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Southeast Asia and Pacific Islands: The Impact of 

Climate Change to 2030 

 A Commissioned Research Report 

Jointly prepared by: 

Joint Global Change Research Institute 

Battelle Memorial Institute, Pacific Northwest Division 

Scitor Corporation 

The National Intelligence Council sponsors workshops and research with 

nongovernmental experts to gain knowledge and insight and to sharpen debate on 

critical issues. The views expressed in this report do not reflect official US 

Government positions. 

NIC 2009-006D 

August 2009 
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Scope Note 

Following the publication in 2008 of the National Intelligence Assessment on the National 

Security Implications of Global Climate Change to 2030, the National Intelligence Council 

(NIC) embarked on a research effort to explore in greater detail the national security implications 

of climate change in six countries/regions of the world: India, China, Russia, North Africa, 

Mexico and the Caribbean, and Southeast Asia and the Pacific Island states. For each 

country/region, we are adopting a three-phase approach. 

• In the first phase, contracted research—such as this publication—explores the latest scientific 

findings on the impact of climate change in the specific region/country. 

• In the second phase, experts from outside the Intelligence Community (IC) will meet at a 

workshop or conference to determine if anticipated changes from the effects of climate 

change will force inter- and intra-state migrations, cause economic hardship, or result in 

increased social tensions or state instability within the country/region. 

• In the final phase, the NIC Long-Range Analysis Unit (LRAU) will lead an IC effort to 

identify and summarize for the policy community the anticipated impact on U.S. national 

security. 

To support research by the National Intelligence Council (NIC) on the National Security Impacts 

of Global Climate Change, this assessment of the impact of Climate Change on Southeast Asia 

and Pacific Islands through 2030 is being delivered under the Global Climate Change Research 

Program contract with the Central Intelligence Agency’s Office of the Chief Scientist. 

This research identifies and summarizes the latest peer-reviewed research related to the effects of 

climate change on Southeast Asia and Pacific Islands, drawing on both the literature summarized 

in the latest Intergovernmental Panel on Climate Change (IPCC) assessment reports and on other 

peer-reviewed research literature. It includes such impacts as sea-level rise, water supply and 

demand, agricultural shifts, ecological disruptions and species extinctions, infrastructure at risk 

from extreme weather events (severity and frequency), and disease patterns. The research 

addresses the extent to which Southeast Asia and Pacific Islands are vulnerable to climate change 

impacts. The timeframe of this analysis extends through 2030, although various studies 

referenced in this report have diverse timeframes and extend through the 21st century. 

The research also identifies (Annex B) deficiencies in climate change data that would enhance 

the IC understanding of potential impacts on Southeast Asia and Pacific Islands and other 

countries/regions of interest. 
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Executive Summary 

Southeast Asia and Pacific Islands are at risk from the impact of climate change in the next 20 

years due to the region’s large and growing population, long coastlines, abundant low-lying 

areas, reliance on the agricultural sector, and dependence upon natural resources. This report 

focuses on the nations of Thailand, Cambodia, Laos, Vietnam, the Philippines, Malaysia, 

Singapore, and Indonesia. These countries have a diverse range of governments, populations, 

religions, economic growth, development, and allocation of natural resources, but they all have a 

similar tropical maritime climate and face similar threats from climate change. 

The effects of climate change have already begun in the Southeast Asia and Pacific Islands 

region: 

• Average annual surface temperatures in the region increased by 0.5-1.1 °C during the period 

1901-2005.i

• Precipitation patterns are changing regionally, with increases in some locations and decreases 

in others. For example, annual rainfall decreased across most of the southern regions of 

Indonesia (Java, Lampung, South Sumatra, South Sulawesi, and Nusa Tenggara) and 

increased across most of the northern regions of the country (Kalimantan and North 

Sulawesi) during the period 1931-1990.ii

• Sea level is rising, and the magnitude varies regionally. During the period 1993-2001, the 

largest increases in sea level (15-25 mm per year) in the region occurred near Indonesia and 

the Philippines, while only moderate changes (0-10 mm per year) occurred along the coasts 

of Thailand, Cambodia, and Vietnam.iii 

Global circulation model projections indicate that climate change will continue to occur in the 

region over the course of the 21st century: 

• Climate model simulations clearly indicate that average annual temperatures are likely to 

increase across the region by approximately 1°C through 2030, and they will keep increasing 

through the remainder of the 21st century. 

• The magnitude, location, and trends of future precipitation changes are much less certain due 

to the inherent difficulty of modeling such changes. Furthermore, future precipitation 

changes due solely to climate change are difficult to resolve because they are superimposed 

on significant interannual variations that occur naturally in the region. Climate model 

simulations suggest that net precipitation rates will increase across the region in the next 20 

years, but there will likely be local decreases that will vary geographically and temporally. 

• It is difficult to project future changes in monsoon patterns and the effects of El NiñoSouthern Oscillation (ENSO) on precipitation in the region, due to the difficulty of modeling 

these phenomena. Climate model results suggest that the onset of the monsoon in Thailand, 

Laos, Cambodia, and Vietnam may be delayed by 10-15 days for the period 2030-2070, but 

the duration of the monsoon will not change.iv There is no evidence from climate model 

simulations that ENSO events will become more frequent due to climate change, but it is 

possible that their intensity may increase.v

 
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• Sea level will continue to rise, although rates will vary across the region. By the end of the 

21st century, sea level is projected to have risen by approximately 30-40 cm.vi 

There is overwhelming evidence that climate change will impact a variety of sectors in Southeast 

Asia and Pacific Islands through 2030. All of the major effects of climate change on the region 

are interrelated, so it is impossible to assess one impact independently of the others. The most 

high-risk impacts of climate change in the region are related to fresh water and ocean water 

resources, and include the following: 

Sea Level Rise. Throughout the region, rising sea level causes a number of devastating effects 

in the region, including saltwater intrusion into estuaries and aquifers, coastal erosion, 

displacement of wetlands and lowlands, degradation of coastal agricultural areas, and increased 

susceptibility to coastal storms. These effects are interrelated with impacts on agriculture, 

natural disasters, river deltas, water resources, coastal ecosystems, human livelihoods and 

infrastructure, and national security. Sea level rise has overarching socioeconomic impacts as 

well, due to loss in income associated with degradation of agricultural areas and loss of housing 

associated with coastal inundation, for example. 

Water Resources. Future changes in regional water resources are closely tied to changes in 

precipitation. Individual areas under severe water stress in the region are projected to increase 

dramatically in the next few decades, although model results suggest that the region as a whole 

will not be at risk for water shortages. Fresh water resources on all island nations in the region 

are especially vulnerable to any variability in precipitation because many rely on rainwater 

collection for their supply of fresh water. The management of water issues is one of the most 

challenging climate-related issues in the region, as it is central to health and sustainable 

development. The impacts of climate change on water resources are interrelated with impacts on 

agriculture, river deltas, forests, coastal ecosystems, diseases and human health, and national 

security. 

Agriculture. Assessment of the specific impacts of climate change on agriculture is challenging 

because it is difficult to reliably simulate the complicated effects of future variations in 

temperatures, precipitation, and atmospheric CO2 concentrations on crop growth. Temperature 

increases associated with climate change could result in a northward expansion of growing areas 

and a lengthening of the growing season. Rising atmospheric CO2 levels are expected to 

stimulate plant photosynthesis, which would result in higher crop yields. Studies show that the 

beneficial effects of CO2 on plants may be offset by average temperature increases of more than 

2°C, however. Overall, it is likely that future crop yields will vary by region and by crop, with 

yield increases in some locations but decreases in others. Management of the agricultural sector 

by regional nations is critical to their economic growth and national security. The impacts of 

climate change on agriculture are interrelated with impacts on sea level, river deltas, natural 

disasters, water resources, and national security. 

Coastal Regions. Coastal regions are some of the most at-risk areas for the impacts of climate 

change in the region due to their prevalence and high population density. 

Mangroves and coral reefs across the region are two key coastal ecosystems that are expected to 

be significantly impacted by climate change. Many coastal areas are already degraded by 

pollution, sediment-laden runoff, and destructive fishing practices. Climate change-related 

destruction and degradation of mangroves and coral reefs will only exacerbate these effects, and 

result in long-term economic repercussions, since these ecosystems are central to the tourism, 
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agriculture, fishing, and aquaculture industries. The area’s coastal regions are also susceptible to 

inundation associated with sea level rise and destruction of infrastructure from flooding and 

storm surges, which are likely to increase as a result of future climate change. Careful 

management and safeguarding of coastal regions by regional governments is therefore essential 

in the next 20 years, as the effects of climate change manifest themselves. Impacts on coastal 

regions are interrelated with sea level, river deltas, natural disasters, water resources, agriculture, 

forests, and human livelihoods and infrastructure. 
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Contents 

Page 

Scope Note 1 

Executive Summary 3 

Introduction and Background 9

Projected Regional Climate Change 

Current Climatology of Southeast Asia 

Climate Predictions (Modeling) 

Projections of Future Changes in Temperature and Precipitation 

Projections of Future Changes in Monsoons and ENSO 

Projections of Future Changes in Water Resources 

Projections of Future Changes in Sea Level 

Projections of Future Changes in River Delta Flooding and Salinity 

 Intrusion 

13 

13 

15 

20 

23 

24 

25 

26 

Impacts of Climate Change on Human and Natural Systems 

Impacts of Sea Level Rise 

Impacts on River Deltas 

Impacts of Natural Disasters 

Impacts on Water Resources 

Impacts on Agriculture 

Impacts on Forests and Biodiversity 

Impacts on Coastal Ecosystems 

Impacts on Coral Reefs 

Impacts on Diseases and Human Health 

Impacts on Electricity Demand in Urban Areas 

Impacts on Human Livelihoods and Infrastructure 

27 

28 

28 

29 

29 

30 

33 

35 

35 

37 

38 

39 

Adaptive Capacity 

The adaptive Capacity of Southeast Asia in a Global Context 

Strengths and Weaknesses in Adaptive Capacity Assessments 

40 

40 

43 

Specific Adaptive Capacity Considerations for Southeast Asia 

Summary of Possible Adaptive Strategies for Climate Change in Southeast 

Asia 

43 

44 

Conclusions: High-Risk Impacts 47 

Annexes 

A: Accuracy of Regional Models 51 

B: Knowledge Deficiencies that Preclude a Full Evaluation of Climate 

 Change Impacts on Southeast Asia/Pacific Islands and Southeast 

 Asia/Pacific Islands' Adaptive Strategies 

53 
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Introduction and Background 

The Southeast Asia and Pacific Islands region is one of the most vulnerable areas in the world to 

climate change, due to its large and growing population, its long coastlines and low-lying areas, 

the economic importance of its agricultural sector, and its high dependence upon natural 

resources for development.vii This report summarizes the latest peer-reviewed research on 

projected climate change in Southeast Asia and Pacific Islands and the associated impacts on 

human and ecological systems across the region. Literature sources include the latest 

Intergovernmental Panel on Climate Change (IPCC) assessments, peer-reviewed journal articles, 

and reports generated by governments and scientific organizations. 

The nations that constitute the focus of this assessment are Thailand, Cambodia, Laos, Vietnam, 

the Philippines, Malaysia, Singapore, and Indonesia. For convenience, these countries are 

referred to as "Southeast Asia" in this report, unless otherwise noted. The geographic scope of 

this report includes the region of approximately 23.5 °N to 10 °S latitude and 97 °E to 141 °E 

longitude. A map of the region, including the countries and their capitals, is given in Figure 1. 

Specific information about the geography, economy, and society of the countries of interest is 

summarized below.viii 

Thailand: Thailand is a coastal nation that borders Burma, Cambodia, Laos, Malaysia, the 

Andaman Sea, and the Gulf of Thailand. The total area of Thailand is 514,000 km2, composed 

of 511,770 km2 of land and 2,230 km2 of water, and it has 3,219 km of coastline. The 

topography of Thailand includes a central plain, the Khorat Plateau in the east, and mountainous 

regions. It is subject to droughts nationwide and land subsidence in the Bangkok area resulting 

from depletion of the water table. Environmental issues in Thailand include air pollution from 

vehicle emissions, water pollution from organic and factory wastes, deforestation, soil erosion, 

and wildlife populations threatened by illegal hunting. The estimated population in 2009 is 

approximately 66 million, with an annual growth rate of 0.6 percent. The urban population 

constitutes 33 percent of the total population. Life expectancy is approximately 73 years. The 

Thai population is overwhelmingly Buddhist, with approximately 5 percent Muslims. 

Thailand has a well-developed infrastructure, a free-enterprise economy, and generally proinvestment policies, which permitted annual real Gross Domestic Product (GDP) growth of more 

than 6 percent for the period 2002-04. Overall economic growth fell sharply in 2005-2007, 

however, because persistent political crises stalled infrastructure mega-projects, eroded investor 

and consumer confidence, and damaged the country's international image. The GDP (purchasing 

power parity) for 2008 was $553 billion. The labor force includes 43 percent in agriculture, 20 

percent in industry, and 37 percent in services. 
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This Map is UNCLASSIFIED

Figure 1. Map of the geographic region of interest for this report. Colored areas indicate the eight nations of focus. 

Laos: Laos is a landlocked nation that borders Burma, Cambodia, Thailand, China, and 

Vietnam. Laos’ total area is 236,800 km,2

 composed of 230,800 km2 of land and 6,000 km2 of 

water. Laos is subject to droughts and floods. Current environmental issues include 

deforestation and soil erosion; most of the population does not have easy access to potable water. 

Laos’ estimated 2009 population is about 6.8 million, with a 2.3 percent annual growth rate. The 

urban population makes up 31 percent of the total population. Life expectancy is approximately 

57 years. The religious make-up of the population is approximately 67 percent Buddhist, 1.5 

percent Christian, and other unspecified religions. 

Laos is one of the few remaining one-party Communist states. It has an underdeveloped 

infrastructure, particularly in rural areas: the nation has no railroads, a rudimentary road system, 

and limited external and internal telecommunications. Subsistence agriculture, dominated by 

rice, accounts for about 40 percent of GDP and provides 80 percent of total employment. The 

GDP (purchasing power parity) for 2008 was $14 billion. The labor force includes 80 percent in 

agriculture and 20 percent in industry and services. 
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Cambodia: Cambodia is a coastal nation that borders Thailand, Laos, Vietnam, and the Gulf of 

Thailand. Cambodia’s total area is 181,040 km,2 composed of 176,520 km2 of land and 4,520 

km2 of water. Its terrain is mostly low, flat plains, with mountains in the southwest and north. 

Cambodia is subject to flooding and occasional droughts. Current environmental issues include 

soil erosion, lack of potable water in rural areas, and declining fish stocks due to illegal fishing 

and overfishing. In addition, illegal logging activities throughout the country and strip mining 

for gems in the western region along the border with Thailand have resulted in habitat loss and 

declining biodiversity. In addition, destruction of mangrove swamps threatens natural fisheries. 

Cambodia’s 2009 population is estimated at 14.5 million, with a 1.8 percent annual growth rate. 

The urban population makes up 22 percent of the total population. Life expectancy is 

approximately 62 years, and 95 percent of the population is Theravada Buddhist. 

Cambodia’s economy grew about 10 percent per year during 2004-2008, driven mainly by 

construction, agriculture, tourism, and an expanding garment industry. As of 2008, the garment 

industry employed more than 320,000 people and accounted for more than 85 percent of 

Cambodia's exports. The global financial crisis is weakening demand for Cambodian exports, 

however. Tourism has grown rapidly in recent years, with more than 2 million foreign visitors 

per year during 2007-2008. The GDP (purchasing power parity) for 2008 was approximately 

$28 billion. The labor force includes 75 percent in agriculture, with the remaining 25 percent in 

unspecified areas. 

Vietnam: Vietnam is a coastal nation that borders China, Laos, Cambodia, the Gulf of Thailand, 

the Gulf of Tonkin, and the South China Sea. Vietnam’s total area is 329,560 km,2

 composed of 

325,360 km2

 of land and 4,200 km2 of water. Vietnam is subject to occasional typhoons from 

May to January that can cause extensive flooding, especially in the Mekong River delta. Current 

environmental issues include deforestation and soil degradation due to logging and slash-andburn agricultural practices, water pollution, over-fishing, limited potable water supply due to 

groundwater contamination, growing urban industrialization, and increasing population 

migration to Hanoi and Ho Chi Minh City. Vietnam’s estimated 2009 population is about 87 

million, with a 0.9 percent annual growth rate. The urban population makes up 28 percent of the 

total population. Life expectancy is approximately 72 years. The distribution of religions 

includes 9.3 percent Buddhist, 6.7 percent Catholic, 1.5 percent Hoa Hao, 1.1 percent Cao Dai, 

0.5 percent Protestant, and 0.1 percent Muslim, with 80 percent reporting no religion. 

Vietnam has an export-oriented economy, with 68 percent of GDP in 2007 coming from exports. 

The agriculture sector has shrunk from 25 percent of the nation’s economic output in 2000 to less 

than 20 percent in 2008. The global financial crisis will constrain Vietnam's ability to reduce 

poverty and create jobs in coming years. The nation’s GDP (purchasing power parity) for 2008 

was $242 billion. The labor force includes 56 percent in agriculture, 19 percent in industry, and 

26 percent in services. 
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Philippines: The Philippines is an archipelago of 7,107 islands located east of Vietnam, between 

the Philippine Sea and the South China Sea. The total area of the Philippines is 300,000 km,2

composed of 298,170 km2 of land and 1,830 km2 of water. The Philippines is mostly mountains 

with narrow to extensive coastal lowlands. It is subject to typhoons, landslides, active volcanoes, 

destructive earthquakes, and tsunamis. Environmental issues in the Philippines include 

uncontrolled deforestation (especially in watershed areas), soil erosion, air and water pollution in 

major urban centers, coral reef degradation, and increasing pollution of coastal mangrove 

swamps that are important fish breeding grounds. The estimated 2009 population is 

approximately 98 million, with an annual growth rate of 2 percent. The urban population 

constitutes 65 percent of the total population. Life expectancy is approximately 71 years. The 

Philippine population is overwhelmingly Catholic, with approximately 5 percent Muslims. 

Economic growth in the Philippines has averaged 5 percent since 2001. The economy faces 

several long-term challenges, such as improving employment opportunities and alleviating 

poverty. In the coming years, the Philippines will need sustained economic growth to make 

progress in alleviating poverty, given the nation’s high population growth and unequal 

distribution of income. The Philippine economy grew at its fastest pace in three decades in 2007 

with real GDP growth exceeding 7 percent, but growth slowed to 4.5 percent in 2008 as a result 

of the world financial crisis. The GDP (purchasing power parity) for 2008 was $320.6 billion. 

The labor force includes 35 percent in agriculture, 15 percent in industry, and 50 percent in 

services. 

Malaysia: Malaysia occupies the southern portion of the Malay Peninsula and the northern onethird of the island of Borneo. Malaysia borders Indonesia, Brunei, and Thailand. Malaysia’s 

total area is 329,750 km,2

 composed of 328,550 km2 of land and 1,200 km2 of water. Malaysia is 

subject to flooding, landslides, and forest fires. Current environmental issues include air 

pollution from industrial and vehicular emissions, water pollution from raw sewage, 

deforestation, and smoke and haze from Indonesian forest fires. Malaysia’s estimated 2009 

population is about 26 million, with a 1.8 percent annual growth rate. The urban population 

makes up 70 percent of the total population. Life expectancy is approximately 73 years. The 

religious make-up of the population is approximately 60 percent Muslim, 19 percent Buddhist, 

9 percent Christian, 9 percent Hindu, and other less populous religions. 

Malaysia is a middle-income country that has transformed from a producer of raw materials into 

an emerging multi-sector economy. It has recently attracted investments in high technology 

industries, medical technology, and pharmaceuticals. As an oil and gas exporter, Malaysia has 

profited from higher world energy prices, although the rising cost of domestic gasoline and 

diesel fuel forced Kuala Lumpur to reduce government subsidies. Real GDP growth has 

averaged about 6 percent per year over the past few years, although regions outside of Kuala 

Lumpur and the manufacturing hub of Penang are growing less robustly. Decreasing worldwide 

demand for consumer goods is expected to hurt economic growth. The GDP (purchasing power 

parity) for 2008 was $387 billion. The labor force includes 10 percent in agriculture, 45 percent 

in industry, and 45 percent in services. 

