Abstract
Global radiative feedbacks exhibit large dependence on the spatial structure of sea surface temperature (SST) changes, which is referred to as the “pattern effect.” A Green’s function (GF) approach has been demonstrated to be useful in identifying and understanding contributions of regional SST changes to global radiative feedbacks. Here, we explore the ability of the GF approach in quantifying the pattern effect in an atmospheric model (AM4) and a coupled model (CM4) recently developed at NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL), including the impact of SST changes on global-mean and local responses of key variables important to climate. Given historical SST patterns, the GF derived from idealized experiments with SST warming patches can largely reproduce AM4 simulated global-mean and regional responses. When AM4 is forced by SST patterns retrieved from the CM4 abrupt quadrupling of carbon dioxide experiment, the same GF captures interannual variations of AM4 simulated global-mean responses but falls short of reproducing the magnitude of the responses. A decomposition of such SST patterns into global-mean values plus remaining anomalies helps reduce biases. Additional idealized experiments are conducted to examine the sensitivity of the GF to the amplitude and sign of SST perturbations and to the integration time and the confidence level of the significance test. Impacts of these factors on the performance of the GF are discussed.
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