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

low-resolution CCSM4 (henceforth called T31x3) uses a T31 spectral dynamical core for the atmospheric and land components (horizontal grid of 3.75° × 3.75°) with 26 atmospheric layers in the vertical. The ocean and ice components employ a nominal 3° irregular horizontal grid (referred to as x3) with 60 ocean layers in the vertical. The intermediate-resolution CCSM4 utilizes the finite-volume (FV) dynamical core ( Lin 2004 ) with a nominal 2° atmosphere and land horizontal grid (1.9° × 2

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Semyon A. Grodsky, James A. Carton, Sumant Nigam, and Yuko M. Okumura

1. Introduction Because of its proximity to land and the presence of coupled interaction processes, the seasonal climate of the tropical Atlantic Ocean is notoriously difficult to simulate accurately in coupled models ( Zeng et al. 1996 ; Davey et al. 2002 ; Deser et al. 2006 ; Chang et al. 2007 ; Richter and Xie 2008 ). Several recent studies, including those referenced above, have linked the ultimate causes of the persistent model biases to problems in simulating winds and clouds by the

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

1. Introduction Permafrost and seasonally frozen ground are key components of the Arctic and global climate system because of their influence on energy, water, and carbon cycles. The freeze–thaw status of the ground is a critical threshold in the terrestrial system that is closely linked to the timing and length of the vegetation growing season ( Black et al. 2000 ), boreal plant productivity ( Kimball et al. 2006 ), the seasonal evolution of land–atmosphere carbon dioxide ( Goulden et al. 1998

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Kerry H. Cook, Gerald A. Meehl, and Julie M. Arblaster

monsoon regions, CAM4 produces excessive monsoon precipitation that is brought down in closer agreement to observations in the CCSM4. The CAM4 simulation is better than CCSM4 for the aspects of the West African monsoon in which atmosphere–ocean coupling dominates because there are still systematic errors in the seasonal evolution of tropical SSTs (e.g., the tropical eastern Atlantic). But CCSM4 shows a marked improvement over CCSM3 when atmosphere–land surface interactions dominate (JAS

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

atmosphere/land version of the CCSM . Climate Dyn. , 34 , 819 – 833 , doi:10.1007/s00382-009-0614-8 . Gent , P. R. , and Coauthors , 2011 : The Community Climate System Model version 4 . J. Climate , 24 , 4973 – 4991 . Gettelman , A. , and Coauthors , 2010 : Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model . J. Geophys. Res. , 115 , D18216 , doi:10.1029/2009JD013797 . Gibson , J. K. , P. Kallberg , S

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Matthew C. Long, Keith Lindsay, Synte Peacock, J. Keith Moore, and Scott C. Doney

available observational data. Two configurations of CESM1 are considered: 1) the fully coupled Earth system model, including ocean, sea ice, land, and atmosphere models; and 2) the ocean-ice component models forced by atmospheric reanalysis data. Our analysis is aimed at identifying model biases and examining the model's twentieth-century mean state, seasonal cycle, interannual variability, and transient response. Furthermore, we explicitly test the degree to which the fully coupled model is able to

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

Boucher 2000 ; Myhre 2009 ), indirect effects ( Lohmann and Feichter 2005 ), and semidirect effects ( Hansen et al. 1997 ; Koch and Del Genio 2010 ). The term aerosol direct effects refers to the direct impact of anthropogenic aerosol particles on the planetary energy balance through scattering, absorption, and emission of radiation in the atmosphere, without consideration of the aerosol effects of the radiative heating on clouds. Aerosol indirect effects refer to the impact through the

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Laura Landrum, Bette L. Otto-Bliesner, Eugene R. Wahl, Andrew Conley, Peter J. Lawrence, Nan Rosenbloom, and Haiyan Teng

: Antarctic sea ice climatology, variability, and late twentieth-century change in CCSM4 . J. Climate , 25 , 4817 – 4838 . Lawrence , D. M. , P. E. Thornton , K. W. Oleson , and G. B. Bonan , 2007 : The partitioning of evapotranspiration into transpiration, soil evaporation, and canopy evaporation in a GCM: Impacts on land–atmosphere interaction . J. Hydrometeor. , 8 , 862 – 880 . Lawrence , D. M. , K. W. Oleson , M. G. Flanner , C. G. Fletcher , P. J. Lawrence , S. Levis

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Laura Landrum, Marika M. Holland, David P. Schneider, and Elizabeth Hunke

climatologically important water masses are formed in the Southern Ocean: Antarctic Intermediate Water (AAIW), Subantarctic Mode Water (SAMW; both formed near and north of the Antarctic Circumpolar Current), and Antarctic Bottom Water (AABW; formed over continental shelves along the Adelie coast and in the Ross and Weddell Seas). These water masses form as a result of complex interactions of atmosphere–ocean–cryosphere processes. Despite its relatively small surface area (~10% of the global ocean), 20%–30% of

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

and the coupled climate response, six integrations were performed. These use a coupled atmosphere–land–sea ice–slab ocean model configuration. The Community Atmosphere Model, version 4 (CAM4) is described by R. B. Neale et al. (2011, unpublished manuscript). Here we use the atmospheric model resolution of 2°. The land model is discussed in Lawrence et al. (2012) . The sea ice model uses a nominally 1° resolution with the northern pole smoothly displaced into Greenland. The slab ocean model (SOM

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