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5
Measuring the Consequences of Climate Variability and
Forecasts
Although the potential benefits of improved
seasonal-to-interannual climate forecasts are not known precisely,
they are widely believed to be substantial. Government agencies
spend money to improve the skill of climate forecasts, presuming
that society will benefit and that markets may not allocate scarce
resources to supply useful forecast information. Agencies have an
implicit interest in measuring the effects of climate variations
and the potential and actual benefits of climate forecasts in order
to direct research to where the potential benefit is greatest,
evaluate past research and communication efforts, and improve the
delivery of forecast information. This chapter examines the
concepts, data, and analytical methods needed and available for
assessing the effects of climate variability and the value of
improved climate forecast information. It considers how to define
and measure the effects of climatic variations and estimate the
value of improved forecasts, examines the state of scientific
capability to make such estimates, and considers the availability
of the data needed to estimate the actual and potential benefit of
improved forecasts.
It is useful to distinguish two related analytical tasks:
estimating the effects of climatic variation and estimating the
value of climate forecasts. Climatic variations alter the outcomes
for actors engaged in activities that are sensitive to weather or
other climate-related environmental conditions, such as fires and
floods, in ways that depend on the coping systems those actors use.
Climate forecasts can have value by allowing these
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actors to use their coping systems differently in order to
improve their outcomes relative to what they would have been
without the forecast.
Estimating the Effects of Climate
Variations
An effect of climatic variations in a particular time period for
a particular actor, activity, or region can be defined as the
difference between an outcome for that period and the long-term
average of similar outcomes, net of nonclimatic influences and of
longer-term changes in average climate. According to this
definition, each region or activity has climate-induced good and
bad years, compared with long-term averages.
Using this definition to measure the effects of climatic
variations is not a simple matter. It requires first that the
effects of climatic variability on a range of outcomes be
identified and measured for each sensitive activity in each region.
Monetary effects and deaths and serious injuries from extreme
weather events are relatively easy to identify and measure, but
many other effects are not. For extreme events, they include
uninsured injuries and property losses, as well as other effects
that are harder to quantify, such as increased community cohesion
in the immediate period of disaster recovery and in the longer
term, community recognization and shifts in employment patterns,
with some people benefiting and others losing.
The effects of nonextreme climatic variations can be
particularly difficult to measure. Although many extreme negative
events are routinely tallied, few nonextreme events are. The
effects of such climatic variations are often subtle or distant in
time from their causes, and, for these reasons, causality may be
hard to establish. Some effects are deleterious and others are
beneficial. It is necessary to model many of these effects rather
than measuring them directly, as can be done with storm damage.
Econometric models have been used in attempts to value commonplace
weather events (e.g., Center for Environmental Assessment Services,
1980), but with mixed success.
Estimating the effects of climatic variation requires that data
be developed on the various outcome variables and on things that
may affect them, both in the time periods of interest and over a
long enough past to establish historical averages. In any
weather-sensitive sector, many outcome variables may be affected by
climatic variability either directly or indirectly. In agriculture,
for example, weather-sensitive outcomes include not only crop
yields and income from crop sales, but also the costs (in money and
time) of crop selection, water management, crop hazard insurance,
participation in the futures market, government disaster payments,
and so forth. Each of these activities may be affected by
climatic
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variation or the anticipation of it, and each may benefit from
appropriate kinds of climate forecast information.
It is important to have data available at sufficient levels of
disaggregation and resolution to examine the effects of climatic
variations and of forecasts on particular sectors, types of actors
or activities within sectors, and geographic regions. For example,
agriculture's gain from improved forecasts might be the insurance
industry's loss, if farmers gamble by adopting seed varieties that
will do well under forecast climatic conditions, in the expectation
that insurance will pay if the crop fails. Or, as with the
1997-1998 El Niño, the costs of a climatic event along the
U.S. Pacific coast may be tied to benefits in the Northeast. There
is need to understand the regional and sectoral effects as well as
the aggregate effects. Even if the aggregate effect of a set of
climatic events is zero, better prediction might improve outcomes
in every region.
The distribution of costs and benefits of climatic variations
within a sector is also important to recognize and measure. A major
climatic event may affect people very differently depending on
whether they have access to disaster insurance, on precisely where
they are located in a flood plain, on their previous economic
condition, or on other specific factors.
A major difficulty in estimating the effects of climatic
variations is constructing appropriate baselines. Baselines are
intended to capture important social and environmental outcomes
that may be altered by climatic variability. It is important that
the defining characteristics of such baselines be described to
reflect outcomes in the absence of the climatic variability being
examined, in order to provide a benchmark against which to compare
the outcomes after particular climatic variations.
Choosing the appropriate temporal scale of baselines is
critical. Social and environmental outcomes must be corrected to
take into account longer-term climatic change and various
nonclimatic factors that have influenced them and that are likely
to be different in the present and the future from what they were
in the past. But a baseline period can be too long. Episodic tastes
and preferences, technological eras, and stages of economic
development often distinguish societies temporally. It is important
to capture in a baseline the elements of society that are most
homogeneous over time scales of seasonal-to-interannual climate
variability. In sum, estimating the effects of any one season's
climate on a particular activity or region requires significant
efforts to conceptualize the relevant outcomes and the range of
climate-related and other factors that affect them, to measure all
these variables, to develop data bases, and to build and validate
models.
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A Conceptual Model of the Effects of
Climate Variability
To model the effects of climatic variability, one must simplify
a very complex system of human-environment interactions. Numerous
conceptual modeling schemes have been previously proposed to
portray the interactions of human systems and climate variability.
