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Appendix E
Foundations for Monitoring
Climate–Security Connections
C
onclusion 6.1 sets out the five types of key phenomena for which
monitoring to anticipate national security risks related to climate
events is needed:
1. Climate and other biophysical environment phenomena;
2. The exposures of human populations and the systems that provide
food, water, health, and other essentials for life and well-being;
3. The susceptibilities of people, assets, and resources to harm from
climate events;
4. The ability to cope with, respond to, and recover from shocks; and
5. The potential for outcomes of inadequate coping, response, and
recovery to rise to the level of concern for U.S. national security.
There is substantial variation in the degree to which the expert com-
munities that study and analyze these phenomena have achieved consensus
about a set of key variables that are essential for monitoring. In many
cases the foundation for identifying, collecting, and analyzing basic data
is still under construction. The sections in this appendix reflect the com-
mittee’s judgment about some of the major data and monitoring needs
and capacities related to the five types of key phenomena above. In addi-
tion, as discussed in Chapter 6, there are significant challenges associated
with developing and applying the analytic techniques needed to turn ever-
increasing amounts of data into useful information.
203
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204 CLIMATE AND SOCIAL STRESS
CLIMATE AND OTHER BIOPHYSICAL
ENVIRONMENT PHENOMENA
The effects of human activity on the natural environment coupled with
natural changes in the physical climate system make long-term monitoring
of the global climate system crucial both to understanding the variability
and changes in the Earth system and for providing inputs to model-based
prediction schemes. As the human population continues to increase, so too
will global demands on agricultural, water, and energy resources. Analyzing
regional climate impacts and assessing human vulnerabilities will require
high-frequency and spatially dense observations as well as information on
the change and rate of change of the global climate system. Regional and
national networks must be developed, particularly in regions currently
experiencing an increased demand on natural resources. If sufficient obser-
vations are not collected, the ramifications will be serious, including less
accurate weather forecasts and an inability to monitor natural hazards.
As discussed in Chapter 6, in 1998 the Intergovernmental Panel on
Climate Change (IPCC) and the United Nations Framework Convention on
Climate Change (UNFCCC) established specific requirements for systematic
climate observations and a sustained observing system. Those requirements
included supporting research to understand more fully the causes of cli-
mate change, to predict future global climate change, and to characterize
extreme events important to impact assessments, adaptation, risks, and
vulnerability. In 2003 the Second Adequacy Report on the Global Observ-
ing System (Global Climate Observing System, 2003) concluded that while
improvements had been made in the global observing system, deficiencies
remained in the global coverage and quality of ocean, atmosphere, and
terrestrial measurements. The report concluded that satellite observations
over all domains were essential to the global observing system and that they
must continue uninterrupted. Despite such urging, however, U.S. satellite
observation capabilities are expected to decline by 25 percent over the next
8 to 10 years, according to a recent National Research Council (NRC)
r
eport (National Research Council, 2012) on NASA’s implementation of
the Decadal Survey. (See further discussion below.)
The Global Climate Observing System, sponsored by the World Me-
teorological Organization, the United Nations (UN) Educational, Scientific
and Cultural Organization, the UN Environmental Program, and the Inter-
national Council for Science, is charged with advising the community on
global climate observations and overseeing implementation based on UN-
FCCC standards. In 2010 the organization developed a list of 50 essential
climate variables (ECVs) that are possible to implement globally and whose
observation could yield significant progress toward meeting the UNFCCC
requirements (Global Climate Observing System, 2010). These ECVs are
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APPENDIX E 205
concerned with atmospheric conditions over land, sea, and ice and include
such variables as temperature, wind speed and direction, pressure, cloud
properties, and carbon dioxide levels. Ocean ECVs include sea surface and
subsurface temperature and salinity, ocean color, sea ice, sea level, oxygen,
and nutrients. Most terrestrial observations focus on ground water, water
use, snow cover, land cover, and soil moisture.
The ECVs represent a consensus on a broad and comprehensive set of
parameters to document Earth’s climate system. Designed primarily to char-
acterize key aspects of the Earth system, they are aimed more at informing
scientific analysis more than policy decisions. There have, however, been
several attempts to develop indices based on the ECVs that would yield an
integrated measure of climate impacts that could be more useful for policy
analysis. One such index is the U.S. Climate Extremes Index (CEI). It was
developed as a “monitoring and communications tool to help U.S. citizens
and policymakers identify possible trends or long-term variations in a vari-
ety of climate extremes indicators” (Gleason et al., 2008). The CEI is com-
posed of five parameters: monthly minimum and maximum temperatures,
daily precipitation, days with and without precipitation, and the Palmer
Drought Severity Index (PDSI) (Palmer, 1965). Temperature is important
for monitoring a variety of phenomena, including heat waves, cold waves
(including freeze events such as late spring freezes), and even unusually
warm or cold months and seasons that can have effects on a variety of sec-
tors. Precipitation is the basic building block for monitoring precipitation
deficits and excess, including drought and heavy rain events. Air pressure is
important for monitoring storms, heat waves, and other events, while water
vapor is important for monitoring the potential for heavy rain events and
drought. The PDSI is a dryness indicator based on a combination of recent
temperature and soil moisture observations.
The CEI was tailored for the continental United States. Currently there
is an effort to develop a CEI that would provide a more globally integrated
picture of the current state of climate extremes. However, the development
of such a global CEI has been hampered by a lack of data availability
in many regions and for most of the ECVs. A white paper prepared for
the 2011 World Climate Research Programme Open Science Conference
(Trenberth et al., 2012) concluded that although the data for ocean and
atmospheric ECVs have adequate global coverage over a long enough
time and sufficient quality, data for terrestrial measurements are seriously
deficient. It also concluded that although existing in-situ data cover most
of the high-priority regions, spatial and temporal coverage could be im-
proved. It called for more integration of satellite data as well as for new
observations that would provide information about such areas as climate
change mitigation and adaptation efforts. Data at regional and local scales
on such factors as soil moisture, stream flow, and sea surface temperature
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206 CLIMATE AND SOCIAL STRESS
that are related to climate events, are extremely inadequate. Soil moisture,
for example, is important for monitoring drought as well as for estimating
the potential for flooding. Streamflow is a good large-area integrator of
short- and longer-term moisture conditions for a basin, a region, or even a
continent. Sea surface temperature is especially important for tracking El
Niño–Southern Oscillation events.
The white paper found that such data, as well as detailed hourly
information on variables such as precipitation intensity, distribution, fre-
quency, and amount of precipitation, are necessary for predicting extreme
events on regional scales. Because there are not yet adequate data in these
areas, it may be necessary to develop global indicators in an incremental
fashion—for instance, by adding one or two parameters at a time or by be-
ginning with specific regions that allow early development, such as Europe
or Australia.
