Journal
October of2012
the Meteorological Society of Japan, Vol.
H. 90,ICHIKAWANo. 5, pp.et629−646,al.
2012
DOI: 10.2151/jmsj.2012-504

629

Analysis of Cloud Properties Associated with Tropical Convection
in Climate Models and Satellite Data
Hiroki ICHIKAWA
Department of Earth and Environmental Sciences, Graduate School of Environmental Studies,
Nagoya University, Nagoya, Japan

Hirohiko MASUNAGA
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan

Yoko TSUSHIMAMet Office Hadley Centre, United Kingdom

andHiroshi KANZAWA
Department of Earth and Environmental Sciences, Graduate School of Environmental Studies,
Nagoya University, Nagoya, Japan(Manuscript received 2 April 2012, in final form 3 July 2012)

Abstract
Cloud properties associated with tropical convection are analyzed for 11 models participating in Cloud Feedback
Model Intercomparison Project Phase 1 (CFMIP1) in comparison with International Satellite Cloud Climatology
Project (ISCCP) and other satellite observations and reanalysis datasets. Cloud properties are analyzed for different regimes of large-scale circulation field sorted by monthly mean of pressure coordinated vertical velocity
at 500 hPa as an index of large-scale circulation. The present analysis is focused on warm oceanic regions withsea surface temperatures above 27°C where convection is active. The warm oceanic regions cover the verticalmotion regimes ranging from strong ascent to weak descent. The ISCCP simulator outputs are used to evaluate
cloud properties in the models. Cloud amount of optically thick high-clouds with optical thicknesses (τ) ≧ 3.6
and cloud-top pressure (CTP) ≦ 440 hPa is overestimated in the strong ascent regime while that of optically thin
high-clouds with τ < 3.6 is underestimated for all the regimes. Cloud amount of optically thick low-clouds with
CTP ≧ 680 hPa is overestimated in the weak vertical motion regime as well in some models. The relevance ofcloud amount bias to cloud radiative effect bias is discussed.
Observations show that optically thick clouds in the strong ascent regime often have tops around 180–310 hPa.
In many models, the cloud top often reaches higher altitude compared to the observations. The tendency canespecially be seen in the models adopting the moisture accumulation type scheme presumably due to excessively
deep convection. Comparison of upward motion strength among the models and reanalyses suggests that cumulus
parameterization performs better when entrainment rate is varied with large-scale environmental fields to reducethe convection deepness where necessary.
Corresponding author: Hiroki Ichikawa, Okada PatentService Co. Ltd., 7th floor of Nagoya Chamber ofCommerce & Industry Building, 10-19, Sakae 2-chome,
Naka-ku, Nagoya 460-0008, Japan
E-mail: ichikawahtrr@gmail.com©2012, Meteorological Society of Japan

1.

Introduction

This study mainly analyzes clouds associated withconvective activity over oceanic regions in the tropics(30S-30N). Clouds of this type as well as those of other630

Journal of the Meteorological Society of Japantypes play an important role in regulating radiativebalance/imbalance at the top-of-atmosphere (TOA)
over the region. One of the properties of the cloudradiative effect, often called cloud radiative forcing(CRF), in association with convective activity over theregion is that longwave CRF (LWCRF) and shortwave
CRF (SWCRF) nearly cancel each other (Ramanathan
et al. 1989; Kiehl and Ramanathan 1990; Kiehl 1994;
Hartmann et al. 2001). Yuan et al. (2008) examined theeffect of large-scale circulation field on the relationship of LWCRF and SWCRF over the region with highsea surface temperatures (SSTs), and showed that thenear cancellation between the two CRFs occurs irrespectively of different large-scale circulation regimes
sorted by pressure-coordinated vertical motion at 500hPa (ω500).
The response of CRF associated with convective
activity in the tropics to an imposed climate perturbation in model experiments shows no consistency
in either sign or magnitude among different climate
models (Bony et al. 2004; Wyant et al. 2006; Williams
and Tselioudis 2007; Williams and Webb 2009). A
detailed analysis of model physics for clouds associated with convective activity is warranted to obtainphysical insights on what gives rise to the differencesin the CRF response. The analysis may involve model
evaluation and unraveling model bias as was done inseveral studies (e.g., Su et al. 2006; Ichikawa et al.
2009; Waliser et al. 2009; Williams and Webb. 2009).
Ichikawa et al. (2012), hereafter IMTK12, evaluatedthe relationship between LWCRF and SWCRF overwarm oceanic regions with SSTs above 27°C in thetropics for different ω500 regimes in coupled atmosphere-ocean general circulation models (AOGCMs)
participating in Coupled Model Intercomparison
Project Phase 3 (CMIP3). They revealed that theratio of LWCRF to SWCRF in the models is close to
the observed ratio in the strong ascent regime, but issystematically underestimated in the weak verticalmotion regime. In IMTK12, however, cloud properties
responsible for the CRF bias were not investigated in
depth.
The International Satellite Cloud Climatology
Project (ISCCP) simulator (Klein and Jokob 1999;
Webb et al. 2001), which mimics the ISCCP cloudclassification, is one of the major tools to evaluate eachmodel’s cloud properties. It classifies model’s clouds
into several types based on the optical thickness (τ)
and cloud-top pressure (CTP) using the model’s radiation routine. Several studies evaluated cloud amountfor different types of model clouds using the ISCCP
simulator (e.g., Lin and Zhang 2004; Ringer and AllanVol. 90, No. 5

