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India: The Impact of Climate Change to 2030 

A Commissioned Research Report 

Prepared By 

Joint Global Change Research Institute and 

Battelle Memorial Institute, Pacific Northwest Division 

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 2009-03D 

April 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, a workshop or conference composed of experts from outside the 

Intelligence Community (IC) will 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 US national 

security. 

The Joint Global Change Research Institute (JGCRI) and Battelle, Pacific Northwest Division 

(Battelle, PNWD), developed this assessment on the climate change impact on India through 

2030 under a contract with SCITOR Corporation. The Central Intelligence Agency’s Office of 

the Chief Scientist, serving as the Executive Agent for the DNI, supported and funded the 

contract. 

This assessment identifies and summarizes the latest peer-reviewed research related to the impact 

of climate change on India, drawing on both the literature summarized in the latest 

Intergovernmental Panel on Climate Change (IPCC) assessment reports and on other peerreviewed research literature and relevant reporting. It includes such impact as sea level rise, 

water availability, agricultural shifts, ecological disruptions and species extinctions, 

infrastructure at risk from extreme weather events (severity and frequency), and disease patterns. 

This paper addresses the extent to which regions within India are vulnerable to climate change 

impact. The targeted time frame is to 2030, although various studies referenced in this report 

have diverse time frames. The research does not draw inferences about the potential for internal 

or interstate conflict arising out of changes, e.g., in water supply or in likely migration from 

Bangladesh; such analyses will be conducted in the subsequent efforts described above. 

This assessment also identifies (Annex B) deficiencies in climate change data that would 

enhance the IC understanding of potential impact on India and other countries/regions. 
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Executive Summary 

India is both a major greenhouse gas emitter and one of the most vulnerable countries in the 

world to projected climate change. The country is already experiencing changes in climate 

and the impacts of climate change, including water stress, heat waves and drought, severe 

storms and flooding, and associated negative consequences on health and livelihoods. With a 

1.2 billion but growing population and dependence on agriculture, India probably will be 

severely impacted by continuing climate change. Global climate projections, given inherent 

uncertainties, indicate several changes in India’s future climate: 

• Global observations of melting glaciers suggest that climate change is well under way in 

the region, with glaciers receding at an average rate of 10–15 meters per year. If the rate 

increases, flooding is likely in river valleys fed by these glaciers, followed by diminished 

flows, resulting in water scarcity for drinking and irrigation. 

• All models show a trend of general warming in mean annual temperature as well as 

decreased range of diurnal temperature and enhanced precipitation over the Indian 

subcontinent. A warming of 0.5o

C is likely over all India by the year 2030 (approximately 

equal to the warming over the 20th century) and a warming of 2-4oC by the end of this 

century, with the maximum increase over northern India. Increased warming is likely to 

lead to higher levels of tropospheric ozone pollution and other air pollution in the major 

cities. 

• Increased precipitation⎯including monsoonal rains⎯is likely to come in the form of 

fewer rainy days but more days of extreme rainfall events, with increasing amounts of rain 

in each event, leading to significant flooding. Drizzle-type precipitation that replenishes 

soil moisture is likely to decrease. Most global models suggest that the Indian summer 

monsoons will intensify. The timing may also shift, causing a drying during the late summer 

growing season. Climate models also predict an earlier snowmelt, which could have a 

significant adverse effect on agricultural production. Growing emissions of aerosols from 

energy production and other sources may suppress rainfall, leading to drier conditions with 

more dust and smoke from the burning of drier vegetation, affecting both regional and global 

hydrological cycles and agricultural production. 

Uncertainties about monsoonal changes will affect farmers’ choices about which crops to plant 

and the timing of planting, reducing productivities. In addition, earlier seasonal snowmelt and 

depleting glaciers will reduce river flow needed for irrigation. The large segment of poor people 

(including smallholder farmers and landless agricultural workers) may be hardest hit, 

requiring government relief programs on a massive scale. Finally, migration, especially from 

Bangladesh, may strain resources and India-Bangladesh relations. 

The most important impacts of climate change will likely include the following: 

• Agriculture. High-input, high-output agriculture will be negatively affected even as 

demands for food and other agricultural products rise because of an increasing population 

and expectations for an improved standard of living. Millions of subsistence and smallholder 
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farmers will experience hardship and hunger through being less able to predict climate 

conditions.1 To a certain extent, trade may compensate for these deficits. 

• Water: Glacier melt may yield more runoff in the short term but less in the medium and long 

terms. More severe storms (especially cyclones) will cause more damage to infrastructure 

and livelihoods and exacerbate salt water intrusion in storm surges. Changes in the timing 

and amount of monsoon rains will make the production of food and other agricultural 

products more uncertain, so that, even in good-weather years, farmers will be more likely to 

make decisions leading to lower-productivity. 

• Exacerbation of Inequality: The welfare of those who are affected by climate change and 

who have limited means to adapt may act as a force that can change governments, strain 

public budgets, and foster unrest. About one-third of Indians are extremely poor, and 60 

percent depend upon agriculture for their livelihoods. 

• Energy: As India searches for additional sources of energy to meet rising demand, climate 

change mitigation efforts may constrain its use of indigenous and imported coal, oil, and gas, 

while development of nuclear energy will be slow at best and likely to encounter opposition. 

Other non-emitting technologies will require technology transfer and capacity-building. 

• Migration: India receives immigrants from a number of countries. Under climate change 

conditions, it may be flooded with many more, particularly from Bangladesh. Such 

migration may exacerbate tension between the two countries as well as putting a strain on 

Indian central and state governments. 

Adaptive capacity in India varies by state, geographical region, and socioeconomic status. 

Studies point to influential factors such as water availability, food security, human and social 

capital, and the ability of government (state and national levels) to buffer its people during tough 

times. Where adaptive capacity is low, the potential is greater for impacts to result in displaced 

people; deaths and damage from heat, floods, and storms; and conflicts over natural resources 

and assets. 

 

1

 The current accuracy of even current forecasts is in doubt. For example, the Indian Medium Range Weather 

Forecasting Center is not allowed to issue such forecasts in public media–that is the responsibility of the Indian 

Meteorological Department. 
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Contents 

Page 

Scope Note 1 

Executive Summary 3 

Introduction and Background 7

Projected Regional Climate Change 

Climate Observations 

Climate Predictions (Modeling) 

Projections of Future Temperature and Precipitation 

Seasonal Weather Patterns (Monsoons) 

Sea Level Changes 

Agricultural Growing Periods 

Climatic Events 

9 

11 

12 

15 

15 

17 

17 

18 

Impacts of Climate Change on Natural Ecosystems 

Observed Changes 

Projected Changes: The Example of Forests 

19 

19 

20 

Impacts of Climate Change on Human Systems 

Energy System 

Economic Growth and Development 

Food Production and Drinking Water Supply 

Human Health 

Coping Capabilities in Facing Natural Disasters 

Climate Change in Neighborhoods 

20 

20 

22 

22 

25 

26 

26 

Adaptive Capacity 

Strengths/Weaknesses in Adaptive Capacity Assessments 

27 

30 

Conclusions: High-Risk Impacts 

Agriculture 

Water 

Energy 

Exacerbation of Inequality 

Migration 

32 

32 

32 

32 

32 

32 

Annexes 

A: Accuracy of Regional Models 33 

B: Knowledge Gaps That Preclude a Full Evaluation of Climate Change 

 Impacts Affecting India and India's Adaptive Capacities 

37 

C: State/District Vulnerability and Adaptive Capacity in India 39 
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Introduction and Background 

Global climate projections, given inherent uncertainties, indicate several changes in India’s 

future climate: 

• Global observations of melting glaciers suggest that climate change is well under way in the 

region, with glaciers receding at an average rate of 10–15 meters per year.i

• If the rate of glacial melt increases, flooding is likely in the river valleys fed by these 

glaciers, followed by a diminished flow, resulting in a scarcity of water for drinking and 

agricultural irrigation. 

• All models show a trend of general warming in mean annual temperature as well as 

decreased range of diurnal temperature and enhanced precipitation over the Indian 

subcontinent.ii

• A warming of 0.5oC is likely over all India by the year 2030 (approximately equal to the 

warming over the 20th century) and a warming of 2-4oC by the end of this century, with the 

maximum increase over northern India.iii

• Increased precipitation is likely to come in the form of fewer rainy days but more days of 

extreme rainfall events, with increasing amounts of rain in each event, leading to significant 

flooding.iv Fine precipitation (drizzle-type) that replenishes soil moisture is likely to 

decrease. 

• Increased warming is likely to lead to higher levels of tropospheric ozone pollution and other 

air pollution in India’s major cities.v

• Most global models suggest that the Indian summer monsoons will intensify with a warming 

climate. The timing may also shift, causing a drying during the late summer growing 

season.vi

• Climate models also predict an earlier snowmelt, which could have significant adverse 

effects on agricultural production, both irrigated and non-irrigated.vii

• Growing emissions of aerosols from energy production and other sources may suppress 

rainfall, leading to drier conditions with more dust and smoke from the burning of drier 

vegetation, affecting both regional and global hydrological cycles and agricultural 

production.viii 

In both its greenhouse gas emissions and its vulnerability to climate change, India is one of the 

most significant countries in the world. With a large and growing population, India’s emissions 

of greenhouse gases are increasing. Moreover, potential climate impacts in India are severe: sea 

level rise, changes in the monsoon, increased severe storms and flooding, more drought, and 

severe water stress. Recently, climate variability in the form of floods and cyclones has resulted 

in destruction of crops, property and infrastructure, as well as in negative impacts on human 

health and well-being. All of these impacts set back general socio-economic development. 

Rural dwellers’ continuing dependence upon agriculture for food and livelihood (17.5 percent of 

gross domestic product (GDP) and more than 60 percent of the labor force)ix makes the Indian 

people particularly vulnerable to climate variability and change. Nowhere is this more evident 
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than in the linkage of the annual monsoon cycle and agricultural production, commonly referred 

to as, “Indian agriculture gambles with monsoon.” 

The diversity and extremes of India’s climate and geography are characteristic of its society as 

well. Religious and cultural diversity is a major feature of Indian life. The strong Hindu 

traditions have been synthesized with and challenged by other religions, notably Islam, 

Christianity, and Sikhism. India has at least 300 known languages, 24 of which have at least one 

million speakers each. There are differences, sometimes amounting to estrangement, between 

the North, with its history of grand-scale invasions, and the relatively stable South. Religious 

divisions became geographical divides when Muslim Pakistan (1947), then Bangladesh (1971), 

were created, and ethnic and caste-related strife continues among groups. However, the 

connectedness of the extended family is a core feature of Indian life. Together with a sense of 

civil society’s claims on individuals and families, the extended family knits the society together 

and emphasizes interdependence.x 

Diversity and extremes are evident in India’s patterns of economic development as well. In this 

sphere, two themes stand in contrast: modernist, democratic, and technical development, 

intensified by the economic liberalization beginning in the early 1990s; and persistent poverty, 

subsistence agriculture, and caste-based discrimination. Various models of development have 

been advanced. Dasxi characterizes the country-level model thus: “Rather than adopting the 

classic Asian strategy—exporting labor-intensive, low-priced manufactured goods to the West—

India has relied on its domestic market more than exports, consumption more than investment, 

services more than industry, and high-tech more than low-skilled manufacturing.” Kerala’s 

model of human development emphasizes education, health services, and equality; however, 

slow economic development has somewhat tarnished this model, as incomes remain low and the 

contributions of Keralans working abroad continue to be very much needed.xii The Karnataka 

model focuses on technology, centered in Bangalore, and historically participatory local 

governance by Panchayat. Yet Karnataka also has “enduring gender inequity and regional 

disparities, and a visibly increasing gap between urban and rural areas.”xiii 

Despite substantial economic and general development progress, poverty, malnutrition, illiteracy, 

and inequalityxiv continue to plague India, as well as serious environmental issues. India has not 

only several Silicon Valleys but also several Nigerias.xv In addition, the ongoing dispute with 

Pakistan over Kashmir and ethnic strife (e.g., in Assam) claim national attention. Conflict with 

Pakistan has lessened by confidence-building measures since 2002, when nuclear war was hinted 

at, but such hints arose again after the recent terrorist attack in Mumbai, although the response so 

far has been relatively temperate. 

India’s broad spectrum of highly articulated national policies includes inclusive growth goals in 

the areas of economic development, human development, and environmental protection. 

National goals are, of course, differentially implemented in each of India’s states, which exhibit 

widely varying degrees of social and economic development. Limited growth has occurred in 

the areas of fiscal policy, privatization, small-scale industry, agriculture, and labor law.xvi 

At the national level, India’s climate change policies are subsumed in its economic-industrial and 

human development policies, which come first. Local policies have had some success in limiting 

significant urban air pollution problems. Substantial improvements in local air quality in Delhi, 

for example, have resulted from recent government programs to improve the quality of petrol 

and diesel fuels, introduction of emissions standards for vehicles, and conversion of buses and 
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three-wheelers to compressed natural gas (CNG) fuel—but Kolkata and other metropolitan 

centers experience worsening air quality, with increasing combustion of fossil fuels contributing 

to carbon dioxide emissions. Generally speaking, climate change policy has been reactive rather 

than proactive and focused largely on the energy sector. According to reports from the 

Organization for Economic Cooperation and Development (OECD) and the Pew Center,xvii India, 

through normal policy developments, is “making significant progress in limiting greenhouse 

emissions” (i.e., from what emissions might have been) through energy efficiency improvements 

and environmentally friendly energy development. Also, India is participating in the Clean 

Development Mechanism of the United Nations Framework Convention on Climate Change 

(UNFCCC) and actively participating in the development of a proposed UNFCCC mechanism 

called Reducing Emissions from Deforestation and forest Degradation (REDD). The National 

Clean Development Mechanism Authority (NCDMA) is housed in the Ministry of Environment 

and Forests (MoEF); CDM India has operated since December 2003 as the Designated National 

Authority (DNA). More aggressive measures, India feels, should be financed by developed 

nations as they lead by reducing their own emissions and engaging in clean technology transfer, 

in accordance with the 1992 UNFCCC (ratified by 192 countries). 

