Journal of the Meteorological Society of Japan, Vol. 87, No. 4, pp. 771--789, 2009.

771

DOI:10.2151/jmsj.87.771

Evaluation of Precipitation and High-Level Cloud Areas Associated with Large-Scale
Circulation over the Tropical Paciﬁc in the CMIP3 Models

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

andHiroshi KANZAWA
Department of Earth and Environmental Sciences, Graduate School of Environmental Studies, Nagoya University,
Nagoya, Japan(Manuscript received 29 September 2008, in ﬁnal form 23 April 2009)

Abstract
Precipitation and high-level cloud (HLC) areas in association with the large-scale circulation over the tropicalPaciﬁc are analyzed for simulations of nineteen Coupled Model Intercomparison Project Phase 3 (CMIP3) models
with observations for 16 years of 1984–1999. The distribution of rainfall and HLC areas are composited aroundthe geographical center of tropospheric upper-level (200 hPa) divergence (DIV) along Intertropical Convergence
Zone (ITCZ) using monthly anomaly data. Datasets with a ﬁner temporal sampling than monthly means were
not available for the present purposes. The most notable feature is that the horizontal spread of enhanced circulation and the related rainfall and HLC areas are all underestimated around the DIV center in the models compared to the observation. Particularly, the underestimation is pronounced in HLC, presumably owing to di‰culties in the physical processes relevant to the spatial distribution of HLC area. In general, a model with a higher
correlation between the large-scale circulation ﬁeld and rainfall tends to have a wider spread of HLC area aroundthe DIV center.

1. Introduction
Deep and frequent convection and the associated precipitation over the tropics activate thevigorous hydrological cycle in the earth climate
system. A huge amount of latent heat release asCorresponding author: Hiroki Ichikawa, Departmentof Earth and Environmental Sciences, Graduate
School of Environmental Studies, Nagoya University,
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.
E-mail: ichikawa@hyarc.nagoya-u.ac.jp
6 2009, Meteorological Society of Japansociated with the convection and precipitation
makes the tropical region a major atmosphericheat source (e.g., Yanai and Tomita 1998) thatplays an essential role in driving global generalcirculation. The better understanding of spatiotemporal cloud/rainfall variability and its background dynamical/thermodynamical mechanism
would help improve the current skill in the climate
model for reproducing the present and past climate and for predicting the future climate change.
Tropical deep convection and the associated precipitation respond both to large-scale dynamical

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forcing and local thermodynamic changes. Because
the large-scale circulation is tied to the pattern ofunderlying sea surface temperature (SST) thatslowly varies from a season to another, deeper convection and heavier rainfall occur over regions withhigher SST and stronger upward motion (e.g., Del
Genio and Kovari 2002), accounting for visible
appearance of Intertropical Convergence Zone
(ITCZ) in satellite imagery. Over the western andcentral Paciﬁc ITCZ regions, deep convection systems accompany more abundant anvil cirrus clouds
compared to the eastern Paciﬁc region for a given
rainfall rate (Hartmann et al. 2001; Kubar et al.
2007). The cloud-circulation-SST relationships
characterize the regional di¤erence of convection/
rainfall systems along the Paciﬁc ITCZ (Hartmann
et al. 2001; Berg et al. 2002; Masunaga et al. 2005;
Back and Bretherton 2006; Kubar et al. 2007).
The representation of cloud and precipitationin the climate models has been a big challenge tothe climate research community. Dai (2006) analyzed the temporal and horizontal variation of
precipitation in the latest generation of coupled
Atmosphere-Ocean General Circulation Models(AOGCMs), and revealed that most models reproduced the observed broad spatial pattern and yearto-year variability of precipitation. On the other
hand, the models fail to di¤erentiate precipitation
types in a realistic manner, with too much convective precipitation compared with the Tropical Rainfall Measuring Mission satellite observation in thetropics and subtropics. While it is di‰cult to compare precipitation types in the models depending
on convection schemes with those in the satellite
estimate depending on the original algorithm, thecomparison of precipitation types would be conveyed in useful information. Zhang et al. (2005)
evaluated the cloud property in several Atmospheric General Circulation Models (AGCMs),
and found the signiﬁcant biases where optically intermediate and thin clouds are underestimatedwhile thick clouds are overestimated. Compensation of the errors in representing cloud fraction at
various heights and cloud thickness in the models
results in relatively good representation in radiativeﬂux at the top of the atmosphere. Associated withthe deﬁciency of a well-stacked cloud layer, themodels tend to simulate the cloud albedo moderately well, but not the cloud water path (Wear
2004).
How the tropical climate system responds to an
external forcing is an important outstanding prob-

Vol. 87, No. 4

lem (e.g., Stephens 2005). A recent satellite observation indicated that the deep convective activity over the ocean with SST above @27–28 C
greatly enhances the upper-tropospheric water vapor greenhouse e¤ect (Su et al. 2006a). The latest
AOGCMs well simulate qualitatively the observed
relationships of larger amount of upper-level ice
clouds and water vapor with higher SST, but theyquantitatively disagree with the observation andeach other (Su et al. 2006b). For the climate
models, clouds and precipitation associated withtropical convection should be further evaluated because they play a determining role in the global
general circulation and hydrological cycle thateventually inﬂuence them.
In this study, precipitation and high-level cloudareas associated with large-scale circulation mainlyin the horizontal pattern over the Paciﬁc Ocean incoupled AOGCMs are evaluated. Previously, theevaluation of clouds and precipitation in the climate model has been made on a global or zonalmean basis (e.g., Dai 2006; Zhang et al. 2005), or
using regime-sorted approaches (e.g., Bony et al.
2004; Wyant et al. 2006; Williams and Tsekioudis2007) on the climatological mean basis. While theclimatological mean pattern of rainfall in the latest
AOGCMs has been evaluated by Dai (2006), thatof high-level clouds and large-scale circulation, andthe linkage of rainfall and high-level cloud withlarge-scale circulation has not been evaluated yet.
First, this study evaluates the climatological mean
relationships of rainfall and high-level cloud areas
with large-scale circulation. However, the direct intercomparisons of climatological mean ﬁelds alone
are not necessarily informative to a suitable evaluation of physics in the climate model. Here we focus
on the interaction of rainfall and high-level cloudareas with large-scale circulation associated withsubseasonal disturbances that are deﬁned as disturbances on a time scale shorter than a season but
longer than a few weeks, and analyze the spatial
linkage of rainfall and high-level cloud areas withthe large-scale circulation associated with the disturbances. In order to accomplish the purpose, weinvestigate the horizontal patterns of rainfall andhigh-level cloud areas around large-scale divergence centers in the upper-troposphere in association with subseasonal disturbances through a composite analysis. Large-scale circulation associatedwith subseasonal disturbances may have a strong
impact on seasonal-to-climatological mean largescale circulation. The relationship of reproducibilityAugust 2009

