15 FEBRUARY 2012

ICHIKAWA ET AL.

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Reproducibility by Climate Models of Cloud Radiative Forcing Associated with
Tropical Convection
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, Exeter, United Kingdom

HIROSHI KANZAWA
Department of Earth and Environmental Sciences, Graduate School of Environmental Studies,
Nagoya University, Nagoya, Japan(Manuscript received 23 February 2011, in final form 26 July 2011)
ABSTRACTIn this study, cloud radiative forcing (CRF) associated with convective activity over tropical oceans isanalyzed for monthly mean data from twentieth-century simulations of 18 climate models participating inphase 3 of the Coupled Model Intercomparison Project (CMIP3) in comparison with observational andreanalysis data. The analysis is focused on the warm oceanic regions with sea surface temperatures (SSTs)
above 278C to exclude the regions with cold SSTs typically covered by low stratus clouds. CRF is evaluated for
different regimes sorted by pressure-coordinated vertical motion at 500 hPa (v500) as an index of large-scale
circulation. The warm oceanic regions cover the regime of vertical motion ranging from strong ascent to weakdescent. The most notable feature found in this study is a systematic underestimation by most models of theratio of longwave cloud radiative forcing (LWCRF) to shortwave cloud radiative forcing (SWCRF) over the
weak vertical motion regime defined as 210 , v500 , 20 hPa day21. The underestimation of the ratio
corresponds to the underestimation of LWCRF and the overestimation of SWCRF. Clouds in models seem tobe lower in the amount of high clouds but more reflective than those in the observations in this regime.
In the weak vertical motion regime, the lower free troposphere is dry. In the large-scale environment
condition, the reproducibility of LWCRF is high in models adopting the scheme where the relative humidity–
based suppression for deep convection occurrence is implemented. Models adopting the Zhang and McFarlane
scheme show good performance without such a suppression mechanism.

1. Introduction
The radiative effect of clouds, often called cloud radiative forcing (CRF), associated with convective activity largely controls the radiative balance–imbalance

Corresponding author address: Hiroki Ichikawa, Dept. of Earthand Environmental Sciences, Graduate School of Environmental
Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 4648601, Japan.
E-mail: ichikawa@hyarc.nagoya-u.ac.jp
DOI: 10.1175/JCLI-D-11-00114.1
Ó 2012 American Meteorological Society

at the top of the atmosphere (TOA) over the tropics
through the horizontal extension of high clouds that
accompany deep convection (Ramanathan and Collins
1991; Lindzen et al. 2001; Hartmann et al. 2001). The
response of CRF associated with convective activity
to an imposed climate perturbation is thus fundamental
for our understanding of climate change, but shows no
consistency in either sign or magnitude among different
climate models in climate perturbation experiments (Bonyet al. 2004; Wyant et al. 2006; Williams and Tselioudis
2007; Williams and Webb 2009). A detailed evaluation

1248

JOURNAL OF CLIMATEof CRF associated with convective activity is warranted
to provide physical insights into particular biases of themodels in the CRF response.
The model reproducibility of CRF associated withconvective activity depends on how the model deals withmoist convection and its associated cloud properties.
Previous studies evaluated different elements of clouds
associated with convective activity in conjunction withdynamic and thermodynamic environments in models.
Ichikawa et al. (2009) showed that the horizontal spreadof high clouds around the center of upper-tropospheric
divergence is underestimated over the large-scale ascending branch in models. Su et al. (2006) showed thatthe sensitivity of the cloud ice amount to sea surface temperature (SST) is underestimated in models and largely
diverse among models. These biases would cause biases
in the CRF.
Observational evidence has shown that, in the tropics,
the longwave cloud radiative forcing (LWCRF) andshortwave cloud radiative forcing (SWCRF) are bothstrong and nearly cancel each other over the region where
deep convection is active (Ramanathan 1989; Kiehl and
Ramanathan 1990; Kiehl 1994; Hartmann et al. 2001).
Recently, Yuan et al. (2008, hereafter Y08) reexamined
the observed relationship between LWCRF and SWCRF
in the tropics with consideration of the effects of thelarge-scale circulation. They revealed that, over warm
oceanic regions with SST above 288C, the two components of CRF nearly cancel each other regardless of thelarge-scale environment.
This study evaluates CRF associated with convective
activity over tropical oceans in coupled atmosphere–
ocean general circulation models (AOGCMs), considering the effects of the dynamic and thermodynamic
environments. The area domain analyzed in this study
is warm oceanic regions where shallow to deep convection develops in response to large-scale circulationin the troposphere and/or in conjunction with highmoist static energy in the lower troposphere. First, weanalyze the reproducibility of CRF for different regimes of large-scale circulation in climate models. The
main focus of the analysis is on evaluating the relationship of the balance between LWCRF and SWCRF withlarge-scale circulation that was revealed observationally by Y08. We find a bias in the balance common to
the models for particular regimes of large-scale circulation, and then devote our efforts to unraveling thephysical causes for this common bias. This study employs a wide range of models with different types ofcumulus parameterization schemes and large-scalecondensation schemes, so that we can undertake asystematic intercomparison of the parameterization
schemes among the models. Such an intercomparison

VOLUME 25

may provide useful information helpful for improving
the model physics.
Section 2 outlines the observational and reanalysis
data and model simulations used in this study. Section 3
describes a method for the analysis. Section 4 presents
the results on the reproducibility of CRF associated withconvective activity in the models as described above.
Section 5 is devoted to a discussion on the result. Section
6 presents our concluding remarks.

