulated maximum in early summer isslightly earlier. Over the North Atlantic and eastern
North Pacific, the model attempts to reproduce active
and inactive TC seasons, though the seasonal cycle isshifted toward a later season.
NICAM captures the behaviors of TCs with objec-

Vol. 93, No. 4

tively determined thresholds of maximum windspeed due to the intensity of simulated TCs. In particular, the reproducibility of the seasonal march of TC
genesis is encouraging. TC biases are also found; few
genesis in the North Atlantic, more moving from theeastern to the Central Pacific, and the delay of theseasonal cycle of TC genesis over the North Atlantic
and eastern North Pacific. These may be related tothe biases in basic fields, the use of the slab ocean
model, cloud microphysics scheme configuration,
horizontal resolution, etc. Serra et al. (2010) foundthat the strength of the Caribbean low-level jet is positively (negatively) correlated with the TC frequency
over the eastern North Pacific (North Atlantic). Therefore, we speculate that the TC genesis biases over
the North Atlantic and eastern North Pacific in the
NICAM simulation are related to the stronger Caribbean low-level jet (not shown) associated with more
active ITCZ convection (Fig. 1) over the tropicaleastern North Pacific. Robert et al. (2015) recently
found that the simulated TC track becomes more
frequent over the Central Pacific as horizontal resolution is increased from 130 to 25 km (resolution at
midlatitude). The lack of SST reduction by upwellingassociated with strong surface winds, which seems
to be favorable for maintaining TC intensity, may
primarily contribute to the higher intensity bias inhigher latitudes.
4.2 MJO and convectively coupled equatorial waves
This section addresses tropical intraseasonal (i.e.,
up to 90 days) variability with particular focus on
two components: 30–90 day variability, often referred
to as the MJO (Madden and Julian 1971, 1972) oras the tropical intraseasonal oscillation (ISO) andthe other so-called convectively coupled equatorial
waves (CCEWs) (Kiladis et al. 2009). Together they
are responsible for a substantial portion of the tropical intraseasonal variability in various fields such as
precipitation and winds.
Since the detailed analysis of the statisticalbehavior of ISOs will be addressed in another study(Kikuchi et al. in revision), we summarize the results.
Kikuchi et al. (in revision) made a detailed assessment of the performance of NICAM in reproducing
the ISOs in the context of the bimodal ISO representation concept proposed by Kikuchi et al. (2012).
Many fundamental features of the ISOs such as theevolution of cloudiness patterns and seasonal cycles
are relatively well reproduced, although the simulated ISOs are weaker in amplitude than observations
by a factor of approximately 2. We show the essen-August 2015

C. KODAMA et al.

407

Fig. 11. Monthly mean climatological number of TC genesis and its standard deviation in each ocean basin definedin Fig. 10. IBTrACS results are shown in black, and NICAM results are shown in blue.

tial features of the simulated ISO in a simpler mannerwith the results that are consistent with Kikuchi et al.
(in revision).
Figure 12 shows the time against longitude sectionsof a lagged correlation of 25–90 day OLR anomalies
during boreal winter when eastward propagation is
pronounced (Kikuchi et al. 2012; Kiladis et al. 2014).
Although the simulated ISO amplitude is weaker by afactor of approximately 2, which is in agreement withthe results of Kikuchi et al. (in revision), NICAM
reproduces the lifecycle of the ISO convection relatively well: maximum intensity over the central Indian
Ocean, slowdown in phase speed to the east and westof the Maritime Continent, and weakening in amplitude over the Maritime Continent.
NICAM also captures the statistical properties of
the CCEWs well. Figure 13 shows the normalized
space–time power spectra. Here we followed themethod developed by Wheeler and Kiladis (1999) to
calculate the normalized spectra. The observation andsimulation show significant spectral peaks along thedispersion curves corresponding to Matsuno’s (1966)
solutions of the shallow water equations on an equatorial beta plane with equivalent depths of 12–50 m(Kiladis et al. 2009), including Kelvin, equatorial
Rossby, n = 1 westward inertia–gravity, and mixedRossby gravity (MRG) waves and n = 0 eastward
inertia–gravity (EIG) waves. However, simulated
MRG and n = 0 EIG wave peaks are less significant,and their equivalent depths seem slightly deeper. The
detailed analysis of the CCEWs will be conducted infuture to explain the similarities and differences inspectral characteristics between the observations and
NICAM simulation, especially from a nonstationarity
viewpoint (Kikuchi 2014).
4.3 Seasonal cycle of Asian summer monsoon
Asian monsoon characterizes the seasonal cycleof the Indo-Pacific domain. The Asian monsoon isdivided into several sub-systems, including Indian,western North Pacific, and East Asian monsoons suchas Baiu (Section 4.4), and their stepwise seasonal

408

Journal of the Meteorological Society of Japan

Vol. 93, No. 4

Fig. 12. Lagged regression of 25–90 day OLR anomalies averaged over 7.5°S and 7.5°N against 25–90 day OLR
anomalies over the equatorial Indian Ocean (85–95°E, 5°S–5°N) during boreal winter (DJF). NOAA (Table 1)
OLR values are shown in (a), and NICAM results are shown in (b). Note that the contour levels for (a) and (b)are different, as indicated in the color bars. The solid line represents a reference phase speed of approximately 3.6
m s−1.

evolution and mutual connections have been intensively investigated (Murakami and Matsumoto 1994;
Wang et al. 2001, 2004; Wang 2006). This section
discusses the reproducibility of the climatology andinterannual variation of the Asian summer monsoon.
In particular, the monsoonal circulation over thewestern North Pacific, which is closely related to tropical cyclogenesis in summer (Holland, 1995; Ritchieand Holland 1999; Yoshida and Ishikawa 2013), ishighlighted.
The Asian summer monsoon typically appears in
the low-level wind fields as a response to the warming
over the Eurasian continent. Figure 14 shows theJune–September (JJAS) mean climatology of the 850
hPa wind field in the JRA-25 reanalysis (Table 1)
and the NICAM simulation. Westerly winds over thetropical Indo-Pacific domain, connected to the Somali
jet along the eastern coast of Africa extending to thePhilippine Sea, is reproduced in the model (Figs.
14a, b). The westerlies form part of a cyclonic circulation over the continent, and their simulated magnitudes along the peak axis are comparable to JRA-25
results. This is a significant improvement over theeight-year boreal summer hindcasts (Satoh et al.
2012), which used previous versions of NICAM with
7 km mesh; the monsoonal westerlies were poorly
maintained in those cases. Model biases are also
found in this study. For example, the northward

displacement of the westerly axis occurs, which leads
to easterly biases over the equatorial Indian Ocean,and the northwestern Pacific anti-cyclone is displaced
northward with higher mean intensity (Fig. 14c). The
biases of weaker westerlies over the equatorial Indian
Ocean and a stronger North Pacific subtropical high
(NPSH) were also detected in Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel
ensemble mean (Sperber et al. 2013). In the present
simulation, the biases can be attributed to the warmer
surface air temperature over the land during boreal
summer (Fig. 9b). The northward error in the latitudes of the westerly axis leads to excessive Himalayan precipitation (Fig. 14c). The annual cycle of
the western North Pacific Monsoon Index (WNPMI),
defined by the meridional shear of zonal wind at 850
hPa averaged in the tropical (100–130°E, 5–15°N)
and subtropical (110–140°E, 20–30°E) subdomains(Wang et al. 2001), is presented in Fig. 14d. The
tropical subdomain includes the South China Sea(SCS), and the WNPMI corresponds to the convective activity over the SCS and Philippines with associated wind fields. The 20-year climatology of theannual cycle is reproduced in the simulation with a
0.83 correlation coefficient with JRA-25 results (Fig.
14d). Note that the climatological monsoon onset (i.e.,
WNPMI > 0, followed by continuous positive values)in mid-June is well captured, as well as a sharpAugust 2015

C. KODAMA et al.

409

Fig. 13. Zonal wavenumber–frequency power spectrum estimates of CLAUS (Table 1) (top) and NICAM (bottom)
brightness temperature (Tb). CLAUS results were calculated from July 1983 to June 2006, and NICAM resultsfrom 1979 to 1998 for the equatorially symmetric (left) and antisymmetric (right) components. Each spectrum
was averaged between 15°S–15°N and normalized by a red noise background power spectrum. Solid lines denote
dispersion curves for the Kelvin, n = 1 equatorial Rossby, n = 1 and n = 2 westward inertia–gravity, n = 0 eastward inertia–gravity, and mixed Rossby-gravity waves with equivalent depths of 8, 12, 25, 50, and 90 m.