2004; Zhang et al. 2005; Wyant et al. 2006; Williams
and Tselioudis 2007; Williams and Webb 2009). Wyant
et al. (2006) analyzed the simulated cloud properties fordifferent ω500 regimes in the tropical land and ocean.
They showed that models tend to make the mistake ofgiving optically thick clouds over the region where
satellites of ISCCP observe optically thin clouds.
This study evaluates cloud properties associated withconvective activity in the models participating in Cloud
Feedback Model Intercomparison Project (CFMIP,
McAvaney and Le Treut 2003; http://www.cfmip.net)
which provides the ISCCP simulator outputs. The
result of IMTK12 motivates us to conduct an analysisof the cloud properties. While IMTK12 analyzed thetwentieth-century simulations conducted by coupled
AOGCMs, this study analyzes the equilibrium control
experiment conducted by coupled atmosphere-mixed
layer slab ocean models. Because ocean heat transport
in the equilibrium control experiment is prescribed inorder to maintain a seasonal cycle of SSTs which is
as close as possible to the observed climatology, anymodel bias that appears in the current analysis may beascribed mainly to an intrinsic bias of the atmosphericgeneral circulation model (AGCM). Thus, this study
can reveal a model bias of cloud properties mainly
caused by the AGCM component, not largely contaminated by the biases caused by the oceanic generalcirculation model (OGCM) component and coupling
between the AGCM and the OGCM.
Following IMTK12, this study focuses on warm
oceanic regions with SST above 27°C in the tropics.
Cloud properties are evaluated for different regimes
using ω500 as an index of large-scale circulation.
First, the model reproducibility of CRF is analyzed
in the same way as IMTK12. Next, the model reproducibility of cloud amount for different cloud types
is analyzed. Finally, how the model reproducibility ofcloud amount for different cloud types affects that of
CRF is discussed. This study employs a wide range ofmodels with different types of cumulus parameterization schemes to facilitate a systematic intercomparisonof the parameterization schemes among the models.
Such an intercomparison may provide useful information to improve model physics.
Section 2 outlines the observational and reanalysis
data and model simulations used in this study. Section
3 describes the method for the present analysis. Section
4 presents the results of the reproducibility of CRF andcloud amount associated with convective activity inthe models. Section 5 is devoted to a discussion on the
result. Section 6 presents conclusions of this study.

October 2012

2.

H. ICHIKAWA et al.

Data

Following IMTK12, all the data used in this study
are interpolated to the common 2.5° × 2.5° grid in horizontal, the same coordinate system as reanalysis datasets, using the method by Kosaka et al. (2009).
2.1 Observational and reanalysis data
Three observational and reanalysis datasets used inthis study are the same as used in IMTK12. The Earth
Radiation Budget Experiment (ERBE) S-9 data (Barkstrom1984; Barkstrom and Smith 1986) are used to
calculate CRF. The 40-year European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis(ERA-40) data (Uppala et al. 2005) are used for atmospheric fields. The Hadley Centre Global Sea Ice and
Sea Surface Temperature (HadISST) analyses (Rayner
et al. 2003) are used for SST.
The followings are observational and reanalysis
datasets used in this study but not in IMTK12. The
ISCCP D1 VIS/IR cloud data (Rossow and Schiffer
1999) are used for cloud amount in this study instead of
ISCCP D2 cloud data used in IMTK12. This is because
cloud amount is provided for finer τ-CTP categories in
the ISCCP D1 data than in the ISCCP D2 data. The
ISCCP D1 data provide cloud amount in six categoriesof τ partitioned by 1.3, 3.6, 9.4, 23, and 60 and in sevencategories of CTP partitioned by 800, 680, 560 440,
310, and 180 hPa at every 3 hour for day-light hours
only, while the ISCCP D2 data provide cloud amountin three categories of τ partitioned by 3.6 and 23 andin three categories of CTP partitioned by 680 and 440
hPa on a monthly mean basis. Considering potential
differences in atmospheric fields among reanalysis
datasets, ECMWF Interim reanalysis (ERA-Interim;
Dee et al. 2011) and Japanese 25-yr reanalysis (JRA25; Onogi et al. 2007) data are used in this study aswell as ERA-40 data.
Monthly averages for 5 years and 1 month ofFebruary 1985 – February 1990 of the above datasetsare mainly used for the analysis. The period of the analysis is limited by the observational period of ERBE.
In addition, following datasets based on satellite
observation are used to discuss the result of analysis.
For the vertical profile of cloud amount, the monthly
mean product of CloudSat Reflectivity Data (available
in CFMIP website: http://climserv.ipsl.polytechnique.
fr/cfmip-obs/) which is generated from the Level 2
GEOPROF product (Mace et al. 2008; Marchand et al.
2008) is used. The data contain reflectivity-height histograms with the dBZ values binned by 5 dBZ intervals
in the range of –50 dBZ to 25 dBZ and the altitude631