We rely here on a scheme modified from one proposed by Kates
(1985). Kates's general scheme is shown in Figure 5-1, and our
scheme, which focuses on the major factors affecting the human
consequences of climatic variations and forecasts, is in Figure
5-2. Our scheme differs from the more general one in providing more
detail on particular kinds of human activity and human-environment
relationships and in omitting some of the feedbacks in the general
model for a more focused presentation.
Most analyses of the human consequences of climatic variability
include one or more elements of the scheme in Figure 5-2, with some
parts better represented than others. Climatic averages and
variations affect various biophysical systems on which people
depend; they also influence human activities designed to cope with
climate. The human consequences of climatic variations are shaped
by climatic, biophysical, and social factors, including both the
coping activities and more general social forces. For example, farm
income is affected not only by climatic events and their
biophysical consequences, but also by the coping behaviors of
farmers
FIGURE 5-1 A schematic model of factors
responsible for the human consequences
of climatic variability. Source: Kates (1985). Reprinted by
permission of SCOPE.
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FIGURE 5-2 A schematic model of the human
consequences of climatic variability
emphasizing the roles of coping mechanisms and of climate
forecasts.
and the institutions that support them (reviewed in Chapter 3)
and by other social phenomena. Among them are those that respond
directly to climatic events, such as the prices and availability of
agricultural commodities, timber, and other climate-sensitive goods
and services, and relatively climate-independent social phenomena,
such as changes in food preferences and in society's willingness to
underwrite the stability of farm income with transfer payments.
It is important to emphasize that, although climatic variations
affect people directly (e.g., through the impacts of temperature on
human health and the demand for energy), many of the most important
effects operate indirectly through biological systems (e.g., water
supplies, agricultural ecosystems). In fact, the first indications
of climatic variability of consequence to human systems are
environmental: for instance, changes in streamflow, reservoir
levels, incidence of fire, water in soils and plants, and crop
yields. These environmental systems are also influenced by human
coping mechanisms, such as management of flood-control dams,
choices of crop species and cultivars, soil management choices, and
so forth. Moreover, the effects of climate-induced biophysical
changes are shaped by human coping mechanisms and other social
phenomena (e.g., food and insurance prices and availability,
emergency preparedness, income distribution). Thus, the
consequences or impacts attributed to climatic variability are in
fact dependent not only on climatic processes but also on their
interactions with other biophysical processes, human coping
mechanisms, and various other social phenomena.
Of special interest for the present purpose is the fact that
people typically intervene in valued biophysical systems when they
believe that climatic events may adversely affect them. Thus, each
of these systems is
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human-managed to some extent, as indicated in Figure 5-2 by the
arrow from ex ante coping to nonclimatic environmental systems.
Climate forecasts, through their influence on ex ante coping, can
be expected to induce people to intervene in biophysical and social
systems to increase their well-being under expected climatic
conditions. Consequently, it is important for analyses of the
impacts of climatic variations and forecasts to recognize that
forecasts may affect ecological and other biophysical systems
through human interventions initiated in response to the forecasts.
When forecasts are skillful and provide decision-relevant
information, acting on them can improve social well-being.
A key to understanding the consequences of climatic variability
for society lies in understanding the dynamic interplay of people's
preferences and the constraints on those preferences, and how
climate variability affects this interplay. These preferences and
constraints influence human behavior in the face of uncertainties,
such as those related to climatic variability. Preferences matter
because, for example, decision makers' aversion to absorbing risk
will affect what they do in risky environments. Constraints matter
because they bound the set of possible actions by which people
exercise their preferences. Among the major kinds of constraints
are the biophysical (e.g., the amount of rainfall affects seed
growth), the technological, ''income'' constraints (e.g., the
ability of the decision maker to borrow or to obtain formal or
informal insurance), and constraints imposed by societal
institutions.
Current Scientific Capability
We presume, for purposes of discussion, that the ideal model of
the effects of climatic variability on society is one that
explicitly represents all the structural elements shown in Figure
5-1 based on knowledge obtained from observation. With knowledge of
these fundamental elements and their relationships, and assumptions
about decision making (e.g., the assumption of economically
rational behavior), it is possible for researchers to predict what
decision makers will do given particular technological
capabilities, amounts and types of information, and institutional
regimes (e.g., insurance). Use of data to construct and validate
their models lends credibility to the resulting predictions.
Current scientific capability reflects progress in understanding
key structural elements of decision making in response to climate
variability, but we are a long way from understanding all aspects
of the problem and are particularly deficient in modeling based on
direct observation. Some models and analytical approaches contain
little or no information about structural elements of decision
making. Many such models rely on reduced-form statistical
relations. Some models are little more than assumption-driven
simula-
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tions with no connection to observations. In the following
section, we review examples of research progress, pointing out
strengths and weaknesses of different approaches to estimating the
societal effects of climatic variability. Our review first
considers research on how climatic variability affects other
biophysical systems that impose constraints on human systems. We
turn our attention to research on decision making based on
observation and then consider research that simulates decision
making in the absence of observation.
Estimating Biophysical Impacts That
Constrain Human Systems
The biophysical impacts of climatic variability pose a
formidable constraint on decision making. A considerable amount of
modeling research has focused on estimating the biophysical effects
of climatic variability and change. The modeling approaches used in
this research fall into two broad classes: reduced-form and
deterministic. Reduced-form modeling has relied on correlations
between highly aggregate climatic and other biophysical data and
has used them to predict biophysical outcomes of a range of climate
scenarios. Deterministic modeling specifies causal relations
linking climate variability to biophysical outcomes, sometimes
deriving the causal relations from theoretical principles, such as
well-understood mechanisms in plants that partition sensible and
latent heat fluxes to maintain viable internal temperatures in the
presence of stressful external temperatures.