increase in the WNPMI in mid-May, followed by atentative reduction for several weeks. The negative
bias in the WNPMI during July–October is due to thenorthern displacement of the westerly axis (Fig. 14c).
The 20-year time series of the normalized WNPMI
during boreal summer also shows general agreement

between JRA-25 and NICAM results (Fig. 15a). Close
examination finds that the interannual variation during
the latter half of the analysis period is better reproduced than those in the former period. Such reproducibility difference does not correspond to the ENSO.
Because the low-level circulation is closely associated410

Journal of the Meteorological Society of Japan

Vol. 93, No. 4

Fig. 14. 850 hPa zonal wind and wind vectors for JRA-25 (a), NICAM (b), and NICAM − JRA-25 (c) results averaged during June–September (JJAS) (1979–1998). Colored regions represent the zonal wind, and arrows represent wind vectors. Colored outlines in (c) show NICAM − GPCP in precipitation rates with green representing 2, 5,
and 10 and gray −2 and −5 mm day−1. A time series of the western North Pacific Monsoon Index (WNPMI) (Wanget al. 2001) for JRA-25 (black) and NICAM (red) is shown in (d) using the 5-day running mean. Purple boxes in (a)–(c) indicate WNPMI subdomains.

with convective activity, the interannual variationsof observed and simulated OLR anomalies over thewestern North Pacific domain (110–160°E, 0–25°N)
were also examined (Fig. 15b). The time series of the
WNPMI and OLR anomalies are negatively correlated(at a correlation coefficient of −0.67 in the observation and −0.65 in the model), and the WNPMI isbetter simulated when the OLR anomalies (Fig. 15b)
are better reproduced. Lau et al. (1997) argued thatthe anomalous convection in the tropics is related tolarge-scale circulation rather than local SST on interannual timescales. Further model evaluation of thebasin-scale SST anomalies and convective responses,
including the remote effects, is warranted to improvethe model performance.
4.4

Baiu front as a boundary between the tropics
and mid-latitudes
The Baiu front (BF) is a part of the large-scale

Asian summer monsoon (Murakami and Matsumoto
1994; Wang and LinHo 2002). It stays near Japan
during June and July, and sometimes causes serious
natural disasters such as drought or torrential rain in
East Asia. The BF is defined as a boundary between
the air masses of the tropics and mid-latitudes and issuitable to evaluate the dynamical and thermodynamical performance of NICAM at the edge of the tropics.
This section identifies the differences in the spatial
patterns (Fig. 16) and the time progress (Fig. 17) of
the BF between objective analysis data (JRA-25,
GPCP) and NICAM.
The air masses are classified by temperature andhumidity. Therefore, this study used the lower tropospheric (925–700 hPa) equivalent potential temperature (<θe>), which was estimated using temperature as well as humidity. The meridional gradient of<θe> (−∂<θe>/∂y) was then employed to detect theboundary of the air masses, i.e., the BF. A 330 K <θe>August 2015

C. KODAMA et al.

411

Fig. 15. Interannual variations of WNPMI (a) and OLR (b) averaged during JJAS normalized by the variances ofeach time series. JRA-25 (black) and NICAM (red) values are shown in (a). NOAA (Table 1) (black) and NICAM(red) values, calculated between 110–160°E and 0–25°N, are shown in (b).

contour was also used to determine the boundary
between the tropical and mid-latitude air masses in the
East Asia (EA)–western North Pacific (WNP) sector(Saito 1966), although this position is different fromthe BF estimated using −∂<θe>/∂y (Fig. 16a). See
Tomita et al. (2011) for the methodology details usedin this section.
Figure 16 exhibits the climatological mean fieldsof <θe> and −∂<θe>/∂y (upper panels) and precipitation and vertically integrated water vapor flux (lower
panels) during the Baiu season near Japan (May 31–
July 29). The period of which was estimated basedon the GPCP data. It is known that the precipitation
corresponds with the convergence of the verticallyintegrated water vapor flux (Chen et al. 2012). In
the fields of <θe> and –∂<θe>/∂y, the climatological
BF in JRA-25 extends along the latitudes of Japanin the EA–WNP sector (Fig. 16a), while it is weak
and has already reached near 40°N in NICAM (Fig.
16b, thick solid line). In this period, the monsoon air
mass (MoAM) and heating in East Asia are stronger
in NICAM than in JRA-25. In particular, <θe> ishigh in the zonal band between 40°N and 50°N in the
EA–WNP sector (Fig. 16c), which seems to be related
to the northward displacement and stronger intensity of the westerly winds over the EA–WNP sector,
as mentioned in Section 4.3. The higher <θe> in thiszonal band makes the BF weaker and the latitude

412

Journal of the Meteorological Society of Japan

Vol. 93, No. 4

Fig. 16. Climatological (1979–1998; 20 years) mean fields of lower tropospheric (925–700 hPa) equivalent potential temperature (<θe>) and −∂<θe>/∂y in K and 10−5 K m−1 respectively, averaged during the Baiu season near
Japan (May 31–July 29) for (a) JRA-25, (b) NICAM, and (c) NICAM − JRA-25. Colored regions indicate <θe>,and gray contours indicate –∂<θe>/∂y. The contour interval is 0.3 × 10−5 K m−1. In (a) and (b), only regions larger
than 0.3 × 10−5 K m−1 are contoured, while dotted contours in (c) identify the regions with a negative −∂<θe>/∂y.
A thick solid line is drawn on the 330 K <θe> in (a) and (b). In the lower panels, the corresponding climatological mean fields of precipitation (mm day–1) and vertically integrated (300–1000 hPa) water vapor flux (kg m–1s–1) are shown for (d) GPCP and (e) NICAM. The differences ((e) – (d)) are shown in (f), where the divergenceof water vapor flux (10–5 kg m–2 s–1) is also depicted by contours. The colored regions show precipitation, and thevectors show the vertically integrated water vapor flux. The scale vector is placed at the bottom center.

higher. We also found higher <θe> along 20°N in the
WNP in NICAM (Fig. 16c).
The corresponding precipitation fields (Figs. 16d–f)
support the above results in <θe> and −∂<θe>/∂y(Figs. 16a–c). Even larger precipitation is observed
along 20°N in the WNP in NICAM (Fig. 16e), whichindicates that the WNP summer monsoon (WNPSM)
leading the end of Baiu season (Tomita et al. 2011)
has already started during this period. The vectorsof water vapor flux also demonstrate the northward
expansion of NPSH in NICAM (Fig. 16e). The differences (Fig. 16c) show that the Baiu season near Japan
has already ended in May 31–July 29 in NICAM withthe onset of WNPSM, the northward expansion of
NPSH, and the northward shift of the BF. The diver-gence in water vapor flux is strong in NICAM in the
EA–WNP sector (Fig. 16f). In the following, we will
examine the temporal development of the BF in theobjective analysis data and NICAM.
Figure 17 shows the differences in the northward movement of the BF near Japan (−∂<θe>/∂yaveraged over 125–145°E), which determines the
beginning and ending of the Japanese Baiu season.
The beginning occurs when the BF reaches 30°N,
and the ending occurs when it reaches 40°N, whichcorrespond to June 2 and July 31, respectively, in
the JRA-25 reanalysis (Fig. 17a; Tomita et al. 2011).
During this period, the relatively fast northwardadvance occurs in June in the JRA-25 reanalysis,
while the speed slows in July. At the end of July, theAugust 2015

C. KODAMA et al.

413

Fig. 17. Climatological mean time series of maximum –∂<θe>/∂y latitude averaged between 125°E and 145°E(stepwise solid line; left axis), <θe> averaged from 125°E to 145°E and 30°N to 40°N (smoothed dotted line;
right axis), and <θe> averaged from 125°E to 145°E and 20°N to 30°N (smoothed dashed line; right axis).
JRA-25 results are shown in (a), and NICAM results in (b). Left axis shows maximum –∂<θe>/∂y latitude in
degrees north, and the 30°N and 40°N latitudes are indicated as horizontal solid lines. Right axis shows <θe> in K,
and the 335 K <θe> is indicated as the horizontal dashed line.

