SOLA, 2016, Vol. 12A, 1−6, doi:10.2151/sola.12A-001

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Future Changes in Rainfall Extremes Associated with El Niño Projectedby a Global 20-km Mesh Atmospheric Model
Akio Kitoh1, and Hirokazu Endo2
University of Tsukuba, Tsukuba, Japan
2
Meteorological Research Institute, Tsukuba, Japan
1

Abstract
A global high-resolution atmospheric general circulation
model with grid size about 20 km is used to project future changes
in rainfall extremes associated with El Niño at the end of the 21st
century. In the future climate projections, hypothetical sea surface
temperature (SST) is assumed where the interannual variability of
SST remains same as in the present climate. The annual maximum
1-day precipitation total (R1d) over the western North Pacific islargely associated with tropical cyclone activity and positively
correlated to the Niño3.4 SST anomalies. It is found that climatological mean R1d will only modestly increase in the western North
Pacific in the future, but interannual variability of R1d will largely
increase compared to the present due to enhanced association with
El Niño. This implies an increasing risk of heavier rainfall events
by global warming around the western North Pacific countries.
(Citation: Kitoh, A., and H. Endo, 2016: Future changes inrainfall extremes associated with El Niño projected by a global
20-km mesh atmospheric model. SOLA, 12A, 1−6, doi:10.2151/
sola.12A-001.)

1. Introduction
El Niño/Southern Oscillation (ENSO) is one of the biggest
variability of the atmosphere-ocean coupled system, and influences most parts of the tropics and even some mid-latitudes.
Present-day reproducibility of ENSO and its future change varies
much among the models. By analysing 34 climate models participating in the Coupled Modeling Intercomparison Project phase 5(CMIP5), Taschetto et al. (2014) find no consistent changes acrossthe models in the location and magnitude of maximum sea surface
temperature (SST) anomalies, frequency, or temporal evolution of
El Niño events in a warmer world. IPCC (2013) assessed that natural modulations of the variance and spatial pattern of ENSO are
so large in models that confidence in any specific projected change
in its variability in the 21st century remains low. It is also assessedthat ENSO will still be the most dominant year-to-year variationsof the future tropical climate system and due to increased moistureavailability, the associated precipitation variability on regional
scales likely intensifies. Cai et al. (2015) showed that mean statechange of the tropical Pacific will cause an increased frequency of
extreme El Niño and La Niña events and an eastward shift of the
ENSO rainfall teleconnections with a likely increased intensity.
The CMIP5 models project a strengthening ENSO effect on thenorthwest Pacific and East Asia summer climate (Hu et al. 2014).
The western North Pacific is the most active region of tropical
cyclones. There are numerous studies on the variability of tropical
cyclones. Under the global warming, global maximum wind speed
and precipitation rates are projected to increase (IPCC 2013).
The ENSO is known to affect the western North Pacific tropical
cyclone (typhoon) activity in its location (Takaya et al. 2010) andintensity (Camargo and Sobel 2005). Therefore, changes in both
ENSO and tropical cyclones on local precipitation in a warmer
Corresponding author: Akio Kitoh, Faculty of Life and Environmental
Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058572, Japan. E-mail: kito.akio.ff@u.tsukuba.ac.jp. ©2016, the Meteorological Society of Japan.

world should be investigated to assess changes in natural hazards
to cope with possible risk increases.
A global high-resolution atmospheric general circulation
model with grid size about 20 km is used to project future changes
in rainfall extremes associated with El Niño at the end of the 21st
century. As reliable projections of future ENSO characteristics
are not available, we use hypothetical SST in the future climate
projections where the interannual variability of SST is assumed
to remain as in the present climate. This strategy is used to assess
future changes in weather and climate extremes including tropical
cyclones (Murakami et al. 2011, 2012) and heavy precipitation(Kitoh et al. 2009; Kitoh and Endo 2016) as well as changes inteleconnections such as the ENSO-forced Pacific-North Pacificpattern (Zhou et al. 2014).

