SOLA, 2014, Vol. 10, 167−171, doi:10.2151/sola.2014-035

167

Classification of CMIP5 Future Climate Responses
by the Tropical Sea Surface Temperature Changes
Ryo Mizuta1, Osamu Arakawa2, Tomoaki Ose1, Shoji Kusunoki1, Hirokazu Endo1, and Akio Kitoh2
1
Meteorological Research Institute, Tsukuba, Japan
2
University of Tsukuba, Tsukuba, Japan

Abstract
Climate changes for the end of the 21st century projected by
Coupled Model Intercomparison Project phase 5 (CMIP5) models
are classified into three clusters by a cluster analysis of annual-mean tropical sea surface temperature (SST) change patterns.
The classified SST change patterns are featured by the zonal gradient of the change in the equatorial Pacific and inter-hemisphericcontrast of the warming. Precipitation and atmospheric circulation
responses are composited for the clusters, and their relationships
to the SST changes are examined. Precipitation increase is larger
where SST warming is larger than surroundings and vice versa.
Common precipitation and atmospheric circulation responses foreach cluster are found also over tropical lands and the extratropics
as well as in the tropical oceans, suggesting that some remote
effects of the tropical SST change patterns could be one reason forless agreement among CMIP5 models in climate changes.
(Citation: Mizuta, R., O. Arakawa, T. Ose, S. Kusunoki H.
Endo, and A. Kitoh, 2014: Classification of CMIP5 future climate
responses by the tropical sea surface temperature changes. SOLA,
10, 167−171, doi:10.2151/sola.2014-035).

1. Introduction
Based on the Coupled Model Intercomparison Project phase
5 (CMIP5; Taylor et al. 2012) models projections, the futurechanges in surface air temperature and sea surface temperature(SST) for the end of the 21st century are not spatially uniform andalso not seasonally constant (Collins et al. 2013). For the tropicalocean surface temperature, the strongest warming is projected on
the equator and in the Northern Hemisphere subtropical regions.
Precipitation change also varies in space. While models showthat global precipitation will increase with increased global meansurface temperature, changes in average precipitation in a warmer
world exhibit substantial spatial variation (Collins et al. 2013).
The spatial distribution of tropical precipitation changes will
depend on the current precipitation climatology and also on future
SST warming pattern (Christensen et al. 2013). The first effect is
to increase precipitation near the currently rainy regions (“wet-getwetter” effect; Held and Soden 2006; Chou et al. 2009), and thesecond effect is to increase precipitation where warming of SST
exceeds the tropical mean and vice versa (“warmer-get-wetter”
mechanism; Xie et al. 2010; Chadwick et al. 2013; Ma and Xie
2013).
Tropical SST changes vary among the models. Variances of
SST change patterns in the CMIP5 models are large as well as in
the CMIP3 models, in particular for zonal SST gradient changes
along the equatorial Pacific (Christensen et al. 2013). This isrelated to the variance of mean precipitation and tropical circulation change through the effect mentioned above, which affect theextratropics, mainly through the changes in the teleconnection
patterns from the tropics and changes in tropical cyclone genesis.
This study investigates how the inter-model difference of thetropical SST changes for the end of 21st century is related to that
Corresponding author: Ryo Mizuta, Meteorological Research Institute, 1-1
Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: rmizuta@mri-jma.
go.jp. ©2014, the Meteorological Society of Japan.

of the precipitation and circulation changes. Horizontal patternsof the tropical SST changes projected by the CMIP5 models
are classified using a cluster analysis method, and associatedchanges in the precipitation and circulation for each cluster are
investigated. These analyses will give some information on whatpart of the uncertainty in the projected regional climate change
could be attributed to the uncertainty of the tropical SST change.
The classified SST change patterns can be used as the lower
boundary change for atmospheric models to study on what part ofthe climate change could depend solely on the pattern of the SSTchange. We show a cluster analysis method and its results in Section 2. Composites of climate changes for the three SST clustersof the CMIP5 models will be given in Section 3. Summary andsome remarks will be made in Section 4.

