One strategy the model is likely to use is to break down mountainous regions into elevation bands rather than small, uniform grid boxes. Mountain areas, with their myriad microclimates, are particularly difficult to model. Mountains can cause winds to shift and clouds to form; snow-covered north faces, warm south faces, and cold valleys can give rise to strikingly different conditions. L. Ruby Leung and Steve Ghan, climate physicists at the Pacific Northwest National Laboratory in Richland, WA, are pioneers in the elevation-band approach, which they say can be as accurate as nesting techniques that zoom into smaller regions to resolve mountain effects, yet less computationally expensive. Their models provide, among other things, detailed pictures of how global warming will cause snow lines to move to higher altitudes, making it possible to estimate the resulting diminution of the snowpack that now provides most of the fresh water in the western United States. The state of California has already estimated that under scenarios assuming medium to high levels of future greenhouse-gas emissions, higher temperatures could eat away between 70 percent and 80 percent of the Sierra snowpack by century’s end.
But so far, the models Leung and her colleagues have developed are not reliable enough to dictate specific measures for addressing snowpack loss–like building new reservoirs. Water-resource managers want more certainty, Leung says, so she is pushing the research on several fronts by running multiple global and regional models. Such efforts are both labor and computation intensive, but they are also critical to reducing uncertainty. “We know that the climate is actually changing, but they are managing the water system based on rules devised 50 to 100 years ago,” Leung says. “If what we project in the future is correct, we expect a pretty big problem.”
Visualizing Water Shortages
Critical to understanding future water shortages in the western United States, the model that generated this image depicts springtime snowpack at a resolution of 1 kilometer, far better than the 150-kilometer resolution of the average global climate model. Red peaks indicate the deepest snowpack; purple areas indicate none. (The vertical scale is exaggerated; California’s central valley is in the foreground.) This visualization incorporates data on clouds, surface temperatures, and precipitation, broken down by topographical elevation in a technology pioneered at Pacific Northwest National Laboratory. Zooming in on different regions reveals future snowpack loss on specific mountains (see “Vanishing Yosemite Snowpack,” p. 70).