2. Model and experiment
2.1 MRI AGCM
We used a global 20-km mesh Meteorological Research Institute (MRI) atmospheric general circulation model (AGCM) version 3.2 (MRI-AGCM3.2, Mizuta et al. 2012). The 20-km meshversion uses a triangular truncation at wave number 959 (TL959)
in the horizontal, which has 1920 × 960 grid points. There are 64
layers in the vertical with a top at 0.01 hPa. For cumulus parameterization scheme, a new mass-flux type scheme (Yoshimura et al.
2015) is used.
This model has superiority than the previous version in reproducing global distribution of strong tropical cyclones (TC) such as
categories 4 and 5 (Murakami et al. 2012). In a warmer climate,genesis frequency of TC is projected to significantly decrease in
the western North Pacific and South Pacific, and increase in thetropical central Pacific (Murakami et al. 2012). Endo et al. (2012)
showed an increase in extreme precipitation in South Asia andSoutheast Asia in future climate. Kitoh and Endo (2016) further
investigated future changes in precipitation extremes. They foundthat South Asia is the region of the largest extreme precipitation
increase, while heavy precipitation increases in all regional
domains even where mean precipitation decreases.
2.2 Experiment method
The present climate simulation used the observed interannually varying monthly mean SST and sea-ice concentration during
1979−2003 based on the HadISST1.1 data (Rayner et al. 2003).
Two 25-year ensemble simulations were made from different
atmospheric initial conditions, thus we have 50-year present-day
simulation data.
For the future climate (2075−2099), the boundary SST data
were prepared by superposing the future change in the multimodel ensemble of SST projected by CMIP5 models to thepresent-day observed SST. See Mizuta et al. (2008) for the details.
In order to assess the uncertainty in projections, we made otherthree simulations with different SST spatial patterns. Three SST
patterns are obtained by a cluster analysis of 28 CMIP5 RCP8.5
experiments (Mizuta et al. 2014): cluster 1 is characterized bya nearly uniform warming in the both hemispheres, cluster 3 isdominated by a larger warming in the Northern Hemisphere thanin the Southern Hemisphere, and cluster 2 shows a larger warming
over the central equatorial Pacific (El Niño-like pattern). Overall,four-member 25-year simulations were performed for the future,2

Kitoh and Endo, Future Changes in Rainfall Extremes Associated with El Niño
the future period (0.91°C for 2075−2099) is larger than that in thepresent (−0.13°C for 1979−2003).
2.3 Observation data
In order to investigate precipitation extremes associated with
ENSO, we use daily precipitation data. For observed precipitation
data, we used Tropical Rainfall Measuring Mission (TRMM)
3B42 product in version 6 (Huffman et al. 2007), which covers
1998−2013. The TRMM data resolution is 0.25 degree and is very
close to the model resolution.
For the observed TC tracks, we used International Best Track
Archive for Climate Stewardship (IBTrACS) v03r06 (Knapp et al.
2010).

Fig. 1. Time-series of annual mean Niño3.4 SST (°C). Solid line: presentday experiment for 1979−2003. Dash line: future experiment for 2075−
2099.

3. Present-day R1d associated with El Niño andtropical cyclones

resulting in 100-year data for future climate.
Kitoh and Endo (2016) investigated projected changes inregional precipitation extremes such as annual maximum 5-day
precipitation total (R5d) and annual maximum 1-day precipitation
total (R1d) over 22 regional land domains using the above ensemble simulation data, showing an increase in all regional domains,even where mean precipitation decreases. They also noted that different SST patterns resulted in large precipitation changes in somedomains, possibly related to changes in large-scale circulations in
the tropical Pacific.
Figure 1 shows the time-series of the prescribed mean Niño3.4
SST for the present and the future climate experiments. For clarity, annual mean values are shown, although monthly mean SST is
used for the experiments. The 25-year mean difference is 3.03°C.
Years of warm SST at year 4 (1982), year 9 (1987), year 19 (1997),
and year 24 (2002) are clearly seen in the present climate experiment. Due to our experiment strategy, the same peaks are found in
the future climate experiment. It is also noted that a linear trend in

MRI-AGCM3.2 shows good skill in simulating monsoon
circulations and precipitation. The model also reproduces various
characteristics of TC such as their intensity and global distribution(Mizuta et al. 2012). Kitoh and Endo (2016) showed that the
20-km mesh MRI-AGCM3.2 has good skill in reproducing precipitation extremes such as R1d in its present-day simulation.
Figure 2a shows the model climatology of R1d based on 50year simulations corresponding to the 25-year period 1979−2003(two ensembles with different atmospheric initial conditions).
Note that all figures in the portrait format are drawn with 1 degreeby 1 degree box averaging for visual easiness. Observation is
given in Fig. 3a, which shows large R1d over the western Tropical
Pacific and South China Sea, the Bay of Bengal, South PacificConvergence Zone (SPCZ) and Inter-Tropical Convergence Zone
(ITCZ). The 20-km mesh MRI-AGCM3.2 well reproduces spatial
distribution as well as intensity of R1d compared with TRMM
3B42 data, although the model overestimates R1d over SPCZ and
ITCZ. Area average R1d over 50°S−50°N, 0°−360° is 74.5 mm by
the model, which is close to the observation (76.7 mm).