Numerous analyses have documented the linkages between global cli-
mate change and environmental sustainability (e.g., National Research
Council, 2010a, 2010b, 2010c). One example is the way in which changes
in ocean and atmospheric circulation force corresponding changes in ocean
temperatures, which reach the ice shelves, resulting in land ice loss, sea level
rise, and, ultimately, coastal erosion. Likewise, changes in precipitation
patterns can affect the snowpack and surface hydrology, thereby affecting
agricultural productivity. And increased carbon dioxide emissions absorbed
by the ocean lead to increased ocean acidity, which destroys the marine
organisms that provide food sources for other marine life and thus nega-
tively affects fisheries. To understand these relationships it is necessary to
measure a broad and diverse set of the variables that connect global climate
change with human life-supporting systems such as those providing for
food, energy, water, and health. Global-scale indicators and metrics based
on a broad spectrum of observations can provide some advance warning of
the impacts of global climate change (National Research Council, 2010b).
Sea level rise is one such indicator, for example, because it is a function
of oceanic, land ice, and hydrologic processes. However, to obtain better
estimates and projections of rising sea levels it will be necessary to make
better sustained observations of such variables as sea state, atmospheric
wind speed and direction, sea-ice extent, the mass balance of mountain
glaciers and ice sheets, and river discharge.
The United States and other countries do not currently have national
strategies for sustaining long-term environmental observations. Joint efforts
in support of the UNFCCC and IPCC are encouraging international col-
laboration, and many nations have recognized the need for a fully imple-
mented observing system. However, funding is a major obstacle. Major
gaps in satellite and in-situ observations are developing because of the loss
of several key satellites, such as Cryosat, the Orbiting Carbon Observa-
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APPENDIX E 207
tory, and Glory, and because of the reduced maintenance on the Tropical
Ocean Atmospheric Array of ocean buoys and on land-based carbon tow-
ers. Delays or cancellation of other satellites will further increase the data
gap. Lack of data will severely impair the ability of climate scientists to
understand and forecast changes from interactions and feedbacks in the
Earth system and will limit the information available to users and decision
makers. Serious consequences will include less accuracy in forecasting and
in the projecting of natural hazards and extreme events; as these events af-
fect a growing population that is already placing significant stress on Earth’s
systems and is expanding into areas exposed to likely climate hazards, the
consequences will only grow with time.
The national and international efforts noted above have focused mainly
on understanding and anticipating climate changes, climate events, and
some of their direct consequences. The field has made progress in setting
priorities, but its emphasis has not been on security issues. Such an empha-
sis would require types of monitoring not mentioned above. A consider-
able number of the types of monitoring relevant to security issues were
identified in a previous report from the NRC, Monitoring Climate Change
Impacts (National Research Council, 2010b). We note that many of the
environmental variables that are important to human life-supporting sys-
tems cannot be measured readily or with sufficient resolution and accuracy
from satellites and instead require place-based measurement and automated
interrogation and assimilation. Examples of such variables include the
q
uality of agricultural land, the condition of aquifers, and the ranges of
key pests and pathogens.
In addition, to our knowledge there has as yet been no serious priority
setting among environmental measurements, looking at the issue through
the lens of security analysis. Thus the priorities noted here and in the previ-
ous NRC effort (National Research Council, 2010b) provide only a start-
ing point toward prioritizing climate and environmental monitoring needs
for security analysis purposes. Efforts to develop priorities should aim at
identifying a small number of composite indices designed for the specific
purposes of analysis or early warning. Progress will require additional
work, which should be conducted through collaborations involving climate
scientists, environmental scientists, social scientists, and security analysts.
An effective monitoring system is feasible in principle, but it will require
an authoritative setting of priorities, the integration of climate and social
and political stress indicators, and the development of reliable protocols for
international collaboration.
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208 CLIMATE AND SOCIAL STRESS
EXPOSURE TO POTENTIALLY DISPRUPTIVE CLIMATE EVENTS
Exposure refers to the presence of people, livelihoods, environmental
services and resources, infrastructure, or economic, social, or cultural as-
sets in places that could be adversely affected (Intergovernmental Panel on
Climate Change, 2012:3). For security analysis it is important to recognize
that different climate hazards matter for different places because each popu-
lation and location of interest has a particular pattern of exposures and sus-
ceptibilities to harm as well as of abilities to cope and respond. Therefore,
monitoring of exposures needs to include both changes in the frequency and
intensity of potentially disruptive climate events and changes in the societal
conditions that affect which people and things are exposed to harm from
specific events and the different exposures of different segments of societies.
This section focuses on the monitoring of people and things. It is, of course,
equally important to monitor the hazards. Although some hazards, such
as large-scale floods, are easy to monitor, others (e.g., droughts) are not
presently well monitored. The following are, in our judgment, some of the
monitoring requirements that are most important for estimating exposures
to potentially disruptive climate events.
Population Estimates on Spatially Disaggregated Scales
Understanding how hazards might affect human societies requires a
basic understanding of where people are located with respect to the haz-
ards, and estimates of exposed populations form a fundamental building
block of any vulnerability assessment. The most basic indicator is the
number of people residing in a given area, sometimes called the headcount.
In some cases one wants to know more demographic detail than simply
the headcount, for example, the age structure, the number of orphans, the
percentage of female-headed households, and so on. Such information is
often available in national censuses.
In some cases the most important variable to estimate is not the number
of residents in an area, but the total number of people located in the area
at a given time, regardless of their residence. This total number of people
in an area, including both residents and nonresidents, is sometimes referred
to as the ambient population. For example, many of the people exposed to
the Asian tsunami in 2004 were tourists, who were not counted in the lo-
cal censuses. In other cases a region may have large numbers of migratory
workers whose exposure is relevant to understanding risk but who are not
counted in the censuses. An example is China prior to the 2008 financial
crisis, when a large number of people located in vulnerable areas along the
coast were migrant workers from the interior.
In many climatically fragile areas, people respond to shifting climatic
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APPENDIX E 209
patterns by taking part in seasonal migration. Some massive population
movements (e.g., the Hajj) also take place at times of major holidays. Such
dynamics can not only affect estimates of exposure, but also influence the
emergence of a hazard (e.g., infectious disease). For some hazards it may
be helpful to estimate population distributions on an even finer time-scale
(e.g., hourly); in some large urban areas the number of people present varies
by orders of magnitude over the course of a day.
There are a handful of databases that have been used to generate esti-
mates of these phenomena. Of these the Gridded Population of the World
(GPW), produced by Columbia University, and LandScan, produced by
Oak Ridge National Laboratory, are the most widely used. Regional col-
lections are also available. (For a recent review see Tatem et al., 2012.)
GPW integrates national census data using the highest spatial resolution
available; LandScan estimates ambient population using census data along
with spatial correlates of population location.
