Wednesday, 26 January 2011: 10:45 AM
608 (Washington State Convention Center)
Radiative feedbacks associated with effects of changes in water vapor, temperature, albedo and clouds on the Earth's radiation budget are a major source of uncertainty in estimates of climate sensitivity and, thus, the response of climate to anthopogenic forcing. The radiative kernel technique is a computationally efficient new method to quantify radiative feedbacks in climate models. This technique splits feedbacks into a radiative kernel - the response of top-of-atmosphere (TOA) radiative fluxes to incremental changes in feedback variables - and the climate response of feedback variables to changes in global average surface temperature. If we assume a linear relationship between TOA radiative fluxes and feedback variables, one kernel computed using an offline radiative transfer algorithm can be applied to a range of model simulations. Here global climate model simulations forced with 2x, 4x and 8x present day concentrations of carbon dioxide are used to investigate both the impact of forcing magnitude on feedbacks and the accuracy of the radiative kernel at large perturbations from present day climate. While climate sensitivity remains relatively constant, individual feedbacks do vary with forcing magnitude. The largest changes are associated with the atmospheric temperature response. At large forcings, the kernel, calculated from a present day base climate, underestimates longwave flux changes in the model, thus producing biased estimates of feedbacks. To eliminate this bias, we combine this kernel with a kernel based on an 8xCO2 climate. Changes in water vapor feedback and in the overall sum of feedbacks due to the choice of kernel exceed changes due to varying forcing, highlighting the importance of using the appropriate base climate in kernel calculations.
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