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

Bengtsson 2007 ). Artificial variance inflation can be used to counter both this variance loss and to simulate other model errors that are not captured by the variance in atmospheric forcing. That being said, it was not clear in the design of this system whether artificial inflation would be necessary. So, while the option to artificially inflate the ensemble spread can potentially be exercised, in the current configuration we do not use any explicit variance inflation beyond the boundary forcing

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Semyon A. Grodsky, James A. Carton, Sumant Nigam, and Yuko M. Okumura

explanation for why this bias occurs is the fact that there is a strong SST front at the latitude of the boundary between the warm Angola and cold Benguela Current systems (which should be at ~17.5°S) ( Rouault et al. 2007 ; Veitch et al. 2010 ). The position of this front is maintained partly by local wind-induced upwelling, and thus local wind errors will cause errors in its position and strength. Also, even if the local winds are correct, the coastal currents must be resolved numerically ( Colberg and

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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

deep western boundary currents east of the Kerguelen Plateau ( Fukamachi et al. 2010 ) and off the Antarctic Peninsula in the Weddell Sea ( Gordon et al. 2001 ; Fahrbach et al. 2001 ). AAIW and SAMW are found near and equatorward of the Antarctic Circumpolar Current (ACC). Increased anthropogenic carbon and transient tracer concentrations are associated with these water masses ( Sabine et al. 2004 ; Fine et al. 2008 ; Hartin et al. 2011 ). SAMW and AAIW enter the subtropical gyre at the base of

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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

CCSM3, these coefficients no longer depend on either the local deformation rate or the grid Reynolds number. Instead of the latter, elevated viscosities at the western boundaries are used in both directions. This follows the Munk (1950) criterion, resolving the viscous western boundary currents as well as diminishing numerical noise. The minimum east–west viscosity is 600 m 2 s −1 . In the north–south direction, the minimum viscosity increases from an equatorial value of 600 to 1200 m 2 s −1

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Jenny Lindvall, Gunilla Svensson, and Cecile Hannay

1. Introduction To understand and predict future climate, we rely on using numerical models of the climate system. Previous generations of global climate models (GCMs) only described the physical–dynamical climate system; whereas the current generation, Earth system models (ESMs), expand to interactively couple key biogeochemical cycles, such as the carbon and nitrogen cycles, into the physical–dynamical GCMs. However, the reliability of simulations of present and future climate is dependent

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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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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

new satellite observations and corresponding diagnostics available in COSP provides new opportunities to understand and quantify climate model cloud biases. As described in Bodas-Salcedo et al. (2011 , hereafter B11 ), COSP currently produces climate model diagnostics that can be compared to observations from six satellite projects: 1) ISCCP, 2) Multiangle Imaging SpectroRadiometer (MISR), 3) Moderate Resolution Imaging Spectroradiometer (MODIS), 4) CloudSat —a spaceborne radar, 5) a spaceborne

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

variability are easily recognizable as related to known biases in the mean state such as western boundary current and double ITCZ errors ( Figs. 1 and 2 ). However, some of the errors in the annual and interannual bands differ in location and magnitude relative to each other and the mean errors. Fig . 6. Maps of (left) CORE standard deviation and (right) the ratio of CCSM4 to CORE variance for the 9- to 15-month bandpassed variability for 1984–2006. Only regions where CORE standard deviation falls

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Markus Jochum, Alexandra Jahn, Synte Peacock, David A. Bailey, John T. Fasullo, Jennifer Kay, Samuel Levis, and Bette Otto-Bliesner

[see, however, Stommel (1961) for a classic study of multiple equilibria in idealized systems]. Even if one is forced to accept the current time-slice comparison as inevitable but reasonable, it would be preferable to use the last rather than the present interglacial as the control, in particular for the analysis of the transient ocean response. Again, costs are a problem. More importantly though, it is the main result of the ocean transient study that the meridional ocean heat transport is quite

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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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