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

observational data used. Section 4 summarizes explicit climate changes related to model changes between CAM3 and CAM4, and section 5 details improvements in more general aspects of the model climate in prescribed-SST and fully coupled experiments. Conclusions are presented in section 6 . 2. Overview of model formulation and dataset changes A number of modifications have been implemented in CAM4, the most impactful of which are summarized below. A more comprehensive description of CAM4 can be found in

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

1. Introduction Data assimilation (DA) has long been recognized as an indispensable tool in numerical weather forecasting for generating realistic initial and boundary conditions, for melding diverse observations into gridded analyses that have been used for model forecast verification ( Lynch 2006 ) and for added quality control of observational systems. Until recently, its usefulness for climate model development has not been compelling enough to warrant the effort of implementing the best

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

determine buoyancy and related cloud closure properties. The modification is based on the conservation of moist entropy and mixing methods of Raymond and Blyth (1986 , 1992) . It replaces the standard nonentraining plume method used in CAM3 with a DPA to increase convection sensitivity to tropospheric moisture and to reduce the amplitude of the diurnal cycle of precipitation over land. Mixing occurs at all levels (not only at the cloud top) between the lowest model level and the neutral buoyancy level

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J. E. Kay, B. R. Hillman, S. A. Klein, Y. Zhang, B. Medeiros, R. Pincus, A. Gettelman, B. Eaton, J. Boyle, R. Marchand, and T. P. Ackerman

parameterizations in CAM5 and CAM4. Local modifications to the COSP v1.3 code were necessary both to ensure compatibility with the CESM code and software engineering requirements and to incorporate the influence of radiatively active snow, a modification that only influences the ISCCP, MISR, MODIS, and lidar diagnostics in the CAM5 simulations. Snow in this context represents the population of large ice crystals with appreciable fall velocities. Because it incorporates the impact of snow on radiative fluxes

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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

than observed ( Danabasoglu et al. 2012 ): zonally averaged zonal wind stress T x peaks at about 0.20 N m −2 , as compared with approximately 0.15 N m −2 for the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) ( Fig. 3b ; Uppala et al. 2005 ). Fig . 3. Time series of (a) SAM, (b) maximum of the zonally averaged zonal wind stress, (c) the Niño-3.4 index, and (d) net SHF Q f averaged over the domain south of 55°S. Plotted are 11-yr running means for the

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

residual. Using this methodology, Serreze et al. (2007) compared energy budgets from both the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (NRA) products. They found F SFC significantly impacts the atmospheric (and oceanic) energy budget and showed that in a mean sense the ERA-40 atmosphere demonstrated a net loss of energy between August

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

; Lin 2004 ) replaces the spectral Eulerian core as the default dynamical core in CAM4. The deep convection scheme has two major modifications: the calculation of convective available potential energy (CAPE) is now based on a dilute-entraining parcel ( Neale et al. 2011 ), and the subgrid-scale vertical transport of momentum by deep convection is included according to Richter and Rasch (2008) . Minor modifications are also made to the calculation of cloud fraction in very dry and cold conditions

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

scheme ( Richter and Rasch 2008 ; Neale et al. 2008 ) and a “freeze dry” parameterization to improve Arctic low cloud cover ( Vavrus and Waliser 2008 ). The CAM4 model is described more fully in Neale et al. (2011, manuscript submitted to J. Climate ), and coupled simulations from the CCSM4 model are presented in Gent et al. (2011) . The land model component of CCSM4 is the Community Land Model 4.0. New improvements include modifications to the hydrology and canopy integrations, as discussed in

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C. Kendra Gotangco Castillo, Samuel Levis, and Peter Thornton

modified by terrestrial and ocean carbon cycle processes. The CCSM4CNDV was run for 60 yr to allow sea surface temperatures to equilibrate, after which history files containing weather data (half-hourly solar and 3-hourly precipitation, temperature, wind, etc.) were generated by the CCSM4 coupler over a period of 30 yr. These coupler history files were used to drive a multicentury offline CLM4CNDV spinup until net ecosystem exchange (NEE) fluxes (calculated by the model from net ecosystem production

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

Denmark Strait, Faroe Bank Channel, and the Ross and Weddell Seas' continental shelves are represented by an explicit parameterization ( Danabasoglu et al. 2010 , 2012 ). Ocean carbon biogeochemistry in CESM1(BGC) is represented by the ocean Biogeochemical Elemental Cycle model embedded within the CESM ocean component. The BEC model has been described in a number of earlier papers (see Moore et al. 2004 ; Doney et al. 2006 ) and has been implemented within CESM1 with limited modification. The

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