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Marika M. Holland, David A. Bailey, Bruce P. Briegleb, Bonnie Light, and Elizabeth Hunke

this forcing is effective at driving variations in climate because it excites the surface albedo feedback. Here we document new radiative transfer aspects of the sea ice model component of the Community Climate System Model, version 4 (CCSM4) and assess their influence on the climate and climate response of CCSM4. The improvements include a multiple scattering calculation with inherent optical properties (IOPs) for ice and snow based on physical measurements ( Briegleb and Light 2007 , hereafter

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S. J. Ghan, X. Liu, R. C. Easter, R. Zaveri, P. J. Rasch, J.-H. Yoon, and B. Eaton

) , Morrison and Gettelman (2008) , and Gettelman et al. (2008 , 2010 ). Stratiform cloud macrophysics is described by Gettelman et al. (2010) and S. Park et al. (2012, unpublished manuscript). The treatment of shallow cumulus clouds is described by Park and Bretherton (2009) . The Zhang and McFarlane (1995) parameterization of deep convective clouds has been modified as described by Neale et al. (2008) . c. Radiation Longwave and shortwave radiative transfer are parameterized with the Rapid

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A. Gettelman, J. E. Kay, and K. M. Shell

( Soden et al. 2008 ; Shell et al. 2008 ) factors the feedback parameter for each climate variable X into two parts by approximating the change in ( Q − F ) in response to Δ X as linear around some base state. The quantity is the radiative kernel , the change in TOA fluxes due to a standard change in a physical climate variable (the adjoint radiative response). It is calculated using an offline radiative transfer model and depends on the radiative transfer code, as well as the base state

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Jennifer E. Kay, Marika M. Holland, Cecilia M. Bitz, Edward Blanchard-Wrigglesworth, Andrew Gettelman, Andrew Conley, and David Bailey

atmosphere (TOA) radiative fluxes. For each control integration, we then ran a sensitivity experiment by instantaneously doubling the CO 2 concentration from the 1850 value of 284.7 to 569.4 ppmv. Because the TOA radiative forcing resulting from a CO 2 doubling ( Q 2xCO2 , W m −2 ) depends both on the radiative transfer code and on the climate state, we estimated the Q 2xCO2 separately for CAM4 and CAM5 using one year of offline radiative transfer calculations. We allowed temperatures above the

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J. E. Kay, B. R. Hillman, S. A. Klein, Y. Zhang, B. Medeiros, R. Pincus, A. Gettelman, B. Eaton, J. Boyle, R. Marchand, and T. P. Ackerman

optically thick cloud ( Fig. 3 , Fig. 7d ), and 3 ) the underestimation of midlevel cloud ( Fig. 7c ). The CAM5 midlevel cloud results suggest that climate models underestimate midlevel cloud fraction when the impact of snow on radiative transfer in the atmosphere is neglected. In contrast, CAM4 has large compensating biases in cloud optical properties and cloud amount, biases that are similar to those found in many climate models analyzed using the ISCCP simulator and observations (e.g., Z05 ). The

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Esther C. Brady, Bette L. Otto-Bliesner, Jennifer E. Kay, and Nan Rosenbloom

same horizontal grid as the ocean component. It includes an improved treatment of the surface albedo and shortwave radiative transfer in the ice and overlying snowpack ( Briegleb and Light 2007 ). These improvements to the ice model have resulted in a better simulation of Arctic sea ice extent and thickness than in CCSM3 ( Jahn et al. 2012 ). Antarctic sea ice is more extensive in CCSM4 than in observations; however, its simulated variability compares well with observations ( Landrum et al. 2012

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C. M. Bitz, K. M. Shell, P. R. Gent, D. A. Bailey, G. Danabasoglu, K. C. Armour, M. M. Holland, and J. T. Kiehl

and are therefore candidates to affect climate sensitivity. However, we tested the sensitivity to CN cycling in isolation and found it had a negligible effect on ECS. We do not present these results below because the tests used a predecessor to the Q flx used in all the other CCSM4 integrations described in this paper. The primary change to CICE4 is the new multiple-scattering radiative transfer scheme of Briegleb and Light (2007) . This scheme requires a melt pond fractional coverage, which

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Stephen J. Vavrus, Marika M. Holland, Alexandra Jahn, David A. Bailey, and Benjamin A. Blazey

Lawrence et al. (2010, manuscript submitted to J. Adv. Model. Earth Syst. ). CCSM4 uses the Los Alamos National Laboratory Community Ice CodE (CICE4.0; Hunke and Lipscomb 2004 ). New sea ice model physics and capabilities include a shortwave radiative transfer scheme that incorporates a melt pond parameterization and the deposition and cycling of aerosols such as black carbon. These improvements are described in Holland et al. (2012) . The ocean model component uses the Parallel Ocean Program

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David M. Lawrence, Keith W. Oleson, Mark G. Flanner, Christopher G. Fletcher, Peter J. Lawrence, Samuel Levis, Sean C. Swenson, and Gordon B. Bonan

(not shown) simulations, which indicates that biases in the CCSM4 climate are contributing to the CCSM4 LH bias. However, even when CLM4CN and CLM4SP is forced with observed meteorology, LH is too high ( Table 1 ). The excessive LH in CLM4 appears to be due to several factors: a substantial high bias in LAI simulated in CLM4CN ( Lawrence et al. 2011 , see section 4f for further discussion), structural errors in the canopy radiative transfer, leaf photosynthesis, and stomatal conductance submodels

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Jenny Lindvall, Gunilla Svensson, and Cecile Hannay

, and we trust that we have not lost any data quality in doing so. b. Models In this study, we examine CAM4 ( Neale et al. 2010a ) and CAM5 ( Neale et al. 2010b ), two versions of the atmospheric component of CESM1. CAM5 has been substantially modified as compared to CAM4 and contains a range of new parameterizations. Improvements include updated schemes for cloud microphysics, radiative transfer, macrophysics, aerosol formations, ice clouds, and shallow convection and a new moist turbulence

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