Singapore: Singapore is a small city-state that borders Malaysia on the southern tip of the 

Malay Peninsula. Singapore’s total area is 692.7 km,2

 composed of 682.7 km2 of land and 10 

km2

 of water. Singapore’s terrain includes lowlands and a gently undulating central plateau that 

contains a nature preserve. Current environmental issues include industrial pollution, limited 
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natural fresh water resources, limited land availability that causes waste disposal problems, and 

seasonal smoke and haze from forest fires in Indonesia. Singapore’s estimated 2009 population 

is about 4.7 million, with a 1 percent annual growth rate. The population is 100 percent urban. 

Life expectancy is approximately 82 years. The distribution of religions includes 42.5 percent 

Buddhist, 14.9 percent Muslim, 8.5 percent Taoist, 4 percent Hindu, and 4.8 percent Catholic. 

Singapore has a free-market economy that features a per capita GDP equal to that of the four 

largest West European countries. Singapore’s economy depends primarily on exports, 

particularly consumer electronics, information technology products, pharmaceuticals, and a 

growing service sector. Real GDP growth for the period 2004-2007 averaged 7 percent, but it 

dropped to 1.2 percent in 2008 due to the global financial crisis. The nation’s GDP (purchasing 

power parity) for 2008 was $240 billion. The labor force includes 33 percent in industry and 67 

percent in services. 

Indonesia: Indonesia is an archipelago between the Indian Ocean and the Pacific Ocean. 

Indonesia’s total area is 1,919,440 km,2 composed of 1,826,440 km2 of land and 93,000 km2 of 

water. Its terrain is mostly coastal lowlands, although larger islands have interior mountains. 

Indonesia is subject to occasional floods, severe droughts, tsunamis, earthquakes, volcanoes, and 

forest fires. Current environmental issues include deforestation, water pollution from industrial 

wastes and sewage, air pollution in urban areas, and smoke and haze from forest and agricultural 

fires. Indonesia’s estimated 2009 population is about 240 million, with a 1.1 percent annual 

growth rate. The urban population makes up 52 percent of the total population. Life expectancy 

is approximately 71 years. About 86 percent of the population is Muslim, with smaller Christian 

and Hindu populations. 

Indonesia has made significant economic advances in recent years, but the nation faces 

challenges stemming from the global financial crisis and world economic downturn. Indonesia's 

debt-to-GDP ratio in recent years has declined steadily because of increasingly robust GDP 

growth and sound fiscal stewardship. Indonesia still struggles with poverty and unemployment, 

inadequate infrastructure, corruption, a complex regulatory environment, and unequal resource 

distribution among regions. As global demand slows and prices for Indonesia’s commodity 

exports fall, Indonesia faces the prospect of growth significantly below the 6-plus percent 

recorded in 2007 and 2008. The GDP (purchasing power parity) for 2008 was $916 billion. The 

labor force includes 42 percent in agriculture, 19 percent in industry, and 39 percent in services. 

Projected Regional Climate Change 

Current Climatology of Southeast Asia 

Southeast Asia has a tropical maritime climate featuring relatively high temperatures, high 

relative humidity, and abundant precipitation. Figures 2-4 shows the monthly average daily 

minimum temperatures, monthly average daily maximum temperatures, and rainfall amounts for 

the capital cities of the Southeast Asian countries that are the focus of this report. There are 

three basic seasons in Southeast Asia: the rainy season, winter, and summer, although the 

characteristics, duration, and timing of the seasons vary widely based on latitude and geography. 

Thailand has a rainy, warm, and cloudy southwest monsoon season from mid-May to 

September, a dry, cool winter northeast monsoon season from October to mid-March, and a 

warm and relatively dry summer season from mid-March to mid-May; Thailand’s southern 

isthmus on the Malay Peninsula is generally hot and humid year-round.ix 
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Laos and Cambodia both have two main seasons: the rainy season from May to November and 

the dry season from December to April.x 

Vietnam has a hot, rainy season under the influence of the monsoon from May to September and 

a warm, dry season from October to March.xi 

The Philippines has a northeast monsoon season from November to April and a southwest 

monsoon season from May to October.xii 

Malaysia has a southwest monsoon season from April to October and a more rainy northeast 

monsoon season from October to February.xiii 

Singapore has a southwest monsoon season from June to September and a more rainy northeast 

monsoon from December to March; during the inter-monsoon period, afternoon and early 

evening thunderstorms are common.xiv 

Indonesia’s climate is hot and humid year-round, although it is more moderate in the 

highlands.xv 

The general pattern of rainy, winter, and summer seasons is typical except during El NiñoSouthern Oscillation (ENSO) events. ENSO is a global climate phenomenon that recurs 

irregularly every 2-7 years and is associated with changes in sea surface temperature and 

prevailing winds. During a period of El Niño, the Northeast Trade Winds slacken, which 

increases the length of the dry season and creates drought conditions. La Niña is the 

complementary phenomenon; La Niña events are marked by a strengthening of the Northeast 

Trade Winds and a concomitant increase in the length of the rainy season. La Niña years are 

characterized by widespread flooding, landslides, and surface runoff from higher than average 

rainfall. 

There is a great deal of natural climate variability in Southeast Asia. The regular pattern of 

seasonal monsoons can cause extreme weather events, such as floods and droughts. Overlaid on 

the monsoon variability is the periodic shift in global climate caused by ENSO, which can create 

or intensify existing floods and droughts. 

Drought in El Niño years can have wide-reaching impacts on countries in Southeast Asia. For 

example, during the 1992 El Niño episode, total inflow to the Angat Reservoir in the Philippines, 

a major source of domestic and irrigation water supplies, was 69 percent less than average for the 

first six months of the year. As a result, there was a 20 percent reduction in the domestic water 

supply for the Manila metropolitan region, which necessitated water rationing in many areas. 

The cropping season was also delayed due to water shortages in June and July, and there were 

negative impacts on the national rice yield due to lack of irrigation water.xvi 
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10

12

14

16

18

20

22

24

26

28

30

January

February

March

April

May

June

July

August

September

October

November

December

Month

Minimum Temperature (°C)

Bangkok

Phnom Phen

Vientiane

Hanoi

Manila

Kuala Lumpur

Singapore

Jakarta

The Chart is UNCLASSIFIED 

Figure 2. Monthly averaged daily minimum temperatures for the capital cities of the Southeast Asian countries that 

are the focus of this report. Climatology values for Bangkok, Thailand are averaged over the period 1961-1990; 

values for Phnom Penh, Cambodia are averaged over the period 1997-2001; values for Vientiane, Laos are averaged 

over the period 1951-2000; values for Hanoi, Vietnam are averaged over the period 1898-1990; values for Manila, 

Philippines are averaged over the period 1971-2000; values for Kuala Lumpur, Malaysia are averaged values over 

the period 1971-2000; values for Singapore are averaged values over the period 1961-1990; and values for Jakarta, 

Indonesia are averaged over the period 1994-1990. Source: World Meteorological Organization, World Weather 

Information Service, http://www.worldweather.org/ (accessed April 15, 2009). 

Tropical cyclones, called typhoons in the Pacific, also bring a great deal of precipitation to 

regions of Southeast Asia north of approximately 10° latitude. Tropical cyclones do not occur 

near the equator because Coriolis force is required to impart rotation to developing systems; thus 

Indonesia, Malaysia, Singapore, and southern portions of the Philippines are generally spared 

from their devastating effects. Typhoons usually form in the Central Pacific and move in a 

westerly direction across Southeast Asia. The typhoon season typically runs from April to 

December, with the peak number of typhoons generally occurring in September and October. 

Climate Predictions (Modeling) 

General circulation models (GCMs) are the main tool used by scientists to project future climate 

change. These models simulate atmospheric and oceanic circulations, as well as processes that 

occur on land. As a result, GCMs are very complex models, and they tend to have rather low 
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15

20

25

30

35

January

February

March

April

May

June

July

August

September

October

November

December

Month

Maximum Temperature (°C)

Bangkok

Phnom Phen

Vientiane

Hanoi

Manila

Kuala Lumpur

Singapore

Jakarta

This chart is UNCLASSIFIED 

Figure 3. Monthly averaged daily maximum temperatures for the capital cities of the Southeast Asian countries that 

are the focus of this report. Climatology values for Bangkok, Thailand are averaged over the period 1961-1990; 

values for Phnom Penh, Cambodia are averaged over the period 1997-2001; values for Vientiane, Laos are averaged 

over the period 1951-2000; values for Hanoi, Vietnam are averaged over the period 1898-1990; values for Manila, 

Philippines are averaged over the period 1971-2000; values for Kuala Lumpur, Malaysia are averaged values over 

the period 1971-2000; values for Singapore are averaged values over the period 1961-1990; and values for Jakarta, 

Indonesia are averaged over the period 1994-1990. Source: World Meteorological Organization, World Weather 

Information Service, http://www.worldweather.org/ (accessed April 15, 2009). 

spatial resolutions, on the order of 400 to 125 km. To obtain model information on the local and 

regional scales, such as for Southeast Asia, at higher resolutions than native GCM grid sizes, 

"downscaling" is used. There are two main downscaling methods, dynamical and statistical. 

Dynamical downscaling involves the use of high-resolution climate models with observed or 

simulated data as boundary conditions. This approach has high credibility, but it is 

computationally expensive. In contrast, statistical downscaling, which involves application of 

established relationships between observed data to modeled data, is computationally inexpensive, 

and it can replicate finer scales than dynamical downscaling. Statistical downscaling methods do 

not accurately simulate regional feedback effects, however.xvii 

In general, GCM predictions of temperature changes for a given region are consistent, but 

predictions of precipitation changes can vary widely due to the difficulty in simulating the 

myriad of factors that influence precipitation frequency, duration, and intensity. An additional 
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0

50

100

150

200

250

300

350

400

450

500

January

February

March

April

May

June

July

August

September

October

November

December

Month

Total Precipitation (mm)

Bangkok

Phnom Phen

Vientiane

Hanoi

Manila

Kuala Lumpur

Singapore

Jakarta

This chart is UNCLASSIFIED 

Figure 4. Monthly averaged total precipitation values for the capital cities of the Southeast Asian countries that are 

the focus of this report. Climatology values for Bangkok, Thailand are averaged over the period 1961-1990; values 

for Phnom Penh, Cambodia are averaged over the period 1997-2001; values for Vientiane, Laos are averaged over 

the period 1951-2000; values for Hanoi, Vietnam are averaged over the period 1898-1990; values for Manila, 

Philippines are averaged over the period 1971-2000; values for Kuala Lumpur, Malaysia are averaged values over 

the period 1971-2000; values for Singapore are averaged values over the period 1961-1990; and values for Jakarta, 

Indonesia are averaged over the period 1994-1990. Source: World Meteorological Organization, World Weather 

Information Service, http://www.worldweather.org/ (accessed April 15, 2009). 

complication for Southeast Asia is the fact that precipitation varies naturally on an interannual 

time scale due to ENSO and other natural variability. Any precipitation changes associated with 

future climate change in model simulations are overlaid on this natural variability, and it can be 

very difficult for GCMs to resolve the natural and anthropogenic contributions. A more detailed 

discussion of the ability of GCMs to project regional climate changes is given in Annex A. 

GCMs simulate changes in climate under scenarios of future greenhouse gas and aerosol 

emissions. The 2000 IPCC Special Report on Emission Scenarios (SRES)xviii laid out the four 

basic scenario families used by IPCC scientists to predict future climate change; they are 

summarized in Table 1. This set of scenarios is designed to represent the range of possible future 

global conditions that will influence greenhouse gas emissions. The scenarios are based on 

consistent and reproducible assumptions about global forces that impact greenhouse gas 

emissions, including economic development, population, and technological change. 
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The following excerpt from the 2000 IPCC Special Report on Emission Scenarios (SRES) 

describes the emissions scenarios in more detail: 

• A1. The A1 storyline and scenario family describes a future world of very rapid economic 

growth, global population that peaks in the mid-21st century and declines thereafter, and the 

rapid introduction of new and more efficient technologies. Major underlying themes are 

convergence among regions, capacity building and increased cultural and social interactions, 

with a substantial reduction in regional differences in per capita income. The A1 scenario 

family develops into three groups that describe alternative directions of technological change 

in the energy system. The three A1 groups are distinguished by their technological emphasis: 

fossil-intensive (A1FI), non-fossil energy sources (A1T) or a balance across all sources 

(A1B), where balance is defined as not relying too heavily on one particular energy source, 

on the assumption that similar improvement rates apply to all energy supply and end use 

technologies. 

• A2. The A2 storyline and scenario family describes a very heterogeneous world. The 

underlying theme is self reliance and preservation of local identities. Fertility patterns across 

regions converge very slowly, which results in continuously increasing population. Economic 

development is primarily regionally oriented and per capita economic growth and 

technological change are more fragmented and slower than other storylines. 

• B1. The B1 storyline and scenario family describes a convergent world with the same global 

population, that peaks in the mid-21st century and declines thereafter, as in the A1 storyline, 

but with rapid change in economic structures toward a service and information economy, 

with reductions in material intensity and the introduction of clean and resource-efficient 

technologies. The emphasis is on global solutions to economic, social and environmental 

sustainability, including improved equity, but without additional climate initiatives. 

• B2. The B2 storyline and scenario family describes a world in which the emphasis is on local 

solutions to economic, social and environmental sustainability. It is a world with 

continuously increasing global population, at a rate lower than A2, intermediate levels of 

economic development, and less rapid and more diverse technological change than in the B1 

and A1 storylines. While the scenario is also oriented toward environmental protection and 

social equity, it focuses on local and regional levels. 

Climate researchers frequently use GCMs from the UK Met Office Hadley Centre for Climate 

Prediction and Research to investigate future changes in temperature and precipitation. These 

models are representative of many GCMs used to simulate the effects of climate change. The 

HadCM2 model has four different integrations that represent the climate effects of greenhouse 

gases and sulfate aerosols. Greenhouse gases, such as carbon dioxide, water vapor, ozone, 

methane, and nitrous oxide, absorb infrared radiation emitted from the Earth and subsequently 

emit it back into the atmosphere, which results in a net warming of the Earth’s surface. HadCM2 

includes the combined forcing of all greenhouse gases as an equivalent CO2 concentration of 0.5 

percent or 1 percent, depending on the integration. HadCM2 can also incorporate the negative 

direct forcing of sulfate aerosols by means of an increase in clear-sky albedo; sulfate forcing is 

0.5 percent or 1 percent, depending on the model integration. The influence of sulfate aerosols is 
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Emission 

Scenario 

Economic 

Development 

Global 

Population 

Technology 

Changes Theme 

A1 Very rapid 

Peaks around mid21st century and 

declines thereafter 

Rapid introduction of 

new and more 

efficient technologies 

Convergence among 

regions; increased 

cultural and social 

interactions 

A2 Regionally-oriented Continuously 

increasing 

Slower and more 

fragmented than A1, 

B1, and B2 

Self-reliance and 

preservation of local 

identities 

B1 

Rapid change toward 

service and 

information economy 

Same as A1 Introduction of clean 

and resource-efficient 

technologies 

Global solutions to 

economic, social, 

and environmental 

sustainability 

B2 

Intermediate levels of 

economic 

development 

Continuously 

increasing, but not 

as fast as A2 

Less rapid and more 

diverse changes than 

A1 and B1 

Local solutions to 

economic, social, 

and environmental 

sustainability 

This table is UNCLASSIFIED

Table 1. Summary of IPCC emissions scenarios. Source: Intergovernmental Panel on Climate Change (IPCC), 

Special Report on Emissions Scenarios (SRES), eds. Nebojsa Nakicenovic and Rob Swart (Cambridge: Cambridge 

University Press, 2000), http://www.ipcc.ch/ipccreports/sres/emission/index.htm. 

important because they reflect incoming solar radiation; thus less reaches the surface of the 

Earth, which results in a net cooling of the Earth’s surface. Each integration of HadCM2 has 

four ensembles, from which an ensemble mean can be calculated.xix Ensembles are used to 

represent the range in uncertainty of model predictions. In this case, the same model, HadCM2, 

is run four times using different initial conditions. The average of a series of ensembles is 

always more accurate than any single model run. 

HadCM3 is the successor to HadCM2. Its integrations are run under six different climatology 

scenarios of population growth, greenhouse gas emissions, and technology development through 

the 21st century. Both HadCM2 and HadCM3 have spatial resolutions of 2.5° latitude by 3.75° 

longitude. In general, this resolution is sufficient to resolve climate changes on a country-level 

scale in Southeast Asia, without the need for downscaling or temporal smoothing.xx To simulate 

local climate changes, however, downscaling is required. 

In contrast to the most recent GCMs, which are run under conditions matching the various IPCC 

emissions scenarios, many GCMs prior to approximately 2000 were run under more simplistic 

conditions. The most common method of simulating climate change in the older models was 

with an equivalent doubling of atmospheric CO2 concentrations (2×CO2), which represented the 

net radiative effect of increases in CO2 and other greenhouse gases since pre-industrial times 

(typically equivalent to 560 ppm of CO2). Models established a baseline using "current" CO2

concentrations (1×CO2), and the change between 1×CO2 and 2×CO2" in model output was 

considered representative of future climate change. Under this type of scenario, researchers 

often neglected to frame the model results in terms of specific decadal changes, so the exact 

timeframe for projected climate changes was not specified. 

Additional information on the GCMs mentioned in this report is available from the IPCC Data 

Distribution Centre (http://www.ipcc-data.org/). 
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Projections of Future Changes in Temperature and Precipitation 

Rising concentrations of greenhouse gases in the global atmosphere during the 21st century are 

expected to cause a net warming of the Earth’s surface. Higher surface temperatures will likely 

increase surface evaporation and thus global precipitation. To quantify regional future changes 

in temperature and precipitation, the IPCC uses a coordinated set of climate model simulations 

archived at the Program for Climate Model Diagnosis and Intercomparison (called the multimodel dataset, or MMD). Figure 5 shows the projected increase in temperature for Southeast 

Asia for the 21st century in the context of observed warming during the 20th century. This 

distinction is important because if GCMs cannot accurately reproduce observed climatic data, 

then they cannot be relied upon to simulate future climate changes. To obtain the temperature 

information shown in Figure 5, a subset of 58 simulations from 14 models of the MMD was used 

for the observed period and 47 simulations from 18 models for the future projections; the future 

projections were calculated for the A1B emissions scenario. The width of the shading and the 

bars in Figure 5 represent the 5-95 percent range of the model output. Results show that by the 

end of the 21st century, the annual mean temperature across Southeast Asia is expected to 

increase by a median value of 2.5°C, with little seasonal variation. This increase compares to a 

historical warming of 0.5-1.1°C in Southeast Asia for the period 1901-2005. The future 

temperature increase is expected to vary by location, with stronger warming in the interior of 

countries on the Asian mainland and less warming along coastal regions and in island nations.xxi 

IPCC also used the MMD models to estimate precipitation changes for the 21st century under the 

A1B scenario. In contrast to the temperature projections, which are consistent in predicting a 

temperature increase across Southeast Asia, future precipitation changes are less straightforward 

to quantify. Results from the MMD simulations show a net increase in precipitation across the 

region, with a median increase in average annual precipitation of 7 percent; these increases 

generally follow the location of the Intertropical Convergence Zone (ITCZ).xxii The ITCZ is a 

region of low pressure and thus rising air near the equator that occurs where the Northeast Trade 

Winds meet the Southeast Trade Winds. As air rises, it cools and moisture condenses out, 

causing clouds and precipitation to form. Consequently, the ITCZ is marked by a band of heavy 

precipitation that moves northward and southward seasonally, following the warmest surface 

temperatures. The MMD models predict that the strongest and most consistent future increases 

in precipitation will follow the movement of the ITCZ, and thus will occur over Thailand, 

Cambodia, Laos, Thailand, the Philippines, Malaysia, Singapore, and northern Indonesia in June, 

July, and August; and will occur over southern Indonesia and Papua New Guinea in December, 

January, and February. During the times when these regions are not under the influence of the 

ITCZ, the models predict that concomitant decreases in precipitation will occur. The overall 

trend forecasted by the MMD models is for precipitation to increase during the rainy season and 

decrease during the dry season.xxiii 
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This chart is UNCLASSIFIED 

Figure 5. Temperature changes in °C predicted by the IPCC MMD models for Southeast Asia (SEA). Temperature 

anomalies for the region with respect to 1901-1950 are shown for 1906-2005 (black line), as simulated by the MMD 

models using known forcings (red envelope), and as projected for 2001-2100 by the MMD models for the A1B 

scenario (orange envelope). The colored horizontal bars on the right side of the figure represent the range of 

projected changes for 2091-2100 for the B1 scenario (blue), the A1B scenario (orange), and the A2 scenario (red). 