2. Dataa. Observational and reanalysis data
The data used to evaluate the model outputs include both observational and reanalysis datasets. The
Earth Radiation Budget Experiment (ERBE) S-9 data
(Barkstrom1984; Barkstrom and Smith 1986) are used to
calculate CRF. The data contain monthly mean valuesof the reflected shortwave and emitted longwave fluxes
at the TOA obtained from broadband scanner measurements. The International Satellite Cloud Climatology Project (ISCCP) D2 visible–infrared (VIS–IR) clouddata (Rossow and Schiffer 1999) are used for cloudamount, cloud-top pressure, and cloud water path. The
40-yr European Centre for Medium-Range Weather
Forecasts (ECMWF) Re-Analysis (ERA-40) dataset(Uppala et al. 2005) is used for the atmospheric variables. Pressure-coordinated vertical velocity at 500 hPa,denoted by v500, is used as an index of the large-scale
circulation field. The Hadley Centre Global Sea Ice and
Sea Surface Temperature (HadISST) analyses (Rayner
et al. 2003) are used for SST. For a comparison witha model output, all these data are interpolated onto thecommon 2.58 3 2.58 grid, the same coordinate system as
in ERA-40, using the method of Kosaka et al. (2009).
Monthly averages for 5 yr and 1 month during February
1985–February 1990 are used for the analysis. The period of the analysis is limited by the observational periodof ERBE.

b. Climate models
1) OVERVIEWThis study uses outputs from the twentieth-century
climate simulations conducted with the 18 AOGCMs
listed in Table 1. These models were included in the
Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC; Solomon et al.
2007), and their data were submitted to the Programfor Climate Model Diagnosis and Intercomparison(PCMDI). These archived data constitute phase 3 of theWorld Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project (CMIP3).

Third climate configuration of the Met Office
Unified Model (HadCM3)
Hadley Centre Global Environmental Model,
version 1 (HadGEM1)

Hadley Centre for Climate Prediction andResearch/Met Office1.38 3 1.98

2.58 3 3.88

2.88 3 2.88

Zhang and McFarlane(1995) (ZM)
Gregory and Rowntree(1990) (CBB)Gregory and Rowntree(1990) (CBB)

Wilson and Ballard (1999); no

Rasch and Kristjansson(1998); no
Smith (1990); no

Yukimoto et al. (2006); no

Le Trent and Li (1991); no

Le Trent and Li (1991); no

Diansky and Volodin (2002); no
Bony and Emanuel (2001); yes

Del Genio et al. (1996); yes

Del Genio et al. (1996); yes

Tiedtke (1993); yes

Lohmann and Roeckner(1996); yes
Rasch and Kristjansson(1998); no
Tiedtke (1993); yes

Ricard and Royer (1993); no

McFarlane et al. (2005); no

McFarlane et al. (2005); no

Zhang et al. (2003); yes

LSC* parameterization
scheme; coupling with cumulus
parameterization scheme?

ICHIKAWA ET AL.

* LSC 5 large-scale condensation.

NCAR

2.88 3 2.88

1.18 3 1.18

Model for Interdisciplinary Research on
Climate 3.2, high-resolution version(MIROC-hi)
MIROC3.2, medium-resolution version(MIROC-me)
MRI Coupled General Circulation Model,
version 2.3.2 (MRI)
Parallel Climate Model, version 1 (PCM1)

Center for Climate System Research, National
Institute for Environmental Studies, andFrontier Research Center for Global Change

Meteorological Research Institute (MRI)

4.08 3 5.08
2.58 3 3.88

4.08 3 5.08

GISS Model E-R (GISS-ER)
INM Coupled Model, version 3.0 (INM)
IPSL Coupled Model, version 4 (IPSL)

4.08 3 5.08

GISS Model E-H (GISS-EH)

2.88 3 2.88

1.98 3 1.98

Institute for Numerical Mathematics (INM)
Institute Pierre Simon Laplace (IPSL)

NASA Goddard Institute for SpaceStudies (GISS)

Pan and Randall (1998)/
Emori et al. (2001) (AS)Pan and Randall (1998) (AS)2.08 3 2.58

GFDL Climate Model version 2.1 (GFDL2.1)

2.88 3 2.88

2.08 3 2.58

NOAA–Geophysical Fluid Dynamics
Laboratory (GFDL)

Flexible Global Ocean–Atmosphere–Land
System Model gridpoint version 1.0 (FGOALS)GFDL Climate Model version 2.0 (GFDL2.0)

LASG/Institute of Atmospheric Physics

Tiedtke (1989)/Nordeng(1994) (MC)Zhang and McFarlane(1995) (ZM)
Moorthi and Suarez (1992)/
Tokioka et al. (1988) (AS)
Moorthi and Suarez (1992)/
Tokioka et al. (1988) (AS)
Del Genio and Yao(1993) (CBB)Del Genio and Yao(1993) (CBB)
Betts (1986) (BE)
Emanuel (1991)/
Grandpeix et al.
(2004) (EM)Pan and Randall (1998)/
Emori et al. (2001) (AS)1.98 3 1.98

CNRM Coupled Global Climate Model,
version 3 (CNRM)
ECHAM5/MPI Ocean Model (ECHAM5)

Météo-France/Centre National deRecherches Metéorologiques (CNRM)
Max Planck Institute for Meteorology (MPI)

1.98 3 1.98

2.88 3 2.88

Zhang and McFarlane(1995) (ZM)
Zhang and McFarlane(1995) (ZM)
Zhang and McFarlane(1995) (ZM)
Bougeault (1985) (MC)Cumulus parameterization
scheme/modification(label in this paper)

1.48 3 1.48

Community Climate System Model,
version 3 (CCSM3)
CCCma Coupled General Circulation Model,
version 3.1 (CGCM-T47)
Same as above (CGCM-T63)

Model (label in this paper)

National Center for AtmosphericResearch (NCAR)
Canadian Centre for Climate Modelingand Analysis (CCCma)

Modeling group

Atmosphericresolution(lat 3 lon)

TABLE 1. Descriptions of the 18 coupled AOGCMs analyzed in this study (more information available online at http://www-pcmdi.llnl.gov/.)

