NSFGEO-NERC: Analyses of the Pattern Effect on Radiative Feedbacks: Gaining Physical Insights from Statistical Methods; Testing the Role of Land Surface Temperatures

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 benefit society by informing our understanding of how much climate change is likely to occur for a given change in radiative forcing. It will also provide mentoring and funding for two graduate students at Colorado State University, as well as outreach activities in the local school district. The project proceeds in two stages. In the first, the investigators develop and test a hierarchy of statistical methods that provide practical and physically-meaningful estimates of the pattern effect in observations and existing coupled climate simulations. The statistical methods will complement existing results from targeted numerical experiments, and have the advantage that they are computationally quick and can be determined entirely from observations or existing simulations. In the second stage, the investigators will probe how the pattern of land surface temperature change influences local and remote feedbacks. The investigators will develop and conduct the first numerical experiments that independently test the role of surface temperature changes over ocean and land areas. Previous work on the pattern effect has focused on warming patterns in ocean surface temperature, thus the exploration of pattern effects over land is a novel aspect of the research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.