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Lazaros Oreopoulos, Robert F. Cahalan, and Steven Platnick

framework for studying the PPH bias by using a fractal cloud model but restricted the quantitative analysis of cloud inhomogeneity on marine stratocumulus clouds with properties derived from surface microwave radiometer observations. Cloud microphysics (i.e., droplet effective radius r e ) was assumed constant ( r e = 10 μ m), surface and atmospheric effects were neglected, and the radiative transfer did not extend beyond monochromatic calculations. For typical marine stratocumulus observed during

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Roy W. Spencer and John R. Christy

operating satellites reveals single-satellite precisiongenerally better than 0.07°C in the tropics and better than 0.15°C at higher latitudes. Monthly anomalies inradiosonde channel 2 brightness temperatures computed with the radiative transfer equation compare veryclosely to the MSU measured anomalies in all climate zones, with correlations generally from 0.94 to 0.98 andstandard errors of 0.15°C in the tropics to 0.30°C at high latitudes. Simplification of these radiative

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Howard W. Barker, Zhanqing Li, and Jean-Pierre Blanchet

. Regional examinationshows that these biases are confined largely to oceans. Tropical oceans have excessive shortwave CRF despitegood total cloud amounts. This may be due to neglect of cloud geometry effects on solar radiative transfer.1. Introduction Earth's climate is complicated by radiative propertiesthat fluctuate on many different time and space' scales.These fluctuations are largely because the three phasesof water interact with radiation distinctly. Indeed, someof the most pressing problems

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Fang Pan, Xianglei Huang, L. Larabbe Strow, and Huan Guo

using two datasets as input to a radiative transfer model: one is simulations by a free-running Geophysical Fluid Dynamics Laboratory (GFDL) AM3 model forced by the observed SST over the same period, and the other is the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). This section will introduce the data processing of AIRS radiances, the GFDL AM3 model and ERA-Interim, as well as the radiative transfer tools used in following sections. a. AIRS

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Faisal S. Boudala, George A. Isaac, N. A. McFarlane, and J. Li

the ice particle sizes ( D ≤ 100 μ m) that are not reliably measured with the current available instruments such as the PMS 2D-C and 2D-P probes, but this definition may differ for other researchers. The method for determining the size spectra from small to large ice particles, recognizing all the above problems, has been described by Boudala et al. (2002) , and a brief summary is given in section 2 . Small ice crystals can be important for both solar and thermal IR radiative transfer ( Platt

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Xin Huang, Yu Song, Chun Zhao, Xuhui Cai, Hongsheng Zhang, and Tong Zhu

1. Introduction Atmospheric aerosol, mainly comprising sulfate, nitrate, ammonium, black carbon (BC), organic carbon (OC), dust, and sea salt, is generated from primary anthropogenic and natural emissions as well as by secondary transformation. Aerosol has impacts on radiative transfer directly through scattering and absorbing solar radiation and indirectly by modifying microphysical properties of clouds, thereby exerting a cooling or heating effect on the planet ( Rosenfeld et al. 2008

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William B. Rossow, Leonid C. Garder, and Andrew A. Lacis

infrared radiance measurements from the NOAA-5 Scanning Radiometer (SR) areanalyzed for the months of January, April, July and October 1977 to infer cloud and surface radiative properties.In this first paper in a three part series, the data and analysis method are described. A unique feature of themethod is that it utilizes radiative transfer models that simulate the SR measurements using explicit parametersrepresenting the properties of the surface, atmosphere, and clouds. The simulations also account

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Neil P. Barton, Stephen A. Klein, and James S. Boyle

forcing, is a measure of how clouds interact with radiation ( Ramanathan et al. 1989 ). We define CRE LW at the surface, as in Eq. (2) : where is the surface downwelling longwave radiation for all-sky conditions, and is the surface downwelling longwave radiation assuming clear-sky conditions. While and are both readily available from standard model output, is not an observed variable. Thus, we calculate it using the Rapid Radiative Transfer Model for GCMs (RRTMG; Mlawer et al. 1997 ) for

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A. Bodas-Salcedo, P. G. Hill, K. Furtado, K. D. Williams, P. R. Field, J. C. Manners, P. Hyder, and S. Kato

recently used to estimate the climatological impact of clouds on the atmospheric radiative heating ( L’Ecuyer et al. 2008 ; Haynes et al. 2013 ). Here we use satellite data and radiative transfer simulations to quantify the contributions of different cloud types and cloud thermodynamic phase to the TOA radiation budget. We also analyze data from the most recent multimodel ensemble simulations to understand the implications of the present-day biases observed in the current generation of models over the

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Keith M. Hines, David H. Bromwich, Philip J. Rasch, and Michael J. Iacono

located in the Arctic during winter and in the Antarctic for all seasons. The simulated seasonal cycle of cloud amount in the Arctic was improved by the change. The simulation of Predicted Cloud Water is for 14 yr over the same time period as AMIP SST. These simulations are listed in Table 1 . Also, Atmospheric and Environmental Research, Inc., has provided a version of the Rapid Radiative Transfer Model (RRTM; Mlawer et al. 1997 ; Iacono et al. 2000 ), a longwave radiation code that can be

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