What is "downscaling"

vb. (present participle of downscale English)

Global Climate Models (GCMs) used for climate studies and climate projections are run at coarse spatial resolution (in 2012, typically of the order ) 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.