H. ICHIKAWA et al.

of the climatological mean ﬁeld with that of subseasonal disturbances in the models is ﬁnally discussed.
The paper is organized as follows. Section 2 outlines the data used in this study and provides a brief
overview of climate models. In Section 3, ﬁrst, general features of the climatological mean of rainfall
and high-level cloud areas in the models are evaluated (Section 3.1). Second, the composite patternsof rainfall and high-level cloud areas associatedwith large-scale circulation are analyzed for the observation and models (Section 3.2). Third, the reproducibility of the climatological mean ﬁeld andthat of subseasonal disturbances in the models arecompared (Section 3.3). Section 4 presents a summary and discussion.
2. Data and methods
2.1 Observational and reanalysis data
The data used in this study includes the international satellite cloud climatology project (ISCCP)
D2 VIS/IR cloud data (Rossow and Schi¤er 1999),
obtained from the NASA Langley Research Center
Atmospheric Science Data Center. The data contain the monthly mean cloud fraction for threeheight categories. In particular, high clouds with
top pressures between 440 and 50 hPa are used inthis study to investigate the variability of uppertropospheric clouds. The high clouds are further
classiﬁed into three cloud types; deep convection(DC), cirrostratus (CS), and cirrus (CI) on the basisof the optical thickness in the product. The highlevel cloud (HLC), deﬁned as these three types ofclouds all together, is analyzed in most of this paper, but each classiﬁed cloud (DC, CS, CI) is partly
analyzed for the physical interpretation of observational evidence. The observed precipitation from
Global Precipitation Climatology Project (GPCP)(Adler et al. 2003) is utilized. The large-scale circulation ﬁeld is derived from European Centre for
Medium-Range Weather Forecasts (ECMWF)
ERA-40 reanalysis data (Uppala et al. 2005). The
outgoing longwave radiation (OLR) datasets (Liebmann and Smith 1996) and monthly mean SST
using optimum interpolation (Reynolds and Smith
1994), both of which are distributed by the National Oceanic and Atmospheric Administration(NOAA) Climate Prediction Center (CPC), arealso used. For comparisons with AOGCMs outputs, all these data with the same horizontal resolution of 2:5  2:5 grid on a monthly average areused for 16 years of 1984–1999, limited by the observational period of ISCCP.

773

2.2 AOGCMs simulations
This study uses the outputs from the twentiethcentury climate simulations conducted by 19
AOGCMs listed in Table 1. At the time of writing,
only these 19 model outputs make three dimensional cloud amounts available among the registered 25 model outputs. Our analysis is based on
the monthly mean data, because cloud amount is
not available for a ﬁner temporal sampling thanmonthly means. These models participated in the
Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC)(IPCC 2007), and their data were submitted to Program for Climate Model Diagnosis and Intercomparison (PCMDI). These archived data constitutephase 3 of the World Climate Research Programme(WCRP) Coupled Model Intercomparison Project(CMIP3).
The spatial resolution of model ranges fromthe highest resolution of 1:1  1:1 (MIROC3.2hires) to the lowest resolution of 5:0  4:0 (GISSEH/ER). Most of the models do not use thesurface ﬂux corrections except for 4 models(CCCMA-CGCM3.1-t47/t63, INMCM3.0, MRICGCM2.3.2a). Clouds and precipitation associatedwith tropical convection are strongly a¤ected byconvection schemes adopted in the models. In
most models analyzed in this study, the adopted
parameterizations are based on Arakawa and Schubert (1974) or bulk ﬂux schemes while the ﬁnal
implementations are di¤erent from a model toanother (see for details Table 2 in Dai 2006). In
the model using the Arakawa-Schubert scheme,
MIROC3.2-hires/medres and MRI-CGCM2.3.2a
use prognostic cumulus kinetic energy as a closure while GFDL-CM2.0/2.1 use relaxed quasiequilibrium between large-scale forcing and convection. In the bulk ﬂux scheme, stability-dependent
closure is adopted in three models of CSIROMK3.5, GISS-EH/ER, and UKMO-HadCM3,
while the original Kuo-type moisture convergence is used in a model of BCCR-BCM2.0. The
Zhang and McFarlane (1995) scheme, used by
CCCMA-CGCM3.1, IAP-FGOALS-g1.0, NCARCCSM3.0, and NCAR-PCM1, is designed primarily for well reproducing deep convection and adopts
the consumption of convective available potential
energy (CAPE) by convection as a closure. Additionary, further modiﬁcations such as relative humidity suppression applied in MIROC3.2 (Emoriet al. 2001) have been made in most of the models.
More information on aspects of the physical pa-

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Table 1. Description of 19 coupled AOGCMs, the output data of which are analyzed in this study. For more information such as resolution of the ocean, see http://www-pcmdi.llnl.gov/.
Label
a
b
c

Model name
BCCR-BCM2.0
CCCMA-CGCM3.1-t47
CCCMA-CGCM3.1-t63

Resolution (Atmosphere)(horizontal/vertical)
1.9  1:9 / L31

Deque et al. (1994)





Flato (2000)





Flato (2000)



2.8 @2:8 / L31
1.9 @1:9 / L31

d

CSIRO-MK3.5

1.9 @1:9 / L18

Gordon et al. (2002)e

GFDL-CM2.0

2.5  2:0 / L24

Delworth et al. (2006)f

GFDL-CM2.1

2.5  2:0 / L24

Delworth et al. (2006)g

GISS-EH

Schmidt et al. (2006)

5.0  4:0 / L20h

GISS-ER

5.0  4:0 / L20

Schmidt et al. (2006)

i

IAP-FGOALS-g1.0

2.8 @2:8 / L26

Yu et al. (2004)j

INGV-ECHAM4

1.1  1:1 / L19

Gualdi et al. (2006)k

INMCM3.0

5.0  4:0 / L21

Diansky and Volodin (2002)l

IPSL-CM4

3.75  2:5 / L19

Marti et al. (2005)

m

MIROC3.2-hires

1.1 @1:1 / L56

K-1 model developers (2004)

n

MIROC3.2-medres

2.8 @2:8 / L20

K-1 model developers (2004)op

MPI-ECHAM5
MRI-CGCM2.3.2a

Roeckner et al. (2003)





