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Steven M. Cavallo, Jimy Dudhia, and Chris Snyder

) and H 2 O, while O 3 , although reaching a maximum ~5 hPa ( Fig. 2b ), is a relatively weak absorber in the longwave bands (e.g., Manabe and Strickler 1964 ). Since CO 2 is well mixed, and since it is evident from Fig. 2c that H 2 O is well mixed in the stratosphere, we hypothesize that assuming a more realistic thermal structure between the model top and TOA can improve the accuracy of radiative flux calculations. Fig . 2. The standard MLW (blue), MLS (red), SAW (cyan), TROP (green), and mean

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

and correlations between different feedback variables. There are two widely used, but very different, approaches for quantifying climate feedbacks in GCMs. The first method, introduced by Wetherald and Manabe (1988) , uses offline calculations to compute the change in radiative fluxes that results from substituting one variable at a time from the perturbed climate state into the control climate. This procedure can be computationally expensive, and there are complexities in its implementation that

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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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Karen M. Shell, Jeffrey T. Kiehl, and Christine A. Shields

, the climate changes in response to restore the energy balance. In a steady state, imposed top-of-the-atmosphere (TOA) radiative flux changes, G , must be balanced by changes in outgoing longwave radiation, F , and absorbed solar radiation, Q : The climate sensitivity determines how much the climate, represented by the surface temperature, T s , needs to change in order for the TOA fluxes to return to equilibrium: where γ is the feedback parameter, the inverse of the climate sensitivity. From

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Joseph Sedlar, Laura D. Riihimaki, Kathleen Lantz, and David D. Turner

1. Introduction Clouds are a crucial aspect of the climate system via their direct connection to the hydrological cycle and their influence on Earth’s energy balance. Because clouds significantly interact with solar (shortwave) and infrared (longwave) fluxes, their influence on the surface and top-of-atmosphere radiation budgets drive weather and climate across a wide range of temporal and spatial scales (e.g., Peixoto and Oort 1992 ; Trenberth et al. 2009 ; Ahrens 2012 ). Cloud–radiative

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Ehrhard Raschke, Stefan Kinne, William B. Rossow, Paul W. Stackhouse Jr, and Martin Wild

et al. 2005 ) on the geostationary Meteosat platforms since 1998. The required accuracy for atmospheric radiative fluxes depends on the spatial and temporal scales considered as well as the applications ( Smith et al. 1986 , 2006 ). Accuracy requirements range from 15 W m −2 for weather scales to less than 1 W m −2 for climate scales ( Ohring et al. 2005 ). Direct satellite determinations of the broadband radiative fluxes at the top of the atmosphere (TOA) face uncertainties related to

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Tyler J. Thorsen, Seiji Kato, Norman G. Loeb, and Fred G. Rose

1. Introduction Understanding the variability and response of Earth’s radiation budget to perturbations requires isolating individual contributions from each component of the atmosphere and surface. Isolating radiative effects has been strongly motivated by the need to evaluate feedbacks—processes that can amplify or dampen the response to a radiative forcing—in global climate models (GCMs). A feedback is defined as the change in top-of-the-atmosphere (TOA) radiative flux for a given change in

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J. C. B. Hoedjes, A. Chehbouni, J. Ezzahar, R. Escadafal, and H. A. R. De Bruin

, a similar effect will be seen. In this study, an effort is made to quantify differences between sensible heat fluxes, obtained from an LAS and an EC system, caused by differences between the characteristics of the respective footprints. The approach is based on the use of the radiative surface temperature, obtained from thermal infrared satellite data, as indicator of the spatial variability of soil humidity. First, a model for the estimation of sensible heat fluxes from radiative surface

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