3. Potential Impacts of Global Climate Change
Hideo Harasawa1, Yuzuru Matsuoka2, Kiyoshi Takahashi1, Yasuaki Hijioka1,
Yoko Shimada3, Yosuke Munesue4, and Murari Lal5
Summary. AIM/Impact model, an integrated assessment model of climate change
impacts, has been developed in order to evaluate future climate change impacts
and to support decision making on the global/Asia scale. AIM/Impact model consists of sub-models for evaluating impacts on major vulnerable sectors (water, agriculture, ecosystem, human health) and linkages among them. In this chapter, thegeneral framework of AIM/Impact and examples of model outputs are introducedwith a brief description of the sub-models.

3.1 Introduction
There is concern that anticipated climate change will cause significant negative
damage on ecosystems and various sectors of human life. The degree of climatechange and its damage depends on the pattern of future greenhouse gases (GHGs)
emissions. The spatial and temporal distribution of climate change impact will beunequal, since the degree of climate change varies spatially and the adaptive capacity in relation to climate change is quite different among those affected according to their physical, economic, and social environments. The direct physical impacts of climate change on each sector may be interrelated and cause higher-order
impacts. In order to evaluate alternative policies on GHGs mitigation, the consequent impacts, including higher-order effects, need to be assessed, while analysis
on adaptation for the mitigation of future impacts is also important.
The AIM/Impact model, an integrated assessment model of climate change impacts, has been developed in order to evaluate future climate change impacts considering these complicated interrelationships and to support decision making on
the global/Asia scale. The AIM/Impact model consists of sub-models for evaluating impacts on major vulnerable sectors (water, agriculture, ecosystem, humanhealth) and linkages among them.
In this chapter, the general framework of AIM/Impact is presented first. Secondly, examples of model outputs are provided with a brief description of the submodels.

1 National Institute for Environmental Studies, Tsukuba 305-8506, Japan
2 Kyoto University, Kyoto 606-8501, Japan
3 Kobe Institute of Health, Kobe 650-0046, Japan
4 Tokyo Institute of Technology, Tokyo 152-8552, Japan
5 Indian Institute of Technology, New Delhi 110016, India

38

Harasawa, H. et al.

3.2 Framework of AIM/Impact Model
Figure 1 shows the linkages between the sub-models developed for the
AIM/Impact study. Some sub-models have already been developed and sectoral
impacts of climate change are estimated for various future climate scenarios projected using General Circulation Models (GCMs).
The FOOD sub-model consists of a productivity model for 12 crops and an agricultural trade model. The potential productivity changes caused by climatechange are estimated using a 5o x 5o spatial resolution. Then, based on the estimated changes in crop productivity, the agricultural trade model calculates the allocation of the production of, and demand for, crops and other commodities thatmaximize social welfare.
The HEALTH sub-model examines the impact of malaria infection. It evaluates
the suitability of climatic factors for the malaria mosquito to reproduce, and estimates the extent of different possible levels of malaria infection.
The VEG sub-model estimates the impact of climate change on several forest
and other vegetation types. The model simulates forest collapse in regions where
the rate of climate change is too high for the existing vegetation patterns to continue. The VEG sub-model also determines the value of human services provided
by forests that are lost due to climate change. Work to modify the model so that it
simulates dynamic changes to the vegetation is continuing.
The HYDRO sub-model uses information on climate, soil and terrain to simulate surface runoff and river discharges. The WATER sub-model estimates the future water demand at the national level and assigns that demand to each grid
block, so creating a spatial distribution of water demand. Sub-models of the
AIM/Emission model and of the AIM/Climate model such as the ENERGY andthe CLIMATE ones are also interrelated with the sub-models of AIM/Impact invarious ways. For example, the CLIMATE sub-model provides future climate
scenarios for the sub-models of AIM/Impact with processing of the spatial GCM
projections and observed climatology.
The sub-models that have been developed and will be developed are now related in a complex way. This complexity did not exist during the initial development stage of the project and the reasons for this are as follows:1.

2.