The width of the shading and the bars represent the 5-95 percent range of the model results. Source: 

Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. 

Variations in future regional precipitation are expected. Although a net increase in future 

precipitation is expected globally in a warmer world, regional precipitation rates in Southeast 

Asia will vary due to a variety of reasons, including shifts in the ITCZ, changes in the monsoons, 

variations in ENSO, local hydrological feedbacks, and alterations in atmospheric stability.xxiv 

Future climate change-induced variations of regional precipitation will be superimposed on the 

natural interannual variations that occur in Southeast Asia. For example, analysis of monthly 

rainfall from 210 stations across Indonesia for the periods 1931-1960 and 1961-1990 showed 

that annual rainfall has decreased across most of the southern regions of Indonesia (Java, 

Lampung, South Sumatra, South Sulawesi, and Nusa Tenggara) and increased across most of the 

northern regions of the country (Kalimantan and North Sulawesi).xxv 

In addition to the global simulations of future temperature and precipitation changes executed by 

the IPCC, several other researchers have conducted localized simulations for the Southeast Asian 

region. Lal and Harasawaxxvi used four GCMs to estimate the effect of climate change on 

temperature and precipitation in Asia for the periods 2049-2069 and 2070-2099. Their analysis 

of Southeast Asia included the region of 10°S – 20°N latitude and 95°E – 155°E longitude, 

which encompasses the countries that are the focus of this report, except for the northern-most 

sections of Laos and Vietnam. The authors ran the Japanese CCSR/NIES model, the Australian 

CSIRO model, the German ECHAM4 model, and HadCM2 under conditions of greenhouse gas 

forcing only and combined greenhouse gas and sulfate aerosol forcings. Ensemble results for the 

four GCMs showed a consistent increase in both temperature and precipitation across Southeast 
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Asia for both time periods. Due to increases in greenhouse gases only, the annual average 

temperature was predicted to increase by 2.15°C, the average "winter" season (December, 

January, and February) temperature was predicted to increase by 2.28°C, and the average 

"summer" season (June, July, and August) temperature was predicted to increase by 2.01°C for 

the 2049-2069 period. The inclusion of sulfate aerosol forcing in the GCMs caused a slight 

reduction in the magnitude of the expected temperature increases, to 1.72°C, 1.73°C, and 1.6°C 

for annual, winter, and summer average temperatures, respectively. In addition, the models 

predicted a uniform increase of 4.6 percent, 3.5 percent, and 3.4 percent in annual, winter, and 

summer average precipitation, respectively, for the period 2049-2069 due to increases in 

greenhouse gases. As with the temperature predictions, inclusion of sulfate aerosol forcing in the 

four GCMs resulted in a moderate reduction in the expected precipitation increase, to 1.0 

percent, 2.9 percent, and 2.6 percent for annual, winter, and summer average precipitation, 

respectively. The temperature results of Lal and Harasawa are consistent with those of the IPCC 

MMD models, which also predicted little seasonal variation in the expected temperature 

increases across Southeast Asia. The seasonally uniform precipitation results of Lal and 

Harasawa are in contrast to those of the IPCC MMD models, however, which showed a variation 

in future precipitation changes for the rainy and dry seasons. The inconsistency of the 

precipitation estimates from these two studies underscores the difficulty in obtaining consistent 

precipitation forecasts from GCMs under conditions of future climate change. 

Lal et alxxvii continued their earlier work by simulating the temperature and precipitation effects 

resulting from climate change over small island states, including those of the Pacific Ocean, for 

the periods 2010-2029, 2049-2069, and 2070-2099. The authors ran the CCSR/NIES, CSIRO, 

ECHAM4, CCCma, and HadCM2 models under conditions of greenhouse gas forcing only and 

combined greenhouse gas and sulfate aerosol forcings. See M. Lal, H. Harasawa, and K. 

Takahashi, "Future climate change and its impacts over small island states," Clim. Res., 19 

(2002):179–192 for results from the ensemble of five models for the Pacific Ocean Islands (the 

region 23°S – 23°N latitude and 120°E – 145°W longitude, which includes the Philippines and 

eastern Indonesia). Model results projected annual mean temperature increases of 0.93±0.12°C 

and 0.79±0.05°C due to increases in greenhouse gases and greenhouse gases plus sulfate aerosol, 

respectively, by 2010-2029. The models also predicted annual mean precipitation increases of 

2.9±1.0 percent and 1.9±0.8 percent due to increases in greenhouse gases and greenhouse gases 

plus sulfate aerosol, respectively, by 2010-2029. Similar to the authors’ earlier work in the Asia 

region,xxviii model results for the Pacific Ocean Islands showed a mostly uniform increase in both 

temperature and precipitation for the three simulated time periods of 2010-2029, 2049-2069, and 

2070-2099 with little seasonal variation. The exception was slightly larger increases in 

precipitation during the summer season for the periods 2049-2069 and 2070-2099. The authors 

noted that regional precipitation is strongly dependent on local topography, particularly elevated 

terrain, which is typically not well represented in GCMs, and consequently there is significant 

uncertainty in their model projections of regional precipitation due to future climate change. 

Although the IPCC MMD model resultsxxix and the ensemble mean results of Lal and 

Harasawaxxx and Lal et alxxxi differed slightly from each other regarding the magnitude of 

projected seasonal precipitation in Southeast Asia, the models were generally internally 

consistent in the sense that they all predicted precipitation increases. Other researchers who have 

used a series of GCMs to estimate the future effects of climate change on precipitation in specific 

countries in Southeast Asia have obtained conflicting results. 
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For example, Boer and Faqihxxxii examined the potential changes in future temperature and 

precipitation in Indonesia through 2080 using the CCSR, CSIRO, ECJAM4, CGCM1, and 

HadCM3 GCMs under the A2 and B2 emissions scenarios. Results showed an expected uniform 

increase in average temperature across the country of approximately 0.0344°C per year for the 

A2 scenario and approximately 0.0211°C per year for the B2 scenario. Precipitation changes 

varied by model and emissions scenario, however, as CCSR and CSIRO predicted rainfall 

increases, ECHAM4 and CGCM1 predicted rainfall decreases, and HadCM3 varied depending 

on the emissions scenario. Based on these results, the authors concluded that it was not possible 

to determine the specific effects of climate change on precipitation across Indonesia. 

Variations in the intensity and occurrence of tropical cyclones (typhoons) due to climate change 

will also impact precipitation changes in regions of Southeast Asia north of approximately 10 °N 

latitude. There is significant uncertainty in future changes in tropical cyclone occurrence and 

intensity, however, due to the fact that few GCMs have the spatial resolution necessary to 

simulate synoptic scale patterns. The IPCC reports that in general, higher-resolution models that 

can simulate tropical cyclone characteristics predict that mean and peak precipitation intensities 

will increase in future tropical cyclones. 

Projections of Future Changes in Monsoons and ENSO 

Regional precipitation in Southeast Asia will be affected by future variations in monsoons due to 

climate change. Giorgi et alxxxiii analyzed the global effects of climate change for the period 

2071-2100 relative to 1961-1990 using nine GCMs run under the A2 and B2 emissions 

scenarios. The model results predicted a consistent increase in summer monsoon precipitation 

across Southeast Asia relative to the global mean average precipitation. Overall precipitation 

across the region was not expected to change, however, which suggests that rainfall will decrease 

in some areas and/or during seasons that are not impacted by the monsoon. 

Bhaskaran and Mitchellxxxiv examined the changes in Southeast Asian monsoon precipitation due 

to climate change for the period 1990 to 2100 using the Hadley Centre HadCM2 global climate 

model. Two scenarios of HadCM2 were used: forcing only from greenhouse gases represented 

by a 1 percent per year increase in CO2 concentration, and with the addition of sulfate aerosol 

forcing. Overall, the model simulations indicated that monsoon-related precipitation will 

increase as CO2 concentration increases, and monsoon genesis and intensity are sensitive to 

sulfate aerosol forcing. Specific results for the region between 5 °N and 25 °N latitude and 90 °E 

and 105 °E (roughly including Thailand, Cambodia, Laos, and Vietnam) indicated an expected 

10-15 day delay in the monsoon onset date, but with no extension of the monsoon season for the 

period 2030 to 2070. The model simulations also predicted an increase of approximately 3-6 

mm day-1 in total daily precipitation and a substantial increase in frequency of intensity of areal 

mean precipitation for the same period, which, if realized, could lead to an increase in monsoonrelated flooding across the region. 

As previously noted, natural variations in monsoon frequency and intensity are tied to ENSO, so 

any future enhancement of ENSO associated with climate change will likely affect monsoon 

characteristics. Future changes in ENSO are very uncertain, however, because GCMs cannot 

accurately simulate past observed ENSO variations and thus simulations of future changes are 

suspect.xxxv Paeth et alxxxvi studied potential variations in ENSO and monsoons in the 21st

century using a suite of 12 GCMs under six IPCC emissions scenarios. Model results projected a 

substantial warming of the eastern tropical Pacific Ocean of more than 5°C, which represented a 

change in the background state of ENSO. There was no compelling evidence in the model 
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simulations for wholesale changes in the frequency of El Niño and La Niña events in the 21st

century, although results suggested that the intensity of ENSO events may increase. The authors 

concluded that the intensity of summer monsoons in South Asia may increase as a result of 

changes in ENSO due to future climate change, with the caveat that model uncertainty in 

simulating ENSO makes it impossible to draw any definitive conclusions. 

Projections of Future Changes in Water Resources 

Changes in temperature and precipitation in Southeast Asia will affect water resources. Tao et 

alxxxvii studied the impact of climate change on global water resources for the period 2021-2030 

using two Hadley Centre models. HadCM2 was run with forcing from greenhouse gases 

represented by a 1 percent per year increase in CO2 concentration and sulfate aerosols, and 

HadCM3 was run under the same conditions as HadCM2 but with the addition of ozone changes 

and the indirect forcing of sulfate aerosols. The model results projected that annual mean 

temperatures will increase uniformly by 0.5-1.5°C across Southeast Asia by 2021-2030. 

Precipitation projections across the region for the same period were not consistent due to 

variations between the models. The HadCM2 ensemble mean predicted that annual mean 

precipitation will increase by 0-100 mm in southern Philippines, western Malaysia, Singapore, 

and western Indonesia; increase by 0-50 mm in northern Vietnam, Thailand, and Laos; decrease 

by 0-50 mm in Cambodia; decrease by 0-100 mm in southern Vietnam and eastern Malaysia; and 

decrease by 100-150 mm in northern Philippines and eastern Indonesia. HadCM3 results were 

similar, with the exception of a projected increase in annual mean precipitation of greater than 

150 mm for most of Indonesia. The models were consistent in predicting an increase in annual 

mean potential evapotranspiration1

 of 0-100 mm across Southeast Asia by 2021-2030, which 

suggests that climate change may cause a net decrease in ecosystem water demand across the 

region. Furthermore, model projections showed little change in annual soil moisture deficit 

across Southeast Asia by 2021-2030, indicating that the region as a whole may not be susceptible 

to water shortages or water stress on local vegetation due to climate change. The models 

indicated that net increases in precipitation across parts of Southeast Asia due to climate change 

are likely to lead to increased surface runoff, however, which in turn may cause increased 

erosion, flooding, and water pollution. Surface runoff occurs when soil moisture exceeds soil’s 

water-holding capacity. Changes in annual surface runoff predicted by the GCMs mirrored the 

precipitation predictions, with variations across Southeast Asia and among the models. Areas 

with the largest predicted increases in surface runoff included parts of Thailand, Philippines, 

Malaysia, Vietnam, Laos, and Indonesia. 

Arnellxxxviii estimated the global effects of climate change on water resources using the Hadley 

Centre HadCM2 and HadCM3 global climate models. All simulations included forcing from 

greenhouse gases represented as a 1 percent per year increase in CO2 concentration from 1990 to 

2100, with no sulfate aerosol forcing. Analysis focused on surface runoff, and model projections 

showed that by 2040, annual surface runoff will increase by greater than 150 mm per year in 

Thailand, Malaysia, Singapore, and the Philippines and decrease by 50 to 150 mm per year in 

Laos, Cambodia, Vietnam, and Indonesia. See Nigel W. Arnell, "Climate change and global 

water resources," Global Environmental Change 9 (1999): S31-S49 for a graphic representation 

of the change in monthly average runoff for Thailand as predicted by the HadCM2 ensemble 

mean (thick black line) compared to baseline conditions (shaded region). The increases in runoff 

 

1

 The combined process of water evaporation from the Earth’s surface and transpiration from vegetation. 
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were predicted to occur during the rainy season, from June through October. The magnitude of 

the predicted changes in surface runoff generated in this study is consistent with that of Tao et 

al,xxxix although the specific locations where increases and decreases in surface runoff were 

predicted varied among the two studies. 

On a regional scale, Jose et alxl investigated the effects of climate change on water resources in 

the Philippines using the CCCM, UKMO, and GFDL GCMs run under conditions of 2×CO2. 

Analysis focused on two major reservoirs, Lake Angat and Lake Lanao, which provide water for 

domestic use, irrigation, and hydroelectric power in the Philippines. The models predicted a 

uniform increase in average annual temperature of approximately 2-3°C at both reservoir 

locations. Predictions of precipitation change were not consistent, however; CCCM estimated a 

slight decrease in annual average rainfall while the UKMO and GFDL models predicted a slight 

increase at both reservoirs. The authors used the temperature and precipitation simulations in 

conjunction with a hydrological model to estimate the changes in future rainfall runoff to the two 

reservoirs. For Lake Angat, the predicted changes in runoff were -12 percent, 5 percent, and 32 

percent for the CCCM, UKMO, and GFDL models, respectively. Predicted changes at Lake 

Lanao were -2 percent, -12 percent, and 7 percent for the CCCM, UKMO, and GFDL models, 

respectively. As might be expected, projected changes in runoff roughly correlated with 

projected changes in precipitation. The variations in the model projections underscore the 

difficulty GCMs have in simulating precipitation changes, particularly on a local scale. Any 

areas that see an increase in runoff could experience an increase in flooding, while decreased 

runoff could lead to water shortages. These results are comparable to an earlier study by the 

same authors that focused only on the effect of climate change on water resources at Lake 

Angat.xli 

A study published as part of the Assessments of Impacts and Adaptations to Climate Changexlii

used the HadCM3, CCC, and CSIRO GCMs with downscaling to determine the effect of climate 

change in 2020, 2050, and 2080 for the Pantabangan-Carranglan Watershed (PCW) on the island 

of Luzon in the Philippines. Model results showed that by 2020, mean daily precipitation is 

expected to increase by an average of 6.7 percent and mean daily maximum temperature is 

expected to increase by an average of 1.6 percent, compared to observed daily values for the 

period 1960-1990. These changes are expected to result in an increase in stream flow during the 

rainy season and a decrease during the dry season. The authors concluded that there will be a 

higher likelihood of floods during the rainy season and water shortages during the dry season in 

the PCW Watershed associated with future climate change. 

Projections of Future Changes in Sea Level 

As the global ocean warms, its volume will increase, and as a result, sea level will increase. Net 

global sea level rose by approximately 17 cm in the 20th century.xliii The rate of sea level rise 

varies regionally, however, principally due to local variations in the balance between the density 

and circulation of the oceans. Changes in local ocean momentum flux are also important in the 

tropical Pacific.xliv Patterns of coupled ocean-atmosphere variability, such as ENSO, also 

influence local sea level rise. Recent analysis of tide gauge records and data from the 

TOPEX/Poseidon satellite altimeter indicate that changes in sea level have varied across the 

tropical Pacific for the period 1950-2001.xlv See J.A. Church, N.J. White, and J.R. Hunter, "Sea 

level rise at tropical Pacific and Indian Ocean islands," Global Planet. Change, 53, no. 3 (2006): 

155–168 for trends in sea level for 1993-2001 using a blend of the tide gauge and satellite data. 

The largest recent increases in sea level in Southeast Asia have been observed near Indonesia 
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and the Philippines, while only moderate changes have been observed along the coasts of 

Thailand, Cambodia, and Vietnam. These recent rates of change in sea level are characteristic of 

natural interannual climate variability and are consistent with the trend toward more frequent and 

intense ENSO events that have been observed in the past 20 years. 

The IPCCxlvi has estimated regional future changes in sea level due to ocean density and 

circulation changes for the 21st century using the ensemble mean of a subset of 14 models from 

the MMD. Results for the projected sea level change in Southeast Asia for the period 2080-2099 

relative to 1980-1999 for the A1B emissions scenario are shown in Figure 6. The regional sea 

level projections are given in relation to the average global sea level increase of 35 cm expected 

over the same period by IPCC for the A1B emissions scenario. Thus, Figure 6 shows that most 

of the seas around Southeast Asia are expected to rise approximately 0-5 cm above the global 

average value by the end of the 21st century, for a net increase of 35-40 cm. A few areas, such as 

the Southern Philippines and parts of Indonesia, are expected to observe decreases in sea level of 

0-5 cm below the global average value, for a net increase of 30-35 cm. Any increase in sea level 

represents a significant change for Southeast Asia, which is comprised of low-lying coastal and 

island nations that will be severely impacted by rising ocean waters. 

This map is UNCLASSIFIED 

Figure 6. Projected local sea level changes in meters during the 21st century relative to the global average. Sea level 

change is calculated as the difference between averages for 2080-2099 and 1980-1999 from the ensemble mean of 

16 GCMs using the A1B emissions scenario. The local seal level changes shown are above/below the global 

average sea level rise of 35 cm expected in the 21st century under the A1B emissions scenario. Regional sea level 

changes are attributed to changes in ocean density and circulation. Source: Intergovernmental Panel on Climate 

Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. Solomon, D. Qin, M. Manning, M. 

Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: Cambridge University Press, 2007), 

http://www.ipcc.ch/ipccreports/ar4-wg1.htm. 

Projections of Future Changes in River Delta Flooding and Salinity Intrusion 

Sea level rise resulting from climate change will affect flooding and salinity intrusion of major 

rivers and deltas across Southeast Asia. Several researchers have examined these effects on the 

Mekong River Delta in southern Vietnam. The health of the Mekong Delta is important because 

it supports agriculture, particularly rice production, in Vietnam and Cambodia. Since 1997, 50 
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27 

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percent of rice production in Vietnam has been supported by the Mekong Delta.xlvii Flooding of 

the Mekong Delta can be devastating on rice crops, while salinity intrusion reduces the amount 

of fresh water available for crop irrigation. 

Khang et alxlviii studied the effect of sea level rise on salinity intrusion in the Mekong River Delta 

during the dry season (December to June) using a coupled hydro-dynamic and advection 

dispersion model. Salinity intrusion during the dry season is a problem because fresh water from 

the Delta permits a dry season rice crop via irrigation, and this contribution is essential to 

Vietnam’s rice production and export. The authors ran the model under conditions of the B2 

emissions scenario, specifically considering the effects of a 20 cm increase in sea level and a 15 

percent decrease in Mekong River flow by the mid-2030s. The authors measured salinity 

intrusion by identifying the extent of water with a salinity concentration of 2.5 g/L, which is the 

threshold value for reduction in rice yield by approximately 25 percent. Results from the model 

showed that by the mid-2030s, the 2.5 g/L saline front is likely to shift upstream by 10 km in the 

main Mekong River and by 20 km in the rice paddy fields during the dry season. Consequently, 

due to the associated loss in fresh water from the river for irrigation, the total reduction in land 

area for dry season rice cropping was estimated at approximately 71,000 ha. 

