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

, the ensemble analyses drift away from the observations and toward the model bias. This is partly due to the absence of ensemble spread inflation in the DART/POP2 assimilations, so that the model becomes overconfident and rejects an increasing number of observations. Further analysis and verification of decadal-scale, coupled, hindcast forecasts is available in Yeager et al. (2012) , where techniques such as “bias correction” are applied to improve decadal forecasts. c. Evaluating the utility of

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

Kalman filters are scalable to large state spaces and can be made parallel for computation on multiple computer processors ( Anderson and Collins 2007 ). Methodologically, they fall into the class of advanced data assimilation techniques that allow for time-varying model-determined background states. This enables the use of prior knowledge that is potentially more complex and informative than would be possible with either stationary background statistics or parameterized covariance forms. It also

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

1. Introduction Society would benefit tremendously if climate scientists could produce reliable forecasts, years to decades in advance, of changes in regional hurricane activity, rainfall, or the likelihood of extreme events such as severe heat waves. In the relatively new field of decadal climate prediction, work is under way to assess the feasibility of using coupled general circulation models (CGCMs) to generate such forecasts, but significant scientific challenges must be overcome if this

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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

1. Motivation a. Using satellite simulators to evaluate climate model clouds Cloud feedbacks dominate uncertainty in model climate projections (e.g., Cess et al. 1990 ; Bony and Dufresne 2005 ; Williams and Webb 2009 ; Medeiros et al. 2008 ), but the quantification of model cloud biases is often confounded by poor model–observational comparison techniques. In the last decade, data from a number of new cloud-observing satellite platforms have become available. Given this context, the use of

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K. J. Evans, P. H. Lauritzen, S. K. Mishra, R. B. Neale, M. A. Taylor, and J. J. Tribbia

cases ( Taylor et al. 1997 ; Thomas and Loft 2002 ), three-dimensional dry dynamical test cases ( Taylor et al. 1998 ; Thomas and Loft 2005 ; Dennis et al. 2005 ; Taylor et al. 2007 ; Lauritzen et al. 2010 ), multicloud simulations ( Khouider et al. 2011 ), aquaplanet experiments that include full physics ( Taylor et al. 2008 ; Mishra et al. 2011a , b ), and realistic simulations with CAM2 physics ( Wang et al. 2007 ). The SE method has also been pursued for global forecast modeling, as in

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Ernesto Muñoz, Wilbert Weijer, Semyon A. Grodsky, Susan C. Bates, and Ilana Wainer

. 2002 ; Nobre and Shukla 1996 ; Hastenrath and Heller 1977 ). Another mode of variability is the so-called zonal mode or Atlantic Niño and is predominant in the boreal summer ( Servain 1991 ; Tokinaga and Xie 2011 ; Carton and Huang 1994 ; Zebiak 1993 ; Shannon et al. 1986 ). Yet, the identification of tropical Atlantic modes of SST variability has also benefited from the use of statistical techniques, such as rotated empirical orthogonal functions (rEOFs). The resulting modes using rEOFs have

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

wind anomalies (contours) correlated against intraseasonal OLR at the Indian Ocean reference box (10°S–5°N, 75°–100°E) for (a) observations and (b) CCSM4. We next use the Wheeler and Hendon (2004 , hereafter WH04 ) technique to extract the dominant MJO spatial and temporal modes. Combined EOFs (CEOFs), using OLR, U850, and U200, each bandpassed to the 20–100-day-period band, are computed. This multivariate approach isolates the convective and baroclinic zonal wind signature of the MJO. We

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Jenny Lindvall, Gunilla Svensson, and Cecile Hannay

is in the midlatitudes, it is located on a high altitude and therefore qualifies as a boreal climate site ( Li et al. 2005a ). There are three hot, semiarid sites used in our study. One is Maun, a semiarid shrubland site in Botswana. On the Northern Hemisphere we have included two semi-arid sites: Audubon Grasslands and Santa Rita Mesquite both located in Arizona. At the flux tower sites, turbulent fluxes are measured in 30- or 60-min intervals using the eddy-covariance technique (e.g., Aubinet

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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

evaluate the multiyear ice cover simulated by the CCSM4, we make use of the sea ice “age” dataset of Fowler et al. (2003) and described further by Maslanik et al. (2007 , 2011) , Tschudi et al. (2010) and Stroeve et al. (2011) . To summarize the Fowler et al. (2003) approach briefly, ice movement is calculated using a cross-correlation technique applied to sequential, daily satellite images acquired by the SMMR and SSM/I passive microwave brightness temperature sensors, and visible and thermal

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