Making Climate Forecasts Matter (1999) / Chapter Skim
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5 Measuring the Consequences of Climate Variability and Forecasts
5 Measuring the Consequences of Climate Variability and Forecasts
Pages 95-123
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From page 95... ...
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.
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From page 96... ...
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.
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From page 97... ...
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.
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From page 98... ...
For example, farm income is affected not only by climatic events and their biophysical consequences, but also by the coping behaviors of farmers I nteractive Model/Feedback/Underlying Process fir change biophysical characteristics Climate > Variation/ Change Jar Society Population Activity Region Nation Societal Variation, Change ~ ) ~ Impact change societal characteristics FIGURE 5-1 A schematic model of factors responsible for the human consequences of climatic variability.
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From page 99... ...
Ex Post Coping 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)
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From page 100... ...
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.
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From page 101... ...
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.
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From page 102... ...
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)
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From page 103... ...
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.
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From page 104... ...
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.
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From page 105... ...
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.
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From page 106... ...
showed that land reform in Mexico specifically, the creation of the ejido land tenure system characterized by communal land holding led to higher agricultural losses from drought compared with privately held land. Simulations of Decision Making Firm-level economic decision models have been used to track the effects of climatic variability on economic agents' expectations of climate risk.
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From page 107... ...
Comparison of ex post studies of farmer decision making throughout a period of climate variability with projections from a decision model focusing on the same climate event could greatly enhance the interpretation of such a model. Computable general equilibrium economic models attempt to simulate the effects of climatic variation on economies by balancing supply and demand so that a new equilibrium state is achieved in the wake of climatic perturbation to resource supplies (e.g., Adams et al., 1990; Kane et al., 1992~.
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From page 108... ...
And, like the firm-level approaches noted above, the structural elements of computable general equilibrium models are rarely evaluated in light of observed human behavior. Challenges in Estimating the Impacts of Climate Variability Considerable attention has been devoted to estimating the effects of climatic variability on ecosystems and society.
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From page 109... ...
Skillful forecasts can help individuals and organizations prepare better both for extreme negative climatic events and for less dramatic but more common climate variations, both negative and positive. Preparedness for the latter climatic variations can be quite valuable because the consequences of nonextreme and positive climatic events can be very large in the aggregate.
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From page 110... ...
The value of a climate forecast can be defined as the difference between the outcomes experienced by actors in weather-sensitive sectors with and without the forecast, or the difference between their outcomes with forecasts of different levels of skill. The value of a forecast might also be estimated by the expenditures made for it: public expenditures for climate forecasting research, mass media time devoted to presenting forecast information, private-sector expenditures on climate forecasts, and so forth.
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From page 111... ...
Thus, like the effects of climate variability, the value of a climate forecast cannot be directly measured. It can only be modeled, based on assumptions for estimating what the outcomes might have been in the counterfactual situation.
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From page 112... ...
To assess the value of a climate forecast, it is important to understand the kinds of information the forecast provides in relation to the kinds of forecast information that can benefit forecast users. The users, of course, desire information of relevance to their decisions.
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From page 113... ...
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)
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From page 114... ...
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~.
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From page 115... ...
Simulations of Climate Forecast Value Johnson and Holt (1997) state that the theoretical basis for valuing forecast information lies in Bayesian decision theory.
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From page 116... ...
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.
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From page 117... ...
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.
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From page 118... ...
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)
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From page 119... ...
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.
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From page 120... ...
FINDINGS Scientific capability to measure and model the effects of seasonal-tointerannual 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)
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From page 121... ...
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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From page 122... ...
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.
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From page 123... ...
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 climateforecasts.
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