binned by 480m intervals from surface to 20 km inheight. For humidity field in the lower and upper troposphere, Aqua Atmospheric Infrared Sounder (AIRS)/
Advanced Microwave Sounder Unit (AMSU) version
5 level-3 monthly product (Olsen et al. 2007) is used.
In addition, for humidity field in the upper troposphere,
Aura Microwave Lim Sounder (MLS) version 3.3
level-3 monthly product (Livesey et al. 2011) which isprepared for the comparison with GCM outputs (avail­able at ftp://mls.jpl.nasa.gov/pub/outgoing/fullerr/cmip/)
is used. Monthly averages for 4 years and 7 month ofJune 2006 – December 2010 of the above datasets areused. The period of the analysis is set to the period
available for CloudSat data.
2.2 Climate models
a. Overview
This study uses outputs from the equilibrium
control experiments conducted by 11 coupled atmosphere-mixed layer slab ocean models (Table 1). Fully
coupled model versions of the models (AOGCMs) areparticipating in CMIP3, except CCCMA4.0, HadSM4,
and UIUC. Three of the Met Office Hadley Centre
models are included in our study. HadGSM1 and
HadSM3 have considerable structural differences fromeach other, including different dynamical cores, spatial
resolutions, and physical parameterizations. HadSM4
is different from HadSM3 with respect to physicalparameterizations and vertical resolution. Two versionsof MIROC3.2 with different climate sensitivities areincluded in our study. The higher and lower sensitivity
versions of the model are referred as ‘‘MIROC-s-hi’’and ‘‘MIROC-s-lo’’, respectively. More information of
all the models analyzed in this study can be found in
Webb et al. (2006) and Williams and Webb (2009).
ISCCP simulator outputs are included in CFMIP
model outputs. ISCCP simulator outputs provide cloudamount in seven categories of τ partitioned by 0.3, 1.3,
3.6, 9.4, 23, and 60 and seven categories of CTP partitioned in the same way as the ISCCP observations. The
data are available for day-light hours only. The ISCCP
simulator output has one additional category of τ < 0.3,
which is below the least detectable limit of τ in the
ISCCP product. This study evaluates clouds with τ >0.3 in the models. However, the choice of the thresholdvalue of 0.3 is somewhat arbitrary (Zhang et al. 2005).
Thus, careful interpretation is needed for discussing thedifference of optically thin clouds between the ISCCP
simulator output and the ISCCP observations.
Monthly mean outputs for 5 years are analyzed foreach model except ECHAM5 and MIROC-s-hi wheremonthly outputs are unavailable. Five years is the632

Journal of the Meteorological Society of Japan

Vol. 90, No. 5

Table 1. Description of 11 models analyzed in this study. ‘T’ and ‘N’ in the column of resolution means the truncation ofspectral models and half the number of east–west points for grid-point models, respectively. ‘L’ in the column means thenumber of vertical atmospheric levels.
Modeling group

Model nameResolution(Horizontal/
Vertical)Canadian Centre for Cli- CCCMA4.0mate Modeling and Analysis
National Center for AtmoCCSM3
spheric Research
Max Planck Institute for ECHAM5
Meteorology
NOAA/Geophysical Fluid
GFDL2.1
Dynamics Laboratory
Hadley Centre for Climate HadGSM1
Prediction and Research/
Met Office
Hadley Centre for Climate
HadSM3
Prediction and Research/
Met Office
Hadley Centre for Climate
HadSM4
Prediction and Research/
Met OfficeMeteo-France/Centre NaIPSL4
tional de Recherches Meteorologiques
Center for Climate System MIROC-s-hi
Research, National Insti- (MIROC3.2)
tute for Environmental
studies, and Frontier Research Center for Global
ChangeSame as above
MIROC-s-lo(MIROC3.2)
Climate Research GroupUIUCof the University of Illinois at Urbana-Champaign

T47/L35

Main references

Modification of
cumulusparameterization
schemevon Salzen et al. (2005) Zhang and McFarlane(1995)/ZM

T85/L26

Collins et al. (2006)

T63/L31

Roeckner et al. (2003)

N72/L24

Delworth et al (2006)

N96/L38

Martin et al. (2006)

N48/L19

Pope et al. (2003)

Gregory and Rowntree(1990)/CBB

N48/L38

Webb et al. (2001)

Gregory and Rowntree(1990)/CBB

N48/L19

Hourdin et al. (2006)

Emanuel (1991)/EM

T42/L20

K-1 Model Developers(2004)

Pan and Randall (1998)/
AS

Emori et al. (2001)

T42/L20

K-1 Model Developers(2004)
Yang et al. (2000)

Pan and Randall (1998)/
AS
Yang et al. (2000)/CBB

Emori et al. (2001)

N36/L24

longest period where monthly outputs are available forall models. Monthly mean data are constructed from 5
years of daily mean data for ECHAM5 and 2 years ofdaily mean data for MIROC-s-hi.
b.

Cumulusparameterization
scheme/Labelin this paper

Cumulus parameterization schemes
Subgrid-scale cloudiness associated with tropicalconvection is largely controlled by the cumulus parameterization scheme adopted in each model. Several
types of schemes are used as described in Table 1. This
study classifies cumulus parameterization schemes into
5 of the 6 types in IMTK12. The Betts (1986) convective adjustment scheme is not represented in CFMIP
and thus is not analyzed in this study. The remaining

Zhang and McFarlane(1995)/ZM
Tiedtke (1989)/MC
Moorthi and Suarez(1992)/AS
Gregory and Rowntree(1990)/CBB

Nordeng (1994)
Tokioka et al. (1988)

Grandpeix et al. (2004)

5 types of scheme analyzed in this study are briefly
described as follows.
Three models (GFDL2.1, MIROC-s-hi, MIROCs-lo) adopt a scheme based on a mass flux approach with
spectral cloud models similar to Arakawa and Schubert(1974). This type of scheme is hereafter named AS.
The triggering mechanism and closure assumption forconvection activity (hereafter referred to as convective
trigger/closure) of this scheme are linked to convective
available potential energy (CAPE). The modification
proposed by Tokioka et al. (1988) and Emori et al.
(2001), both of which set a threshold for convection
occurrence, acts to relate the accumulation of moisture
to convection occurrence in model simulations.