BF advances to 45°N, which marks the end of theJapanese Baiu season.
The NICAM climatological BF progresses northward much faster during the earlier and shorter
period from early May to the end of June (Fig. 17b)
compared with the JRA-25 BF. This seems to becaused by the faster and stronger northward expansion of the MoAM (Figs. 16a, b) and stronger surface
heating in the eastern part of the Asian continent (Fig.
16c). Note that time progression pattern in NICAM(Fig. 17b) and JRA-25 (Fig. 17a) is slightly similar.
In particular, the rapid northward advance occurs in
the early period, then the BF remains near 35°N laterin the period, and then finally jumps and passes 50°N
at the end of the period. Thus, the correction of the
MoAM northern expansion in East Asia and of thefaster and stronger higher latitude heating over the
Asian continent could improve the NICAM BF reproduction.
4.5 Diurnal precipitation cycle
This section summarizes the climatological diurnal
precipitation cycle over the Southeast Asian monsoon
region, the Maritime Continent, and the Central
America during boreal summer. The diurnal precipitation cycle simulation over the Maritime Continent and Asian monsoon regions was difficult whena GCM with a cumulus convection scheme (Dai and
Trenberth 2004; Dai 2006; Hara et al. 2009; Loveet al. 2011) was used. Previous studies suggested
that NICAM has an advantage in reproducing diurnal
precipitation cycles (Sato et al. 2009; Dirmeyer et al.
2011; Noda et al. 2012). Sato et al. (2009) performed

NICAM for one month with 14, 7, and 3.5 km resolution meshes and showed that the diurnal peak inprecipitation becomes more realistic over land as thehorizontal resolution increases. Dirmeyer et al. (2011)
showed that high resolution models including updated
NICAM have simulated nocturnal precipitation overa continent, for example, precipitations along thesouthern slope of the Tibetan Plateau. However, evenin their project, the reproducibility of the nocturnal
precipitations over the Southeast Asian monsoon
region and the Maritime Continent is still a challenging issue. As we have seen, simulated precipitation climatology is improved, which can also improve
the nocturnal precipitations. In addition, the analysis of the characteristic nocturnal precipitation overCentral America, which propagates offshore, offers agood example to examine how “20-year” experiment
improves diurnal precipitation signals. Though diurnal
precipitation cycle over the Central America wassimulated in the NICAM monthly and season-scale
experiments (Sato et al. 2009; Noda et al. 2012), its
signal-to-noise ratio seems to be weak, possibly due
to its shorter analysis period.
The diurnal precipitation cycle is well simulated in
the NICAM climate simulation (Fig. 18). The diurnal
precipitation signals around the Tibetan Plateau,Southern China, and North America (not shown) were
simulated, which was similar to the previous studies(Dirmeyer et al. 2011). Note that the simulated diurnal
peaks in precipitation correspond to those observedby the TRMM-PR (Table 1) over the Asian monsoon
and Maritime Continent regions (Fig. 18, left). Early
morning precipitation is well simulated west of the414

Journal of the Meteorological Society of Japan

30N(a) Diurnal Peak in Precip(TRMM-PR)

30N

24N

24N

18N

18N

12N

a’

a

6N

EQ

EQ

30N

80E

90E

100E

110E

120E

130E

18N

18N

12N

12N

6N

6N

EQ

EQ

24

100E

110E

120E

130E

110W

140E

(e) Diurnal Peak in Precip a-a’

24

18

90W

100W

70W

80W

60W

(d) Diurnal Peak in Precip(NICAM)

30N
24N

90E

b
110W

24N

80E

b’

140E

(c) Diurnal Peak in Precip(NICAM)

70E(b) Diurnal Peak in Precip(TRMM-PR)

12N

6N

70E

Vol. 93, No. 4

21

90W

100W

60W

70W

80W

(f) Diurnal Peak in Precip b-b’b’

18

LatitudeLocal Time

12
6

24

15

18

12
12
9

6
0

a

70

75

80

85

90

95

Longitude

a’

100

105

6
110

b
0

6

12

18

24

6

Local Time

12

18

24

Fig. 18. Local time of climatological diurnal peaks in precipitation during JJAS in h. Longitude–latitude plots of theTRMM-PR observations between 1998 and 2012 (top) and NICAM simulations between 1979 and 1998 (middle)
are shown for Asian monsoon and Maritime Continent (left) and Central America (right) regions. A diurnal peakin precipitation is defined as the first harmonic of the diurnal precipitation cycle in each grid box. Diurnal peaks in
precipitation along the line a–a’ in (a) and along the line b–b’ in (b) are shown in (e) and (f), respectively, with black
circles indicating simulated results and gray symbols indicating TRMM-PR observations. Horizontal and vertical axesin (e) are longitude and local time (repeated for 2 days), respectively, and those in (f) are local time and latitude.

August 2015

C. KODAMA et al.

Indochina Peninsula and over the interior of Indochina Peninsula (Satomura 2000; Takahashi et al.
2010) and Borneo Island. In addition, diurnal precipitation systems, which propagate from coastal regions
toward the ocean, are in good agreement with the
TRMM-PR observations over the Bay of Bengal (Fig.
18, left). The simulated nocturnal precipitation propagating offshore over the western coast of Central
America (Fig. 18, right) is more systematic comparedwith the previous monthly and seasonal-scale simulations (Sato et al. 2009; Noda et al. 2012). The increasein the simulation period improves the diurnal precipitation signals over this region. The improvement
in diurnal precipitation cycles, especially over theSoutheast Asian monsoon region, can be associatedwith the improvements in the reproduction of mean
climatological features such as monsoon westerlies
and precipitation (Sections 4.3 and 3.1, respectively).
Similar to the previous studies (Sato et al. 2009; Noda
et al. 2012), the simulated diurnal peaks in precipitation are delayed by 2–4 h compared with the observations, especially over land. These systematic biases
may be attributable to a poor representation of thevertical moisture transport at the lower atmosphereassociated with the coarse horizontal resolution (Petchet al. 2002; Mass et al. 2002).
Fujita et al. (2011) analyzed the observational
datasets and NICAM output and found a prominent diurnal convection cycle over the Indian Ocean
during the MJO phases 2 and 3. As we have shownin Section 4.2 and here, NICAM simulates realistic
climatology for the MJO as well as diurnal precipitation cycles, and thus is favorable for investigating
their interaction.
4.6 Lower stratospheric variability
The importance of the troposphere–stratosphere
coupling has been recognized because stratosphericvariability is closely linked with tropospheric weather
and climate (Baldwin and Dunkerton 2001). In thissection, we demonstrate the stratospheric variability
in monthly mean zonal-mean zonal wind in the
NICAM simulation. Figures 19a and 19b show the 30
hPa zonal wind frequency power spectra as a functionof latitude for ERA-I reanalysis (Table 1) and NICAM
simulation. NICAM simulates the annual and semiannual variability with its maximum values and latitudinal positions. In addition, variability over 7–11
months, as well as interannual variability, resembles
ERA-I results.
Figures 19c and 19d illustrate the time-latitude
cross-sections of interannual anomaly of the 30415

hPa zonal wind. To extract interannual variability,
the mean seasonal cycle (i.e., annual cycle) was
calculated using data from 1979 to 1998, and thensubtracted from the raw data. The resulting timeseries were then smoothed using five-month runningmeans (Kawatani and Hamilton 2013). The irregularoccurrence of strong easterly/westerly anomalies near
60°N and 70°S are found in both the ERA-I reanalysis
and NICAM simulation, although the phases of theseanomalies are slightly different. The latitude–height
distributions of interannual variability in NICAM arenormally comparable with those in the ERA-I (notshown) in the mid- to high-latitudes, despite slightly
weaker variability.
The most prominent interannual variability in theequatorial stratosphere is the quasi-biennial oscillation(QBO). Most comprehensive global models do not
simulate this feature well (Kawatani and Hamilton2013). Since the QBO is driven by interactions among
the mean zonal wind and vertically propagatinginternal waves generated in the tropical troposphere(Baldwin et al. 2001), high horizontal and verticalresolution and appropriate representation of tropicalconvective variations are required to adequately simulate the QBO (Kawatani et al. 2009, 2010).
In the ERA-I data, large spectrum peaks are found
20–40 months in the tropical regions (Fig. 19a), andzonal winds change from strong easterlies to westerlies with a mean period of approximately 28 months(Fig. 19c). This QBO signal does not appear in thepresent NICAM simulation (Figs. 19b, d), possibly
due to the coarse vertical resolution (i.e., approximately 2 km around 30 hPa). However, NICAM
attempts to simulate the westerly/easterly changes inthe tropical stratosphere despite much smaller amplitude. As shown in Fig. 13, NICAM succeeds in simulating CCEWs. Kawatani et al. (2009, 2010) demonstrated that CCEWs are the main source of equatorial waves with less than 90 m equivalent depths,
which propagate from the troposphere into stratosphere. These equatorial waves comprise up to 50 %of the force driving the westerly phase of the QBO.
These results reveal that NICAM has the potential toadequately simulate the QBO with additional stratospheric vertical layers, and that it would be beneficial to further investigate troposphere–stratosphere
coupling processes.
5.

