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Clara Deser, Adam S. Phillips, Robert A. Tomas, Yuko M. Okumura, Michael A. Alexander, Antonietta Capotondi, James D. Scott, Young-Oh Kwon, and Masamichi Ohba

2008 ), the pan-Pacific mode ( Guan and Nigam 2008 ), and the North Pacific gyre oscillation ( Di Lorenzo et al. 2008 ). Investigation of these additional patterns in the Community Climate System Model version 4 (CCSM4) is beyond the scope of this study. Various mechanisms have been hypothesized to account for PDV, including ENSO and associated atmospheric teleconnections to the North Pacific, forcing by random fluctuations in the Aleutian low pressure system, oceanic Rossby wave adjustment to wind

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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

time step. Thus, the restoring flux does not contribute to the global salt budget. For OCN, we use the 20-yr mean corresponding to yr 1986–2005 (as in CCSM4) from the fourth forcing cycle in our analysis. 3. Model solutions a. Model drift and deep θ and S By the end of the 1300-yr CCSM4 1850 CONTROL simulation, the global volume-mean potential temperature 〈 θ 〉 is down to 3.13°C in the ocean model, representing a cooling of 0.42°C from the initial conditions. This cooling largely reflects the TOA

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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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Wilbert Weijer, Bernadette M. Sloyan, Mathew E. Maltrud, Nicole Jeffery, Matthew W. Hecht, Corinne A. Hartin, Erik van Sebille, Ilana Wainer, and Laura Landrum

significant reductions in sea ice extent in most of the 20C ensemble members, although even the late twentieth-century sea ice extent is still significantly larger than observed ( Landrum et al. 2012 ). Significant trends are present in many other metrics ( Fig. 3 ; Table 1 ). Despite large ensemble spread and interannual variability, several features appear consistently among ensemble members. This suggests a common response to external forcing variability like greenhouse gasses or ozone concentrations

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

Dai et al. (2004) discharge estimates. The model was forced by four cycles of the CORE version 2 interannual forcing data ( Large and Yeager 2009 ), which span the 60-yr period from 1948 to 2007. The POP-CORE data analyzed are from the fourth (last) forcing cycle. No data assimilation is done in this POP-CORE simulation. The SSTs were not restored to observations. Although, as described in Griffies et al. (2009) , bulk formulas are used to compute turbulent heat fluxes from a prescribed, time

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

Budget of the Arctic (SHEBA) ( Uttal et al. 2002 ). 3. Overview of simulations Two T31x3 simulations are presented—a preindustrial control simulation ( a.d. 1850 forcing) of 500 years in length and a modern twentieth-century transient simulation ( a.d. 1850–2005 forcing). In sections 4 and 6 , the T31x3 preindustrial simulation is compared to the FV2x1 preindustrial control as a means to show the viability of the T31x3 model as an alternative to the FV2x1 model. However, so as to evaluate the T

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Gretchen Keppel-Aleks, James T. Randerson, Keith Lindsay, Britton B. Stephens, J. Keith Moore, Scott C. Doney, Peter E. Thornton, Natalie M. Mahowald, Forrest M. Hoffman, Colm Sweeney, Pieter P. Tans, Paul O. Wennberg, and Steven C. Wofsy

-dimensional structure of CO 2 in earth system models. Atmospheric CO 2 is an integrator of surface fluxes, and its variations on seasonal and longer time scales reflect the terrestrial and oceanic processes that control carbon–climate feedbacks ( Randerson et al. 2009 ; Cadule et al. 2010 ). Although CO 2 perturbations in the atmosphere persist long enough to be well mixed in terms of radiative forcing, small horizontal and vertical gradients in atmospheric CO 2 allow for the diagnosis of regional to

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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

. A closer analysis of the sea ice changes in the individual ensemble members reveals that in one of the two ensemble members where the sea ice extent does not show a significant negative trend over 1981–2005, the ice in the Arctic stays relatively old up until 2005, resulting from the specific variability of the atmospheric forcing in this run. This older and thicker Arctic ice cover leads to a smaller decrease of the September ice extent because the area covered by less than 15% sea ice does not

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Kerry H. Cook, Gerald A. Meehl, and Julie M. Arblaster

Program (POP) with a nominal latitude–longitude resolution of 1° (down to ¼° in the equatorial tropics) and 60 levels in the vertical. No flux adjustments are used in either CCSM3 or CCSM4. Experiments analyzed include twentieth-century simulations with a combination of anthropogenic and natural forcings and a multicentury preindustrial control run ( Gent et al. 2011 ). Atmospheric Model Intercomparison Project (AMIP) simulations with CAM4 were run with observed monthly mean SSTs from 1979 to 2005 and

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