143

The Reliability of Future Climate Change Projection by
High-Resolution Climate Models
Seita EMORI
Frontier Research Center for Global Change
National Institute for Environmental Studies,
16-2 Onagawa, Tsukuba, Ibaraki 305-8506, Japan
Japan Agency for Marine-Earth Science and Technology,
Center for Climate System Research, The University of Tokyoe-mail: emori@nies.go.jp

Abstract
Recent advancements in the spatial resolution of climate models enable us to provide detailed
future climate change scenarios including changes in regional climate and extreme events for impact assessment research. However, impact researchers should be aware that climate models are still burdenedwith sizable and inherent uncertainty caused by the incompleteness in the representation of physicalprocesses, or “parameterizations.” The prediction of the quantitative amplitude of climate change, suchas “climate sensitivity,” remains uncertain because it depends more on physical parameterizations thanon resolution. However, some qualitative characteristics of projected climate change, such as geographical patterns, can be regarded as relatively reliable, though they remain at the level of “expert judgment”
by climate researchers rather than fact. Criteria for determining the reliable aspects of projections include: a. statistical significance, b. inter-model consistency, c. interpretability, and d. the validity of key
processes. It is difficult for impact researchers alone to judge whether these criteria are met by the projected climate change in question for their impact assessments. Therefore, active mutual communication
between climate researchers and impact researchers is highly desirable to improve the overall reliabilityof impact assessment based on the results of high-resolution climate modeling.
Key words: extreme events, high-resolution climate models, impact assessment of climate change,
regional climate change, reliability of projection

1. Introduction
Future climate change scenarios produced by climate models have been and, for the foreseeable future,
will continue to be the only credible input as thepremise for impact assessments of climate change.
Hence, the reliability of impact assessments depends
largely on the performance of climate models. Climate
models are developed at various climate centers in theworld and are constantly being improved. The recent
advancement of climate models, especially the enhancement of the models’ spatial resolution (thesmallest spatial scale that can be resolved by a model)
owing to the use of larger computing facilities, enables more advanced, and possibly more reliable, impact assessments. One of the major improvements inrecent climate models is a more realistic simulation of
extreme events, such as extremely hot days and torrential rain (e.g., Meehl et al., 2000). The analysis ofpossible future changes in extreme events has also
Global Environmental Research
10(2)/2006:143-149
printed in Japan

experienced progress. Extreme events are considered
to be critical in certain kinds of impact assessments.
For example, the impact of heat waves on health andthe societal damage of floods can be assessed only byconsidering extreme events in some way. In this article, I will highlight the advantages of using highresolution climate modeling in impact assessments,
while also bringing up a caution regarding uncertainty
issues, which need to be addressed by impact
researchers.

2. Recent Advancements in Climate Models
Coupled atmosphere-ocean global climate models(AOGCMs) consist of components such as atmosphere, ocean, land and sea-ice. The atmospheric andoceanic components can each be further divided into
two parts; that is, the part that handles the dynamics(dynamical core) and the part that deals with the physics (physical parameterizations). The dynamics part©2006 AIRIES144

S. EMORI

solves a well-defined physical principle, i.e., a set of
fluid dynamic equations. The physics part is an assortment of treatments of various physical processes thatcannot be represented by the dynamics part, such asradiation, cloud and sub-grid scale mixing. When thespatial resolution of a model is increased, the performance of the dynamics part is invariably improved,
while the physics part is not necessarily enhanced.
This is because the physics part is not purely based on
physical principles and includes empirical assumptions and approximations (i.e., parameterizations).
Improvement of the physics part is done by improving
the equations through consideration of more realistic
processes taking place in nature.
In recent years, since the IPCC Third Assessment
Report (TAR) (IPCC, 2001), considerable efforts havebeen made to improve the climate models used invarious climate centers in the world. Improvements inphysics include more realistic parameterizations forradiation, clouds, convection, boundary-layer, ocean
mixing and so on, and more realistic interactions
among them (e.g., GAMDT, 2004). Some climate
models also incorporate an on-line aerosols moduleand its interaction with cloud and radiation (e.g.,
Takemura et al., 2005), which is a completely new
feature since TAR.
As for the resolution, all AOGCMs at the time of
TAR had horizontal resolutions of approximately 300
km or coarser for the atmosphere and approximately
100 km or coarser for the ocean. Recently, however,the ‘Earth Simulator,’ one of the world’s largest computing facilities developed in Japan, dramatically enhanced the computing power that can be used for climate modeling. It enabled a Japanese AOGCM (the‘MIROC’ model developed by CCSR1)/NIES2)/FRCGC3)) to run at the resolution of approximately
100 km for the atmosphere and approximately 20 km
for the ocean (K-1 Model Developers, 2004). A global
20 km-mesh atmosphere-only model developed by
MRI/JMA4) has also been running on the Earth
Simulator (Mizuta et al., 2006). These high-resolution
climate models running on the Earth Simulator havesignificantly enhanced our ability to represent regional
characteristics and relatively small-scale phenomena(such as tropical cyclones, especially for the 20kmmesh atmospheric model (Oouchi et al., 2006)).