Internationally, India has played a key role in climate negotiations at several points. India broke 

the impasse at the first Conference of the Parties by leading the development of a common 

statement that became the basis for the Berlin Mandate. More recently, India hosted the eighth 

Conference of the Parties to the Framework Convention on Climate Change in 2002. India, 

bolstered by nongovernmental organizations such as the Tata Energy Research Institute (TERI) 

and the Centre for Science and Environment (CSE), focuses on per capita emissions (low in 

India and high in most developed countries) and on cumulative emissions (also low in 

developing countries and high in developed countries), as the indicators that developed countries 

should undertake mitigation first. Government officials press developed nations to establish and 

conform to emissions reduction goals and to engage in technology transfer to developing 

countries. 

CSE rebutted the second World Resources Report,xviii making the distinction between 

“subsistence emissions” of the poor (mostly in developing countries) and the “luxury emissions” 

of the rich (mostly in developed countries). CSE has also characterized “green” policies dictated 

by the North (e.g., debt-for-nature swaps) as unwarranted interference in other nations, as 

exacerbating inequality among nations, and as likely to foster unsustainable management. 

Projected Regional Climate Change 

The current climate of India is highly diverse, ranging from the subfreezing Himalayan winters 

to the tropical climate of the south. The states of Assam and West Bengal experience extremely 

damp, rainy, and humid conditions, while the regions of Rajasthan and Gujarat make up part of 

the arid Great Indian Desert. Based on precipitation and temperature, India can be divided into 

six climatic regions: the Himalayas, Assam and West Bengal, the Indo-Gangetic Plain, the 

Western Ghats and coast, the Deccan (the interior of the Peninsula south of the Narmada River), 

and the Eastern Ghats and coast.xix 

The Indian Meteorological Service divides the year into four seasons, two of which are 

characterized by monsoon conditions. Winter occurs from December through February, when 

conditions are generally relatively dry and cool. March through May is considered to be 
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summer, as the conditions are usually hot and dry. During this period temperatures throughout 

non-Himalayan India reach the upper 30s°C and can reach as high as 48°C during the day in the 

pre-monsoon months. 

The southwest monsoon season occurs from June through September, when the predominating 

southwest maritime winds bring rains to most of the country. One branch of the southwest 

monsoon, known as the Arabian Sea monsoon, generally breaks on the west coast early in the 

season and spreads across South Asia by early July. The other, known as the Bay of Bengal 

monsoon, spreads over Assam during June and travels along the Indo-Gangetic Plain toward 

New Delhi, merging with the Arabian branch to bring rains farther north. The southwest 

monsoon provides almost 80 percent of the annual rainfall to most of the country. It is critically 

important to agricultural production; predictions of its timing are used by agronomists and 

farmers to determine optimal dates for plantings. 

The northeast monsoon occurs in October and November as the southwest monsoon retreats. 

The states of Tamil Nadu, Karnataka, and Kerala receive most of their rainfall from the northeast 

monsoon during November and December. (See Figure 1 for a map of Indian states and 

http://en.wikipedia.org/wiki/File:India_climatic_zone_map_en.svg for climate regions.) 

Interannual climate variability is linked to a global-scale, naturally occurring phenomenon 

known as the El Niño/Southern Oscillation (ENSO) cycle.2 ENSO can explain some of the interannual rainfall variability over the subcontinent of India and can affect the location and activity 

of tropical storms. Analysis of observational data shows a significant correlation between ENSO 

and tropical circulation and precipitation such that there is a tendency for less Indian summer 

monsoon rainfall in El Niño years and above-normal rainfall in La Niña years. However, the 

effect is not linear. The ability to accurately predict the timing and occurrence of the ENSO 

phenomenon is extremely important to agricultural production. 

 

2

 The terms El Niño and La Niña represent opposite extremes of the ENSO cycle. El Niño refers to the aboveaverage sea-surface temperatures that periodically develop across the east-central equatorial Pacific. It represents 

the warm phase of the ENSO cycle and is sometimes referred to as a Pacific warm episode. La Niña refers to the 

periodic cooling of sea-surface temperatures across the east-central equatorial Pacific. It represents the cold phase 

of the ENSO cycle, and is sometimes referred to as a Pacific cold episode. 
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Figure 1. Indian States. 

Climate Observations 

Global observations suggest that climate change is well under way. At continental, regional, and 

ocean basin scales, numerous long-term changes in climate have been observed, including 

widespread changes in precipitation amounts; ocean salinity; wind patterns; and aspects of 

extreme weather including droughts, heavy precipitation, heat waves, and the intensity of 

tropical cyclones. Studies of the tropical Indo-Pacific region show unusual warmth in the 20th

century, and many isotope records show a trend toward warmer conditions in the tropical Indian 

Ocean. In most multi-centennial coral series, the late 20th century is shown to be warmer than 

any time in the past 100 to 300 years. 

Some studies have suggested that the South Asian (Indian) monsoon, in the drier areas of its 

influence (northwest India), has recently reversed its millennia-long orbitally driven, lowfrequency trend toward less rainfall. This recent reversal in monsoon rainfall also appears to 

coincide with a synchronous increase in inferred monsoon winds over the western Arabian Sea, a 

change that could be related to increased summer heating over and around the Tibetan Plateau. 

Globally, estimates of the potential destructiveness of tropical storms and hurricanes show a 

substantial upward trend since the mid-1970s, with a trend toward longer storm duration and 

greater storm intensity. Storm activity is generally correlated with tropical sea surface 

temperature. 

The distributions of global minimum and maximum temperatures have shifted to higher values, 

consistent with overall warming.xx More warm extremes imply an increased frequency of heat 

waves. However, cold extremes have warmed more than the warm extremes over the past 50 
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years. Further indications include the observed trend toward fewer frost days associated with the 

average warming in most mid-latitude regions. A prominent indication of a change in extremes 

is the evidence of increases in heavy precipitation events over the mid-latitudes in the past 50 

years, even in places where mean precipitation amounts are not increasing. For very heavy 

precipitation events, increases are reported as well, but results are available for only a few areas. 

Recent warming in sea surface temperatures (SSTs) is strongly evident at all latitudes, although 

there are inter-hemispheric differences. Much of the surface of the Indian Ocean has warmed 

since 1955, with the major exception of the 5°S to 20°S latitude belt. The Southern Ocean (south 

of 35°S) in the Atlantic, Indian, and Pacific sectors has generally warmed. The regions that 

exhibit cooling are mainly in the shallow equatorial areas and in some high-latitude regions. In 

the Indian Ocean, cooling occurs at subsurface depths centered on 12°S (South Equatorial 

Current) at 150 m depth and in the Pacific centered on the equator and 150 m depth. In the 

tropical and eastern subtropical Indian Ocean (north of 10°S), warming in the upper 100 m is 

consistent with the significant warming of the sea surface from 1900 to 1999. The surface 

warming trend during the 1900 to 1970 period was relatively weak but increased significantly in 

the 1970 to 1999 period, with some regions exceeding 0.2°C per decade. Models suggest that 

upper-ocean warming in the South Indian Ocean can be attributed to a reduction in the strength 

of the southeast trade winds and associated decrease in the southward transport of heat from the 

tropics to the subtropics. 

Local and regional changes in the character of precipitation also depend a great deal on 

atmospheric circulation patterns determined by El Niño, the North Atlantic Oscillation (NAO),xxi

and other patterns of variability. India’s rainfall features show strong variability but little in the 

way of a century-scale trend, even as the linear trends of rainfall decreases for 1900 to 2005 were 

7.5 percent in western Africa and a similar decrease was observed when averaged over the 

broader southern Asia region as a whole. Over much of northwestern India, the 1901 to 2005 

period shows precipitation increases of more than 20 percent per century, but the same area 

shows a strong decrease in annual precipitation in the 1979 to 2005 period. 

Very dry land areas across the globe have more than doubled in area since the 1970s, an 

observation that has been associated with precipitation decreases related to ENSO and with 

subsequent increases primarily due to surface warming. The tendency of the warming to be 

more pronounced in winter is a conspicuous feature of the observed temperature trends over 

India, one that is likely to continue. 

Climate Predictions (Modeling) 

While Global Circulation (or Climate) Models (GCMs) can be used to infer climate changes in 

specific regions, it is far preferable to develop models that have a high resolution sufficient to 

resolve local and regional scale changes. There are many challenges in reliably simulating and 

attributing observed temperature changes at regional and local scales. At these scales, natural 

climate variability can be relatively greater, making it harder to distinguish long-term changes 

expected due to external radiative forcings.3

 

 

3

 Radiative forcings are changes in the net irradiance at the tropopause resulting from a change in an external driver 

of climate change, such as carbon dioxide or the output of the Sun. These changes in net irradiance are expressed as 

watts per meter squared (Wm2

). 
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The procedure of estimating the response at local scales based on results predicted at larger 

scales is known as “downscaling.” The two main methods for deriving information about the 

local climate are (1) dynamical downscaling (also referred to as “nested modeling” using 

“regional climate models” or “limited area models”) and (2) statistical downscaling (also referred 

to as “empirical” or “statistical-empirical” downscaling).xxii Chemical composition models 

include the emission of gases and particles as inputs and the simulation of their chemical 

interactions; global transport by the winds; and removal by rain, snow and deposition to the 

earth’s surface. 

Downscaled regional scale climate models rely on global models to provide boundary conditions 

for the region to be modeled. There are three primary approaches to numerical downscaling: 

(1) limited-area models, (2) stretched-grid models, and (3) uniformly high resolution 

atmospheric GCMs (AGCMs). 

Regional Climate Model (RCM) projections for climate change in India were compared for eight 

models using several different IPCC scenarios at time intervals of 20 years. All models show 

positive trends of widespread warming (Figure 2), with warming more pronounced during winter 

and post-monsoon months compared to the rest of the year (i.e., seasonal changes) consistent 

with recent observations. 

Figure 2. Temperature anomalies with respect to 1901-1950 for six Asian land regions 1906-2005 (black 

line) and as simulated (red envelope) by multi-model dataset (MMD) models. 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. Miller Jr. and Z. Chen (Cambridge: 

Cambridge University Press 2007): 882. 

Although some models show a slight decrease in precipitation over all India during the first few 

decades of the study, all models show increased precipitation during the monsoon season by the 

year 2100, especially over the northwestern parts of India, an area that is currently very dry. One 

model projects that by 2050 there will be an overall decrease in the number of rainy days over all 

India but an increase in the number of one-day extreme rainfall events. The magnitudes and 

patterns of the projected rainfall changes differ significantly between models, probably due to 

their coarse resolution. 

The Atlantic and Pacific Oceans are strongly influenced by natural variability occurring on 

decadal scales, but the Indian Ocean appears to be exhibiting a steady warming. Natural 
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variability (from ENSO, for example) in ocean-atmosphere dynamics can lead to important 

differences in regional rates of surface-ocean warming that affect the atmospheric circulation and 

hence warming over land surfaces. New modeling efforts have shown improvements in climate 

forecasts in the southern hemisphere when the incidence of the Indian Ocean Dipole (IOD) is 

included.xxiii The IOD occurs periodically when there is a cold water upwelling in the eastern 

Indian Ocean, during which the eastern half of the ocean becomes much cooler than the western 

half. 

The recent IPCC assessment report suggests that, in the future, circulation associated with the 

monsoon may slow down, but the moisture in the air may increase. However, the representation 

of the observed monsoon maximum rainfall along the west coast of India, northern parts of Bay 

of Bengal, and north India is poor in many models. 

A comparison of four different GCM models applied to the Indian subcontinentxxiv found a 

significant spread in the summer monsoon precipitation anomalies. Soil moisture was 

determined to be an important parameter in model projections. Most models project increased 

precipitation during the monsoon season, particularly over the northwestern parts of India, with 

differences in the magnitudes of the increase from one model to the other. West central India 

shows maximum overall increase in rainfall. Extremes in both temperature and precipitation 

events are likely to increase out to 2030. 

Including sulfate aerosols in the models reduces the regional climate sensitivity, but the 

greenhouse warming still dominates the changes at the larger scale and longer time scale. 

Models that include emissions of short-lived radiatively active gases and particles suggest that 

future climate changes could significantly increase maximum ozone levels in already polluted 

regions. Projected growth of emissions of radiatively active gases and particles in the models 

suggest that they may significantly influence the climate, even out to year 2100.xxv Atmospheric 

brown clouds, plumes of polluted air moving from the Asian continent out over the Pacific and 

Indian Ocean, may cause changes in the monsoon circulation and reduce summer monsoon 

precipitation in parts of South and East Asia. 

Global models that are unable to include small-scale spatial geographical features and distortions 

of albedo feedbacks are also unable to project precipitation over the Himalayas. It is expected 

that the net effect of a warming climate will be an increase in the rainfall associated with the 

monsoon. Several models suggest that throughout Asia there will be an overall decrease in the 

December-February precipitation, an increase in the remaining months, and more intense rainfall 

over larger areas in the future. 