Most reduced-form studies establish correlations between
observed climatic elements and observed measures of biophysical
system performance. For example, historical time series of observed
temperature and precipitation may be related to time series of crop
yields using regression techniques (Thompson, 1969; Bach, 1979).
The resulting regression coefficients are then used to predict the
effects of current climate variability on crop yields. A similar
approach can be used to analyze streamflow and ecosystem zonation
(e.g., Holdridge, 1967). In a National Research Council report,
Waggoner (1983) used such an approach to predict a 2 to 12 percent
decline in yields of major U.S. Midwest crops (corn, soybeans,
wheat) relative to their current averages as a result of an assumed
1°C increase in annual temperature and a 10 percent decline in
summer precipitation. Such approaches are also being used to
examine the possible effect of El Niño outbreaks on crop
yields. Cane et al. (1994) found that maize yields in Zimbabwe
seemed to vary regularly with El Niño cycles.
Reduced-form models bypass obtaining information characterizing
the structure underlying decision making and examine instead the
empirical relationship between changes in one or another dimension
of the biophysical environment—e.g., variations in
rainfall—and the outcomes
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of decisions. The chief advantage of reduced-form approaches is
that they greatly simplify the relationship between climate and
biophysical outcomes. They are practical when data limitations are
large. However, collinearity among explanatory climate variables is
often large. Also, the climatic variations used to force the models
are often outside the range of climatic observations from which the
model coefficients were estimated. Spurious relationships between
climate predictors and their predicted outcomes, and statistical
overfitting of the data, are frequent (Katz, 1977). Because such
models do not specify many variables that may affect the
relationship between climatic variables and human outcomes, they
are not useful for making predictions about what would happen if
the missing variables (e.g., seed technology, forecasting skill)
changed over time.
Deterministic models of plant growth and other ecological
processes permit detailed estimates of the effects of climate
variability to be made under a wide range of climate conditions.
Examples include mathematical simulation models of forest growth
and composition (Botkin et al., 1972; Shugart, 1984) and
agricultural crop growth and yield (Williams et al., 1984; Jones
and Kiniry, 1986). Such models realistically couple climatic
determinants (e.g., temperature, precipitation, solar radiation,
humidity, wind speed) with biophysical processes (e.g., plant water
use, photosynthesis) that regulate biophysical outcomes (e.g., crop
yields). For example, forest composition models have simulated the
retreat of maple forests poleward in northeastern North America in
response to climate change (Davis and Zabinski, 1992). They have
also been used to estimate the impact of sustained drought on
timber productivity in the central United States (Bowes and Sedjo,
1993). In the Missouri-Iowa-Nebraska-Kansas (MINK) study (Rosenberg
et al., 1993; Easterling et al., 1993), a crop model simulated a
contemporary crop response to a recurrence of the Dust Bowl
droughts of the 1930s. MINK researchers found that such droughts,
absent human intervention, would reduce current yields of maize,
soybeans, and wheat by as much as 30 percent below current
averages. The model revealed that crop development rates were
abnormally increased by the high heat of the droughts, which led to
premature termination of grainfill.
Deterministic approaches are richly detailed in causal
explanation of biophysical impacts. They provide detailed
diagnostic information on why a certain type of outcome was
predicated. However, they require massive amounts of data and are
highly location-specific, which requires the scaling of results to
represent surrounding regions. Acquisition of the necessary data to
run the models can be difficult, especially in nations and regions
with less developed scientific infrastructure.
Modeling can be improved by joining together the strengths of
reduced-form and deterministic models. Promising work on this front
seeks
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to use a geographic information system to organize data sets
spatially for use in deterministic crop growth models (Lal et al.,
1993). This will facilitate realistic aggregation of the results
from multiple modeling sites into regional estimates of crop
response to climate variability. However, in regions where reliable
data for running the models are too sparse or even
nonexistent—including many developing countries—there is
little prospect for using deterministic models to estimate
large-area response to climate variability.
Neither of the above approaches adequately takes into account
human coping with climatic variability. They must be coupled with
social and economic analyses based on observations to make the
effects of human intervention explicit and realistic. For example,
data on how farmers adapt to climatic variations by changing crop
production practices are necessary to model the phenomena that make
outcomes for farms less sensitive to climatic events than outcomes
for individual plants or farm animals.
Research Based on Observations of
Decision Making
A basic research challenge is to obtain adequate knowledge of
human decision making to allow for empirically based assessments of
the consequences of climatic variations that take into account
human adaptations. Recent advances in computer power and the
availability of data that track decision makers over time have led
to a number of studies of the structure of individual decision
making in a variety of contexts. These studies, which have looked
at such decisions as the replacement of bus engines, the purchases
and sales of bullocks by farmers, the adoption of new seed
varieties, and teenagers' decisions to leave school, assume
particular functional forms for preferences, technological changes,
and income constraints. They also assume that individuals are
forward-looking, taking into account that their current decisions
will affect future outcomes. The basic approach is to start with
initial values of the parameters characterizing preferences and
constraints, solve the model using well-known solution techniques
for dynamic stochastic models, and compare the dynamic decisions
(outcomes) predicted by the model with what is observed in the
data. This process is repeated until a set of parameters
characterizing the structure is found, providing the best fit
between the model outputs and the observational data.