Such information is useful across the entire life cycle of hazard assess-
ment, preparedness, and response. Well before an event of concern, such
information is relevant for risk assessment and planning for reducing sus-
ceptibility among exposed populations. As an expected event approaches,
the information is relevant for preparedness and crisis response planning
(evacuation, resource mobilization, etc.). And in post-crisis periods, it is
relevant for damage assessment and reconstruction planning; at this stage,
census-derived data are no longer relevant, and estimates must be made
afresh using ground surveys and satellite imagery.
The main weaknesses of the available data are the temporal lags (de-
cennial censuses are inadequate for many purposes) and the lack of spatial
resolution.
Location and Characteristics of Critical Infrastructure
Some risks are more a function of the exposure of critical infrastructure
than of the exposure of populations. In such cases, it is important to moni-
tor the locations and key characteristics of such infrastructure as power
plants, roads, railroads, ports, telecommunications centers, hospitals, pris-
ons, government buildings, and key manufacturing facilities. It is important
that, in addition to monitoring, there should be an ongoing assessment of
the adequacy of this infrastructure and of whether plans are in place for
backup procedures if the adequacy standards are not yet met.
Publicly available global databases on such infrastructure are not con-
sidered adequate. Data on roads, for example, are available but are out-
dated and of very low accuracy, while other infrastructure information is
not available except in commercial proprietary databases (mainly from the
insurance industry and large engineering firms) and from classified military
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210 CLIMATE AND SOCIAL STRESS
databases. There is an effort under way to compile a global exposure data
base for use in earthquake modeling1 that will represent the most compre-
hensive public database of this type when complete. However, because it
is oriented toward modeling economic loss rather than modeling security
threats, it cannot be expected to meet all needs for security assessments.
Information on infrastructure exposures would be useful at all stages
of the life cycle of potentially disruptive events: early on for risk assess-
ment and planning, and later on for damage assessment and response
prioritization.
Land Use and Land Cover
Patterns of land use affect vulnerability to hazards and also constitute
an integral aspect of exposure. Whether a given area is devoted to commer-
cial agriculture, subsistence agriculture, grazing, dense urban settlement, or
wilderness will make a difference on how a given hazard affects security
dynamics. For example, a prolonged drought in an area with significant
subsistence agriculture and grazing will likely result in population move-
ments, whereas such a response would be less likely in an area with other
land uses.
There are standard classification schemes for characterizing land use
and land cover, and these form the basis for generating indicators. Indica-
tors that characterize different types of agricultural activity and different
levels of urbanization would be particularly useful.
A number of global databases measure land use and land cover, with
satellite imagery providing the most important input, along with significant
validation from ground-based observations. There are a number of sources
of global data on land cover change, including NASA’s Land-Cover/Land-
Use Change Program,2 the Global Land Cover Facility at the University of
Maryland,3 and the U.S. Geological Survey.4 Other sources provide land
use data for specific counties or regions. For example, the Department of
Global Ecology of the Carnegie Institution for Science, based at Stanford
University, compiles high-resolution data on forest cover and related vari-
ables for tropical regions.5
These databases are considered accurate enough for use in global mod-
eling and other broad exercises, but their accuracy and precision within
specific regions is low. Their precision is low enough, for example, that they
1
GED4GEM. See http://bit.ly/KCzpyj (accessed November 15, 2012).
2
See http://lcluc.umd.edu/data_information.php (accessed November 15, 2012).
3
See http://esip.umiacs.umd.edu/index.shtml (accessed November 15, 2012).
4
See http://landcover.usgs.gov/index.php (accessed November 15, 2012).
5
See http://claslite.ciw.edu; http://cao.stanford.edu (accessed November 15, 2012).
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APPENDIX E 211
tend not to discriminate between subsistence and commercial agriculture or
between different levels of urbanization. They also tend to have difficulty
in characterizing pasture, a critical land use category for understanding
climate impacts. Moreover, the databases tend to disagree widely in delin-
eating urban extents.
Data on land use and land cover are most useful when used for risk
assessment and planning in the early stages of the life cycle of potentially
disruptive events.
SUSCEPTIBILITY TO HARM
Susceptibility is the likelihood of immediate harm to a population,
community, society, or system as the result of exposure to a climate event.
Thus, susceptibility is an indicator of the extent that an event would create
disruptive change in the short term in that population, community, society,
or system. For security analysis, it is important to identify factors that influ-
ence the general susceptibility of populations to all types of climatic shocks
and stresses as well as the susceptibility to specific types of climate-induced
events, such as a flood or a pandemic. This section discusses monitoring
needs for measures of general susceptibility before turning to a few key
areas of specific susceptibility: food, water, and health.
Monitoring of General Susceptibility
A number of factors influence general susceptibility, including various
economic, demographic, social, and environmental conditions in a region;
the form and quality of the infrastructure and the built environment (In-
tergovernmental Panel on Climate Change, 2007, 2012); and the presence
or a recent history of violent conflict (Barnett, 2006; Barnett and Adger,
2007; Brklacich et al., 2010). In most cases it is not only the values of the
variables that are relevant but also the direction and rate of change in those
variables. For example, while the level of poverty in a region is an indicator
of general human well-being and therefore of susceptibility to harm, there
is additional value in knowing whether the level of poverty is increasing.
If it is—even if poverty in the population does not seem particularly high
to start with—this would suggest that well-being is deteriorating and that
the potential for harm from climatic shocks may be increasing because fi-
nancial and other assets that previously would have allowed households to
cope with exposure to climate shocks are being depleted and because the
construction of protective infrastructure is lagging far behind and further
exposing populations to risk (Parry et al., 2009).
In addition to determining which types of susceptibility factors should
be monitored, it is also critically important to consider the appropriate scale
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212 CLIMATE AND SOCIAL STRESS
or level of analysis for assessing and monitoring the susceptibility of specific
populations. A susceptibility assessment may apply to an entire population
or to a defined subset of a population (such as a particular ethnic group)
within a politically bounded region, such as a city, state, or country, or
within various types of functional regions, such as urban neighborhoods,
border zones, agricultural areas, hydrologic basins, and so forth. As a
general rule, susceptibility monitoring should emphasize locations that are
currently or potentially of security concern, but it should also pay attention
to regions where major humanitarian or other types of crises may arise or
where large migration inflows or outflows may be likely.
It is also important to recognize that many of the factors that influ-
ence susceptibility to future climatic shocks and stress are already in flux
as the result of ongoing environmental and climatic changes (Paavola,
2008) and of non-climatic processes, including globalization and urbaniza-
tion (O’Brien and Leichenko, 2007; Leichenko and O’Brien, 2008). For
example, rapid rates of deforestation on the hill slopes surrounding a city,
which are often associated with population immigration, will exacerbate
the susceptibility of a region to extreme precipitation events. Areas where
susceptibility indictors are rapidly changing—especially when these changes
suggest that susceptibility is increasing—also merit special attention because
these are regions where social and political turmoil may be more likely,
particularly if the regions have a recent history of violent conflict.