3. Uncertainty in Climate Change
Projections
However, one should be conscious of general uncertainty issues in climate change projections. Any
climate model is inherently incomplete mainly because of the incompleteness of the parameterizationsof various physical processes. As mentioned above,
the parameterizations do not necessarily improve due
to enhancement in resolution. Hence, no matter how
high a climate model’s resolution becomes, you cannot naively expect that the uncertainty in climatechange scenarios projected by the model will be reduced accordingly.
One major long-standing issue is the uncertainty inthe overall amplitude of the change, known as the ‘climate sensitivity’ issue. The climate sensitivity definedby the global mean surface air temperature change in
response to a doubling of atmospheric carbon dioxide
concentrations is different for different models, ranging from approximately 1.5°C to 4.5°C (an even wider
range is suggested in some recent studies (e.g.,
Stainforth et al., 2005)). Note that a higher-resolution
model does not necessarily give a more reliable estimate of climate sensitivity, since the climate sensitivity of a model is largely dependent on physical parameterizations.
However, some of the qualitative aspects of the
projected changes, such as the spatial pattern ofchanges, are considered to be more reliable. Some
criteria for a relatively reliable projection, though theyremain subjective ‘expert judgments’ rather than fact,
can be proposed as follows:a. The climate change signal is distinguishable fromnatural variability (statistical significance),b. The change is found in many different models(inter-model consistency),c. The physical interpretation of the change is clear
and reasonable (interpretability),d. The key processes to the change are realistically
represented in the model and well validated against
observational data in the current climate (validityof key processes).
Still another point may be added for some cases.
e. A signal similar to the projected change is found inrecent observed record (consistency with observation).
Note that a special care is needed in applying this last
criterion because an observed trend may be a part ofnatural variability and not necessarily indicative of
climate change.
Generally, larger-scale spatial patterns of thechange can meet these criteria more easily. For example, the well-known patterns in TAR (IPCC, 2001),
including greater continental warming than over thesea, greater warming over northern higher latitudes,
and an increase in precipitation over high latitudes,
can easily be found to meet the criteria in a-d (or a-e).
On the other hand, regional changes and the changes
in extreme events are generally less likely to meetthese criteria. Therefore, we must be especially careful
about the uncertainty issue when discussing these detailed changes.

4. Future Climate Change Projection by
a High-resolution Model
Some recent work based mainly on the MIROC
model results (~100 km atmosphere and ~20 km
ocean resolution) is shown here as an example offuture climate change projection with a high-The Reliability of Future Climate Change Projection by High-Resolution Climate Models

resolution climate model and the related discussion on
uncertainty.
4.1 Validation of extreme daily precipitation
Before talking about future projections, I will
demonstrate the relationship between the validity ofthe processes (criterion d) and the model’s spatial
resolution. Figure 1 shows the resolution dependenceof the simulation of daily precipitation intensity(Kimoto et al., 2005). The frequency of daily precipitation greater than 50 mm/day, as an index of extreme
precipitation, simulated by the high-resolution
MIROC model (~100 km atmosphere) compares wellwith a satellite estimate (GPCP (Huffman et al.,2001)), while a lower-resolution version of the samemodel (~300 km atmosphere) seriously underestimates the frequency of heavy rain events.
Figure 2, on the other hand, shows the physics de-(a)pendence of the simulation of daily precipitation
intensity (Emori et al., 2005). The 99th percentile daily
precipitation (approximately the 4th largest event in ayear), as another index of extreme precipitation, simulated by the high-resolution MIROC model (precisely,
its atmospheric part driven by observed sea surface
temperature) compares well with the GPCP satellite
estimate. However, the same model at the same
resolution (~100 km) seriously fails to simulate heavy
rainfall events over low latitudes, if one criticalparameter in the convection parameterization is set toa different value, while the mean climate values of thetwo models are quite similar (figure not shown).
These two results clearly demonstrate that highresolution is a necessary but not sufficient condition
for the realistic simulation of extreme precipitation, as
it is also strongly dependent on parameterization.