15 FEBRUARY 2012

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1250

JOURNAL OF CLIMATEThe twentieth-century simulations by the models areevaluated by using monthly mean outputs for the 20 yr
during 1980–99 so that the data period covers that of theobservations (February 1985–February 1990) and is longenough to evaluate the climatological features of themodel reproducibility. All the variables are interpolated
onto the common 2.58 3 2.58 grid, the same as the observational and reanalysis datasets, using the methodof Kosaka et al. (2009). Most models do not use thesurface flux corrections, with four exceptions (CGCMT47, CGCM-T63, INM, MRI; see Table 1 for modelacronyms).

2) CUMULUS PARAMETERIZATION SCHEMES
Subgrid-scale cloudiness associated with tropical convection is largely controlled by a cumulus parameterization scheme adopted in each model. Several types ofschemes are used as described below and in Table 1.
More information on aspects of the cumulus parameterizations can be found in Dai (2006) and Lin (2007).
Some models adopt a scheme based on mass flux approaches with spectral cloud models similar to Arakawa
and Schubert (1974). The scheme is hereafter named AS(GFDL2.0, GFDL2.1, MIROC-hi, MIROC-me, MRI).
The convective trigger–closure of this scheme links basically to the convective available potential energy (CAPE).
Some models adopting this scheme incorporate thethreshold of relative humidity proposed by Emori et al.
(2001) (MIROC-hi, MIROC-me) to trigger the convection or, alternately, the threshold of the entrainment rateby Tokioka et al. (1988) (GFDL2.0, GFDL2.1). The incorporation of these thresholds acts to relate an accumulation of moisture to the convection occurrence in themodel simulations.
Two types of schemes adopted in some models are
based on the bulk cloud model, where an ensemble ofclouds is approximated by one single cloud. For one type,the convective trigger–closure is based on the cloud-basebuoyancy proposed by Gregory and Rowntree (1990)
and Del Genio and Yao (1993). The scheme is hereafter
named CBB (GISS-EH, GISS-ER, HadCM3, HadGEM1).
For the other type of scheme, the convective trigger–
closure is based on the moisture convergence concept
originally proposed by Kuo (1965). The scheme is hereafter named MC (CRNM, ECHAM5).
Some models adopt the scheme 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 the cloud-base massflux. The scheme is hereafter named ZM (CCSM3,
CGCM-T47, CGCM-T63, FGOALS, PCM1). The convective trigger–closure of this scheme links basically to
CAPE. This scheme is primarily designed for deep rather

VOLUME 25

than shallow cumulus convection. The top of the shallowest plume among the spectral rising plumes is higher
than the height where the moist static energy exhibits its
minimum in its vertical profile at each grid point. In thetropics, the minimum typically appears in the middle
troposphere in association with a deep conditionally unstable layer.
A model (INM) adopts the lagged convective adjustment scheme proposed by Betts (1986). This scheme ishereafter named BE. The BE scheme adjusts the thermodynamic field toward a reference thermodynamic
field for shallow and deep convection. For deep convection adjustment, the total enthalpy between thecloud base and cloud top is needed to be conserved.
Because of the enthalpy constraint, the deep convection adjustment is not activated when dry warm layers
exist in the model atmosphere (see Baldwin et al. 2002
for details).
A model (IPSL) adopts the scheme proposed by
Emanuel (1991) where the collective effects of the various subparcels in the cloud are represented by a buoyancy sorting approach. This scheme is hereafter named
EM. To achieve the inhomogeneous and episodic occurrence of mixing of the environmental air into a cloud,a finite number of subparcels that contain different fractions of environmental and cloudy air are assumed using
a probability distribution for the mixing fraction. The
IPSL model adopts a probability distribution that makes
the convection sensitive to the relative humidity profiles
in the free troposphere, as proposed by Grandpeix et al.
(2004). The probability distribution acts to suppress deep
convection when the free troposphere is under dry conditions in the model simulations.

3) COUPLING BETWEEN THE CUMULUS
PARAMETERIZATION AND LARGE-SCALECONDENSATION PARAMETERIZATION SCHEMES

Representations of cirrus clouds detrained from deep
convection in models may be classified into two distinctgroups as follows. In some models, the cumulus parameterization scheme and the large-scale condensation
scheme are allowed to interact. More specifically, detrained cloud water from cumulus convection is madeavailable for the calculation of stratiform cloud condensates. This type of model is hereafter named coupled(CCSM3, ECHAM5, GFDL2.0, GFDL2.1, GISS-EH,
GISS-ER, IPSL). In other models, stratiform cloud condensates are calculated only from large-scale variables
without being related directly to the subgrid-scale convective activity. This type of model is hereafter named decoupled (CCCMA-T47, CCCMA-T63, CNRM, FGOALS,INM, MIROC-hi, MIROC-me, MRI, PCM1, HadCM3,
HadGEM1).

15 FEBRUARY 2012

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FIG. 1. Horizontal distributions of annual mean R (in grayscale) for the ERBE observations and the 15 models. Contours of v500 at
215 hPa day21 (white solid line) and SST at 278C (black solid line) are superposed. Three models of CGCM-T47, GFDL2.0, and
MIROC-me are not shown because the horizontal patterns of these three models are similar to the other versions of the same models(i.e., CGCM-T63, GFDL2.1, and MIROC-hi).

