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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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Page 95 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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Page 96 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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Page 97 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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Page 98 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 image 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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Page 99 image 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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Page 100 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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Page 101 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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Page 102 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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Page 103 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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Page 104 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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Page 105 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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Page 113 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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Page 114 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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Page 115 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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Page 116 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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Page 117 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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Page 118 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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Page 119 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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Page 120 • 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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Page 121 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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Page 122   • 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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Page 123   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.