Yukimoto et al. (2006)

1.9 @1:9 / L31
2.8 @2:8 / L30q

NCAR-CCSM3.0

1.4  1:4 / L26

Collins et al. (2006)r

NCAR-PCM1

2.8 @2:8 / L26

Washington et al. (2000)s

UKMO-HadCM3



3.75  2:5 / L19

rameterization can be found in Dai (2006).
The twentieth century simulations by AOGCMs
are evaluated by using monthly mean outputs for
1984–1999 so that the data period matches that of
the satellite observation. All the variables are interpolated to the common 2:5  2:5 grid, the same as
the observational data set.
2.3

References

Cloudiness in the observation and model
simulations
Comparing cloudiness of the observation withthat of model outputs is challenging because oflarge di¤erences in nature between the two. One of
the fundamental problems for the comparison isthat the models are run at far lower horizontal resolutions than the 4–10 km pixel of the raw ISCCP
data. Clouds in the models need to be parameterized from the prognostic grid-mean variables such

Gordon et al. (2000)

as the upward mass ﬂux and/or the relative humidity of the grid box. In addition, the deﬁnition ofcloudiness in the models using a threshold of cloudwater/ice concentration or an optical depth of thecloud condensate is not consistent with that in theobservation. Given these limitations in the model
representation of cloudiness, cloud radiative e¤ect(CRE) might be a suitable measure for the comparison between the observation and the models. However, the evaluation of CRE does not necessarily
provide the physical insight on particular model
biases arising from individual types of clouds because CRE only measures the accumulative e¤ects
of clouds.
This study tries to evaluate clouds in the models.
Several objective methods for quantitatively characterizing the clouds in the models have been developed. The ISCCP simulator, which stratiﬁes theAugust 2009

H. ICHIKAWA et al.

model’s cloud types into the same cloud types as
ISCCP using the model’s radiation scheme, is oneof the major tools for evaluating clouds in themodels (e.g., Klein and Jakob 1999; Webb et al.
2001). However, ISCCP simulator needs to be run
online with the model run itself, but the online run
products are unavailable for our analysis. This
study adopts, therefore, the method of maximum
overlap assumption developed by Wear (2004) for
deﬁning HLC. The method has been used for evaluating clouds in climate models (e.g., Karlsson et al.
2008). In the models, cloud fraction has been givenby the modeling groups at ﬁxed pressure levels
which vary from a model to another. In order tocompare the model output with the ISCCP observation as seen from satellites, model cloud fraction
has to be dealt with in a way consistent with the observation. Concretely, the HLC amount is deﬁned
as the largest value of cloud fraction among alllevels with pressures between 440 and 50 hPa. The
detection of HLC by this deﬁnition closely follows
the HLC of the ISCCP estimate. Unfortunately,
cloud type classiﬁcation such as DC, CS, and CI
as in ISCCP cannot be made because of the unavailability of information about optical thickness
in the models. The model outputs and ISCCP estimate are, thus, compared for HLC without being
further divided into subcategories. The maximum
overlap assumption has the advantage that the results are nearly independent of the di¤erences between model radiation codes.
While the detected HLC for the models is considered to follow the HLC of the ISCCP estimate, it isdi‰cult to evaluate the HLC for the models exactly
because of a large di¤erence in the nature of cloudiness between the observation and the models as
is described above. Considering the limitation in
the model representation of cloudiness, OLR isalso analyzed as well as HLC in the composite
analysis in Section 3.2 as a more objective measurefor the comparison between the observation andthe models.
3. Results
3.1

General features of climatological mean
precipitation and high-level cloud areasa. Spatial patterns
First of all, observational climatologies of rainfall and high-level cloud areas are analyzed andcompared with AOGCMs outputs.
Figure 1a shows the observed spatial distributionof annual mean rainfall area from GPCP for 16

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Fig. 1. Annual mean (1984–1999) spatial
distribution of (a) Rainfall, (b) DC, (c) CI,
and (d) HLC (in grey scale). DIV (divergence at 200 hPa) is shown by white solid
contours with 0.0, 2.0 [106 s1 ]. SST at
27 C is indicated by a black dotted contour.

years (1984–1999). Divergence at 200 hPa, hereafter designated by DIV (white solid contours), andSST at 27 C (a black dotted contour) are superposed. The DIV, associated with the tropical largescale circulation, is closely related to SST in the
climatological mean pattern because changes inlarge-scale upward motion, inducing DIV, often
occur in association with changes in the spatial
distribution of SST (e.g., Lindzen and Nigam
1987). Areas with intense rainfall in the tropics arestrongly related to the large-scale circulation ﬁeld
as inferred by DIV. Particularly, heavily raining
areas are observed over the Maritime Continent,
Paciﬁc ITCZ, and South Paciﬁc Convergence Zone
(SPCZ). The spatial distribution of each type ofhigh-level cloud areas is shown in Fig. 1b (DC)and 1c (CI) for comparison with that of the rainfall
area. The distribution of DC area (Fig. 1b) resembles the distribution of the rainfall area (Fig. 1a),indicating that most of the rainfall is yielded by

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Fig. 2. Same as Fig. 1d but for selected 16 coupled AOGCMs among 19 models. Three models of CCCMACGCM3.1-t47, GFDL-CM2.0, and MIROC3.2-medres are not shown because the spatial patterns of these3 models are similar to the other version of the same models (i.e., CCCMA-CGCM3.1-t63, GFDL-CM2.1,
and MIROC3.2-hires).

deep convective clouds. Particularly, the DCamount is large over the western to central/southcentral Paciﬁc, mostly conﬁned in the region withstrong DIV (contour at 2  106 s1 ). Spatial characteristics similar to DC area can also be observedin the distribution of CS area (not shown). On theother hand, the distribution of CI area (Fig. 1c) ismore widely spread from the Indian Ocean to the
Paciﬁc than that of the other types of high-levelcloud and rainfall areas. CI area does not exceed
25% in amount over the eastern Paciﬁc. Figure 1dshows the annual mean distribution of HLC area,that is, the summation of DC, CS, and CI areas.
HLC area is enlarged along ITCZ and SPCZ as visualized by the heavily raining area and large DIV
region of Fig. 1a, particularly over the western Paciﬁc where DIV is also strong.
Here, we brieﬂy analyze the mean spatial patternof HLC area in AOGCMs. The annual mean spatial distributions of HLC area (in grey scale), DIV(white solid contours), and SST (a black dotted
contour) are shown in Fig. 2. As inferred from theDIV ﬁeld, the models well simulate the overalllarge-scale circulation pattern while the ITCZ and