2. Cluster analysis of CMIP5 SST
The names of the CMIP5 models used in this analysis arelisted in Table S1 in the auxiliary material. We used 28 model
results from CMIP5 for which the historical, RCP2.6, RCP4.5,
and RCP8.5 scenario experiments are available. Only one member
is used even if the model has multiple experiments with different
initial conditions. Cluster analysis is made for the SST difference
between the historical and RCP8.5 experiments. This classification
is used for other scenarios. Additionally, as we obtained 22 out of
the 28 models for the RCP6.0 scenario experiments, we also madeanalysis for the 22 RCP6.0 experiments.
We use the same cluster analysis method as Murakami et al.
(2012) and Endo et al. (2012), which was applied to the CMIP3
SRES A1B scenario runs. The detailed procedure for the cluster
analysis is as follows: (1) for each of the 28 CMIP5 models, theannual-mean SST change from the present-day (1979−2003 of
historical experiment) to the end of the 21st century (2075−2099
of RCP8.5 experiment) is computed; (2) the computed mean SSTchange is normalized by the tropical (30°S−30°N) mean SSTchange; (3) the multi-model ensemble mean of the normalizedchange is subtracted from that for each model experiment; (4)then, the inter-model pattern correlation r over the tropics (30°S−
30°N) is computed between each pair of the models; and (5)norms (or distances) are defined as 2 × (1 − r) for every pair, andthe cluster analysis is performed using these norms. A small
distance between two models indicates they share similar spatial
patterns in the tropical SST change. Clustering is based on the
single-linkage (or minimum-distance) method (e.g., Wilks 2011),
in which the smallest distance between two models (or groups) is
joined step-by-step. The clustering procedure is terminated when
the final three groups are obtained. The effect of the mid-latitude
ocean to the atmosphere is smaller, and there could be otherclassifications of the models in the north and south polar regions.
Therefore, we use the SST only in the tropics to focus on theeffect of the tropical ocean.
Figure S1 in the auxiliary material shows the normalized SSTchanges of all 28 models between the 1979−2003 mean of thehistorical experiment and the 2075−2099 mean of the RCP8.5
experiment. For each model, SST was normalized by the tropical(30°S−30°N) mean SST change, and then multiplied by the tropical SST change averaged for the total 28 models (2.74K). Figure
1 shows the dendrogram of the cluster analysis. It is noted thatmodels from the same institute have a smaller distance than other168

Mizuta et al., Classification of CMIP5 Future Climate Responses

models, and thus tend to be clustered earlier. This characteristic
has been also found in the cluster analysis of CMIP5 modelclimatology of surface temperature and precipitation (Knutti et al.
2013). As we ended up as three clusters, 8, 14, and 6 models areclassified as Cluster 1, Cluster 2, and Cluster 3, respectively.
Based on the result of the cluster analysis above, we composed
three kinds of the future SST change by averaging each clusterof the models, after normalized by the annual-mean tropicalmean (30°S−30°N) SST change. Figure 2 shows the annual-mean
normalized SST change between the present-day and the end of
21st century, for the composite of the total 28 models and thecomposites of the three clusters of the models, together with thedeviations for each cluster from the total mean. The SST changes
are multiplied by the annual-mean tropical SST change averaged
for the total 28 models (2.74K) in order that the annual-mean
tropical SST change becomes the same value as that in the 28
CMIP5 model mean. Note that the resultant three kinds of the
SST change pattern have almost the same values of SST for not
only the tropical mean but also the global mean. It is shown that
SST warming even in the total mean is spatially inhomogeneous(Fig. 2a): warming is larger in the Northern Hemisphere than in
the Southern Hemisphere, and the largest SST warming occurs in
the northern North Pacific. Relatively larger warming is found in

Fig. 1. Dendrogram of SST cluster analysis based on 28 CMIP5 SSTchanges in the tropics (30°S−30°N).