3. Methoda. Calculation of the cloud radiative forcing
Climatological features of CRF associated with cloudtypes prevalent for different regimes of large-scale circulation are analyzed on a monthly mean basis. Following
Charlock and Ramanathan (1985), CRF is calculated as
LWCRF [ OLRcl 2 OLR,SWCRF [ TRScl 2 TRS, and
NetCRF [ LWCRF 1 SWCRF.
Here, OLR and TRS refer to the outgoing longwave radiation and the total reflected solar radiation, respectively, at the TOA for all-sky conditions. Both OLRcl andTRScl refer to the radiation for clear-sky conditions. Notethat clouds have the effect of warming the atmosphere–
surface system if their NetCRF is positive.
The analysis in this study includes a comparison of themagnitudes of LWCRF and SWCRF, as has been studied previously (e.g., Hartmann and Doelling 1991; Kiehl
1994; Cess et al. 2001). We analyzes the ratio of LWCRF
to SWCRF calculated as
R[2

LWCRF
,
SWCRF

where R is a relative measure comparing the magnitudesof LWCRF and SWCRF. The quantity R equals one
when LWCRF and SWCRF completely compensate
each other to make NetCRF equal to zero. In this study,
R is calculated from the climatologies of LWCRF and

SWCRF, obtained as the mean value of the full period
analyzed in this study (ERBE observations, February
1985–February 1990; models, 1980–99), for each grid point(Fig. 1) or each regime of large-scale circulation (Fig. 3d).

b. Calculation of high-cloud amountThis study analyzes the model reproducibility of thecloud amount in the ISCCP observations. Considering
the fact that subgrid-scale clouds in models are expressedby a prescribed parameterization, it is difficult to evaluate clouds in models exactly. Nevertheless, evaluationof cloud amount would be necessary to provide physicalinsights into the model bias of CRF.
In section 5a, we compare high-cloud amounts in models
with the ISCCP observations. Several objective methods
for quantitatively characterizing the clouds in the models
have been developed. The ISCCP simulator, which classifies the model’s clouds into the same cloud types as in
ISCCP using the model’s radiation schemes, is useful forthis characterization (e.g., Klein and Jakob 1999; Webb
et al. 2001). However, the ISCCP simulator products are
not available for the twentieth-century climate simulationsin CMIP3 model outputs. This study adopts the maximum
overlap assumption developed by Weare (2004) to calculate the high-cloud amount for models where the cloudamount is available for multiple vertical layers. Highcloud amount is assumed to be the largest value of thecloud fraction among all levels, with pressures between 440
and 50 hPa. The detection of high clouds based on thisdefinition in the models closely follows the detection of highclouds in the ISCCP observations as seen from satellite.
In section 4b, we compare the total cloud amount in
the CMIP3 model outputs with the ISCCP observations.

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JOURNAL OF CLIMATETABLE 2. Definition of regimes for v500 (hPa day21) in this study.

Strong ascentv500 , 240

Moderate
ascentWeak verticalmotionModerate tostrong descent

240 , v500
, 210

210 , v500
, 20

v500 . 20

The total cloud amount in the models is defined as thecloud fraction for the whole atmosphere column as seenfrom the surface or the top of the atmosphere.

c. Analysis domain
This study analyzes the tropics (i.e., the range of latitude between 308S and 308N). In particular, this study is
focused on oceanic regions with SSTs above 278C to
exclude the regions with cold SSTs typically dominatedby low-level stratus clouds. Over the warm oceanic regions, LWCRF and SWCRF turn out to nearly cancel
each other independently of different regimes of largescale circulation in the ERBE observations (cf. Fig. 3d),
as was revealed by Y08. We intensively evaluate themodel reproducibility of the observations in section 4a.
The near cancellation is not seen over the region witha cold sea surface because the impacts of low-levelstratus clouds on SWCRF are much larger than are thoseon LWCRF (Y08).

4. Results

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1) HORIZONTAL DISTRIBUTION OF R, v500, AND
SSTFirst, we evaluate the general features of the CRF,
large-scale circulation, and SST. Figure 1 shows the horizontal distributions of R (in grayscale) for the ERBE
observations and models, superposed by v500 contoured
at 215 hPa day21 (white solid lines) and SST at 278C
(black solid lines). The horizontal distributions of v500
and SST for most models generally agree with ERA-40
and HadISST. The models reproduce well a wide area ofascent motion around the Maritime Continent surrounded by warm oceanic regions and a zonal band of
ascent motion associated with the intertropical convergence zone (ITCZ) over the Pacific. This study focuses on
the region with SSTs above 278C, corresponding to regions enclosed by the black solid lines. Close examinations reveal that the area of the warm SST region varies
among the models. Considerable differences between the
ERBE observations and models, and among the models
themselves, exist for the horizontal distribution of R. In
the ERBE observations, R has nearly homogeneous
values of about 0.8–0.9 within the warm SST region. In
most models, within the warm SST region, R largely
varies spatially (e.g., FGOALS, GFDL2.1, MIROC-hi,
MRI, HadCM3) or is nearly homogeneous but underestimated (e.g., GISS-EH, GISS-ER).

2) RELATIONSHIP OF CRF WITH v500

a. Analysis for the whole oceanic regions with warm
SST
In this section, we analyze the model reproducibilityof CRF over warm oceanic regions for the entirety of thetropics.

It is well known that large-scale circulation largely
controls the cloudiness and consequently CRF (Hartmann
and Michelsen 1993; Bony et al. 1997; Williams et al.
2003). Thus, evaluating CRF for different regimes oflarge-scale circulation may be quite useful to unravel the

FIG. 2. PDF of v500 for each bin of 10 hPa day21 width within the domain of SSTs above 278C
for ERA-40 (black solid line) and the 18 models (colored solid lines).