Stabilization emissions scenarios assume future emissions based on an internally consistent set of 

assumptions about driving forces (such as population, socioeconomic development, and 

technological change) and their key relationships. These emissions are constrained so that the 

resulting atmospheric concentrations of the substance level off at a predetermined value in the 

future. For example, if one assumes the global CO2 concentrations are stabilized at 450 parts per 

million (ppm) (the current value is about 380 ppm), the climate models can simulate the climate 

consistent with the emissions of greenhouse gases and short-lived species of this scenario. The 

climate model predictions can be used to assess specific regional impacts at this stabilization 

level. 
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A recent study of models from the Coupled Model Intercomparison Project showed that the 

models’ response to a +1 percent/yr increase in CO2 includes a substantial warming of tropical 

storm basin SSTs, enhanced upper-tropospheric warming relative to the surface warming, and 

little change in lower-tropospheric relative humidity. The study included climate change 

scenarios from nine different global coupled climate models as inputs to an idealized hurricane 

model. The results demonstrate that there is a significant sensitivity in the models to the choice 

of climate model and convective parameterization. 

A more detailed discussion of the ability of the models to project regional climate changes can be 

found in Annex A.xxvi 

Projections of Future Temperature and Precipitation 

Current projections of climate change from the latest IPCC Assessmentxxvii suggest increases in 

temperatures, precipitation rates, and the intensity of tropical storms over the Indian Ocean. 

Most models either assume a doubling of CO2 and their results can be compared using this 

assumption, or the models are run to the year 2100 and the results of the climate projections are 

assessed for that date. The IPCC has not included numerical model results specifically for the 

year 2030. Kumar et al.xxviii have modeled climate changes on the Indian subcontinent for 

intermediate scenarios at 2020 and 2050; however, only the results for the final scenario for 2080 

have been published. An earlier studyxxix used eight global models to project temperature and 

precipitation changes over all India per decade for three different emission scenarios. Although 

the tables presented in this reference are useful, the uncertainties, especially in the prediction of 

precipitation changes, are great. 

Most AGCMS predict general warming and enhanced rainfall over India, with these changes 

becoming particularly significant by 2040.xxx Kumar et al. use the model Providing Regional 

Climates for Impacts Studies (PRECIS), developed by the Hadley Centre for Climate Prediction 

and Research, to yield projections for climate change during intermediate time scales (every 10 

years until 2100); these projections can be averaged over model results to estimate climate 

change in 2030.xxxi The models project an average temperature increase over all India by the 

year 2030 of around 0.5o

C, an increase comparable with that that has occurred over the globe 

during the 20th century. The maximum temperature increase (i.e., annual mean temperature 

change) is expected to occur over northern India, with a secondary maximum over the eastern 

peninsula. By the year 2100, the temperature increase could be on the order of 2-4o

C, with a 

maximum increase in the northern region of 4o

C. 

A regional model recently developed at the Indian Institute of Tropical Meteorology (IITM) 

projected that, averaged over the country, India could face a temperature increase of 4°C by 

2100. However, the model predicts that, because of the longer time scales for system-wide 

changes in atmosphere-ocean interactions, a delay in the increase in monsoon rainfall will 

occur,xxxii resulting in drought in some areas. 

Seasonal Weather Patterns (Monsoons) 

Monsoon rains account for most of India’s annual rainfall. Monsoons are generally defined as 

tropical and subtropical seasonal reversals in both the surface winds and associated precipitation. 

The strongest monsoons occur over the tropics of southern and eastern Asia and northern 

Australia as well as parts of western and central Africa. Rainfall is the most important monsoon 

variable because the associated latent heat release drives atmospheric circulations and because 
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rainfall plays a critical role in the global hydrological cycle and is vital to socioeconomic 

impacts. 

Most global models suggest that the Indian monsoons will intensify with a warming climate.xxxiii 

Since the continental-scale land-sea thermal contrast is expected to become larger in summer and 

smaller in winter, one would expect that in the future the summer monsoon will be stronger and 

the winter monsoon will be weaker than they are presently. However, some models predict that 

a pronounced warming over the tropics will result in a weakening of the Asian summer monsoon 

circulations due to a reduction in the meridional thermal gradients between the Asian continent 

and adjacent oceans. 

The global monsoon system embraces an overturning circulation that is intimately associated 

with the seasonal variation of monsoon precipitation over all major continents and adjacent 

oceans. The Asian monsoon can be divided into the East Asian and the South Asian or Indian 

monsoon systems. Although the Indian monsoons recur each year, their irregularity at a range of 

time scales from weeks to years depends on feedback from the ocean in ways that are not fully 

understood. Intra-seasonal variability is associated with the Monsoon Intra-Seasonal Oscillation 

(MISO) and the Madden-Julian Oscillation (MJO), which are long-lasting weather patterns that 

evolve in a systematic way for periods of four to eight weeks. On an interannual and decadal 

scale, statistical methods have shown that, while there are periods of high correlation between 

ENSO and monsoon variation, there are decades in which little or no association is apparent. 

The influence of ENSO on the position and strength of the subtropical high in the North Pacific 

influences both typhoons and other damaging heavy rainfall events and has been implicated in 

observed inter-decadal variations in typhoon tracks. This suggests that the spatial structure of 

warming in the Pacific will be relevant for changes. 

For South Asia, the monsoon depressions and tropical cyclones generated over the Indian seas 

modulate the monsoon anomalies. For East Asia, the monsoonal circulations are strengthened by 

extratropical cyclones energized in the lee of the Tibetan Plateau and by the strong temperature 

gradient along the East Coast. 

Although attention is often focused on the frequency or number of storms, the intensity, size, and 

duration are much more important. From an observational perspective, then, key issues are the 

tropical storm formation regions; the frequency, intensity, duration, and tracks of tropical storms; 

and associated precipitation. All of these can be influenced by climate teleconnections,xxxiv

especially those such as the Indian dipole oscillation (IOD) and the Southern Annular Mode 

(SAM) Index. Annular modes are hemispheric scale patterns of climate variability.xxxv SAM is 

linked to variations in temperatures over Antarctica, sea-surface temperatures throughout the 

Southern Ocean, and the distribution of sea-ice around the perimeter of Antarctica. 

New evidence indicates that increased aerosol loading4 in the atmosphere may also have strong 

impacts on monsoon evolution through changes in local heating of the atmosphere and land 

surface. Polluted air can also have an effect on local circulation patterns. Heating of a lofted 

dust layer in the Tibetan Plateau could act as an elevated heat pump to strengthen the Asian 

summer monsoon circulation and cause a local increase in precipitation, despite the global 

reduction of evaporation that accompanies the aerosol-induced reduction in shortwave radiation 

 

4

 Aerosols are very small particles that influence the climate in several ways. They are both emitted (e.g., via dust 

storms or from smokestacks) and form in the atmosphere. 
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at the surface. The dust-induced thermal contrast changes between the Eurasian continent and 

the surrounding oceans are postulated to trigger or modulate a rapidly varying or unstable Asian 

winter monsoon circulation, with a feedback to reduce the dust emission from its sources. 

Sea Level Changes 

A significant fraction of sea level rise is due to thermal expansion of a warmed ocean (as much 

as 0.3 to 0.8 m over the past century, according to the 2007 IPCC report). Geographic patterns 

of sea level rise are due mainly to changes in the distribution of heat and salinity in the ocean, 

resulting in changes in ocean circulation. Precise satellite measurements since 1993 show that 

the largest sea level rise since 1992 has taken place in the western Pacific and eastern Indian 

Oceans, with the potential for significant impacts on the east coast of India. The 2004 Indian 

National Communication to the UNFCCC states that sea level rise is highest in the Gulf of Kutch 

(Gujarat) and on the coast of West Bengal.xxxvi There is a large interannual variability in sea 

level rise associated with patterns of coupled ocean-atmosphere variability, including ENSO and 

the NAO. 

Projected patterns of sea level rise display more similarity across models than in past 

assessments. Common features include a narrow band of pronounced sea level rise stretching 

across the southern Atlantic and Indian Oceans. Sea level is projected to rise between the 

present (1980-1999) and the end of this century (2090-2099) by 0.35 m (0.23 to 0.47 m). Due to 

ocean density and circulation changes, the distribution will not be uniform. 

Agricultural Growing Periods 

Many regions of India already face water scarcity. Productive agricultural regions in the North 

depend on the spring snowmelt to replenish regional water supplies. Climate models predict an 

earlier snowmelt, which could have a significant effect on agricultural production, especially if 

the levels of moisture in the soils are reduced during the growing season. 

The retreat of snow and ice cover in and around the Himalayas is already having a drying effect 

on these regions. A recent study of the melting Naimona'ny glacier in the Himalayas, which 

provides water to the Indus, and Brahmaputra Rivers shows that the glacier has melted so much 

that the exposed surface of the glacier dated to 1944.xxxvii 

A study of glaciers in the region shows that they are now receding at an average rate of 10-15 

meters per year.xxxviii Himalayan glaciers collect water during the monsoon season and release it 

during the dry season, providing irrigation water for crops. If the rate of glacial melt increases, 

flooding is likely to occur in the river valleys fed by the glaciers. Later, as the river flows 

decrease to below previous rates, many people may be left without sufficient drinking water or 

water for irrigating crops. 

Decreasing trends in evapotranspiration during recent decades are evident in records, even 

though such records are sparse. This is likely due to decreased sunshine duration related to 

increases in air pollution, atmospheric aerosols, and increases in cloud cover. An accelerating 

trend in sulfate deposition has been observed in Himalayan glaciers and is probably due to 

increased sulfur dioxide emissions from the increasing energy demand throughout Asia. The 

concentration of sulfate deposited in the glaciers in the past 50 years exceeded that for any prior 

50-year period in the last millennium.xxxix 

A model to predict changes in river flows due to future glacial melt from climate change was 

developed by the Centre for Ecology and Hydrology in the United Kingdom. Under different 
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climate scenarios, the model predicts that in the upper Indus there will be an initial increase of 

between +14 percent and +90 percent in mean flows (compared to baseline) over the first few 

decades of the 100-year incremental scenario runs. By decade 10, river flows will decrease 

between –30 percent and –90 percent of baseline.xl 

During the Indian Ocean Experimentxli that focused on emissions of human-generated aerosols 

from the Indian sub-continent, local forcing at the surface was observed to be significantly 

stronger than that at the top of the atmosphere. These results indicate that absorption of solar 

radiation by aerosols, primarily black carbon in the atmospheric column, is of great significance. 

It has been suggested that absorbing aerosols may have masked up to 50 percent of the surface 

warming in South Asia from the global increase in greenhouse gases. In cases where aerosols 

act to suppress rainfall (the second aerosol indirect effect), drier conditions tend to induce more 

dust and smoke due to the burning of drier vegetation, affecting both regional and global 

hydrological cycles and agricultural production. However, more research is needed to 

understand the combined effects of aerosols and dusts, which may influence the monsoon 

circulation and hydrological cycle in different ways. 

Climatic Events 

Extremes are the infrequent events at the high and low end of the range of values of a particular 

variable. The probability of occurrence of values in this range is called a probability distribution 

function (PDF) that for some variables is shaped similarly to a “Normal” or “Gaussian” curve 

(the familiar bell-shaped curve). 

People affected by an extreme weather event (e.g., the heavy rainfall in Mumbai in July 2005) 

wonder whether climate changes due to human influences are responsible. It is difficult to 

attribute any individual event to a change in the climate. In most regions, instrumental records of 

variability typically extend only over about 150 years, so there is limited information to 

characterize how extreme rare climatic events could be. Further, several factors usually need to 

combine to produce an extreme event, so linking a particular extreme event to a single, specific 

cause is problematic. In some cases, it may be possible to estimate the anthropogenic 

contribution to such changes in the probability of occurrence of extremes. 

As the climate changes and SSTs continue to increase, the conditions that cause tropical storms 

to form are no longer the same. Higher SSTs are generally accompanied by increased water 

vapor in the lower troposphere; thus, the moist static energy that fuels convection and 

thunderstorms is also increased. Hurricanes and typhoons currently form from pre-existing 

disturbances only where SSTs exceed about 26°C; so, as SSTs have increased, the areas over 

which such storms can form are potentially expanded. However, many other environmental 

factors also influence the generation and tracks of disturbances. 

The 2007 IPCC assessment concluded that there was a risk of increased temperature extremes in 

India, with more extreme heat episodes in a future climate. This result has been confirmed and 

expanded in more recent studies. Future increases in temperature extremes are projected to 

follow increases in mean temperature over most of the world except where surface properties 

(e.g., snow cover or soil moisture) change. There is still much debate over whether there is 

likely to be an increase in tropical cyclone intensity. 

Changes in tropical storm and hurricane frequency and intensity are often masked by large 

natural variability. ENSO greatly affects the location and activity of tropical storms around the 
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world. Globally, estimates of the potential destructiveness of hurricanes show a substantial 

upward trend since the mid-1970s, with a trend toward longer storm duration and greater storm 

intensity, and this activity is strongly correlated with tropical SSTs. One study found a large 

increase in numbers and proportion of hurricanes reaching categories four and five globally since 

1970, even as the total number of cyclones and cyclone days decreased slightly in most basins. 

The largest increase was in the North Pacific, Indian and Southwest Pacific Oceans. 