Measurements of economic conditions provide an indication of the
financial and material well-being within a population or region and of
the capacity to withstand climate-related shocks and stresses. Regions or
population subgroups with low or deteriorating levels of per capita income
and financial assets, high levels of poverty, high levels of unemployment,
or high levels of income inequality can be expected to be more susceptible
to harm from climatic risks and hazards. Economic diversity also has an
important influence on susceptibility. Regions or populations that depend
on a single agricultural commodity for their livelihoods and where alterna-
tive livelihood options are limited also tend to be more susceptible to harm
from all types of climatic shocks and stresses, particularly those affecting
their main source of livelihood. Potential indicators of economic well-being
and economic diversity include per capita income, poverty rates, levels of
inequality, unemployment rates (overall and by age cohort), percentage of
the labor force in agriculture, and share of agricultural production by crop.
Many of these basic economic variables are regularly catalogued by
international organizations such as the World Bank, which maintains data
bases on variables at the level of the nation-state for most countries.6
National-level information on agricultural production and crop yields is
6
See http://databank.worldbank.org/data/home.aspx (accessed November 15, 2012).
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APPENDIX E 213
available from the International Food Policy Research Institute7; other
data sources are discussed later. Many national governments also collect
economic data at both the national and subnational levels, but for less-
developed countries the availability and reliability of these data are limited,
and many countries do not release their routinely collected economic data.
Subnational data are also available in datasets of the Center for Interna-
tional Earth Science Information Network at Columbia University; these
datasets contain subnational estimates of indicators of economic well-being,
including poverty, inequality, unmet basic needs, and food security, for a
large number of countries.8 One important limitation of using existing
data from many publicly available secondary sources is that the underlying
source data, such as national population censuses, are updated relatively
infrequently. These datasets provide reasonable estimates of baseline condi-
tions, but they would need to be supplemented with primary data collected
for specific regions of interest in order to gauge current conditions or to
assess changes over shorter periods of time.
Demographic and social conditions are another group of factors that
influence susceptibility to harm from climatic shocks and stresses. Regions
with high rates of population growth, particularly from immigration, can be
expected to be more susceptible to many climate risks because, as discussed
in Chapter 5, new immigrants to a region are often poorer than long-time
residents, are likely to lack knowledge of local environmental conditions,
and tend to live in hazard-prone areas such as flood plains. Highly urban-
ized areas concentrate people and may therefore increase the disruption that
would result from events experienced there; on the other hand, it may be
easier in highly urbanized areas for response efforts to reach affected people.
Regions with high shares of elderly residents or of young children also tend
to be more susceptible to multiple hazards because these groups rely on
others for their safety and well-being. Regions with low levels of education
and literacy—both of which are indicators of human capital—would also
be expected to be more susceptible to harm from a variety of climate events
(Cavallo and Noy, 2010). In regions with high levels of gender inequality,
females could be expected to be more susceptible to harm from climate
events than males because of a relative lack of assets and a lack of access
to resources that may be available to men. The health status of populations
also influences susceptibility to harm from such hazards as food insecurity,
poor water quality, and exposure to infectious disease agents.
Potential demographic and social variables to monitor include popula-
tion growth rate, rate of immigration, share of the population that is over
7
See http://www.ifpri.org/dataset/agro-maps-mapping-agricultural-production-systems (accessed
November 15, 2012).
8
See http://sedac.ciesin.columbia.edu/theme/poverty (accessed November 15, 2012).
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228 CLIMATE AND SOCIAL STRESS
ratist, or independence movements), (b) the right of the current government
or administration to rule (as evidenced by insurrections, insurgencies, or,
in the case of opposition to authoritarian regimes, popular or democratic
movements), or (c) the methods or processes by which the incumbent au-
thorities came to be holding office (as evidenced by, on an increasing scale
of violence, anti-government protests, demonstrations, or riots). The situ-
ation most likely to lead to problems would be a combination of all three
rejections by multiple groups that agree on short-term goals (even if they
differ on long-term goals), including at a minimum the ouster of the cur-
rent authority figures. Such conditions would be conducive to the collapse
or fall of the government or administration and possibly even to a regime
change in the aftermath of the disaster. As discussed in Chapter 4, the case
of Myanmar after 2008’s Cyclone Nargis, where a type of slow democra-
tization is currently occurring, is a possible example, depending upon the
final outcome of the process.
Many countries at serious risk of disasters, including several of national
security interest to the U.S. government, are in the substantial and com-
plicated middle area between these two ideal types of high-legitimacy and
low-legitimacy regimes. Turkey, for example, is closer to the first type—a
country in which a major natural disaster (e.g., a great earthquake affecting
Istanbul) would likely not pose a threat to the regime (although perhaps
to the incumbent authorities and possibly the government of the time).
Pakistan, with a major earthquake or flood, could be closer to the second
type because, although the regime survived devastating floods in 2010, the
aftermath of such an event could provide the occasion for an Islamist or
military usurpation of power that would change not only the authorities
but also the government and, quite likely, the entire regime.
Considering that in the 21st century the standard for legitimacy in most
countries is some form or approximation of a democracy, it is worth con-
sidering and monitoring the characteristics of a democratic political system
that might make it resistant to erosion or collapse in the wake of a major
disaster. That set would include (1) sustained levels of high socioeconomic
development without stark inequities; (2) regularized and accepted mecha-
nisms and channels to organize and express grievances; (3) low levels of
violence, particularly political violence; (4) a sustained record of low public
corruption; (5) full civil liberties, a free media, and regular and fair elections
with peaceful turnovers of post-election positions; and (6) effective gover-
nance capacities from the national to the local level, including for extreme
event emergency response and recovery.23
23
An example of a regime’s stability under disaster can be seen in the situation that arose
in Germany in August 2002 when devastating floods affected large areas and many cities and
towns in the eastern part of the country. Interestingly, before the floods Chancellor Gerhard
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APPENDIX E 229
A nation-state whose characteristics were a mirror image of these
would likely have its stability threatened by a major disaster. Such a state
would have: (1) low to middle levels of socioeconomic development, with
stark inequities; (2) restricted, ineffective, or nonexistent mechanisms and
channels to organize and express grievances; (3) high levels of violence,
either currently or in the recent past; (4) sustained high levels of public
corruption; (5) restricted civil liberties and media, fraudulent or non-com-
petitive elections, and problematic turnovers of governing authority; and
(6) ineffective or very limited governance capacities from the national to the
local level, including for extreme event emergency response and recovery.24
Specific Monitoring Needs
These considerations suggest some specific monitoring needs for as-
sessing the short-, medium-, and long-term effects of a major disaster or
a sequence of disasters on social and political stability. In addition to the
four factors introduced at the beginning of the section, monitoring should
also be aimed at the following baseline and social and political conditions:
1. The coping capacities of the principal groups affected directly and
indirectly by the event or sequence of events and the differences in
coping capacity among the principally affected groups. Some exist-
ing indicators of social capital may serve as indicators of coping
capacity (Aldrich, 2012).