SPCZ patterns are unrealistic in some models.
Similar to the observational dataset, the spatial distribution of high HLC area in most models is
closely linked to the distribution of strong DIV
and high SST. Simulations by MPI-ECHAM5,
NCAR-CCSM3.0, UKMO-HadCM3 tend to produce most realistic patterns in HLC area as well as
DIV and SST. The simulated patterns of HLC
area, DIV, and SST of CCCMA-CGCM3.1 arealso similar to those of the observation, moresimilar to SPCZ than to ITCZ. The simulated
patterns of HLC area, DIV and SST of MRICGCM2.3.2a are similar to those of the observation while HLC area is underestimated. In somemodels (e.g., GISS-EH, INMCM3.0, NCARPCM1), the regions of large DIV and the associated large HLC over the western Paciﬁc stay near
15 N while those in the real ITCZ are found aroundthe equator (Fig. 1). In these models, areas withthe large DIV and HLC south of the equator also
tend to extend east-west, without exhibiting thenorthwest-southeast tilt typical of those in the real
SPCZ in the observation. In the case of CSIRO3.5,
INGV-ECHAM4, IPSL-CM4, MIROC3.2-hires,

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777

Fig. 3. Observed (a) Rainfall–DIV and (b) HLC–DIV relationships stratiﬁed by SST categories (I: 24@25,
II: 25@26, III: 26@27, IV: 27@28, V: 28@29, VI: 29@30 [ C]). Observed (C) Rainfall–SST and (d) HLC–SST relationships stratiﬁed by DIV categories (I: 3.5@2.5, II; 2.5@1.5, III: 1.5@0.5, IV:0.5@0.5, V: 0.5@1.5, VI: 1.5@2.5, VII: 2.5@3.5 [106 s1 ]).

HLC area widely spreads from the Maritime Continent to the Paciﬁc Ocean, and does not show aband-like structure of HLC area reﬂecting the real
ITCZ and SPCZ in spite of the good representationof the DIV ﬁeld. In particular, in MIROC3.2, ahigh HLC area spreads extensively owing to theoverestimation of HLC1, especially in medres (notshown). Overall, the inter-model di¤erences in
HLC are larger than those in rainfall as analyzed
by Dai (2006).
1 MIROC3.2 makes available in PCMDI data base an
output of the whole cloud amount (‘‘clt’’) or cloudarea fraction for the whole atmospheric column as
seen from the surface or the top of the atmosphere.
In MIROC3.2, the fraction of HLC identiﬁed by thecurrent maximum overlap assumption method exceeds clt. The overestimation of the HLC in the
MIROC3.2 using the maximum overlap assumption
method is also indicated in Karlsson et al. (2008).
The distribution and absolute amount of clt in
MIROC3.2 is much closer to the observation.

b. Relationships of rainfall and HLC with DIVand SST
Both of the large-scale circulation and SST play
important roles in the occurrence of tropical rainfall and deeply developed high-level clouds on the
climatological mean basis as shown in Fig. 1. The
climatological relationships of rainfall and HLCwith DIV (SST) as a function of the SST (DIV)
are next investigated. Figures 3a and 3b show theobserved rainfall–DIV and HLC–DIV relationships, respectively, for di¤erent SST categories.
The rainfall–DIV and HLC–DIV curves are obtained by computing the mean of rainfall and
HLC values at every 1 C SST bin from 24 C to30 C. As an overall feature, rainfall and HLC increase almost linearly with DIV. Taking a closer
look at them, the sensitivities of rainfall and HLC
to DIV (i.e., gradient in Figs. 3a, b) are more pronounced in positive DIVs than negative DIVs. Aninteresting feature is that the sensitivities of rainfall

778

Journal of the Meteorological Society of Japanand HLC to DIV are quite independent of SST
while, in general, the magnitude of each parameterat the same DIV is larger for higher SST.
Figures 3c, d show the observed rainfall–SST
and HLC–SST relationships, respectively, for thedi¤erent DIV categories. Both rainfall and HLC
do not vary signiﬁcantly in the range below 26 C,
exhibiting a nearly ﬂat distribution, in each category of DIV. On the other hand, it is apparent
that each variable is enhanced dramatically around@27 C. Similar characteristics of other indices fortropical convection as a function of SST have been
indicated by previous studies (e.g., Zhang 1993;
Waliser and Graham 1993; Masunaga and Kummerow 2006). It should be noted that the abrupt enhancement around 27 C in rainfall and HLC isseen not only in the positive DIVs but also in negative DIVs. In this respect, the relationships of rainfall and HLC with SST are qualitatively similar
among di¤erent DIV categories. Meanwhile, thesensitivities of rainfall and HLC to SST vary
among di¤erent DIV categories. The sensitivities
of rainfall and HLC to SST from 26–28 C andfrom 26–29 C, respectively, are higher in the positive DIVs than in the negative DIVs. The sensitivities of rainfall and HLC to SST become weakfrom 28–30 C and from 29–30 C, respectively,
and negative above 30 C in the positive DIVs while
they keep almost constantly above 26 C in the negative DIVs. Our result implies that the reduction in
rainfall and HLC above 30 C is most pronounced
in a small DIV (DIV IV), and does not occur inthe negative DIVs. On the other hand, previous
studies indicated that the reduction of convection
in extreme SSTs (30 C@) may occur in regions ofstrong subsidence (negative DIVs in this study)
forced by convection generated elsewhere, causinga very high SST (Waliser 1996; Lau et al. 1997).
The further discussion of this issue of inconsistency
between their results and ours is beyond the scopeof this paper.
Figure 4 compares the relationships of rainfalland HLC with DIV (Figs. 4a, b) and with SST(Figs. 4c, d) for the observation (a thick solid line)
and models (marked thin lines). Figs. 4a, b andFigs. 4c, d are not subdivided by SST and DIV categories, respectively, because the relationships of
rainfall and HLC with DIV (SST) are qualitatively
similar in that the sensitivities of both variables to
DIV (SST) are almost the same among di¤erent
SST (DIV) categories in the observation. As a general feature, climate models simulate the observed