Improved models, ones that prescribed convection constraints based on the relative humidity, 

were able to simulate the variability and extremes of rainfall quite well over most of India when 

compared to satellite-derived rainfall but had a tendency to overestimate heavy rainfall events in 

central India. 

Several recent studies have addressed possible future changes in heat waves and found that in a 

future climate, heat waves are expected to be more intense, longer-lasting and more frequent. 

Based on an eight-member multi-model ensemble, heat waves are simulated to have increased 

for the latter part of the 20th century and are projected to increase globally and over most regions. 

Impacts of Climate Change on Natural Ecosystems 

Observed Changes 

An analysis of seasonal and annual surface air temperatures for India, using data for 1881–2001 

for 25 or more stations, shows a significant annual mean warming of 0.68°C per hundred years. 

Most of the warming occurs in the post-monsoon and winter seasons. The monsoon 

temperatures do not show a significant trend in most parts of the country except for a significant 

negative trend over northwest India. Maximum daytime temperatures show more of a trend than 

minimum nighttime temperatures,xlii in contrast to general expectation. 

Water supply is changing. Almost 67 percent of the glaciers in the Himalayan mountain ranges, 

the source of major rivers in India, have retreated in the past decade.xliii 

For India, the IPCCxliv reports increased frequency of hot days and multiple-day heat waves in 

the past century, with more deaths attributable to heat stress in recent years. Consecutive 

droughts in 1999 and 2000 led to a sharp decline in water tables in the northwest, and 2000-2002 

droughts caused crop failures, leading to mass starvation and impacts on ~11 million people in 

Orissa. 

Management decisions about natural hazards can cause conflicts. In 2002, under drought 

conditions in Andhra Pradesh, the state released dam water for electricity generation but not for 

irrigation. Poor farmers responded to such policies by smashing the pumps of their richer 

neighbors. Quarrels over water rights between states can be bitter, too. Tamil Nadu claimed that 

its neighboring state, Karnataka, violated agreements about sharing water from the Cauvery 

River.xlv Bangalore is facing acute water scarcity as it attempts to meet the drinking water needs 

of 7 million people in the city.xlvi 

Public health is affected by currently experienced climate variability and change in the forms of 

heat and flooding. Between 1980 and 1998, 18 heat waves were reported in India; one in 1988 

affected ten states and caused 1,300 deaths. Heat waves in Orissa, in the 1998-2000 period 

caused an estimated 2,120 deaths, and heat waves in 2003 in Andhra Pradesh caused more than 

3,000 deaths.xlvii Flood-related increases in diarrheal disease have also been reported in India.

xlviii 
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Observed trends in the mean sea level along the Indian coast indicate a rising trend of about 1 cm 

per decade, which is close to that recorded in other parts of the globe. Today, coastal regions in 

India and Bangladesh are subjected to stronger wind and flood damage than in the past because 

of stronger storm surges associated with tropical storms.xlix 

Projected Changes: The Example of Forests 

Based on the IPCC’s Special Report on Emission Scenarios, a study by Ravindranath et al. 

(2006)l

 investigated the impacts of climate change on Indian forests into the year 2085 under two 

emission scenarios: A2 (740 ppm CO2) and B2 (575 ppm CO2). Globally, A2 is the more 

extreme scenario, representing a growing human population and slower and inequitable 

economic development whereby atmospheric CO2 concentration is projected to double by 2050 

and is likely to increase to 740 ppm by 2085. B2 represents moderate population growth, 

intermediate levels of economic development, adoption of environmentally sound technologies, 

and greater social equity. 

Using the B2 scenario projections and Forest Service of India’s categories of forests, a pattern 

emerges: colder forests are subject to a larger increase of about 3°C, while the Western Ghat 

evergreen forests become warmer by only about 2.4°C on average—compared to the national 

average of 2.9°C. For the A2 scenario, the magnitudes of the impacts are larger. Most of the 

forests show an increase of about 4°C with the northern temperate forests undergoing a 

temperature increase of around 4.6°C. Western Ghats evergreen, semi-evergreen, and mangrove 

forest types show the least impacts under both A2 and B2 scenarios. 

Even with a conservative temperature increase of 1–2°C, most ecosystems and landscapes will 

be impacted through changes in species composition, productivity and biodiversity. Impacts to 

nearly 200,000 forest villages—naturally heavily dependant on forest resources—will be 

innumerable. Impacts to the country as a whole are also projected by way of economically 

important forest types, such as Tectona grandis, Shorea robusta, bamboo, upland hardwoods and 

pine. A clear possibility of a large-scale shift in forest types in India is projected for the period 

2070 to 2100, with adverse implications for biodiversity and a nearly 70 percent increase in net 

primary productivity of forest types, with implications for biomass production and timber 

markets. 

India’s forests are already changing because of socioeconomic pressures; virgin forest areas are 

less dense and monocultures and plantations are preferred to native species. These conditions 

will be greatly exacerbated by climate change. Specifically, biodiversity is likely to be reduced 

under the projected climate scenarios representing changes or shifts in forest or vegetation types, 

forest dieback during the transient phase, and different response changes of species to climate 

changes even when there is no change in forest type.li

 

Impacts of Climate Change on Human Systems 

Energy System 

Primary energy demand in 2005 was roughly equivalent to that of Japan—but of course with 

many more people, India’s per capita demand remains at about one-tenth of the OECD average. 

However, Indian demand is growing at a fast pace, 3.2 percent per year (2000-2005).lii The 

International Energy Agency (IEA)liii projects India’s primary energy demand to more than 

double by 2030 in a reference scenario (i.e., no policies to slow demand); this projection is 

attributable largely to a projected annual growth rate for GDP of 6.3 percent. The primary 
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energy demand growth areas will be industrial (mostly steel) and transport (although two- and 

three-wheelers will still be over half of vehicle stock). In this scenario, India will become the 

world’s third-largest carbon dioxide emitter by 2030, although per-capita emissions will still be 

comparatively low. 

Inequality is such an issue in the energy sector that the IEA devoted a chapter to it, “Focus on 

Energy Poverty.” liv Approximately 412 million people are still without access to electricity in 

India. The use of fuelwood and dung for cooking and heating causes more than 400,000 

premature deaths annually, mostly of women and children. Income disparities account for most 

of the energy access disparities, but other barriers include “unreliable energy service delivery, 

ineffective and regressive subsidies, gender discrimination in policy planning, inadequate 

information about the health impacts of current fuels and technologies, and administrative 

hurdles in getting connections.” lv “Energy poverty” is one aspect of a concern in India that its 

booming economic conditions have benefited the “haves” but not the “have-nots.” 

India’s current electrical system runs mostly on domestic coal: 82.7 percent fossil fuel, 14.5 

percent hydropower, and 3.4 percent nuclear.lvi The transportation sector runs mostly on 

imported oil; domestic production is 785,000 bbl/day against a demand of 2.45 million bbl/day 

(2004 estimate).lvii OECD/IEA characterized the overall energy system as fueled “largely by 

coal and combined renewables and waste, with much smaller but growing shares of oil, gas, 

nuclear, and hydro.”lviii Future increases in energy production will likely include the following: 

• Domestic coal—although deposits are located at some distance from population centers and 

the coal is of low quality. (Higher quality coal is imported for steel making; India is the 

seventh-largest steel producer in the world.lix) Coal gasification could also be used to make 

diesel fuel.lx However, the use of coal contributes to anthropogenic climate change and bad 

air quality, so pressures to reduce greenhouse gas and other emissions could limit both 

options for coal use in India, unless carbon dioxide capture and storage technologies could be 

implemented.lxi

• Imported oil and gas—with associated issues of investing in oil fields, exploitation rights, 

and refineries in Myanmar, Sudan, Iraq, Russia, Vietnam, Venezuela, and Libya,lxii as well as 

raising the problematic prospect of a gas pipeline running through either Iran and Pakistan or 

Turkmenistan, Afghanistan, and Pakistan. 

• Nuclear power—in part as a fruit of the 2005 “strategic partnership” with the United States, 

raising concerns about nuclear nonproliferation. 

• Renewable sources—such as biomass fuels (waste and purpose-grown crops) and 

hydropower. Residential use of biomass (fuelwood, dung, and agricultural waste) is 

projected to change from a current 54 percent share to 12 percent in urban households, but a 

smaller 92-to-79 percent decline in rural households still means that the absolute amount of 

biomass used will increase. Biofuel crops, like other crops, will be affected by climate 

change;lxiii hydropower, like other water uses, will be subject to changes in precipitation 

under climate change. 

• Domestic gas reserves—with 2002 and subsequent discoveries in the Krishna-Godavari basin 

(in Andhra Pradesh on the east coast) and more discoveries expected.lxiv 
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In all of these areas, advanced technologies will be essential to increasing production and 

meeting environmental regulations, including those related to climate change mitigation and 

adaptation. The Indian Government has introduced clean coal technologies like coal washing 

and the use of cleaner and less carbon-intensive fuels, e.g., liquefied petroleum gas (LPG) for 

automobiles and motor spirit-ethanol blending projects in selected states.lxv 

Economic Growth and Development 

Since 1990, India as a country has moved aggressively from a centrally planned economy to 

private ownership of businesses and trade liberalization. It has “developed a diversified 

industrial base and sophisticated financial sector. Its software subsector—one of the most 

dynamic in the world—has experienced a sustained and rapid growth.”lxvi Over the past 25 

years, the annual growth rate has been 6 percent, accelerating to a five-year annual average of 

8.8 percent,lxvii investment at 30 percent of GDP, and booming foreign direct investment. India 

has made substantial strides in fostering human capital by reducing infant mortality, increasing 

life expectancy, and improving literacy. The central, democratic government provides stability 

and some curbs to unbridled free market-ism (or barriers to growth in an alternative 

characterization), and hordes of entrepreneurs provide the impetus for growth. 

On the positive side, India’s democracy results in equity slightly higher than the global average. 

The dependency ratio (the percentage of the population dependent on the percentage of the 

population in the work force) is relatively high, indicating that many people are available for the 

work force, supporting relatively few people other than themselves. 

However, the poor condition of people engaged in agriculture and/or born into lower castes 

reduces the robustness of the overall economy. Climate change, adding to existing problems of 

the agricultural system, may worsen conditions for the large poor segment of the population 

enough to severely tax the economic and industrial resources of the central and state 

governments. 

Thus, the impacts of climate change are likely to be felt first and foremost in the agricultural 

sector and associated water availability, with many people affected by lower food productivity 

(e.g., hunger, malnutrition, and its consequences for education and productive economic life) and 

burdens on the central and state governments in dealing with smallholders and landless workers 

who will require assistance. Educational and employment inequalities will exacerbate these 

conditions. Some (or many) of these workers will migrate to urban areas, placing stress on 

cities. The need to add to or replace infrastructure affected by climate change (e.g., in the energy 

and transportation sectors, as well as irrigation systems) will present additional economic costs. 

Finally, migrants, particularly from Bangladesh, will affect India’s economy by providing 

competition for low cost labor. 

Food Production and Drinking Water Supply 

Agriculture and water are inseparable issues, as almost all the water use in India is for irrigation 

to support high productivity in agriculture. Thus, both the monsoon onset and active/break phase 

mean a great deal to the agriculture sector. Research indicates that the monsoon active/break 

phase is related to a tropical phenomenon called the Madden Julian Oscillation (MJO), which has 

a periodicity of 30-70 days, with a predictability of 20-30 days. Predicting the MJO has 
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important implications to predicting subseasonal5 rainfall variability. If farmers sow too early 

and the crop needs water when the monsoon break phase occurs, the whole crop is lost. 

At the national level, India has abundant water resources for agriculture and drinking, as well as 

for cooking and sanitation uses. India withdraws 34 percent of its available water annually.lxviii 

However, at state and local levels, there are wide-ranging differences; overexploitation is already 

problematic and will likely become worse under climate change conditions. 

Increasing temperatures and increased seasonal variability will likely cause Himalayan glaciers 

to melt more and more quickly, also leading to increasing danger from floods as glacial lakes 

burst out of their natural bounds. Changes over decades will include less flow in rivers fed today 

by snow and ice, with impacts on hydropower, urban water supply, and agriculture.lxix Although 

there is clear evidence of de-glaciation across the Himalayas, the effect on river flows is likely to 

be substantially different in different areas. The extent of the changes is very uncertain because, 

as Sengupta puts it, “River flow data is so scant and recent that it is impossible for scientists to 

predict how the current rates of glacial retreat will affect river volume.”lxx 

There are major regions, including many of the most highly productive agricultural and industrial 

regions of India, where water scarcity is already felt in day-to-day life. The retreats of snow and 

ice cover are important for several local climates, especially those near the Himalayas. The 

drying effect of an earlier spring snowmelt and, more generally, the earlier reduction in soil 

moisture is a continuing concern. Already 15 percent of aquifers are in critical condition, a 

figure that is projected to increase to a “frightening” 60 percent by the year 2030.lxxi 

Especially in important agricultural areas that have benefited from the Green Revolution, 

irrigation has led to exploitation of groundwater resources—e.g., 94 percent in Punjab, 84 

percent in Haryana, 60 percent in Tamil Nadu, and 51 percent in Rajasthan.lxxii Farmers who 

borrow to dig bore wells for crop irrigation may find that when groundwater tables decline, they 

cannot repay their loans; some probably will commit suicide. Groundwater depletion has been 

accelerated by the provision of free or subsidized electricity for agriculture: subsidized rates on 

irrigation pumps, some state-level free power policies, and supports for agricultural 

consumers.

lxxiii In many places (northwest India as well as Pakistan) irrigation has resulted in not 

only overexploitation of water resources, but also increased soil salinity, waterlogging, and 

siltation of river basins. These problems have been intensified by declining soil fertility, 

increasing pest damage, and decreasing genetic diversity in crops.lxxiv 

Environmental and economic leaderslxxv have called for a second Green Revolution as rates of 

growth in agricultural production have slowed. This would place more pressure on water 

resources for irrigation. To partially compensate for increased water demand, the efficiency of 

irrigation systems can be improved. And in recent years, Water Users Associations (WUAs) 

have formed to conduct participatory irrigation management; lxxvi these may promote 

conservation and water use efficiency. 