Discussion

Some of the biases found in this study seem to becommon among most climate models. The doubleITCZ is still a problem in the NICAM simulation,

416

Journal of the Meteorological Society of Japan

Vol. 93, No. 4

Fig. 19. Frequency power spectra of 30 hPa zonal mean zonal wind in m2 s−2 month as a function of latitude for
ERA-I (a) and NICAM (b) results. Time-latitude cross-section of the interannual anomaly of 30 hPa zonal mean
zonal wind in m s−1 is also shown for ERA-I (c) and NICAM (d).

as discussed in Section 3.1. Over the continent, thewarmer surface air temperature bias is evident, especially during summer (Section 3.4). This may berelated to the stronger monsoonal flow over South
Asia (Section 4.3) and rapid northward advancement of the BF, i.e., shorter Baiu season, in East Asia
(Section 4.4). Such a temperature bias is related to theunderestimated cloud amount and associated weaker
SW cooling (Section 3.2). Insufficient simulated MJO
amplitude (Section 4.2) is also a common bias among
climate models. The delay in the diurnal precipitation
peak (Section 4.5) may be partly caused by insufficient spatial resolution. These biases may be quantitatively emphasized due to the lack of model parametertuning by a series of climate scale experiments. For
example, TOA net radiation imbalance (Section 3.2) isrelatively large compared with AMIP simulations byother models (Wang and Su 2013).

In the next phase of NICAM climate simulations,orographic gravity wave drag, aerosol, and sophisticated cloud microphysics schemes are planned to
be introduced to better reproduce zonal mean basic
states. Seiki and Nakajima (2014) developed a double
moment cloud microphysics (NDW6) scheme that
reduced the warmer temperature bias in the tropicalupper troposphere in the NICAM simulation (Seiki
et al. 2015a). Another cloud microphysics scheme
choice is the modified NSW6 scheme proposed by
Roh and Satoh (2014), which can reproduce bettercloud statistics compared with the original one. As
stated in Section 2.1, we adopted a slab ocean model,
nudged toward observed SSTs rather than fixed SST
conditions, to obtain a better geographic precipitation
distribution (Appendix A) and the MJO. However,nudging the model in this way causes SST drift fromits boundary conditions. The flux correction method,August 2015

such as McFarlane et al. (1992), may be necessary to
avoid this problem.
6.

417

C. KODAMA et al.

Conclusion

This study evaluates 20-year climatological meanstate and variability simulated by a 14-km mesh
NICAM. NICAM reasonably simulates many aspects
of atmospheric climatology and variability. The
geographical distributions of precipitation, including
ENSO related, seasonal, and diurnal variations arereasonably reproduced (Sections 3.1 and 4.5). Simulated zonal mean temperature, zonal wind, mean
meridional circulation, clouds, and TOA radiation
balance are qualitatively simulated, though somesevere biases such as underestimated low clouds andshortwave reflection and warmer surface and tropicalupper troposphere exist (Sections 3.2–3.4). Note that
we tuned model parameters in advance by performinga series of seasonal- or shorter-scale experiments
without any prior climate scale experiments with 14
km mesh NICAM.
The analysis of the individual phenomena in
Section 4 also revealed a potential for NICAM tosimulate multiscale atmospheric phenomena, especially TCs and the MJO. TCs are captured with
objective thresholds of maximum wind speed due tothe realistic intensity of simulated TCs. The seasonal
march of TC genesis in each ocean basin, as well as
TC intensity, is well simulated (Section 4.1). These
results are obtained without any cumulus convection scheme, and future TC projection is expected tobe more reliable if NICAM is performed under thewarmer climate conditions. Wavenumber–frequency
power spectrum analysis for the tropical disturbances shows that the MJO and tropical waves arewell reproduced (Section 4.2). This implies, basedon how tropical waves influence the stratosphere, thestratospheric variability is also well reproduced, as
partly revealed in Section 4.6. Asian monsoon analysis shows that climatological western North Pacificmonsoon onset agrees closely with the observations(Section 4.3), and that the BF is reproduced to someextent (Section 4.4). Some significant model biases
still exist, which indicates a need for further model
improvements. The topics chosen here are subjective and not comprehensive but may provide insights
into model capabilities. Given the NICAM’s ability
to explicitly resolve multiple multiscale atmosphericphenomena at a time, the results shown here warrant
further the studies of several weather and climate
phenomena and their interactions (e.g., TC genesis–
MJO, CCEWs–stratospheric dynamics, MJO–diurnal

cycle, MJO–ENSO) in a seamless manner.
Acknowledgments
The authors express their thanks to H. Miura,
M. Nakano, T. Seiki, T. Miyakawa, Y.-W. Chen,
K. Fukutomi, M. Fujita, and K. Oouchi for theirfruitful discussions. They also appreciate R.
Mizuta at the Meteorological Research Institute forproviding external conditions for the AMIP experiment. Constructive comments from two anonymous
reviewers helped improve our manuscript. We thankthe providers of the data used in this study. Amongthem, CMAP precipitation and NOAA OLR data wereobtained from http://www.esrl.noaa.gov/psd/, ERA-40
and ERA-I from the European Centre for Medium
Range Weather Forecasts (ECMWF), ISCCP datafrom the National Aeronautics and Space Administration (NASA) Langley Research Center Atmospheric
Science Data Center, JRA-25 data from the cooperative research project performed by the Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry (CRIEPI), and JRA-55
data from the JMA. All the simulations analyzed
in this study were performed on the K computer at
the RIKEN Advanced Institute for Computational
Science (Proposal number hp120279, hp130010,
and hp140219). 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, Scienceand Technology (MEXT), Japan. KK acknowledges
the support of NOAA Grant NA130AR4310165
and JAMSTEC through its sponsorship of research
activities at the IPRC. School of Ocean and Earth
Science and Technology Contribution Number 9422
and International Pacific Research Center Contribution Number 1118. YK acknowledges the support ofJapan Society for the Promotion of Science (JSPS)
KAKENHI Grant Number 26287117.
Appendix A: Impact of SST nudging on
the precipitation bias
In this study, we employed the slab ocean model
nudged toward reference SSTs rather than fixed SST
condition. This means that the simulated SSTs areslightly different from the reference SSTs. Figure A1
shows the simulated SST anomaly from the reference SST for each season. In the summer (winter)hemisphere, the simulated SST is higher (lower) than
the reference SST by approximately 1.5 K. In addition, the warm bias remains throughout nearly the418

Journal of the Meteorological Society of Japan

Fig. A1.

Vol. 93, No. 4

Seasonal mean climatology of NICAM − HadISST SST in K from March 1979 to February 1999.

Fig. A2. Seasonal mean precipitation rates in mm day−1 during JJA and SON (2004). GPCP results are shown in (a),NICAM results using the slab ocean model in (b), and NICAM results with fixed SSTs in (c).

entire year in the stratocumulus regions, off the coastof Peru, Namibia, and California, and in the deep
tropics, for example, all of which are consistent withthe underestimated cloud amount (Fig. 4).
Using the slab ocean model helps to simulate
precipitation patterns with less bias than fixed SSTs
would produce. Figure A2 shows the precipitation

rate over the tropical western Pacific produced by
GPCP, NICAM with the slab ocean model (SLAB),
and NICAM with the fixed SST (FIXED). Thesesensitivity experiments began in June 1, 2004, andthe integrations were performed for six months. In the
FIXED run, dry bias is significant around the Philippines, especially during JJA with a hole-like outline.

