Downscaling

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Global Climate Models (GCMs) used for climate studies and climate projections are run at coarse spatial resolution (in 2012, typically of the order 50 kilometres (31 mi))[citation needed] and are unable to resolve important sub-grid scale features such as clouds and topography. As a result GCM output can not be used for local impact studies.

To overcome this problem downscaling methods are developed to obtain local-scale weather and climate, particularly at the surface level, from regional-scale atmospheric variables that are provided by GCMs. Two main forms of downscaling technique exist. One form is dynamical downscaling, where output from the GCM is used to drive a regional, numerical model in higher spatial resolution, which therefore is able to simulate local conditions in greater detail. The other form is statistical downscaling, where a statistical relationship is established from observations between large scale variables, like atmospheric surface pressure, and a local variable, like the wind speed at a particular site. The relationship is then subsequently used on the GCM data to obtain the local variables from the GCM output.

In 1997, Wilby and Wigley divided downscaling into four categories: regression methods, weather pattern-based approaches, stochastic weather generators, which are all statistical downscaling methods, and limited-area modeling. Among these approaches regression methods are preferred because of its ease of implementation and low computation requirements.

Climate downscaling projections[edit]

In 2007 Reclamation collaborated with U.S. Department of Energy’s National Energy Technology Laboratory (DOE NETL), Santa Clara University (SCU), Lawrence Livermore National Laboratory (LLNL), and University of California’s Institute for Research on Climate Change and Its Societal Impacts (IRCCSI) to apply a proven technique called “Bias Correction Spatial Disaggregation” BCSD—Wood et al., 2004; see also “About on the Web site” to 112 contemporary global climate projections made available through the World Climate Research Program Couple Model Intercomparison Project, Phase 3 (WCRP CMIP3). These projections represent 16 GCMs simulating climate responses to three GHG scenarios from multiple initial climate system conditions.

The effort resulted in development of 112 monthly temperature and precipitation projections over the continental U.S. at 1/8º (12 kilometres (7.5 mi)) spatial resolution during a 1950–2099 climate simulation period.

References[edit]

  • Hessami, M., Quarda, T.B.M.J., Gachon, P., St-Hailaire, A., Selva, F. and Bobee, B., “Evaluation of statistical downscaling method over several regions of eastern Canada”, 57th Canadian water resources association annual congress, 2004.
  • Kim, J.W., Chang, J.T., Baker, N.L., Wilks, D.S., Gates, W.L., 1984. The statistical problem of climate inversion: determination of the relationship between local and large-scale climate. Monthly Weather Review 112, 2069–2077.
  • von Storch, H., Zorita, E., Cubasch, U., 1993. Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime. Journal of Climate 6, 1161–1171.
  • Wilby, R.L. and Wigley, T.M.L., (1997) Downscaling general circulation model output: a review of methods and limitations, Progress in Physical Geography, 21, 530–548.
  • Wilby, R.L., Dawson, C.W. and Barrow E.M., (2002) SDSM - a decision support tool for the assessment of regional climate change impacts, Environmental Modelling & Software, 17, 147– 159.
  • Wood, A. W., Leung, L. 5 R., Sridhar, V., and Lettenmaier, D. P.: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Climatic Change, 62, 189–216, 2004.
  • Reclamation et al. “Bias Correction and Downscaled WCRP CMIP3 Climate and Hydrology Projections” <http://gdo-dcp.ucllnl.org/ downscaled_cmip3_projections/>