Rainfed agriculture (65 percent of cropped land)lxxvii will experience climate change impacts, too. 

Without irrigation, smallholder farmers and landless agricultural workers will likely face 

household food shortages and loss of livelihoods, requiring government assistance or even 

international aid, as well as pressures on cities to accommodate migrants from rural areas. 

 

5

 “Subseasonal” refers to any timescale shorter than a season. 
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Cultural Willingness to Change 

Even if scientists knew unequivocally how climate change will manifest itself, traditional 

knowledge and practices would need to be accounted for and integrated into responses. 

Change is never as simple as providing information that immediately alters people’s 

worldviews and ways of doing things. Selvaraju et al. (2004) explored approaches to 

help smallholder farmers to benefit from seasonal forecasting. Traditional knowledge in 

Avinashi (southern India) includes such rules of thumb as, “If it rains on the 10th of Adi 

(July) ... the rainfall in the succeeding season will be good” and, “If the breeze is towards 

the east during July, the winter monsoon will be good; if towards south, the summer 

monsoon will be successful.”lxxviii 

Researchers found that farmers could use their knowledge about on-farm conditions in 

combination with probabilistic forecasts of rainfall (derived from Southern Oscillation 

Index data) to better meet their production objectives. The most important, and 

potentially largest-yielding decisions involve crop choice, sowing season, and planting 

density. However, these decisions can be the riskiest, coming at the beginning of the 

growth cycle, and “communicating the risk and opportunities of alternative management 

options is a major challenge.” lxxix The final decisions always rest with the farmers, so 

participatory approaches are essential for this kind of cultural shift. 

The Indo-Gangetic Plain, the “bread basket” of the region, comprises both highly modernized 

and traditional agriculture. The western region (the Punjab in Pakistan and India) is 

characterized by high productivity, high investment in infrastructure, and widespread use of 

fertilizers and groundwater irrigation. The eastern region (northeastern India and Bangladesh) 

exhibits low productivity, with poor infrastructure and low inputs of fertilizer and water, high 

risk of flooding, and chronic poverty. Rising population and climate change will have 

acombined impact in each of these regions.lxxx Changes and uncertainties in water supply 

(especially monsoonal changes) will affect irrigated and rainfed crops alike. 

During the “wheat panic” of 2007, India purchased nearly 800,000 tonnes of wheat to buffer its 

reserves of this essential grain.lxxxi India sees keeping a buffer stock as a necessity in a country 

that, although theoretically food sufficient, has historically experienced shortages. Climate is 

one of the key components influencing agricultural production in small Indian farming systems, 

accounting for two-thirds of the variation in production. ENSO can explain some of the interannual rainfall variability that affects overall production. ENSO-based climate forecasts may be 

used to aid vulnerable smallholder agriculture production systems in India (see box above). 

However, unless projections of future climate change are able to account for how ENSO may 

change in a warming climate, these forecasts may not be able to prevent the significant 

agricultural losses that are possible. 

Over the longer term, most studies project decreased yields in non-irrigated wheat and in rice and 

a loss in farm-level net revenue between 9 and 25 percent for a temperature increase of 2.0–

3.5°C.lxxxii Considering a range of equilibrium climate change scenarios which project a 

temperature rise of 2.5°C to 4.9°C for India, Kumar and Parikh (2001) estimated that the impacts 

of climate change on Indian agriculture would be significant across the scenarios.lxxxiii They 

estimated that, with a temperature change of +2°C and an accompanying precipitation change of 
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25

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+7 percent, farm-level total net revenue would fall by 9 percent, whereas with a temperature 

increase of +3.5°C and precipitation change of +15 percent, the fall in farm-level total net 

revenue would be nearly 25 percent. Aggarwal et al. explored agricultural adaptations to global 

environmental change in the Indo-Gangetic Plan, concluding that “new information and tools are 

needed to analyze the trade-offs between the joint socioeconomic and environmental goals, and 

possible adaptation strategies.”lxxxiv 

Livestock, too, will be affected by climate change. India has the largest livestock population in 

the world,lxxxv with animals used as milk producers (especially cattle and buffalo), draft animals, 

nutrient recycling (manure) and seeding, and as household capital, particularly in landless 

households. Heat stress lowers production and reproduction, reduces feed and fodder, and 

increases conditions favorable to disease. For example, outbreaks of foot and mouth disease in 

cattle are explained 52 percent (in Andhra) and 84 percent (in Maharashtra) by temperature, 

humidity, and rainfall; mastitis increases in dairy animals during hot and humid weather, which 

also is associated with increases in flies and cattle ticks.lxxxvi 

Water availability will be a crucial factor in the continued viability of both crops and livestock, 

but management is diffuse. Under the Indian constitution, water is the responsibility of the 

states, not the central government. Thus, water management institutions at the state level have 

the biggest say in planning and allocation. lxxxvii At the national level, at least five ministries 

(Water Resources, Environment and Forests, Rural Development, Power, and Urban 

Development and Poverty Alleviation) are concerned with water, but no organizational 

mechanism exists to coordinate water management among these ministries. Moreover, efforts to 

adapt to more variability in water flows by constructing dams and catchments may increase 

tensions with Bangladesh and Pakistan. 

While freshwater resources will be affected, the marine environment is and will be changing too. 

Fisheries stocks may collapse or move in the already-contested waters of the Indian Ocean, 

potentially affecting livelihoods and food supply for millions of people. The international ocean 

management regimes will require renegotiation.lxxxviii 

Human Health 

Currently, India’s public health care system produces relatively poor health outcomes.lxxxix For 

India, Healthy Life Expectancy (HALE), which includes adjustment for time spent in poor 

health, is 53 years for children born in 2003.xc Despite perceived strengths of the national-level 

public health system (good expertise, written guidelines and standards, and network of research 

and training institutions), implementation and monitoring of services are weak. xci Funding for 

control of communicable diseases has been deemphasized since the 1980s; several infectious 

diseases, such a tuberculosis and malaria, have reemerged as public health care concerns.xcii In 

India, unplanned urbanization has contributed to the spread of Plasmodium vivax malaria and 

dengue.xciii 

Climate change impacts on health include an expected increase in communicable diseases, such 

as malaria. Malaria is projected to move to higher latitudes and altitudes in India.xciv An 

assessment in India projected shifts in the geographical range and duration of the transmission 

window for Plasmodium falciparum and Pvivax malaria.xcv 
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Coping Capabilities in Facing Natural Disasters 

India is highly vulnerable to natural disasters, and people live on marginal lands or in coastal and 

delataic cities where they are at greater risk. Floods, regional droughts, cyclones, and 

earthquakes affect millions of Indians.xcvi 

In the area of disaster mitigation, much has been done to document conditions leading to 

vulnerability. For example, a Flood Atlas of India and a Vulnerability Atlas of India (1997, 

revised 2006) have been produced; the latter assesses the vulnerability of housing and 

infrastructure to earthquakes, cyclones and floods to improve zoning and construction. 

In India, as in the United States, the primary responsibility for responding to disasters lies at the 

state level. The Disaster Management Act of 2005 set up the Natural Disaster Management 

Division in the Ministry of Home Affairs and proclaimed a new emphasis on disaster prevention. 

The United Nations Development Programxcvii has developed a plan for disaster preparedness 

and response that attempts to integrate government and other activities. 

Many Indian states have limited resources and lack their own disaster management plans. 

Because of these factors, India's disaster response record has been mixed, with delayed response, 

lack of early warning systems and resources to undertake measures like mass evacuation, 

inadequate coordination among various government departments, failure to keep essential stores 

(e.g., sandbags, medicines, and life-saving equipment) on hand, and inadequate coordination 

with the Army and other service organizations, as well as donors. 

As an example of disaster response, in 2007 the New York Times reportedxcviii a United Nations 

official as saying that about 2,800 people in India, Bangladesh, Nepal, and Pakistan had died in 

monsoonal floods (“the worst in living memory”), from drowning, waterborne illnesses, 

snakebites or hunger. For survivors, the land damage meant there would be no near-term 

agricultural work for millions of landless laborers, leaving them to rely on the sporadic support 

of aid agencies and government relief organizations. The condition of the 31 million people 

affected in India was covered internationally but was not featured either in New Delhi 

newspapers or on national news channels. 

Infrastructure is affected by climate hazards as well. For instance, 14 percent of the annual 

repair and maintenance budget of the newly built 760 km Konkan Railway in India is spent 

repairing damage to track, bridges, and cuttings due to extreme weather events such as raininduced landslides. In spite of preventive targeting of vulnerable stretches of the line, operations 

must be suspended for an average of seven days each rainy season because of such damage.xcix 

Systematic disaster preparedness at the community level has helped reduce death tolls; for 

instance, new warning systems and evacuation procedures in Andhra Pradesh after 1977 reduced 

deaths from coastal tropical cyclones to 10 percent of the 1977 total by 1997.c

 

Climate Change in the Neighborhood 

The most obvious regional climate change impacts will likely concern water. The Indo-Gangetic 

Plain stretches from Pakistan’s coast across northern India to almost the whole of Bangladesh. 

The Indus River drains in Pakistan, the Ganges in India, and Bangladesh. Rivers fed by 

Himalayan snow and ice provide irrigation water for this important agricultural area. India has 

water agreements with both neighboring countries, and alleged violations of these agreements 

have caused international disputes. 
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Bangladesh, in particular, depends upon India to allow sufficient fresh water to flow into 

Bangladesh for every water use and for preventing salt water intrusion. Sea level rise and 

management decisions have reduced freshwater flows and increased salt-water intrusion in the 

Indus delta and Bangladesh. This adversely affects the Sundarbans, the largest mangrove forest 

in the world, which is shared by India and Bangladesh. In addition, flooding has displaced 

thousands to millions of Bangladeshis, who are subject to outbreaks of xenophobic violence if 

they resettle in India. 

Also potentially important are changes in Arabian Sea (e.g., loss of fresh water input due to 

channeling of Indus river water to agriculture in Pakistan, leading to biogeochemical changes in 

the Western Arabian Sea); the impacts of aerosols (the “Atmospheric Brown Cloud”), which 

may diminish winter precipitation in western India and in Pakistan); and changes in the carbon 

cycle in the Bay of Bengal. 

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, that is, its adaptive capacity. Adaptive capacity is defined 

by the IPCC as, “The ability of a system to adjust to climate change (including climate 

variability and extremes) to moderate potential damages, to take advantage of opportunities, or to 

cope with the consequences.”ci 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, e.g., flooding or droughts. Other components 

include a high enough level of education so that people can 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 Tolcii 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. 
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Researchers have only recently taken on the challenge of assessing adaptive capacity in a 

comparative, quantitative framework. A global comparative studyciii of resilience to climate 

change (including adaptive capacity) was conducted using the Vulnerability-Resilience 

Indicators Model (VRIM—see box below). 

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: 

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. 

• 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 
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— 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). 

Adaptive capacity for a sample of 11 countries from the 160-country study is shown in Figure 3 

(base year of 2005). There is a wide range of adaptive capacity represented by these countries; 

India ranks low, both in the sample and overall: 

• Russia ranks 32nd and Libya 34th (in the highest quartile). 

• Indonesia ranks 45th, Belize 48th, Mexico 59th, and China 75th (in the second quartile). 

• The Philippines ranks 91st and India 119th (in the third quartile). 

• Morocco ranks 136th and Haiti 156th (in the lowest quartile). 

Any country-level analysis must take into account the comparative ranking of the country. 

Figure 3. Sample of 11 countries’ rankings of adaptive capacity (2005). 

Figure 4 shows the contribution of each variable to the overall ranking (slight differences 

occurring because of the methodology (see box on page 25). India ranks low in comparison with 

Russia and China because of lower human resources (dependency ratio and literacy levels) than 

China, and both lower human resources and environmental capacity (non-managed land, 

emissions per total land area, and population density) than Russia. 

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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Figure 4. Variables’ contributions to adaptive capacity rankings. 

Figure 5 shows projected adaptive capacity growth over time for the 11-country sample. 

Projections are made for two scenarios; rates of growth are based on the IPCC’s A1 scenario in 

its Special Report on Emissions Scenariosciv. Both scenarios feature moderate population growth 

and a tendency toward convergence in affluence (with market-based solutions, rapid 

technological progress, and improving human welfare). The scenarios used in this study differ in 

the rate of economic growth, one modeling high-and-fast economic growth, the other delayed 

growth. In the high-growth scenario, China overtakes Russia in adaptive capacity, but India 

retains its relatively low position throughout the entire period in both scenarios. 

Strengths/Weaknesses in Adaptive Capacity Assessments 

Even comparative measures of adaptive capacity only allow analysts to ask improved, more 

focused questions about area or local conditions that contribute to or reduce resilience. It is 

likely, for instance, that for particular places (e.g., states in India) important variables or domains 

are not included. For agricultural regions, this might include the extent of irrigation; for urban 

areas, better measures of education could be important. The measure of unmanaged land does 

not account for the potential usefulness of that land. 

