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Samantha Stevenson, Baylor Fox-Kemper, Markus Jochum, Richard Neale, Clara Deser, and Gerald Meehl

-amplified stochastic anomalies ( Thompson and Battisti 2001 ; Gebbie et al. 2007 ; Jin et al. 2006 ). Approaching the problem from a different direction, there have been numerous studies of ENSO-relevant dynamics that are likely to be affected by global warming ( Bony and Dufresne 2005 ; Liu and Philander 1995 ; McPhaden and Zhang 2002 ; Sun 2003 ). It is possible that external forcing might lead to a change in the types of El Niño–La Niña events that could be expected in the future, and much attention has

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

–sea flux fields should become a necessary part of any comprehensive assessment of coupled model skill, because they reflect coupling mechanisms. Our approach to the problems with observed fluxes is to compare model fields with the best available component flux data obtained from multiple sources as advocated by Gleckler et al. (2008) . The flux dataset computed from the forcing developed for the Coordinated Ocean-Ice Reference Experiments (CORE) combined with the Hurrell et al. (2008) SST product is

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

package computes an overall error measure from the Taylor diagram ( Taylor 2001 ) from 10 key diagnostics (including sea level pressure, cloud forcing, rainfall, temperature, wind stress, zonal wind, and relative humidity) and normalized by the results from the previous CAM, version 3.5. The root-mean-square error (RMSE), not including the bias, is 0.920 for CAM-SE and 0.937 for CAM-FV, showing that CAM, version 4, represents an 8% improvement in RMSE over version 3.5, and CAM-SE obtains a slight

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

that this under representation of shortwave energy at the surface is linked to high-latitude cold surface temperatures because this missing tropical energy is not available to be transported to higher latitudes. To test this hypothesis, cloud properties were tuned to allow more shortwave radiation to reach the tropical surface, thus forcing the model to agree better with observational estimates of the surface shortwave budget (to locally within 10 W m −2 of observations). The tropical surface air

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

that the magnitude of the SST bias is related to differences in the convection parameterization in atmospheric models. In addition to remote mechanisms, the impact of local meridional winds and upwelling on the Benguela SST has been discussed by Large and Danabasoglu (2006) . They show that the warm bias in eastern boundary upwelling regions in the Community Climate System Model (CCSM) is due to a combination of weak ocean currents, weak upwelling, weak alongshore wind, too little stratus cloud

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Gerald A. Meehl, Julie M. Arblaster, Julie M. Caron, H. Annamalai, Markus Jochum, Arindam Chakraborty, and Raghu Murtugudde

flux adjustments are used in either CCSM3 or CCSM4. Experiments analyzed will include twentieth-century simulations with a combination of anthropogenic and natural forcings and a multicentury preindustrial control run ( Gent et al. 2011 ). AMIP simulations with CAM4 were run with observed monthly mean SSTs from 1979 to 2005. For validating the ENSO–monsoon association we use the observed all-India rainfall (AIR) index of Parthasarathy et al. (1994) and Australian land-based monsoon indices from

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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

atmospheric model is the Community Atmospheric Model, version 4 (CAM4), which has a horizontal resolution of 1.25° × 0.9° and 26 layers in the vertical [see R. B. Neale et al. (2011, personal communication) for a detailed documentation of CAM4]. Among the many important improvements in CAM4, the most important for the Arctic climate is the addition of a freeze-dry modification, which reduces the amount of wintertime low clouds in the Arctic ( Vavrus and Waliser 2008 ). The land model of the CCSM4 is the

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Synte Peacock

are identical. For the simulations analyzed in this study, all model components have a nominal horizontal resolution of 1°. For the twenty-first century, simulations were carried out using four different RCPs ( Moss et al. 2010 ): RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The numerical value assigned to each RCP indicates the approximate radiative forcing in the year 2100 in the absence of climate feedbacks (e.g., RCP8.5 has specified greenhouse gases and aerosol trajectories consistent with a radiative

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