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Patrick C. Taylor

monthly diurnal cycle composites? A discussion of the implications, physical meaning, and cause of diurnal cycle variability is presented in section 5 followed by a summary and conclusions in section 6 . 2. Data The CERES SYN Ed2Rev1 dataset contains all-sky OLR and RSW fluxes and clear-sky OLR (OLR CLR ) and RSW (RSW CLR ) fluxes extending from March 2000 through October 2005 with 1° × 1° spatial and 3-hourly temporal resolution ( Loeb et al. 2009 ; Doelling et al. 2013 ). The radiative forcing

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Hua Zhang, Feng Zhang, Qiang Fu, Zhongping Shen, and Peng Lu

on the source function technique proposed by Davies (1980) and Toon et al. (1989) . Fu et al. (1997) showed that they are suitable for the radiative flux and heating rate calculations in the infrared, with an accuracy close to the δ -four-stream method but a computational efficiency only about 50% more than the δ -two-stream methods. Unfortunately, for the solar radiation, when the single scattering albedo is equal to 1, these approaches do not necessarily yield conserved radiative fluxes

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Nadir Jeevanjee and Stephan Fueglistaler

approximation hold? How do these conditions break down (as they must in PRE), and how can this be reconciled with the double cancellation argument given above? The goal of this paper is to shed light on these questions. A key ingredient in our analysis will be a refinement of the canonical decomposition of radiative flux divergence given by Green (1967) into a new decomposition which naturally captures the double cancellation described above, and which also isolates the contributions which do not cancel

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Zhonghai Jin and Andrew Lacis

1. Introduction Clouds have very important effect on Earth’s radiation budget of the atmosphere, thus making clouds one of the fundamental issues in the study and modeling of the climate. Accurate computation of radiative fluxes and absorption in clouds is needed to assess their impact on climate, but rigorous radiative transfer computations (e.g., Hansen and Travis 1974 ) are only feasible in the framework of plane-parallel homogeneous (PPH) geometry. In contrast, surface and space

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Feng Zhang, Zhongping Shen, Jiangnan Li, Xiuji Zhou, and Leiming Ma

are given in section 5 . 2. Single-layer solution of four-stream approximation The azimuthally averaged solar radiative transfer equation is (e.g., Chandrasekhar 1950 ) where I ( τ , μ ) is diffuse intensity; μ is the cosine of the zenith angle; τ is the optical depth; ω is the single-scattering albedo; P ( μ , μ ′) is the azimuthally averaged scattering phase function, defining the light incidence at μ ′, and scattered away at μ ; F 0 is the solar flux at the top of the atmosphere

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Feng Zhang and Jiangnan Li

radiative transfer method does not appear to make a big difference. The corresponding results in flux are also shown in Tables 2 – 4 ; both δ -4DDA and δ -4SDA are very accurate with relative error less than 0.8%. Different from the result of heating rate, δ -4SDA is not inferior to δ -4DDA in flux. Fig . 5. As in Fig. 3 , but for the sky containing (top two rows) high clouds and (bottom two rows) all the three cloud types. In Fig. 5 , the results of heating rate for all the low, middle, and high

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Haruma Ishida and Shoji Asano

can lead to larger errors, especially in radiances at optically thin points. The direction of the incident solar radiation was set to be parallel to the x – z plane. For the boundary condition, we assumed that there was no inflow of diffusive radiance through all of the domain boundaries, and no reflection occurred at the ground surface. Hereafter, we will use the normalized radiative fluxes and intensities relative to the incident downward solar flux. b. Calculated results and comparison The

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Virendra P. Ghate, Mark A. Miller, Bruce A. Albrecht, and Christopher W. Fairall

) Earth System Research Laboratory (ESRL)’s Physical Sciences Division (PSD), primarily during the Northern Hemisphere fall period. Instrumentation at the ARM sites is described in detail by Mather and Voyles (2013) and that on board the NOAA cruises by de Szoeke et al. (2012) . Described below is the subset of the instrumentation used in this study. The radiative transfer model used to simulate the radiative fluxes is described in appendix A . a. Instrumentation 1) ARM Southern Great Plains

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Laura M. Hinkelman, K. Franklin Evans, Eugene E. Clothiaux, Thomas P. Ackerman, and Paul W. Stackhouse Jr.

; Di Giuseppe and Tompkins 2005 ). The 3D radiative transfer effect on domain-averaged solar fluxes has been divided into two physical processes, as summarized by Várnai and Davies (1999) . The first, which is termed the “one-dimensional (1D) heterogeneity effect,” arises from the nonlinear relationship between cloud optical depth and albedo. The mean transmission of a cloud with horizontally varying optical depth is more than the transmission of a uniform cloud with the mean optical depth. As a

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John W. Bergman and Harry H. Hendon

1. Introduction Cloud variability exerts a strong influence on radiative transfer within the earth’s atmosphere. That influence, or cloud radiative forcing, affects circulations of the atmosphere and ocean by altering surface energy fluxes and atmospheric heating rates. For example, investigations with atmospheric general circulation models (GCM) find that tropical circulations are substantially altered if cloud radiative forcing is neglected (e.g., Slingo and Slingo 1988 , 1991b ; Randall

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