One example of this technique, applied to longitudinal data on
poor Indian farmers, looked at how variations in rainfall, under
conditions of borrowing constraints and the absence of insurance,
affected the decisions of farmers to buy and sell bullocks. The
structural estimates of the model—which among other things
revealed how risk-averse the farmers
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were—were used to assess how farmers' welfare would have
been improved and stocks of bullocks increased if farmers had had
weather insurance or increased capabilities to borrow.
Such models can also incorporate learning. A standard method for
doing this is to incorporate Bayesian learning. Model estimation
then reveals, along with preference parameters and standard
technology parameters, how fast learning takes place and how it is
affected by the underlying uncertainty of the economy. Such
estimable dynamic models have shed new light on behavior and
reveal, among other results, how important it is to achieve an
understanding of the consequences of technological change to
understand the constraints facing decision makers. Because the
techniques involve iterative estimation and model solution,
obtaining estimates of the structure underlying dynamic decisions
requires a great deal of computing power. To obtain estimates in
realistic time frames, the number of parameters characterizing the
structure is kept to a minimum, so that a common criticism of such
models is that they are too simple. Absent substantial innovations
in dynamic solution techniques or computing power in the near
future, hybrid estimable models that take estimates of biophysical
processes from other studies and fix them for purposes of
estimation may be a promising technique in coming years.
Input-output models have been used to trace flows of costs and
revenues among linked sectors of regional and national economies.
Such models (e.g., Bowes and Crosson, 1993) fully replicate
interindustry exchanges of capital and labor costs embodied in
producer and consumer goods and show how such exchanges are
affected by changes in final demand for goods and services. They
enable climate-induced changes in supplies of basic materials
(e.g., agricultural production, fish harvests) to ramify throughout
the connected industries in an affected economy. In the MINK study
mentioned above, an input-output model was used to compute the
overall effect of a recurrence of the Dust Bowl droughts of the
1930s on the MINK region's economy. Absent adjustments to on-farm
production, the droughts prompted a 9.7 percent ($29.9 billion)
decrease in total regional production.
The main strength of input-output models is their ability to
track interindustry exchanges in great detail. Intersectoral
linkages are realistic—that is, they are based on observation.
The main disadvantage of input-output models is their static
nature. The coefficients used to represent interindustry exchanges
are constants, with the result that the models are unable to
represent the reinvestment of underused resources induced by
climatic events (e.g., unemployed agricultural labor) in other
sectors of the economy. Consequently, input-output models tend to
overstate the negative impacts of climatic events.
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A wide spectrum of retrospective, case-study-based methods is
available to estimate the societal impacts of climate variability.
Past climatic variations create natural experiments that allow for
a quasi-experimental case-control design for studying the effects
of climate fluctuations. For example, regions experiencing
localized climate fluctuations can be compared with adjacent
regions with similar physiographic and socioeconomic
characteristics that experience average climatic conditions.
Riebsame (1988) studied a decade-long period of high precipitation
in northern California that was preceded and followed by periods of
normal precipitation. Nearby regions experienced no noticeable
change in precipitation. Operating rules on major reservoir
impoundments in the affected area were systematically altered to
avoid flood risk at the expense of maintaining water supplies for
summer irrigation needs. No such altered behavior was evident in
the control region. Such methods provide a way of systematically
separating social and economic impacts of climate variability from
the vast array of nonclimate-related influences on social and
economic behavior.
Comparative case studies employing carefully coordinated field
survey methods and documentary analysis provide key insights into
the causal mechanisms that determine the adaptations and
vulnerability of populations, regions, and sectors to climatic
variability. Survey methods may include implementation of detailed
questionnaires, participant interviews, and participant
observation. A key to the success of such case studies is the
orchestration of research questions, assumptions, data sets, and
analytical approaches to provide comparability among case studies
and make generalization possible. Comparative case studies are
being used in the International Geosphere-Biosphere Program's Land
Use/Cover Change core project (Turner et al., 1995) to parse out
proximate causes and driving forces of land use change in a variety
of locations globally. An illustration of an exemplary use of the
comparative case study approach for the study of the consequences
of climatic variability is provided in Box 5-1.
Liverman (1992) argues that the "political economy" approach
offers an alternative to mechanistic methods of gauging climate
impacts. Drawing from Marxist social theory, the political economy
approach seeks to understand the impacts of climate in the larger
context of political, social, and economic conditions of society.
Those conditions either ameliorate or exacerbate climate
vulnerability, which is defined as the degree to which different
classes of society are at risk from climate variability. The
trappings of underdevelopment (flows of resources out of a region,
political oppression, land expropriations, exploitative labor
practices) combine to force the impoverished into unsustainable
environmental management, which leads often to greater
vulnerability to drought and other climate
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to find when the outcomes result from nonextreme climatic events
and when the outcome variables are difficult to quantify.
Social and economic data are widely collected, however, on
outcomes that are affected by seasonal-to-interannual climate
variation and that might therefore be improved by skillful
forecasts. For example, most countries collect data on agricultural
production, human morbidity and mortality from various causes,
streamflows in important rivers, yields from fishing and lumbering,
and various other phenomena that are sensitive to weather and
climate. Such data can be used to model the effects of climate
variability and the value of forecasts. However, their usefulness
for this purpose depends on the extent to which sufficiently long
time series are available, data are comparable across time and
geographical regions, measurement procedures are constant, and
other such factors. There is reason to believe that the data
available in many countries on many of these variables fall short
of the necessary quality and comparability. However, the extent of
this shortfall is not well understood.