Fig. 2. Model climatology of the 20-km mesh MRI-AGCM3.2 for 1979−2003 of (a) R1d (mm), (b) R1d (mm) associated with tropical cyclones, (c) regression coefficients of R1d (mm) on Niño3.4 SST anomalies, and (d) regression coefficients of R1d (mm) associated with tropical cyclones on Niño3.4 SST
anomalies. All figures are drawn by 1 degree × 1 degree box averaging for visual easiness.

SOLA, 2016, Vol. 12A, 1−6, doi:10.2151/sola.12A-001

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Fig. 3. Same as Fig. 2 except for the TRMM3B42 climatology for 1998−2013.

Heavy precipitation events in the tropics often occur associated with TC. We define TC-associated precipitation as that within
a 500 km radius of TC centers because precipitation concentrates
within a 5-degree radius around the TC centers (Kamahori 2012).
As TC track data is available every 6 hours, calculation is made6 hourly, and then accumulated daily precipitation is used to calculate TC-associated R1d. The criteria defining simulated TC aremodel-dependent in such a way that the present-day global annual
mean TC number matches the observed number (84 per year) as
shown in Murakami et al. (2012). In our definition, in years when
TC does not exist within a 500 km radius of a given grid, TC-associated R1d becomes zero.
Figures 2b and 3b show that the 20-km mesh MRI-AGCM3.2
reproduces well the observed TC-associated R1d quantitatively.
The ratio of the TC-associated R1d to the total R1d is greater than
80% over the western Pacific (the Philippines Sea and the South
China Sea), off Mexico, northwest of Australia and east of Madagascar. There exist the years in which non-TC-associated rainfall
contributes the R1d. Over the western Pacific, simulated TCassociated R1d is displaced northward compared to the observation. This may be related to the model’s bias of a northward shiftof typhoons (Murakami et al. 2012).
Next we investigate interannual variations of precipitation
extremes associated with El Niño. Figure 2c shows regression
coefficients of R1d on Niño3.4 SST anomalies. Here annual meandata in calendar year is used because R1d is calculated in anannual basis. As is well known, total precipitation anomaly in El
Niño years is positive over the central equatorial Pacific and ITCZ
in the eastern Pacific, and is negative over the western Pacific andmaritime continent. This feature is well reproduced by the model(not shown). Simulated spatial regression map of R1d on Niño3.4
SST anomalies is overall similar to that of total precipitation,
except for the western Pacific where R1d is positively correlated
to Niño3.4 SST anomalies (Fig. 2c). This feature is also seen in
the observed data for a period 1998−2013 (Fig. 3c), although astrict comparison cannot be made because of different data periods
between the observation and the model.
Positive R1d anomalies over the western Pacific in El Niño

years come from heavy precipitation associated with TC. Figure
2d shows the regression coefficients of TC-associated R1d on
Niño3.4 SST anomalies in the present-day experiment. Simulated
TC-associated R1d in El Niño years is large positive to the east of
the Philippines while negative in the South China Sea. It is also
positive over the oceans northeast of Australia, while negative
northwest of Australia. Over the Indian Ocean, it is positive over
the western Indian Ocean and negative over the eastern Indian
Ocean. Simulated TC-associated R1d in El Niño years is generally
negative in the North Atlantic Ocean. Most of these features, inparticular large contribution of TC for heavy precipitation in thewestern North Pacific, are seen in the observation (Fig. 3d).

4. Future changes
Here we investigate future changes in R1d, TC-associatedR1d, and their regressions on Niño3.4 SST anomalies. R1d in the
future climate will increase almost everywhere (Fig. 4a). Global
average R1d increases about 25% from 61.6 mm to 77.5 mm. As
the global annual mean surface air temperature increases 3.49°C
in this experiment, the rate of R1d change is 7.4%/°C, which is
close to the change of saturated vapor pressure by the ClausiusClapeyron relationship.
Large R1d change is projected over the central-eastern equatorial Pacific and the equatorial Atlantic Ocean. India, central
China, Sahel, equatorial South America and southern Brazil are
the region of large R1d increases over land. The change is not significant over the western North Pacific. Future R1d is projected todecrease in some regions such as the South China Sea, the southeastern Pacific Ocean and the southern Atlantic Ocean. The lattertwo regions correspond to the oceanic regions where climatological R1d is small (Fig. 2a). Similar to the spatial pattern of the total
precipitation changes, which roughly follows the “wet-gets-wetter”(Held and Soden 2006) or “warmer-gets-wetter” pattern (Xie et al.
2010; Chadwick et al. 2013), future changes in R1d also follow
the climatological R1d pattern (Fig. 2a). The exception is smallchange or reduction of R1d over the western North Pacific where4

