The usefulness of climate information for agricultural risk management hinges on its availability and relevance to the producer when climate-sensitive decisions are being made. Climate information providers are challenged with the task of balancing forecast availability and lead time with acceptable forecast skill, which requires an improved understanding of the timing of agricultural decision making. Achieving a useful balance may also require an expansion of inquiry to include use of non-forecast climate information (i.e. historical climate information) in agricultural decision making. Decision calendars have proven valuable for identifying opportunities for using different types of climate information. The extent to which decision-making time periods are localized versus generalized across major commodity-producing regions is yet unknown, though, which has limited their use in climate product development. Based on a 2012 survey of more than 4770 agricultural producers across the U.S. Corn Belt region, we found variation in the timing of decision-making points in the crop year based on geographic variation as well as crop management differences. Many key decisions in the cropping year take place during the preceding fall and winter, months before planting, raising questions about types of climate information that might be best inserted into risk management decisions at that time. We found that historical climate information and long term climate outlooks are less influential in agricultural risk management than current weather, short term forecasts, or monthly climate projections, even though they may, in fact, be more useful to certain types of decision making

A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.

Recent wind farm studies have revealed elevated nighttime surface temperatures but have not validated physical mechanisms that create the observed effects. We report measurements of concurrent differences in surface wind speed, temperature, fluxes, and turbulence upwind and downwind of two turbine lines at the windward edge of a utility‐scale wind farm. On the basis of these measurements, we offer a conceptual model based on physical mechanisms of how wind farms affect their own microclimate. Periods of documented curtailment and zero‐power production of the wind farm offer useful opportunities to rigorously evaluate the microclimate impact of both stationary and operating turbines. During an 80 min nighttime wind farm curtailment, we measured abrupt and large changes in turbulent fluxes of momentum and heat leeward of the turbines. At night, wind speed decreases in the near wake when turbines are off but abruptly increases when turbine operation is resumed. Our measurements are compared with Moderate Resolution Imaging Spectroradiometer Terra and Aqua satellite measurements reporting wind farms to have higher nighttime surface temperatures. We demonstrate that turbine wakes modify surface fluxes continuously through the night, with similar magnitudes during the Terra and Aqua transit periods. Cooling occurs in the near wake and warming in the far wake when turbines are on, but cooling is negligible when turbines are off. Wind speed and surface stratification have a regulating effect of enhancing or decreasing the impact on surface microclimate due to turbine wake effects.

Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.

The Climate Indicators for Agriculture report presents 20 indicators of climate change, carefully selected across multiple agricultural production types and food system elements in the United States. Together, they represent an overall view of how climate change is influencing U.S. agriculture and food systems. Individually, they provide useful information to support management decisions for a variety of crop and livestock production systems. The report includes multiple categories of indicators, including physical indicators (e.g., temperature, precipitation), crop and livestock (e.g., animal heat stress), biological indicators (e.g., pests), phenological indicators (e.g. seasonality), and socioeconomic indicators (e.g., total factor productivity).