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FIG. 3. Composites of (a) LWCRF, (b) SWCRF, (c) NetCRF, and (d) R as functions of v500 within the domain of SSTs above 278C for the
ERBE observations (black solid line), the 18 models (colored solid lines), and the model mean for the models (black dashed line).

biases in the individual cloud types and the associated
CRF in the models. We compute a composite of CRF foreach regime of large-scale circulation sorted by bins of
10 hPa day21 width in v500. Table 2 shows the regime
definitions in this study.
Figure 2 shows the probability density function (PDF)
of v500 in the domains of warm SSTs above 278C for
ERA-40 and the models. In ERA-40, the PDF exhibits
at the descent, a peak of ;110 hPa day21, declining gradually to stronger ascents and sharply to stronger descents.
The frequency of occurrence is high for the weak verticalmotion regime of 210 , v500 , 20 hPa day21, contributing to roughly 35% of the total in the domains of thewarm SSTs. In most models, the PDF overall agrees withthat in ERA-40. A closer examination reveals that thefrequency of occurrence seems to be larger in the strong
ascent regime while smaller in the moderate-to-strong descent regime in most models than in ERA-40.
Figure 3 shows the composites of (a) LWCRF, (b)SWCRF, (c) NetCRF, and (d) R as functions of v500.
LWCRF and SWCRF vary almost linearly with v500 for
both the ERBE observations and the models while they
spread within a range of ;30 W m22 at each v500 in themodels. The model biases of LWCRF are largely canceledout in the strong ascent regime, and the multimodel meanof LWCRF in the regime stays close to the ERBE

observations. In most models, LWCRF is underestimated in the weak vertical motion and moderate-tostrong descent regimes. SWCRF tends to be overestimated in many models; that is, it exceeds the observations
in negative magnitude for all values of v500.
The NetCRFs of the models are largely dispersed,
suggesting a large uncertainty in the simulated CRF. In
most models, NetCRF is underestimated for the weakvertical motion and moderate-to-strong descent regimes. For all values of v500 in the ERBE observations,
R is about 0.85. The model reproducibility of R worsens
for the weak vertical motion and moderate-to-strong
descent regimes where R is largely underestimated inmost models. The model underestimation of R increases
with increasing v500. The underestimation of R is, bydefinition, ascribed to the underestimation of LWCRF
and/or the overestimation of SWCRF.

b. Analysis for the weak vertical motion regime withwarm SSTIn this section, we analyze details of the model biases of
LWCRF and SWCRF at v500 . 210 hPa day21, where R
is apparently underestimated in most models to unravel
the physical causes of the biases. In particular, we focus
on the weak vertical motion regime of 210 , v500 ,20 hPa day21, where the frequency of occurrence is high1254

JOURNAL OF CLIMATEVOLUME 25

FIG. 4. Contribution of observed (a) LWCRF and (b) SWCRF for different v500s for thedomain of SSTs above 278C to those for the whole of the tropical oceans. The contribution is
calculated as (PDFv500CRFv500)/CRFall, where PDFv500 and CRFv500 refer to the area normalized by the total area of the tropical oceans and the averaged value of CRF, respectively, foreach bins of 10 hPa day21 width of v500 over the domain of SSTs above 278C. The quantity
CRFall refers to the mean value of CRF for all of the tropical oceans.

in ERA-40, as shown in Fig. 2. Figure 4 shows the contribution of LWCRF and SWCRF for different v500s for
SSTs above 278C to those for the whole of the tropicaloceans in the ERBE observations. LWCRF and SWCRF
for these regions contribute to 16% and 13% of those forthe whole tropical oceans, respectively.

1) RELATIONSHIP BETWEEN LWCRF AND
SWCRF
The model reproducibility of LWCRF and SWCRF isevaluated individually in the previous section but jointly
in this section. We analyze the frequency occurrence ofthe two CRFs jointly, and compare the preferred combination of the two CRFs between the models and the
ERBE observations. Figure 5 shows the joint PDF of
LWCRF and SWCRF (in grayscale) for bins of 5 W m22
width, calculated from data of each individual month ateach grid point within the weak vertical motion regime.
Contours of the observed PDF at 0.1%, 0.5%, and 2%(black solid contours) are superposed in each panel.
Calculated from the climatologies of LWCRF and
SWCRF for the weak vertical motion regime, R is also
given in each panel. In the ERBE observations, the joint
PDF distribution extends from the bottom left to the top
right in the domain around the diagonal, indicating that
LWCRF and SWCRF with roughly similar magnitude
but opposite sign appear at each individual month. In
the models, the joint PDF distribution extends from thebottom right to the top left similarly to the observation,
but shows notable biases. In general, the joint PDF distribution tends to shift toward lower right compared to
the ERBE observations for the models underestimatingR, except CCSM3 and CNRM. It is noted that the frequency of occurrence in the range of large LWCRF and
SWCRF in the top-right part of the panel is underestimatedfor many models underestimating R. The underestimation of the frequency of occurrence is presumably associated with model bias of the cloudiness. Figure 6 shows
the cloud-top pressure as a function of LWCRF and
SWCRF. The domain of current interest near the topright part is mainly dominated by high-top clouds withpressures lower than 440 hPa, indicating that large
LWCRF and SWCRF are attributed mainly to hightop clouds. It follows that the models generally do not
produce as many high clouds as are observed. We further discuss the model reproducibility of high clouds in
section 5a.