Vol. 87, No. 4

relationships of rainfall and HLC with DIV fairly
well (Figs. 4a, b). In the case of rainfall, the relationship of rainfall with DIV is well captured in
the models (Fig. 4a). The sensitivity of rainfall to
DIV is small in BCCR-BCM2.0 (designated bysymbol ‘‘a’’ in Fig. 4a) and large in positive DIV
in INMCM3.0 (‘‘k’’), respectively, compared to theobservation and other models. In the case of HLC,
the sensitivity to DIV in the models is similar tothat in the observation, but the magnitude is diverse among the models. The magnitude of HLC isoverestimated in MIROC3.2 (both hires ‘‘m’’ andmedres ‘‘n’’), while it is underestimated in GISSEH (‘‘g’’), GISS-ER (‘‘h’’), and MRI-CGCM2.3.2a(‘‘p’’). Similar to rainfall, in INMCM3.0 (‘‘k’’), thesensitivity of HLC to DIV in INMCM3.0 (‘‘k’’) is
large for positive DIV. The sensitivity of HLC to
DIV in INGV-ECHAM4 (‘‘j’’) is relatively small
through all DIVs.
Compared to the reasonable agreement in the relationships of rainfall and HLC with DIV, the relationships of rainfall and HLC with SST exhibit
larger di¤erences among the models (Figs. 4c, d).
Most models capture the sudden enhancement in
rainfall around the threshold value at 27–28 C.
However, the rainfall amount tends to be overestimated, particularly above @27–28 C, accompanied
by high sensitivity of rainfall to SST comparedto the observation. The sensitivity of rainfall to
SST above 30 C is almost constant in somemodels [CSIRO3.5 (‘‘d ’’), GFDL-CM2.0 (‘‘e’’),IAP-FGOALS-g1.0 (‘‘i’’), NCAR-PCM1 (‘‘r’’),
UKMO-HadCM3 (‘‘s’’)]. Abrupt enhancement in
rainfall occurs at a lower SST in BCCR-BCM2.0(‘‘a’’), INMCM3.0 (‘‘k’’), and Miroc3.2-medres(‘‘n’’). Compared to rainfall, HLC behaves di¤erently from a model to another in both sensitivity
to SST and magnitude. Most models underestimate
the sensitivity to SST around 27 C. The unrealistic
representation in the relationship of HLC with SST
would be related to the model deﬁciency to simulate ice cloud amount as was pointed out by Su
et al. (2006b). CCCMA-CGCM3.1 (‘‘b’’, ‘‘c’’),MPI-ECHAM5 (‘‘o’’) and NCAR-CCSM3.0 (‘‘q’’)
exhibit results similar to the observation.
3.2 Spatial relationships of rainfall and HLC
areas with DIV associated with subseasonal
disturbances over ITCZ
In the previous section, it is conﬁrmed that thegeneral relationships of rainfall and HLC with
DIV as an index of large-scale circulation are wellAugust 2009

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Fig. 4. Comparison of the observation (a thick solid line) and AOGCMs (marked thin lines: alphabets atright legend indicate each model as labeled in Table 1) for (a) Rainfall–DIV and (b) HLC–DIV, and for(c) Rainfall–SST and (d) HLC–SST relationships.

captured in the AOGCMs as shown in Figs. 4a, b;
the sensitivities of rainfall and HLC to DIV are
well captured in the models. The result encourages
further analyses for the models. In this section, thespatial relationships of rainfall and HLC areas with
DIV associated with subseasonal disturbances areanalyzed for ITCZ through a composite analysis.
The composite analysis is focused on the westernand central Paciﬁc where the dominant rainfalland HLC band appear in each model (rainfall and
HLC areas over the eastern Paciﬁc tend to be faintin some models; see Fig. 2 in this study and also
Fig. 1 in Dai 2006).
a. Method of composite analysis
The spatial patterns of rainfall and HLC areas
around the DIV centers associated with subseasonal disturbances over ITCZ are investigatedthrough a composite analysis. OLR is also included
for the present analysis as an index for deep con-vection. Convective activity and the associatedrainfall and HLC are tied not only to subseasonallarge-scale circulation but also to slowly varying
environmental conditions including the underlying
SST pattern. We try to isolate the impact of largescale circulation associated with subseasonal disturbances on rainfall, HLC, and OLR from that of theslowly varying environmental conditions. To exclude such environmental conditions, the seasonal
cycle is ﬁrstly removed by subtracting monthly climatologies from monthly mean data for each variable to yield monthly anomalies, which are used forthe composite analysis. In addition, months corresponding to El Nino and La Nina phases that accompany ENSO are excluded from the composite.
The both phases are determined by the deﬁnition
used in Vecchi and Harrison (2004) where periods
corresponding to monthly anomalies of NINO.3(150 W–90 W, 5 S–5 N) SST above 0.75 C andbelow 0.75 C are identiﬁed as El Nino and La

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Fig. 5. Spatial distributions of DIV (white solid contours) and Rainfall (grey scale) in October for (a) climatology (1984–1999), and those for monthly mean and monthly anomaly from monthly climatology in (b)1991 and (c) 1993. DIV at 1.0, 3.0, 5.0 [106 s1 ] are contoured for climatology and monthly mean, andDIV at 2.5, 4.0 [106 s1 ] are contoured for monthly anomaly. A DIV center is plotted by a black crossed
mark in (b) and (c).

Nina phases, respectively. El Nino and La Nina
phases are determined for the observation and foreach model individually.
More speciﬁcally, the composite technique is ap-

plied as follows. At ﬁrst, a geographical center of
DIV for raw monthly mean data is sought for every
month during 1984–1999 except El Nino and
La Nina phases in area domains containing cli-August 2009

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781

Fig. 6. Spatial distribution of (a) Rainfall, (b) HLC, and (c) OLR (grey scale) composited around the DIV
center over the western and central Paciﬁc. DIV at 0.75, 2, 4 [106 s1 ] (white solid contours) and SST at
0.1 [ C] (a black dashed contour) are superposed. (d) Detailed SST pattern (grey scale), and horizontal
wind (vector) and convergence (grey dotted contours; 1, 0.5, 0.25 [106 s1 ]) at 1000 hPa. X and Y
axes indicate the relative distances from the DIV center of eastward and northward, respectively. Portions
with statistically signiﬁcance exceeding the 95% conﬁdential level are only described. See the text about thecomposite technique for detail.

