By Melanie Lenart | The University of Arizona | September 14, 2008
Bringing the results of Global Climate Model projections down to the size of a state remains challenging. But it has become more feasible now that the resolution of Global Climate Models (GCMs) has increased.
Regional projections and related assessments generally take one of these approaches:
Figure 1.Statistical and dynamical downscaling techniques allow scientists to use global climate model outputs as inputs into regional climate and weather models.
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Credit: The University Corporation for Atmospheric Research
Statistical downscaling uses equations to convert global-scale output to regional-scale conditions. Instead of maintaining a dynamic climate model at the higher resolution of a region, this approach applies the information from GCMs to the region by using a series of equations to relate variations in global climate to variations in local climate.
Because the statistical approach requires less computational effort than dynamic downscaling, it offers the opportunity for testing scenarios for many decades or even centuries, rather than the brief “time slices” of the dynamical downscaling approach.
Advantages of statistical modeling also include the opportunity to use “ensemble” GCM results. Modelers recommend using results from an ensemble of numerous GCMs rather than a single model, since ensemble values, which average results from many models, tend to match overall observations better than results from any individual model.
With statistical downscaling, the ensemble average for a region can be applied using equations that relate the larger-scale observations to regional climate parameters. So, for instance, a northern shift in the subtropical jet stream at the global scale may translate into more winter precipitation in one area and less in another.
The number of weeks featuring snow-capped mountains in Ruidoso, New Mexico is likely to change as climate does. Regional models incorporating elevation differences can help define how changes in snow cover will affect regional rivers.
Source: ©Brandon Seidel, istockphoto.com
Equations based on past observations of how the jet stream position affects local precipitation could serve to interpret how the shift might affect the snow on specific mountain ranges and perhaps even the amount of water reaching local rivers.
For instance, Niklas Christensen and Dennis Lettenmaier used statistical downscaling to consider how Colorado River streamflow might change with climate.1 Their detailed analysis involved considering which high-elevation sites might remain cold enough to retain mountain snowpack despite projected warming (Figure 2).
Christensen and Lettenmaier found the extent of a river’s decline really depends on changes in winter precipitation. When their input shifted the timing of precipitation slightly from winter to summer, their model showed a 16 percent decline in river flow on average this century. But when they used models that showed a slight shift from summer to winter precipitation, the decline amounted to about half of that, with an 8 to 11 percent decrease by the end of the century.
Statistical downscaling often involves “bias removal,” the correction of factors inaccurately modeled by GCMs. Many models overestimate precipitation in the Southwest, for instance, on the order of a millimeter a day, or roughly an inch a month.2 So, for instance, a downscaling effort would typically correct that bias before modeling future rainfall.
Dynamical downscaling fits output from GCMs into regional meteorological models.
Rather than using equations to bring global-scale projections down to a regional level, dynamic downscaling involves using numerical meteorological modeling to reflect how global patterns affect local weather conditions.
The level of detail involved strains computer capabilities, so computations can only tackle individual GCM outputs and brief time slices. Yet climatologists generally consider three decades about the minimum for deducing climatic conditions from the vagaries of weather.
The amount of computations involved in dynamical downscaling makes it “essentially impossible” to produce decades-long simulations with different GCMs or multiple emissions scenarios.3 As a result, most research aimed at producing regional projections involves statistical downscaling—or another approach known as sensitivity analysis—to consider potential impacts on specific regions or sectors.
Sensitivity analysis is an evolving field that involves bringing climate projections down to the scale of a sector or business (e.g., water providers, a chain of grocery stores). Such analyses can take several approaches to consider the impacts of changing climate on a specific sector or institution.
One approach popular with water providers uses the general output from GCMs, such as specific temperature and precipitation changes, in existing models to test a sector’s vulnerability to different climate scenarios.
For instance, a water provider may use its existing model estimating freshwater availability given specific climatic conditions, adjusting results to reflect the warmer temperatures and precipitation changes projected for the future. As with statistical downscaling, this approach lends itself to testing a variety of different projections.
- Christensen, N. and D.P. Lettenmaier. 2006. A multimodel ensemble approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River basin. Hydrology and Earth System Sciences Discussions. 11(4):1417-1434.
- Bader, D.C., et al. 2008. Climate models: An assessment of strengths and limitations. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Department of Energy, Office of Biological and Environmental Research, Washington, D.C.
- Maurer, E.P. and H.G. Hidalgo. 2007. Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods. Hydrology and Earth Systems Sciences Discussions 12: 551-563.