2) RELATIONSHIP OF LWCRF WITH SST
We next analyze the possible factors for the systematic underestimation of LWCRF. The analysis is focusedon the model reproducibility of the dependency of
LWCRF on SST. Previous studies showed a drastic enhancement of deep convection and its associated highclouds, and the resultant increase in LWCRF over theregions with SSTs above ;278C (e.g., Gadgil et al. 1984;
Graham and Barnett 1987; Bony et al. 1997; Lau et al.
1997; Del Genio et al. 2005; Masunaga and Kummerow2006). In the ERBE observations, as shown in Fig. 7, theincrease in LWCRF with increasing SST from 268 to
298C occurs not only in the strong and moderate ascentregimes but also in the weak vertical motion regime
while LWCRF itself is smaller in the latter regime. The
sensitivity of LWCRF to SST (i.e., gradient in Fig. 7) for
the weak vertical motion regime is quite similar to thatfor other regimes for the range of SSTs above ;258C. The
decrease in LWCRF with increasing SST above 308C isseen, as was revealed previously (Bony et al. 1997).
Figure 8 shows LWCRF as a function of SST for the
weak vertical motion regime for the ERBE observations

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FIG. 5. Joint PDF of LWCRF and SWCRF (gray scale) over the weak vertical motion regime within the domain of SSTs above 278C for
the ERBE observations and the 18 models. Contours of PDF at 0.1%, 0.5%, and 2% for the observations (black solid lines) are superposedin each panel. The horizontal and vertical axes for the models are the same as those for the observations. Models are sorted by R, which is
given in the top-left corner in each panel.

and models. The performance of the simulated LWCRF
differs among models, particularly when SST is high.
Most models fail to simulate the high sensitivity of
LWCRF to SST from 268 to 298C, although a few models successfully simulate it, and thus suffer from a significant underestimation of LWCRF over warm oceanic
regions.

3) POTENTIAL RELEVANCE TO CUMULUS
PARAMETERIZATIONThe reproducibility of LWCRF is affected by that ofdeep convection, which is closely related to cumulus
parameterization schemes adopted by models. Figure 9shows the mean LWCRF for the weak vertical motion

regime for the observations and models. The model reproducibility of LWCRF is assessed in terms of six typesof cumulus parameterization schemes as described in
section 2b. The reproducibility of LWCRF varies depending on the type of scheme adopted. In general,
LWCRF is well reproduced in models adopting the BE,
EM, and ZM schemes. On the other hand, LWCRF issystematically underestimated in models adopting the
CBB scheme. The reproducibility of LWCRF differs
among models adopting the AS scheme: the reproducibility tends to be good for the models adopting thethreshold for triggering convection, and is particularhigh for the models adopting relative humidity–based
suppression for their triggering convection.

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FIG. 6. Mean cloud-top pressure for each magnitude of LWCRF
and SWCRF over all of the tropical oceans from the ISCCP observations.

4) RELATIONSHIP OF SWCRF WITH CLOUDAMOUNT

In Fig. 3b, the absolute value of SWCRF in models in
the weak vertical motion regime is overestimated. SWCRF
increases with cloud amount and/or cloud reflectivity,
corresponding to the cloud albedo effect. Figure 10ashows the PDF of the total cloud amount in the weakvertical motion regime for the ISCCP observations andmodels. The PDFs largely differ among the models. In
general, the PDFs of the models tend to be biased leftward against those of the ISCCP observations, indicatingthat the models underestimate the total cloud amount.
The models’ overestimation of SWCRF (Fig. 3b) in spiteof the underestimation of total cloud amount (Fig. 10a)

VOLUME 25

FIG. 7. Composite LWCRF as a function of SST over the strong
ascent regime (blue line), the moderate ascent regime (green line),
and the weak vertical motion regime (red line) for the ERBE observations. Vertical bars represent 61 standard deviation aroundthe mean at each SST.

suggests that clouds in the models are more reflective
than clouds in the observations. To confirm this result,Fig. 10b shows SWCRF as a function of total cloudamount for the ERBE observations and the models. The
models overestimate SWCRF at any given value of thetotal cloud amount. In particular, the overestimation islarge at total cloud amounts between 20% and 70%
where the PDFs are high in most models.

5. Discussion
a. Reproducibility of high-cloud amountIn section 4, we found that LWCRF is underestimatedfor the weak vertical motion regime with high SSTs in

FIG. 8. Composite LWCRF as a function of SST over the weak vertical motion regime for the
ERBE observations (black solid line) and the 18 models (colored solid lines).

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ICHIKAWA ET AL.

FIG. 9. Mean LWCRF over the weak vertical motion regime
within the domain of SSTs above 278C for the ERBE observations(nonshaded bar) and the 18 models (shaded bar). Models are classified by cumulus parameterization scheme (see text for details).

most models. Observational evidence showed that, in thisregime, deep convection is occasionally activated while
shallow convection is generally more favored (e.g.,
Takayabu et al. 2010). The lower free troposphere(around 600–800 hPa) in this regime seems to be dry in
different reanalysis datasets [Fig. 11 in this paper andFig. 14 in Takayabu et al. (2010)]. The dry air above theboundary layer would be unfavorable for the occurrenceof deep convection (e.g., Gregory and Miller 1989; Blyth1993; Derbyshire et al. 2004). While deep convection is
only occasional, the cloud statistics in Fig. 12 indicate
frequent occurrences of high clouds, in particular cirrus
clouds (see Fig. 12’s caption for the definition), compared
to middle and low clouds in this regime. Extensive anvil
clouds, which accompany deep convection, are suggested
to prevail. Cirrus clouds formed away from deep convection have also been known to exist from observations(e.g., Comstock and Jakob 2004; Luo and Rossow 2004).
The results from Figs. 5 and 6 suggest that the observedhigh-cloud amount seems to be underestimated in themodels, resulting in the underestimation of LWCRF in
the models. In this section, we actually analyze the model
reproducibility of the high-cloud amount in the ISCCP
observations. Figure 13 shows the high-cloud amount as