2. The general response capacity, which can be monitored in part by
observing the response capacities of responsible formal organiza-
tions. Response capacity is partly a matter of budgets and supplies
(e.g., emergency shelter materials, water purification equipment, and
food stocks). It also encompasses logistical capacities, the availability
and training of general response personnel, and access to special-
ized personnel (e.g., first responders or medical personnel). Because
capacity is meaningful only in relation to the costs of providing
a
ssistance—some of which, such as food prices, can be volatile—
the costs of price-volatile supplies and equipment should also be
monitored.
Schröder was behind in the polls for the September 2002 elections. His opposition to the
oncoming U.S. invasion of Iraq combined with an effective disaster response and then a
carefully financed recovery plan brought him back electorally, and he won reelection, albeit
with a much reduced majority (from 21 to 9) in the Bundestag.
24
These pre-conditions characterized Haiti when it was struck by a major earthquake in
2010, except that the United Nations and the community of nongovernmental organizations
were providing most of the services normally associated with a state—and all Haitians were
well aware of it.
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230 CLIMATE AND SOCIAL STRESS
3. Expectations of response on the part of the general public, key
socioeconomic and cultural groups, and disaster-affected popula-
tions. This includes expectations of government emergency response
capabilities and effectiveness as well as perceptions of government
capabilities and effectiveness versus those of other domestic actors
(e.g., the military or religious organizations in many countries), non-
governmental organizations in general, or outside donors. Expecta-
tions provide an important baseline indicator in advance of extreme
events and can be measured by survey methods, perhaps as part of
the more general efforts to survey public attitudes toward govern-
ment discussed below. We are unaware, however, of any efforts to
do such measurement systematically across countries for disaster-
related issues. Perceptions of response after a disruptive event can
be assessed in the same way and compared with the expectations
to provide an indicator of the perceived adequacy of the response.
The key dimensions of response perception include the effectiveness,
transparency, and honesty of the responding organizations.
4. Surge capacity, one indicator of which is the availability of desig-
nated standby personnel and the funds to support them in the event
of an emergency.
5. The likelihood of effective response. A baseline assessment could be
provided by examining the track records of government and other
response organizations in providing support to the affected popula-
tions or areas both generally and after past disruptive events, the his-
tory of national governments in allowing outside resources to flow
to affected areas, and the record of national and local governments
in delivering normal services to affected or potentially affected areas.
6. The direct and indirect social and economic impacts of the disaster
(numbers killed, injured, and made homeless as well as the numbers
of those whose livelihoods have been destroyed or jeopardized).
The monitoring of these impacts, which is done in the aftermath of
an event, could be done by class, race, ethnicity, gender, geography,
political orientation, and so on. These factors are most important
to monitor at the geographic level of the event, rather than at the
national level. We recognize that some of this information may be
difficult to collect, particularly in countries where some of these is-
sues are likely to be sensitive and governments are either reluctant
to ask the questions or else to make the information public. In such
cases, it may be necessary to rely on indirect measures.
7. The impacts of actual response and recovery, which can be moni-
tored through a number of attitudinal variables. These variables
range from attitudes about specific responses to a disaster to atti-
tudes about more general political and social conditions and capaci-
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APPENDIX E 231
ties. These are normally measured by public opinion surveys asking
for approval ratings, but they can also be assessed by examining
the treatment of the government in free mass media. The attitudinal
variables that can be monitored to assess the impacts of response
and recovery can include
• Expectations of government disaster recovery capabilities and
effectiveness by the general public, by key socioeconomic groups,
and in the disaster-affected population.
• Perceptions of the effectiveness of government efforts in disaster
recovery compared with efforts of other actors, institutions, or
donors, assessed in the general public, key socioeconomic groups,
and disaster-affected populations.
• Mobilization of disaster-related grievances by location, degree,
type of grievance, population subgroup, and apparent purposes
or goals. This monitoring should be particularly sensitive to con-
centrations of grievances by region, class, race, ethnicity, religious
orientation, or other pre-existing societal cleavage.
• Attitudes toward incumbent authorities (i.e., the leaders or leader-
ship group) from local to national levels. This can be assessed by
approval ratings collected from general public samples, key socio-
economic groups, and disaster-affected populations, and should
be sensitive to the above sets of pre-existing societal cleavage.
• Attitudes toward the government or administration, measured by
approval ratings among the general public, key socioeconomic
groups, and disaster-affected populations from local to national
levels. This monitoring should also be sensitive to the above sets
of pre-existing societal cleavage.
• Attitudes about the underlying legitimacy or “rightness” of the
nation, the state, the current government, and the incumbent
authorities, monitored in the general public, key socioeconomic
groups, and disaster-affected populations, and sensitive to the
above pre-existing societal cleavages. In surveys or focus groups,
legitimacy can be assessed directly by asking whether the current
form of government is appropriate for solving national problems.
It can be assessed indirectly by examining such indicators as pro-
vision of basic health and security services.
8. Social or political instability over the longer term. Potential indica-
tors include
• Pre-existing levels of internal violence and political instability in
the country of interest.
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232 CLIMATE AND SOCIAL STRESS
• Pre-existing levels of violence in neighboring countries and
changes in those levels after the event or sequence of events in
the country of interest.
• Attitudes toward or approval of the nation, state, current govern-
ment, and incumbent authorities within key institutions (e.g., the
military, private-sector commercial and industrial organizations,
and churches or religious organizations and movements). This
is normally assessed with standard forms of political analysis,
although there may also be a role for monitoring general or spe-
cialized media.
• Mobilization of opposition by parties, groups, or movements that
may attempt to take advantage of the post-impact disaster period
and the recovery period to advance their interests and agendas.
This monitoring should be particularly sensitive to differences be-
tween largely non-violent “in-system” opposition parties, groups,
or movements versus “out-system” and more violence-prone enti-
ties, especially if the latter are organized along the lines of major
pre-existing societal cleavages. This is assessed by standard forms
of political analysis.
• The repressive capacities of the state and the degree to which
these are enhanced, remain static, or are degraded by the climate
event or sequence of disasters.