Four models adopt a scheme based on a mass flux
approach with a bulk cloud model. For three models(HadgSM1, HadSM3, HadSM4, UIUC), the convective trigger/closure is based on cloud-base buoyancy
proposed by Gregory and Rowntree (1990) or Yanget al. (2000). This type of scheme is hereafter named
CBB. While the details of the parameterizations are
significantly different between Gregory and Rowntree(1990) and Yang et al. (2000), convection in both of thetwo schemes depends on the stability of atmosphere.
For one model (ECHAM5), the convective trigger/
closure is based on moisture convergence originallyproposed by Kuo (1965). This scheme is hereafter
named MC.
Two models (CCCMA4.0, CCSM3) adopt thescheme proposed by Zhang and McFarlane (1995)
which is based on the spectral rising plume concept
as in the AS scheme but assumes a constant spectral
distribution in cloud-base mass flux. The scheme ishereafter named ZM. The convective trigger/closureof this scheme links basically to CAPE. This scheme
is primarily designed for deep rather than shallow
cumulus convection by applying an assigned minimum
depth of convection.
A model (IPSL4) adopts the scheme proposed by
Emanuel (1991) which is based on a buoyancy sorting
approach. This scheme is hereafter named EM. The
convective trigger/closure of this scheme links basically to convective available potential energy (CAPE).
The modification proposed by Grandpeix et al. (2004)
acts to suppress deep convection when the free troposphere is dry in model simulations.
3.

633

H. ICHIKAWA et al.

Method

3.1 Calculation of cloud radiative forcing
Following IMTK12, LWCRF, SWCRF, NetCRF(LWCRF plus SWCRF), and R (the ratio of LWCRF to
SWCRF) are calculated as
LWCRF ≡ OLRclr － OLR,SWCRF ≡ TRSclr － TRS,NetCRF ≡ LWCRF + SWCRF,
LWCRF
R≡ SWCRF
where OLR and TRS refer to the outgoing longwave
radiation and the total reflected solar radiation, respectively, at the TOA for all-sky conditions. OLRclr and
TRSclr refer to those for clear-sky conditions. CRF
for different ω500 regimes is calculated on a monthly
mean basis. R is calculated from the climatologies of
LWCRF and SWCRF calculated with the full period
analyzed in this study for each ω500 regime.

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Fig. 1.

Definition of cloud types in this study.

3.2 Definition of cloud types
This study compares cloud amount for different
cloud types in ISCCP simulator outputs with that in
the ISCCP observations. While the ISCCP simulator
is a valuable tool for evaluating model reproducibilityof cloudiness, definitions of τ and CTP in the ISCCP
simulator are not exactly consistent with those in the
ISCCP observation (Mace et al. 2011). This point should
be kept in mind through this study. Nevertheless, theanalysis of cloud amount for different cloud types in
conjunction with that of CRF reflecting cloud properties could be quite helpful to unraveling intrinsic model
biases.
This study evaluates cloud amount for 4 cloud types(Fig. 1). At first, clouds are categorized into 3 cloudtypes of high, middle, and low clouds. The definitionsof the 3 cloud types are based on the ISCCP classification where detected CTP is smaller than 440 hPa forhigh clouds, between 680 hPa and 440 hPa for middle
clouds, and larger than 680 hPa for low clouds. The
evaluation of cloud amounts in this study is particularly
focused on high clouds, which largely control CRF
over the warm oceanic regions in the tropics (e.g., Yuan
et al. 2008). High clouds are subdivided into opticallythick high-clouds (TckHCs) with τ ≧ 3.6 and opticallythin high-clouds (TnHCs) with τ < 3.6. In the ISCCP
classification, TckHCs include deep convective clouds
and cirrostratus clouds while TnHCs correspond to
cirrus clouds (Rossow and Schiffer 1999). High clouds
are subdivided because of the following two points.
The first point is a difference in physical mechanism
for cloud formation. In the observations, the occurrence of cirrus clouds does not always coincide withthat of deep convective clouds and cirrostratus clouds,

634

Journal of the Meteorological Society of Japan

while the occurrence of deep convective clouds andcirrostratus clouds are closely related with each other(e.g., Luo and Rossow 2004; Del Genio et al. 2005).
The second point is a difference in CRF. Cirrus clouds
act to primarily warm the atmosphere-surface system
while other high clouds act to cool it (e.g., Hartmann
et al. 2001; Kubar et al. 2007). When the impact ofmodel reproducibility of cloudiness on that of CRF is
discussed as will be done in section 5b, this subdivision
is quite useful to identify the linkage of cloud biases
with CRF biases.
4.