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a function of v500 for the observations and for the 16 of
the 18 models where the cloud amount is available formultiple vertical layers. It is noted that, for the weakvertical motion regime, the high-cloud amount is underestimated for most models. Because of the intrinsic
inconsistency in the definition of cloudiness between theobservations and models, it is quite difficult to properly
define the cloud amount in a model. Although this pointshould be kept in mind, when we consider the strong
impacts of high clouds on LWCRF (e.g., Hartmann et al.
2001; Kubar et al. 2007), our results possibly suggest thatthe models underestimate the high-cloud amount andthus underestimate LWCRF in the weak vertical motion
regime.
Two models of MIROC-hi and MIROC-me overestimate the high-cloud amount in the whole regime(Fig. 13) while these models underestimate LWCRF(Fig. 3a) in the weak vertical motion regime. In the twomodels, the high-cloud amount identified by the current
maximum overlap assumption method exceeds the total
cloud amount, which is available in CMIP3 model outputs
for different regimes of v500 with high SSTs. The highcloud amount is likely overestimated in the two models as
was pointed out by Karlsson et al. (2008) and Ichikawa et al.
(2009). An ongoing analysis by the authors using ISCCP
simulator outputs from slab-ocean experiments in the
Cloud Feedback Model Intercomparison Project (CFMIP)
indicates that one of the two versions of MIROC, which is
identical to MIROC-me, simulates a large amount ofclouds with an optical thickness of less than 0.3. The model
does not overestimate the high-cloud amount compared tothe observations when clouds with optical thicknesses ofless than 0.3, which are thought to be a detectable limit of
the ISCCP observations, are omitted.

b. The impacts of the physical parameterizations
We have seen that LWCRF biases are somewhat related to cumulus parameterization schemes adopted in

FIG. 10. (a) PDF of total cloud amount and (b) composite of SWCRF as a function of total cloud amount binned
every 5% in the weak vertical motion regime within the domain of SSTs above 278C for the ISCCP and ERBE
observations (black solid line) and the 18 models (colored solid lines).

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FIG. 11. Composite of the vertical distribution of the relative
humidity as a function of v500 within the domain of SSTs above
278C for ERA-40.

models. The reproducibility of LWCRF is high in thetwo models adopting the BE and EM schemes. The
relative frequency of occurrence of the magnitudes of
LWCRF (Fig. 5), as well as the mean value (Fig. 9) in thetwo models, are quite close to the observations. In addition to the models adopting the two schemes, the reproducibility is generally high in the models adopting
the ZM scheme, as judged from the mean value (Fig. 9).
The reproducibility tends to also be high in the models
using the AS scheme with a relative humidity threshold.
In the BE, EM, and AS schemes with a relative humidity threshold, the relative humidity–based suppression mechanism is implemented for the occurrence ofdeep convection (see section 2b for details). The relative
humidity–based suppression may effectively accumulatemoisture at low levels of the atmosphere (from theVOLUME 25

FIG. 12. Cloud amount (%) for nine types of clouds based on the
ISCCP D2 estimate over the weak vertical motion regime within
the domain of SSTs above 278C. In the ISCCP classification, thedetected cloud-top pressure is lower than 440 hPa for high clouds,
between 680 and 440 hPa for middle clouds, and higher than
680 hPa for low clouds. High clouds with optical thicknesses lessthan 3.6 are referred to as cirrus clouds in the text.

surface to the lower free troposphere) by suppressing
excessive convection that ventilates the lower atmosphere
too quickly. The accumulation of moisture at the low levelsof the atmosphere would eventually be favorable for theoccurrence of vigorous deep convection. The humiditybased suppression, on the other hand, is not necessarily
critical for other cumulus parameterization schemes. In
the case of the ZM scheme, a certain minimum depth ofconvection is intrinsically ensured (see section 2b for
details). This assumption may be favorable for the occurrence of deep convection. Previous studies showedthat the ZM scheme tends to produce dry biases in the

FIG. 13. Composite high-cloud amount as a function of v500 within the domain of SSTs
above 278C for the ISCCP observations (black solid line) and the 16 available models(colored solid lines).

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ICHIKAWA ET AL.

FIG. 14. Mean column-integrated CCI normalized by high-cloudamount over the weak vertical motion regime within the domain of
SSTs above 278C for the ISCCP D2 estimate (nonshaded bar) andthe 14 available models (shaded bar). Models are classified by the
coupling–decoupling of the cumulus parameterization scheme andthe large-scale condensation scheme (see text for details). For
GISS-EH and GISS-ER, the value is divided by 4.0.

lower troposphere, probably through frequent convection (Zhang et al. 1998; xie and Zhang 2000; Zhang 2002).
Our results suggest that the parameters such as CAPE
or CBB alone are not necessarily responsible for suitably reproducing deep convection over the weak verticalmotion regime with high SSTs where the lower freetroposphere is dry. It is supposed that, unless a suitablesuppression mechanism is imposed to promote deep convection occurrence in the models, convection is forced tooccur in rapid response to heat flux from a warm sea
surface without sufficiently accumulating moisture at
low levels of the atmosphere. Thus, convection would beless vigorous than expected for models without a realistic suppression mechanism incorporated. This may
support Derbyshire et al. (2004), who showed that the
upward mass flux of parameterized deep convection in
the middle to upper levels of the atmosphere is weakerin the scheme where the occurrence of deep convection
is insensitive to the free-tropospheric relative humidity
than in the same scheme with large sensitivity.
The reproducibility of high-cloud amount is roughly
similar to that of LWCRF and depends on the cumulus
parameterization scheme (not shown). A detailed evaluation of the deep convection and associated clouds inthe models involves an analysis of the condensed cloudice (CCI) content as well as the high-cloud amount. To
approximate the CCI density within individual cloud
cells, the CCI content normalized by the high-cloudamount is examined for the weak vertical motion regime(Fig. 14). The ISCCP estimates of the cloud ice pathobtained from the combination of the observed optical
thickness with the prescribed particle size distribution
are used for the plot in Fig. 14. The model reproducibility of the CCI content is assessed in terms of two
types of models, as described in section 2b. The reproduced tendency of the CCI content varies between the
two types of models. In general, the CCI content is