It was necessary and efficient to consider long-term climate change problems
simultaneously with other short-term environmental problems to develop realistic policies, especially for emissions abatement and impact adaptation. Initially, using individual sub-models with future climate scenarios was sufficient for estimating the damage caused solely by climate change in eachregion for each sector. However, the problems have become more complicated and recent policy requirements meant that the sub-models developed in
the earlier stages had to be revised.
Since the initial stage, the significance of the feedback effect caused bychanges in vegetation patterns and other sectors affected by climate change
has been recognized. Nevertheless, due to the limited computer resources and38

3. Potential Impacts of Global Climate Change

39

AIM/Climate

HEALTHPOP

Health impacts ofenvironmental
changePopulation, fertility and mobility

CLIMATE
Radiation,energy balance,
temperature andsea level rise

ENV

ENERGYCYCLE

Environmental pressure andcounter-measure

Energy technology
and resources

Chemistry of
GHGs

CGE
AIM/Emission

Supply and demand equilibrium of goods,
energy, water, land and labor

FOOD
Production anddemandLAND

WATER
Supply infrastructures
DemandLand-use allocation
and GHGs emission

HYDRO
Surface water balance
Routing module

OCEAN
Energy andcarbon budget of
ocean

VEG
Vegetation dynamics

AIM/Impact

Fig. 1. Framework of AIM/Impact model (see color plates)3.

the complexity of the feedback processes, this issue had not received muchdetailed attention when reassessing the model. As climate models are refinedand computer technology improves, it is becoming easier and more feasible toconsider the feedback processes for carbon and other GHGs within GCM
simulations. The importance of studies on the impact of climate change on
vegetation is growing.
In order to investigate efficient strategies for mitigating and adapting to theimpacts of climate change, it is necessary to estimate and compare themonetary value of the damage caused by climate change, the monetary valueof the damage alleviated by various adaptation strategies, and the cost ofadaptation strategies. A framework for this economic assessment, mainly
relying on the CGE model, is required to quantitatively assess the various
impacts of climate change in financial terms.

3.3 Agriculture
The productivity of agricultural land will be greatly influenced by future environmental changes. For example, climate-induced changes are expected to have profound impacts on potential crop yields, and so influence the distribution of cropping patterns in the Asia-Pacific area.
In order to assess the impact of climate change on agriculture, studies arenecessary using a framework that can evaluate related direct and indirect effects.

39

40

Harasawa, H. et al.
Primary Impact Module

Climate change
Growth season

RunoffIrrigation

Possible/potential
production system

Technological inputs
Labor and capital inputs

Soilconstraints

Agricultural supply

Actual production

Domestic trade

Consumer demand

World trade

Consumer preferences
Life style

Secondary Impact Module

Fig. 2. Framework of agricultural impact model

CLIMATE, WATER, HYDROCrop productivity part
Temperature, precipitation, PET, PAR, soil characteristics
Crop Parameters

Estimation of growing period

Threshold temperature
Normal growing period

Biomass production rate

Photosynthesis path
Normal leaf area index

Net biomass production
Normal harvest index
Soil constraints

Soil data

Potential productivity

Fig. 3. Model used to estimate potential crop productivity

Figure 2 shows the framework of the agricultural impact study (Takahashi et al.
1997). The two basic assumptions for this study are: (1) climatic change will directly affect land and water resources (the primary effects); and (2) changes inland and water resources will affect economic activities (the secondary effects).
A potential crop productivity model was developed and linked to regional
climate and environmental soil data to illustrate the first assumption. The method
used for estimating potential crop productivity is based on that used by the Foodand Agriculture Organization (FAO 1978-1981). The number of days suitable forcrop cultivation (the growing period) are counted using climate data, then the crop
growth during the growing period is simulated using the growth parameters for40

