The response of the global atmosphere/ocean system to radiative forcing depends on a range of feedback processes. For example: Melting sea ice increases the amount of sunlight absorbed at the surface, a positive feedback since warming temperature causes sea ice melt which in turn causes more sunlight to be absorbed, amplifying the initial warming. Conversely, increases in surface temperature lead to increases in the amount of infrared radiation emitted to space, a cooling effect which counteracts the surface temperature increase, thereby producing a negative feedback. In the past decade, it has become clear that the net effect of all feedback processes acting on the Earth system depends not only on the change in globally-averaged surface temperature but on the geographical pattern of the temperature change as well. The relationship between the pattern of surface temperature change and the resulting net radiative feedback is referred to as the "pattern effect". Work performed here develops tools that can be used to estimate the pattern effect from statistical methods alone, without the use of climate model simulations. The advantage of statistical methods is that they can be applied directly to the observational record, thereby avoiding the biases and uncertainties inherent in model simulations. Statistical methods can also take advantage of the wealth of observations available from satellites, weather balloons, and other observing systems. The project will directly bene