matological ITCZ over the western and central
Paciﬁc (140 E–120 W, 1.25 S–12.5 N). Once the DIV
center is detected, monthly anomalies for rainfall,
HLC, and OLR as well as DIV are composed
against the horizontal location of the DIV center.
b. Spatial pattern in the observation
The spatial patterns of DIV (white solid contours) and rainfall (in grey scale) areas in Octoberfor climatology are shown in Fig. 5a, and those forthe monthly mean and monthly anomaly in 1991
and 1993 are shown in Figs. 5b and c, respectively,
as examples. The horizontal position of the DIV
center over the western and central Paciﬁc is plotted by a thick cross (‘þ’) in Figs. 5b, c. The horizontal position of the DIV center for each year
does not stay at a ﬁxed position but varies from ayear to another. DIV and rainfall patterns also
vary from a year to another around the DIV center.
When the monthly climatology as a backgroundmean ﬁeld is subtracted, DIV and rainfall patterns
are found to be spatially conﬁned around the DIV
center in the monthly anomaly ﬁeld. We compose
the monthly anomaly ﬁeld with respect to the DIV
center, representing a subseasonal disturbance, foreach year, and investigate the mean spatial patternof subseasonal disturbances shown in composite
anomalies. As such, the dynamical linkage of rainfall and HLC areas with subseasonal large-scale circulation can be investigated with a year-to-year
variability of the DIV center properly taken into
account.
The results of the composite analysis for rainfall,
HLC, and OLR anomalies are shown in Figs. 6a,b, c (in grey scale). The anomalies of DIV (whitesolid contours) and SST (a black dashed contour)
are also plotted. The positive DIV anomaly has its
maximum over the center of DIV, corresponding to
the maximum location in the distribution of the
raw DIV in each month, and spreads around thecenter with stretching more zonally, i.e., more toeast-west than north-south. The spatial distributionof each variable (rainfall, HLC, and OLR) spreads
more zonally in a manner similar to the DIV. It isnoted that highly positive anomalies of the variables spread more to the eastern part of the DIV
center than to the western part. This might be associated with a positive SST anomaly spreading
to the east of the DIV center, particularly with ahighly positive anomaly nearly along at 0 in thereference latitude (0 km in the vertical axis of Fig.
6). To the west of the DIV center, a negative SST
anomaly spreads from the DIV center. At a lowlevel (1000 hPa), the wind anomaly pattern shownby Fig. 6d is presumably related to the atmospheric
response to the heat source due to the convection
located north of the equator within ITCZ (Hecky
and Gill 1984) where the DIV centers are identiﬁed
through all the period of this analysis. The resultant
pattern in a low-level wind anomaly might belinked to the SST anomaly pattern. To the east of
the DIV center, a westerly anomaly becomes
weaken near the reference latitude of 0 to which awind anomaly tends to converge from both northand south. To the west of the DIV center, counterclockwise and clockwise turns of wind anomalies
are seen at north and south of the DIV center, respectively.

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782

Table 2. Zonal width [100 km] of Rainfall, HLC, and
OLR around the DIV center identiﬁed in the westernand central Paciﬁc in the observation.
Rainfall

HLC

OLR

9.39

10.70

10.23

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Table 3. Same as Table 2, but for each cloud type of
DC, CS, CI.
DC

CS

CI

9.37

9.76

11.84

(976 km) areas are smaller than those of HLC and
OLR areas. The zonal width of DC area is close tothat of the rainfall area. The broad zonal width of
HLC area, a characteristic in Table 2, is thus due
largely to CI area.

Fig. 7.

c.

Same as Fig. 6b, but for CI.

Zonal widths of horizontal pattern of rainfall,
HLC, OLR, and DIV areas in the observation
In order to evaluate the zonally extended feature
quantitatively, the ‘‘zonal width’’ is deﬁned here.
First, zonal distribution of each variable averaged
meridionally between G400 km from the DIV center is obtained. Then, the standard deviation of thecomposite anomalies at the zonal distance from theDIV center is calculated within G2000 km zonal
distance from the DIV center. The resultant standard deviation of zonal distance of each variable
is deﬁned as the zonal width. Interestingly, thezonal width varies from a variable to another asshown in Table 2. The zonal width of HLC area isthe largest (1070 km), followed by that of OLR(1023 km). Compared to HLC and OLR areas, thezonal width of the rainfall area (939 km) is somewhat narrower. The di¤erence of the zonal widthamong the variables is compared with that of eachtype of high clouds, i.e., DC, CS, and CI, as mentioned below. Figure 7 shows the composite mapof CI area around the DIV center. It is apparent
that CI area exhibits a more widespread distribution than rainfall, HLC, and OLR areas aroundthe DIV center, particularly to the east of DIV center. Compared to this particular pattern in CI area,
the spatial pattern of DC and CS areas in the composite (not shown) is quite similar to that of therainfall area. As shown in Table 3, the zonal widthof CI area (1184 km) is larger than any other variable while the widths of DC (937 km) and CS

d. Evaluation of the zonal widths in the models
In order to evaluate the patterns of rainfall,
HLC, OLR, and DIV anomalies along ITCZ inthe models, the same composite analysis as applied
to the observation is applied to the outputs of themodels to calculate the zonal widths. Figure 8
shows the inter-model comparison of the zonal
width of each variable as well as the observation(a: DIV, b: rainfall, c: HLC, d: OLR). The horizontal axis in each ﬁgure shows the temporal correlation between rainfall and DIV at the DIV center
which is derived from the time sequence of monthly
anomaly data except El Nino and La Nina phases
in each model. Because the rainfall amount isstrongly related to the column integrated latentheating, the correlation between rainfall and DIVat the DIV center indicates the degree of relationship of the large-scale circulation ﬁeld with thecolumn integrated latent heating consisting mainlyof the aggregation of cumulus latent heating. The
large correlation means that the latent heatingstrongly inﬂuences subseasonal large-scale circulation by enhancing the upward motion throughthe atmospheric warming, potentially leading toupper-level divergence at the DIV center. The
large-scale circulation may in turn induce low-level
convergence to help trigger the convection. A mostprominent feature in Fig. 8 is the systematic model
bias that the zonal width is underestimated in all
variables compared to the observation (a ﬁlled circle) with a few exceptions that are particularly seenin the rainfall area. The zonal widths of models reproduce those of the observation better in the rainfall area than in HLC area. The zonal widths of
HLC area in the models tend to be close to thoseof the rainfall area or even smaller in some models
while the zonal width of HLC area is larger thanthat of the rainfall area in the observation.
It is noted that, in the models, the zonal widthAugust 2009