the central and eastern tropical Pacific. This spatial pattern in thetropics is emphasized in Cluster 2 (Figs. 2c, f). The pattern in Fig.
2f is similar to the observed interannual variation pattern associated with ENSO, consistent with the CMIP3 result that projects
El Niño-like response in many models (Yamaguchi and Noda2006). In contrast, Cluster 1 (Figs. 2b, e) shows smaller warmingin the eastern tropical Pacific, which is an opposite characteristic
to Cluster 2. In addition, warming in the Southern Hemisphereis larger in Cluster 1, to the extent that the warming in the midlatitudes (~40°) is about the same between the both hemispheres.
Cluster 3 (Figs. 2d, g) has large warming in the western North
Pacific. Warming is larger than the other clusters also aroundnorthern Indian Ocean and the North Atlantic. In contrast,
warming in the Southern Hemisphere is smallest, thus the interhemispheric contrast is large.
More specifically, the clustered SST change patterns are featured by the zonal gradient of the change in the equatorial Pacificand inter-hemispheric contrast of the warming. Figure 3 shows thescatter diagram of the annual-mean normalized SST change of eachmodel, for the difference between 15°S−30°S and 15°N−30°N
versus the difference between 5°S−5°N, 150°W−90°W (called
Niño 3 region) and 0°N−15°N, 130°E−150°E (called Niño West

Fig. 3. Scatter diagram of the annual-mean SST change (K) of each model,
for the difference between 15°S−30°S and 15°N−30°N versus the difference between 5°S−5°N, 150°W−90°W (called Niño 3 region) and 0°N−
15°N, 130°E−150°E (called Niño West region). The change is normalized
by the tropical mean for each model and then multiplied by 28 models
mean tropical SST change (2.74K). The digits denote the cluster numberof the models.

Fig. 2. Annual-mean sea surface temperature changes (K) from the present-day (1979−2003, historical experiment) and the end of the 21st century(2075−2099, RCP 8.5 experiment), for (a) the composite of total 28 models, and (b−d) the composites of the three clusters of the models. (e−g) Differences
for each cluster from the total mean. The regions where over 75% of the models agree with the sign of the difference are colored. Contours denote 0. Thechange is normalized by the tropical (30°S−30°N) mean for each model before making composition, and then multiplied by 28 models mean tropical SST
change (2.74K).

SOLA, 2014, Vol. 10, 167−171, doi:10.2151/sola.2014-035
region). The three clusters are separated clearly, although there are
two exceptional models. Cluster 1 is characterized by the small
zonal gradient change and the small (or negative) inter-hemispheric gradient change, Cluster 2 is characterized by the large zonal
gradient decrease and the modest inter-hemispheric contrast, andCluster 3 is characterized by the small zonal gradient change andthe large inter-hemispheric contrast.
The composites of the annual-mean normalized SST change
for the three clusters have been made also for the other RCP experiments. The pattern correlation coefficients between the different experiments are shown in Table 1 (top right). In any clusters,
pattern correlation between the changes for the RCP2.6 and thosefor the other RCP experiments are commonly lower than the otherpairs. Since amplitudes of the changes from the historical to the
RCP2.6 experiments are so small, internal decadal variability or
the effects of aerosol changes could be comparable to the effects
of CO2 increase, and the ratio of warming over land to that overocean could be different from the other scenarios. While there aresome exceptions for the Cluster 1, the correlation coefficients areover 0.9 in most of the pairs. This suggests that the annual-mean
normalized SST changes could be treated as the same to a large
extent among the four RCP scenarios. If the normalized SSTchanges are the same among the scenarios, we have possibilityof applying “pattern scaling” (e.g., Mitchell et al. 1999) to obtainmany future changes from one experiment just by multiplying theamplitude of the change.

3. Composites of climate changes for the three SST
clusters of the CMIP5 models
Tropical SST distribution has large impacts not only on in situ
precipitation but also on various remote responses in precipitation
and atmospheric fields. In this section, composites of climate