Wassmann et alxlix investigated the effect of sea level rise on flooding of the Mekong Delta 

during the flood season (August to November) using a hydraulic model. Similar to the work of 

Khang et al,l

 the authors ran the model under a scenario of 20 cm increase in sea level by 2030. 

Results showed that in October, at the peak of the flood season, high water discharge into the 

Mekong River is expected to increase water levels in the Mekong Delta by 11.9 cm, leading to 

substantial aggravation of annual flooding in the Delta region. The authors concluded that 

increases in sea level due to climate change will cause excessive flooding in tidally inundated 

areas of the Mekong Delta and longer periods of flooding in the central portion of the Mekong 

Delta, both of which will negatively impact rice production. 

Impacts of Climate Change on Human and Natural Systems 

Southeast Asia is susceptible to climate change for a variety of reasons. It has major populations 

concentrated along low-lying coastal areas, including the poor who subsist as smallholder 

farmers and fishermen,li and it contains fragile and unique ecosystems. The economy of much of 

the region is dependent on vulnerable industries, such as agriculture, aquaculture, fishing, and 

tourism. In addition, many nations in Southeast Asia are undergoing rapid economic 

development, which will likely exacerbate the effects of climate change on human and natural 

systems. 

Although the precise extent and magnitude of future climate change in Southeast Asia is 

uncertain, it has already begun to impact the following human and natural systems: 

• Sea level 

• River deltas 

• Natural disasters 

• Water resources 

• Agriculture 

• Forests and biodiversity 
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• Coastal ecosystems 

• Coral reefs 

• Diseases and human health 

• Electricity demand in urban areas 

• Human livelihoods and infrastructure 

Nearly all of these systems are interrelated. For example, rising sea level causes saltwater 

intrusion into river deltas, which limits available fresh water and in turn impacts agricultural 

irrigation, drinking water availability, and population patterns. Climate change impacts on 

natural and human systems also have socioeconomic consequences on both micro and macro 

scales, which will affect the economic vitality of individual countries and the region as a whole. 

Impacts of Sea Level Rise 

As documented in the Model Projections section, sea level rose in the Southeast Asia region at 

rates of up to 3 cm per year for the period 1993-2001,lii and sea level is projected to rise up to 40 

cm in the 21st century.liii Increases in sea level associated with climate change are particularly 

problematic for Southeast Asia, which is comprised of low-lying coastal and island nations. In 

fact, approximately 20 percent of the world’s population of low-lying coastal regions is in 

Southeast Asia.liv The effects of rising sea level on island nations, including the Philippines, 

Malaysia, Singapore, and Indonesia, whose borders are mostly or entirely coastline, will be most 

pronounced.lv Although limited studies exist, recent estimates indicate that Indonesia could lose 

2,000 small islands to sea level rise by 2030.lvi The primary impacts of sea level rise are 

saltwater intrusion into estuaries and aquifers, coastal erosion, displacement of wetlands and 

lowlands, degradation of coastal agricultural areas, and increased susceptibility to coastal storms. 

Climate change-induced sea level rise in Southeast Asia will likely have significant economic 

effects as well. A recent study estimates that rising ocean waters could cause the loss of 40,000 

km2 of land in Vietnam, particularly rice paddies, and necessitate re-engineering of port facilities 

and transportation systems. Taken together, these impacts could result in economic losses of up 

to 80 percent of the yearly Vietnamese GDP.lvii In addition, coastal inundation of Indonesian 

cities associated with potential sea level rise in the 21st century is estimated to cause total 

economic losses of 1.8-2.3 billion.lviii Sea level rise is expected to inundate 38 km2

 of the total 

land area of Jakarta, Indonesia, by 2030, resulting in economic losses of US$1 billion.lix 

Impacts on River Deltas 

Increases in sea level due to climate change will impact river deltas in a variety of ways, 

particularly by increasing flooding, coastal erosion, and salinity intrusion. The Red River and 

Mekong River Deltas in Vietnam appear to be particularly vulnerable to the impacts of climate 

change. Saltwater intrusion into the Red River Delta is already occurring, and as outlined in the 

Model Projections section, saltwater intrusion is expected to move farther inland as a result of 

climate change. Saltwater intrusion is a serious problem for agricultural regions, particularly rice 

paddies, since fresh water from river deltas is a primary source of irrigation water in Vietnam. 

Saltwater intrusion also damages coastal ecosystems and modifies fish and wildlife habitats.lx 

A recent studylxi of the impacts of sea level change on global river deltas found that by 2050 

there will be serious challenges to human occupancy of delta regions in Vietnam and Indonesia. 

Table 2 shows that almost 2 million people will be at risk from the impacts of sea level rise on 
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the Mekong River Delta in Vietnam, which will constitute approximately 6.5 percent of the total 

population dependent on the delta. Furthermore, the Vietnam coast is an area of active economic 

development that is experiencing a rapid increase in population density, which will exacerbate 

future effects of climate change on the health of the Mekong and Red River Deltas.lxii 

River Delta Country Population at 

Risk 

% of Delta 

Population at Risk

% Delta Area 

Potentially Lost 

Chao Phraya Thailand 12,300 0.01 0.34 

Mahakam Indonesia 64,800 7.06 6.29 

Mekong Vietnam 1,910,000 6.51 5.82 

Red (Hong) Vietnam 70,500 0.85 0.95 

This table is UNCLASSIFIED 

Table 2. Impacts associated with climate change on Southeast Asian river deltas in 2050. Source: Jason P. Ericson, 

Charles J. Vorosmarty, S. Lawrence Dingman, Larry G. Ward, and Michel Meybeck, "Effective sea-level rise and 

deltas: Causes of change and human dimension implications." Global and Planetary Change 50 (2006): 63-82. 

Impacts of Natural Disasters 

Natural disasters, such as floods, droughts, and typhoons, can have far-reaching impacts on a 

variety of sectors in Southeast Asia, including agriculture, water resources, coastal ecosystems, 

and infrastructure. The Philippines appears to be particularly susceptible to impacts associated 

with climate change-induced increases in natural disasters. The nation experiences a wide range 

of climate-related hazards, including tropical cyclones, floods, droughts, and landslides.lxiii As a 

result, flood-prone settlements, agricultural lands susceptible to drought, highly erodible and 

unstable areas on steep slopes, and grasslands and forests near settlements that are susceptible to 

fire have been identified as regions in the Philippines that are at risk from climate-change 

enhanced natural disasters.lxiv 

Incidents of flooding have grown worse recently in the Philippine capital of Manila. Flooding in 

Manila is caused by a combination of climate-change induced sea level rise (1 to 3 mm per year), 

land subsidence (3 to >10 mm per year) associated with over-extraction of groundwater, and 

natural deltaic subsidence. Currently, even moderate rains can cause flooding in the Manila area, 

and some locations are left inundated for weeks or months at a time.lxv 

Impacts on Water Resources 

Climate change is expected to impact the supply and quality of fresh water in Southeast Asia, 

which in turn will have far-reaching impacts on agriculture, coastal ecosystems, and population 

growth. Heavy seasonal monsoon rainfall in Southeast Asia provides abundant fresh water, but 

increases in agricultural use, inadequate planning and misuse, and deforestation have led to water 

shortages in recent years, particularly in dry seasons.lxvi Climate change will likely exacerbate 

these effects. Recent observed increases in temperature have already contributed to increased 

evapotranspiration in water bodies, which results in decreased fresh water availability. 

Much of Southeast Asia’s water supply and water quality are sensitive to small changes in the 

frequency and distribution of precipitation. Recent changes in precipitation patterns have 

already been linked with increases in runoff, erosion, flooding, and associated impacts on surface 

water and groundwater in Southeast Asia.lxvii Climate change-induced variations in precipitation 

patterns, such as delays in the onset of the monsoon or a shift toward heavier, less frequent 

precipitation events, are therefore expected to have significant impacts on water resources in the 
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region.xxxiv Falling water levels in many constructed reservoirs, associated with changes in 

precipitation patterns and droughts in ENSO years, have already led to decreased water 

availability and decreased hydroelectric power production.lxviii 

Fresh water resources on island nations of Southeast Asia are especially vulnerable to any 

variability in precipitation because many island populations rely on rainwater collection for their 

supply of fresh water. Droughts that are associated with periodic ENSO events disrupt fresh 

water supplies, and any increase in frequency or intensity of droughts associated with future 

climate change could have severe impacts on water resources.lxix Watersheds on the island of 

Java are more vulnerable to the effects of climate change than other islands in Indonesia.lxx 

Although not an island, Singapore is also vulnerable to variability in precipitation patterns, 

because its water supply is based upon reservoirs which are significantly impacted by short-term 

decreases in precipitation.lxxi 

Impacts on Agriculture 

Agriculture is a major component of the economy in many nations of Southeast Asia. Recent 

increases in population and incomes have changed demands for agricultural products, which has 

led to increases in production of domestic consumption crops, such as grains and animal feed, 

and industrial crops, such as palm oil and natural rubber. These changes have been accompanied 

by the conversion of huge areas of land for agricultural purposes. 

Future climate change will have a significant impact on agriculture in the region, particularly rice 

and corn production. Rice is the most important food crop across Southeast Asia and accounts 

for more than 80 percent of total grain production in nearly every country in the region;lxxii corn 

is also grown in some key locations, such as the Philippines. Rice and corn crops are very 

susceptible to climate variability, particularly due to flooding, tropical cyclones, El Niño-induced 

droughts, and the salinization of coastal waters.lxxiii For example, for the period 1968-1990, 48 

percent of the losses in rice and corn production in the Philippines were caused by tropical 

cyclones and floods, 33 percent were caused by drought, and 18 percent were caused by weatherrelated incidents of pests and diseases.lxxiv 

In theory, temperature increases associated with climate change should cause a northward 

expansion of growing areas and a lengthening of the growing season. Rising atmospheric CO2

levels are expected to stimulate plant photosynthesis, which would result in higher crop yields. 

However, increased heat stress on plants in a warmer climate may negate the effects of higher 

CO2 levels, since plants are very sensitive to air temperature. Many crops in the tropics are 

already grown in conditions near the maximum temperatures they can endure, and even small 

changes in temperature will decrease their productivity.lxxv For example, Peng et allxxvi found 

that during the period 1979-2003, a 1°C increase in growing season minimum temperature 

during the dry cropping season (January – April) decreased rice yield by 10 percent at Laguna, 

Philippines. Higher minimum temperatures may benefit some crops, however, particularly those 

in upland regions and higher latitudes. Any increases in coastal or river delta flooding and 

salinity intrusion due to climate change will likely reduce future crop yields. 

In an attempt to quantify the effects of climate change on crop yield in Southeast Asia, several 

researchers have used GCM climate simulations in conjunction with crop models. Matthews et 

allxxvii investigated the possible changes on rice production in Asia caused by increases in 

atmospheric CO2 and temperature due to climate change using the GFDL, GISS, and UKMO 

GCMs. The models were run using 300 ppm (GFDL, GISS) and 323 ppm (UKMO) of CO2 as a 
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baseline concentration (1×CO2), and future climate change was simulated as 2×CO2. Changes in 

rice crop production were estimated using two rice crop simulation models in conjunction with 

the GCMs. The crop models were calibrated for the indica rice ecotype that is prevalent in 

Indonesia, Malaysia, the Philippines, and Thailand. Overall, results indicated that rice yields 

across Asia are directly correlated with atmospheric CO2 concentrations but indirectly correlated 

with temperature. In Southeast Asia, the models predicted clear increases in total rice production 

of up to 25 percent for Indonesia and Malaysia, compared to 1993 production levels. Model 

predictions were split for the Philippines and Thailand, however, with a modest increase in rice 

production of approximately 6-14 percent predicted by the GFDL model and a decrease in rice 

production of approximately 1-12 percent predicted by the GISS and UKMO models. These 

results suggest that the enhancing effect of increasing atmospheric CO2 concentrations on rice 

growth is likely to be more important in Indonesia and Malaysia, while the negative effect of 

increasing temperatures on rice growth is likely to dominate in the Philippines and Thailand. 

Buan et allxxviii studied the potential effects of climate change on rice and corn crops in the 

Philippines using the CCCM, GFDL, GISS, and UKMO GCMs in conjunction with rice and corn 

crop models. The GCMs simulated changes in total rainfall, solar radiation, and maximum and 

minimum temperature for the first and second cropping seasons at six locations in the Philippines 

based on 2×CO2. The six sites used in the study represented the major rice and corn growing 

areas in the Philippines, with a range in elevation of 22-302 meters above sea level. The GCMs 

predicted an increase in temperature at all six sites for both cropping seasons, but estimations of 

rainfall and solar radiation varied by model, location, and cropping season. Model results 

projected consistent decreases in corn yield of approximately 11-15 percent at all locations for 

both cropping seasons, but the predictions of changes in rice production were very inconsistent, 

with increases at some locations and decreases at others. 

Amien et allxxix studied the effects of climate change on rice yields in Java, Indonesia, through 

2050 using the GISS GCM in conjunction with a rice crop growth simulation model. Rice is a 

staple food in Indonesia, and approximately 60 percent of rice grown in the country comes from 

the fertile volcanic ash soils on Java.lxxx Analysis focused on Ngawi in East Java, an inland 

location on the flood plain of the Bengawan Solo River, and Sukamandi in West Java, a lowland 

coastal location. These sites are two principal rice production areas in Indonesia. Climate 

change was represented in the GCM by 2×CO2 equal to 555 ppm. The GISS simulations predict 

that in the 2030s decade, monthly rainfall at Sukamandi and Ngawi will increase by 10.3 percent 

and 6.2 percent, respectively; maximum temperature will increase by 7.2 percent and 6.9 percent, 

respectively; and solar radiation will increase by 1.1 percent at both locations. Based on these 

projected changes in meteorological conditions, rice yield at Sukamandi is expected to decrease 

by 18 percent in normal years and remain unchanged in El Niño years; at Ngawi, rice yield is 

expected to decrease by 17 percent and 11 percent in normal years and El Niño years, 

respectively. 

In a follow-up study, Amien et allxxxi continued their analysis of rice yields in Java, Indonesia, 

using the GFDL and UKMO GCMs in addition to the GISS GCM. Analysis shifted to Mojosari 

in East Java, an inland site on the flood plain of the Brantas River, and Pusakanegara in West 

Java, a lowland coastal site. As in their previous study, climate change was represented in the 

GCMs by 2×CO2 equal to 555 ppm. Climate projections from the GCMs were incorporated into 

a crop growth simulation model to determine the effects on crop yield. Rice yield results were 

similar to the earlier study, with reductions in yield predicted in both East and West Java due to 
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32 

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climate change, although higher yield reductions were projected due to natural interannual 

climate variability, such as that associated with ENSO. The authors concluded that adoption of 

heat-tolerant rice varieties by farmers in Indonesia could compensate for expected yield losses 

due to climate change. 

Most recently, Naylor et allxxxii investigated the potential effect of climate change on rice 

agriculture in Indonesia through 2050 using a suite of GCMs and a risk assessment model. 

Analysis focused on the regions of West/Central Java and East Java/Bali, which account for 

approximately 55 percent of Indonesia’s annual rice production. The authors examined the 

impact of climate change on natural climate variability, particularly ENSO. El Niño events 

typically cause a delay of up to 2 months in the onset of the monsoon in Indonesia, which delays 

planting of the rice crop. As a result, a 30-day delay in monsoon onset was selected as a critical 

threshold in the study. 

Analysis of observed data for 1983-2004 showed that the probability of a 30-day delay in 

monsoon onset was 18.2 percent for West/Central Java and 9.1 percent for East Java/Bali, which 

corresponded to 11 percent and 6.5 percent reductions in rice production, respectively. The 

authors used a set of 20 empirically downscaled CCMs from the IPCC MMD to simulate future 

changes in precipitation under the A2 and B2 emissions scenarios through 2050. Results showed 

that, in most cases, a delay in monsoon onset is likely to occur more frequently in 2050 than it 

has for the past 30 years. For example, under the A2 scenario, the probability of a 30-day delay 

in monsoon onset in 2050 is expected to be 23-33 percent for West/Central Java and 19.8-40 

percent for East Java/Bali. Model projections also suggested that there will be about a 10 

percent increase in precipitation later in the crop year (April – June) and a large decrease of up to 

75 percent in precipitation later in the dry season (July – September), which could have serious 

negative consequences for rice agriculture in Indonesia. 

As these modeling studies demonstrate, crops in Southeast Asia are very susceptible to drought, 

flooding, and storms associated with natural interannual climate variability caused by ENSO. In 

recent years, delayed onset of the rainy season, by up to a month in ENSO years, has been 

observed to cut rice yields by up to 11 percent in parts of Indonesia.lxxxiii Potential increases in 

the frequency and severity of climate extremes under future climate change are expected to cause 

greater losses, which will be exacerbated if extremes occur during critical stages of crop growth. 

In order to address these issues, Lansigan et allxxxiv investigated the potential impacts of climate 

extremes on agriculture by comparing crop-sowing yields for El Niño and non-El Niño years in 

the Philippines. Results indicated that during El Niño years, the variability in weather patterns 

moved the sowing date for rice (normally near Julian day 173) to as early as Julian day 137 (midMay) or as late as Julian date 229 (mid-August), representing a major change in the cycle of 

planting and harvest. Results also showed that the variability of precipitation in El Niño years 

reduced crop yield. Typically, an El Niño year was marked by a shorter, more intense wet 

season, which decreased crop growing time, subjected crops to stress from excess water, and 

consequently caused a crop loss of 52-81 percent. High temperatures also significantly 

decreased rice production, particularly if they occurred during early phases of development. 

Crop water requirements were significantly altered under El Niño regimes, because changes in 

temperature and precipitation altered rates of plant evapotranspiration. In the Philippines, results 

showed that El Niño was associated with a 20-50 percent reduction in rainfall, requiring 

increased crop irrigation. These results are consistent with the modeling simulations of Amien et 

allxxxv that projected similar changes on rice yields in Java, Indonesia due to ENSO-related 
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33 

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climate variability. Thus, it is likely that natural climate variability will continue to cause 

significant impacts on Southeast Asian agriculture in coming decades, and any increases in the 

frequency or severity of climate variability due to climate change will serve to aggravate these 

natural effects. 

On a more local scale, a recent studylxxxvi found that water availability is the key parameter 

affecting mangosteen growth in Thailand. Higher mangosteen yields are associated with 

increased water available to plants during fruiting, which suggests that impacts on precipitation 

associated with future climate change will be important for the mangosteen industry. 

Increases in temperature have also been related to increased outbreaks of agricultural pests and 

diseases, such as rice stem borers and twisting disease. Climate change is expected to exacerbate 

these outbreaks and necessitate the more frequent use of stronger pesticides to protect crops.lxxxvii 

Impacts on Forests and Biodiversity 

In 2005, the biologically diverse forests of Southeast Asia represented 5.1 percent of the total 

forest area in the world, and they were a major source of global forest products, accounting for 

50 percent of total forestry exports from Asia and the Pacific.lxxxviii Degradation and 

unsustainable practices, such as illegal logging and conversion of native forests to palm oil 

plantations or agricultural lands, have made many Southeast Asian forests particularly vulnerable 

to climate change. Burning and clearing of forests has increased the likelihood that they will not 

be able to adapt to projected changes in temperature and precipitation. The combination of 

human stresses and climate change is expected to increase the incidence of forest fires and make 

recovery from fires more difficult.lxxxix In addition, habitat destruction in Asian boundary forests 

is increasing. Individual plant and animal species are often located in narrow bands of suitable 

environmental conditions, and their ability to tolerate new conditions associated with climate 

change will determine their future survival.xc 

Teak wood is an important economic product in Indonesia and the Philippines, and the teak tree 

is known to be especially sensitive to increases in temperature and changes in precipitation. 

