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Y. Gu, J. Farrara, K. N. Liou, and C. R. Mechoso

). The parameterization of cloud–radiation processes in climate models is a complex task. Radiative transfer in the atmosphere is determined by spectrally dependent optical properties. Calculation of the radiative heating/cooling in clouds is complicated due to difficulties in parameterizing their single-scattering properties, especially those of ice clouds due to complexities in the ice crystal size, shape, and orientation, which cannot be determined from the models ( Liou 1986 ). Furthermore

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Ting Chen, Yuangchong Zhang, and William B. Rossow

Project (ISCCP; Rossow and Schiffer 1991 ) cloud dataset. In addition to providing the distribution of cloud-top locations, ISCCP also reports the total column cloud optical thicknesses. This extra information provides a strong constraint on simple cloud layer overlap schemes that reduces the magnitude of its effect. In section 2 , after describing the radiative transfer model and datasets used in the calculations, we consider several constrained versions of the simple overlap assumptions that have

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Yulan Hong and Guosheng Liu

their lidar observation studies, that is, subvisual cirrus ( < 0.03), thin cirrus (0.03 < < 0.3), and opaque cirrus 0.3 < < 3.0. Based on radiative transfer calculations with inputs of in situ measurement data, subvisual cirrus clouds were found to have a positive radiative forcing of approximately 1.6 W m −2 in the tropics ( McFarquhar et al. 2000 ), while tropical cirrus clouds with 0.02 < < 0.3 tend to have a small influence on shortwave radiation (instantaneous forcing < 2 W m −2 ) but

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Qing Yue, Brian H. Kahn, Eric J. Fetzer, Mathias Schreier, Sun Wong, Xiuhong Chen, and Xianglei Huang

into two factors: the radiative kernel and the climate response pattern. Scaling the response with the appropriate radiative kernels then yields the climate feedback parameter. As stated in Soden and Held (2006) , because of the strong nonlinearities in the calculation of the radiative adjoint arising from the vertical structure of cloud, cloud radiative kernels (CRKs) were not computed by perturbing cloud property vertical profiles in radiative transfer calculations. These noncloud radiative

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Melanie F. Fitzpatrick, Richard E. Brandt, and Stephen G. Warren

Gardiner's cannot accurately relate transmittance of radiation through a cloud to cloud optical depth. Previous work on inferring cloud optical depth using only broadband measurements was carried out by Leontyeva and Stamnes (1994) . They used broadband measurements of downwelling irradiance and surface albedo together with a multilayer radiative transfer model to iteratively determine cloud optical depth. A further study by Leontyeva and Stamnes (1996) determined cloud optical depth from spectral

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Brian J. Soden, Isaac M. Held, Robert Colman, Karen M. Shell, Jeffrey T. Kiehl, and Christine A. Shields

the effects of changes in water vapor, for example, from changes in other inputs to the radiative transfer, the standard procedure, introduced by Wetherald and Manabe (1988) , is to take water vapor from state B and substitute it into the instantaneous flux computation for A , holding all other inputs ( T , c , a ) fixed, and then averaging: Everything here is a function of μ . Attention is often focused on averages over μ , in particular on the global, annual mean of δ w R . Assuming

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Daniel M. Gilford, Susan Solomon, and Robert W. Portmann

effects of TTL concentration changes (both short term and long term) are investigated using raw Aura MLS observations and the combined Stratospheric Water Vapor and Ozone Satellite Homogenized (SWOOSH) dataset ( Davis and Rosenlof 2013 ), along with two radiative transfer models. The largest perturbations in water vapor and ozone during the 2011 abrupt drop are found at or just below the CPT altitude. The “substratosphere” ( Folkins et al. 1999 , 2000 ; Thuburn and Craig 2002 )—a region of the TTL

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Karen L. Smith, Gabriel Chiodo, Michael Previdi, and Lorenzo M. Polvani

satellites. It has been recently examined in S15 , who computed the nearly instantaneous to a quadrupling of CO 2 concentrations using both a line-by-line radiative transfer model and an atmospheric general circulation model (GCM). In this paper we confirm and extend the findings of S15 . Specifically, using both an offline radiative transfer model and ensembles of fully coupled atmosphere–ocean–land–sea ice GCM integrations, we demonstrate that the while the is indeed negative over Antarctica in

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William B. Rossow, Carl Delo, and Brian Cairns

1. Introduction Calculating radiative transfer in cloudy atmospheres is challenging because clouds introduce inhomogeneities of the optical medium, and hence, of the absorption, emission, and scattering of radiation, over a very wide range of time- and space scales. Visual observations of clouds from the surface and aircraft have emphasized some basic characteristics in the representation of cloud variations in radiative transfer models (RTMs). The most basic division of cloud-type names

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T. Zhang, K. Stamnes, and S. A. Bowling

2110 JOURNAL OF CLIMATE VOLUME9Impact of Clouds on Surface Radiative Fluxes and Snowmelt in the Arctic and Subarctic T. ZHANG, K. STAMNES, AND S. A. BOWLINGGeophysical Institute, University of Alaska, Fairbanks, Alaska(Manuscript received 18 September 1995, in final form 26 February 1996)ABSTRACT A comprehensive atmospheric radiative transfer model combined with the surface

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