Examples of Monitoring Resources for Coping, Response, and Recovery
Response capacity. The increasing international attention in recent
years to reducing disaster risks rather than simply responding when disas-
ters occur has led to a number of major communication and coordination
initiatives that also offer sources of information about national, regional,
and international capabilities. For example, the UN International Strategy
for Disaster Reduction Secretariat (UNISDR), as part of its initiative to
create national platforms for disaster risk reduction (81 countries at the
time of this report) is developing a database of national response capacity.25
Another UNISDR initiative is the Integrated Research on Disaster Risk’s
Forensic Investigations of Disasters,26 launched in partnership with the
International Social Science Council and the International Council for
Science. Its goals are (1) to provide a baseline of the current state of the
science in integrated research on disaster risk in order to measure the ef-
fectiveness of multiple programs, (2) to identify and support a long-term
25
See http://www.unisdr.org/partners/countries (accessed November 15, 2012).
26 See http://www.irdrinternational.org/about-irdr/scientific-committee/working-group/
forensic-investigations/ (accessed November 15, 2012).
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APPENDIX E 233
science agenda for the research community and funding agencies, and (3) to
provide a scientific basis to support policy and practice. All of these various
activities are yielding data on, or at least relevant to, national disaster re-
sponse capabilities and offer relatively inexpensive monitoring possibilities
for the U.S. intelligence community.
Perceptions and attitudes. In addition to traditional political analysis
and research, both inside and outside the government, there are a number
of resources for data on public perceptions and attitudes. In particular,
public opinion survey projects such as Afrobarometer,27 the mericas Ba-
A
rometer survey from the Latin American Public Opinion Project,28 Asian
Barometer,29 and Arab Barometer30 are accumulating evidence about at-
titudes toward democracy, government performance, and a range of social
and political issues.
Regime types and governance. The longstanding Polity project, now
part of the Political Instability Task Force effort, provides data that support
quantitative and comparative analysis of regime authority characteristics
and transitions. The types of governing authority range from fully insti-
tutionalized autocracies through mixed, or incoherent, authority regimes
(termed “anocracies”) to fully institutionalized democracies. The “Polity
Score” captures this regime authority spectrum on a 21-point scale ranging
from –10 (hereditary monarchy) to +10 (consolidated democracy) (Polity
4 Project, 2012).
Assessing the quality of governance has become a major focus for devel-
opment assistance in recent years. Hundreds of datasets have emerged from
donor agencies, governments, nongovernmental organizations, think tanks,
and academia. (For a review of some of the “uses and abuses” of governance
indicators, see Arndt and Oman, 2006.) Often used for research purposes
because of their independence and freedom from member government in-
fluences, the annual datasets from Transparency International and Human
Rights Watch are readily accessible and include various indicators for gov-
ernance. The Worldwide Governance Indicators Project, produced by a team
of scholars at the Brookings Institution with funding from the World Bank,
is an example of a widely cited set of indicators from the world of intergov-
ernmental organizations. It reports aggregate and individual indicators for
six dimensions of governance: voice and accountability, political stability
and the absence of violence, government effectiveness, regulatory quality,
rule of law, and control of corruption. As explained on the World Bank’s
27
See http://www.afrobarometer.org (accessed November 15, 2012).
28
See http://www.vanderbilt.edu/lapop (accessed November 15, 2012).
29
See http://www.asianbarometer.org/newenglish/introduction/default.htm (accessed November
15, 2012).
30
See http://www.arabbarometer.org/about.html (accessed November 15, 2012).
OCR for page 234
234 CLIMATE AND SOCIAL STRESS
website, “These aggregate indicators combine the views of a large number
of enterprise, citizen, and expert survey respondents in industrial and de-
veloping countries. The individual data sources underlying the aggregate
indicators are drawn from a diverse variety of survey institutes, think tanks,
non-governmental organizations, and international organizations” (World
Bank, 2012). The data cover 213 economies for the period 1996–2010. A
number of donor agencies also support national assessments. For example,
as part of its Democracy, Human Rights, and Governance Program, USAID
occasionally funds surveys that capture public experience with and percep-
tions of corruption.
Political instability and violence. A number of these resources were
already noted in Chapter 5 as part of the discussion on assessing the evi-
dence base for connections between climate change and security risk. The
resources include the Uppsala Conflict Data Program,31 the Political Insta-
bility Task Force (Box 5-1), and the Peace and Conflict Instability Ledger
(Hewitt et al., 2012). A description of major datasets on international
conflict and cooperation is maintained as an online appendix to the Com-
pendium Project of the International Studies Association.32
Cross-cutting projects. In addition to the Political Instability Task
Force, another U.S. government effort to examine a number of factors that
affect coping, response, and recovery is the USAID Alert List. Since 2004
the USAID has produced an annual report that ranks countries according
to (1) their current level of fragility and (2) a forecast of their risk for con-
flict or political instability and then combines the two rankings to produce
an assessment of the most vulnerable countries. The seventh report was
produced in 2011. The report is based on quantitative analysis of unclas-
sified, open source information, and although the actual list of rankings is
not made public, details about the methodology and some general findings
are available. For example, in 2009, 23 of the 29 most vulnerable countries
were in Africa (Moore, 2010:4)
The concept of “fragility” refers to the extent to which interactions
between state and society produce outcomes that are (1) effective and (2)
legitimate; the USAID measure assesses states on these two dimensions in-
dependently, and the two assessments for a state are often very different.33
Of the 33 political, security, economic, and social indicators used to assess
fragility, 16 are related to effectiveness and 17 to legitimacy. The forecasts
of instability use a model developed by the Center for International De-
31
See http://www.pcr.uu.se/research/UCDP/ (accessed November 15, 2012).
32
See http://www.paulhensel.org/compendium.html (accessed June 23, 2102).
33
Principal components analysis was used to calculate weightings based on the relative influ-
ence each indicator exerts on empirical relationships in the data, and those weights are then
used to compute country scores.
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APPENDIX E 235
velopment and Conflict Management at the University of Maryland. Its
forecasts rely on a limited number of explanatory variables. In 2010 the
project included an assessment of vulnerability to climate change based
on a statistical model that analyzes global weather data and projects them
forward. This approach permits physical climate hazards and human vul-
nerability to conflict to be disaggregated from the effects of climate change
(Hewitt, 2012).
SUMMARY
The multitude of possible sources of data and indicators that could
be applied to understanding climate-security connections is potentially
overwhelming. This appendix attempts to provide an overview of major
data sources and monitoring projects across the phenomena of interest of
this project as well as a sense of the degree of consensus across research
communities about key variables and indicators. It serves to illustrate the
complexity and challenges associated with one of the report’s findings:
Monitoring systems will require the integration of quantitative indicators
of both environmental and social phenomena with traditional security and
intelligence analytic methods.
REFERENCES
Aldrich, D.P. 2012. The politics of natural disasters (pre-print). In Oxford bibliogra-
phies in political science. Available: http://works.bepress.com/cgi/viewcontent.
cgi?article=1019&context=daniel_aldrich (accessed October, 4, 2012).
Arndt, C., and C. Oman. 2006. Uses and abuses of governance indicators. Paris, France:
OECD, Centre for Development.