Such dependence of individual species on existing climate conditions is a major threat to forest 

health, as flora, fauna, and pests are expected to adapt at different rates, and with varying levels 

of success.xci Overall, climate change is expected to transform the types and species of forest 

vegetation in Indonesia.xcii 

Although variations in temperature and precipitation due to future climate change will impact 

forests across Southeast Asia, only a few limited studies are available that project the potential 

effects on specific species and regions. Boonpragob and Santisirisomboonxciii investigated the 

potential changes in forest type and distribution in Thailand due to climate change using the 

UK89, UKMO, and GISS GCMs. The models were run using 320 ppm of CO2 as a baseline 

concentration (1×CO2), and future climate change was simulated as 2×CO2. Model projections 

indicated that increases in temperature and variations in precipitation in Thailand due to climate 

change will reduce areas of subtropical forest and subtropical wet forest, and increase the areas 

of tropical dry forest, tropical moist forest, and tropical wet forest. Overall, the model 

simulations suggested that climate change will likely cause the expansion of tropical dry forest 

into subtropical moist forest in the northern part of the country, and the cause replacement of 

subtropical forests with tropical forests in the southern part of the country. 

Koskelaxciv examined the effects of predicted climate change on the growth rate of Merkus pine 

trees in Thailand. Merkus pines grow across Southeast Asia at elevations less than 1200 meters, 
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34 

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including parts of Thailand, Laos, Cambodia, Vietnam, and the Philippines. Koskela used a gasexchange model to estimate changes in trees’ gross photosynthesis, transpiration, and stomatal 

conductance, and a carbon- and nitrogen-balance model to simulate the growth of young trees. 

The models were run under several climate change scenarios involving variations in temperature, 

precipitation, and atmospheric CO2. The models were used to simulate Merkus pine growth rate 

changes at two representative locations in Thailand: Ban Wat Chan, a high-altitude site in the 

northern highlands and Surin, a low-altitude site on the northeastern Khorat Plateau. Results 

showed that rising temperature and atmospheric CO2 levels associated with climate change are 

expected to increase annual photosynthesis and transpiration rates in pine seedlings, and cause 

concurrent large increases in total tree biomass and height. These results suggest that future 

climate change could enhance growth of Merkus pine trees and shorten the seedling stage. The 

author noted that actual tree growth in the future will be dependent on rainfall variability, since 

prolonged periods of drought could offset any enhancement in tree growth due to higher 

atmospheric CO2 levels. 

Booth et alxcv assessed the possible effects of climate change through 2050 on two tree species in 

Vietnam using a climatic mapping program model, a simple plant growth simulation model, and 

a process-based tree growth simulation model. Each model was run under a range of climate 

change scenarios involving variations in temperature, precipitation, evapotranspiration, and 

atmospheric CO2. The authors analyzed two tree species that they considered to be 

representative of those important for forestry in Vietnam: Styrax tonkinensis, a native species 

used for pulp and perfume ingredients, and Acacia mangium, an introduced species used for pulp 

and fuel. Results from the climatic mapping program indicated that areas in Vietnam which are 

climatically suitable for growing S. tonkinensis will likely decrease as a result of future climate 

change. The simulation models projected that increases of 1-2°C in mean annual temperature in 

the vicinity of Hanoi will decrease S. tonkinensis growth rate by approximately 5-22 percent, 

since the species is not well adapted to warmer growing conditions. The authors noted that it 

was not possible to identify which of the four variables modeled in their study—temperature, 

precipitation, evapotranspiration, or atmospheric CO2—had the most dominant effect on plant 

growth under future climate change conditions. They concluded that climate change is likely to 

cause significant alterations in the Vietnamese forestry industry, and these effects will be related 

to shifts in suitable growing regions rather than variations in tree yield. 

Changes in forestry and vegetation due to climate change will likely impact species biodiversity 

in Southeast Asia. To investigate the possible future changes in global biodiversity, Malcolm et 

alxcvi calculated changes in habitat areas and associated extinctions of endemic plant and 

vertebrate species in 25 biodiversity "hotspots," including the Philippines. The authors used a 

suite of 14 GCMs run under conditions of 2×CO2 in conjunction with two global vegetation 

models. Biodiversity hotspots were defined as regions that are home to a large number of the 

world’s species per unit land area and have suffered significant natural habitat loss. Biomes 

(vegetation types) were used as proxies for natural habitats in the study. Modeled habitat loss for 

the Philippines was 3-32 percent and associated mean required migration rate was 59-736 meters 

per year, depending on the vegetation model and biome type. Although these changes in habitat 

area and migration rate are from one limited study for a specific area in Southeast Asia, the 

results suggest that climate change likely poses a threat to biodiversity in the region. 

Impacts on Coastal Ecosystems 

Rapid development of coastal megacities has led to highly concentrated population surges and 
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economic activity in recent years, with roughly 80 percent of the population of Southeast Asia 

living within 100 km of the coasts in 2005. Coastal regions are major contributors to the 

regional economy because they are central to tourism, fishing, and aquaculture.xcvii 

Mangroves are a key component of Southeast Asian coastal ecosystems that will be affected by 

future climate change. Mangroves are critical to the region because they provide habitats for 

fish, help maintain coastal water quality, protect shorelines from erosion and storm surges, and 

provide products and services for human communities. Because of their importance, mangroves 

are very valuable. For example, the value of Malaysian mangroves for storm protection and 

flood control is estimated at US$300,000 per kilometer of coastline, based on the cost of 

replacing the mangroves with rock walls.xcviii 

There are several climate change-related effects that are expected to impact mangroves, both 

positively and negatively. Sea level rise will impact mangroves to some degree, depending on 

the rate of mangroves’ ability to migrate landward to maintain their preferred hydroperiod. 

Increases in intensity and frequency of storms related to climate change may intensify damage to 

mangrove forests through defoliation, stress, and soil impacts. Increased precipitation in tropical 

areas due to climate change may increase mangrove range and biodiversity. In addition, 

mangroves are likely to experience increased growth rates in response to increased atmospheric 

CO2 levels.xcix 

On Olango Island in the Philippines, climate change is expected to cause destruction of 

mangroves, deaths of coral reefs, and loss of feeding grounds for migratory birds.c Other 

ecosystem impacts in the Philippines associated with future climate change include coral 

bleaching, seaweed and sea grass impacts, shoreline erosion, and aggravation of marine diseases. 

Ecosystem effects will be driven by changes in ocean circulation, marine biogeochemistry, and 

increasing sea surface temperatures.ci 

Along the Vietnam coast, climate change is expected to result in loss of habitats for many rare 

and endemic species; destruction of coral reefs, mangroves, and sea grass beds; and a resulting 

decrease in living resources for residents.cii 

Increases in temperature are also projected to negatively impact fishing and aquaculture, with 

decreased abundance of large predator fish and significant decreases in the viability of fish larvae 

in warmer waters.ciii 

Impacts on Coral Reefs 

Sustainable fisheries and coral reef systems are critical to the livelihoods of millions of people 

across Southeast Asia. The region has more coral reefs than any other part of the world, with a 

total of over 100,000 km,2

 equivalent to 34 percent of the world’s total. The reefs have 

substantial economic importance—the value of the region’s sustainable coral reef fisheries alone 

is estimated at US$2.4 billion per year. Coral reefs are also important for food security, 

employment, tourism, pharmaceutical research, and shoreline protection.civ 

Increases in atmospheric CO2 concentration associated with climate change affect ocean water 

chemistry and have a direct impact on the growth of corals. Silverman et alcv studied these 

effects using a coupled climate/carbon cycle model. Coral reefs grow by precipitating calcium 

carbonate (CaCO3) from ocean water, but the chemistry of the oceans is being changed by rising 

atmospheric CO2 levels that are causing the oceans to become more acidic. Results from the 

model study show that most coral reefs are currently precipitating 20-40 percent less CaCO3
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compared to pre-industrial times, and reefs located in the warm water pool of the Western Pacific 

show the strongest response to changes in water chemistry and temperature associated with 

climate change. When atmospheric CO2 concentrations reach 560 ppm, which represents a 

doubling of CO2 levels since pre-industrial times, all coral reefs are expected to stop growing and 

begin to dissolve. Present atmospheric CO2 concentrations are approximately 380 ppm, and if 

emissions continue at the current rate, atmospheric levels will reach 560 ppm by the mid-22nd

century.cvi The potential widespread reduction in the number and extent of coral communities 

due to the climate change-induced decline in coral skeleton growth rates will have devastating 

impacts on coastal marine ecosystems and the various industries that are dependent on coral 

reefs. 

Increasing sea surface temperatures associated with climate change are also a significant threat to 

the health of coral reefs in Southeast Asia. Elevated sea surface temperatures cause coral 

bleaching, which is the loss of color due to stress-induced expulsion of algae or loss of 

pigmentation of algae within the corals. The 1997-1998 ENSO caused the largest worldwide 

coral bleaching event ever recorded, damaging or destroying an estimated 18 percent of 

Southeast Asia’s coral reefs. An increase in ocean water temperatures of only 1-2°C above the 

normal threshold temperature for a few weeks can cause a bleaching event, and extreme or 

prolonged temperature anomalies, such as those expected from future climate change, can cause 

significant coral mortality.cvii Increased temperatures in recent years have significantly increased 

coral reef bleaching in the region, notably in the vast and diverse reefs of Indonesia, the 

Philippines, and Thailand.cviii 

Mass bleaching and mortality of corals has been widespread since the 1980s, but the magnitude 

varies significantly and is correlated with "hot spots" of sea surface temperature. Corals in 

accelerated warming regions will likely be impacted sooner than predicted by global climate 

change models. Impacts will be less severe and occur more slowly for corals in waters that are 

anomalously cooler than the global average, such as those around Indonesia.cix 

The reefs of Southeast Asia, particularly near Indonesia and the Philippines, are already 

degraded by pollution, sediment-laden runoff, destructive fishing practices, and other human 

impacts;cx all of these effects will be compounded by climate change. Southeast Asian reefs do 

have several characteristics that make them more favorable for recovery compared to other ocean 

reefs, however, such as high levels of biodiversity, large-scale through-flow of Pacific Ocean 

water, and nearby reef systems to aid in reproduction. Impacts on humans as a result of coral 

damage and destruction due to climate change include lost or reduced tourism and fishery 

activities, and more difficult to quantify aspects such as shoreline damage.cxi 

Guinotte et alcxii studied "marginal" coral reefs in the Pacific basin, which are those reefs 

considered to be living at the extremes of tolerable environmental limits. Results showed that 

corals in Southeast Asia that are most likely to become marginal due to increasing sea surface 

temperatures by 2069 are those in the Philippines, Gulf of Thailand, and Andaman Sea. 

Recent country-specific impacts on coral reefs include the following:cxiii

• Thailand has an estimated 1,800 km2 of coral reefs in the Gulf of Thailand and the Andaman 

Sea. Sea surface temperature increases along Thailand’s coasts have been particularly 

pronounced, such as near Phuket, where the sea surface temperature increased at a rate of 

more than 0.02°C per year between 1981 and 1999. As a result of the warming waters, coral 

reefs along Thailand’s coasts have experienced significant bleaching episodes, especially in 
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the Gulf of Thailand. Existing pressures due to economic development, such as 

sedimentation and wastewater pollution associated with tourism, are expected to compound 

climate change pressures on reefs. Some coral reefs around small islands are currently under 

only minimal threat from existing pressures. 

• The coral reefs of Cambodia and Vietnam are some of the most threatened in Southeast Asia; 

over 95 percent are threatened by existing pressures such as overfishing, land use changes, 

and high population density. 

• There are over 350 species of coral in Malaysia. Over 85 percent of Malaysia’s reefs are 

currently threatened by existing pressures such as shipping lanes, harmful fishing practices, 

and land use changes. 

• Singapore has many fringing and patch reefs around both its main island and small offshore 

islands. Singapore’s total coral reef area is estimated to be about 54 km2 and includes more 

than 197 hard coral species. These reefs are also some of the most threatened in Southeast 

Asia, with over 95 percent threatened by existing pressures, including land reclamation and 

development. 

• Indonesia has approximately 51,000 km2

 of coral reefs, and over 85 percent are currently 

threatened by existing pressures such as overfishing and land use changes. Indonesia’s coral 

reefs provide annual economic benefits estimated at US$1.6 billion per year. The greatest 

diversity of coral reef fish in the world is found in Indonesia, with more than 1,650 species in 

eastern Indonesia. Monitoring shows that the nation’s reef conditions are currently declining. 

The 1997-1998 El Niño killed up to 95 percent of coral around certain Indonesian islands. 

Some areas have rebounded remarkably, possibly due to anomalously cooler waters in the 

area around Indonesia. 

• The Philippines’ coral reefs are severely threatened by existing pressures including 

overfishing, destructive fishing practices, agriculture, and aquaculture. The Philippines’ 

approximately 26,000 km2

 of coral reefs contain extremely diverse species, and they provide 

annual economic benefits estimated at US$1.1 billion per year. The first ever mass-bleaching 

event in the Philippines was reported in 1998-1999; it proceeded nearly clockwise around the 

Philippines and was correlated with anomalously high sea-surface temperatures. 

Impacts on Diseases and Human Health 

Climate-related health risks in Southeast Asia include increases in vector-borne disease, heat 

stress, food stress, and air pollution. Studies suggest that disease outbreaks in the region, 

including malaria, dengue, diarrhea, and cholera, are linked with climate events such as droughts 

and floods, which are in turn strongly related to ENSO events. Changes in temperature and 

precipitation patterns have already been linked to increases in dengue fever and malaria in 

Indonesia, Thailand, and Vietnam.cxiv 

Mosquito populations will be affected by variations in temperature and precipitation associated 

with future climate change, and thus the incidence of mosquito-borne diseases such as dengue 

fever and malaria are likely to be affected as well. Environmental conditions strongly influence 

the growth and survival of mosquitoes. The optimum temperature range for mosquito survival is 

20-25°C, and rainfall is critical for mosquito reproduction because standing water is necessary 

for several of their life stages. Flooding due to excessive precipitation however, can disrupt 

mosquito larvae development.cxv 
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Hopp and Foleycxvi modeled the response of Ae. aegypti, the principal mosquito carrier of dengue 

fever, to observed climate variations for the period 1958-1995 in order to determine how future 

climate change may impact the incidence of worldwide dengue fever cases. They found that 

there was a strong relationship between mosquito larvae density and temperature, precipitation, 

and relative humidity. Results showed that for countries in Southeast Asia, cases of dengue 

fever are strongly dependent on climate-induced variations in mosquito densities. As a result, it 

is likely that increases in temperature and precipitation expected from future climate change in 

Southeast Asia will cause increased incidence of dengue fever across the region, in the absence 

of preventive measures to control the spread of the disease. 

Martens et alcxvii conducted a similar study that investigated the potential impact of global 

climate change on malaria risk. The authors predicted changes in malaria epidemic potential for 

the 21st century by using temperature and precipitation output from the UKMO GCM run under 

two emissions scenarios in conjunction with an integrated linked-system model. Analysis 

focused on the P. vivax and P. falciparum mosquito species. The authors concluded that 

temperature and precipitation are the main climate variables that affect malaria transmission. 

Model results indicated that the incidence of malaria infection is sensitive to future climate 

change in Southeast Asia, and as a result, an increased rate of malaria infection is likely across 

the region in the 21st century. 

Projected increases in flooding due to changes in precipitation patterns and sea level rise are 

expected to increase the risk of water-borne disease such as dermatosis, amoebiasis, cholera, 

giardia, shigellosis, and typhoid. Residents of Southeast Asia already have higher risks of 

mortality and morbidity from water-borne diseases than in many other parts of the world, and 

climate change is expected to exacerbate these risks.cxviii 

Heat is also a public health threat, especially among the elderly and very young. Chronic 

exposure to excessive heat has been linked to increased incidence of cardiovascular and 

respiratory diseases. Researchers have noted that humans may be capable of adapting to heat 

associated with climate change, but the response time cannot be predicted.cxix 

A recent studycxx found that human health impacts from climate change in the Philippines 

include blooms of toxic marine micro-organisms which can lead to dietary constraints and even 

poisoning, increases in heat stroke and vector-borne diseases, and population dislocation. 

Although this study was focused on the Philippines, the impacts are applicable across the 

Southeast Asian region. 

Impacts on Electricity Demand in Urban Areas 

Future increases in temperature and relative humidity associated with climate change will impact 

electricity demand across Southeast Asia, particularly in urban areas where air conditioning is 

more common. In order to determine the effect of climate change on temperature and associated 

electricity demand in Thailand through 2080, Parkpoom and Harrisoncxxi ran the HadCM3 

general circulation model under four integrations that corresponded to the A1, A2, B1, and B2 

emissions scenarios. These four scenarios were selected in order to represent the range of future 

socioeconomic developments in Thailand. Model analysis focused on the Bangkok metropolitan 

area, since 70 percent of Thailand’s electricity demand is concentrated there. An initial linear 

regression analysis found that temperature is the most significant weather variable that affects 

electricity demand in Thailand. The model simulations were run to predict temperature increases 

due to climate change, and that information was fed into an electricity demand sensitivity model 
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in order to estimate the concomitant increases in electricity demand. Results showed that annual 

mean temperature is expected to increase by approximately 0.46-0.67°C in the 2020s (2011-

2040) and 1.1-1.9°C in the 2050s (2041-2070) in Thailand under the four climatology scenarios. 

These temperature increases translate into an expected increase in peak electricity demand of 

1.0-3.1 percent in the 2020s and 2.8-8.5 percent in the 2050s, with the highest demand during the 

summer season. Variations among model output corresponding to the four climatology scenarios 

became more pronounced as time increased. The authors concluded that the potential changes in 

electricity demand predicted by the model runs are significant and likely necessitate substantial 

investment in electrical power plant capacity to meet future demand. Nevertheless, these 

projected increases may only be a small portion of what is expected to occur over the next few 

decades due to economic development. For example, electricity consumption in Malaysia has 

been increasing on the order of 4-8 percent per year since 2001, and this rate is expected to 

continue.cxxii Thus, a 1-8 percent increase in electricity demand in Thailand over the next 60 

years associated with climate change may be only a small fraction of the total increase in demand 

due to general economic development, urbanization, and increases in manufacturing. 

Impacts on Human Livelihoods and Infrastructure 

Livelihoods in many parts of Southeast Asia, particularly in less urban areas, are heavily 

dependent on natural resources. Whether derived from fisheries, coral reefs, forests, agriculture, 

or tourism, residents are sensitive to impacts on these natural resources from climate change.cxxiii 

In the Philippines, climate change will heavily impact coastal-dwelling Filipinos, who are highly 

dependent on coastal resource related livelihoods, such as fishing, seaweed cultivation, 

aquaculture, shell collecting, and tourism.cxxiv Population resettlement options in Manila are 

hindered by residents’ strong cultural connections to lifelong homelands, particularly among 

impoverished populations.cxxv 

Climate-related increases in sea level and ecological perturbations related to extreme weather 

events will compound anthropogenic and climate change-related pressures, such as shortages in 

drinking water on small islands. These pressures could increase human migration to the 

mainland or larger islands.cxxvi 

Coastal erosion and inundation of coastal zones has increased across the region in recent years, 

damaging infrastructure and natural resources. Thailand’s coastline is already observed to be 

eroding at a rate of 15-25 meters per year in some places.cxxvii In Indonesia, adaptation to new 

livelihoods and resettlement as a result of increased coastal inundation associated with sea level 

rise will be particularly difficult for very low-income households with little social safety net and 

other cultural constraints. Impacts from inundation include physical damage to houses, and 

social damage such as interruption of school and commerce. In the city of Makassar, for 

example, the estimated damage to 4,000 houses from sea level rise in 2000-2002 was 

approximately US$11 million. Entire sections of communities in coastal Indonesia are already 

being abandoned because of frequent inundation, often within "unplanned" housing communities 

containing highly impoverished residents. Climate change is expected to exacerbate these 

problems.cxxviii 

Adaptive Capacity 

The impacts of climate change will be felt differentially, depending upon how well a society can 

cope with or adapt to climate change. Adaptive capacity is defined by the IPCCcxxix as "the 

ability of a system to adjust to climate change (including climate variability and extremes), to 
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moderate potential damages, to take advantage of opportunities, or to cope with the 

consequences." Thus, adaptive capacity is distinguished from both climate change impacts and 

the degree to which those impacts affect the systems that are in place, as discussed in the 

previous sections. 