(c)

(b)

Fig. 1 Frequency of daily precipitation greater than 50 mm/day in summer (June-July-August) over andaround Japan.
(a) observed (GPCP satellite estimate), (b) simulated by high-resolution model, and (c) simulated by
low-resolution model. (Reproduced from Kimoto et al., 2005)

(a)

(b)

145

(c)

Fig. 2 Intensity of the 99th percentile daily precipitation.
(a) observed (GPCP satellite estimate), (b) simulated by the atmospheric part of the MIROC model, and(c) simulated by the same model at the same resolution but with the ‘cumulus suppression parameter’ set to zero(set to 0.8 in (b)). (Reproduced from Emori et al., (2005) with modification)

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S. EMORI

3.5 [mm/day]pressure anomaly located to the northeast of thePhilippines, which is likely caused by El Niño-liketropical Pacific warming, provides moist air to the rain
belt. Moreover, another high-pressure anomaly overeastern Siberia and the Sea of Okhotsk, caused byenhanced continental warming, tends to hinder thenorthward migration of the rain belt (Kimoto, 2005).
The El Niño-like warming and the enhanced continental warming are relatively reliable large-scalefeatures, as they are commonly found in many different modeling results (criterion b). Hence, the regional
climate change pattern derived from them is also
considered to be relatively reliable.
Incidentally, this result suggests that the pressure
pattern in the future climate over Japan is more likethat in anomalous cool summers (like in 1993 or2003). However, this does not mean that the summer
will be cooler over Japan in the future climate, as theair, continent and sea surface around Japan will bewarmed. Figure 4(a) shows the number of ‘tropicaldays’ for Japan (a day with maximum surface air temperature higher than 30°C in any grid box of Japan)
simulated in the MIROC model, which indicates a
striking increase in extremely hot days during the 21st
century. Figure 4(b) shows the number of ‘torrential
rain days’ (days with precipitation in summer greaterthan 100 mm in any grid box of Japan) for Japan in
the summer, which indicates a significant increase intorrential rain frequency in the late 21st century,
though the inter-annual variability is large. Note thatthe magnitude of the changes seen in Fig. 4 is quite
uncertain, though the qualitative tendencies areprobably robust.

Fig. 3 Climate change pattern over the East-Asian region
projected by the MIROC model.
Difference between the mean of 2071-2100 (based on
IPCC SRES A1B scenario) and the mean of
1971-2000. The contours, arrows and shades denotethe changes in 500 hPa height, 850 hPa wind and precipitation rate, respectively. H and L denote anticyclonic and cyclonic changes, respectively, relative tosurrounding areas. (Reproduced from Kimoto (2005)
with modification)

4.3 Future projection of mean and extreme
precipitation
The next example is given to try to interpret the
projection of changes in extreme events. The geographical pattern of projected change in annual mean
precipitation is shown in Fig. 5(a), while that in extreme(99th percentile) precipitation is shown in Fig. 6(a)(Emori & Brown, 2005). These are multi-model en-

4.2 Regional climate change over JapanThe next example demonstrates “interpretable” regional climate change (criterion c). Figure 3 shows thepattern of climate change from the late 20th century
to the late 21st century, over the East Asian region in
the summer (June-July-August) projected by the
MIROC model (Kimoto, 2005). The contours, arrows
and shades denote the changes in 500 hPa height,
850 hPa wind and precipitation rate, respectively.
Increased rainfall in the East Asian monsoon frontal
rain belt is projected (blue-colored areas extending
from Southeast China to Korea, Japan and further tothe east). The changes in pressure pattern causing thisincreased rainfall are identified as follows. A high-

−3.5

0

(a)

(b)

Fig. 4 The changes in the numbers of (a) ‘tropical days’ and (b) ‘torrential rain days’ over Japan during the20th century and 21st century simulated by the MIROC model. The projection for the 21st century isbased on the IPCC SRES A1B scenario. A ‘tropical day’ is defined as a day with maximum surface airtemperature higher than 30°C in any grid box of Japan, while a ‘torrential rain day’ is defined as a daywith precipitation in summer greater than 100 mm in any grid box of Japan.

The Reliability of Future Climate Change Projection by High-Resolution Climate Models

147

Fig. 5 Projected percentage change in annual mean precipitation due to climate change and its components: (a) total, (b) dynamic and (c) thermodynamic. Areas where separation into components isdifficult are in gray. (Reproduced from Emori &Brown (2005))