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783

Fig. 8. Scatter plots of zonal width of (a) DIV, (b) Rainfall, (c) HLC, and (d) OLR against correlation between rainfall and DIV at the DIV center over the western and central Paciﬁc on the basis of the composite
for the observation (a ﬁlled circle) and for 19 AOGCMs (alphabets; each model as labeled in Table 1).
Inter-model correlations (cor) are indicated at the upper left in each panel. A regressed slope among themodels is described by a dotted line for each panel. Plots of GISS-ER and INGV-ECHM4 are omitted in(c) HLC because signiﬁcant positive anomalies cannot be seen around the DIV center.

of DIV area (Y-axis) increases with the correlation
between rainfall and DIV (X-axis) in Fig. 8a. Theinter-model correlation of the rainfall-DIV relationship and the zonal width of DIV area is 0.49, exceeding the 95% conﬁdence level. As is describedabove, the stronger correlation between rainfall
and DIV at the DIV center implies that subseasonallarge-scale circulation interacts more closely withcumulus latent heating. Figure 8a, thus, indicatesthat a model with a tighter coupling of the subseasonal large-scale circulation ﬁeld with the cumulus
latent heating tends to have a wider spread of DIV
area around the DIV center. Similar inter-model
positive correlations between the zonal width (Yaxis) and rainfall-DIV relationship (X-axis) can bealso seen in HLC (Fig. 8c) and OLR (Fig. 8d)

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Fig. 9. Same as Fig. 6a, b, c, but for each climate model (a: UKMO-HadCM3, b: NCAR-CCSM3.0,
c: BCCR-BCM2.0, d: CCCMA-CGCM3.1-t63).

areas. Figures 8a, c, d show that a model better reproducing the zonal width of DIV, HLC, and OLR
areas tends to have stronger coupling of the largescale circulation ﬁeld with the cumulus latent heating than the observation. In the case of rainfall(Fig. 8b), the zonal width does not strongly dependon the correlation between rainfall and DIV. In
Fig. 8, the reproducibility of the zonal width ineach variable has no clear relation to the horizontal
resolutions and convection schemes that are used ineach model.
Figure 9 shows the composite spatial pattern ofeach variable in the anomaly around the DIV center for two pairs of models showing the contrast of
the performances of DIV (Fig. 8a). Pair A shownby Figs. 9a, b is the two models of UKMO-

HadCM3 (‘‘s’’ in Fig. 8) and NCAR-CCSM3.0(‘‘q’’) that are very close to the observation in Fig.
8a while Pair B shown by Figs. 9c, d is BCCRBCM2.0 (‘‘a’’) and CCCMA-CGCM3.1-t63 (‘‘c’’)that are far from the observation in Fig. 8a. The
composite three variables of rainfall, HLC, and
OLR as well as DIV at the DIV center in Pair A(Figs. 9a, b) exhibit a zonally wide spread distribution, close to those of the observation (Figs. 6a, b,c), while they exhibit a meridionally narrower distribution than the observation. Their strengths ofanomalies of rainfall, HLC, and OLR at the DIV
center are also close to those of the observation(Figs. 6a, b, c). It should be noticed that Pair A reproduces well the observed SST anomaly distribution in the composite. In contrast to Pair A, theAugust 2009

H. ICHIKAWA et al.

composite three variables as well as DIV in Pair B(Figs. 9c, d) tend to be signiﬁcantly more concentrated around the DIV center than those of theobservation (Figs. 6a, b, c). The strength of positive anomalies at the DIV center is underestimatedfor rainfall (rainfall, HLC, and OLR) in BCCRBCM2.0 (CCCMA-CGCM3.1-t63) of Pair B. In
addition, Pair B does not reproduce the observed
SST anomaly distribution. BCCR-BCM2.0 and
CCCMA-CGCM3.1-t63 of Pair B exhibit negativeand small positive anomalies of SST, respectively,
at the DIV center (the negative and small positive
value contours, respectively, do not appear in Figs.
9c, d because they are out of range of the selected
contours that are the same as those of the observation shown in Fig. 6).
3.3

Reproducibility of the climatological meanﬁeld and that of subseasonal disturbances in
the models
In this section, we compare the reproducibility ofthe simulated climatological mean ﬁeld described in
Section 3.1 and that of the composite anomalies,
which represent subseasonal disturbances, in themonthly mean ﬁeld described in Section 3.2.
The model’s performance of the horizontal
spread in the composite analysis in Section 3.2 isclosely related to its capability of simulating theobserved climatological mean pattern of DIV andHLC areas over ITCZ in Section 3.1. For example,
UKMO-HadCM3 and NCAR-CCSM3.0 of Pair
A, which produce a realistic composite pattern,well simulate the climatological mean pattern of
DIV and HLC areas along the location of the observed ITCZ (Figs. 1d, 2) while NCAR-CCSM3.0
tends to slightly underestimate HLC amount over
the western Paciﬁc as shown by Fig. 2. In addition to these two models of Pair A, the models the
positions of which for variables studied (DIV, rainfall, HLC) in Fig. 8 locate relatively close, depending on variables, to the observation, e.g., IAPFGOALS-g1.0 (‘‘i’’), MIROC3.2-hires (‘‘m’’), and
MRI-CGCM2.3.2a (‘‘p’’) indeed capture well the
climatological mean ITCZ pattern as is shown by
Fig. 2 or Dai (2006).
In BCCR-BCM2.0 of Pair B, where the negative
SST anomaly appears around the DIV center in thecomposite analysis, DIV is not linked to SST in aproper manner in the climatolotical mean pattern
as shown in Fig. 2. In the case of CCCMACGCM3.1 of Pair B, where the distribution of
DIV and HLC areas is too much concentrated