However, comparative measures such as these can be an important first step toward determining 

where to direct resources—for further analysis or additional factors. 

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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Figure 5. Projections of adaptive capacity for 11 countries. 

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

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
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Conclusions: High Risk Impacts 

Agriculture 

There are impacts to high-input, high-output agriculture as a result of various changes in the 

Indian climate, which will affect productivities negatively even as demand for food and other 

agricultural products rises because of both rising population and standard of living. On the other 

hand, millions of subsistence and smallholder farmers will experience immediate hardship and 

hunger because they will be less able to predict climate well enough to make adequate decisions 

about when to sow, what to grow, and how to time inputs. 

Water 

Glacier melt may yield more runoff in the short term but less in the medium and long terms. 

More severe storms (especially cyclones) will cause more damage to infrastructure and 

livelihoods and exacerbate saltwater intrusion6 in storm surges. Changes in the timing and 

amount of monsoon rain will make the production of food and other agricultural products more 

uncertain, so that, even in good-weather years, farmers will be more likely to make decisions 

resulting in lower-productivity outcomes. 

Energy 

As India searches for additional sources of energy to meet rising demand, climate change 

mitigation efforts may constrain its use of indigenous and imported coal, oil, and gas, while 

development of nuclear energy will be slow at best and likely to encounter opposition. Other 

non-emitting technologies will require technology transfer and capacity-building. 

Exacerbation of Inequality 

In a country so prone to natural hazards, the welfare of those who are affected and who have 

limited means to recover will loom large under climate change, as a force that can change 

governments, strain public budgets, and foster unrest. The proportions are significant: about a 

quarter of the population lives on less than $1 a day; at least one-third face poverty, 

discrimination (often scheduled castes or tribes and Muslims), and lack of educational 

opportunities; and 60 percent are dependent upon agriculture for their livelihoods. Those who 

lack financial resources and adequate education and who depend on agriculture for sustenance 

and livelihood will be disadvantaged under climate change because they have few choices but 

continued dependence on shrinking or uncertain resources. India is attempting to reduce 

inequality by using quotas in government jobs and educational institutions;cv the National Rural 

Employment Guarantee Act (2005) also guarantees that at least one member of a rural household 

will have 200 days of work per year; and India’s 11th five-year plan adopts the policy of sectoral, 

spatial, and socioeconomic growth that includes all people. 

Migration 

India receives immigrants from a number of countries. For instance, since 1960 the government 

has hosted approximately 110,000 de facto refugees from Tibet. The government considers 

Tibetans and Sri Lankans to be refugees and provides assistance to them. However, the 

government regards most other groups, especially Bangladeshis, as economic migrants and does 

not provide them with aid.cvi 

 

6

 Saltwater intrusion is the process in which saltwater flows inland into fresh water because saltwater is denser. 
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Annex A: 

Accuracy of Regional Models 

This is an excerpt from IPCC (2007), Chapter 11, Regional models; see IPCC 2007 for 

references. 7

11.4.2 Skill of Models in Simulating Present Climate 

Regional mean temperature and precipitation in the multi-model dataset (MMD) models show 

biases when compared with observed climate (Table 1). 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. 

South Asia 

Over South Asia, the summer is dominated by the southwest monsoon, which spans the four 

months from June to September and dominates the seasonal cycles of the climatic parameters. 

While most models simulate the general migration of seasonal tropical rain, the observed 

maximum rainfall during the monsoon season along the west coast of India, the north Bay of 

Bengal, and adjoining northeast India is poorly simulated by many models (Lal and Harasawa, 

2001; Rupa Kumar and Ashrit, 2001; Rupa Kumar et al., 2002, 2003). This is likely linked to 

the coarse resolution of the models, as the heavy rainfall over these regions is generally 

associated with the steep orography. However, the simulated annual cycles in South Asian mean 

precipitation and surface air temperature are reasonably close to the observed. The MMD 

models capture the general regional features of the monsoon, such as the low rainfall amounts 

coupled with high variability over northwest India. However, there has not yet been sufficient 

analysis of whether finer details of regional significance are simulated more adequately in the 

MMD models. 

Recent work indicates that time-slice experiments using an AGCM with prescribed SSTs, as 

opposed to a fully coupled system, are not able to accurately capture the South Asian monsoon 

response (Douville, 2005). Thus, neglecting the short-term SST feedback and variability seems 

to have a significant impact on the projected monsoon response to global warming, complicating 

the regional downscaling problem. However, May (2004a) notes that the high-resolution (about 

 

7

 Some references in this section have been changed to be internally consistent with this document and other 

references have been removed to avoid confusion. 
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1.5 degrees) European Centre-Hamburg (ECHAM4) GCM simulates the variability and extremes 

of daily rainfall (intensity as well as frequency of wet days) in good agreement with the 

observations (Global Precipitation Climatology Project, Huffman et al., 2001). 

Three-member ensembles of baseline simulations (1961–1990) from an RCM (PRECIS) at 50 

km resolution have confirmed that significant improvements in the representation of regional 

processes over South Asia can be achieved (Kumar, 2006). For example, the steep gradients in 

monsoon precipitation with a maximum along the western coast of India are well represented in 

PRECIS. 

East Asia 

Simulated temperatures in most MMD models are too low in all seasons over East Asia; the 

mean cold bias is largest in winter and smallest in summer. Zhou and Yu (2006) show that over 

China, the models perform reasonably in simulating the dominant variations of the mean 

temperature over China but not the spatial distributions. The annual precipitation over East Asia 

exceeds the observed estimates in almost all models and the rain band in the mid-latitudes is 

shifted northward in seasons other than summer. This bias in the placement of the rains in 

central China also occurred in earlier models (e.g., Zhou and Li, 2002; Gao et al., 2004). In 

winter, the area-mean precipitation is overestimated by more than 50 percent on average due to 

strengthening of the rain band associated with extratropical systems over South China. The bias 

and inter-model differences in precipitation are smallest in summer, but the northward shift of 

this rain band results in large discrepancies in summer rainfall distribution over Korea, Japan, 

and adjacent seas. 

Kusunoki et al. (2006) find that the simulation of the Meiyu-Changma-Baiu rains in the East 

Asian monsoon is improved substantially with increasing horizontal resolution. Confirming the 

importance of resolution, RCMs simulate more realistic climatic characteristics over East Asia 

than AOGCMs, whether driven by re-analyses or by AOGCMs (e.g., Ding et al., 2003; Oh et al., 

2004; Fu et al., 2005; Zhang et al., 2005a, Ding et al., 2006; Sasaki et al., 2006b). Several 

studies reproduce the fine-scale climatology of small areas using a multiply nested RCM (Im et 

al., 2006) and a very-high resolution (5 km) RCM (Yasunaga et al., 2006). Gao et al. (2006b) 

report that simulated East Asia large-scale precipitation patterns are significantly affected by 

resolution, particularly during the mid- to late-monsoon months, when smaller-scale convective 

processes dominate. 

Southeast Asia 

The broad-scale spatial distribution of temperature and precipitation in December, January, 

February (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 SST. 

Simulation was good over the maritime continent compared to the simulation for other tropical 

regions. Wang et al. (2004) assess the ability of 11 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 AOGCMs continue to have some significant shortcomings in 
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representing 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. (2004a) conducted a number of simulations with 

the Max-Planck Institute (MPI) regional model for an Indonesian domain, forced by reanalyses 

and 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. 

Central Asia and Tibet 

Due to the complex topography and the associated mesoscale weather systems of the highaltitude and arid areas, GCMs typically perform poorly over the region. Importantly, the GCMs, 

and to a lesser extent RCMs, tend to overestimate the precipitation over arid and semi-arid areas 

in the north (e.g., Small et al., 1999; Gao et al., 2001; Elguindi and Giorgi, 2006). 

Over Tibet, the few available RCM simulations generally exhibit improved performance in the 

simulation of present-day climate compared to GCMs (e.g., Gao et al., 2003a,b; Zhang et al., 

2005b). For example, the GCM simulation of Gao et al. (2003a) overestimated the precipitation 

over the northwestern Tibetan Plateau by a factor of five to six, while in an RCM nested in this 

model, the overestimate was less than a factor of two. 
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Table 1. Biases in present-day (1980-1999) surface air temperature and precipitation in the MMD 

simulations. The simulated temperatures are compared with the HadCRUT2v (Jones, et al., 2001) 

data set and precipitation with the CMAP (update of Xie and Arkin, 1997) data set. Temperature 

biases are in °C and precipitation biases in percent. Shown are the minimum, median (50 percent) 

and maximum biases among the models, as well as the first (25 percent) and third (75 percent) 

quartile values. Colors indicate regions/seasons for which at least 75 percent of the models have 

the same sign of bias, with orange indicating positive and light violet negative temperature biases 

and light blue positive and light brown negative precipitation biases.
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Annex B: 

Knowledge Gaps That Preclude a Full Evaluation of 

Climate Change Impacts Affecting India and India’s 

Adaptive Capacities 

To increase the likelihood that this evaluation represents a reasonable assessment of India’s 

projected climate changes and their impacts, and the country’s adaptive capacity, the following 

gaps would need to be addressed: 

• In physical science research, regional analyses will continue to be limited by the inability to 

model regional climates satisfactorily, including complexities arising from the interaction of 

global, regional, and local processes. One gap of particular interest is the lack of mediumterm (20-30 years) projections that could be relied upon for planning purposes. Similarly, 

scientific projections of water supply and agricultural productivity are limited by inadequate 

understanding of various climate and physical factors affecting both areas. 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 

types of issues exist for the biological and ecological systems that are affected. 

• In social science research, scientists and analysts have only partial understandings of the 

important factors in vulnerability, resilience, and adaptive capacity – much less their 

interactions and evolution. Again, research agendas on vulnerability, adaptation, and 

decision-making abound (e.g., (http://books.nap.edu/ catalog.php?record_id=12545). 

• Important factors are unaccounted for in research; scientists know what some of them are, 

but there are likely factors whose influence will be surprising. An example from earlier 

research on the carbon cycle illustrates this situation. The first carbon cycle models did not 

include carbon exchanges involving the terrestrial domain. Modelers assumed that the 

exchange was about equal, and the only factor modeled was deforestation. This assumption, 

of course, made the models inadequate for their purposes. In another example, ecosystems 

research models are only beginning to account for changes in pests, e.g., the pine bark beetle. 

• Social models or parts of models in climate research have been developed to simulate 

consumption (with the assumption of well-functioning markets and rational actor behavior) 

and mitigation/adaptation policies (but without attention to the social feasibility of enacting 

or implementing such policies). As anthropogenic climate change is the result of human 

decisions, the lack of knowledge about motivation, intent, and behavior is a serious gap. 

Moreover, the long time scale of climate-related research introduces uncertainties about 

social behavior in the future as well as weaknesses in scientists’ ability to predict the social 

conditions under which mitigation and adaptation will be undertaken. 
This paper does not represent US Government views. 

38

This paper does not represent US Government views. 

Overall, research about climate change impacts on India has been undertaken piecemeal: 

discipline by discipline, sector by sector, with political implications considered separately from 

physical effects. This knowledge gap can be remedied by integrated research into energyeconomic-environmental-political conditions and possibilities. 
This paper does not represent US Government views. 

39

This paper does not represent US Government views. 

Annex C: 

State/District Vulnerability and Adaptive Capacity in 

India 

Note: The original source data used for derivation of state and district adaptive capacity was 

from 1990-1991 sources and may not reflect current circumstances. 

Results from the Vulnerability-Resilience Indicators Model (VRIM) 

The vulnerability of India and Indian states to climate change was assessed using the 

Vulnerability-Resilience Indicators Model (VRIM) (see box on page 25 for description).cvii

Vulnerability here includes both adaptive capacity and climate sensitivity (i.e., potential negative 

impacts of climate change). 

As in other quantitative rankings, India is ranked as more vulnerable than most countries to 

climate change; in a VRIM-assisted study of 160 countries,cviii India ranks low in the third 

quartile. The largest contributions to current vulnerability of India in the VRIM analysis are food 

security (as represented by total protein intake in the sensitivity aspect) and water availability 

(also in the sensitivity aspect). Rapid expansion in the use of groundwater, primarily for 

irrigation, has contributed significantly to agricultural and overall economic development in 

India, but in many arid and hard-rock zones, increases in overdraft areas and associated waterquality problems are emerging. 

Specific comparative levels of adaptive capacity are shown in Figure 6. Adaptive capacity is 

lowest along the Indo-Gangetic Plain, higher in the northwest and south. 

When sensitivity and adaptive capacity are combined into an overall comparative measure of 

vulnerability, small mountainous northern inland states tend to be ranked higher than coastal 

states, and only five states are ranked higher than the world average. 

Interestingly, most of the variability in state-level sensitivity proxies (i.e., proxies that represent 

how large climate impacts are) results from variability in settlement and food sensitivity, i.e., 

social and economic factors. On the other hand, most of the variability in state-level proxies for 

coping and adaptive capacity results from environmental rather than economic or human 

resource factors. These results imply that social policies would be more likely to be effective in 

reducing sensitivity, while environmental protection policies would likely be more effective in 

increasing coping and adaptive capacity. 

