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Alexandra Jahn, Kara Sterling, Marika M. Holland, Jennifer E. Kay, James A. Maslanik, Cecilia M. Bitz, David A. Bailey, Julienne Stroeve, Elizabeth C. Hunke, William H. Lipscomb, and Daniel A. Pollak

several satellite-derived products to assess how well the CCSM4 simulates the late-twentieth-century Arctic sea ice concentration, sea ice extent, sea ice thickness, multiyear sea ice cover, sea ice motion, and the timing of melt onset and freeze up. a. Sea ice concentration and extent To assess how well the CCSM4 simulates the sea ice concentration, we use the sea ice concentration from Comiso (1999) , which is derived using the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS

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Alicia R. Karspeck, Steve Yeager, Gokhan Danabasoglu, Tim Hoar, Nancy Collins, Kevin Raeder, Jeffrey Anderson, and Joseph Tribbia

minimize the difference between an ocean model solution and a set of observations. Broadly speaking, they are distinguishable from one another by the constraints levied upon the solution, the way that prior knowledge of the state of the system is formulated, and whether observations can influence state estimates in the past. For example, adjoint [four-dimensional variational data assimilation (4DVAR)] methods, for example, the Estimating the Circulation and Climate of the Ocean (ECCO) 2 products

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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

, suggestive that the models are capturing the key first-order processes correctly. This is mostly fortuitous since climate sensitivity is not a tuned parameter and is only determined at the end of the model development process. However, later in this paper the consequences of the magnitude of the forcings in combination with the climate sensitivity will be explored when the time evolution of the model-produced twentieth-century temperature is compared to observations. Observed global surface temperatures

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

T31x3 and FV2x1 simulations in boreal winter ( Fig. 6 , upper panels) compared to the Special Sensor Microwave Imager (SSM/I) satellite observations ( Cavalieri et al. 1996 ) (solid black line) and by contrast well simulated in the FV1x1 simulation. When evaluating sea ice extent time series across the entire length of the 1850 control simulations, we find that the FV2x1 simulation produces a sea ice pattern in the Labrador Sea very similar to FV1x1 (not shown) during some time periods, while

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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

. North Atlantic MOC (Sv) for the (a) CCSM3 1870 control 〈345–364〉 and (b) CCSM4 1850 control 〈871–900〉, and global MOC for (c) CCSM3 1870 control and (d) CCSM4 1850 control. c. Arctic sea ice concentration Figure 3 shows the mean sea ice concentration in the Arctic from the CCSM4 1850 and CCSM3 1870 control runs, and the black lines are the 10% mean concentration values from recent Special Sensor Microwave Imager (SSM/I) satellite observations. The figure shows that the sea ice was much too

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

twentieth-century CCSM3 average (dashed), and the hindcast ice–ocean simulation (dotted). Satellite-derived observations ( Fetterer et al. 2009 ) are shown by the asterisks. In contrast to the Arctic, large-scale trends in Antarctic sea ice cover have been small and slightly increasing since satellite records began in 1979 (e.g., Cavalieri and Parkinson 2008 ). This small change is due to regionally compensating trends that are present since 1979. Significant ice loss has been observed in the

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

experiments The configuration for CAM experiments consists of fully interactive atmosphere and CLM components. A data ocean model is used that allows prescribed SSTs and a version of the CICE in thermodynamic-only mode where ice extent is prescribed from observations and ice thickness is specified. The prescribed SSTs and sea ice properties are derived from the Atmosphere Model Intercomparison Project (AMIP; Taylor et al. 2001 ), whereby linearly interpolated daily values are obtained from adjusted

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

characteristics of permafrost under the simulated present-day climate are assessed and compared to available observations. Projections of permafrost conditions out to 2100 are shown and the results are compared and contrasted to those obtained with CCSM3 ( Lawrence and Slater 2005 ). There is some a priori expectation that the representation of permafrost will be superior in CCSM4 compared to CCSM3 because of improvements in the land model [CLM version 4 (CLM4)]—including principally an extension of the

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

temperature and sea ice melting rates (e.g., Eisenman et al. 2007 ; Gorodetskaya et al. 2008 ) and precipitation, both of which regulate surface albedo. Comparisons between GCMs and Arctic observations are challenging to complete due to inconsistencies between cloud fraction definitions between simulations and sensors. Despite these challenges, Walsh et al. (2002) demonstrated improvement in simulation of Arctic Ocean cloud cover between Atmospheric Model Intercomparison Project version 1 (AMIP1) and

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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

albedo feedback has increased by a factor of 1.5 compared to CCSM4. Finally, there are enhanced positive short wave cloud feedbacks in CESM1(CAM5), mostly having to do with complex interactions of the moist physics parameterizations ( Gettelman et al. 2013 ) on the equatorward branches of the storm tracks and in the subtropics that contribute to greater climate sensitivity in CESM1(CAM5) compared to CCSM4. d. Observations Observed global surface temperatures considered shortly are from version 3 of

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