3. Potential Impacts of Global Climate Change

41

Fig. 4. Change in the potential productivity of rice from 1990 to 2050 under the climaticconditions projected using the CCSR/NIES GCM (see color plates)

each crop. Figure 3 shows the framework for estimating potential crop
productivity. This model requires data for: daily mean temperatures; mean
daytime temperatures; precipitation; potential evapotranspiration (PET);
photosynthetically active radiation; and soil characteristics. The physical andchemical properties of soil such as soil texture and soil slope were used to estimate
the suitability of land areas for agriculture. The calculations used data for theseproperties on a 5-minute resolution grid to account for their high spatial
variability. Figure 4 shows the change in the potential productivity of rice from1990 to 2050 under the climate conditions projected with CCSR/NIES GCM. In
general, the productivity of rice crops will increase in areas of high-latitude anddecrease in areas of low-latitude.
An agricultural trade model based on the GTAP general equilibrium model developed at Purdue University (Hertel 1997) was used to assess the impact of climate change on the economy through changes in crop productivity within each region (Takahashi et al. 1999). Regions and production sectors treated in the model
are shown in Tables 1 and 2. In the agricultural trade model, potential changes in
the productivity of rice, winter wheat, tropical maize and other crops - as estimated in the potential crop production model - are taken as the changes in the
Hicks-neutral technology parameters for their respective production sectors.
Table 3 shows the changes in producer prices, agricultural production and social welfare in some regions under changed climatic conditions (IS92a emissionsfor 2100). Comparing the changes in social welfare per capita, India is found to be
the country likely to suffer the most damage. This reflects the significant decline
in the productivity of wheat in India and the comparatively large share of agricultural products purchased using private funds. Whilst a rich country can use trade
to mitigate the economic damage caused by decreases in crop productivity, a poor
country cannot adapt in the same way, as a result, its social welfare deteriorates.

41

42

Harasawa, H. et al.

Table 2. The eight production sectors in the
agricultural trade model

Table 1. The thirty countries and regions in the agricultural trade modelCountry
Australia
New ZealandJapan
Korea, Republic
Indonesia
Malaysia
Philippines
Singapore
Thailand
China
Hong Kong
Taiwan (China)
India
Other South Asia
CanadaU.S.A.
MexicoCentral America and Caribbean
Argentina
BrazilChile
Other South America
EU
Austria, Finland, Sweden
European Free Trade Area
Central European Associates
Former USSR
Middle East and North Africa
South Africa
Rest of the world

Code
AUS
NZL
JPN
KOR
IDN
MYS
PHL
SGP
THA
CHN
HKG
TWN
IDI
RAS
CAN
USA
MEX
CAM
ARG
BRA
CHL
RSM
E_U
EU3
EFT
CEA
FSU
MEA
SSA
ROW

Sectors
Paddy
Wheat
Other grains

Description
Paddy
Wheat
Other grains

Other crops

Non-grain crops
Wool
Other livestock
Processed rice
Meat products
Milk products
Other food products
Beverages and tobacco
TextilesLeather
ApparelLumber
Pulp paper
Petroleum and coalChemicals, rubbers and plastics
Nonmetallic minerals
Primary ferrous metals
Nonferrous metals
Fabricated metal products
Transport industries
Machinery and equipment
Other manufacturing
Electricity, water and gas
Construction
Trade and transport
Other private services
Other governmental services
Ownership of dwellings

Livestock

Other agricultural
products

Manufacture

Services

Table 3. Changes in producer prices, agricultural production and social welfare in someregions under changed climate (IS92a emissions, the year 2100 compared to 1990)
JPN
Producer price change (%)
Rice
Wheat
Other grains
Other crops
Livestock
Other agricultural products
Manufacture
Services
Production change (%)
Rice
Wheat
Other grains
Other crops
Livestock
Other agricultural products
Manufacture
Services
Consumer price index (%)
Income change per capita (%)
Social welfare change (%)

CHN

IDI

CAN

USA

E_U

-0.01
4.91
1.81
-0.01
-0.19
-0.15
0.03
0.03

-1.58
8.47
0.79
-0.28
-0.09
-0.01
-0.12
-0.16

17.96
125.11
1.80
1.90
2.84
0.30
-1.10
-0.93

-40.16-13.10-43.59
2.76
-1.22
-0.35
0.61
0.69

-0.06
4.76
-1.46
-0.10
-0.59
-0.07
0.03
0.02

-4.93
8.92
-3.36
-0.05
-0.04
0.04
-0.02
-0.02

0.11
-6.60
-15.56
0.11
0.09
0.11
-0.01
0.00
0.001
0.026
0.022

-0.25
-3.97
-1.39
-0.07
-0.24
-0.27
0.31
0.00
0.001
-0.236-0.219

-1.76
-7.64
-1.33
-4.25
-2.27
-4.73
-0.37
-2.62
6.047
-0.617-4.892105.99115.07
89.41
-2.26
0.94
0.69
-1.62
-0.02
0.513
0.833
0.343