Many analyses attempt to measure these kinds of differences by using GDP per capita or some 

income measure as a summary proxy. However, this study shows no meaningful correlation 

between net domestic product (NDP) per capita in states and vulnerability of a state to climate 

impacts. Differences other than economic ones are of great importance. For example, literacy 

rates in the states range from 44 to 91 percent, life expectancy from 58 to 70 years. There are 

great disparities in natural resources and climate hazards among Indian states. 
This paper does not represent US Government views. 

40

This paper does not represent US Government views. 

Figure 6. Coping and adaptive capacity comparative levels of India’s states (average of four 

periods, 1990-2035). Source: based on Antoinette L. Brenkert and Elizabeth L. Malone, 

“Modeling vulnerability and resilience to climate change: a case study of India and Indian states,” 

Climatic Change 72 (2005): 57-102. 

Adaptive Capacity of Indian Districts 

O’Brien et al.cix performed a detailed analysis of adaptive capacity at the district level in India; 

likely because of the focus on agriculture and the difference in variables measured, the results 

differ from the VRIM analysis. To measure adaptive capacity, the researchers used the 

following indicators: 

• Biophysical indicators: soil conditions (the depth of the soil cover and severity of soil 

degradation) and groundwater availability (based on estimates of the total amount of 

replenishable groundwater available annually). 

• Human and social capital: adult literacy rates, degree of gender equity in a district, and the 

presence of alternative economic activities (an indicator of the ability of farmers in a district 

to shift to other economic activities). 

• Infrastructure: irrigation rates and quality of infrastructure (measured using the Infrastructure 

Development Index of the Center for Monitoring of Indian Economy). 

The capacity index map shows higher degrees of adaptive capacity in districts located along the 

Indo-Gangetic Plains (except Bihar) and lower adaptive capacity in the interior portions of the 

country, particularly in the states of Bihar, Rajasthan, Madhya Pradesh, Maharashtra, Andhra 

Pradesh, and Karnataka. 
This paper does not represent US Government views. 

41

This paper does not represent US Government views. 

O’Brien et al. measured sensitivity under exposure to climate change. They found the areas with 

high to very high climate sensitivity for agriculture to be located in the semi-arid regions of the 

country, including major parts of the states of Rajasthan, Gujarat, Punjab, Haryana, Madhya 

Pradesh, and Uttar Pradesh. Under the HadRM2 (the Hadley Centre’s Regional Model) scenario, 

district climate sensitivity noticeably increased in Uttar Pradesh, Madhya Pradesh, and 

Maharashtra. 

Vulnerability in India was assessed by summing a district-level index of adaptive capacity with 

an index of climate sensitivity under exposure. The districts with the highest/lowest sensitivity 

were not necessarily the most/least vulnerable. For example, most districts in southern Bihar 

exhibited only medium sensitivity to climate change, yet were still highly vulnerable as the result 

of low adaptive capacity. By contrast, most districts in northern Punjab showed very high 

sensitivity to climate change, yet were found to be only moderately vulnerable as the result of 

high adaptive capacity. 

Last, the research team added factors representing exposure to globalization. Liberalization of 

agricultural trade may provide new opportunities for some Indian farmers to engage in 

production for export market, but also may expose many other farmers to competition from 

imported agricultural products. One example is the liberalization of trade in edible oils and 

oilseeds which led to a crash in domestic oilseed prices in the late 1990s due to imports of 

inexpensive Malaysian palm oil. For farmers in southern India, particularly in the state of 

Andhra Pradesh, this price crash, perhaps exacerbated by such factors as the inability to afford 

imported hybrid seeds, proved devastating and is associated with the beginning of a long wave of 

suicides by bankrupt farmers. 

O’Brien et al. focused on exposure to import competition, with the result that high vulnerability 

was shown in most of Rajasthan and Karnataka, as well as in substantial portions of Bihar, 

Madhya Pradesh, Maharashtra, Gujarat, and Assam. Notable areas of low vulnerability occurred 

along the Indo-Gangetic plains. Districts, mostly concentrated in Rajasthan, Gujarat, Madhya 

Pradesh, as well as in southern Bihar and western Maharashtra, may be interpreted as areas of 

“double exposure,” where globalization and climate change are likely to pose simultaneous 

challenges to the agricultural sector. 

Several short case studies complement the broader-scale research. The case studies showed the 

effect that institutional barriers or support systems have on local-level vulnerability; this is not 

visible in the district-national profiles. In the cases of Jhalawar (Rajasthan) and Anatapur 

(Andhra Pradesh), institutional barriers leave farmers who are “double exposed” poorly equipped 

to adapt to either of the stressors, let alone both simultaneously. In Chitradurga (Karnataka), on 

the other hand, institutional support appears to facilitate adaptation to both climatic change and 

globalization. However, these supports tend to disproportionately benefit the district’s larger 

farmers. 
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This paper does not represent US Government views. 

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43

This paper does not represent US Government views. 

i

 World Wildlife Fund (WWF), “An Overview of Glaciers, Glacier Retreat and Subsequent Impacts in Nepal, India 

and China,” WWF Nepal Programme (March 2005). 

ii Rex Victor Cruz, Hideo Harasawa, Murari Lal and Shaohong Wu, “Asia,” in 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). 

iii P.R. Shukla, ed. 2003. Climate Change and India: Vulnerability Assessment and Adaptation. Orient Blackswan. 

ISBN 8173714711, 9788173714719, Chapter 3. 

iv Rex Victor Cruz, Hideo Harasawa, Murari Lal and Shaohong Wu, “Asia,” in 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). 

v

 Keqin Duan, L.G. Thompson and T. Yao, “A 1000 year history of atmospheric sulfate concentrations in southern 

Asia as recorded by a Himalayan ice core,” Geophysical Research Letters 34 (2007): L01810, 

doi:10.1029/2006GL027456. 

vi Rex Victor Cruz, Hideo Harasawa, Murari Lal and Shaohong Wu, “Asia,” in 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); K. Ruosteenoja, T.R. Carter, K. Jylhä and H. 

Tuomenvirta. “Future climate in world regions: an intercomparison of model-based projections for the new IPCC 

emissions scenarios,” The Finnish Environment 644, (Helsinki: Finnish Environment Institute 2003). 

vii Rex Victor Cruz, Hideo Harasawa, Murari Lal and Shaohong Wu, “Asia,” in 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). 

viii Climate Change Science Program (CCSP), “Climate projections based on emissions scenarios for long-lived and 

short-lived radiatively active gases and aerosols,” eds. H. Levy II, D.T. Shindell, A. Gilliland, M.D. Schwarzkopf, 

L.W. Horowitz (Washington, D.C.: Department of Commerce, NOAA’s National Climatic Data Center 2008), 

http://www.climatescience.gov/Library/sap/sap3-2/final-report/sap3-2-final-report-all.pdf 

ix Central Intelligence Agency: The World Factbook, s.v. “India,” January 22, 2008, 

https://www.cia.gov/library/publications/the-world-factbook/geos/in.html; Robert W. Fogel, “Capitalism & 

democracy in 2040,” Daedalus (Summer 2007): 87-95. x

 M.J. Gannon and Associates, Understanding Global Cultures (Thousand Oaks, CA: Sage 1994). xi Gurcharan Das, “The India model” Foreign Affairs 85 no. 4 (2006): 2-16. xii Govindan Parayil, “The ‘Kerala Model’ of development: development and sustainability in the third world,” Third 

World Quarterly 17, no. 5 (1996): 941-957. xiii Gopal Kadekodi, Ravi Kanbur and Vijayendra Rao, “Governance and the ‘Karnataka Model of Development,’” 

Economic and Political Weekly (24 February 2007). 

xiv World Bank, “Country Brief: South Asia Region (SAR)–India,” (2002) http://www.Worldbank.org; Somini 

Sengupta, “Education push yields little for India’s poor,” New York Times (17 January 2008). xv Ashutosh Varshney, “India’s democratic challenge,” Foreign Affairs 86, no. 2 (2006): 93-106. 

xvi Ashutosh Varshney, “India’s democratic challenge,” Foreign Affairs 86, no. 2 (2006): 93-106. 

xvii J.K. Parikh and K. Parikh, Climate Change: India’s Perceptions, Positions, Policies and Possibilities. (Paris: 

Organization for Economic Cooperation and Development 2002); W. Chandler, R. Schaeffer, Z. Dadi, P.R. Shukla, 

F. Tudela, O. Davidson and S. Alpan-Atamer, “Climate Change Mitigation in Developing Countries: Brazil, China, 

India, Mexico, South Africa, and Turkey,” (Washington, D.C.: Pew Center on Global Climate Change 2002). 

xviii A. Agarwal and S. Narain, Toward a Green World: Should Global Environmental Management Be Built on 

Legal Conventions or Human Rights? (New Delhi: Centre for Science and Environment 1992). xix Ghats are the two coastal mountain ranges that converge in southern India, separated by the Deccan Plateau. xx 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. Miller Jr. and Z. Chen (Cambridge: 

Cambridge University Press 2007). 

xxi For more information on the NAO, see http://transcom.colostate.edu/Data/index.html 
This paper does not represent US Government views. 

44

This paper does not represent US Government views. 

xxii Rasmus Benestad and Mauri Pelto, “Regional climate change assessment,” in Encyclopedia of Earth. Ed. Cutler 

J. Cleveland (Washington, D.C.: Environmental Information Coalition, National Council for Science and the 

Environment, 2008), first published in the Encyclopedia of Earth July 2, 2008; last revised August 28, 2008. 

xxiii T.N. Krishnamurti, C.M. Kishtawal, Timothy E. LaRow, David R. Bachiochi, Zhan Zhang, C. Eric Williford, 

Sulochana Gadgil and Sajani Surendran, “Improved weather and seasonal climate forecasts from multimodel 

superensemble,” Science 285 no. 5433 (1999): 1548-1550. xxiv H. Douville, F. Chauvin, and H. Broqua, “Influence of soil moisture on the Asian and African monsoons. Part I: 

mean monsoon and daily precipitation,” Journal of Climate 14 (June 2001): 11. xxv CCSP, “Climate projections based on emissions scenarios for long-lived and short-lived radiatively active gases 

and aerosols,” eds. H. Levy II, D.T. Shindell, A. Gilliland, M.D. Schwarzkopf, L.W. Horowitz (Washington, D.C.: 

Department of Commerce, NOAA’s National Climatic Data Center 2008), 

http://www.climatescience.gov/Library/sap/sap3-2/final-report/sap3-2-final-report-all.pdf 

xxvi Rex Victor Cruz, Hideo Harasawa, Murari Lal and Shaohong Wu, “Asia,” in 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). 

xxvii Rex Victor Cruz, Hideo Harasawa, Murari Lal and Shaohong Wu, “Asia,” in 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). 

xxviii Rupa K. Kumar, A.K. Sahai, K. Krishna Kumar, S.K. Patwardhan, P.K. Mishra, J.V. Revadekar, K. Kamala and 

G.B. Pant, “High-resolution climate change scenarios for India for the 21st century,” Current Science 90 no. 3 (10 

February 2006): Special Section: Climate Change and India. 

xxix Rupa K. Kumar, K. Krishna Kumar, V. Prasanna, K. Kamala, N.R. Deshpande, S.K. Patwardhan, and G.B. Pant, 

“Future climate scenarios,” in Climate Change and India: Vulnerability Assessment and Adaptation, eds. P.R. 

Shukla, K. Subodh, K. Sharma, N.H. Ravindranath, Amit Garg and Sumana Bhattacharya, (Hyderabad: Universities 

Press, 2003), 69–127. 

xxx P.R. Shukla, ed. 2003. Climate Change and India: Vulnerability Assessment and Adaptation. Orient 

Blackswan. ISBN 8173714711, 9788173714719, Chapter 3. 

xxxi P.R. Shukla, ed. 2003. Climate Change and India: Vulnerability Assessment and Adaptation. Orient 

Blackswan. ISBN 8173714711, 9788173714719, Chapter 3. 

xxxii Rupa K. Kumar, A.K. Sahai, K. Krishna Kumar, S.K. Patwardhan, P.K. Mishra, J.V. Revadekar, K. Kamala 

and G.B. Pant, “High-resolution climate change scenarios for India for the 21st century,” Current Science 90 no. 3 

(10 February 2006): Special Section: Climate Change and India. 

xxxiii Rex Victor Cruz, Hideo Harasawa, Murari Lal and Shaohong Wu, “Asia,” in 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); K. Ruosteenoja, T.R. Carter, K. Jylhä and H. 

Tuomenvirta. “Future climate in world regions: an intercomparison of model-based projections for the new IPCC 

emissions scenarios,” The Finnish Environment 644, (Helsinki: Finnish Environment Institute 2003). 

xxxiv Teleconnections are statistically significant correlations between weather events that occur at different places on 

the Earth. 

xxxv See http://transcom.colostate.edu/Data/index.html for more information on annular modes. 

xxxvi India’s First National Communication to the Framework Convention on Climate Change (UNFCCC, 2004) 

http://unfccc.int/resource/docs/natc/indnc1.pdf. 

xxxvii L.G. Thompson, T. Yao, E. Mosley-Thompson, M.E. Davis, K.A. Henderson and P.N. Lin, “A high resolution 

millennial record of the South Asian Monsoon from Himalayan ice cores,” Science 289 (2006): 1916-1919. 

xxxviii World Wildlife Fund (WWF), “An Overview of Glaciers, Glacier Retreat and Subsequent Impacts in Nepal, 

India and China,” WWF Nepal Programme (March 2005). 

xxxix Keqin Duan, L.G. Thompson and T. Yao, “A 1000 year history of atmospheric sulfate concentrations in 

southern Asia as recorded by a Himalayan ice core,” Geophysical Research Letters 34 (2007): L01810, 

doi:10.1029/2006GL027456. 
This paper does not represent US Government views. 

