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Sunwook Park and Xiaoqing Wu

(2004) also examined the relationship between surface albedo and cloud radiative forcing (CF) over an Arctic region using the cloud and radiation dataset from the Surface Heat Budget of the Arctic (SHEBA) program. For middle latitude cases, some research groups have investigated various surface-albedo-related phenomena. Grant et al. (2000) examined the dependence of clear-sky albedo on the SZA by observing the daily variation of surface albedo at Uardry in southeastern Australia. Considering the

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Hanjun Kim, Sarah M. Kang, Yen-Ting Hwang, and Young-Min Yang

TOA ; Fig. 3a ), which is primarily determined by the response of cloud radiative forcing ( δ CRF, Fig. 3b ), exclusively from changes in SW cloud reflection (not shown). For example, the largest northern subtropical warming in BC950 is consistent with the least SW cloud reflection. The hemispheric asymmetry in δR TOA leads to a shift of the rising branch of the Hadley circulation (HC) toward the warmer hemisphere (see Fig. 5 ) in order to transport more energy toward the cooler hemisphere

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Bingqi Yi, Ping Yang, Bryan A. Baum, Tristan L'Ecuyer, Lazaros Oreopoulos, Eli J. Mlawer, Andrew J. Heymsfield, and Kuo-Nan Liou

thickness using the SR ice particle model. Similar results were obtained for RRTMG but are not shown here. The CRE is highly sensitive to the changes in ice cloud optical thickness, while the dependence on D eff is weak. These results are in agreement with the study by Hong et al. (2009) . Fig . 2. (top) SW, (middle) LW, and (bottom) total cloud radiative forcing (CRF) at solar zenith angle (SZA) = 60° as a function of effective diameter and cloud optical thickness as simulated by the Fu–Liou RTM for

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Michael S. Town, Von P. Walden, and Stephen G. Warren

and weather simulations ( Hines et al. 1999 , 2004 ). The problems associated with these parameterizations are due in part to the lack of adequate ground-truth data. Therefore, accurate information about clouds and cloud radiative forcing could provide a more solid foundation on which to test the models. Prior work on the subject of cloud cover over the South Pole by Town et al. (2005) confirmed a low wintertime bias in visual observations of cloud cover found by Hahn et al. (1995) . The

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Brian J. Soden, Anthony J. Broccoli, and Richard S. Hemler

consistent with observable quantities. The two methods, however, are not the same. Zhang et al. (1994) point out that the cloud feedback obtained from the PRP differs from that inferred due to a change in cloud radiative forcing (ΔCRF) because the latter does not account for potential differences in the temperature and water vapor distributions between a clear-sky and a cloudy atmosphere, leading to a “small but non-negligible difference” between the two. More recently, Colman (2003) compared offline

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Mark D. Zelinka, Stephen A. Klein, and Dennis L. Hartmann

the available diagnostics archived by the modeling centers. Three primary methods have been used previously to attribute modeled cloud feedbacks to the cloud changes from which they arise. In all cases, the cloud feedback is quantified as the change in cloud radiative forcing per unit change in global mean surface air temperature, where cloud radiative forcing is defined as the difference between clear- and all-sky TOA fluxes (e.g., Charlock and Ramanathan 1985 ). First, Bony et al. (2004

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Trude Eidhammer, Hugh Morrison, David Mitchell, Andrew Gettelman, and Ehsan Erfani

1. Introduction The parameterization of ice microphysics is an important component of climate modeling. In general circulation models (GCMs), it directly affects cloud radiative forcing by impacting the microphysical and optical properties of ice-containing clouds (e.g., Gettelman et al. 2010 ). It also affects circulation through latent heating and cooling and the hydrologic cycle through precipitation formation, growth, and fallout. Ice microphysics has a critical impact on mixed-phase cloud

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Woogyung Kim, Jhoon Kim, Sang Seo Park, and Hi-Ku Cho

in the UV forcing factors and trends in the UV irradiance caused by these forcing factors are also evaluated with the time series of fractional deviation of daily UV irradiance from the reference values obtained by a superposition of sinusoids fitted to the daily data in this study. Changes in the amount of UV radiation reaching the earth’s surface depend mainly on changes in the UV forcing factors of ozone, atmospheric turbidity (aerosols), and clouds, except for geometric factors such as

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Rosemary Auld Miller and William M. Frank

MONTHLY WEATHER REVIEW VOLUME I21Radiative Forcing of Simulated Tropical Cloud Clusters ROSEMARY AULD MILLER AND WILLIAM M. FRANK Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania (Manuscript received 15 November 1991, in final form 25 May 1992) ABSTRACT A number of field experiments and subsequent studies in the 1970s and 1980s have led to the belief

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Aaron D. Kennedy, Xiquan Dong, Baike Xi, Patrick Minnis, Anthony D. Del Genio, Audrey B. Wolf, and Mandana M. Khaiyer

observations ( Klein and Del Genio 2006 ). SCM versions of GCMs typically have been used to simulate the atmosphere over limited time periods, driven by field experiment data or enhanced soundings during intensive observing periods (IOPs; see the 2005 special issue of J. Geophys. Res., volume 110, issue D15). These exercises have proven difficult to interpret because model–data discrepancies can be due to inaccurate large-scale advective forcing, inaccurate model physics, or problems with the cloud data

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