August 2015

C. KODAMA et al.

Such a bias is also seen in the SLAB run but withlower magnitude and unclear outline. Though both the
FIXED and SLAB runs produce double-ITCZ during
SON, the separation of north and south high precipitation regions is clearer in the FIXED run. In addition, precipitation along the coastal areas in the Bayof Bengal is overestimated in the FIXED run. It isbeyond the study’s scope to determine why the SLAB
run reproduces better geographical distributions ofprecipitation. The relationship between the ocean
model and precipitation pattern may be of interest in acloud system resolving simulation.
References
Adler, R. F., G. J. Huffman, A. Chang, R. Ferraro, P. P.
Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis,
D. Bolvin, A. Gruber, J. Susskind, P. Arkin, and E.
Nelkin, 2003: The version-2 global precipitation
climatology project (GPCP) monthly precipitation
analysis (1979–present). J. Hydrometeor., 4, 1147–
1167.
Baldwin, M. P., and T. J. Dunkerton, 2001: Stratosphericharbingers of anomalous weather regimes. Science,
294, 581–584.
Baldwin, M. P., L. J. Gray, T. J. Dunkerton, K. Hamilton,
P. H. Haynes, W. J. Randel, J. R. Holton, M. J. Alexander, I. Hirota, T. Horinouchi, D. B. A. Jones, J. S.
Kinnersley, C. Marquardt, K. Sato, and M. Takahashi,
2001: The quasi-biennial oscillation. Rev. Geophys.,
39, 179–229.
Camargo, S. J., 2013: Global and regional aspects oftropical cyclone activity in the CMIP5 models. J.
Climate, 26, 9880–9902.
Cess, R. D., G. L. Potter, J. P. Blanchet, G. J. Boer, S. J.
Ghan, J. T. Kiehl, H. Letreut, Z. X. Li, X. Z. Liang,
J. F. B. Mitchell, J. J. Morcrette, D. A. Randall, M.
R. Riches, E. Roeckner, U. Schlese, A. Slingo, K.
Taylor, W. M. Washington, R. T. Wetherald, and I.
Yagai, 1989: Interpretation of cloud-climate feedback
as produced by 14 atmospheric general-circulation
models. Science, 245, 513–516.
Chen, T.-C., J.-D. Tsay, M.-C. Yen, and J. Matsumoto, 2012:
Interannual variation of the late fall rainfall in central
Vietnam. J. Climate, 25, 392–413.
Chu, J.-H., C. R. Sampson, A. S. Levine, and E. Fukada,
2002: The joint typhoon warning center tropical
cyclone best-tracks, 1945–2000. Naval ResearchLaboratory Rep., NRL/MR/7540-02-16, 22 pp.
Dai, A., 2006: Precipitation characteristics in eighteen
coupled models. J. Climate, 19, 4605–4630.
Dai, A., and K. E. Trenberth, 2004: The diurnal cycle and its
depiction in the Community Climate System Model.
J. Climate, 17, 930–951.
Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P.
Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G.

419

Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars,
L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R.
Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B.
Healy, H. Hersbach, E. V. Holm, L. Isaksen, P. Kallberg, M. Kohler, M. Matricardi, A. P. McNally, B. M.
Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey,
P. de Rosnay, C. Tavolato, J.-N. Thepaut, and F.
Vitart, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system.
Quart. J. Roy. Meteor. Soc., 137, 553–597.
Dirmeyer, P. A., B. A. Cash, J. L. Kinter III, T. Jung, L.
Marx, M. Satoh, C. Stan, H. Tomita, P. Towers, N.
Wedi, D. Achuthavarier, J. M. Adams, E. L. Altshuler,
B. Huang, E. K. Jin, and J. Manganello, 2011: Simulating the diurnal cycle of rainfall in global climate
models: resolution versus parameterization. Climate
Dyn., 39, 399–418.
Ebita, A., S. Kobayashi, Y. Ota, M. Moriya, R. Kumabe,
K. Onogi, Y. Harada, S. Yasui, K. Miyaoka, K. Takahashi, H. Kamahori, C. Kobayashi, H. Endo, M.
Soma, Y. Oikawa, and T. Ishimizu, 2011: The Japanese 55–year reanalysis “JRA-55”: An interim report.
SOLA, 7, 149–152.
Emanuel, K. A., 2013: Downscaling CMIP5 climate models
shows increased tropical cyclone activity over the
21st century. PNAS, 30, 12219–12224.
Eyring, V., M. P. Chipperfield, M. A. Giorgetta, D. E. Kinnison, E. Manzini, K. Matthes, P. A. Newman, S.
Pawson, T. G. Shepherd, and D. W. Waugh, 2008:
Overview of the new CCMVal reference and sensitivity simulations in support of upcoming ozone
and climate assessments and the planned SPARC
CCMVal report. SPARC Newsl., 30, 20–26.
Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, andJ. B. Edson, 2003: Bulk parameterization of air-sea
fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571–591.
Fudeyasu, H., Y. Q. Wang, M. Satoh, T. Nasuno, H. Miura,
and W. Yanase, 2008: Global cloud-system-resolving
model NICAM successfully simulated the lifecyclesof two real tropical cyclones. Geophys. Res. Lett., 35,
L22808, doi:10.1029/2008GL036003.
Fujita, M., K. Yoneyama, S. Mori, T. Nasuno, and M. Satoh,2011: Diurnal convection peaks over the eastern
Indian Ocean off Sumatra during different MJO
phases. J. Meteor. Soc. Japan, 89A, 317–330.
Gates, W. L., 1992: AMIP - The atmospheric model intercomparison project. Bull. Amer. Meteor. Soc., 73,
1962–1970.
Grabowski, W. W., 1998: Toward cloud resolving modelingof large-scale tropical circulations: A simple cloud
microphysics parameterization. J. Atmos. Sci., 55,
3283–3298.
Grabowski, W. W., 2001: Coupling cloud processes withthe large-scale dynamics using the Cloud-Resolving
Convection Parameterization (CRCP). J. Atmos. Sci.,

420

Journal of the Meteorological Society of Japan

58, 978–997.
Grabowski, W. W., 2006: Impact of explicit atmosphere-ocean coupling on MJO-like coherent structures in idealized aquaplanet simulations. J. Atmos.
Sci., 63, 2289–2306.
Hansen, J., and M. Sato, 2004: Greenhouse gas growth
rates. Proc. Natl. Acad. Sci. USA, 101, 16109–16114.
Hara, M, T. Yoshikane, H. G. Takahashi, F. Kimura, A.
Noda, and T. Tokioka, 2009: Assessment of thediurnal cycle of precipitation over the Maritime
Continent simulated by 20-km mesh GCM using
TRMM PR data. J. Meteor. Soc. Japan, 87A,
413–424.
Hodges, K. I., D. W. Chappell, G. J. Robinson, and G. Yang,
2000: An improved algorithm for generating global
window brightness temperatures from multiple satellite infrared imagery. J. Atmos. Oceanic Technol, 17,
1296–1312.
Holloway, C. E., S. J. Woolnough, and G. M. S. Lister,
2013: The effects of explicit versus parameterized
convection on the MJO in a large-domain high-resolution tropical case study. Part I: Characterization oflarge-scale organization and propagation. J. Atmos.
Sci., 70, 1342–1369.
Holland, G, J., 1995; Scale interaction in the western Pacificmonsoon. Meteor. Atmos. Phys., 56, 57–79.
Hung, M. P., J. L. Lin, W. Q. Wang, D. Kim, T. Shinoda,
and S. J. Weaver, 2013: MJO and convectively
coupled equatorial waves simulated by CMIP5
climate models. J. Climate, 26, 6185–6214.
Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and K.
Okamoto, 2000: Rain-profiling algorithm for the
TRMM precipitation radar. J. Appl. Meteor., 39,
2038–2052.
Inness, P. M., J. M. Slingo, E. Guilyardi, and J. Cole, 2003:
Simulation of the Madden-Julian oscillation in acoupled general circulation model. Part II: The role ofthe basic state. J. Climate, 16, 365–382.
Iwasaki, T., 1989: A diagnostic formulation for wave-mean
flow interactions and Lagrangian-mean circulation
with a hybrid vertical coordinate of pressure and isentropes. J. Meteor. Soc. Japan, 67, 293–312.
Iwasaki, T., H. Hamada, and K. Miyazaki, 2009: Comparisons of Brewer-Dobson circulations diagnosed fromreanalyses. J. Meteor. Soc. Japan, 87, 997–1006.
Jarvinen, B. R., C. J. Neumann, and M. A. S. Davis, 1984:
A tropical cyclone data tape for the North Atlanticbasin, 1886–1983: Contents, limitations and uses.
NOAA Tech. Memo., NWS NHC-22, 21 pp.
Kawatani, Y., and K. Hamilton, 2013: Weakened stratospheric quasibiennial oscillation driven by increased
tropical mean upwelling. Nature, 497, 478–481.
Kawatani, Y., M. Takahashi, K. Sato, S. P. Alexander, and
T. Tsuda, 2009: Global distribution of atmosphericwaves in the equatorial upper troposphere andlower stratosphere: AGCM simulation of sources