45

This paper does not represent US Government views. 

xl “FRIEND - A Global Perspective 2002-2006,” in Chapter 8, “Hindu Kush Himalaya,” FRIEND – Flow Regimes 

from International Experimental and Network Data (Koblenz: United Nations Education, Scientific and Cultural 

Organization, International Hydrological Programme VI 2006). 

xli V. Ramanathan, P.J. Crutzen, J. Lelieveld, et al., “Indian Ocean Experiment: An integrated analysis of the climate 

forcing and effects of the great Indo-Asian haze,” Journal of Geophysical Research 106 (2001): D22, 28, 371–28, 

398. 

xlii M. Lal, “Global climate change-India’s monsoon and its variability: vulnerability and adaptation issues,” Report 

on Country Studies Vulnerability and Adaptation, Work Assignment 402 – Task 11 under Stratus Consulting 

Contract 68-W6-0055 (Washington D.C.: Environmental Protection Agency 2001). 

xliii M. Lal, “Global climate change-India’s monsoon and its variability: vulnerability and adaptation issues,” Report 

on Country Studies Vulnerability and Adaptation, Work Assignment 402 – Task 11 under Stratus Consulting 

Contract 68-W6-0055 (Washington D.C.: Environmental Protection Agency 2001). 

xliv Rex Victor Cruz, Hideo Harasawa, Murari Lal and Shaohong Wu, “Asia,” in 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). 

xlv The Economist, “Water in India: nor any drop to drink (24 August 2002): 364:8287, 31–32. 

xlvi C. Nanjundaiah, State of Environment Report for Bangalore City-2008 (Center for Economic Studies and Policy, 

Bangalore, forthcoming). 

xlvii Ulisses Confalonieri and Bettina Menne, “Human health,” in 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). 

xlviii Ulisses Confalonieri and Bettina Menne, “Human health,” in 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). 

xlix M. Lal, “Global climate change-India’s monsoon and its variability: vulnerability and adaptation issues,” Report 

on Country Studies Vulnerability and Adaptation, Work Assignment 402 – Task 11 under Stratus Consulting 

Contract 68-W6-0055 (Washington D.C.: Environmental Protection Agency 2001). 

l

 N.H. Ravindranath, N.V. Joshi, R. Sukumar and A. Saxena, “Impact of climate change on forests in India,” Current 

Science 90 (2006): 354-361. li N.H. Ravindranath, N.V. Joshi, R. Sukumar and A. Saxena, “Impact of climate change on forests in India,” 

Current Science 90 (2006): 354-361. lii International Energy Agency, World Energy Outlook 2007: China and India Insights (Paris: International Energy 

Agency 2007). 

liii International Energy Agency, World Energy Outlook 2007: China and India Insights (Paris: International Energy 

Agency 2007). 

liv International Energy Agency, World Energy Outlook 2007: China and India Insights (Paris: International Energy 

Agency 2007). 

lv International Energy Agency, World Energy Outlook 2007: China and India Insights (Paris: International Energy 

Agency 2007). 

lvi Central Intelligence Agency: The World Factbook, s.v. “India,” January 22, 2008, 

https://www.cia.gov/library/publications/the-world-factbook/geos/in.html 

lvii Central Intelligence Agency: The World Factbook, s.v. “India,” January 22, 2008, 

https://www.cia.gov/library/publications/the-world-factbook/geos/in.html 

lviii Organization for Economic Cooperation and Development, International Energy Agency, Statistics on the web 

(2006) http://www.iea.org/statist/indux.htm 

lix International Energy Agency, World Energy Outlook 2007: China and India Insights (Paris: International Energy 

Agency 2007). 

lx Anshu Bharadwaj, Rahul Tongia and V.S. Arunchalam, “Scoping technology options for India’s oil security: Part 

II—coal to liquids and bio-diesel,” Current Science 92 no. 9 (2007b): 1234-1241. lxi See the first assessment of this potential in International Energy Agency, Greenhouse Gas Programme, A Regional 

Assessment of the Potential for CO2 Storage in the Indian Subcontinent (Paris: IEA Report 2008/2 2008). 
This paper does not represent US Government views. 

46

This paper does not represent US Government views. 

lxii Ingolf Kiesow, and Nicklas Norling, The Rise of India: Problems and Opportunities (Washington, D.C.: Johns 

Hopkins University-SAIS 2007). 

lxiii Anshu Bharadwaj, Rahul Tongia and V.S. Arunchalam, “Scoping technology options for India’s oil security: Part 

I—ethanol for petrol,” Current Science 92 no. 8 (2007a): 1071-1077. 

lxiv International Energy Agency, World Energy Outlook 2007: China and India Insights (Paris: International Energy 

Agency 2007). 

lxv Sumana Bhattacharya, C. Sharma, R.C. Dhiman and A.P. Mitra, “Climate change and malaria in India,” Current 

Science 90 no. 3 (2006): 369-375. 

lxvi World Bank, “Country Brief: South Asia Region (SAR)–India,” (2002) http://www.Worldbank.org 

lxvii The Economist, “Suddenly vulnerable,” (13 December 2008), 15. 

lxviii World Wildlife Fund, “An Overview of Glaciers, Glacier Retreat and Subsequent Impacts in Nepal, India and 

China,” WWF Nepal Programme (March 2005); 

Food and Agriculture Organization. AQUASTAT, http://www.fao.org/nr/water/aquastat/main/index.stm 

lxix Somini Sengupta, “Glaciers in retreat,” New York Times (17 July 2007); 

Roger F. McLean, S.K. Sinha, M.Q. Mirza and Murari Lal, “Tropical Asia,” in The Regional Impacts of Climate 

Change: An Assessment of Vulnerability, eds. Robert T. Watson, Marufu C. Zinyowera and Richard H. Moss 

(Cambridge: Cambridge University Press 1998). 

lxx Somini Sengupta, “Glaciers in retreat,” New York Times (July 17, 2007). lxxi John Briscoe and R.P.S. Malike, India's Water Economy: Bracing for a Turbulent Future (New York: Oxford 

2006). 

lxxii Ulka Kelkar, “Indian agriculture: growth, sustainability, and vulnerability,” in Designing Policies in a World of 

Uncertainty, Change, and Surprise: Adaptive Policymaking for Agriculture and Water Resources in the Face of 

Climate Change (International Institute for Sustainable Development and The Energy and Resources Institute 2006), 

http://www.iisd.org/pdf/2006/climate_designing_policies.pdf 

lxxiii Ulka Kelkar, “Indian agriculture: growth, sustainability, and vulnerability,” in Designing Policies in a World of 

Uncertainty, Change, and Surprise: Adaptive Policymaking for Agriculture and Water Resources in the Face of 

Climate Change (International Institute for Sustainable Development and The Energy and Resources Institute 2006), 

http://www.iisd.org/pdf/2006/climate_designing_policies.pdf 

lxxiv P.K. Aggarwal, P.K. Joshi, J.S.I. Ingram and R.K. Gupta, “Adapting food systems of the Indo-Gangetic plains 

to global environmental change: key information needs to improve policy formulation,” Environmental Science and 

Policy 7 (2004): 487-498. 

lxxv See, for example, the Food and Agriculture Organization’s press release, “FAO Director-General appeals for 

second Green Revolution: Vast effort needed to feed billions and safeguard environment,” (September 13, 2006), 

http://www.fao.org/newsroom/en/news/2006/1000392/index.html 

lxxvi Ulka Kelkar, “Indian agriculture: growth, sustainability, and vulnerability,” in Designing Policies in a World of 

Uncertainty, Change, and Surprise: Adaptive Policymaking for Agriculture and Water Resources in the Face of 

Climate Change (International Institute for Sustainable Development and The Energy and Resources Institute 2006), 

http://www.iisd.org/pdf/2006/climate_designing_policies.pdf 

lxxvii Roger F. McLean, S.K. Sinha, M.Q. Mirza and Murari Lal, “Tropical Asia,” in The Regional Impacts of 

Climate Change: An Assessment of Vulnerability, eds. Robert T. Watson, Marufu C. Zinyowera and Richard H. 

Moss (Cambridge: Cambridge University Press 1998). 

lxxviii Ramasamy Selvaraju, Holger Meinke and James Hansen, “Approaches allowing smallholder farmers in India to 

benefit from seasonal climate forecasting,” in New Directions for a Diverse Planet, Proceedings of the 4th

International Crop Science Congress (Brisbane: 26 September to 1 October 2004) 

lxxix Ramasamy Selvaraju, Holger Meinke and James Hansen, “Approaches allowing smallholder farmers in India to 

benefit from seasonal climate forecasting,” in New Directions for a Diverse Planet, Proceedings of the 4th

International Crop Science Congress (Brisbane: 26 September to 1 October 2004) 

lxxx Global Environmental Change and Food Systems, “GECAFS Indo-Gangetic Plan Science Plan and 

Implementation Strategy,” Report 5 (Oxford: GECAFS 2008), 

http://www.gecafs.org/publications/documents/IGPSPISGECAFSRpt5.pdf 

lxxxi Chidanand Rajghatta, “India sparks wheat price rise,” Times of India, (8 September 2007). lxxxii Tata Energy Research Institute. 2002. lxxxiii K.S. Kavi Kumar, and Jyoti Parikh, “Indian agriculture and climate change sensitivity,” Global Environmental 

Change 11 no. 2 (2001): 147-154. 
This paper does not represent US Government views. 

47

This paper does not represent US Government views. 

lxxxiv P.K. Aggarwal, P.K. Joshi, J.S.I. Ingram and R.K. Gupta, “Adapting food systems of the Indo-Gangetic plains 

to global environmental change: key information needs to improve policy formulation,” Environmental Science and 

Policy 7 (2004): 487-498. 

lxxxv Food and Agriculture Organization Statistics, FAOSTAT, 

http://www.fao.org/statistics/census/wcares/2000indiaweb.pdf. 

lxxxvi Smita Sirohi and Axel Michaelowa, “Sufferer and cause: Indian livestock and climate change,” Climatic 

Change 85, 285-298. 

lxxxvii Ulka Kelkar, “Indian agriculture: growth, sustainability, and vulnerability,” in Designing Policies in a World of 

Uncertainty, Change, and Surprise: Adaptive Policymaking for Agriculture and Water Resources in the Face of 

Climate Change (International Institute for Sustainable Development and The Energy and Resources Institute 2006), 

http://www.iisd.org/pdf/2006/climate_designing_policies.pdf 

lxxxviii Nick Mabey, “Delivering climate security: international security responses to a climate-changed world,” 

Whitehall Papers (April 2008), 1-7. http://dx.doi.org/10.1080/2681300802012886 lxxxix Monica Das Gupta, “India’s Public Health System: How Well Does It Function at the National Level?” World 

Bank Policy Research Working Paper 3447 (Washington, D.C.: World Bank 2004). 

xc World Health Organization, “WHO Statistical Information System” (2008) 

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xci Monica Das Gupta, “India’s Public Health System: How Well Does It Function at the National Level?” World 

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xcii Sai Ma and Neraj Sood, A Comparison of the Health Systems in China and India (Santa Monica, CA: RAND 

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xciii Ulisses Confalonieri and Bettina Menne, “Human health,” in Climate Change 2007: Impacts, Adaptation and 

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on Climate Change, eds. M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson 

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xciv Ulisses Confalonieri and Bettina Menne, “Human health,” in 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 

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xcv Sumana Bhattacharya, C. Sharma, R.C. Dhiman and A.P. Mitra, “Climate change and malaria in India,” Current 

Science 90 no. 3 (2006): 369-375; 

Ulisses Confalonieri and Bettina Menne, “Human health,” in 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 

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xcvii United Nations Development Program India, Disaster Preparedness and Response Plan: Version 45i (2007)

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xcviii Amelia Gentleman, “After the deluge, the reality of deprivation,” New York Times (22 August 2007). xcix Tom Wilbanks, and Patricia Romero Lankao, “Industry, settlement and society,” in Climate Change 2007: 

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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). 

c

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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); 

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from two decades of research,” Disasters 24 (2000) 18-37. ci Intergovernmental Panel on Climate Change, “Glossary,” Climate Change 2007: Impacts, Adaptation and 

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(Cambridge: Cambridge University Press 2007). 
This paper does not represent US Government views. 

48

This paper does not represent US Government views. 

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adaptive capacity,” Global Environmental Change 12 (2002): 25-40 ciii 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). 

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cv U.S. Department of State, “India: country reports of human rights practices – 2006,” (2007) 

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cvii Antoinette L. Brenkert and Elizabeth L. Malone, “Modeling vulnerability and resilience to climate change: a 

case study of India and Indian states,” Climatic Change 72 (2005): 57-102; 

Elizabeth L. Malone and Antoinette L. Brenkert, “Uncertainty in resilience to climate change in India and Indian 

states,” Climatic Change 91 (2008): 451-476. 

cviii Elizabeth L. Malone and Antoinette L. Brenkert, “Vulnerability, Sensitivity, and Coping/Adaptive Capacity 

Worldwide,” in The Distributional Effects of Climate Change: Social and Economic Implications, eds. M. Ruth and 

M. Ibarraran (Edward Elgar, in press). 

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This paper does not represent US Government views. 

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