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

m. The WOD09 corrects the XBT standard level data for the known errors in the drop rate equation ( Hanawa et al. 1995 ) and the time-dependent temperature biases described in Levitus et al. (2009) . Some of the profiling floats in the WOD09 were adjusted by the Argo project for drifts in their pressure sensors ( Barker et al. 2011 ). Only those data that pass all WOD09 quality control standards are included in the assimilation. See Johnson et al. (2009) for a complete description of the WOD09

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Gokhan Danabasoglu, Susan C. Bates, Bruce P. Briegleb, Steven R. Jayne, Markus Jochum, William G. Large, Synte Peacock, and Steve G. Yeager

ensemble simulations described in Gent et al. (2011) . Prior to 1850 CONTROL, a preliminary preindustrial simulation was integrated for 130 yr, starting with the January-mean climatological Polar Science Center Hydrographic Climatology (PHC2) potential temperature ( θ ) and salinity ( S ) data [PHC2 dataset represents a blending of the Levitus et al. (1998) and Steele et al. (2001) data for the Arctic Ocean] and state of rest in the ocean model. After updating to new datasets (e.g., ozone) and

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

2x1 control and twentieth-century simulations can be found in Gent et al. (2011) . The T31x3_1850 control simulation was initialized using the Polar Science Center Hydrographic Climatology dataset (PHC2) of potential temperature and salinity data [representing a blending of the Levitus et al. (1998) and Steele et al. (2001) data for the Arctic Ocean], and state of rest in the ocean model. The twentieth-century simulation was integrated for 150 years using aerosol, greenhouse gas, volcanic

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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

with available data for this period. Most of the data products used for this comparison have only recently become available, making a detailed Arctic-wide validation of the simulated sea ice and ocean properties possible for the first time. The analysis shows that the model captures the mean state of the sea ice and ocean in the Arctic as well as changes in the sea ice cover during recent decades reasonably well. Shortcomings in the model are mainly found in the sea ice motion field, the location

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Jenny Lindvall, Gunilla Svensson, and Cecile Hannay

of FLUXNET, as well as by the Coordinated Energy and Water Cycle Observation Project (CEOP) archived by the NCAR Earth Observing Laboratory (EOL; ). None of the data used for this study was gap filled. Quality control and flux corrections of the data were left to the individual principal investigators (PIs) that supplied the data. We have chosen not to omit any data, except for what has already been done by the individual working groups. All of the data included are most

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

variability from a nontransient preindustrial (year 1850) control run. Mechanisms driving simulated sea ice variability and linkages to large-scale modes of atmospheric variability are discussed. We then investigate sea ice changes in transient twentieth-century model runs from six ensemble members including the seasonality of simulated sea ice change and provide comparisons to observed twentieth-century change. 2. Experiments and observational data We assess the Antarctic sea ice mean state, variability

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David M. Lawrence, Keith W. Oleson, Mark G. Flanner, Christopher G. Fletcher, Peter J. Lawrence, Samuel Levis, Sean C. Swenson, and Gordon B. Bonan

on an annual basis according to data from a global historical transient land use and land cover change dataset ( Hurtt et al. 2006 ) that has been interpreted for use in CLM4 ( D. M. Lawrence et al. 2012 ). Further details about the configuration and forcing fields for these simulations are described in Gent et al. (2011) . Where applicable, the CCSM4 simulations are compared to T85 resolution CCSM3 simulations (1870 preindustrial control and a five-member ensemble of 1870–1999 historical

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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

properties To evaluate the representation of the major Southern Ocean water masses (AABW, AAIW, and SAMW) in CCSM4, a comparison is made with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Atlas of Regional Seas 2009 (CARS2009) climatology. CARS2009 is a climatology of ocean water properties, and consists of a gridded average seasonal cycle based on a quality-controlled archive of all available historical subsurface ocean data ( Ridgway et al. 2002 ). Details of CARS2009 can be

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

CCSM4 climate bias) and a simulation with the climate bias removed by the method described above. Mean permafrost extent values for several 20-yr periods are also reported in Table 3 . Throughout the twentieth century, there is about 1 × 10 6 km 2 more near-surface permafrost, which brings the amount of permafrost in line with that seen in control offline CLM4 simulations forced with observed data. This agreement indicates that the method to remove the climate bias from the CCSM4 data has worked

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