Vol. 93, No. 4

and propagation. J. Geophys. Res, 114, D01102,
doi:10.1029/2008JD010374.
Kawatani, Y., K. Sato, T. J. Dunkerton, S. Watanabe, S.
Miyahara, and M. Takahashi, 2010: The roles ofequatorial trapped waves and internal inertia-gravity
waves in driving the quasi-biennial oscillation. Part I:
Zonal mean wave forcing. J. Atmos. Sci. 67, 963–980.
Khairoutdinov, M. F., and D. A. Randall, 2001: A cloud
resolving model as a cloud parameterization in the
NCAR Community Climate System Model: Preliminary results. Geophys. Res. Lett., 28, 3617–3620.
Khairoutdinov, M., C. DeMott, and D. Randall, 2008: Evaluation of the simulated interannual and subseasonalvariability in an AMIP-Style simulation using the
CSU multiscale modeling framework. J. Climate, 21,
413–431.
Kikuchi, K., 2014: An introduction to combined Fourier-wavelet transform and its application to convectively coupled equatorial waves. Climate. Dyn.,
1339–1356.
Kikuchi, K., B. Wang, and Y. Kajikawa, 2012: Bimodal
representation of the tropical intraseasonal oscillation.
Climate Dyn., 38, 1989–2000.
Kikuchi, K., C. Kodama, T. Nasuno, H. Miura, and M.
Satoh, 2015: Tropical intraseasonal oscillation in an
AMIP-type experiment by NICAM. J. Adv. Model.
Earth Syst., in revision.
Kiladis, G. N., M. C. Wheeler, P. T. Haertel, K. H. Straub,and P. E. Roundy, 2009: Convectively coupledequatorial waves. Rev. Geophys., 47, RG2003,
doi:10.1029/2008RG000266.
Kiladis, G. N., J. Dias, K. H. Straub, M. C. Wheeler, S. N.
Tulich, K. Kikuchi, K. M. Weickmann, and M. J.
Ventrice, 2014: A comparison of OLR and circulation-based indices for tracking the MJO. Mon. Wea.
Rev., 142, 1697–1715.
Kim, D., K. Sperber, W. Stern, D. Waliser, I. S. Kang, E.
Maloney, W. Wang, K. Weickmann, J. Benedict, M.
Khairoutdinov, M. I. Lee, R. Neale, M. Suarez, K.
Thayer-Calder, and G. Zhang, 2009: Application of
MJO simulation diagnostics to climate models. J.
Climate, 22, 6413–6436.
Kinter, J. L., B. Cash, D. Achuthavarier, J. Adams, E.
Altshuler, P. Dirmeyer, B. Doty, B. Huang, E. K. Jin,
L. Marx, J. Manganello, C. Stan, T. Wakefield, T.
Palmer, M. Hamrud, T. Jung, M. Miller, P. Towers, N.
Wedi, M. Satoh, H. Tomita, C. Kodama, T. Nasuno,
K. Oouchi, Y. Yamada, H. Taniguchi, P. Andrews,
T. Baer, M. Ezell, C. Halloy, D. John, B. Loftis, R.
Mohr, and K. Wong, 2013: Revolutionizing climate
modeling with project Athena: A multi-institutional,
international collaboration. Bull. Amer. Meteor. Soc.,
94, 231–245.
Klein, S. A., and C. Jakob, 1999: Validation and sensitivities
of frontal clouds simulated by the ECMWF model.
Mon. Wea. Rev., 127, 2514–2531.

August 2015

C. KODAMA et al.

Kodama, C., A. T. Noda, and M. Satoh, 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.
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond,and C. J. Neumann, 2010: The International Best
Track Archive for Climate Stewardship (IBTrACS).
Bull. Amer. Meteor. Soc., 91, 363–376.
Lau, K. M., H. T. Wu, and S. Bony, 1997: The role of largescale atmospheric circulation in the relationshipbetween tropical convection and sea surface temperature. J. Climate, 10, 381–392.
Liebmann, B., and C. A. Smith, 1996: Description of acomplete (interpolated) outgoing longwave radiation
dataset. Bull. Amer. Meteor. Soc., 77, 1275–1277.
Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith,
D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong,
2009: Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748–766.
Louis, J. F., 1979: A parametric model of vertical eddy
fluxes in the atmosphere. Bound.-Layer Meteor., 17,
187–202.
Love, B. S., A. J. Matthews, and G. M. S. Lister, 2011:
The diurnal cycle of precipitation over the Maritime
Continent in a high-resolution atmospheric model.
Quart. J. Roy. Meteor. Soc., 137, 934–947.
Madden, R. A., and P. R. Julian, 1971: Detection of a 40–50
day oscillation in the zonal wind in the tropical
Pacific. J. Atmos. Sci., 28, 702–708.
Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation cells in tropics with a 40–50 day
period. J. Atmos. Sci., 29, 1109–1123.
Manganello, J. V., K. I. Hodges, J. L. Kinter III, B. A. Cash,
L. Marx, T. Jung, D. Achuthavarier, J. M. Adams, E.
L. Altshuler, B. Huang, E. K. Jin, C. Stan, P. Towers,
and N. Wedi, 2012: Tropical cyclone climatology in a
10-km global atmospheric GCM: Toward weather-resolving climate modeling. J. Climate, 25, 3867–3893.
Manganello, J. V., K. I. Hodges, B. Dirmeyer, J. L. KinterIII, B. A. Cash, L. Marx, T. Jung, D. Achuthavarier,J. M. Adams, E. L. Altshuler, B. Huang, E. K. Jin,
P. Towers, and N. Wedi, 2014: Future changes in
the western North Pacific tropical cyclone activity
projected by a multidecadal simulation with a 16-km
global atmospheric GCM. J. Climate, 27, 7622–7644.
Mass, C. F., D. Ovens, K. Westrick, and B. A. Colle, 2002:
Does increasing horizontal resolution produce moreskillful forecasts? Bull. Amer. Meteor. Soc., 83,
407–430.
Matsueda, M., and H. Endo, 2011: Verification of medium-range MJO forecasts with TIGGE. Geophys. Res.
Lett., 38, L11801, doi:10.1029/2011GL047480.
Matsuno, T., 1966: Quasi-geostrophic motions in the equatorial area. J. Meteor. Soc. Japan, 44, 25–43.
McFarlane, N. A., 1987: The effect of orographically421