Although the specific determinants (or "drivers") of adaptive capacity are a matter of debate 

among researchers, there is broad agreement that economic, human, and environmental resources 

are essential elements. Some components of this adaptive capacity are near-term, such as the 

ability to deliver aid swiftly to those affected by, for example, floods or droughts. Other 

components include a level of education sufficient for people to change livelihoods, a quantity of 

unmanaged land that can be brought into food production, and institutions that provide 

knowledge and assistance in times of change. For instance, Yohe and Tolcxxx identified eight 

qualitative "determinants of adaptive capacity," many of which are societal in character, although 

the scientists draw on an economic vocabulary and framing: 

1. The range of available technological options for adaptation. 

2. The availability of resources and their distribution across the population. 

3. The structure of critical institutions, the derivative allocation of decision-making 

authority, and the decision criteria that would be employed. 

4. The stock of human capital, including education and personal security. 

5. The stock of social capital, including the definition of property rights. 

6. The system’s access to risk-spreading processes. 

7. The ability of decisionmakers to manage information, the processes by which these 

decisionmakers determine which information is credible, and the credibility of the 

decisionmakers themselves. 

8. The public’s perceived attribution of the source of stress and the significance of exposure 

to its local manifestations. 

The Adaptive Capacity of Southeast Asia in a Global Context 

Researchers have only recently taken on the challenge of assessing adaptive capacity in a 

comparative, quantitative framework. A global comparative studycxxxi of resilience to climate 

change (including adaptive capacity) was conducted using the Vulnerability-Resilience 

Indicators Model (VRIM – see box on page 44). 

Adaptive capacity, as assessed in this study, consists of seven variables (in three sectors), chosen 

to represent societal characteristics important to a country’s ability to cope with and adapt to 

climate change: 
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Human and Civic Resources 

• Dependency ratio: proxy for social and economic resources available for adaptation after 

meeting basic needs. 

• Literacy: proxy for human capital generally, especially the ability to adapt by changing 

employment. 

Economic Capacity 

• GDP (market) per capita: proxy for economic well-being in general, especially access to 

markets, technology, and other resources useful for adaptation. 

• Income equity: proxy for the potential of all people in a country or state to participate in the 

economic benefits available. 

Environmental Capacity 

• Percent of land that is unmanaged: proxy for potential for economic use or increased crop 

productivity and for ecosystem health (e.g., ability of plants and animals to migrate under 

climate change). 

• Sulfur dioxide per unit land area: proxy for air quality and, through sulfur deposition, 

other stresses on ecosystems. 

• Population density: proxy for population pressures on ecosystems (e.g., adequate food 

production for a given population). 

Methodological Description of the Vulnerability-Resilience Indicator Model (VRIM) 

The VRIM is a hierarchical model with four levels. The vulnerability index (level 1) is derived 

from two indicators (level 2): sensitivity (how systems could be negatively affected by climate 

change) and adaptive capacity (the capability of a society to maintain, minimize loss of, or 

maximize gains in welfare). Sensitivity and adaptive capacity, in turn, are composed of sectors 

(level 3). For adaptive capacity these sectors are human resources, economic capacity, and 

environmental capacity. For sensitivity, the sectors are settlement/infrastructure, food security, 

ecosystems, human health, and water resources. Each of these sectors is composed of one to 

three proxies (level 4). The proxies under adaptive capacity are as follows: human resource 

proxies are the dependency ratio and literacy rate; economic capacity proxies are GDP (market) 

per capita and income equity; and environmental capacity proxies are population density, sulfur 

dioxide divided by state area, and percent of unmanaged land. Proxies in the sensitivity sectors 

are water availability, fertilizer use per agricultural land area, percent of managed land, life 

expectancy, birthrate, protein demand, cereal production per agricultural land area, sanitation 

access, access to safe drinking water, and population at risk due to sea level rise. 

Each of the hierarchical level values is comprised of the geometric means of participating values. 

Proxy values are indexed by determining their location within the range of proxy values over all 

countries or states. The final calculation of resilience is the geometric mean of the adaptive 

capacity and sensitivity. 
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The adaptive capacities for a sample of 10 countries from the 160-country study are shown in 

Figure 7 for the base year of 2005. These countries represent a wide range of adaptive capacity; 

of note for the Southeast Asian region, Indonesia ranks 45th (high in the second quartile) and the 

Philippines ranks 91st (high in the third quartile). Any country-level analysis must take into 

account the comparative ranking of the country in the 160-country group. 

Figure 8 shows the contribution of each variable in the model to the overall ranking of adaptive 

capacity. Slight differences occur in the contribution of the variables among the countries due to 

the overall methodology, as described in the box below. Indonesia ranks third because of its 

relatively high rankings in most areas; the exceptions are a very low ranking in GDP per capita 

and a moderately low ranking in non-managed land. The lower overall rank of the Philippines 

stems principally from low rankings in the equity index and dependency ratio. 

Figure 9 shows projected adaptive capacity growth over time for the 10-country sample. 

Projections are made for two scenarios, with rates of growth based on the A1 emissions scenario. 

Both modeled scenarios feature moderate population growth and a tendency toward convergence 

in affluence, featuring market-based solutions, rapid technological progress, and improving 

human welfare. The scenarios used in this study differ in the rate of economic growth: one 

models high-and-fast economic growth and the other models delayed growth. In the delayedgrowth scenario, Indonesia showed strong growth in adaptive capacity and is projected to 

This chart is UNCLASSIFIED 

Figure 7. Adaptive capacity for a sample of 10 countries for the base year of 2005. Source: Based on E.L. Malone 

and A.L. Brenkert, "Vulnerability, sensitivity, and coping/adaptive capacity worldwide," The Distributional Effects 

of Climate Change: Social and Economic Implications, M. Ruth and M. Ibarraran, eds., Elsevier Science, Dordrecht 

(in press). 

Ranking of Adaptive Capacity in Base Year 

0 20 40 60 80 100

Haiti

Morocco

India

Philippines

China

Mexico

Belize

Indonesia

Libya

Russian Federation
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43 

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overtake Libya by 2035. The Philippines showed good growth in adaptive capacity and is 

projected to catch up with Mexico by 2065. In the high-growth scenario, Indonesia ranks in the 

top three countries; however, China is predicted to move past Indonesia by 2020. By 2065, the 

model projects that the Philippines will have moved past Mexico and caught up to Belize. 

Strengths and Weaknesses in Adaptive Capacity Assessments 

Comparative measures of adaptive capacity, such as the model results outlined in the previous 

section, only allow analysts to ask improved, more focused questions about area or local 

conditions that can contribute to or reduce resilience. It is likely, for instance, that important 

variables or domains for particular sub-national areas are not included. For agricultural regions, 

a key domain might be the extent of irrigation; for urban areas, it might be better measures of 

education. In addition, the measure of unmanaged land does not account for the potential 

usefulness of that land. Comparative measures are an important first step toward determining the 

allocation of resources, either for further analysis or to address additional factors. 

Specific Adaptive Capacity Considerations for Southeast Asia 

Climate change is expected to exacerbate the effects of natural disasters, such as floods, 

droughts, and typhoons. Community-based disaster preparedness focuses on building adaptive 

and coping capacities at the local level, such as construction of flood control infrastructure and 

emergency shelters, and providing community training. A community-based disaster 

preparedness pilot project in the Philippines in 1998-1999 demonstrated the promise of this 

approach to overcoming impacts associated with climate change, while also highlighting the 

need to simultaneously increase local capacity and responsibility.cxxxii Expansion of this type of 

disaster preparedness program to other nations in Southeast Asia could help mitigate the 

expected impacts of climate change on the region. 

This chart is UNCLASSIFIED

Figure 8. The contributions of several variables to adaptive capacity rankings. Source: Based on E.L. Malone and 

A.L. Brenkert, "Vulnerability, sensitivity, and coping/adaptive capacity worldwide," The Distributional Effects of 

Climate Change: Social and Economic Implications, M. Ruth and M. Ibarraran, eds., Elsevier Science, Dordrecht 

(in press). 

Aspects of Adaptive Capacity in Base Year

0 200 400 600

Haiti

Morocco

India

Philippines

China

Mexico

Belize

Indonesia

Libya

Russian Federation

GDP per capita

equity index

dependency ratio

literacy levels

non-managed land (%)

emssions/total land

population density
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Many Southeast Asian countries are facing a water crisis due more to poor management than to 

water scarcity. Rapid industrialization and increases in population and wealth have led to 

dramatic increases in demands for water and energy in many Southeast Asian nations. With the 

additional stresses expected from climate change, Asian countries will need to consider new 

methods for water management to ensure sustainability of water resources for human and 

ecological requirements.cxxxiii

• The recent sustained regional economic growth of Southeast Asian countries is energyintensive and has relied heavily on consumption of fossil fuels. International policy 

responses to global climate change in the next several decades may restrict use of fossil 

fuels,cxxxiv which will necessitate that Southeast Asian governments invest in development of 

alternative energy sources, such as solar and wind power. 

• Improvement in the coordination of policy and planning to address adaptive capacity in 

Southeast Asia is vital because climate change will have impacts far beyond the purview of a 

single governmental ministry or organization. To improve adaptive capacity, inter-agency 

and inter-ministry coordination is required and must include integration with national 

disaster risk management.cxxxv

• A holistic approach to building adaptive capacity, which will include the needs of vulnerable 

groups and vulnerable locations, is recommended for Southeast Asia. Part of this holistic 

approach is the consideration of the potential impacts of adaptation measures. For example, 

a dam and reservoir may increase the adaptive capacity of one region, but it may negatively 

influence the adaptive capacity of downstream communities.cxxxvi 

Summary of Possible Adaptive Strategies for Climate Change in Southeast Asia 

The following adaptive strategies were recently summarized in a review report on climate 

change in the Asia/Pacific region.cxxxvii The effectiveness and suitability of these strategies will 

vary for the different countries in Southeast Asia, depending on national priorities and specific 

susceptibilities to the impacts of climate change, but all of these strategies are potentially 

relevant for the region. 

Disasters and Emergency Management 

• Diversify economic activity to reduce reliance upon climate-sensitive sectors 

• Develop emergency management plans for climate hazards 

• Develop early warning systems for extreme weather events (e.g., flood, cyclones, heat waves) 

• Expand availability and use of risk-spreading institutions (e.g., insurance, government 

assistance) 

• Identify critical activities and infrastructure for protection (e.g., health services, energy, 

transport, communication) 
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This chart is UNCLASSIFIED 

Figure 9. Projections of adaptive capacity for 10 countries under a delayed growth scenario and a high growth 

scenario. Source: Based on E.L. Malone and A.L. Brenkert, "Vulnerability, sensitivity, and coping/adaptive 

capacity worldwide," The Distributional Effects of Climate Change: Social and Economic Implications, M. Ruth 

and M. Ibarraran, eds., Elsevier Science, Dordrecht (in press). 

Adaptive Capacity in the High Growth Scenario

20

30

40

50

60

70

80

90

100

110

1990 2005 2020 2035 2050 2065

Russian Federation

Libya

Indonesia

Belize

Mexico

China

Philippines

India

Morocco

Haiti

Adaptive Capacity in the Delayed Growth Scenario

20

30

40

50

60

70

80

90

100

110

1990 2005 2020 2035 2050 2065

Russian Federation

Libya

Indonesia

Belize

Mexico

China

Philippines

India

Morocco

Haiti
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Water Resources 

• Develop new water resources and storages (where possible) 

• Invest in climate and catchment monitoring and research 

• Rehabilitate existing water supply and transport systems 

• Implement water management measures 

• Increase recycling and reuse of waste water 

• Invest in water saving technologies/methods 

Agriculture 

• Change farming practices 

• Change timing of farm operations 

• Use different crop varieties (i.e., heat resistant) 

• Review governmental and institutional policies and programs 

• Research new practices and technologies (e.g., land-use planning, biotechnology) 

• Develop drought management and relief protocols 

Forests and Biodiversity 

• Establish conservation areas and networks 

• Invest in natural resource management plans 

• Manage land use to reduce environmental harm 

• Identify at-risk ecosystems and species 

• Develop aquaculture and plantation forestry instead of exploiting native resources 

Coastal Communities 

• Identify vulnerable areas, communities, and infrastructure 

• Channel future development around "high," "moderate," and "low growth" areas 

• Develop coastal zone management plans 

• Construct new, or modify existing, coastal defenses 

• Design infrastructure to accommodate sea-level rise 

• Manage progressive retreat from the coastline 

Public Health 

• Develop early warning systems for extreme weather events (e.g., flood, cyclones, heat waves) 

• Establish or bolster public health institutions 
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• Engage in research and development regarding disease transmission and prevention 

• Improve access of individuals and communities to medical and public health agencies 

• Provide education in disease prevention 

Conclusions: High-Risk Impacts 

There is overwhelming evidence that climate change will impact a variety of Southeast Asian 

sectors through 2030. The timing and magnitude of climate change impacts are difficult to 

quantify due to limitations in projections of future trends in temperature and precipitation in 

Southeast Asia. Studies using GCMs indicate that average annual temperatures across the region 

will rise by approximately 1°C through 2030, and they will keep rising through the remainder of 

the 21st century. The magnitude, location, and trends of future precipitation changes are much 

less certain, however, due to the inherent difficulty in modeling such changes. Future alterations 

in precipitation patterns associated with climate change are complicated by a strong natural 

variability in local climate, associated with ENSO, that routinely causes flooding and droughts 

across the region. Model simulations suggest that net precipitation rates will increase across 

Southeast Asia in the next 20 years, but there will likely be local decreases. 

A recent studycxxxviii found that the areas in Southeast Asia that are most vulnerable to climate 

change are: 

• All regions of the Philippines 

• The Mekong River Delta in Vietnam 

• Most regions of Cambodia 

• North and East Laos 

• The Bangkok region of Thailand 

• West Sumatra, South Sumatra, West Java, and East Java in Indonesia. 

The most high-risk impacts of climate change in Southeast Asia are related to fresh water and 

ocean water resources, due to the region’s heavy dependence on precipitation for supplies of 

fresh water and its close proximity to the ocean. All of the major effects of climate change on 

Southeast Asia are interrelated, so it is impossible to assess one impact independently of the 

others. 

Sea Level Rise 

• Sea level rose in the Southeast Asia region at rates up to 3 cm per year during 1993-2001, and 

GCM projections indicate that it will continue to rise up to 40 cm by the end of the 21st

century. 

• Since Southeast Asia is composed entirely of low-lying coastal and island nations, rising sea 

level causes a number of devastating effects, including saltwater intrusion into estuaries and 

aquifers, coastal erosion, displacement of wetlands and lowlands, degradation of coastal 

agricultural areas, and increased susceptibility to coastal storms. 
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• Impacts from sea level rise are interrelated with impacts on agriculture, natural disasters, 

river deltas, water resources, coastal ecosystems, human livelihoods and infrastructure, and 

national security. 

• Sea level rise also has overarching socioeconomic impacts, due to loss in income associated 

with degradation of agricultural areas and loss of housing associated with coastal inundation, 

for example. 

Water Resources 

• Future changes in Southeast Asian water resources are closely tied to changes in 

precipitation. 

• GCM results suggest that there will be a net increase in surface runoff across the region, but 

local trends will vary, with increases in some areas and decreases in others. Any areas that 

see an increase in runoff could experience an increase in erosion, flooding, and water 

pollution, while decreased runoff could lead to water shortages. 

• Individual areas under severe water stress in Southeast Asia are projected to increase 

dramatically in the next few decades, although model results suggest that the region as a 

whole will not be at risk for water shortages. 

• Fresh water resources on island nations of Southeast Asia are especially vulnerable to any 

variability in precipitation because many island populations rely on rainwater collection for 

their supply of fresh water. 

• The management of water issues is one of the most challenging climate-related issues in 

Southeast Asia, as it is central to health and sustainable development. 

• Water resource impacts are interrelated with impacts on agriculture, river deltas, forests, 

coastal ecosystems, diseases and human health, and national security. 

Agriculture 

• Agriculture is a major component of the economy in many nations of Southeast Asia, and 

there is no question that it will be significantly affected by climate change in the next 20 

years. 

• Assessment of specific agriculture impacts is challenging, because it is difficult for GCMs 

and crop models to reliably simulate the complicated effects of future variations in 

temperatures, precipitation, and atmospheric CO2 concentrations on crop growth. 

• Overall, it is likely that future crop yields will vary by region and by crop, with yield 

increases in some locations but decreases in others. 

• Climate change-induced impacts on agriculture will be augmented by natural climate 

variability, especially due to ENSO, which is responsible for serious impacts on agriculture 

associated with droughts, floods, and severe storms. 

• Management of the agricultural sector by Southeast Asian nations is critical to their economic 

growth and national security. Food shortages in the region, clearly associated with ENSO 

years in the past and projected to increase with changing climate, will stress poor populations 

across the region who are already susceptible to malnutrition. 
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49 

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• Agriculture impacts are interrelated with impacts on sea level, river deltas, natural disasters, 

and water resources. 

Coastal Regions 

• Coastal regions are some of the most at-risk areas for the impacts of climate change in 

Southeast Asia due to their prevalence and high population density. 

• Many coastal areas in Southeast Asia are already degraded by pollution, sediment-laden 

runoff, and destructive fishing practices. 

• Mangroves and coral reefs are two key coastal ecosystems that are expected to be 

significantly impacted by climate change. Destruction and degradation of mangroves and 

coral reefs will result in long-term economic repercussions for Southeast Asia, since these 

ecosystems are central to the tourism, agriculture, fishing, and aquaculture industries. 

• Coastal regions obviously are susceptible to inundation associated with sea level rise and 

destruction of infrastructure from flooding and storm surges, which are likely to increase as a 

result of future climate change. 

• Careful management and safeguarding of coastal regions by Southeast Asian governments is 

essential in the next 20 years, as the effects of climate change manifest themselves. 

• Impacts on coastal regions are interrelated with impacts on sea level, river deltas, natural 

disasters, water resources, agriculture, forests, and human livelihoods and infrastructure. 
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51 

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Annex A: Accuracy of Regional Models 

This is an excerpt from IPCC Fourth Assessment Report, Working Group 1 Report, Chapter 11: 

Regional Climate Projections; see original text for references.2 

11.4.2 Skill of Models in Simulating Present Climate 

Regional mean temperature and precipitation in the MMD models show biases when compared 

with observed climate. The multi-model mean shows a cold and wet bias in all regions and in 

most seasons, and the bias of the annual average temperature ranges from –2.5°C over the 

Tibetan Plateau (TIB) to –1.4°C over South Asia (SAS). For most regions, there is a 6°C to 7°C 

range in the biases from individual models with a reduced bias range in Southeast Asia (SEA) of 

3.6°C. The median bias in precipitation is small (less than 10 percent) in Southeast Asia, South 

Asia, and Central Asia (CAS), larger in northern Asia and East Asia (NAS and EAS, around +23 

percent), and very large in the Tibetan Plateau (+110 percent). Annual biases in individual 

models are in the range of –50 to +60 percent across all regions except the Tibetan Plateau, 

where some models simulate annual precipitation 2.5 times that observed and even larger 

seasonal biases occur in winter and spring. These global models clearly have significant 

problems over Tibet, due to the difficulty in simulating the effects of the dramatic topographic 

relief, as well as the distorted albedo feedbacks due to extensive snow cover. However, with 

only limited observations available, predominantly in valleys, large errors in temperature and 

significant underestimates of precipitation are likely. 

