Scenarios

These scenarios represent the types of problems that are posed by the new members entering the climate change community. They demonstrate the need for different types of knowledge use and knowledge generation required for problem solving. It is, in general, possible to perform knowledge-based analyses for these projects using archived climate information. However, these analyses are based on re-use of information generated for climate science and rely on a set of both good and bad assumptions. Experience shows that problem solvers often use old information because they have access to old information. Knowledge of and ease of use of new data are limited. These scenarios demonstrate the need for the best information at any given time, which includes the generation of customized information.

Scenario 1

Large-scale agricultural and energy projects. A corporation poses a project to use solar energy to desalinate ocean water to provide irrigation to arid lands. The corporation realizes that large-scale changes to the landscape will change how the Sun’s energy is absorbed and reflected at the surface, as well as change the balance of water in the region. There is some belief that regional climate and perhaps weather pattern will be changed. This might have liability risk or increase the company’s ability to participate in the carbon market. The corporation would like to configure a validated climate model, run numerical simulations, evaluate the simulations, and analyze the results to assess risk and value of the project. An answer for potential investors is needed in six months.

Scenario 2

Climate impact assessment. A significant part of observed global warming is related to changes in the use of land. Likewise, thoughtful planning of use of materials can impact the local climate, for example urban heat, and perhaps regional climate. Rather than climate scientists responding to land-use change and evaluating the impact after the fact, climate impact assessments are needed as part of planning. This requires configurable, validated climate models that can be run, evaluated, and analyzed in eighteen months.

Scenario 3

Planning for more heat waves. Over the years excess heat causes more human fatalities than other environmental extremes. Likewise, there are threats to agriculture realized in both poultry production and extended drought. Global warming will increase the risk of heat waves. Heat wave research requires climate predictions, information about the built environment (cityscapes), and information on vulnerable populations. The spatial scale of the information is small, at the human scale rather than the global climate scale. Validated climate predictions, standardized algorithms to generate small-scale information from these predictions, and interfaces to mapping and analysis routines that are the standard of the specific application community are needed to assure the use of the best climate information at any particular time.

Scenario 4

Water resources for energy generation. Much of the impact of climate change will be felt through increased stress on water resources. Floods and droughts are both expected to increase. Energy production is already the largest user of water resources in the U.S. Many alternatives to fossil fuels demand even more water than current energy sources. Climate change stands to impact the established distributions of rain and snow to which we have adapted (Milly et al., 2008. We are currently planning our energy future, infrastructure investments that will have lifetimes of decades. In the next five years we will need to make decisions about energy infrastructure. Validated climate predictions, standardized algorithms to generate small-scale information from these predictions, and interfaces to mapping and analysis routines that are the standard of specific application community are needed to assure the use of the best climate information at any particular time.