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Susan C. Bates, Baylor Fox-Kemper, Steven R. Jayne, William G. Large, Samantha Stevenson, and Stephen G. Yeager

1. Introduction The coupling between the atmosphere and ocean is a major player in the earth’s climate system and governor of climate change. The former has a limited capacity to store water and heat but is connected to the ocean, which is effectively an infinite reservoir of water and has more heat capacity in only its upper few meters than exists in the entire atmosphere. The direct coupling of the planetary boundary layers (PBLs) is accomplished through the air–sea fluxes. In nature, the

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Esther C. Brady, Bette L. Otto-Bliesner, Jennifer E. Kay, and Nan Rosenbloom

versions of the models [e.g., for the Community Climate System Model (CCSM), see Shin et al. (2003) and Otto-Bliesner et al. (2003 , 2006 ); for PMIP models, see Table 1 and Otto-Bliesner et al. (2009) ]. Using the same version for both past and future climate change experiments should enhance progress in addressing some of the outstanding scientific questions of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). Table 1. Summary of forcings, boundary conditions

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Jenny Lindvall, Gunilla Svensson, and Cecile Hannay

small over the ocean. More recently, the Global Energy and Water Cycle Experiment (GEWEX) Atmospheric Boundary Layer Study (GABLS) has coordinated several model intercomparison studies with focus on PBL parameterizations in numerical weather prediction and climate models. The first intercomparison clearly illuminated problems in simulating weakly stably stratified conditions in terms of too deep boundary layers with not enough wind turning compared to large-eddy simulation results ( Cuxart et al

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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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Stephen Yeager, Alicia Karspeck, Gokhan Danabasoglu, Joe Tribbia, and Haiyan Teng

to a few decades by reducing the forecast uncertainty associated with intrinsic climate variability ( Hawkins and Sutton 2009 ). Initializing CGCM simulations with conditions that reflect the current observed state of the earth system could enhance predictive skill by synchronizing the slowly evolving internal variations in the model, in particular ocean variations, with those in nature. Initialization could also improve skill simply by reducing model bias in the early years of a prediction

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

oceanic internal variability to the ensemble spread is minor, and nearly all of the ensemble variability is imparted by the use of an ensemble of atmospheric boundary forcing. This is essentially a form of variance inflation that can be understood as ocean model error that stems from our uncertainty in the atmospheric conditions. Most ensemble filtering methods underestimate variance because of statistical sampling error stemming from the use of a finite number of ensemble members ( Furrer and

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Keith Oleson

are determined for each urban surface (us) ( Fig. 2 ). The interior boundary conditions for roofs and walls are determined by an interior building temperature held between prescribed minimum and maximum temperatures ( and ), thus simulating space heating and air conditioning (HAC) fluxes. Hydrology on the roof and canyon floor is simulated and walls are hydrologically inactive. A snowpack can form on the active surfaces. Liquid water is allowed to pond on these surfaces, which supports

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

conditions for the period 850–1850, is described in Landrum et al. (2011, manuscript submitted to J. Climate ). Among the LM forcings and boundary conditions included in this simulation are changes in solar irradiance, greenhouse gases, land cover, and volcanic forcing. The volcanic forcing includes several volcanic eruptions that are estimated to be much larger than those that have occurred since 1850 including major eruptions in the years 1258, 1452, and 1815. For the period 1850–2005, the same

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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

of different spinup procedures used in CCSM3 and CCSM4 to obtain initial conditions for the 20C simulations ( Gent et al. 2011 ). The CCSM3 strategy was to obtain a relatively well-balanced top of the atmosphere model (TOA) heat flux in the present-day control integration. The 1870 preindustrial control used the same tuning. The 20C ensemble members subsequently started from various stages of this 1870 control. In contrast, with the CCSM4 simulations, the objective was to get a well-balanced TOA

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

the primary modes of climate variability in the LM simulation, both in comparison to the data reconstructions and to the long CCSM4 control simulation. In section 7 , we summarize the results and identify outstanding issues for future work. 2. Forcing datasets The CCSM4 LM simulation starts at 850 and continues to 1850, where it matches up and is extended as an additional ensemble member of the CCSM4 twentieth-century simulations that end in December 2005. The forcings and boundary conditions for

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