785

around the DIV center in the composite analysis,
the area with large DIV and HLC in the climatological mean pattern is conﬁned to the western
Paciﬁc west of 180 E and ITCZ is faint along
the location of the observed ITCZ as shown in
Fig. 2.
Some of other models are brieﬂy referred to as
follows. In GISS-ER and INGV-ECHAM4, whosemarks are not plotted in Fig. 8c because the signiﬁcant HLC anomaly is not seen in the composite
plane around the DIV center, HLC area is not
linked to DIV in a proper manner in the climatological mean pattern. In GISS-EH (‘‘g’’), which exhibits a relatively smaller zonal width (Fig. 8), thelocation of large DIV over ITCZ in the climatological mean pattern is completely di¤erent from thatof the observation in the way that it is too far northof the equator in latitude and too far east aroundthe central-eastern Paciﬁc in longitude (Fig. 2).
4. Summary and discussion
In this study, relationships of rainfall and highlevel cloud areas with large-scale circulation over
the tropical Paciﬁc Ocean in the latest generation
AOGCMs were evaluated. As an overall feature,most models well represent the observed climatological relationships of rainfall and HLC areas
with large-scale circulation. In order to evaluate
the horizontal relationships of rainfall and highlevel cloud areas with large-scale circulation associated with subseasonal disturbances over thetropical Paciﬁc Ocean, the distribution of rainfall, HLC, and OLR is composed around the DIV
center along ITCZ, using DIV as an index oflarge-scale circulation. Monthly anomalies againstmonthly climatologies are used for the composite
except El Nino and La Nina phases. The composite analysis turned out to be useful for studying thehorizontal relationships of rainfall, HLC, and OLR
areas around the DIV centers associated with subseasonal disturbances over ITCZ.
Figure 10 schematically illustrates the resultof the analysis. A comparison between AOGCMs
and observations reveals that, as one of the systematic model biases, the subseasonal large-scale circulation ﬁeld and the related rainfall, HLC, and OLR
are underestimated for the spread, i.e., more localized around the ascent region in most of the models
than in the observation. Particularly, the horizontal
spread of HLC area is largely underestimated by
the models. The spread tends to be less di¤erent between rainfall and HLC areas in the models than in786

Journal of the Meteorological Society of Japan

Fig. 10. Schematics of large-scale circulationﬁeld and cloud/rainfall associated withsubseasonal disturbances (see the last paragraph but one of Section 1) along the eastwest direction in the (a) observation and(b) AOGCM.

the observation. HLC area spreads more extensively than the rainfall area in the observation
owing to the widespread high-level cirrus clouds,
suggesting that the physical processes relevant tothe spatial distribution of HLC area cannot be
captured well in the models.
The large-scale circulation ﬁeld and related HLC
and OLR areas spread more widely in the models
that exhibit a strong coupling between the largescale circulation and the cumulus latent heating.
The result would be presumably explained by the
positive feedback that the cumulus latent heating
enhances the large-scale upward motion leading
to the spread of upper-level divergence, whichstrengthens the large-scale circulation motion thatinduces the enhanced low-level convergence leading

Vol. 87, No. 4

to the enhanced cumulus convection. On the other
hand, the coupling in the models that reproduce
the observed spread of HLC and OLR areas tends
to be too strong.
The model’s performance of the horizontal
spread in the composite anomalies is closely related
to its capability of simulating the observed climatological mean pattern of each variable. A model well
reproducing the observed spread of each variablearound the DIV center tends to well simulate theclimatological mean pattern of each variable over
ITCZ.
It is interesting to see if the spread of each variable around the DIV center can be associated withmodel speciﬁcations such as the spatial resolutions
and convection schemes. Broadly speaking, the reproducibility of the spread appears not to exhibita systematic dependence on the spatial resolutions
and convection schemes that are used in eachmodel. Probably, we must also consider the dependence not only on the convection schemes but also
on the large-scale condensation clouds schemes.
Nevertheless, certain dependence on convection
schemes might be suggested from the results of thisstudy. Considering the fact that the large-scale
circulation and the related HLC and OLR areas
spread widely in the models where large-scale
circulation is tightly coupled to the cumulus latent heating, the spread could be wide in themodels requiring the quasi-equilibrium state between large-scale forcing and convection as is assumed in the Arawawa-Schubert scheme. That isparticularly true of GFDL-CM2.0 (‘‘e’’), GFDLCM2.1 (‘‘f ’’), and MRI-CGCM2.3.2 (‘‘p’’) adopting the Arakawa-Schubert scheme as is shown in
Figs. 8a, c, and d while the spread of HLC and
OLR areas in GFDL-CM2.1 is rather small compared to those in the other two models. For thesemodels, however, the coupling between large-scale
circulation and cumulus latent heating is toostrong compared to the observation while thespread is close to the observation. In the case of
MIROC3.2-hires (‘‘m’’) and MIROC3.2-medres(‘‘n’’), adopting the Arakawa-Schubert scheme but
applying convection suppression modiﬁcation usinga threshold of relative humidity (Emori et al. 2001),
the coupling is close to the observation while thespread is small compared to the observation. Itshould be noted that the spread of each variable iswider and nearer to the observation in MIROC3.2hires (‘‘m’’) than in MIROC3.2-medres (‘‘n’’), i.e.,in the higher spatial resolution model. In contrast

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H. ICHIKAWA et al.

to the model using the quasi-equilibrium assumption, the model using the assumption of a strong
dependence of convection on relative humidity as
BCCR-BCM2.0 (‘‘a’’) is weaker in the couplingand smaller in the spread in the models; it is also
weaker and smaller than the observation.
Overall, the underestimation of the spread of thehigh-level cloud area in AOGCMs is obvious in our
result, suggesting the systematic bias in the representation of anvil cirrus clouds in AOGCMs.
Luo and Rossow (2004) indicated that tropicalcirrus can advect over large distance, about 600–
1000 km, during its lifetime in association withlarge-scale lifting as the replenishing process tomaintain the cirrus much longer than the particle
lifetime. Because the distance spans more than one
grid box in current AOGCMs, they suggested thatthe advection term needs to be properly designed
for the water vapor and cloud water budget in
AOGCMs. An implementation of such a process
would be of great advantage for the improvementof the current skill of representing clouds and rainfall in AOGCMs.
Acknowledgments
We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and the World Climate
Research Programme’s (WCRP’s) Working Groupon Coupled Modelling (WGCM) for their roles inmaking available the WCRP Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model
dataset. Support of this dataset is provided by theO‰ce of Science, U. S. Department of Energy.
The climatological monthly mean ISCCP D2 data
are obtained from the NASA Langley ResearchCenter Atmospheric Science Data Center. This research was supported by the Global Environment
Research Fund (S-5-2) of the Ministry of the Environment, Japan, and by the ‘‘Data Integration andAnalysis System’’ Fund for National Key Technology from the Ministry of Education, Culture,
Sports, Science and Technology, Japan.
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