Research Based on the Use of Actual
Climate Forecasts
Empirical decision studies attempt to shed light on how decision
makers actually use (or fail to use) and value forecasts. These
studies examine the ways actual forecasts are received,
interpreted, and applied, drawing lessons about forecast value from
actual experiences. The ledger on such studies is thin, but there
are a few deserving of mention here. Stewart (1997) divides
empirical studies of forecast use and valuation into the categories
of: (1) anecdotal reports and case studies; (2) user surveys; (3)
interviews and protocol analysis; and (4) decision experiments. We
add a fifth category of empirical modeling studies.
Case studies on the value of climate forecasts are common in
government publications (e.g., Aber, 1990) and agricultural
cooperative extension circulars. A typical case may recount how
farmers used forecasts to improve the efficiency of operations. A
grain grower might be interviewed and asked how valuable the
forecasts are in managing the crop and may provide a dollar
estimate of how much was saved by using the forecast. The problem
with such reporting is that the information given is subjective and
apt to be unreliable.
Ex post case studies of actual forecasts provide important
insights into how decision makers actually apply climate forecasts.
Stewart cites a case study by Glantz (1982) of the ramifications of
using a faulty streamflow forecast in the Yakima valley in the
state of Washington as an example. As previously noted, Glantz
detailed the costs in terms of the value of legal claims brought by
farmers who, at great cost, had undertaken preemptive actions to
avoid loss due to the erroneously forecast
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water shortage. Though such case studies give some basis for
estimating the value of climate forecasts, they do not separate
climate forecast-related behavior from behavior that may be
determined by other factors.
User surveys ask representative samples of respondents to value
climate forecasts (Easterling, 1986; McNew et al., 1991). Hence,
they are really studies of the perceived value of such forecasts
(Stewart, 1997). Stewart argues that user surveys are reliable
instruments for gauging subjective forecast value.
Several investigators have relied on interviews and closely
related protocol analysis to gain knowledge about how valuable
climate forecasts are to decision makers (e.g., Changnon, 1992;
Sonka et al., 1992). Stewart describes these techniques as the
characterization of forecast users' decision-making protocols based
on extensive interviews. For example, Glantz (1977) interviewed a
wide range of decision makers in Sahelian Africa to determine what
they said they would have done differently had they had available a
perfectly accurate forecast of the recently experienced drought of
1973. He learned that, given the lack of effective possible
response strategies, most Sahelian decision makers were skeptical
that even a perfect forecast would have caused them to do anything
differently. Like most of the other descriptive techniques reviewed
above, interviews and protocol analysis lack a compelling
experimental design that enables causal relations to be
unambiguously defined.
Decision experiments take a gaming approach to eliciting
information about the value of forecasts to decision makers. Actual
decision makers are asked to participate in the experiments.
Participants are presented with detailed forecast scenarios and
requested to explain in detail what their actions and thoughts
would be under each scenario. A regression model is then developed
to ''predict'' participants' hypothetical behavior with respect to
forecast use. Sonka et al. (1988) used decision experiments to
model the behavior of two managers responsible for production
planning in a major seed corn manufacturing company. The main
problem with decision experiments is that behavior in actual
situations may differ systematically from behavior in the
simulation.
Easterling and Mendelsohn (in press) used a Ricardian-based
econometric approach to estimate the cross-sectional relationships
of climate, agricultural land values, and revenues in the United
States. Assuming that this relationship is conditioned by cropping
systems that are strongly, though not perfectly, adapted to their
local average climatic resources (including variability and
frequencies of extreme events), the econometric model provides a
baseline from which to quantify imperfect adaptation to widespread
climate events marked by extreme departure from historic averages.
Easterling and Mendelsohn argue that the revenue differences
between the baseline and drought conditions, net of input
substitutions
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and market adjustments, is the theoretical aggregate value of a
perfect forecast of drought.
The approach used in this study has been criticized by Antle
(1996) and must be viewed in light of fundamental criticisms. It
uses reduced-form relationships between climate and aggregate
decision making and thus does not make the structural elements of
decision making explicit. The approach requires the assumption that
the underlying conditions embedded in the reduced-form model, such
as agricultural policy, must be assumed to be constant between the
period of the data used to generate the model and the period being
simulated. It also requires invariance in the model structure over
time and space (Schneider, 1997). Moreover, the farmers in each
region use coping mechanisms (e.g., hedging against risk, using
seeds that are resilient to climatic fluctuations) based on the
lack of skillful forecasts; thus, unless they are completely
insured, they have lower profits on average than they would if
skillful forecasts were available. This last consideration calls
into question the validity of the assumption that the baseline
condition equates with having a perfect forecast because
technologies and other coping mechanisms will be different with
better forecasts. For instance, farmers with good forecasts will
use seeds that are more sensitive to weather (such as
water-dependent varieties if the forecast is for lots of rain).
Despite the criticisms, Easterling and Mendelsohn (in press)
illustrate some of the defensible approaches to estimating the
value of climate forecasts using the general concept of differences
in outcomes. One value of the concept is that it makes possible a
distinction between the potential value of a forecast and its
actual value: for example, actors who do nothing with forecast
information receive no value from it. The concept also allows for
the possibility that a skillful forecast can have negative value.
This may occur in at least two ways. Actors may do things with the
expectation that the forecast average will be realized, but,
because of residual error in the forecast, their outcomes might
have been better if they had followed normal routines. Or some
actors may take advantage of forecast information in ways that
benefit them at great cost to others, so that the aggregate value
of the forecast is negative.
Simulations of Climate Forecast
Value
Johnson and Holt (1997) state that the theoretical basis for
valuing forecast information lies in Bayesian decision theory.