excited gravity-wave drag on the general-circulationof the lower stratosphere and troposphere. J. Atmos.
Sci., 44, 1775–1800.
McFarlane, N. A., G. J. Boer, J.-P. Blanchet, and M. Lazare,
1992: The Canadian Climate Centre second-generation general circulation model and its equilibrium
climate. J. Climate, 5, 1013–1044.
Miura, H., M. Satoh, T. Nasuno, A. T. Noda, and K. Oouchi,
2007: A Madden-Julian oscillation event realisticallysimulated by a global cloud-resolving model. Science,
318, 1763–1765.
Miyakawa, T., M. Satoh, H. Miura, H. Tomita, H. Yashiro,
A. T. Noda, Y. Yamada, C. Kodama, M. Kimoto,and K. Yoneyama, 2014: Madden-Julian Oscillation prediction skill of a new-generation global
model demonstrated using a supercomputer. Nature
Commun., 5, 3769, doi:10.1038/ncomms4769.
Miyamoto, Y., Y. Kajikawa, R. Yoshida, T. Yamaura, H.
Yashiro, and H. Tomita, 2013: Deep moist atmospheric convection in a subkilometer global simulation. Geophys. Res. Lett., 40, 4922–4926.
Miyamoto, Y., M. Satoh, H. Tomita, K. Oouchi, Y. Yamada,C. Kodama, and J. Kinter, 2014: Gradient windbalance in tropical cyclones in high-resolution global
experiments. Mon. Wea. Rev., 142, 1908–1926.
Miyazaki, H., Y. Kusano, H. Okano, T. Nakada, K. Seki,
T. Shimizu, N. Shinjo, F. Shoji, A. Uno, and M.
Kurokawa, 2012: K computer: 8.162 PetaFLOPS
massively parallel scalar supercomputer built withover 548k cores. Solid-State Circuits Conference
Digest of Technical Papers (ISSCC), 2012 IEEE
International, IEEE, 192–194.
Mizuta, R., K. Oouchi, H. Yoshimura, A. Noda, K. Katayama, S. Yukimoto, M. Hosaka, S. Kusunoki, H.
Kawai, and M. Nakagawa, 2006: 20-km-mesh global
climate simulations using JMA-GSM model - mean
climate states. J. Meteor. Soc. Japan, 84, 165–185.
Mizuta, R., Y. Adachi, S. Yukimoto, and S. Kusunoki, 2008:
Estimation of the future distribution of sea surface
temperature and sea ice using the CMIP3 multimodel ensemble mean. Tech. Rep. Meteorol. Res.
Inst., 56, 1–28.
Mizuta, R., H. Yoshimura, H. Murakami, M. Matsueda, H.
Endo, T. Ose, K. Kamiguchi, M. Hosaka, M. Sugi, S.
Yukimoto, S. Kusunoki, and A. Kitoh, 2012: Climate
simulations using MRI-AGCM3.2 with 20-km grid. J.
Meteor. Soc. Japan, 90A, 233–258.
Moon, I. J., I. Ginis, T. Hara, and B. Thomas, 2007: A physics-based parameterization of air-sea momentum flux
at high wind speeds and its impact on hurricane intensity predictions. Mon. Wea. Rev., 135, 2869–2878.
Murakami, H., and M. Sugi, 2010: Effect of model resolution on tropical cyclone climate projection. SOLA, 6,
73–76.
Murakami, H., Y. Wang, H. Yoshimura, R. Mizuta, M. Sugi,
E. Shindo, Y. Adachi, S. Yukimoto, M. Hosaka, S.

422

Journal of the Meteorological Society of Japan

Kusunoki, T. Ose, and A. Kitoh, 2012: Future change
in tropical cyclone activity projected by the newhigh-resolution MRI-AGCM. J. Climate, 25, 3237–
3260.
Murakami, T., and J. Matsumoto, 1994: Summer monsoon
over the Asian Continent and western North Pacific.
J. Meteor. Soc. Japan, 72, 719–745.
Nakanishi, M., and H. Niino, 2006: An improved
Mellor-Yamada level-3 model: Its numerical stability
and application to a regional prediction of advection
fog. Bound.-Layer Meteor., 119, 397–407.
Nguyen, H., A. Evans, C. Lucas, I. Smith, and B. Timbal,
2013: The Hadley circulation in reanalyses: climatology, variability, and change. J. Climate, 26, 3357–
3376.
Noda, A. T., K. Oouchi, M. Satoh, H. Tomita, S. Iga, and
Y. Tsushima, 2010: Importance of the subgridscale turbulent moist process: Cloud distribution in
global cloud-resolving simulations. Atmos. Res., 96,
208–217.
Noda, A. T., K. Oouchi, M. Satoh, and H. Tomita, 2012:
Quantitative assessment of diurnal variation of tropical convection simulated by a global nonhydrostatic
model without cumulus parameterization. J. Climate,25, 5119–5134.
Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S.
Kobayashi, H. Hatsushika, T. Matsumoto, N.
Yamazaki, H. Kamahori, K. Takahashi, S. Kadokura,
K. Wada, K. Kato, R. Oyama, T. Ose, N. Mannoji,
and R. Taira, 2007: The JRA-25 reanalysis. J. Meteor.
Soc. Japan, 85, 369–432.
Oouchi, K., J. Yoshimura, H. Yoshimura, R. Mizuta, S.
Kusunoki, and A. Noda, 2006: Tropical cyclone
climatology in a global-warming climate as simulated in a 20 km-mesh global atmospheric model:
Frequency and wind intensity analyses. J. Meteor.
Soc. Japan, 84, 259–276.
Oouchi, K., A. T. Noda, M. Satoh, H. Miura, H. Tomita,
T. Nasuno, and S. Iga, 2009: 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.
Pauluis, O., and S. Garner, 2006: Sensitivity of radiative-convective equilibrium simulations to horizontal
resolution. J. Atmos. Sci., 63, 1910–1923.
Petch, J. C., A. R. Brown, and M. E. B. Gray, 2002: The
impact of horizontal resolution on the simulationsof convective development over land. Quart. J. Roy.
Meteor. Soc., 128, 2031–2044.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland,
L. V. Alexander, D. P. Rowell, E. C. Kent, and
A. Kaplan, 2003: Global analyses of sea surface
temperature, sea ice, and night marine air temperaturesince the late nineteenth century. J. Geophys. Res.,108, 4407, doi:10.1029/2002JD002670.
Ritchie, E. A., and G. J. Holland, 1999: Large-scale patterns

Vol. 93, No. 4

associated with tropical cyclogenesis in the western
Pacific. Mon. Wea. Rev., 127, 2027–2043.
Roberts, M. J., P. L. Vidale, M. S. Mizielinski, M. E.
Demory, R. Schiemann, J. Strachan, K. Hodges,
R. Bell, and J. Camp, 2015: Tropical cyclones in
the UPSCALE ensemble of high-resolution global
climate models. J. Climate, 28, 574–596.
Roh, W., and M. Satoh, 2014: Evaluation of precipitatinghydrometeor parameterizations in a single-moment bulk microphysics scheme for deep convective
systems over the tropical central pacific. J. Atmos.
Sci., 71, 2654–2673.
Rossow, W. B., and R. A. Schiffer, 1999: Advances inunderstanding clouds from ISCCP. Bull. Amer.
Meteor. Soc., 80, 2261–2287.
Saito, N., 1966: A preliminary study of the summer
monsoon of southern and eastern Asia. J. Meteor. Soc.
Japan, 44, 44–59.
Sato, T., H. Miura, M. Satoh, Y. N. Takayabu, and Y. Q.
Wang, 2009: Diurnal cycle of precipitation in thetropics simulated in a global cloud-resolving model.
J. Climate, 22, 4809–4826.
Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, andS. Iga, 2008: Nonhydrostatic icosahedral atmosphericmodel (NICAM) for global cloud resolving simulations. J. Comput. Phys., 227, 3486–3514.
Satoh, M., K. Oouchi, T. Nasuno, H. Taniguchi, Y.
Yamada, H. Tomita, C. Kodama, J. Kinter III, D.
Achuthavarier, J. Manganello, B. Cash, T. Jung,
T. Palmer, and N. Wedi, 2012: The intra-seasonal
oscillation and its control of tropical cyclones simulated by high-resolution global atmospheric models.
Climate. Dyn., 39, 2185–2206.
Satoh, M., H. Tomita, H. Yashiro, H. Miura, C. Kodama, T.
Seiki, A. T. Noda, Y. Yamada, D. Goto, M. Sawada,
T. Miyoshi, Y. Niwa, M. Hara, T. Ohno, S. Iga, T.
Arakawa, T. Inoue, and H. Kubokawa, 2014: The
non-hydrostatic icosahedral atmospheric model:
Description and development. Prog. Earth Planet.
Sci., 1, 18, doi:10.1186/s40645-014-0018-1.
Satomura, T., 2000: Diurnal variation of precipitation overthe Indo-China Peninsula: Two-dimensional numerical simulation. J. Meteor. Soc. Japan, 78, 461–475.
Seiki, T., and T. Nakajima, 2014: Aerosol effects of thecondensation process on a convective cloud simulation. J. Atmos. Sci., 71, 833–853.
Seiki, T., C. Kodama, A. T. Noda, and M. Satoh, 2015a:
Impacts of a double-moment bulk cloud microphysics
scheme on global cloud-system resolving simulation.
J. Climate, 28, 2405–2419.
Seiki, T., C. Kodama, M. Satoh, T. Hashino, Y. Hagihara, and H. Okamoto, 2015b: Vertical grid spacing
necessary for simulating tropical cirrus clouds witha high-resolution AGCM. Geophys. Res. Lett, 4150–
4157.
Sekiguchi, M., and T. Nakajima, 2008: A k-distribu-

