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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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