169

changes for the three clusters of the CMIP5 models are shown interms of precipitation and sea-level pressure (SLP). The change
for each model is interpolated to 2.5-degree grid and normalized
by the tropical mean SST change before making the composition.
Figure 4 shows the changes in annual-mean precipitation
between the present-day (1979−2003, historical experiment) andthe end of the 21st century (2075−2099, RCP 8.5 experiment)
composited for the total 28 models and the three clusters of themodels. The December-January-February (DJF) mean and theJune-July-August (JJA) mean are also shown in Figs. S2 andS3 in the auxiliary material, respectively. While it is common to
the all clusters that the precipitation increases in the tropics andextratropics, and decreases in the subtropics, there are systematicdifferences between the clusters in precipitation changes over thetropics, associated with the differences in the SST changes.
In the total mean (Fig. 4a), precipitation increases more overthe central equatorial Pacific and less over the western equatorial
Pacific. Moreover, decrease is found in the western part of themaritime continent. This is associated with the El Niño-like SST
response pattern shown in Fig. 2a. The decrease is conspicuous in
the JJA mean (Fig. S3c). These characteristics are more conspicuous in Cluster 2 (Fig. 4c, f). In Cluster 1 and 3, more increase
than Cluster 2 is found from the maritime continent to the South
Pacific Convergence Zone (SPCZ) region, which is opposite sighfrom Cluster 2. In contrast, precipitation increase over the central
and eastern equatorial Pacific is less than Cluster 2 (Fig. 4b, d, e, g).
This is associated with the less SST warming in this region.
Precipitation is increasing over Amazon and the central Africa
in Cluster 1 (Fig. 4b), although decreasing in the other clusters.
These characteristics are seen also in the DJF mean (Fig. S2b, e)
and the JJA mean (Fig. S3b, e). Cluster 3 is characterized by thelarger decrease over the subtropics of the Southern Hemisphere(Fig. 4d, g). This would be related to the less SST warming ineastern South Pacific (Fig. 2g). Over the Asian monsoon region,

Table 1. The pattern correlation coefficients of (top right) annual-mean normalized SST change and (bottom left) annual-mean normalized precipitation
change between the four RCP experiments for the composites of the three clusters. The values over 0.9, 0.8, and below 0.8 are colored by red, green, andblue, respectively.

Fig. 4. Annual-mean normalized precipitation changes (mm day−1 K−1) from the present-day (1979−2003, historical experiment) and the end of the 21st
century (2075−2099, RCP 8.5 experiment), for (a) the composite of total 28 models, and (b−d) the composites of the three clusters of the models. (e−g)
Differences for each cluster from the total mean. The regions where over 75% of the models agree with the sign of the difference are colored. Contours denote 0. The change for each model is interpolated to 2.5-degree grid and normalized by the tropical mean SST change before making the composition.

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increase of precipitation is larger in Cluster 3, especially around
Indochina and the Philippines, associated with the larger SST
warming over the western North Pacific (Fig. 2g). This is clearer
in the JJA mean (Fig. S3e, g). Over the Caribbean Sea and subtropical North Atlantic, in contrast, decrease of precipitation issmaller in Cluster 3.
The pattern correlation coefficients of the normalized precipitation change between the different experiments are shownin Table 1 (bottom left). Except for the RCP2.6 experiment, thecorrelation coefficients are over 0.8 in all of the pairs, even though
the cluster analysis has been made for the SST change. It is noted
that, in Cluster 1, correlation between the RCP6.0 and the others
are relatively lower, partly because 4 models out of 8 are unavailable for RCP6.0 in Cluster 1 (Table S1).
Figure 5 shows the same future changes as Fig. 4, but for
the annual-mean SLP. The DJF mean and the JJA mean are also
shown in Figs. S4 and S5 in the auxiliary material, respectively.
Mean sea-level pressure responses for the total 28 models (Fig.
5a) are projected to decrease in high latitudes and increase in the
mid-latitudes with poleward shifts of the mid-latitude jets (Collins
et al. 2013). Over the maritime continent, increase of SLP is larger
than surroundings, leading to the reduction of zonal SLP gradientover the tropical Pacific. Cluster 2 has a similar characteristic to
the total mean (Fig. 5c) and a small difference from the total mean
(Fig. 5f). Cluster 1 has a smaller increase than the other clusters(Fig. 5b, e), associated with the larger increase of precipitation
over this region. This anomaly in SLP is extending to the subtropics around Australia and south of Japan (Fig. 5e). A relatively
larger SLP increase is seen over the Eurasian continent in winter(Fig. S4e). In Cluster 3, SLP is increasing larger in the central
equatorial Pacific (Fig. 5d, g) and decreasing in the Northern continents. The increase of SLP is larger also over mid-latitudes of the
Pacific and the Atlantic. This is clear in the Northern Hemispherein DJF (Fig. S4g) and in the Southern Hemisphere in JJA (Fig.
S5g), suggesting that a difference in the change of the wintertime
storm track activity in mid-latitudes. In JJA, larger increase of
SLP is found over the Indian Ocean. This would contribute toan enhancement of the eastward moisture transport in the Asiansummer monsoon, consistent with the larger increase of the Asianmonsoon precipitation in Cluster 3 (Fig. S3g). The JJA situationof Cluster 1 (Fig. S3e) is reverse to that of Cluster 3.
The DJF and JJA mean surface temperature changes areshown in Figs. S6 and S7 in the auxiliary material. The warming
is relatively smaller/larger over the Northern continents in Cluster
1/Cluster 3 (Figs. S6e, g, S7e, g), consistent with the SLP changes. In addition, associated with the El Niño-like/La Niña-like SST
response pattern shown in Fig. 2e/2f, the decrease of cold surge
over the East Asia is enhanced/suppressed, also consistent with
the SLP changes around south of Japan (Fig. S4e).