Bayesian theory treats information as a factor in the decision
process to be used by agents to reduce uncertainty. According to
Bayesian theory the following assumptions hold: (1) prior to having
a forecast available, economic agents have subjective "prior"
probability estimates of a set of possible future
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states of climate based on historical experience; (2) they
attach a set of actions to each of the states of climate, and each
combination of action and state of climate has a consequence; (3)
they have a ranked preference for certain consequences over others
that may be expressed in an expected utility function; and (4)
climate forecast information is assumed to modify agents'
subjective prior probabilities by creating a set of "posterior"
probabilities.
The value of the additional information provided by the forecast
is based on the expected utility resulting from decisions made
after the forecast has been received and before the forecast
climate event occurs compared with the expected utility resulting
from the decision that would be made at the same time without the
forecast information. The agent is faced with choosing from among
two optimal choices, one being to choose the optimal action given
only prior subjective probabilities and the other being to choose
the optimal action given the posterior probabilities.
According to Johnson and Holt (1997), solving the
value-of-information problem for individual decision makers in a
strictly theoretical sense using the above procedures is relatively
straightforward. However, determining the market value of such
information is much more difficult for two reasons. First,
establishing a market equilibrium condition and understanding how
that equilibrium is modified by the introduction of additional
information is problematic. Second, aggregating individual
responses to construct market-level supply and demand relations
necessary for information pricing is equally problematic. A
commonly accepted way to deal with these two problems is to adopt
the hypothesis of rational expectations—the hypothesis that
all individuals possess perfect knowledge of the underlying
structure of the market and act on that knowledge accordingly. This
opens the way to treat the simple case of an individual decision
maker as representative of all decision makers comprising the
market. It forms a benchmark of ideal agent behavior against which
to evaluate other, less ideal behaviors.
Most applications of Bayesian decision theory to weather and
climate forecast valuation are agricultural ones. Wilks (1997)
reviewed several such applications. Specific examples include the
application of forecasts to the decisions on whether to convert
grapes to raisins versus selling them for juice (Lave, 1963); on
whether to take steps to protect orchard crops from potential frost
damage (Katz et al., 1982); on how much hay to store as feed for
the following year (Byerlee and Anderson, 1982); on which crops to
plant in the upcoming year (Tice and Clouser, 1982; Adams et al.,
1995); and on the amount and timing of fertilizer applications
(Mjelde et al., 1988). Examples of applications of climate
forecasts to sectors other than agriculture include, in forestry,
the decision on how to allocate firefighting resources between two
forest fires (Brown and
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Murphy, 1988) and, in transportation, the decision on how much
to invest in snow removal equipment (Howe and Cochrane, 1976).
The two crop choice studies cited above (Tice and Clouser, 1982;
Adams et al., 1995) are illustrative of the issues concerning
rational expectations raised by Johnson and Holt (1997). Tice and
Clouser (1982) examined the use of a seasonal climate forecast by a
farmer to determine relative area planted to corn versus soybeans.
The forecast was assumed to be perfectly accurate. Two allocations
of crops are computed by using historical climatic averages and by
using forecast information. The simple arithmetic difference
between average net revenues per hectare using climatic averages
and that dictated by the forecast was computed. Use of the forecast
to allocate areas planted in corn and soybeans was shown to
increase revenues by $3.65 per hectare per year beyond revenues
using historical climate.
Adams et al. (1995) investigated the use of climate forecasts to
determine allocations of areas planted to cotton, corn, sorghum and
soybeans in the southeastern United States. The chief difference
between their study and that of Tice and Clouser (1982) was that
they computed forecast value in terms of total net social welfare
(combined producer and consumer surplus) for the nation rather than
revenues for the individual farmer. Using a general equilibrium
economic model, they computed welfare using forecast-assisted crop
allocations under an assumption that all southeastern farmers would
plant accordingly. Furthermore, they explicitly considered the case
in which forecast accuracy is imperfect. They found that the use of
a perfect forecast increased social welfare by $145 to $265 million
per year. The use of an imperfect (though still skillful) forecast
increased welfare by $96 to $130 million per year.
Several research problems remain unsolved for Bayesian decision
theory applications to climate forecasts. These applications do not
address how forecast information available in an invariant, and
possibly irrelevant, format is made relevant and incorporated into
individual decision makers' information requirements, which differ
considerably from one decision maker to the next. They do not
adequately explore the possibility that decision makers' utility
functions are nonlinear. Most applications do not estimate the
distributional effects of the use of forecasts (i.e., winners
versus losers). Finally, the lack of data and empirical techniques
for clearly valuing forecasts precludes the testing of Bayesian
models against the real world.
Challenges in Estimating the Value of
Forecasts
There remain some significant challenges in applying the general
concept of the value of forecasts. One is in addressing the
imperfections in
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existing forecasts and the uncertainty about precisely how
skillful they are for specific geographic regions, time horizons,
and climate parameters. Part of this challenge is to develop
acceptable indicators of the concept of skill. Another challenge is
to address users' perceptions of forecast skill, which certainly
affect their willingness to act on forecasts and are probably
shaped by various factors in addition to forecast skill itself (for
example, the most recent forecast's accuracy, trust in the sources
of forecast information, nonclimatic events that affect users'
outcomes in the forecast period).
Yet another challenge for modeling the value of forecasts is to
take into account the ways improved forecast skill may change
existing systems for coping with climate variability.