Kitoh and Endo, Future Changes in Rainfall Extremes Associated with El Niño

Fig. 4. Same as Fig. 2 except for the differences between the future (2075−2099) and the present (1979−2003).

the present-day R1d is large climatologically. This peculiar change
may be related to changes in TC activity there.
Murakami et al. (2012) investigated the TC changes by the
MRI-AGCM3.2 using the mean SST warming by the CMIP3
models under the SRES A1B scenario. They found a 15% decreaseof global TC number at the end of the 21st century compared tothe present-day conditions. Regionally, they noted a decreaseof TC frequency in the western North Pacific and in the South
Pacific Ocean, and a marked increase in the central Pacific. Using
the same experiment results, Murakami et al. (2013a, 2013b) discussed physical mechanisms and found that changes in large-scale
environmental conditions are responsible to regional TC genesis
frequency changes.
In our experiment, the global TC genesis number is projected
to decrease by 24%. A larger reduction in TC number than that in
Murakami et al. (2012) is consistent with a larger warming in our
experiment as our experiment is based on the RCP8.5 scenario.
Figure 4b shows the changes of TC-associated R1d in the
future. The TC-associated R1d is projected to decrease in thewestern North Pacific, off the east of Australia, and tropical Indian
Ocean. It is projected to increase near Hawaii, northern Indian
Ocean. In other regions, changes are not statistically significant.
This spatial pattern of TC-associated R1d change resembles the
TC density change in the future (Fig. 5). Therefore, less activityof TCs in the western North Pacific in the future climate may haveresulted in decreased TC-associated R1d.
Next we investigate R1d changes associated with El Niño
(Fig. 4c). Regression coefficients of R1d on Niño3.4 SST anomalies are projected to increase over the northwestern Pacific, theequatorial Pacific and SPCZ. They will decrease off Mexico and
off the east of Australia. Generally, the present-day pattern (Fig.
2c) is enhanced in the future climate, but statistical significanceof changes in regression is low because of large interannual variability and small samples. There are some regional differences.
For example, the positive anomalies in the South China Sea (Fig.
4c) imply weaker regressions in the future climate as compared tolarge negative regression coefficients in the present climate (Fig.
2c).

Fig. 5. Difference of tropical cyclone density (number per 25 years in eachgrid) by the 20-km mesh MRI-AGCM3.2 between the future (2075−2099)
and the present (1979−2003).

In the present climate, TC-associated precipitation is particularly large in the western North Pacific in El Niño years (Fig. 2d).
In the future climate, this TC-associated precipitation in El Niño
years will become much stronger (Fig. 4d). On the other hand,changes in regression coefficients of TC-associated precipitation
to Niño3.4 SST are negative over SPCZ and the Arabian Sea,
and are positive over the Bay of Bengal and the southern Indian
Ocean, tending to weaken the present-day relationship.
We have noted that R1d over the western North Pacific is verylarge in the present climate, but does not increase much in the
future climate. Over there, TC-associated R1d actually decreases.
Figure 6 shows the time-series of R1d averaged over the western
North Pacific (120°E−180°E, 0°N−25°N) for the present and thefuture. Here both the total R1d and TC-associated R1d are shown.
The area average R1d increases by 11% from 106.8 mm to 118.5
mm, which is far less than the global mean R1d change (25%). In
this region, the TC-associated R1d decreases by 25% from 52.4
mm to 39.4 mm, related to the TC density decrease by 47%.
On the other hand, interannual variability (denoted as standard
deviations) increases both for total R1d and TC-associated R1d. A

SOLA, 2016, Vol. 12A, 1−6, doi:10.2151/sola.12A-001

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Acknowledgements
This work was conducted under the Development of BasicTechnology for Risk Information on Climate Change of the
SOUSEI Program of the Ministry of Education, Culture, Sports,
Science, and Technology (MEXT) of Japan. The calculations
were performed on the Earth Simulator of the Japan Agency for
Marine-Earth Science and Technology.
Edited by: M. Sugi

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Fig. 6. Time-series of R1d (mm) averaged over the western tropical Pacific(120°E−180°E, 0°N−25°N) for (a) the present (1979−2003) and (b) the
future (2075−2099). For the future, CMIP5 multi-model mean SSTA experiment is denoted by a thick line, while three cluster experiments aredenoted by thin lines. Solid lines denote R1d, while dashed lines denote
R1d associated with tropical cyclones. Values of 25-year average and its
interannual standard deviation of R1d are shown in the upper left corner,
while values of R1d associated with tropical cyclones are shown withinbrackets.

large increase is found in year 19, which corresponds to El Niño
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5. Summary
A global 20-km mesh MRI-AGCM is used to project futurechanges in rainfall extremes associated with ENSO and TC. About
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