1259

FIG. 15. Composite of R as a function of v500 for different reanalyses (solid line, ERA-40; solid line with closed circles, JRA-25;
solid line with open circles, R-2) within the domain of SSTs above
278C for the ERBE observations and the model mean for the 18
models (dashed line).

higher in the coupled models than that in the decoupledmodels. The difference in the CCI content among thecoupled models is larger than that among the decoupledmodels. Further evaluations of cloud microphysics would
involve an analysis of the vertical distribution of CCI,
but are beyond the scope of this paper.

c. Consistency of results among different
observational and reanalysis datasets
In the analyses described above, v500s of ERA-40 areused to evaluate model outputs. Vertical velocity fields
may differ among reanalysis datasets. Thus, the results
obtained from the analyses using ERA-40 need to becompared with those using other reanalysis datasets.
The same analyses conducted with ERA-40 are performed with data from the Japanese 25-yr Reanalysis(JRA-25; Onogi et al. 2007) and the National Centers
for Environmental Prediction–Department of Energy(NCEP–DOE) Atmospheric Model Intercomparison
Project II (AMIP-II) reanalysis (R-2; Kanamitsu et al.
2002). It is confirmed that the results are consistentamong the three reanalysis datasets. Figure 15 showsthat systematic underestimation of R in the models at
the weak vertical motion regime compared to the ERBE
observations is seen in all three different reanalysis datasets. We also confirmed that the results are generally
consistent in all other aspects of the analyses in thepresent study among three reanalysis datasets.
We further compare the results using the ERBE observation for 5 yr and 1 month from February 1985 toFebruary 1990 with those using the Clouds and Earth’s
Radiant Energy System (CERES) observations (Smithet al. 2004) for 6 yr from 2000 to 2005. The simulations

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also produce unrealistically low values of R when compared to CERES observations (not shown). The absolutevalues of LWCRF and SWCRF in the CERES observations are smaller than those in the ERBE observation
for different regimes of v500 while the difference is not
so large as to undermine the results of this study.

6. Concluding remarks
This study evaluates the CRF associated with convective activity over tropical oceans for the 18 climate
models participating in the CMIP3. CRF is evaluated for
different regimes of the large-scale circulation field
sorted by v500 to consider the effects of large-scale
circulation on CRF in the models. Over warm oceanic
regions with SSTs above 278C where convection is active, LWCRF and SWCRF in the ERBE observations
nearly cancel each other independently of different regimes of large-scale circulation. We intensively evaluate
the model reproducibility of the ERBE observations,focusing on the warm oceanic regions. The warm oceanic regions cover the regime of v500 ranging fromstrong ascent to weak descent. A notable systematic bias
can be found in the weak vertical motion regime definedas 210 , v500 , 20 hPa day21: R, the absolute value ofthe ratio of LWCRF to SWCRF, is underestimated inmost of the models compared to the ERBE observations. The weak vertical motion regime contributes toroughly 35% of occurrence in the domain of the warm
SSTs. The bias of R is attributed to the underestimationof LWCRF and the overestimation of SWCRF in theabsolute value. The models tend to underestimate highclouds but overestimate cloud reflectivity for a given
cloud amount.
The lower free troposphere over the weak verticalmotion regime with high SSTs is dry. The dryness isunfavorable for the occurrence of deep convection,
which is considered to be sensitive to the relative humidity in the free troposphere (e.g., Gregory and Miller
1989; Blyth 1993; Derbyshire et al. 2004). Under largescale environment conditions, deep convection occasionally occurs in this regime (e.g., Takayabu et al. 2010)
presumably through the reduction of the dryness due tothe moisture development in low levels of the atmosphere in some periods. The model reproducibility of
LWCRF, which is affected by deep convection, over thisregime reflects the sensitivity of deep convection occurrences to relative humidity in the free tropospherein cumulus parameterization schemes adopted in eachmodel. The reproducibility is high in the schemes where
the relative humidity–based suppression mechanism isimplemented for the occurrence of deep convection[Arakawa–Schubert-type scheme with a threshold proposed

VOLUME 25

by Emori et al. (2001), Betts scheme, Emanuel scheme witha modification proposed by Grandpeix et al. (2004)], while
the Zhang and McFarlane scheme shows good performance without such a suppression mechanism. The relative
humidity–based suppression may effectively accumulatemoisture at low levels of the atmosphere. Our results are
consistent with previous studies that indicated the importance of convection suppression for the proper representation of deep convection (e.g., Emori et al. 2001; Zhang
2002; Grandpeix et al. 2004; Hirota et al. 2011).
Further analyses would be needed to fully unravel themodel physics responsible for the model bias of CRF
revealed in this study. The analyses would involve theevaluation of cloud properties. The method adopted inthis study would be one of the useful approaches toevaluating the model reproducibility of CRF associatedwith the convective activity, and can be used to evaluate
it in model simulations for the IPCC’s Fifth Assessment
Report (AR5).
Acknowledgments. The authors are grateful to
S. Manabe, K. Nakamura, and Y. N. Takayabu for theirvaluable comments on this study. The authors thankseveral anonymous reviewers for their helpful comments
and suggestions, which improved the manuscript. The
modeling groups, the PCMDI, and the WCRP’s Working Group on Coupled Modeling (WGCM) madeavailable the WCRP CMIP3 multimodel dataset. Support of this dataset is provided by the Office of Science,
U.S. Department of Energy. This research was supportedby the Global Environment Research Fund (S-5-2) of the
Ministry of the Environment, Japan, and by the Data
Integration and Analysis System (DIAS) Fund for National Key Technology from the Ministry of Education,
Culture, Sports, Science and Technology, Japan.
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