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Gretchen Keppel-Aleks, James T. Randerson, Keith Lindsay, Britton B. Stephens, J. Keith Moore, Scott C. Doney, Peter E. Thornton, Natalie M. Mahowald, Forrest M. Hoffman, Colm Sweeney, Pieter P. Tans, Paul O. Wennberg, and Steven C. Wofsy

only on human-mediated fluxes, such as those from fossil fuel combustion or land use change, but also on the response of the natural carbon cycle to changing atmospheric composition and climate ( Fung et al. 2005 ). Currently, 55% of CO 2 emitted by human activities is taken up by the ocean or terrestrial ecosystems, with 45% remaining airborne ( Le Quéré et al. 2009 ). The degree to which the efficiency of these sinks will change with climate is an open question, with implications for the rate of

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

the primary modes of climate variability in the LM simulation, both in comparison to the data reconstructions and to the long CCSM4 control simulation. In section 7 , we summarize the results and identify outstanding issues for future work. 2. Forcing datasets The CCSM4 LM simulation starts at 850 and continues to 1850, where it matches up and is extended as an additional ensemble member of the CCSM4 twentieth-century simulations that end in December 2005. The forcings and boundary conditions for

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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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Peter R. Gent, Gokhan Danabasoglu, Leo J. Donner, Marika M. Holland, Elizabeth C. Hunke, Steve R. Jayne, David M. Lawrence, Richard B. Neale, Philip J. Rasch, Mariana Vertenstein, Patrick H. Worley, Zong-Liang Yang, and Minghua Zhang

now substantially smaller, especially near the equator and the western boundaries of ocean basins ( Jochum et al. 2008 ). This allows the tropical instability waves in the tropical Pacific to be much more energetic and realistic. The vertical mixing terms now have a term that is proportional to the tidal energy ( Jayne 2009 ), which allows a little more cross-isopycnal mixing in the deep ocean. A new parameterization for the effects of submesoscale eddies ( Fox-Kemper et al. 2008 ) has been

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

) and methane ( Mastepanov et al. 2008 ) exchange, and the amplitude and timing of spring snowmelt river discharge peaks ( Rawlins et al. 2005 ; Kane et al. 2008 ). Frozen ground conditions are currently experiencing rapid change in response to late twentieth-century warming and associated climatic changes. Observed terrestrial Arctic changes (see Hinzman et al. 2005 for review) include warming and degrading of permafrost ( Romanovsky and Osterkamp 1997 ; Camill 2005 ; Åkerman and Johansson

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Stephen Yeager, Alicia Karspeck, Gokhan Danabasoglu, Joe Tribbia, and Haiyan Teng

to a few decades by reducing the forecast uncertainty associated with intrinsic climate variability ( Hawkins and Sutton 2009 ). Initializing CGCM simulations with conditions that reflect the current observed state of the earth system could enhance predictive skill by synchronizing the slowly evolving internal variations in the model, in particular ocean variations, with those in nature. Initialization could also improve skill simply by reducing model bias in the early years of a prediction

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Samantha Stevenson, Baylor Fox-Kemper, Markus Jochum, Richard Neale, Clara Deser, and Gerald Meehl

includes a meridional refinement to 0.25° at the equator. A significant reduction in viscosity, however, led to a sharpening of the equatorial currents and a realistic representation of tropical instability waves ( Jochum et al. 2008 ). The latter is responsible for removing the cool bias in the equatorial cold tongue. Below the surface in CCSM3, the maximum equatorial temperature biases at 110°W reached 2.5°C, and they were accompanied by an equatorial undercurrent (EUC) with an overly deep core

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

-dimensional linear remapping scheme ( Lipscomb 2001 ) transfers ice among ITD categories as needed because of thermodynamic, ridging, and advective ice thickness changes. In general, the physical parameterizations in the CCSM3 and CCSM4 ice components are quite similar. Major changes in the CCSM4 ice model include a new radiative transfer scheme, melt ponds and aerosols (all discussed below), a nonzero heat capacity snow cover, and an altered atmospheric boundary layer description that allows sensible heat

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Clara Deser, Adam S. Phillips, Robert A. Tomas, Yuko M. Okumura, Michael A. Alexander, Antonietta Capotondi, James D. Scott, Young-Oh Kwon, and Masamichi Ohba

to answer from the short observational record alone. A recent example is the 2000-yr control simulation of the Geophysics Fluid Dynamics Laboratory (GFDL) model ( Wittenberg 2009 ), which exhibits large decadal-to-centennial modulation of ENSO behavior under constant boundary conditions (e.g., fixed greenhouse gas concentrations). Such internal variability implies that identifying externally forced changes in ENSO behavior from the relatively short observational record may have limited success

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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

sensitivity, transient climate response, and associated processes in CCSM4, see Bitz et al. (2012) . Thus the CCSM4 is somewhat more sensitive to increased CO 2 than the previous model versions. Current estimates of equilibrium climate sensitivity, obtained from multiple lines of modeling, observational, and paleoclimate evidence, range from 2.0° to 4.5°C, with a most likely value of about 3°C ( Meehl et al. 2007 ). Both CCSM3 and CCSM4 fall within the estimated range and are near the most likely value

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