0.23
2.87
-4.04
0.25
0.03
0.04
0.03
0.01
0.017
0.026
0.009

2.03
-3.64
-6.50
-0.03
-0.22
-0.22
0.05
0.01
-0.010-0.0090.003

42

3. Potential Impacts of Global Climate Change

43

3.4 Vegetation
Figure 5 shows the process used to account for the economic impact of forest loss
caused by climate change (Munesue et al. 2000). The impacts of climate change
on several forest types and other vegetation types are projected. These vegetation
types are based on the vegetation climate-zones classified according to theHoldridge scheme (Holdridge 1947) under current and future climatic conditions
and an assumed maximum rate for vegetation transfer that follows the shift insuitable climatic conditions due to climate change. The model simulates forest collapse in regions where the rate of climate change is too great to allow the existing
vegetation types to shift. The black cells (cells are classified to a forest type under
the present climate and to a non-forest type under the future climate) and graycells (cells are classified to a forest type under the present climate and to a different forest type under the future climate, but the shift cannot occur smoothly) in
Fig. 6 indicate forests that collapse under situations of low, medium or high rates
of climate change. The global mean temperature increases between 1990 and 2100
under these scenarios are 0.85oC, 2.08oC, and 3.52oC. The maximum rate of forest
transfer is assumed to be 1 km/yr in this model trial. The forest area will shrink by
2%, 5% and 9% of the total land area, and the replacement of forest types at riskof shrinking will be 3%, 7% and 12% of the total land area. The most damaged
forests are in the northern parts of Eurasia and North America.
The economic value of the loss due to forest collapse caused by climate change
is estimated using values from the National Center for Ecological Analysis andSynthesis (Costanza et al. 1997), and comes to US$49.7 billion, $US126.4 billion,
and $US219.5 billion at 1994 dollars respectively under the three scenarios.
Climate Scenarios

GCMs
Simple climate model

Change of climate zone
Maximum forest
transfer rate

Projection of vegetation considering forest transfer speed- Spatial distribution of vegetation- Estimation of diminished forest area
NCEAS

Value of forest collapsed by climate change

Fig. 5. Process of estimating the economic cost of forest loss from climate change

43

44

Harasawa, H. et al.

IS92c scenario with low climate sensitivity

IS92a scenario with medium climate sensitivity

IS92e scenario with high climate sensitivity
Cells are classified to a forest type under the present climate and to a non-forest
type under the future climate (diminishment of forest).
Cells are classified to a forest type under the present climate and to a different
forest type under the future climate, but the shift cannot occur smoothly(replacement of the forest type with the risk of diminishment).

Fig. 6. Forest collapse under low, mid, high-climate change scenarios (IS92c emissions
with low climate sensitivity, IS92a emissions with medium climate sensitivity, IS92eemissions with high climate sensitivity) (see color plates)44

