B31A-0534
Evaluation of Radiative Kernels for Albedo Radiative Forcing Calculations Using CERES Satellite Observations: Applications for the LULCC Community

Wednesday, 16 December 2015
Poster Hall (Moscone South)
Thomas L O'Halloran, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States and Ryan M. Bright, The Norwegian Institute for Bioeconomy Research, Ås, Norway
Abstract:
Modifications to the land surface that alter surface albedo consequently change the radiation balance of the planet, and therefore have the potential to modify climate. To quantify this “radiative forcing”, researchers in the land use/ land cover change community must calculate the change in the top-of-the-atmosphere (TOA) shortwave radiative flux associated with measured or modeled changes in surface albedo. Conventional solutions to this problem include application of radiative transfer models that require detailed inputs of vertical profiles of cloud properties and atmospheric gas and particle concentrations. Performing these calculations can be logistically complex and computationally intensive. As biogeophysical effects of land use change are increasingly included into climate mitigation strategies, a need has arisen for efficient means of making these calculations. “Radiative kernels”, which are essentially climatologies of the sensitivity of TOA fluxes to changes in surface albedo, as developed using offline calculations of the radiative transfer code inside a global climate model, have become popular options. However, satellite observations of TOA radiative fluxes may be applied with simple models to provide an attractive measurement-based alternative to radiative kernels. The Clouds and the Earth's Radiant Energy System (CERES) instruments provide approximately 15 years (and growing) of remotely-sensed observations of TOA fluxes. Here we evaluate popular radiative kernels with CERES observations, and provide an empirical alternative for calculating TOA radiative forcing from surface albedo change using CERES data.