Results

4.1 Reproducibility of ω500
First of all, the model reproducibility of ω500 is
analyzed. As an important aspect, Fig. 2 shows theprobability density function (PDF) of ω500 in domainsof warm SSTs above 27°C for reanalysis datasets andthe models. The model-line colors are based on cumulus
parameterization scheme. Following IMTK12, thisstudy defines 4 regimes as shown in Table 2. In reanalysis datasets, the PDF exhibits a moderate peak in
the weak vertical motion regime, declining gradually
toward stronger ascending motion and sharply toward
stronger descending motion. In most models, the PDFs
agree overall with those in reanalysis datasets. Close
examination reveals the differences of PDF reproducibility among models. In IPSL4, the PDF peak biases
leftward against the reanalysis datasets, while the
PDF width is reasonable. In UIUC, the PDF width isnarrower than the reanalysis datasets while the PDF
peak is roughly reasonable.
4.2 Reproducibility of CRF
This subsection evaluates CRF over the warm
oceanic regions. Overall features of model reproducibility of CRF described below are generally similar
to those in IMTK12. Figure 3 shows the composite of(a) LWCRF, (b) SWCRF, (c) NetCRF, and (d) R as afunction of ω500. The absolute value of CRF is hereafter referred to as the CRF magnitude. The models
agree qualitatively well with the ERBE observations in
the relationships of LWCRF and SWCRF with ω500:
the two CRFs vary almost linearly with ω500 in themodels as do in the ERBE observations. LWCRF and
SWCRF in the models spread around the ERBE observations over a range of ~40 W m–2 at each ω500.
The magnitude of inter-model spread of NetCRF issimilar to that of each LWCRF and SWCRF. NetCRFs
of the models are generally overestimated in magnitude. Rs in the models are relatively close to the ERBE
observations in the strong ascent regime. The reason-

Vol. 90, No. 5able R results from LWCRF and SWCRF in somemodels (e.g., ECHAM5, GFDL2.1, HadGSM1), while
it occurs as a result of the compensation of overestimated LWCRF and SWCRF in magnitude in somemodels (e.g., CCSM3, HadSM4, IPSL4, MIROCs-lo). The model reproducibility of R worsens withincreasing ω500 for all models. In the weak verticalmotion regime, R is systematically underestimated inmost models. This underestimation of R is associatedmainly with the underestimation of LWCRF in mostmodels, and is also associated with the overestimation of SWCRF in magnitude in some models (e.g.,
CCSM3, MIROC-s-hi, MIROC-s-lo).
4.3 Reproducibility of cloud amountThis subsection evaluates cloud amount for different
cloud types. In the previous subsection, it is shown thatsome models well reproduce CRF as a function of
ω500 (Fig. 3). However, because CRF is determinedfrom the cumulative effect of clouds having a varietyof optical depths and cloud heights, high reproducibility of CRF in a model does not necessarily result
from the high reproducibility of individual clouds inthe model. Analyzing simulated clouds for different
optical depths and CTPs is thus important to fully evaluate model physics. The following analyses are particularly focused on high clouds which largely control
total CRF over the warm oceanic regions (Yuan et al.
2008).
a.

Overall features
Figure 4 shows cloud amount for the 4 cloud types
for the ISCCP observations and the models as a function of ω500. Most models qualitatively agree wellwith the ISCCP observations in the relationships of
TckHC amount and TnHC amount with ω500: bothcloud types’ values decrease with increasing ω500.
However, for TckHC amount, the gradient is steeper
in the models than in the ISCCP observations. Most of
the models overestimate TckHC amount in the strong
ascent regime. TnHC amount is underestimated for all
the regimes in most models. However, this underestimation might be due to the cutoff value of τ at 0.3 inthe ISCCP simulator outputs. When clouds with τ < 0.3
are included in TnHCs in the models, TnHC amountis close to the ISCCP observations or even overestimated. Middle and low cloud amounts (Figs. 4c andd) are underestimated in most models. The underestimation of these cloud amount remains even if clouds
with τ < 0.3 are included in the model outputs. As
shown in the next subsection, while the total low cloudamount is underestimated in the models, optically thickOctober 2012

635

H. ICHIKAWA et al.

Ȱ>K3DGD\@
Fig. 2. PDF of ω500 for each bin of width 10 hPa day–1 for reanalysis data (black lines) and 11 models (colorlines). The model-line colors are based on cumulus parameterization scheme which is denoted in parentheses.

Table 2. Definition of ω500 regimes in this study [hPa day–1].
Strong ascent

Moderate ascent

Weak vertical motion

Moderate-strong descentω500 < –40–40 < ω500 < –10–10 < ω500 < 20

ω500 > 20

low-cloud amount is overestimated in some models.
b.

Vertical distribution
In order to examine the model biases in further depth,
cloud amount for the 7 vertical layers is analyzed. Analysis is particularly focused on optically thick clouds toexamine the detail of overestimation of TckHC amountin the models. Pressure levels between 440–180 hPa
and between 180–50 hPa are hereafter referred to as“the upper tropopshere” and “near the tropopause”
respectively, for the analyses of high clouds.
Figure 5 shows the vertical distribution of cloudamount for optically thick clouds with τ ≧ 3.6, including
TckHCs, as a function of ω500. In the ISCCP observations, the cloud amount is largest at 310–180 hPa in thestrong and moderate ascent regimes. The model performance of reproducing cloudiness varies among models.
At pressure below 440 hPa, the cloud amount is largest
at 310–180 hPa in many models as is in the ISCCP
observations. The cloud amount in the upper troposphere in nearly half of the models is roughly close tothat in the ISCCP observations. On the other hand, thecloud amount near the tropopause is overestimated in
the strong ascent regime in many models. This overes-