3. Potential Impacts of Global Climate Change45

3.5 Health
Air and water pollution, as well as solid and hazardous wastes, impact directly onhuman health. Global climate changes will also affect human health in many
ways. For example, global warming will change the vegetation distribution andincrease ground temperatures. This will allow the habitat of the Anopheles mosquito, which is the malaria vector, to expand. In addition, the time taken for thedevelopment of the malaria protozoan will shorten and its reproductive potential
will increase. As a result, the risk of malaria outbreaks on a global scale is predicted to increase. A sub-model of the AIM/Impact model was developed to quantitatively estimate this risk (Matsuoka et al. 1995).
Figure 7 shows the assessment framework used in this study. The two major
components of the framework are the relationship between sporogony and temperature, and the eco-climatic index model which shows the climatic response ofvectors. These components are supported by the soil moisture sub-model and outputs from the equilibrium experiments of GCMs. The primary climatic variablesof this framework are surface temperature and precipitation distributed spatiallyand temporally for both the current situation and that expected under 2xCO2 climate conditions.
Figure 8 shows the potential for malaria infection calculated under current andexpected future climate conditions. The yellow indicates areas where the risk ofmalaria is mesoendemic and the red highlights areas where it is hyperendemic (seecolor plates).
Table 4 shows the predicted expansion of the population living under endemic
malaria when the global mean temperature increases by 2.0oC. The risk of malaria
in China, Indonesia and India increases due to this change. It is concluded that the
population expected to be at risk of malaria infection in the Asia-Pacific region
will increase by 30%.
Malaria model

Relation between sporogony
and temperature

AIM/Climate +Regional patternby GCMs
Cold
stressGrowth index
Eco-climaticindex

Dry stress

Surface waterrunoff modelBasicreproduction
rate of malaria

Temperature

Soil moisture

Heat
stressHumidity
stressMalaria riskchangeFig. 7. Framework of malaria impact model

45

46

Harasawa, H. et al.

Present(1xCO)
Present(1xCO2) 2

Expected future (2xCO2)
endemic

low receptivity

Fig. 8. Expansion of the area affected by malaria (Upper: current climate, Lower: 2xCO2
climate change, GCM: GFDL q-flux equilibrium experiment) (see color plates)

Table 4. Population at risk of malaria (millions)
Reported values
Country
Afghanistan
Bangladesh
BhutanCambodia
China
India
Indonesia
Iran
Iraq
Laos
Malaysia
Myanmar
Nepal
Oman
Pakistan
Philippines
Saudi Arabia
Sri Lanka
Syria
ThailandTurkeyU.A.E.
Viet Nam
Yemen
Papua New guinea
SolomonVanuatu
Total

Country
Population
18.14
98.66
1.42
7.28
1059.52750.90163.39
44.21
15.90
4.12
15.56
37.15
16.63
1.24
96.18
54.38
11.54
15.84
10.27
51.30
49.27
1.33
59.61
6.85
3.33
0.27
0.14
2594.43

Malarious
Area
10.39
2.29
0.18
2.36
975.82728.33155.63
34.83
15.90
3.30
15.44
33.22
10.76
1.24
96.18
16.82
4.51
11.51
6.38
46.09
49.27
1.33
44.03
3.01
3.33
0.27
2272.42

Present climate
Low
Risk
3.65
64.16
0.79
5.76
646.93721.40122.00
12.92
9.39
3.79
13.14
30.93
14.09
0.92
91.89
30.55
8.68
12.67
1.91
44.61
14.78
1.07
45.96
1.08
2.97
0.08
0
1906.12

High
Risk
0.00
64.16
0.73
5.76
54.60
522.31
74.76
0.00
0.00
3.64
12.16
23.25
4.76
0.00
0.00
30.55
0.00
9.17
0.00
43.98
0.00
0.00
41.37
0.00
2.14
0.00
0.00
893.34

Future climate
Low
Risk
18.14
64.16
0.79
5.76
807.30731.45138.17
29.76
15.90
3.79
13.14
36.06
14.95
0.48
95.86
30.55
7.97
12.67
10.27
44.61
49.27
1.07
45.96
2.38
3.15
0.00
0.00
2183.61

High
Risk
0.00
64.16
0.79
5.76
132.33648.04107.62
0.00
0.00
3.79
12.16
29.39
10.22
0.00
0.00
30.55
0.00
12.67
0.00
43.98
0.00
0.00
45.96
0.00
2.81
0.00
0.00
1150.2346