Barnett, J. 2006. Climate change, insecurity, and justice. Pp. 115–130 in Fairness in adapta-
tion to climate change, W.N. Adger, J. Paavola, M.J. Mace, and S. Huq, Eds. Cambridge,
MA: MIT Press.
Barnett, J., and W. N. Adger. 2007. Climate change, human security and violent conflict.
Political Geography 26(6):639–655.
Brklacich, M., M. Chazan, and H.G. Bohle. 2010. Human security, vulnerability, and global
environmental change. Pp. 35–52 in Global environmental change and human security,
R. Matthew, J. Barnett, B. McDonald, and K. O’Brien, Eds. Cambridge, MA: MIT Press.
Brown, M.E., F. Tondel, T. Essam, J.A. Thorne, B.F. Mann, K. Leonard, B. Stabler, and G.
Eilerts. 2012. Country and regional staple food price indices for improved identification
of food insecurity. Global Environmental Change 22(3):784–794.
Burke, R.L., K.G. Vest, A.A. Eick, J.L. Sanchez, M.C. Johns, J.A. Pavlin, R.G. Jarman, J.L.
Mothershead, M. Quintana, T. Palys, M.J. Cooper, J. Guan, D. Schnabel, J. Waitumbi,
A. Wilma, C. Daniels, M.L. Brown, S. Tobias, M.R. Kasper, M. Williams, J.A. Tjaden,
B. Oyofo, T. Styles, P.J. Blair, A. Hawksworth, J.M. Montgomery, H. Razuri, A. Laguna-
Torres, R.J. Schoepp, D.A. Norwood, V.H. MacIntosh, T. Gibbons, G.C. Gray, D.L.
Blazes, K.L. Russell, and AFHSC-GEIS Influenza Surveillance Writing Group. 2011.
Department of Defense influenza and other respiratory disease surveillance during the
2009 pandemic. BMC Public Health 11(Suppl. 2):S6.
OCR for page 236
236 CLIMATE AND SOCIAL STRESS
Cavallo, E., and I. Noy. 2010. The economics of natural disasters. IDB Working Paper Series
No. IDB-WP-124. Washington, DC: Inter-American Development Bank.
Clinton, W.J. 1996. Presidential decision directive NSTC-7. Washington, DC: The White
House.
Dawe, D. 2009. The unimportance of “low” world grain stocks for recent world price in-
creases. ESA Working Paper No. 09-01. Food and Agriculture Organization. Available:
ftp://ftp.fao.org/docrep/fao/011/aj989e/aj989e.pdf (accessed August 10, 2012).
Diaz-Quijano, F., and E.A. Waldman. 2012. Factors associated with dengue mortality in Latin
America and the Caribbean, 1995–2009: An ecological study. American Journal of Tropi-
cal Medicine and Hygiene 86(2):328–334.
Famine Early Warning Systems Network. 2008. FEWS NET strategy and vision for markets
and trade. Washington, DC: FEWS NET. (Updated January 2008). Available: http://
www.fews.net/docs/special/FEWSNETMarketandTradeStrategy2005-2010.pdf (accessed
December 17, 2012).
Fukuda, M.M., T.A. Klein, T. Kochel, T.M. Quandelacy, B.L. Smith, J. Villinski, D. Bethell,
S. Tyner, Y. Se, C. Lon, D. Saunders, J. Johnson, E. Wagar, D. Walsh, M. Kasper, J.L.
Sanchez, C.J. Witt, Q. Cheng, N. Waters, S.K. Shrestha, J.A. Pavlin, A.G. Lescano,
P.C.F. Graf, J.H. Richardson, S. Durand, W.O. Rogers, D.L. Blazes, K.L. Russell, and
AFHSC-GEIS Malaria and Vector Borne Infections Writing Group. 2011. Malaria and
other vector-borne infection surveillance in the U.S. Department of Defense Armed Forces
Health Surveillance Center-Global Emerging Infections Surveillance program: Review of
2009 accomplishments. BMC Public Health 11(Suppl. 2):S9.
Gleason, K.L., J.H. Lawrimore, D.H. Levison, T.R. Karl, and D.J. Karoly. 2008. A revised
U.S. climate extremes index. Journal of Climate 21:2,124–2,137.
Gleick, P.H. (Ed.). 2011. The world’s water, Volume 7. Washington, DC: Island Press.
Global Climate Observing System. 2003. The second report on the adequacy of the global
observing systems for climate in support of the UNFCCC. April 2003 (GCOS-82,
WMO/TD No. 1143). Available: http://www.wmo.int/pages/prog/gcos/Publications/gcos-
82_2AR.pdf (accessed October 15, 2012).
Global Climate Observing System. 2010. Implementation plan for the global observing system
for climate in support of the UNFCCC (2010 update). Geneva, Switzerland: World Me-
teorological Organization. Available: http://www.wmo.int/pages/prog/gcos/Publications/
gcos-138.pdf (accessed August 6, 2012).
Hayden, M. 2012. The dengue vector mosquito Aedes aegypti at the margins: Sensitivity of
a coupled natural and human system to climate change. Briefing to the Committee on
Assessing the Impacts of Climate Change on Social and Political Stresses, January 12,
Washington, DC.
Hewitt, J.J. 2012. 2011 alert lists: Methodological overview. Briefing to the Committee on
Assessing the Impacts of Climate Change on Social and Political Stresses, March 1,
Washington, DC.
Hewitt, J.J., J. Wilkenfeld, and T.R. Gurr. 2012. Peace and conflict 2012. Boulder, CO:
Paradigm.
Intergovernmental Panel on Climate Change. 2007. Climate change 2007: Impacts, adapta-
tion, and vulnerability. Cambridge, UK: Cambridge University Press.
Intergovernmental Panel on Climate Change. 2012. Managing the risks of extreme events and
disasters to advance climate change adaptation. Special report of working groups I and
II of the Intergovernmental Panel on Climate Change. C.B. Field, V. Barros, T.F. Stocker,
D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen,
M. Tignor and P.M. Midgley, Eds. Cambridge, UK: Cambridge University Press.
OCR for page 237
APPENDIX E 237
Johns, M.C., R.L. Burke, K.G. Vest, M. Fukuda, J.A. Pavlin, S.K. Shrestha, D. Schnabel, S.
Tobias, J.A. Tjaden, J.M. Montgomery, D.J. Faix, M.R. Duffy, M.J. Cooper, J.L. Sanchez,
D.L. Blazes, and AFHSC-GEIS Outbreak Response Writing Group. 2011. A growing
global network’s role in outbreak response: AFHSC-GEIS, 2008–2009. BMC Public
Health 11(Suppl. 2):S3.
Koffi, J.K., P.A. Leighton, Y. Petcat, L. Trudel, L.R. Lindsay, F. Milford, and N.H. Ogden.