timation seems to cause the overestimation of TckHC
amount in Fig. 4a. Models adopting cumulus parameterization schemes sensitive to moisture accumulation(ECHAM5, GFDL2.1, IPSL4, MIROC-s-hi, MIROCs-lo) tend to overestimate the cloud amount near the
tropopause. For reference, cloud amount at pressures
above 800 hPa (part of low clouds) are evidently overestimated in some models (e.g., CCCMA4.0, CCSM3,
MIROC-s-hi, MIROC-s-lo, UIUC).
5. Discussion
5.1 Potential relevance of model bias in cloudinesswith cumulus parameterization schemes
In Section 4, near-tropopause thick cloud amount
were found to be overestimated in many models
compared to the ISCCP observations. However, as
pointed out by Mace et al. (2011), the difference
between ISCCP simulator outputs and the ISCCP
observations may partly come from the retrieval error
in the ISCCP observations. In order to confirm theresult of the ISCCP observations, the CloudSat observations are further analyzed. Figure 6 shows the vertical
distribution of cloud amount observed by the CloudSat
radar as a function ω500. Because the sensitivity of the636

Journal of the Meteorological Society of Japan

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Vol. 90, No. 5

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Fig. 3. Composite of (a) LWCRF, (b) SWCRF, (c) NetCRF, and (d) R as a function of ω500 for ERBE observations sorted by ω500 of reanalyses (black lines) and 11 models (color lines).

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Fig. 4. Composite of (a) TckHC, (b) TnHC, (c) middle cloud, and (d) low cloud amount as a function of ω500 for
ISCCP observations sorted by ω500 of reanalyses (black lines) and 11 models (color lines).

October 2012

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radar is limited to relatively large cloud particles, theCloudSat observation is comparable mainly with optically thick clouds in the ISCCP observation although
the CloudSat observation may partly include opticallythin clouds. A large amount of cloudiness is seen formany pressure levels between 800 hPa and 180 hPa
in the strong ascent regime. In the upper troposphere,a large amount of cloudiness extends from the strong
ascent regime to descent regime. Cloud amount drastically decreases upward near the tropopause, whereclouds are rarely detected. This result suggests thatthe cloud top of deep convection and associated anvil
clouds rarely reaches above 180 hPa, supporting theresults from the ISCCP observations.
The physical causes of the overestimation of thenear-tropopause thick cloud amount in the models arenext examined. TckHC formation is closely related
to deep convection, so that vertical velocity field is

3UHVVXUH>K3D@

Fig. 5. Composite of cloud amount for clouds with τ ≧ 3.6 at each pressure level defined by ISCCP as a functionof ω500 for ISCCP observations sorted by ω500 of ERA-Interim and 11 models. A white solid line in each panel
shows the pressure level at 440 hPa. Models are sorted by cumulus parameterization scheme which is denoted inparentheses.

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Fig. 6. Composite of vertical distribution of cloudamount as a function of ω500 for CloudSat
observations sorted by ω500 of ERA-Interim.
Cloud amount are estimated based on the relative
frequency of occurrence for the dBZ above -25
in the CloudSat observations.

638

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Fig. 7. Composite of vertical distribution of ω as a function of ω500 for available 10 models and 3 reanalyses. The
CFMIP dataset does not provide vertical profile of ω for MIROC-s-hi. Anomaly from multi-model mean, which
is displayed at right bottom corner, is shown for each model and reanalysis. For multi-model mean, multiply thevalues of the scale bar by 3.0. A black solid line in each panel shows the 440-hPa pressure level.

analyzed. Figure 7 shows the anomaly of verticalvelocity in each model and reanalysis against themulti-model mean as a function of ω500. From now
on, the pressure level at 200 hPa is included in thenear tropopause category because upward motion justbelow 180 hPa largely affects cloud formation abovethat level. In the strong ascent regime, a deep layer ofstrong upward motion appears in the upper troposphereand near the tropopause in the many models, overestimating near-tropopause thick clouds (e.g., ECHAM5,
IPSL4, GFDL2.1, MIROC-s-lo).
In order to examine the relationship of near-tropopause thick cloud amount and upward motion
strength, Fig. 8 shows a scatter plot between the twoparameters in the strong ascent regime. In general, atthe near tropopause, a model with stronger upwardOctober 2012

730

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H. ICHIKAWA et al.

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Fig. 8. Scatter plots of near-tropopause thick cloud amount against ω in the strong ascent regime for available 10
models. Plots of ISCCP observations against reanalyses are also shown. Near-tropopause ω is derived by an average of ω between 100 hPa and 200 hPa. Inter-model correlation coefficient between the two components is 0.73,
which exceeds the 95 % level of significance.

motion tends to simulate larger amount of thick cloud.
Close examination reveals that upward motion isstronger in GFDL2.1, IPSC4, and MIROC-s-lo thanin CCCMA4.0 and HadSM4, but optically thick cloudamount is larger in the latter than in the former. Cloudformation processes appear to be linked to upward
motion in different ways from one model to another.
In Fig. 8, the inter-model difference in verticalvelocity field in the strong ascent regime is closely tiedwith the cumulus parameterization scheme adopted
in each model. In general, upward motion strength isstronger in the models adopting a scheme which needs
moisture accumulation for convection occurrence (i.e.,
AS with a threshold, modified EM, and MC) than in themodels adopting the CBB and ZM type schemes. The
upward motion strength of reanalyses is, on average,
close to the two models adopting moisture accumulation type scheme (GFDL2.1 and IPSL4). Compared to
ERA40 and JRA25, upward motion strength is reducedin ERA-interim, where a deep layer of enhanced

