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

Earth models of intermediate complexity ( Goosse et al. 2005 ) to full atmosphere–ocean general circulation models (AOGCMs; Ammann et al. 2007 ; Servonnat et al. 2010 ; Jungclaus et al. 2010 ) have been used to simulate this period [a complete list of last millennium simulations assessed in the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) can be found in Jansen et al. (2007) ]. The simpler models, able to run ensembles of simulations, have allowed more

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Gijs de Boer, William Chapman, Jennifer E. Kay, Brian Medeiros, Matthew D. Shupe, Steve Vavrus, and John Walsh

Model, version 4 (CCSM4) to simulate various components of the present-day Arctic atmosphere. Six major atmospheric characteristics are evaluated because of their significant implications on regional and global climate. Properties are chosen based in part upon guidance from Walsh et al. (2005) and include surface air temperature ( T sfc ), sea level pressure (SLP), cloud distribution and phase, precipitation and evaporation ( P − E ), the Arctic atmospheric energy budget, and lower

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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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Kevin Raeder, Jeffrey L. Anderson, Nancy Collins, Timothy J. Hoar, Jennifer E. Kay, Peter H. Lauritzen, and Robert Pincus

available DA algorithms. That effort has been greatly reduced by the advent of ensemble DA, so that climate model development and research can now benefit greatly and directly from the variety of tools available from DA. Several generations of the Community Atmosphere Model [CAM, the atmospheric component of the Community Earth System Model (CESM)] can now be used with ensemble DA using the Data Assimilation Research Testbed (DART). The DART algorithm and software are described briefly here ( section 2

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Ernesto Muñoz, Wilbert Weijer, Semyon A. Grodsky, Susan C. Bates, and Ilana Wainer

variability of wind stress, sea surface temperature (SST), the Atlantic warm pool (WP), and a head budget of the Benguela region. The seasonal cycle of SST in the tropical Atlantic is related to the seasonal cycles of wind stress and of the intertropical convergence zone (ITCZ). The seasonal cycle is the largest ocean–atmosphere signal in the region. As discussed in Servain et al. (1998) , the timing and characteristics of the seasonal evolution of SST, winds, and the ITCZ depend on the coupled air

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Jenny Lindvall, Gunilla Svensson, and Cecile Hannay

. 2006 ; Svensson and Holtslag 2009 ). The second GABLS study ( Svensson et al. 2011 ) concluded that many models have difficulties representing the diurnal variation in the wind speed and that no significant difference in performance could be seen based on the type of closure. First-order models did not perform worse than turbulent kinetic energy (TKE) types of closures. The most recent release of the NCAR Community Earth System Model version 1 (CESM1) contains the Community Atmosphere Model

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

link between ENSO and changes to the mean state of the atmosphere and ocean is unclear ( Guilyardi et al. 2009 ; Collins et al. 2010 ), possibly due to ENSO’s sensitivity to the balance between different feedbacks ( Philip and van Oldenborgh 2006 , 2010 ; Guilyardi 2006 ). Worryingly, even the models that most closely resemble observations do not respond more similarly to one another than does the full Coupled Model Intercomparison Project phase 3 (CMIP3) ensemble ( Collins et al. 2010 ). As a

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

simulations will be compared to the Atmospheric Model Intercomparison Project (AMIP)-type Community Atmosphere Model, version 4 (CAM4) atmosphere-only runs to show how coupling changes the monsoon simulations. Additionally, comparisons will be made where appropriate to the previous generation of this model (CCSM3) to document any changes or improvements to the monsoon simulations. The monsoons in CCSM3 were previously described by Meehl et al. (2006) and can also be compared to monsoon simulations in a

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William H. Lipscomb, Jeremy G. Fyke, Miren Vizcaíno, William J. Sacks, Jon Wolfe, Mariana Vertenstein, Anthony Craig, Erik Kluzek, and David M. Lawrence

have not been fully explained. Improved process-based models, especially of ice sheets and their interactions with the ocean and atmosphere, are needed to better bound the projections. Ice sheet modelers generally agree on the need for 1) Stokes or higher-order ice flow models with a unified treatment of vertical shear stresses and horizontal plane stresses, 2) grid resolution ~1 km or less near grounding lines (the boundaries between grounded and floating ice) and in other regions where the flow

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