2012. Passive surveillance for I. scapularis ticks: Enhanced analysis for early detection of
emerging Lyme disease risk. Journal of Medical Entomology 49:400–409.
Lagi, M., K.Z. Bertrand, and Y. Bar-Yam. 2011. The food crises and political instability in
North Africa and the Middle East. Available: http://arxiv.org/pdf/1108.2455v1.pdf (ac-
cessed October 4, 2012).
Leichenko, R.M., and K.L. O’Brien. 2008. Environmental change and globalization: Double
exposures. New York: Oxford University Press.
Lindgren, E., Y. Andersson, J.E. Suk, B. Sudre, and J.C. Semenza. 2012. Monitoring EU emerg-
ing infectious disease risk due to climate change. Science 336:418–419.
Money, N.N., R. Maves, P. Sebeny, M. Kasper, M.S. Riddle, and AFHSC-GEIS Enteric Surveil-
lance Writing Group. 2011. Enteric disease surveillance under the AFHSC-GEIS: Current
efforts, landscape analysis and vision forward. BMC Public Health 11(Suppl. 2):S7.
Moore, F. 2010. Climate change in Africa. Testimony before the Subcommittee on African
Affairs and Global Health, Committee on Foreign Affairs, United States House of
Representatives, April 15. Available: http://gopher.info.usaid.gov/press/speeches/2010/
ty100415.html (accessed July 9, 2012).
National Research Council. 2001. Under the weather: Climate, ecosystems, and infectious
diseases. Committee on Climate, Ecosystems, Infectious Diseases, and Human Health.
Washington, DC: National Academy Press.
National Research Council. 2010a. Advancing the science of climate change. Panel on Advanc-
ing the Science of Climate Change. Washington, DC: The National Academies Press.
National Research Council. 2010b. Monitoring climate change impacts: Metrics at the inter-
section of the human and earth systems. Committee on Indicators for Understanding
Global Climate Change. Washington, DC: The National Academies Press.
National Research Council. 2010c. Adapting to the impacts of climate change. Panel on
Adapting to the Impacts of Climate Change. Washington, DC: The National Academies
Press.
National Research Council. 2012. Earth science and applications from space: A midterm as-
sessment of NASA’s implementation of the decadal survey. Committee on the Assessment
of NASA’s Earth Science Program. Washington, DC: The National Academies Press.
O’Brien, K., and R. Leichenko. 2007. Human security, vulnerability, and sustainable adapta-
tion. New York: United Nations Development Programme. Available: http://hdr.undp.
org/en/reports/global/hdr2007-8/papers/O’Brien_Karen%20and%20Leichenko_Robin.
pdf (accessed August 10, 2012).
Office of the Director of National Intelligence. 2012. Global water security: Intelligence com-
munity assessment. Washington, DC: Office of the Director of National Intelligence.
Ogden, N.H., C. Bouchard, K. Kurtenbach, G. Margos, L.R. Lindsay, L. Trudel, S. Nguon,
and F. Milord. 2010. Active and passive surveillance and phylogenetic analysis of Borrelia
burgdorferi elucidate the process of Lyme disease risk emergence in Canada. Environ-
mental Health Perspectives 118:909–914.
Paavola, J. 2008. Livelihoods, vulnerability and adaptation to climate change in Morogoro,
Tanzania. Environmental Science and Policy 11:642–654.
Palmer, W.C. 1965. Meteorological drought. Washington, DC: U.S. Department of Commerce.
OCR for page 238
238 CLIMATE AND SOCIAL STRESS
Parry, M., N. Arnell, P. Berry, D. Dodman, S. Fankhauser, C. Hope, S. Kovats, R. Nicholls,
D. Satterthwaite, R. Tiffin, and T. Wheeler. 2009. Assessing the costs of adaptation to
climate change: A review of the UNFCC and other recent estimates. London, UK: Inter-
national Institute for Environment and Development and Imperial College of London,
Grantham Institute for Climate Change.
Polity 4 Project. 2012. Polity 4 Project: Political regime characteristics and transitions,
1800–2010. Available: http://www.systemicpeace.org/polity/polity4.htm (accessed July
28, 2012).
Russell, K.L., J. Rubenstein, R.L. Burke, K.G. Vest, M.C. Johns, J.L. Sanchez, W. Meyer,
M. Fukuda, and D.L. Blazes. 2011. The Global Emerging Infection Surveillance and
Response System (GEIS), a U.S. government tool for improved global biosurveillance: A
review of 2009. BMC Public Health 11(Suppl. 2):S2.
Singh, R.P., D.P. Hodson, J. Huerta-Espino, Y. Jin, P. Njau, R. Wanyera, S.A. Herrera-Foessel,
and R.W. Ward. 2008. Will stem rust destroy the world’s wheat crop? Advances in
Agronomy 98:271–310.
Tatem, A.J., S. Adamo, N. Bharti, C. Burgert, M. de Castro, A. Dorelien, G. Fink, C. Linard,
J. Mendelsohn, L. Montana, M. Montgomery, A. Nelson, A.M. Noor, D. Pindolia, G.
Yetman, and D. Balk. 2012. Mapping populations at risk: Improving spatial demographic
data for infectious disease modeling and metric derivation. Population Health Metrics
10(1):8.
Trenberth, K.E., R. Anthes, A. Belward, O. Brown, E. Haberman, T.R. Karl, S. Running, B.
Ryan, M. Tanner, and B. Wielicki. 2012. Challenges of a sustained climate observing
system. Paper presented for WCRP Open Science Conference 2011, Denver, CO, October
24–28. (Revised February 15, 2012). Available: http://www.cgd.ucar.edu/cas/Trenberth/
trenberth.papers/Trenberth%20paper%20OSC%20October%202011_v13.pdf (accessed
August 5, 2012).
United Nations. 2012. The Millenium Development Goals report. New York: United Nations.
Witt, C.J., A.L. Richards, P.M. Masuoka, D.H. Foley, A.L. Buczak, L.A. Musila, J.H.
Richardson, M.G. Colacicco-Mayhugh, L.M. Rueda, T.A. Klein, A. Anyamba, J. Small,
J.A. Pavlin, M. Fukuda, J. Gaydos, K.L. Russell, and AFHSC-GEIS Predictive Surveil-
lance Writing Group. 2011. The AFHSC-Division of GEIS Operations Predictive Sur-
veillance Program: A multidisciplinary approach for the early detection and response to
disease outbreaks. BMC Public Health 11(Suppl. 2):S10.
World Bank. 2012. World governance indicators. Available: http://info.worldbank.org/
governance/wgi/index.asp (accessed July 28, 2012).
Wright, B.D. 2011. The economics of grain price volatility. Applied Economic Perspectives
and Policy 33(1):32–58.