4. Summary and concluding remarks
This paper focused on the classification of climate changes
for the end of the 21st century projected by CMIP5 models using
a cluster analysis of annual-mean tropical SST change patterns.
The classified SST change patterns are characterized by thezonal gradient of the change in the equatorial Pacific and interhemispheric contrast of the warming. Associated with different
tropical SST changes among the clusters, characteristic precipitation and atmospheric circulation responses are found in eachcluster. The relationship between the different SST changes andthe different precipitation responses among the clusters seems tofollow the “warmer-get-wetter” mechanism mentioned in the Introduction. The precipitation and atmospheric circulation response
are found also over the tropical lands and the extratropics as wellas in the tropical oceans, suggesting that some remote effects
could be one reason for less agreement among CMIP5 models inclimate changes. It is noted that it should be taken into consideration that, due to the insufficient model number, there could exist
high-latitude surface differences between the clusters (in sea ice
and/or land surface) unrelated to the tropics, which affect extratropical atmosphere.
The obtained SST changes as shown in Fig. 2b, c, d can be usedas the lower boundary change for atmospheric models to study
on what part of the climate change could depend solely on thepattern of the SST change. In particular, the use of high-resolution
atmospheric models can give us information on smaller-scale
extreme events than CMIP5 coupled models themselves. We cancover the range of inter-model spread with smaller number ofexperiments than giving every change pattern from all CMIP5
models. Murakami et al. (2012) showed that regional future
projections of frequency of tropical cyclones are depending on
the SST change pattern. Endo et al. (2012) showed that the uncertainty of future projections in extreme precipitation over Asia islarge and comes from the SST change pattern as well as physicalparameterization schemes of the model. We are now doing
ensemble projections using the SST change patterns obtained inthis study, with a higher-resolution atmospheric model and evenhigher-resolution regional downscaling from it. These experiments
would enable us to discuss more on the uncertainty of the future
projections of smaller-scale and/or extreme phenomena.

Acknowledgments
We acknowledge the modeling groups, the Program for
Climate Model Diagnosis and Intercomparison (PCMDI) and the
WCRP's Working Group on Coupled Modeling (WGCM) for theirFig. 5. As for Fig. 4, but for annual-mean normalized sea-level pressure changes (hPa K−1).

SOLA, 2014, Vol. 10, 167−171, doi:10.2151/sola.2014-035
roles in making available the WCRP CMIP5 multi-model datasets.
This study was supported by the SOUSEI Program of the Ministryof Education, Culture, Sports, Science, and Technology of Japan(MEXT), and the Environmental Research and Technology Development Fund (2A-1201) of the Ministry of Environment of Japan.
We thank two anonymous reviewers for constructive comments.

Supplements
In the auxiliary meterial, list of CMIP5 models used is shownin Table S1. Annual mean SST changes for each model are shownin Fig. S1. DJF and JJA mean normalized precipitation changes
are shown in Figs. S2 and S3. DJF and JJA mean normalized SLPchanges are shown in Figs. S4 and S5. DJF and JJA mean normalized surface temperature changes are shown in Figs. S6 and S7.

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