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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

geographic region (e.g., B11 ). Section 2 describes our methodology including a description of the COSP implementation in CAM, the CAM runs analyzed, and the cloud evaluation strategy. Section 3 contains the results. We identify compensating errors between cloud fraction and cloud optical properties that are not apparent from the radiative fluxes or cloud forcing alone. We also find that, while it has deficiencies, the most recent CAM version, CAM5 ( Neale et al. 2011a ), has significantly reduced

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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

influence of anthropogenic aerosol on the optical properties of clouds by serving as the nuclei for droplets and ice crystals and thereby changing droplet and ice crystal number concentration, which changes cloud particle surface area, influences droplet collisions, and changes the accumulation of liquid water and ice in clouds, all of which affect the reflectivity and emissivity of clouds. Semi-direct effects are changes in the planetary energy balance as clouds respond to radiative heating by

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A. Gettelman, J. E. Kay, and J. T. Fasullo

. (2012a) noted that there is also considerable spread in high cloud feedbacks, but they contribute less to uncertainty in net cloud feedback because of compensating SW and longwave (LW) effects. Tsushima et al. (2006) linked changes in climate sensitivity to changes in cloud water and cloud ice content. Several studies have also looked at the spatial distribution of climate feedbacks. Colman (2002) examined feedbacks spatially (but with fixed cloud optical properties). Ogura et al. (2008

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

net surface energy flux. Assuming identical cloud optical properties, the reduced winter cloud fractions in CAM4 should allow more longwave radiation escape to space and result in a more negative net energy flux in CAM4 than in CAM5. Yet, the winter net energy flux is more negative in CAM5 than in CAM4 ( Figs. 10a,b ), and CAM5 is colder during winter than CAM4 ( Fig. 2a ). Fig . 11. Monthly evolution of Arctic clouds in CAM4 and CAM5: (a) total and low cloud fraction, b) gridbox total and liquid

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

at cooler temperatures, causing warming. Low clouds cool and high clouds warm, with the balance of effects being a net cooling ( Stephens 2005 ). Changes to cloud amount, location, and radiative properties (e.g., optical depth) can exert feedbacks on the system. Any of these feedbacks may significantly alter the magnitude of the response to radiative forcing. The water vapor feedback, for example, is large and positive ( Held and Soden 2000 ). While it is straightforward to calculate the direct

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Gerald A. Meehl, Warren M. Washington, Julie M. Arblaster, Aixue Hu, Haiyan Teng, Claudia Tebaldi, Benjamin N. Sanderson, Jean-Francois Lamarque, Andrew Conley, Warren G. Strand, and James B. White III

al. (2002) , the effects of moisture on the aerosol optical properties were not included in CCSM4. This factor, combined with a lower burden than CCSM3, contributes to reduced carbon optical depths in CCSM4 (globally averaged optical depth of 0.004) compared to CCSM3 (globally averaged optical depth of 0.023). However, there is no indication of a strong negative bias in the atmospheric carbon concentrations or deposition in CCSM4 when compared to other model studies [see Lamarque et al. (2010

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Christine A. Shields, David A. Bailey, Gokhan Danabasoglu, Markus Jochum, Jeffrey T. Kiehl, Samuel Levis, and Sungsu Park

; Holland et al. 2012 ). Adjustments to ice albedos were required to simulate more reasonable ice extent and thickness values. Ice albedos designed for the x1 model are not appropriate for the low-resolution model and were decreased to compensate for excessive ice. In CCSM4, ice albedos are not adjusted directly but are computed using parameters representing optical properties of snow, bare sea ice, and melt ponds. These values are based on standard deviations from data obtained by the Surface Heat

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David M. Lawrence, Andrew G. Slater, and Sean C. Swenson

ground column to a depth of ~50 m ( Lawrence et al. 2008a ) and an explicit treatment of the thermal and hydrologic properties of organic soil ( Lawrence and Slater 2008 )—in conjunction with moderate reductions in Arctic climate biases in CCSM4. We also evaluate how biases in the simulated Arctic climate and initial conditions affect the present-day and future permafrost conditions. In addition, we include an assessment of projected changes in seasonally frozen ground. Finally, based on these

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Richard B. Neale, Jadwiga Richter, Sungsu Park, Peter H. Lauritzen, Stephen J. Vavrus, Philip J. Rasch, and Minghua Zhang

distribution of cloud radiative forcing (W m −2 ) from observations, and CAM3 and CAM4 simulations. (left) SWCF (positive represents cooling) and (right) LWCF (positive represents warming). Although CAM4 suffers from some degradation in cloud the extensive physics parameterization developments in the model's successor CAM5 provides a much improved simulation of cloud and cloud optical properties, particularly when analyzed through the Cloud Observation Simulator Package (COSP) diagnostics available in CAM

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