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

by Meehl et al. (2006) . CCSM4 includes a finite-volume 1° version of the atmospheric model CAM4, with improved components of ocean, land, and sea ice compared to CCSM3 ( Gent et al. 2011 ). Grid points in the atmosphere are spaced roughly every 1° latitude and longitude, and there are 26 levels in the vertical. The ocean is a version of the Parallel Ocean Program (POP) with a nominal latitude–longitude resolution of 1° (down to ¼° in the equatorial tropics) and 60 levels in the vertical. No

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

; Meinshausen et al. 2011 ). 2. Model description and performance The CCSM4 is a considerable advancement from CCSM3 ( Collins et al. 2006 ), with major enhancements in all of the component models. The atmospheric component of CCSM4 is the Community Atmosphere Model, version 4 (CAM4). This component uses the Lin–Rood finite-volume dynamical core ( Lin 2004 ), employing an atmospheric model resolution of 1° horizontally and 26 levels in the vertical. The model includes improvements to the deep convection

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Aneesh C. Subramanian, Markus Jochum, Arthur J. Miller, Raghu Murtugudde, Richard B. Neale, and Duane E. Waliser

parameterization scheme of Zhang and McFarlane (1995) at 3° resolution with prescribed SST; by Kim et al. (2009) with an uncoupled Community Atmosphere Model, version 3 (CAM3.5) at 2° resolution with prescribed SST; and by Zhou et al. (2012) with CCSM3.5 at 2° resolution. Since the MJO is found here to be well represented in CCSM4, we also explore its interaction with other climate phenomena on interannual time scales. These include ENSO, the Indian monsoon, and the Indian Ocean zonal mode (IOZM) of SST

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Gerald A. Meehl, Warren M. Washington, Julie M. Arblaster, Aixue Hu, Haiyan Teng, Jennifer E. Kay, Andrew Gettelman, David M. Lawrence, Benjamin M. Sanderson, and Warren G. Strand

CESM1(CAM5) compared to CCSM4 are discussed in section 5 . Projected changes of Arctic and Antarctic climate are described in section 6 , while discussion and conclusions follow in sections 7 and 8 , respectively. 2. Model and experiments a. Model description The CESM1(CAM5) has the same land, ocean (including an overflow parameterization), and sea ice components as in CCSM4 ( Gent et al. 2011 ), with the biggest change occurring in the atmosphere. General features of the model formulation are

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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

, as well as a listing of observational datasets used in this study. Section 3 documents ENSO and PDV, addressing the issues discussed above. Section 4 summarizes the results and highlights outstanding issues. 2. Model description and observational datasets a. CCSM4 The CCSM4 is a state-of-the art coupled general circulation model comprised of four components (atmosphere, ocean, land, and cryosphere) linked by means of a flux coupler. In this study, we focus on a 1300-yr 1850 control

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K. J. Evans, P. H. Lauritzen, S. K. Mishra, R. B. Neale, M. A. Taylor, and J. J. Tribbia

1. Introduction Here we report on the climate produced by a spectral element atmospheric dynamical core option within the Community Climate System Model (CCSM), version 4. The CCSM is a state-of-the-art climate model with atmosphere, ocean, land, and ice component models that exchange information through a flux coupler ( Gent et al. 2011 ). The spectral element dynamical core comes from the High-Order Method Modeling Environment (HOMME; Dennis et al. 2005 , 2012 ), which has been integrated

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

relationships with atmosphere–ocean mean-state variables are often used to explain various aspects of ENSO dynamics. For example, shifts between surface and thermocline-driven ENSO modes may be related to zonal wind stress as well as the depth and stratification of the mean thermocline ( Fedorov and Philander 2001 ), while the meridional extent of the subtropical wind stress is related to the ENSO period in coupled climate models ( Capotondi et al. 2006 ). Examples of the mean state–ENSO interaction are too

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

) found sensitivity in a single GCM was altered by changes to ice microphysics in the Southern Hemisphere storm track. Soden and Vecchi (2011) looked at the spatial distribution of feedbacks in a multimodel ensemble, and Taylor et al. (2011a) decomposed feedbacks spatially in a single GCM. Taylor et al. (2011b) examined the seasonality of feedbacks. This study uses experiments from an atmosphere–ocean GCM to explore which feedbacks determine climate sensitivity and what processes affect these

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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

these sets of experiments. In particular, the focus will be on the climate system response to various external forcings, both natural (volcanoes and solar) and anthropogenic [greenhouse gases (GHGs), ozone, land use, sulfate aerosols and carbon (both primary organic and black)] aerosols. Where appropriate, comparisons will be made to previous versions of the model, particularly CCSM3, to show where and how the CCSM4 simulations differ from CCSM3. Results from the climate change projections from CCSM

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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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