Weather-sensitive actors act under the presumption of weather
uncertainty, which improved forecasts reduces. Farmers, for
example, choose seeds and make capital investments assuming the
unpredictability of climate variations. They are likely to use
skillful forecasts that arrive with sufficient lead time to invest
differently in insurance and in futures markets to increase
profitability. They may also shift from planting seed varieties
that are tolerant of a variety of climatic conditions—a
traditional strategy for coping with unpredictable growing seasons
by trading some potential for increased yield for a hedge against
disastrous crop failures—to planting more weather-sensitive
varieties, to take advantage of the conditions predicted for each
growing season.
One might estimate the effects of climate predictability by
comparing the profitability and behavior of actors in environments
with different natural degrees of climate variation to suggest how
they would respond to different levels of predictive skill. It
might also be useful to compare farmers facing different average
weather characteristics (e.g., rainfall levels) who, because of
good insurance mechanisms, took little ex ante action to mitigate
risk. This comparison would provide information on the gains from
optimal adjustments to predicted changes in weather because it
compares farmers in different climate regimes who have set in place
the best arrangements for maximizing profits from given average
rainfall levels without regard to risk, which perfect forecasts
would eliminate.
Ideally, models of the value of climate forecasts should treat
coping mechanisms as endogenous variables, to reflect the
possibility that improved predictions may induce innovations
throughout weather-sensitive sectors of the economy. They may even
affect outcomes in a sector by inducing innovation in another
sector. For example, better forecasts may affect agriculture not
only by changing farmers' strategic behavior, but also by inducing
change in the crop insurance and seed industries and even by
creating new industries, for example, climate consulting. We are
suggesting that the theory of induced innovation be employed in
some
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efforts to model the effects of improved climate forecasts. For
summaries of the literature on induced innovation, see Thirtle and
Ruttan (1987) and Ruttan (1997).
Several of the challenges that have been mentioned in connection
with estimating the effects of climate variability are equally
relevant to estimating the value of improved forecasts. One of
these is estimating the effects on outcome variables that are hard
to quantify. For example, decreasing the amount of uncertainty
about next month's or next season's weather may facilitate vacation
planning for some people. It may relieve anxiety about possible
extreme events—or, depending on the content of the
forecast—it may produce anxiety. Improved forecasts will, at
least at first, cause people engaged in weather-sensitive
activities to rethink their usual methods of coping—a
rethinking that may bring long-term benefits but that has
short-term costs, at least in time and effort. It may be difficult
even to identify all the important nonfinancial effects, and it is
always difficult to weigh them against each other and against
monetary outcomes.
It is also important but difficult for models to disaggregate
the estimates of net value and to consider the distributional
effects of improved forecasts. Models should address the likelihood
that some groups may benefit from improved forecasts at the expense
of others. We have already noted some of the possibilities, such as
that commodities speculators, farmers, and consumers are to some
extent competitors in how they use forecasts. There is also the
possibility revealed by the experience of the Green
Revolution—that to the extent that there are fixed costs of
interpreting forecast information, larger operators will benefit
more by spreading those costs over a larger output, leaving smaller
and less economically successful operators at a relative
disadvantage. It is important to estimate the value of climate
forecasts both throughout entire economies and disaggregated by
sector, region, and type of actor.
Addressing many of the challenges alluded to above is made
difficult by a glaring lack of appropriate data sets.
Long-time-scale, comprehensive data sets archived at appropriate
geographic scales (household/firm, local, regional, national) are
nonexistent or not readily accessible to the broader research
community. Data on particular attributes are often of dubious
quality and not comparable over space and time. Moreover, there is
no general agreement about which data are most important to collect
for the purposes of estimating the effects of climate variations or
the value of forecasts.
Because the quality of the relevant data is probably far short
of what is needed for good analysis, it is important to set
priorities for improving the data base. In doing this, it makes
sense to consider at least these factors:
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•
the importance of the outcome variable to society and to
weather-sensitive sectors,
•
the importance of information on the variable for decisions to
be made by governments or actors in weather-sensitive sectors,
•
the need for more detailed outcome data in regions and sectors
that are highly sensitive to climate variability,
•
the need for more detailed data in regions where ENSO
predictions are most skillful,
•
the need to develop data on outcomes in regions and sectors
where insurance is not prevalent,
•
the need to consider the nonmonetary costs and benefits of
climatic events,
•
the need to collect data on socioeconomic, political, and other
factors that may combine with climatic events to determine their
impact,
•
the need for comparability of data in terms of spatial and
temporal resolution, levels of aggregation, timing, and other
factors affecting their use for comparative or time-series
analysis, and
•
the need to examine the distribution of the impact of climate
variability and of the benefits and costs of forecasts.
Finally, it is important to begin to calculate all social costs
in valuing forecasts. The true net value of a forecast is not only
its worth to an individual actor or set of actors; it also includes
the costs to society of its development and dissemination to
actors, including the costs of incorrect forecasts. In addition,
the value of the entire forecasting enterprise may be different
from the sum of the value of individual forecasts because public
reactions to some forecasts, such as early and well-publicized
ones, may affect the response to subsequent forecasts. Estimating
the value of forecasting within a systems approach is fraught with
complications and uncertainties, such as how to properly weight and
value the opportunity costs of investing in the development of
forecasts and how to estimate the effect of one forecast on the use
of future ones. Such challenges must be confronted before such a
systems approach will be feasible.