upward motion in the upper-troposphere and near the
tropopause is almost absent (Fig. 7).
In order to examine the large-scale environment for
deep convection occurrence, moisture fields in the low
and mid levels of troposphere are examined. Figure 9shows relative humidity at 925 hPa and at 700 hPa asa function of ω500. Inter-model difference of relative
humidity is smaller at 925 hPa than at 700 hPa. Relative humidity at 700 hPa in the strong ascent regime
shows systematic differences depending on cumulus
parameterization scheme adopted in each model. The
relative humidity is high in the models adopting moisture accumulation type scheme (blue in Fig. 9), where
near-tropopause upward motion is strong (see Figs. 7,
8). The relative humidity is moderate in the models
adopting the CBB type scheme (orange in Fig. 9), but
is low in the ZM scheme (green in Fig. 9). The relative
humidity in the AIRS observations is lower than thatin the models adopting the moisture accumulation type
and the CBB type schemes. One needs to keep in mind

640

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sorted by ω500 of ERA-interim (a black line with no mark), reanalyses (black lines with marks), and 11 models(color lines).

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Fig. 10. Composite of specific humidity at 150 hPa as a function of ω500 for AIRS and MLS observations sortedby ω500 of ERA-interim (black lines with no mark), reanalyses (black lines with marks), and 11 models (colorlines).

the fact that AIRS retrieval algorithm could be biased
toward clear-sky conditions. The relative humidity inthe two of the three reanalyses is higher than that in the
AIRS observations (Fig. 9).
Near-tropopause thick cloud amount formation links
not only to upward motion but also to moisture supply.
Figure 10 shows specific humidity at 150 hPa as a function of ω500. In the strong ascent regime, the specifichumidity tends to be high in the models adopting moisture accumulation type scheme (blue in Fig. 10) where
near-tropopause thick cloud amount tends to be large(see Figs. 5, 8); Three out of four of the models overestimate the specific humidity compared to the observations. The specific humidity is high and overestimatedalso in CCCMA4.0 where the cloud amount is large(see Figs. 5, 8).

5.2

The relevance of cloud amount bias with the CRF
bias
In this subsection, the impact of cloud amount bias
on the CRF bias in the models is discussed. Figures
11 and 12 show the individual CRF for each cloudtype in the ISCCP observations and the models. The
method for constructing data of Figs. 11, 12 is same asused in Yuan et al. (2008). First, we calculate CRF foreach of the 42 τ-CTP categories of ISCCP: we take theovercast-sky CRF values calculated by Hartmann et al.
(2001), where the radiation model developed by Fu andLiou (1992, 1993) is used, for the 42 categories andthen multiply the CRF values by their cloud amounts.
Second, these results are aggregated to produce CRFs
for 4 cloud types (TckHCs, TnHCs, middle clouds, andlow clouds). Figures 11 and 12 are useful to discuss therelative contribution of each cloud type to CRF. Rela-October 2012

H. ICHIKAWA et al.

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Fig. 11. LWCRF by (a) TckHCs, (b) TnHCs, (c) middle clouds, and (d) low clouds in ISCCP observations sorted by ω500 of reanalyses and 8 models (see the text for details of the calculation method). Reconstructed total
LWCRF combining the effects of all 4 cloud types and total LWCRF which is identical to LWCRF in Fig. 3 areplotted in (e) and (f), respectively. Three models of CCCMA4.0, ECHAM5, and UIUC are omitted because thereconstructed CRF is far different from the total CRF.

tive differences of CRF magnitude between the observation and the models, and among models themselvesin reconstructed total CRF combining the effects of all
4 cloud types (Figs. 11e, 12e), is generally consistent
with those in total CRF (Figs. 11f, 12f).
Figure 11 shows the contribution of 4 cloud types
to LWCRF. TckHCs and TnHCs largely control total
LWCRF both in the observations and the models forall ω500s. Model biases of total LWCRF are related
to those of LWCRF by individual clouds as described

below. In the strong ascent regime where total LWCRF
is overestimated or reasonable, LWCRF by TckHCs
is overestimated in most models. This overestimation is partly compensated by the underestimation of
LWCRF by TnHCs in some models (e.g., HadGSM1,
HadSM4, MIROC-s-hi). In the weak vertical motion
regime where total LWCRF tends to be underestimated, LWCRFs by TckHCs and TnHCs are roughly
reasonable and underestimated in most models, respec­tively. As was described in Subsection 2.2 and Subsec­

642

Journal of the Meteorological Society of Japan

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Fig. 12.

Same as Fig. 11 but for SWCRF.

tion 4.3, accurate evaluation of TnHC amount is difficultin the current analysis. However, unless TnHC amountis underestimated in the models (Fig. 4b), underestimationof the total LWCRF in the weak vertical mo­tion regime
cannot be explained. Thus, it seems to be certain thatradiatively effective TnHC amount is underestimated in
the models.
Figure 12 shows the contribution of 4 cloud types to
SWCRF. In the observations, TckHCs largely control
total SWCRF in the strong ascent regime, but all 4
cloud types equally contribute to total SWCRF in the
weak vertical motion regime. Model biases of total
SWCRF are related to those of SWCRF magnitude
by individual cloud as described below. In the strong

ascent regime where total SWCRF is overestimated orreasonable, SWCRF by TckHCs is overestimated inmost models. This overestimation is partly compensated by the underestimation of SWCRF by TnHCs
and middle clouds. In the weak vertical motion regime
where total SWCRF is overestimated in some models,SWCRF by low clouds is overestimated in those models(e.g., CCSM3, MIROC-s-hi, MIROC-s-lo). Overestimation of cloud amount with optically thick low-clouds(Fig. 5) is responsible for this bias.
6.