3. Potential Impacts of Global Climate Change

47

3.6 Water
Figure 9 shows the model used to predict changes in water resources (Takahashi etal. 2001). In the surface runoff model, the runoff volume is calculated using gridclimate information and surface information, as a result, the volume of water potentially available for use within river basins can be estimated. In the water demand model, the future water demand in each river basin is calculated using estimates of the current water demand and population distribution together witheconomic development scenarios. Moreover, with the changes in the water available for use relative to water demand, future qualitative changes in the balance between water supply and demand can be understood. An assessment of the balance
between supply and demand was carried out for each river basin.
Water demand was estimated for the household, industrial, and agricultural sectors (Fig. 10). For each sector, water demand was first estimated for each country
based on population changes and growth in economic activities as well as scenarios for improvements in water use efficiency. Next, the spatial distribution of thewater demand was estimated using estimates of population and land use distribution (cropland density). Finally, the estimated spatial values were aggregated toprovide a total value for water demand in each river basin.
To estimate the surface runoff in each grid, one of the bucket-type models (Vorosmarty et al. 1989) was used with inputs of monthly mean climate data and surface data at a 0.5o x 0.5o spatial resolution. Surface runoff was calculated as theamount of effective precipitation (precipitation minus evapotranspiration, but including consideration of snow coverage during periods of low temperatures) exceeding the maximum amount of water able to be retained in the soil. Evapotranspiration was calculated as a function of the monthly mean PET using the methodof Penman (FAO 1992) and the level of soil-water saturation.

Water demand module
Current water
withdrawal

Future water demand

Population distribution
Economic scenario
Basin map
Surface Runoff Module

Future balance between
water supply and demand

Grid climate data
Land surface data

SurfacerunoffFuture water supply

Fig. 9. Model used to estimate water resources

47

48

Harasawa, H. et al.
Country A
Withdrawal
in 1990
HouseholdIndustry
Crop yield change
Technology improvement
Population change
GDP growth

Withdrawal
in 2050
Agriculture

HouseholdIndustry

Global cropland density
Global population density
Agriculture

Households

Country level

Agriculture

Industry

Global gridded
5’ x 5’ resolution

Also forCountry B
Country C
and othercountries

Withdrawal inside Basin A
Withdrawal inside Basin B…

Boundary of river basins

Basin level

Total withdrawal (Fig.12)

Fig. 10. Model used to estimate water demand

Figure 11 shows the spatial distribution of water demand density (mm/year, a
total of three sectors) with a 5o x 5o resolution in 1990 and 2050. Areas with a very
high density of water demand exist in various regions, including eastern China,
Southeast Asia, India, Japan, eastern USA and Europe. Of these, eastern China
and India will have extremely high demand due to the rapid increases in demandduring the first half of the 21st century.
When the potential trends for change from the current situation are examined, it
is seen that in the industrialized countries where population should be stable, water demand will increase only in the industrial sector. It will not change significantly in the household sector, and should decline in the agricultural sector. In
contrast, increases will occur simultaneously in all three sectors in the developing
countries, reflecting the trends of both population growth and economic development. The increase in the industrial sector will be the largest.
Figure 12 shows changes in the calculated runoff based on the results of thetransient experiments (atmospheric CO2 concentration is assumed to increase at anannual rate of 1%) of the CCC, ECHAM4, and CCSR/NIES climate models. Thisis equivalent to [mean runoff for the 10 years from 2050 to 2059] minus [meanrunoff for the 10 years from 1980 to 1989]. Thus, it can be seen that some increases in runoff will occur throughout the whole of Siberia. Large increases inrunoff in southern China and the Indian subcontinent are also common features.
On the other hand, completely opposite change patterns are shown by the climate
models for central Africa and northern South America. This shows that the choice48

3. Potential Impacts of Global Climate Change

49

1990

0.3

3

30

300 (mm/year)
2050

Fig. 11. Water demand per unit area in 1990 and 2050 (see color plates)of climate model significantly influences the spatial distribution of changes inrunoff.
As an example of the comparison between water supply and demand, Figure 13
shows the annual changes in the supply and demand balance from 2050 to 2059
for the Ganges and Mekong river basins. For the Ganges river basin, the ratio will
not change greatly since the increase in runoff is expected to match the increase inwater demand due to population growth and economic development. However, the
CCSR/NIES model shows a different behavior for the Ganges river basin compared with the other two models. For the Mekong river basin, the supply - demandratio will increase with the increasing water demand, but the value of the ratio will
not be high compared to other basins where water is scarce.

