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Longwave Band-By-Band Cloud Radiative Effect and Its Application in GCM...

Huang, X., J. N. S. Cole, F. He, G. L. Potter, L. Oreopoulos, D. Lee, M. Suarez, and N. Loeb (2013), Longwave Band-By-Band Cloud Radiative Effect and Its Application in GCM Evaluation, J. Climate, 26, 450-467, doi:10.1175/JCLI-D-12-00112.1.
Abstract: 

The cloud radiative effect (CRE) of each longwave (LW) absorption band of a GCM’s radiation code is uniquely valuable for GCM evaluation because 1) comparing band-by-band CRE avoids the compensating biases in the broadband CRE comparison and 2) the fractional contribution of each band to the LW broadband CRE (fCRE) is sensitive to cloud-top height but largely insensitive to cloud fraction, thereby presenting a diagnostic metric to separate the two macroscopic properties of clouds. Recent studies led by the first author have established methods to derive such band-by-band quantities from collocated Atmospheric Infrared Sounder (AIRS) and Clouds and the Earth’s Radiant Energy System (CERES) observations. A study is presented here that compares the observed band-by-band CRE over the tropical oceans with those simulated by three different atmospheric GCMs—the GFDL Atmospheric Model version 2 (GFDL AM2), NASA Goddard Earth Observing System version 5 (GEOS-5), and the fourth-generation AGCM of the Canadian Centre for Climate Modelling and Analysis (CCCma CanAM4)—forced by observed SST. The models agree with observation on the annual-mean LW broadband CRE over the tropical oceans within 61 W m22. However, the differences among these three GCMs in some bands can be as large as or even larger than 61 W m22. Observed seasonal cycles of fCRE in major bands are shown to be consistent with the seasonal cycle of cloud-top pressure for both the amplitude and the phase. However, while the three simulated seasonal cycles of fCRE agree with observations on the phase, the amplitudes are underestimated. Simulated interannual anomalies from GFDL AM2 and CCCma CanAM4 are in phase with observed anomalies. The spatial distribution of fCRE highlights the discrepancies between models and observation over the low-cloud regions and the compensating biases from different bands.

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