Conclusions

The reproducibility of cloud amount and CRF associated with tropical convection over warm oceanicOctober 2012

H. ICHIKAWA et al.

regions with SSTs above 27°C is evaluated for 11
models participating in CFMIP. Cloud amount and
CRF are evaluated for different regimes of large-scale
circulation field sorted by ω500 as an index of largescale vertical motion field. The warm oceanic regions
cover a variety of vertical motion regimes ranging
from strong ascent to weak descent.
In the warm oceanic regions, LWCRF and SWCRF
nearly cancel each other irrespectively of ω500 in the
ERBE observations. The absolute value of the ratioof LWCRF to SWCRF, R, is well reproduced in themodels in the strong ascent regime: This is broughtby reasonable LWCRF and SWCRF in some models
while by the compensation of opposite overestimationbiases of LWCRF and SWCRF in other models. R issystematically underestimated in the models in the
weak vertical motion regime: This is associated withthe underestimation of LWCRF in most models, whilewith the overestimation of SWCRF in some models.
In order to unravel the model biases of cloudiness
responsible for the CRF biases, ISCCP simulator
outputs are evaluated in comparison with the ISCCP
observations. The evaluation is focused mainly onhigh clouds with cloud-top pressures below 440 hPa,because they have strong impacts on CRF over thewarm oceanic regions. High clouds are classified into
optically thick high clouds (TckHCs) with τ ≧ 3.6 andthin high clouds (TnHCs) with τ < 3.6. TckHC amounttends to be overestimated in the models in the strong
ascent regime. TnHC amount in the models is underestimated irrespectively of ω500. However, this underestimation may be related to the arbitrary cut off valueof τ at 0.3 in the ISCCP simulator outputs. Opticallythick low-cloud amount is overestimated in the weakvertical motion in some models.
The ISCCP and CloudSat observations show thatcloud top of optically thick clouds often appears at
310–180 hPa in the strong ascent regime. On the other
hand, in many models, the cloud-top often reaches
higher altitudes compared to the observations. In
particular, models adopting the moisture accumulation
type scheme exhibit such tendency. In general, TckHC
amount increases in a model as upward motion isenhanced in the models. While the model dependenceof linkage of cloud formation processes with upward
motion needs to be considered, convection in thesemodels is deeper and detrains more condensates a toohigh a level and tends to make more thick clouds there.
Intermodel difference in convection strength strong­lydepends on relative humidity in the lower free troposphere as was pointed out by previous studies (e.g.,
Gregory and Miller 1989, Derbyshire et al. 2004,

643

Takayabu et al. 2010). As relative humidity in thelower free troposphere increases, convection strength
increases presumably through a reduction of dry air
entrainment. The importance of moisture accumulation for proper simulation of convection occurrence
has been pointed out by previous studies (e.g., Tokioka
et al. 1988, Emori et al. 2001, Zhang and Mu. 2005,
Lin et al. 2007, Lin et al. 2008). However, in themodels adopting moisture accumulation type scheme
analyzed in this study, convection seems to be too deep
when relative humidity is high in lower free troposphere. Difference in upward motion strength between
ERA-Interim and ERA-40 may provide important
insight to the convection deepness in the cumulus
parameterization schemes. Upward motion strength isreduced in ERA-Interim where entrainment rate varies
depending on the large-scale environmental field withno prescribed moisture accumulation (Bechtold et al.
2008), compared to ERA-40, where entrainment rateis originally imposed with prescribed moisture accumulation (Gregory et al. 2000). In the models analyzed
in this study, entrainment rate is originally imposedin their cumulus parameterization schemes with pre­scribed moisture accumulation. The introduction ofstate-dependent entrainment rate may be an effective
way to simulate deep convection properly in a model.
The relevance of the cloud amount biases to the CRF
bias in the models is discussed. In the strong ascentregime, the overestimation of TckHC amount causes
the overestimation of LWCRF and SWCRF in mostmodels, while the underestimation of cloud amountfor other cloud types leads to the reduction of CRF insome models. In the weak vertical motion regime, theunderestimation of TnHC amount causes the underestimation of LWCRF in most models, while the overestimation of optically thick low clouds causes the overestimation of SWCRF in some models. It is important
to study different types of clouds individually when
one tries to locate the sources of CRF biases in climate
models.
The method adopted in this study is a useful approach to evaluate model reproducibility of cloudamount and CRF associated with convective activity,and can be used to evaluate it in model simulations for
the CMIP5. In particular, applying the same methodon the CFMIP Observation Simulator Package (COSP;
Bodas-Salcedo et al. 2011) outputs which are available in the CMIP5 models will unravel model biases
in depth, including microphysical properties of modelclouds.

644

Journal of the Meteorological Society of Japan

Acknowledgments
The modeling groups, the PCMDI, and the WCRP’s
Working Group on Coupled Modeling (WGCM) madeavailable the WCRP CFMIP multi-model dataset.
Support to this dataset is provided by the Office of
Science, U.S. Department of Energy. This research was
supported by the Global Environment Research Fund
(S-5-2) of the Ministry of the Environment, Japan, andby the ”Data Integration and Analysis System (DIAS)”
Fund for National Key Technology from the Ministryof Education, Culture, Sports, Science and Technology,
Japan.
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