August 2015

C. KODAMA et al.

tion-based radiation code and its computational
optimization for an atmospheric general circulation
model. J. Quant. Spectrosc. Radiat. Transfer, 109,2779–2793.
Serra, Y. L., G. N. Kiladis, and K. I. Hodges, 2010: Tracking
and mean structure of easterly waves over the
Intra-Americas Sea. J. Climate, 23, 4823–4840.
Shibata, K., M. Deushi, T. T. Sekiyama, and H. Yoshimura,
2004: Development of an MRI chemical transportmodel for the study of stratospheric chemistry. Pap.
Meteor. Geophys., 55, 75–119.
Shukla, J., R. Hagedorn, B. Hoskins, J. Kinter, J. Marotzke,
M. Miller, T. N. Palmer, and J. Slingo, 2009: Revolution in climate prediction is both necessary andpossible a declaration at the world modelling summit
for climate prediction. Bull. Amer. Meteor. Soc., 90,
175–178.
Soden, B. J., and I. M. Held, 2006: An assessment ofclimate feedbacks in coupled ocean-atmospheremodels. J. Climate, 19, 3354–3360.
Sperber, K. R., and H. Annamalai, 2008: Coupled model
simulations of boreal summer intraseasonal (30-50
day) variability, Part 1: Systematic errors and caution
on use of metrics. Climate Dyn., 31, 345–372.
Sperber, K., H. Annamalai, I. S. Kang, A. Kitoh, A. Moise,
A. Turner, B. Wang, and T. Zhou, 2013: The Asiansummer monsoon: an intercomparison of CMIP5 vs.
CMIP3 simulations of the late 20th century. Climate
Dyn., 41, 2711–2744.
Stevens, B., and S. Bony, 2013: What are climate models
missing? Science, 340, 1053–1054.
Sugi, M., A. Noda, and N. Sato, 2002: Influence of the
global warming on tropical cyclone climatology: An
experiment with the JMA global model. J. Meteor.
Soc. Japan, 80, 249–272.
Takahashi, H. G., H. Fujinami, T. Yasunari, and J. Matsumoto, 2010: Diurnal rainfall pattern observedby Tropical Rainfall Measuring Mission Precipitation Radar (TRMM-PR) around the Indochina peninsula. J. Geophys. Res., 115, D07109,
doi:10.1029/2009JD012155.
Takata, K., S. Emori, and T. Watanabe, 2003: Development of the minimal advanced treatments of surface
interaction and runoff. Global. Planet. Change, 38,
209–222.
Taniguchi, H., W. Yanase, and M. Satoh, 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.
Taylor, K. E., D. Williamson, and F. Zwiers, 2000: The
sea surface temperature and sea-ice concentration boundary conditions for AMIP II simulations.
Program For Climate Model Diagnosis And Intercomparison (PCMDI) Report, 60, 28 pp.
Tomita, H., 2008: New microphysical schemes with five and423

six categories by diagnostic generation of cloud ice. J.
Meteor. Soc. Japan, 86A, 121–142.
Tomita, H., and M. Satoh, 2004: A new dynamical framework of nonhydrostatic global model using the icosahedral grid. Fluid Dyn. Res., 34, 357–400.
Tomita, T., T. Yamaura, and T. Hashimoto, 2011: Interannual variability of the Baiu season near Japan evaluated from the equivalent potential temperature. J.
Meteor. Soc. Japan, 89, 517–537.
Uppala, S. M., P. W. Kållberg, A. J. Simmons, U. Andrae,
V. D. C. Bechtold, M. Fiorino, J. K. Gibson, J.
Haseler, A. Hernandez, G. A. Kelly, X. Li, K. Onogi,
S. Saarinen, N. Sokka, R. P. Allan, E. Andersson, K.
Arpe, M. A. Balmaseda, A. C. M. Beljaars, L. Van De
Berg, J. Bidlot, N. Bormann, S. Caires, F. Chevallier,A. Dethof, M. Dragosavac, M. Fisher, M. Fuentes,
S. Hagemann, E. Hólm, B. J. Hoskins, L. Isaksen,
P. A. E. M. Janssen, R. Jenne, A. P. McNally, J.-F.
Mahfouf, J.-J. Morcrette, N. A. Rayner, R. W. Saunders, P. Simon, A. Sterl, K. E. Trenberth, A. Untch,
D. Vasiljevic, P. Viterbo, and J. Woollen, 2005: The
ERA-40 re-analysis. Quart. J. Roy. Meteor. Soc., 131,
2961–3012.
Vial, J., J. L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Climate Dyn., 41, 3339–3362.
Walsh, K. J. E., M. Fiorino, C. W. Landsea, and L. McInnes,
2007: Objectively determined resolution-dependent threshold criteria for the detection of tropical cyclones in climate models and reanalyses. J.
Climate, 20, 2307–2314.
Wang, B., 2002: Rainy season of the Asian-Pacific summer
monsoon. J. Climate, 15, 386–398.
Wang, B., 2006: The Asian Monsoon. Springer Science &Business Media, 838 pp.
Wang B, R. Wu, and K. M. Lau, 2001: Interannual variability of Asian summer monsoon: Contrast between
the Indian and western North Pacific-East Asianmonsoons. J. Climate, 14, 4073–4090.
Wang, B., Y.-S. Zhang, and M. M. Lu, 2004: Definition ofSouth China Sea monsoon onset and commencementof the East Asia summer monsoon. J. Climate, 17,
699–710.
Wang, H. L., and W. Y. Su, 2013: Evaluating and understanding top of the atmosphere cloud radiative effects
in intergovernmental panel on climate change (IPCC)
fifth assessment report (AR5) coupled model intercomparison project phase 5 (CMIP5) models using
satellite observations. J. Geophys. Res., 118, 683–699.
Webb, M., C. Senior, S. Bony, and J. J. Morcrette, 2001:
Combining ERBE and ISCCP data to assess clouds inthe Hadley Centre, ECMWF and LMD atmosphericclimate models. Climate Dyn., 17, 905–922.
Wheeler, M., and G. N. Kiladis, 1999: Convectively
coupled equatorial waves: Analysis of clouds andtemperature in the wavenumber-frequency domain. J.

424

Journal of the Meteorological Society of Japan

Atmos. Sci., 56, 374–399.
Xie, P., and P. A. Arkin, 1997: Global precipitation: A
17-year monthly analysis based on gauge observations, satellite estimates, and numerical model
outputs. Bull. Amer. Meteor. Soc., 78, 2539–2558.
Yamada, Y., and M. Satoh, 2013: Response of ice and liquidwater paths of tropical cyclones to global warming
simulated by a global nonhydrostatic model with
explicit cloud microphysics. J. Climate, 26, 9931–
9945.
Yokokawa, M., F. Shoji, A. Uno, M. Kurokawa, and T.
Watanabe, 2011: The K computer: Japanese next-generation supercomputer development project. Proceedings of the 17th IEEE/ACM international symposium
on low-power electronics and design, IEEE Press,
371–372.
Yoshida, R., and H. Ishikawa, 2013: Environmental factors

Vol. 93, No. 4

contributing to tropical cyclone genesis over thewestern North Pacific. Mon. Wea. Rev., 141, 451–467.
Zhao, M., 2014: An investigation of the connections among
convection, clouds, and climate sensitivity in a global
climate model. J. Climate, 27, 1845–1862.
Zhao, M., and I. M. Held, 2010: An analysis of the effect of
global warming on the intensity of atlantic hurricanes
using a GCM with statistical refinement. J. Climate,23, 6382–6393.
Zhao, M., I. M. Held, S.-J. Lin, and G. A. Vecchi, 2009:
Simulations of global hurricane climatology, interannual variability, and response to global warming using
a 50-km resolution GCM. J. Climate, 22, 6653–6678.
Zhao, M., I. M. Held, and S.-J. Lin, 2012: Some counterintuitive dependencies of tropical cyclone frequency on
parameters in a GCM. J. Atmos. Sci., 69, 2272–2283.