Southeast Asia 

The broad-scale spatial distribution of temperature and precipitation in December-JanuaryFebruary (DJF) and June-July-August (JJA) averaged across the MMD models compares well 

with observations. Rajendran et al (2004) examine the simulation of current climate in the MRI 

coupled model. Large-scale features were well simulated, but errors in the timing of peak 

rainfall over Indochina were considered a major shortcoming. Collier et al (2004) assess the 

performance of the CCSM3 model in simulating tropical precipitation forced by observed sea 

surface temperature (SST). Simulation was good over the maritime continent compared to the 

simulation for other tropical regions. B. Wang et al (2004) assess the ability of 11 atmospheric 

general circulation models (AGCMs) in the Asian-Australian monsoon region simulation forced 

with observed SST variations. They found that the models’ ability to simulate observed 

interannual rainfall variations was poorest in the Southeast Asian portion of the domain. Since 

current atmosphere-ocean general circulation models (AOGCMs) continue to have some 

significant shortcomings in representing El Niño- Southern Oscillation (ENSO) variability, the 

difficulty of projecting changes in ENSO-related rainfall in this region is compounded. 

Rainfall simulation across the region at finer scales has been examined in some studies. The 

Commonwealth Scientific and Industrial Research Organisation (CSIRO) stretched-grid 

Conformal-Cubic Atmospheric Model (CCAM) at 80-km resolution shows reasonable 

precipitation simulation in JJA, although Indochina tended to be drier than in the observations 

(McGregor and Nguyen, 2003). Aldrian et al (2004) conducted a number of simulations with the 

Max-Planck Institute (MPI) regional model for an Indonesian domain, forced by reanalyses and 

 

2

 Some references have been removed to avoid confusion. 
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52 

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by the ECHAM4 GCM. The model was able to represent the spatial pattern of seasonal rainfall. 

It was found that a resolution of at least 50 km was required to simulate rainfall seasonality 

correctly over Sulawesi. The formulation of a coupled regional model improves regional rainfall 

simulation over the oceans (Aldrian et al, 2004b). Arakawa and Kitoh (2005) demonstrate an 

accurate simulation of the diurnal cycle of rainfall over Indonesia with an AGCM of 20-km 

horizontal resolution. 
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53 

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Annex B: Knowledge Deficiencies that Preclude a Full Evaluation 

of Climate Change Impacts in Southeast Asia and Southeast Asia's 

Adaptive Strategies 

To increase the likelihood that this report represents a reasonable assessment of the projections 

and impacts of future climate change in Southeast Asia, as well as the region’s adaptive capacity, 

the following general data gaps must be addressed: 

• In physical science research, regional analyses of climate change are limited by the inability 

of GCMs to model regional climates satisfactorily, including complexities arising from the 

interaction of global, regional, and local processes. For example, uncertainties in changing 

monsoonal activity and ENSO due to natural variations and anthropogenic emissions are 

important information gaps needed for accurate climate projections. A particularly critical 

modeling gap for Southeast Asia is the fact that GCMs cannot consistently project the 

magnitude and location of future precipitation on a country-specific scale. Another important 

data gap is the lack of reliable medium-term climate projections that can be used for planning 

adaptive strategies for the next 20-30 years. Similarly, scientific projections of water supply 

and agricultural productivity are limited by inadequate knowledge of various climate and 

physical factors. Research agendas in these areas can be found in, for instance, the synthesis 

and assessment reports of the US Climate Change Science Program 

(http://www.climatescience.gov) and the National Academy of Sciences (e.g., 

http://books.nap.edu/catalog.php?record_id=11175#toc). Similar issues exist for the 

biological and ecological systems that will be affected by future climate change. 

• In social science research, scientists and analysts have only partial understandings of the 

important factors affecting vulnerability, resilience, and adaptive capacity. As with the 

physical science data gaps, research agendas on vulnerability, adaptation, and decisionmaking abound (e.g., http://books.nap.edu/catalog.php?record_id=12545). 

• Important research factors are still unaccounted for given the current available data. The 

early approach to carbon cycle modeling is a good example of this type of information gap. 

The first carbon cycle models did not include carbon exchanges involving the terrestrial 

domain because modelers assumed that the exchange was about equal. As a result, the only 

factor modeled was deforestation; the omission of terrestrial carbon exchange rendered the 

models inadequate. Ecosystems research models are another example—they are only 

beginning to account for changes in pests populations, such as the pine bark beetle. 

• Social models have been developed to simulate consumption, with the assumption of wellfunctioning markets and rational actor behavior, and mitigation/adaptation policies, but 

without attention to the social feasibility of enacting or implementing such policies. Since 

anthropogenic climate change is the result of human decisions, the lack of knowledge about 

human motivation, intent, and behavior is a serious shortcoming in these social models. 

Overall, research about climate change impacts on Southeast Asia has been undertaken in a 

piecemeal fashion: discipline by discipline, sector by sector, with political implications 

considered separately from physical effects. This knowledge gap can be remedied by integrated 

research into energy-economic-environmental-political conditions and possibilities. 
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54 

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Specific research and data gaps regarding climate change and adaptive capacity in Southeast 

Asia include the following: 

• Wang et alcxxxix found that a suite of 11 GCMs used to simulate observed interannual rainfall 

variations had the poorest skill over the Southeast Asian portion of the domain. The authors 

concluded that current GCM shortcomings in representing natural ENSO variability make it 

difficult to project changes in ENSO-related rainfall in Southeast Asia. 

• Murdiyarsocxl summarized many of the difficulties of using GCMs and crop models to 

accurately simulate the impacts of climate change on agriculture in Southeast Asia. He noted 

that GCMs cannot reliably predict changes in drought and storm frequencies, which makes it 

impossible to model future crop yield changes. 

• Luo and Lincxli noted that continued refinement of dynamic crop simulation models that can 

utilize GCM scenarios is needed. The simulation models must be able to synthesize the 

range of possible crop impacts, including CO2 fertilization, temperature, disease, and changes 

in soil and water. Four specific high-priority research needs were identified, including 

assessment of the crop- and region-specific benefit of CO2 fertilization, development of more 

integrated agricultural models which account for biophysical and socioeconomic factors, 

inclusion of socioeconomic factors in the models, and evaluation of the relative importance 

of climate variability versus changes in mean climate parameters. 

• Sivakumar et alcxlii noted that it is necessary to understand potential crop responses to the 

range of possible climate scenarios. Evaluation of future changes in crop yield associated 

with variations in the frequency and intensity of extreme climate events, such as floods, 

storms, and droughts, is particularly important. Assessment of sustainability practices, 

agricultural productivity, changes in erosion, degradation of soil quality, and ecosystem 

health also need careful consideration.

• Rosensweig et alcxliii noted that improved observation networks are urgently needed in 

Southeast Asia in order to document the sensitivity of physical and biological systems to 

warming in the region.

• Burke et alcxliv noted a need for better information about the location of coral reefs and the 

threats to their survival. 

• Kobayashicxlv noted that there is a need for detailed studies of the magnitude and extent of 

climate change impacts on urban settlements, particularly in developing countries. 

• Sia Sucxlvi noted that future climate change research must incorporate important weather 

factors, such as relative humidity, pressure, and wind speed/direction, which can influence 

the effects of climate on disease outbreaks and associated morbidity and mortality rates. 

• Pennycxlvii identified the need for comprehensive modeling that reflects increased snowmelt 

and monsoon rainfall to assess the implications for food security, wetlands management, and 

biodiversity in Southeast Asia. 

• Preston et alcxlviii identified the need to include the potential for irreversible loss of large ice 

sheets in the Arctic and Antarctic in projections of global sea level rise. Contributions from 

large ice sheets could rapidly increase global sea level, with devastating impacts on the 

coastal and island nations of Southeast Asia. 
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55 

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• Thanh et alcxlix identified research gaps with regard to land-ocean interactions and coastal 

impacts in Vietnam and upland countries. Research needs include synchronization of 

research and applied scale data and greater public availability of data. More detailed, 

integrated and comprehensive investigations are required to obtain information on coastal 

nutrients, pollutants, material fluxes and coastal interaction processes that have been 

impacted by both human activities and climate change. 

• Hanh and Furukawacl identified information gaps on sea level rise and coastal zone 

vulnerability in Vietnam. 

• Mapalocli identified research gaps for evaluating climate change and sea level rise at Olango 

Island in the central Philippines. Research needs include information on storm surge, 

quantification of coral and sand extraction, natural disaster impact data, groundwater salinity 

and transmissibility data, endangered species information, assessment of the impacts of 

mangroves on sea grass beds, and a detailed topographic map of the region. 
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56 

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i

 Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. 

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and Adaptations to Climate Change in Multiple Regions and Sectors) Semi-Annual Report (July to December) 2003, 

http://sedac.ciesin.columbia.edu/aiacc/ (accessed April 8, 2009). 

iii J.A. Church, N.J. White, R. Coleman, K. Lambeck, and J.X. Mitrovica, “Estimates of regional distribution of sea 

level rise over the 1950-2000 period,” J. Clim., 17 (2004): 2609–2625; J.A. Church, N.J. White, and J.R. Hunter, 

“Sea level rise at tropical Pacific and Indian Ocean islands,” Global Planet. Change, 53, no. 3 (2006): 155–168. 

iv B. Bhaskaran and J.F.B. Mitchell, “Simulated changes in Southeast Asian monsoon precipitation resulting from 

anthropogenic emissions,” Int. J. Climatol. 18 (1998): 1455-1462. v

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Water, Air, and Soil Pollution 92 (1996): 191-201. xvii Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. xviii Intergovernmental Panel on Climate Change (IPCC), Special Report on Emissions Scenarios (SRES), eds. 

Nebojsa Nakicenovic and Rob Swart (Cambridge: Cambridge University Press, 2000), 

http://www.ipcc.ch/ipccreports/sres/emission/index.htm. xix Intergovernmental Panel on Climate Change (IPCC), The IPCC Data Distribution Centre, HadCM2 GCM Model 

Information, http://www.ipcc-data.org/is92/hadcm2_info.html (accessed April 1, 2009). xx Intergovernmental Panel on Climate Change (IPCC), IPCC 4th Assessment Report (2007): Model UKMOHADCM3, http://www.ipcc-data.org/ar4/model-UKMO-HADCM3.html (accessed April 1, 2009). xxi Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. 
This paper does not represent US Government views. 

57 

This paper does not represent US Government views. 

xxii Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. xxiii Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. xxiv Filippo Giorgi, Peter H. Whetton, Richard G. Jones, Jens H. Christensen, Linda O. Mearns, Bruce Hewitson, 

Hans vonStorch, Raquel Francisco, and Chris Jack, “Emerging patterns of simulated regional climatic changes for 

the 21st century due to anthropogenic forcings,” Geophysical Research Letters 28, no. 17 (2001): 3317-3320. xxv R. Boer and A. Faqih, “Current and Future Rainfall Variability in Indonesia,” AIACC (Assessments of Impacts 

and Adaptations to Climate Change in Multiple Regions and Sectors) Semi-Annual Report (July to December) 2003, 

http://sedac.ciesin.columbia.edu/aiacc/ (accessed April 8, 2009). xxvi M. Lal, and H. Harasawa, “Future Climate Change Scenarios for Asia Inferred from Selected Coupled 

Atmosphere-Ocean Global Climate Models,” Journal of the Meteorological Society of Japan 79 (2001): 219-227. xxvii M. Lal, H. Harasawa, and K. Takahashi, “Future climate change and its impacts over small island states,” Clim. 

Res., 19 (2002):179–192. xxviii M. Lal, and H. Harasawa, “Future Climate Change Scenarios for Asia Inferred from Selected Coupled 

Atmosphere-Ocean Global Climate Models,” Journal of the Meteorological Society of Japan 79 (2001): 219-227. xxix Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. xxx M. Lal, and H. Harasawa, “Future Climate Change Scenarios for Asia Inferred from Selected Coupled 

Atmosphere-Ocean Global Climate Models,” Journal of the Meteorological Society of Japan 79 (2001): 219-227. xxxi M. Lal, H. Harasawa, and K. Takahashi, “Future climate change and its impacts over small island states,” Clim. 

Res., 19 (2002):179–192. xxxii R. Boer and A. Faqih, “Current and Future Rainfall Variability in Indonesia,” AIACC (Assessments of Impacts 

and Adaptations to Climate Change in Multiple Regions and Sectors) Semi-Annual Report (July to December) 2003, 

http://sedac.ciesin.columbia.edu/aiacc/ (accessed April 8, 2009). xxxiii Filippo Giorgi, Peter H. Whetton, Richard G. Jones, Jens H. Christensen, Linda O. Mearns, Bruce Hewitson, 

Hans vonStorch, Raquel Francisco, and Chris Jack, “Emerging patterns of simulated regional climatic changes for 

the 21st century due to anthropogenic forcings,” Geophysical Research Letters 28, no. 17 (2001): 3317-3320. xxxiv B. Bhaskaran and J.F.B. Mitchell, “Simulated changes in Southeast Asian monsoon precipitation resulting from 

anthropogenic emissions,” Int. J. Climatol. 18 (1998): 1455-1462. xxxv Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm; Heiko Paeth, Anja Scholten, Petra 

Friederichs, and Andreas Hense, “Uncertainties in climate change prediction: El Niño-Southern Oscillation and 

monsoons,” Global and Planetary Change 60 (2008): 265-288. xxxvi Heiko Paeth, Anja Scholten, Petra Friederichs, and Andreas Hense, “Uncertainties in climate change prediction: 

El Niño-Southern Oscillation and monsoons,” Global and Planetary Change 60 (2008): 265-288. xxxvii F. Tao, M. Yokozawa, Y. Hayashi, and E. Lin, “Terrestrial Water Cycle and the Impact of Climate Change,” 

Ambio 32, no. 4 (2003): 295-301. xxxviii Nigel W. Arnell, “Climate change and global water resources,” Global Environmental Change 9 (1999): S31-

S49; Nigel W. Arnell, “Climate change and global water resources: SRES emissions and socio-economic scenarios,” 

Global Environmental Change 14 (2004): 31-52. xxxix F. Tao, M. Yokozawa, Y. Hayashi, and E. Lin, “Terrestrial Water Cycle and the Impact of Climate Change,” 

Ambio 32, no. 4 (2003): 295-301. xl Aida M. Jose and Nathaniel A. Cruz, “Climate change impacts and responses in the Philippines: water resources,” 

Climate Research 12 (1999): 77-84. xli A.M. Jose, L.M. Sosa, and N.A. Cruz, “Vulnerability assessment of Angat water reservoir to climate change,” 

Water, Air, and Soil Pollution 92 (1996): 191-201. xlii Juan M. Pulhin, Rose Jane J. Peras, Rex Victor O. Cruz, Rodel D. Lasco, Florencia B. Pulhin, and Maricel A. 

Tapia, “Vulnerability of Communities to Climate Variability and Extremes: Pantabangan-Carranglan Watershed in 

the Philippines. AIACC Working Paper No. 44, The International START Secretariat, 2006. 
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58 

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xliii Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. xliv Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. xlv J.A. Church, N.J. White, R. Coleman, K. Lambeck, and J.X. Mitrovica, “Estimates of regional distribution of sea 

level rise over the 1950-2000 period,” J. Clim., 17 (2004): 2609–2625; J.A. Church, N.J. White, and J.R. Hunter, 

“Sea level rise at tropical Pacific and Indian Ocean islands,” Global Planet. Change, 53, no. 3 (2006): 155–168. xlvi Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. xlvii Reiner Wassmann, Nguyen Xuan Hien, Chu Thai Hoanh, and To Phuc Tuong, “Sea level rise affecting the 

Vietnamese Mekong Delta: Water elevation in the flood season and implications for rice production,” Climatic 

Change 66 (2004): 89-107. xlviii Nguyen Duy Khang, Akihiko Kotera, Toshihiro Sakamoto, and Masayuki Yokozawa, “Sensitivity of Salinity 

Intrusion to Sea Level Rise and River Flow Change in Vietnamese Mekong Delta – Impacts on Availability of 

Irrigation Water for Rice Cropping,” J. Agric. Meteorl. 64, no. 3 (2008): 167-176. xlix Reiner Wassmann, Nguyen Xuan Hien, Chu Thai Hoanh, and To Phuc Tuong, “Sea level rise affecting the 

Vietnamese Mekong Delta: Water elevation in the flood season and implications for rice production,” Climatic 

Change 66 (2004): 89-107. l

 Nguyen Duy Khang, Akihiko Kotera, Toshihiro Sakamoto, and Masayuki Yokozawa, “Sensitivity of Salinity 

Intrusion to Sea Level Rise and River Flow Change in Vietnamese Mekong Delta – Impacts on Availability of 

Irrigation Water for Rice Cropping,” J. Agric. Meteorl. 64, no. 3 (2008): 167-176. li Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 2009, 

http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. lii J.A. Church, N.J. White, R. Coleman, K. Lambeck, and J.X. Mitrovica, “Estimates of regional distribution of sea 

level rise over the 1950-2000 period,” J. Clim., 17 (2004): 2609–2625; J.A. Church, N.J. White, and J.R. Hunter, 

“Sea level rise at tropical Pacific and Indian Ocean islands,” Global Planet. Change, 53, no. 3 (2006): 155–168. liii Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. liv Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 2009, 

http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. lv E.B. Capili, A.C.S. Ibay, and J.R.T. Villarin, “Climate Change Impacts and Adaptation on Philippine Coasts,” 

Proceedings of the International Oceans 2005 Conference, Washington D.C., USA, September 9-23, 2005. lvi Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 2009, 

http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. lvii Pham Thi Thuy Hanh and Masahide Furukawa, “Impact of sea level rise on coastal zone of Vietnam,” Bull. Fac. 

ScL, Univ. Ryukyus 84 (2007): 45-59. lviii Muh Aris Marfai and Lorenz King, “Potential vulnerability implications of coastal inundation due to sea level 

rise for the coastal zone of Semarang city, Indonesia,” Environ. Geol. 54 (2008): 1235-1245. lix Adianto P. Simamora, “Flood risk rises with sea levels,” The Jakarta Post, January 25, 2007, 

http://www.thejakartapost.com/news/2007/01/25/flood-risk-rises-sea-levels.html. lx Pham Thi Thuy Hanh and Masahide Furukawa, “Impact of sea level rise on coastal zone of Vietnam,” Bull. Fac. 