Findings
Scientific capability to measure and model the effects of
seasonal-to-interannual climatic variability is well developed in
some sectors (e.g., agriculture, water resources) and only
beginning to be developed in others (e.g., human health,
environmental amenities). Scientific capability to judge the value
of climate forecasts is in its infancy. The ability to predict the
ways people cope with climatic variability, with or without a
climate
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forecast, is limited by a number of factors, including lack of
data on the factors affecting decision making. The state of the
science of impacts of climatic variability and the value of climate
forecasts can be summarized in terms of these findings:
1.
A variety of quantitative and qualitative techniques exists
for estimating the human consequences of climatic variations and
the value of climate forecasts. Each of them involves simplifying
assumptions that require validation or relies on data of uncertain
generality. For example, research on climatic impacts makes
many simplifying assumptions about decision makers' preferences and
constraints. Reduced-form approaches, for instance, assume that
these factors are captured by the past empirical relationships
between the biophysical environment and decision makers' outcomes.
Most quantitative methods emphasize the financial costs and
benefits of climate variability and give little attention to other
outcomes for which they lack well-developed and acceptable methods
of measurement. The few models that have been built on observation
of how decision makers deal with risk and uncertain information are
limited in scope and application. Many of these are case studies.
The preponderance of research on the usefulness of climate
forecasts has focused on the simulation of forecast value, absent
observation; relatively little empirical research on the actual use
of forecasts exists, creating an imbalance in need of
attention.
2.
Models currently employed for analyzing the impacts of
climatic variability are limited by important conceptual
deficiencies and methodological limitations. Improvement in
modeling capability over time requires research to address these
major limitations in basic understanding. A serious conceptual
limitation of many current analytic approaches is their presumption
of a chain of causation from climatic variations to natural
(biophysical) systems of importance to humans, and then to the
effects of climate-altered natural systems on society. A more
appropriate way to conceptualize impacts is with a systems approach
in which climatic variations interact simultaneously with natural
systems and society, in which multiple environmental and social
stresses are confronted along with climate stresses, and in which
human activities alter climate-dependent biophysical systems as
well as being altered by them. In addition, current analytic
approaches suffer from imprecision in the definitions of such key
concepts as vulnerability, adaptation, and
sensitivity to climate variability and from inadequate
representation of the range and dynamics of human coping
strategies.
The methodological limitations of the modeling methods currently
used yield analyses that fail to give adequate attention to such
central issues as these:
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•
the distinction between the potential and the actual value of
climate forecasts,
•
effects of climatic events that are nonfinancial and not easily
measured (e.g., damage to ecosystems, changes in social
organization),
•
the effects of skillful forecasts on institutions (e.g., complex
institutional changes that may occur as climatic variability
becomes more predictable),
•
qualitative differences among effects (e.g., costs and benefits
are of qualitatively different kinds)
•
special impacts (e.g., sudden or catastrophically large negative
events, impacts on particularly vulnerable activities or
groups)
•
linkages of social and environmental data collected at the same
spatial and temporal scales.
3.
A lack of reliable strategies for defining baseline
descriptions of society limits the adequacy of current methods for
estimating the effects of climate variability and the value of
climate forecasts. It may be misleading, for example, to
compare outcomes in a particular year or season to the historical
average because if society had always experienced average climate
conditions, it would be a different society—its insurance
institutions, among others, would be quite different. So, comparing
current costs and benefits to historical average conditions might
fail to take proper account of existing disaster insurance
institutions as part of the cost of climate variability.
4.
The ability to detect and model certain consequences of
climate variability depends on the scale of resolution of the
research and of the phenomenon being investigated. For example,
even if analysis shows little aggregate effect of a climatic event
at coarse scales (e.g., state/provincial or national), analysis at
the local scale may reveal that some sectors or groups of decision
makers are greatly disadvantaged and others are greatly advantaged
by the event. Similarly, the effects of a climatic event or the
value of improved forecast skill may look quite different when
analyzed in short-run and long-run modes. There is a great
opportunity to learn about the full range of consequences of
climatic variability and the value of forecasts by conducting
research along a continuum of scales (temporal, economic, and
spatial). For instance, nested-scale climate models can be
integrated with in situ ecological and economic process models in
an effort to link causal mechanisms across a range of spatial and
temporal scales (e.g., Easterling et al., 1998).
5.
Analyses of the value of climate forecasts have paid
insufficient attention to the distribution of benefits and
costs. Experience from analogous situations (see Chapter 4)
suggests that forecast information may benefit some economically at
the expense of others; some past experience suggests that, unless
special efforts are made to change the pattern, the benefits will
go
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disproportionately to a privileged few—large producers,
better-educated individuals, and actors with good access to credit
and insurance markets—and disadvantage may come to many.
However, little is known from direct observation about the
distribution of the benefits from climate forecasts.
6.
Meta-data are nonexistent describing the availability,
quality, resolution, and other essential traits of data relevant
for measuring the effects of climate variability and the value of
climate forecasts. Governments and other organizations around
the world collect data that are relevant to these purposes. In
addition to climatological data, these include data on agricultural
production, insured and uninsured losses from extreme climatic
events, human morbidity and mortality, soil moisture, streamflows,
and so forth. The data are collected for many purposes, but
analysis of the effects of climate variability and its prediction
are rarely, if ever, among them. Potentially useful data are also
collected through various environmental monitoring systems (e.g.,
data from Long-Term Ecological Research sites, Large Marine
Ecosystem Monitoring, and the Global Ocean and Terrestrial
Observing Systems). Again, because the data were collected for
unrelated purposes, their usefulness for addressing research
questions about the consequences of climatic variations and
forecasts needs to be investigated. It remains unknown to what
extent existing relevant data are available in appropriate form and
adequate resolution to address such research questions.
Representative terms from entire chapter:
climate variability
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