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Peter R. Gent, Gokhan Danabasoglu, Leo J. Donner, Marika M. Holland, Elizabeth C. Hunke, Steve R. Jayne, David M. Lawrence, Richard B. Neale, Philip J. Rasch, Mariana Vertenstein, Patrick H. Worley, Zong-Liang Yang, and Minghua Zhang

1850 control and (b) CCSM3 1870 control; both 〈871–900〉. The black lines are the 10% mean concentration values from SSM/I observations ( Cavalieri et al. 1996 ) 〈1979–2000〉. 5. Tropical Pacific climatology and variability a. Precipitation Figure 4 shows frequency versus daily precipitation rate over land in the tropics between 20°N and S. Results from one of the 1° and 2° CCSM4 and T85 CCSM3 twentieth-century runs are plotted with the observational estimates from the Global Precipitation

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Richard B. Neale, Jadwiga Richter, Sungsu Park, Peter H. Lauritzen, Stephen J. Vavrus, Philip J. Rasch, and Minghua Zhang

partitioning of parameterized convective rainfall and resolved stratiform rainfall. Figure 3 shows that CAM3 has a stratiform contribution to tropical rainfall that is below 20% throughout most of the precipitating tropics. This would seem to be low given existing TRMM estimates ranging between 35% and 55% ( Dai 2006 ). The largest impact on increasing the ratio of resolved tropical precipitation comes from the inclusion of DCAPE. This results in an increase in averaged resolved precipitation by around 6

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

1. Introduction Four decades have passed since Madden and Julian made the pioneering discovery of a 40–50-day oscillation in the zonal winds in the tropics ( Madden and Julian 1971 , 1972 ). This discovery has led to numerous studies of a phenomenon now aptly called the Madden–Julian oscillation (MJO). Although MJO dynamics are still not fully understood ( Madden and Julian 1994 ; Zhang 2005 ), MJO is known to interact with a panoply of climate phenomena across different spatial and temporal

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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

winter imparts a “footprint” on the ocean through changes in surface heat fluxes. The SST footprint, which peaks in spring and persists through summer in the subtropical Pacific, impacts the atmospheric circulation including zonal wind stress and surface energy fluxes that extend deep into the tropics. These anomalies excite a response in the central and eastern equatorial Pacific Ocean in the subsequent fall and winter that influence ENSO and decadal tropical variability ( Vimont et al. 2001 , 2003

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

Danabasoglu (2006) and Chang et al. (2007) pointed out that major atmospheric pressure centers and all global-scale surface wind systems are stronger than observed. In the northern tropics, this excess wind forcing results in excess surface heat loss. Despite the excess winds, the SST in the southeastern tropics is too warm. In CCSM3, the SST warm bias in the southeast has been attributed to the remote impact of erroneously weak zonal surface winds along the equator because of a deficit of rainfall

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

°-resolution ocean model going down to about ¼° in the equatorial tropics. As noted above, characteristics of the worldwide monsoon simulations in CCSM3 were described by M06 . CCSM4 includes a finite-volume 1° version of the atmospheric model CAM4, with improved components of ocean, land, and sea ice compared to CCSM3 ( Gent et al. 2011 ). Grid points in the atmosphere are spaced roughly every 1° latitude and longitude, and there are 26 levels in the vertical. The ocean is a version of the Parallel Ocean

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

. (2012) and will be the topic of a future study. 2. Model and observed data descriptions The standard CCSM3 (e.g., Collins et al. 2006 ) will be compared to the new CCSM4 ( Gent et al. 2011 ). The CCSM3 had a T85 atmospheric model with 26 levels in the vertical and was coupled to land and sea ice components as well as a nominal 1°-resolution ocean model going down to about ¼° in the equatorial tropics. As noted above, characteristics of the Asia–Australia monsoon simulations in CCSM3 were described

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A. Gettelman, J. E. Kay, and K. M. Shell

. The vertical structure in the two models is also similar for both feedbacks. Lapse rate feedbacks peak in impact in the tropics at 250 hPa because the change in temperature increases with height up to about 250 hPa ( Santer et al. 2005 ). Temperature feedbacks peak at the level where cloud tops are exposed to space ( Soden et al. 2008 ). The lapse rate feedback difference between the two versions of CAM (−0.08 W m −2 K −1 ) nearly cancels the difference in the water vapor feedback (0.10), in

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A. Gettelman, J. E. Kay, and J. T. Fasullo

; Webb et al. 2012 ). Figure 1 shows the zonal annual mean climatological values of longwave ( Fig. 1a ) and shortwave ( Fig. 1a ) CRE from all 21 base (1 × CO 2 ) runs in comparison with satellite observations from the Energy Balance Adjusted Flux (EBAF) product, version 2.6, from the Clouds and the Earth’s Radiant Energy System satellite instrument ( Loeb et al. 2009 ). There are significant differences between the simulations (up to 40 W m −2 in the SW component in the tropics, partially

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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

same as observations from FLUXNET, although in the tropics there was a significant positive bias ( Bonan et al. 2011 ). The combined soil carbon and productivity estimates from CLM indicated that soil organic turnover times in the model were likely too small, with rapid carbon cycling contributing to the low bias in soil carbon stocks. We also note that the CESM runs analyzed here did not include dynamic vegetation changes, which may have limited the magnitude of simulated changes in regional

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