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Wilbert Weijer, Bernadette M. Sloyan, Mathew E. Maltrud, Nicole Jeffery, Matthew W. Hecht, Corinne A. Hartin, Erik van Sebille, Ilana Wainer, and Laura Landrum

1. Introduction The Southern Ocean is a region of extremes: it is exposed to the most severe winds on the earth ( Wunsch 1998 ), the largest ice shelves ( Scambos et al. 2007 ), and the most extensive seasonal sea ice cover ( Thomas and Dieckmann 2003 ). These interactions among the atmosphere, ocean, and cryosphere greatly influence the dynamics of the entire climate system through the formation of water masses and the sequestration of heat, freshwater, carbon, and other properties ( Rintoul

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Gerald A. Meehl, Julie M. Arblaster, Julie M. Caron, H. Annamalai, Markus Jochum, Arindam Chakraborty, and Raghu Murtugudde

in CCSM4. The improvement in the location of orographic monsoon rainfall is likely associated with the finite-volume dynamical core in CCSM4 compared to the spectral version of CCSM3, and the higher horizontal resolution (about 1° in the atmosphere in CCSM4) compared to the roughly 1.4° atmospheric resolution in the T85 CCSM3. Additionally, though the rainfall over the western Indian Ocean is still a bit too far west in CCSM4, it is improved compared to CCSM3 as noted above. The southern ITCZ

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

-induced advection coefficient varies in space and time according to Danabasoglu and Marshall (2007) . A variable coefficient provides a better representation of changes in eddy activity resulting from variable surface momentum forcing than a constant value; this is a key feature, allowing the model to more realistically capture the circulation response to changing winds, particularly in the Southern Ocean ( Danabasoglu and Marshall 2007 ; Gent and Danabasoglu 2011 ; Gent 2011 ; Farneti and Gent 2011

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Peter R. Gent and Gokhan Danabasoglu

1. Introduction Several recent papers have questioned whether the coarse-resolution ocean components used in climate models can simulate an appropriate response to increasing Southern Hemisphere winds: Hallberg and Gnanadesikan (2006) , Hogg et al. (2008) , Boning et al. (2008) , Screen et al. (2009) , and Spence et al. (2010) . This subject has arisen because the response to increasing Southern Hemisphere winds in ocean models with either eddy-permitting or eddy-resolving resolution, and

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

and change in the Antarctic sea ice is associated with large-scale modes of atmospheric variability, most importantly the El Niño–Southern Oscillation (ENSO) and the southern annular mode (SAM) (e.g., Simmonds and Jacka 1995 ; Yuan and Martinson 2000 ; Liu et al. 2004 ; Yuan and Li 2008 ). Many observational studies suggest that ENSO initiates Southern Hemisphere (SH) variability, including modulation of the SAO, and that ice–ocean coupling prolongs the anomalies (e.g., Harangozo 1997

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

those Southern Hemisphere rivers and enhancing rainfall over Northern Hemisphere river basins, such as the Orinoco, and over the northern tropical ocean. The northward migration of the ITCZ off the west coast of Africa contributes to the sea surface temperature (SST) increase in boreal spring by reducing wind speeds and suppressing evaporation. During this period, the westerly monsoon flow is expanded farther westward and moisture transport onto the continent is enhanced, increasing Sahel rainfall

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

” version; in the “low solar” CCSM4 LM simulation the differential strength is closer overall to the CSM 1.4 medium solar version, but with stronger warm anomalies in the polar and NH interior continental regions. The reconstructed differentials show much more spatial variability than the model runs, in particular indicating significant cooling in the equatorial Pacific, subtropical Indian, and southern Pacific Oceans, and in South America, western Africa, southeastern Australia, and interior and

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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

included that helps to restratify the ocean mixed layer. Finally, a new parameterization for deep ocean overflows, such as the Denmark Strait and Faroe–Scotland Ridge in the North Atlantic, has been implemented ( Danabasoglu et al. 2011b ). This improves the penetration of overflow water into the very deep ocean, which has been a long-standing problem of models that use depth coordinates. The nominal 1° grid uses spherical coordinates in the Southern Hemisphere, but in the Northern Hemisphere the pole

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

. This paper presents a low-resolution CCSM4 as an alternative to the higher resolution versions and highlights both its strengths and weaknesses in comparison with observations and other CCSM4 resolution versions. CCSM4 contains several notable improvements spanning all model components, which include a much improved El Niño–Southern Oscillation (ENSO) representation, improved ocean mixing, a new land carbon–nitrogen (CN) component, more realistic ice albedos, and new coupling infrastructure. The

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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

approximation in the plume calculation ( Neale et al. 2008 ), aspects not included in CAM3. The ocean model component has 60 vertical levels as opposed to 40 in CCSM3, with the number of levels in the upper 200 m increased from 14 to 20. It uses spherical coordinates in the Southern Hemisphere and a displaced-pole grid in the Northern Hemisphere. The ocean model resolution, identical for the 1° and 2° versions of CCSM4, is nearly uniform in longitude (~1.13°) and variable in latitude (0.27° at the equator

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