49

50

Harasawa, H. et al.

CCC

ECHAM4

CCSR/NIES-100

0100 (mm/year)

Fig. 12. Changes in runoff calculated based on the results of the transient experiments of
the CCC, ECHAM4, and CCSR/NIES climate models (mean runoff for the 10 years from2050 to 2059 minus mean runoff for the 10 years from 1980 to 1989) (see color plates)50

3. Potential Impacts of Global Climate Change

51

1.2
0.25
1

Demand/Supply

Demand/Supply

0.2
0.8
0.6
0.4

0.1

0.05

0.2
0
2050(1980)

0.15

02050(1980)

2055(1985)

Year

2055(1985)

Year

GangesMekong
CCC
ECHAM4
CCSR/NIES

LINK (1980-89)
Ten-year average (1980-89)

Fig. 13. Annual change in the supply and demand ratio for water from 2050 to 2059 for
the Ganges and Mekong river basins

3.7 Climate Change Scenarios used in AIM/Impact
The impacts of climate change have been assessed using climate scenarios basedon the latest GCM experiments at each research stage. Currently, the transient experiments with the GCMs listed in Table 5 are mainly used for creating futureclimate scenarios (Takahashi et al. 2001). To compensate for the disadvantages of
GCMs, such as the coarse spatial resolution, historically observed data provided
by LINK (New et al. 1998) is amalgamated with the simulated results of GCMs.
The development of climate scenarios based on regional climate models or statistical downscaling methods have also been investigated, although they have not yet
been used for practical impact assessment.
Research collaboration with the GCM modeling project inside NIES has beenpromoted to take advantage of the fact that NIES has both the impact analysis project and the climate modeling project. AIM/Impact not only receives projectionsof future climatic conditions from GCM experiments, but it also provides the results of the impact modeling to GCM modelers so feedback processes can be depicted.

51

52

Harasawa, H. et al.

Table 5. Transient experiments of the GCMs currently used to create climate scenarios
CCSR

CCCma

CSIRO

GFDL

HADCM2

ECHAM4

NCAR

Institution,
country

Tokyo University andNational
Institute forEnvironmental
Studies, Japan

Resolution(A-GCM)

5.6°×5.6°
20layer

3.7°×3.7°
10layer

3.2°×5.6°
9layer

4.5°×7.5°
9layer

2.5°×3.75°
19layer

2.8°×2.8°
19layer

4.5°×7.5°
9layerResolution(O-GCM)

2.8°×2.8°
17layer

1.8°×1.8°
29layer

3.2°×5.6°
21layer

4.5°×3.75°
12layer

2.5°×3.75°
20layer

2.8°×2.8°
11layer

1°×1°
20layer

CO2 concentration(control run)

345ppmv

295ppmv

330ppmv

300ppmv

323ppmv

354ppmv

330ppmv

Australia's
CommonDeutsches
National CenCanadian CenGeophysical Hadley Centre
wealth ScienKlimarechen- tre for Atmoster for Climate
Fluid Dynamfor Climate
tific andpheric ReModelling andics Laboratory, Prediction and zentrum, GerIndustrial
many
search, USA
Analysis
USA
Research, UK
Research Organization

CO2 concentration(transient run)

1%/yr

1%/yr

0.9%/yr

1%/yr

1%/yr

1%/yr

1%/yr

Simulated period(control run)

1890-2099
210yr

1900-2100
200yr

1881-2100
219yr

1958-2057
100yr

1860-2099
240yr

1860-2099
240yr

1901-2036
136yr

Climate sensitivity

3.5oC

3.5 oC

4.3 oC

3.7 oC

2.5 oC

2.6 oC

4.5 oC

Hirst
et al.

