The simplest form of scaling is represented by the equation: DY(x,t) = DT(t) DŶ(x), where the change in some climate variable of interest (as a function of space and time) equals the global mean temperature change multiplied by some normalized pattern of change. In a more general form of the equation, one can distinguish between global-scale forcings due to well-mixed greenhouse gases and spatially confined forcings due to short-lived species such as aerosols. Scaling is a way to assess whether we can linearly combine different types of forcings to get the overall response.
Most modelers are not aware that these types of spatial scaling techniques were introduced many years ago and are widely applied in the climate impacts community, in software such as SCENGEN and COSMIC, and in several integrated assessment models. These scaling techniques are extremely valuable and should be applied more widely to studies of other forcing agents such as ozone and soot aerosols.
Note, however, that these scaling techniques employ some fundamental assumptions that have not been adequately tested. Questions that may require further investigation include the following: How valid is this assumption that various types of forcings, which exhibit different spatial and temporal patterns, can be added linearly? How much do the normalized patterns of change depend on the sensitivity? To answer such questions we need more information about the climate effects of individual forcing factors, as well as their net effect.