ScL, Univ. Ryukyus 84 (2007): 45-59. lxi Jason P. Ericson, Charles J. Vorosmarty, S. Lawrence Dingman, Larry G. Ward, and Michel Meybeck, “Effective 

sea-level rise and deltas: Causes of change and human dimension implications,” Global and Planetary Change 50 

(2006): 63-82. lxii Tran Duc Thanh, Yoshiki Saito, Dinh Van Huy, Van Lap Nguyen, Thi Kim Oanh Ta, and Masaaki Tateishi, 

“Regimes of human and climate impacts on coastal changes in Vietnam,” Reg. Environ. Change 4 (2004): 49-62. lxiii Arief Anshory Yusuf and Herminia Francisco, “Climate Change Vulnerability Mapping for Southeast Asia,” 

Economy and Environment Program for Southeast Asia (EEPSEA), January 2009. 
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59 

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lxiv Kelvin S. Rodolfo and Fernando P. Siringan, “Global sea-level rise is recognized, but flooding from 

anthropogenic land subsidence is ignored around northern Manila Bay, Philippines,” Disasters 30, no. 1 (2006): 

118-139. lxv Kelvin S. Rodolfo and Fernando P. Siringan, “Global sea-level rise is recognized, but flooding from 

anthropogenic land subsidence is ignored around northern Manila Bay, Philippines,” Disasters 30, no. 1 (2006): 

118-139. lxvi Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 2009, 

http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. lxvii Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 2009, 

http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. lxviii Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 

2009, http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. lxix G.A. Meehl, “Vulnerability of freshwater resources to climate change in the tropical Pacific region,” Water, Air, 

and Soil Pollution 92 (1996): 203-213. lxx Benjamin L. Preston, Ramasamy Suppiah, Ian Macadam, and Janice Bathols, “Climate Change in the 

Asia/Pacific Region: A Consultancy Report Prepared for the Climate Change and Development Roundtable” CSIRO 

Marine and Atmospheric Research, 2006, http://www.csiro.au/resources/pfkd.html. lxxi Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 2009, 

http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. lxxii A. Iglesias, Lin Erda, and C. Rosenzweig, “Climate change in Asia: A review of the vulnerability and adaptation 

of crop production,” Water, Air, and Soil Pollution 92 (1996): 13-27; Yanxia Zhao, Chunyi Wang, Shili Wang, and 

Lourdes V. Tibig, “Impacts of present and future climate variability on agriculture and forestry in the humid and 

sub-humid tropics,” Climatic Change 70 (2005): 73-116. lxxiii R.D. Buan, A.R. Maglinao, P.P. Evangelista, and B.G. Pajuelas, “Vulnerability of Rice and Corn to Climate 

Change in the Philippines,” Water, Air, and Soil Production 92 (1996): 41-51. lxxiv R.D. Buan, A.R. Maglinao, P.P. Evangelista, and B.G. Pajuelas, “Vulnerability of Rice and Corn to Climate 

Change in the Philippines,” Water, Air, and Soil Production 92 (1996): 41-51. lxxv Yanxia Zhao, Chunyi Wang, Shili Wang, and Lourdes V. Tibig, “Impacts of present and future climate 

variability on agriculture and forestry in the humid and sub-humid tropics,” Climatic Change 70 (2005): 73-116. lxxvi Shaobing Peng, Jianliang Huang, John E. Sheehy, Rebecca C. Laza, Romeo M. Visperas, Xuhua Zhong, Grace 

S. Centeno, Gurdev S. Khush, and Kenneth G. Cassman. “Rice yields decline with higher night temperature from 

global warming,” PNAS 101, no. 27 (2004): 9971-9975. lxxvii R.B. Matthews, M.J. Kropff, T. Horie, and D. Bachelet, “Simulating the Impact of Climate Change on Rice 

Production in Asia and Evaluating Options for Adaptation,” Agricultural Systems 54, no. 3 (1997): 399-425. lxxviii R.D. Buan, A.R. Maglinao, P.P. Evangelista, and B.G. Pajuelas, “Vulnerability of Rice and Corn to Climate 

Change in the Philippines,” Water, Air, and Soil Production 92 (1996): 41-51. lxxix Istiqlal Amien, P. Rejekiningrum, A. Pramudia, and E. Susanti, “Effects of interannual climate variability and 

climate change on rice yield in Java, Indonesia,” Water, Air, and Soil Pollution 92 (1996): 29-39. lxxx Istiqlal Amien, P. Rejekiningrum, A. Pramudia, and E. Susanti, “Effects of interannual climate variability and 

climate change on rice yield in Java, Indonesia,” Water, Air, and Soil Pollution 92 (1996): 29-39. lxxxi Istiqlal Amien, Popi Redjekiningrum, Budi Kartiwa, and Woro Estiningtyas, “Simulated rice yields as affected 

by interannual climate variability and possible climate change in Java,” Climate Research 12 (1999): 145-152. lxxxii Rosamond L. Naylor, David S. Battisti, Daniel J. Vimont, Walter P. Falcon, and Marshall B. Burke, “Assessing 

risks of climate variability and climate change for Indonesian rice agriculture,” PNAS 104, no. 119 (2007): 7752-

7757. lxxxiii Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 

2009, http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. lxxxiv F.P. Lansigan, W.L. de los Santos, and J.O. Coladilla, “Argonomic impacts of climate variability on rice 

production in the Philippines,” Agriculture, Ecosystems and Environment 82 (2000): 129-137. lxxxv I. Amien, P. Rejekiningrum, A. Pramudia, and E. Susanti, “Effects of interannual climate variability and climate 

change on rice yield in Java, Indonesia,” Water, Air, and Soil Pollution 92 (1996): 29-39; Istiqlal Amien, Popi 

Redjekiningrum, Budi Kartiwa, and Woro Estiningtyas, “Simulated rice yields as affected by interannual climate 

variability and possible climate change in Java,” Climate Research 12 (1999): 145-152. 
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60 

This paper does not represent US Government views. 

lxxxvi Ornanong Boonklong, Mullica Jaroensutasinee, and Krisandej Jaroensutasinee, “Climate Change Affecting 

Mangosteen Production in Thailand,” Proceedings of the 5th WSEAS International Conference on Environment, 

Ecosystems and Development, Venice, Italy, November 20-22, 2006. lxxxvii Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 

2009, http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. lxxxviii Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 

2009, http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. lxxxix D. Taylor, P. Saksena, P.G. Sanderson, and K. Kucera, “Environmental change and rain forests on the Sunda 

shelf of Southeast Asia: drought, fire and the biological cooling of biodiversity hotspots,” Biodiversity and 

Conservation 8 (1999): 1159-1177; Asian Development Bank, The Economics of Climate Change in Southeast Asia: 

A Regional Review, April 2009, http://www.adb.org/Documents/Books/Economics-Climate-ChangeSEA/default.asp. xc Yanxia Zhao, Chunyi Wang, Shili Wang, and Lourdes V. Tibig, “Impacts of present and future climate variability 

on agriculture and forestry in the humid and sub-humid tropics,” Climatic Change 70 (2005): 73-116. xci Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 2009, 

http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. xcii Benjamin L. Preston, Ramasamy Suppiah, Ian Macadam, and Janice Bathols, “Climate Change in the 

Asia/Pacific Region: A Consultancy Report Prepared for the Climate Change and Development Roundtable” CSIRO 

Marine and Atmospheric Research, 2006, http://www.csiro.au/resources/pfkd.html. xciii K. Boonpragob and J. Santisirisomboon, “Modeling potential changes of forest area in Thailand under climate 

change,” Water, Air, and Soil Pollution 92 (1996): 107-117. xciv Jarkko Koskela, “Responses of gas exchange and growth in Merkus pine seedlings to expected climatic changes 

in Thailand,” Global Change Biology 7 (2001): 641-656. xcv Trevor H. Booth, Nguyen Hoang Nghia, Miko U.F. Kirschbaum, Clive Hackett, and Tom Jovanovic, “Assessing 

possible impacts of climate change on species important for forestry in Vietnam,” Climatic Change 41 (1999): 109-

126. xcvi Jay R. Malcolm, Chanran Liu, Ronald P. Neilson, Lara Hansen, and Lee Hannah, “Global Warming and 

Extinctions of Endemic Species from Biodiversity Hotspots,” Conservation Biology 20, no. 2 (2006): 538-548. xcvii Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 

2009, http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. xcviii Ramsar Secretariat, “Wetland Values and Functions: Climate Change Mitigation,” Gland, Switzerland, 2001. xcix Eric L. Gilman, Joanna Ellison, Norman C. Duke, and Colin Field, “Threats to mangroves from climate change 

and adaptation options: A review,” Aquatic Botany 89 (2008): 237-250. c

 Amuerfino Mapalo, “Vulnerability assessment of major wetlands in the Asia-Pacific region to climate change and 

sea level rise: Part 2: Vulnerability assessment of Olango Island to predicted climate change and sea level rise,” 

Supervising Scientist Report 149, Australian Department of the Environment, Water, Heritage, and the Arts, 2007, 

http://www.environment.gov.au/ssd/publications/ssr/pubs/ssr149-part2.pdf. ci E.B. Capili, A.C.S. Ibay, and J.R.T. Villarin, “Climate Change Impacts and Adaptation on Philippine Coasts,” 

Proceedings of the International Oceans 2005 Conference, Washington D.C., USA, September 9-23, 2005. cii Tran Duc Thanh, Yoshiki Saito, Dinh Van Huy, Van Lap Nguyen, Thi Kim Oanh Ta, and Masaaki Tateishi, 

“Regimes of human and climate impacts on coastal changes in Vietnam,” Reg. Environ. Change 4 (2004): 49-62. ciii Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 2009, 

http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. civ Lauretta Burke, Elizabeth Selig, and Mark Spalding, “Reefs at Risk in Southeast Asia,” World Resources 

Institute, 2002. cv J. Silverman, B. Lazar, L. Cao, K. Caldeira, and J. Erez, “Coral reefs may start dissolving when atmospheric CO2

doubles,” Geophys.Res. Lett., 36 (2009): L05606, doi:10.1029/2008GL036282. cvi Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007: the Physical Science Basis, eds. S. 

Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H.L. Jr. Miller, and Z. Chen (Cambridge: 

Cambridge University Press, 2007), http://www.ipcc.ch/ipccreports/ar4-wg1.htm. cvii Lauretta Burke, Elizabeth Selig, and Mark Spalding, “Reefs at Risk in Southeast Asia,” World Resources 

Institute, 2002. cviii J. Silverman, B. Lazar, L. Cao, K. Caldeira, and J. Erez, “Coral reefs may start dissolving when atmospheric CO2

doubles,” Geophys.Res. Lett., 36 (2009): L05606, doi:10.1029/2008GL036282. 
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This paper does not represent US Government views. 

cix Thomas J. Goreau, Raymond L. Haynes, and Don McAllister, “Regional patterns of sea surface temperature rise: 

Implications for global ocean circulation change and the future of coral reefs and fisheries,” World Resource Review 

17, no. 3 (2005): 350-374. cx Lauretta Burke, Elizabeth Selig, and Mark Spalding, “Reefs at Risk in Southeast Asia,” World Resources Institute, 

2002. cxi Robert W. Buddemeier, Joan A. Kleypas, and Richard B. Aronson, “Coral Reefs & Global Climate Change: 

Potential Contributions of Climate Change to Stresses on Coral Reef Ecosystems,” Pew Center on Global Climate 

Change, 2004. cxii J. M. Guinotte, R.W. Buddemeier, and J.A. Kleypas, “Future coral reef habitat marginality: temporal and spatial 

effects of climate change in the Pacific basin,” Coral Reefs 22 (2003): 551-558. cxiii Lauretta Burke, Elizabeth Selig, and Mark Spalding, “Reefs at Risk in Southeast Asia,” World Resources 

Institute, 2002. cxiv Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 2009, 

http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp; Glenn S. Sia Su, “Correlation 

of Climatic Factors and Dengue Incidence in Metro Manila, Philippines,” Ambio 37, no. 4 (2008): 292-294; S. 

Promprou, M. Jaroensutasinee, and K. Jaroensutasinee, “Climatic factors affecting dengue haemorrhagic fever 

incidence in Southern Thailand,” Dengue Bull. 29 (2005): 41–48. cxv Willem J.M. Martens, Louis W. Niessen, Jan Rotmans, Theo H. Jetten, and Anthony J. McMichael, “Potential 

Impact of Global Climate Change on Malaria Risk,” Environmental Health Perspectives 103 (1995): 458-464. cxvi M.J. Hopp and J.A. Foley, “Worldwide fluctuations in dengue fever cases related to climate variability,” 

Climate Research 25 (2003): 85-94. cxvii Willem J.M. Martens, Louis W. Niessen, Jan Rotmans, Theo H. Jetten, and Anthony J. McMichael, “Potential 

Impact of Global Climate Change on Malaria Risk,” Environmental Health Perspectives 103 (1995): 458-464. cxviii Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 

2009, http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. cxix Robert G. Arnold, David O. Carpenter, Donald Kirk, David Koh, Margaret-Ann Armour, Mariano Cebrian, Luis 

Cifuentes, Mahmood Khwaja, Bo Ling, Irma Makalinao, César Paz-y-Miño, Genandrialine Peralta, Rajendra Prasad, 

Kirpal Singh, Peter Sly, Chiharu Tohyama, Alistair Woodward, Baoshan Zheng, and Todd Maiden, “Meeting 

Report: Threats to Human Health and Environmental Sustainability in the Pacific Basin,” Environmental Health 

Perspectives 115, no. 12 (2007): 1770-1775. cxx E.B. Capili, A.C.S. Ibay, and J.R.T. Villarin, “Climate Change Impacts and Adaptation on Philippine Coasts,” 

Proceedings of the International Oceans 2005 Conference, Washington D.C., USA, September 9-23, 2005. cxxi Suchao Parkpoom and Gareth P. Harrison, “Analyzing the Impact of Climate Change on Future Electricity 

Demand in Thailand,” IEEE Transactions on Power Systems 23, no. 3 (2008): 1441-1448. cxxii Malaysia Energy Center (Pusat Tenaga Malaysia), Energy Information Highlights, Malaysia Energy Database 

and Information System, Selangor, Malaysia, http://medis.ptm.org.my/ (accessed April 28, 2009). cxxiii Thomas J. Goreau, Raymond L. Haynes, and Don McAllister, “Regional patterns of sea surface temperature 

rise: Implications for global ocean circulation change and the future of coral reefs and fisheries,” World Resource 

Review 17, no. 3 (2005): 350-374. cxxiv E.B. Capili, A.C.S. Ibay, and J.R.T. Villarin, “Climate Change Impacts and Adaptation on Philippine Coasts,” 

Proceedings of the International Oceans 2005 Conference, Washington D.C., USA, September 9-23, 2005; 

Amuerfino Mapalo, “Vulnerability assessment of major wetlands in the Asia-Pacific region to climate change and 

sea level rise: Part 2: Vulnerability assessment of Olango Island to predicted climate change and sea level rise,” 

Supervising Scientist Report 149, Australian Department of the Environment, Water, Heritage, and the Arts, 2007, 

http://www.environment.gov.au/ssd/publications/ssr/pubs/ssr149-part2.pdf. cxxv Kelvin S. Rodolfo and Fernando P. Siringan, “Global sea-level rise is recognized, but flooding from 

anthropogenic land subsidence is ignored around northern Manila Bay, Philippines,” Disasters 30, no. 1 (2006): 

118-139. cxxvi Asit K. Biswas and K.E. Seetharam, “Achieving Water Security for Asia,” Water Resources Development 24, 

no. 1 (2008): 145-176. cxxvii Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 

2009, http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. cxxviii Asit K. Biswas and K.E. Seetharam, “Achieving Water Security for Asia,” Water Resources Development 24, 

no. 1 (2008): 145-176. 
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This paper does not represent US Government views. 

cxxix Intergovernmental Panel on Climate Change (IPCC), “Glossary,” Climate Change 2007: Impacts, Adaptation 

and Vulnerability: Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental 

Panel on Climate Change, eds. M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson 

(Cambridge: Cambridge University Press 2007). cxxx G. Yohe and R. Tol, “Indicators for social and economic coping capacity - moving toward a working definition 

of adaptive capacity,” Global Environmental Change 12 (2002): 25-40. cxxxi E.L. Malone and A.L. Brenkert, “Vulnerability, sensitivity, and coping/adaptive capacity worldwide,” The 

Distributional Effects of Climate Change: Social and Economic Implications, M. Ruth and M. Ibarraran, eds., 

Elsevier Science, Dordrecht (in press). cxxxii Katrina M. Allen, “Community-based disaster preparedness and climate adaptation: local capacity-building in 

the Philippines,” Disasters 30, no. 1 (2006): 81-101. cxxxiii Cynthia Rosenzweig, David Karoly, Marta Vicarelli, Peter Neofotis, Qigang Wu, Gino Casassa, Annette 

Menzel, Terry L. Root, Nicole Estrella, Bernard Seguin, Piotr Tryjanowski, Chunzhen Liu, Samuel Rawlins, and 

Anton Imeson, “Attributing physical and biological impacts to anthropogenic climate change,” Nature 453 (2008): 

353-358. cxxxiv Robert G. Arnold, David O. Carpenter, Donald Kirk, David Koh, Margaret-Ann Armour, Mariano Cebrian, 

Luis Cifuentes, Mahmood Khwaja, Bo Ling, Irma Makalinao, César Paz-y-Miño, Genandrialine Peralta, Rajendra 

Prasad, Kirpal Singh, Peter Sly, Chiharu Tohyama, Alistair Woodward, Baoshan Zheng, and Todd Maiden, 

“Meeting Report: Threats to Human Health and Environmental Sustainability in the Pacific Basin,” Environmental 

Health Perspectives 115, no. 12 (2007): 1770-1775. cxxxv Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 

2009, http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. cxxxvi Asian Development Bank, The Economics of Climate Change in Southeast Asia: A Regional Review, April 

2009, http://www.adb.org/Documents/Books/Economics-Climate-Change-SEA/default.asp. cxxxvii Benjamin L. Preston, Ramasamy Suppiah, Ian Macadam, and Janice Bathols, “Climate Change in the 

Asia/Pacific Region: A Consultancy Report Prepared for the Climate Change and Development Roundtable” CSIRO 

Marine and Atmospheric Research, 2006, http://www.csiro.au/resources/pfkd.html. cxxxviii Arief Anshory Yusuf and Herminia Francisco, “Climate Change Vulnerability Mapping for Southeast Asia,” 

Economy and Environment Program for Southeast Asia (EEPSEA), January 2009. cxxxix B. Wang, I.S. Kang, and J.Y. Lee, “Ensemble simulations of Asian-Australian monsoon variability by 11 

GCMs,” J. Clim. 17 (2004): 803–818. cxl D. Murdiyarso, “Adaptation to climatic variability and change: Asian perspectives on agriculture and food 

security,” Environmental Monitoring and Assessment 61 (2000): 123-131. cxli Qunying Lou and Erda Lin, “Agricultural vulnerability and adaptation in developing countries: The Asia-Pacific 

Region,” Climatic Change 43 (1999): 729-743. cxlii M.V.K. Sivakumar, H.P. Das, and O. Brunini, “Impacts of the present and future climate variability and change 

on agriculture and forestry in the arid and semi-arid tropics,” Climatic Change 70 (2005): 31-72. cxliii Cynthia Rosenzweig, David Karoly, Marta Vicarelli, Peter Neofotis, Qigang Wu, Gino Casassa, Annette 

Menzel, Terry L. Root, Nicole Estrella, Bernard Seguin, Piotr Tryjanowski, Chunzhen Liu, Samuel Rawlins, and 

Anton Imeson, “Attributing physical and biological impacts to anthropogenic climate change,” Nature 453 (2008): 

353-358. cxliv Lauretta Burke, Elizabeth Selig, and Mark Spalding, “Reefs at Risk in Southeast Asia,” World Resources 

Institute, 2002. cxlv Hideyuki Kobayashi, “Impact evaluation of sea level rise on Indonesian coastal cities,” Journal of Environment 

Engineering 10 (2004). cxlvi Glenn S. Sia Su, “Correlation of Climatic Factors and Dengue Incidence in Metro Manila, Philippines,” Ambio 

37, no. 4 (2008): 292-294. cxlvii Dan Penny, “The Mekong at Climatic Crossroads: Lessons from the Geological Past,” Ambio 37, no. 3 (2008): 

164-169. cxlviii Benjamin L. Preston, Ramasamy Suppiah, Ian Macadam, and Janice Bathols, “Climate Change in the 

Asia/Pacific Region: A Consultancy Report Prepared for the Climate Change and Development Roundtable” CSIRO 

Marine and Atmospheric Research, 2006, http://www.csiro.au/resources/pfkd.html. cxlix Tran Duc Thanh, Yoshiki Saito, Dinh Van Huy, Van Lap Nguyen, Thi Kim Oanh Ta, and Masaaki Tateishi, 

“Regimes of human and climate impacts on coastal changes in Vietnam,” Reg. Environ. Change 4 (2004): 49-62. 
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63 

This paper does not represent US Government views. 

cl Pham Thi Thuy Hanh and Masahide Furukawa, “Impact of sea level rise on coastal zone of Vietnam.” Bull. Fac. 

ScL, Univ. Ryukyus 84 (2007): 45-59. cli Amuerfino Mapalo, “Vulnerability assessment of major wetlands in the Asia-Pacific region to climate change and 

sea level rise: Part 2: Vulnerability assessment of Olango Island to predicted climate change and sea level rise.” 

Supervising Scientist Report 149, Australian Department of the Environment, Water, Heritage, and the Arts, 2007, 

http://www.environment.gov.au/ssd/publications/ssr/pubs/ssr149-part2.pdf. 
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64 

This paper does not represent US Government views.