Manabeand Stouffer, 1996

Johns et al.,
1997

Roeckner
et al., 1996

Meehl,
2000

ReferenceEmori
et al., 1999

Reader andBoer, 1998

3.8 Future Direction for Model Improvement
The role of studies on the assessment of climate change impacts on a global scale
has been changing in recent years. The main purpose of impact assessments on a
global scale was to alert people to the seriousness of climate change and to prompt
the promotion of policies to mitigate GHG emissions by identifying the regionsthat could suffer severe damage as a result.
Nowadays, however, the roles that impact studies on a global scale are being
expected to fulfill has become diversified. In order to investigate the feasibility
and effectiveness of strategies for dealing with climate change problems in a more
realistic way, the impact on the natural environment and human wellbeing as aconsequence of choices regarding alternative paths to future socioeconomic development should be assessed by taking into account not only climate change itself, but also the wider contributing factors, such as the socioeconomic background and the state of the natural environment. The framework of analysis toachieve a more comprehensive approach is expected to be developed and applied
to the ongoing international activities for comprehensive policy evaluation that focus on wider contexts, such as sustainable development and ecosystem conservation. Moreover, since it is possible for the natural environment and human system
altered by climate change to affect the climate system itself through feedback
mechanisms, impact assessment is expected to be closely linked with climate systems analysis and to provide the information required for considering the consequences of feedback effects.

52

3. Potential Impacts of Global Climate Change

53

Taking into consideration the expected role for impact assessment on a global
scale, our best efforts are being directed towards the creation of linkages among
sectoral assessment models, as explained in section 3.2, as well as to refine thefollowing aspects of the AIM/Impacts model:•••

Development of impact and adaptation assessment methods that consider not
only climate change, but also future changes in socioeconomic and other environmental factors.
Extension of data collection on adaptation strategies and the development ofmethods of evaluating the cost-benefit ratio of the strategies.
Closer collaboration with climate system modelers to take into account feedback effects.

Adding to these refinements, in order to analyze adaptation strategies in more
realistic and effective ways, top-down analysis on a global scale that considers international trade, technology transfer, climate change, and the bottom-up analysis
on a national scale that considers concrete adaptation measures, will be linked bymaintaining consistency in the socioeconomic background in addition to thephysical and monetary variables.

References
Costanza R, d’Arge R, de Groot R, Farber S, Grasso M, Hannon B, Limburg K, Naeem S,
O’Neill RV, Paruelo J, Raskin RG, Sutton P, van den Belt M (1997) The value of theworld’s ecosystem services and natural capital. Nature 387: 253-260
FAO (1978-1981) Report on the Agro-Ecological Zones Project, Vol.1-4, World Soil
Resource Report 48, Food and Agriculture Organization of the United Nations, Rome
FAO (1992) Crop water requirements, Irrigation and Drainage Paper 24, Food and Agriculture Organization of the United Nations, Rome
Hertel TW (1997) Global trade analysis, Cambridge University Press
Holdridge LR (1947) Determination of world plant formations from simple climatic data.
Science 105: 367-368
Matsuoka Y, Kai K (1995) An estimation of climatic change effects on malaria. Journal of
Global Environmental Engineering 1: 43-47
Munesue Y, Takahashi K (2000) Evaluation of climate chnage impact on vegetation and its
economic value. Environmental Science 13(3): 329-337
New M, Hulme M, Jones P (1998) Representing twentieth century space-time climate variability. I: Development of a 1961-1990 mean monthly terrestrial climatology. J. Climate 12: 829-856
Takahashi K, Harasawa H, Matsuoka Y (1997) Climate change impact on global crop production. Journal of Global Environmental Engineering 3: 145-161
Takahashi K, Harasawa H, Matsuoka Y (1999) Impacts of climate change on food production. - An Economic Assessment -. Journal of Global Environmental Engineering 5: 19
Takahashi K, Matsuoka Y, Shimada Y, Harasawa H (2001) Assessment of water resource
problems under climate change - considering inter-annual variability of climate derived from GCM calculations-. Journal of Global Environmental Engineering 7: 17-30

53

54

Harasawa, H. et al.

Takahashi K, Matsuoka Y, Okamura T, Harasawa H (2001) Development of climatechange scenarios for impact assessment using results of General Circulation Model
simulations. Journal of Global Environmental Engineering 7: 31-45
Vorosmarty CJ, Morre III B, Grace AL, Gildea MP (1989) Continental scale model of water and fluvial transport: an application to South America. Global Biogeochemical Cycle 3: 241-25654