Fig. 6 Projected percentage change in extreme precipitation (99th percentile daily precipitation) due
to climate change and its components: (a) total,(b) dynamic and (c) thermodynamic. Areas whereseparation into the components is difficult are ingray. (Reproduced from Emori & Brown (2005))

semble averages (including the MIROC model), as
similar patterns are obtained from all the model resultsthat have been examined. The change in the meanshows general increases over the tropics and mid tohigh latitudes and decreases over some subtropicalregions, a pattern commonly seen in previous studies(IPCC, 2001). The change in the extreme is basicallysimilar to that in the mean. However, there are someareas mainly in the subtropics where the extreme is
projected to increase significantly, while the mean isnot.
In general, increased water vapor in a warmer climate is expected to act to increase precipitation.
Precipitation will also change due to changes inatmospheric circulation, such as intensity, frequency
and track of cyclones. Emori and Brown (2005) called
the former ‘thermodynamic change’ and the latter‘dynamic change,’ and separated the changes in meanand extreme precipitation into these two components.
Figures 5(b) and (c) show the dynamic and thermodynamic changes in mean precipitation, respectively,
while Figs. 6(b) and (c) show those for extreme
precipitation. The distributions of dynamic changes

for mean and extreme precipitation are similar to eachother, showing an increase over the tropical Pacificand a decrease over the subtropics and almost zero for
mid to high latitudes. On the other hand, a clear
difference is found in the thermodynamic changes.
That is, the thermodynamic change in the mean isalmost zero over some areas, mainly in the subtropics,
while that in extreme precipitation shows an overall
increase. This means that the increase in water vapor
does not necessarily increase the mean precipitation,
while it does increase the extreme precipitation.
Though further work is needed to explain thisdifference, the investigation of the mechanisms ofchanges in extremes as demonstrated here is certainly
useful to enhance our understanding of projected climate change, leading to the enhanced reliability of the
projection.

5. Implication for Impact Assessments
Figure 7 shows an example of advanced impact assessments based on daily data from high-resolution
MIROC model results (Hirabayashi et al., 2006).

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S. EMORI

Figure 7(a) is a future projection of drought risk, defined by the number of days with daily river discharge
lower than the 10th percentile, while Fig. 7(b) is a
future projection of flood risk, defined by the 100-year
return value of daily river discharge. If you were an
impact researcher who obtained such results without
any information on the corresponding climate change,
you would not be able to judge the reliability of these
impact assessments. However, if you were given theinformation in Subsection 4.3 above, you would be
able to relate the increased drought risk mainly overthe subtropics with a decrease or only a small increasein annual mean precipitation over the region (Fig. 5(a)),
together with a general expectation of increased
evaporation from the soil in a warmer climate. Similarly, the increased flood risk found mainly over thetropics could be related to the significant increase in
extreme precipitation over the region (Fig. 6(a)). In
particular, the areas with both increased flood anddrought risk roughly correspond to the areas where theextreme precipitation increases but the mean does not.
As a result of amalgamating the analysis of climatechange results with the data from the impact studies,

the resulting impact assessment looks more reasonable.
Note that the high-latitude changes in flood risk do notseem to be explained solely by the change in extreme
precipitation. Further analysis considering the change
in snow processes (e.g., earlier snow melt in a warmer
climate) would be needed.

6. Concluding Remarks
High-resolution climate modeling, together withother recent improvements in physics, is undoubtedly
beneficial to the impact assessment work of climatechange. However, considerable uncertainty still remains and impact researchers must be made aware ofthis fact. Lack of awareness may result in misleading
conclusions in some impact research. In particular,care is needed when using detailed climate change
projections such as those for regional climate and extreme events, as it is generally difficult to obtain reliable projections for them compared to large-scalechanges. In this paper, my goal was not to discuss theuse of high resolution models to predict climatechange, but to emphasize that the comprehension and

(a)

(b)

Fig. 7 (a) Change in drought risk from the late 20th century to the late 21st century, with red showing
increased risk, and (b) Change in flood risk from the whole 20th century to the whole 21st century,with blue showing increased risk, based on daily river discharge data simulated by the MIROC
model. See text for the definition of the risk indices.

The Reliability of Future Climate Change Projection by High-Resolution Climate Models

interpretation of the results of such predictions plays acrucial role in strengthening the reliability of climatechange predictions and, consequently, climate change
impact assessment. An understanding of projectedclimate change as well as other information on uncertainty should be conveyed to impact researchers to
improve the overall quality of climate change and impact research. In other words, enhanced communication between climate researchers and impact researchers is highly desirable not only for transferring data,
but also for transferring knowledge.

Acknowledgments
This article is mainly based on research results of
two projects in which I am involved: Subject 1 of“Kyousei” project, Research Revolution 2002, from
the Ministry of Education, Culture, Sports, Scienceand Technology (MEXT) and B-12 of the Global
Environmental Research Fund (GERF), from the
Ministry of the Environment (MOE). The author
would like to thank the members of both projects.
Notes
1) Center for Climate System Research, the University ofTokyo2) National Institute for Environmental Studies
3) Frontier Research Center for Global Change, Japan Agency
for Marine-Earth Science and Technology
4) Meteorological Research Institute, Japan Meteorological
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(Received 20 December 2006, Accepted 25 December 2006)