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S. J. Ghan, X. Liu, R. C. Easter, R. Zaveri, P. J. Rasch, J.-H. Yoon, and B. Eaton

Boucher 2000 ; Myhre 2009 ), indirect effects ( Lohmann and Feichter 2005 ), and semidirect effects ( Hansen et al. 1997 ; Koch and Del Genio 2010 ). The term aerosol direct effects refers to the direct impact of anthropogenic aerosol particles on the planetary energy balance through scattering, absorption, and emission of radiation in the atmosphere, without consideration of the aerosol effects of the radiative heating on clouds. Aerosol indirect effects refer to the impact through the

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Jennifer E. Kay, Marika M. Holland, Cecilia M. Bitz, Edward Blanchard-Wrigglesworth, Andrew Gettelman, Andrew Conley, and David Bailey

and response . Climate Dyn. , 20 , 415 – 429 . de Boer , G. , and Coauthors , 2012 : A characterization of the present-day Arctic atmosphere in CCSM4 . J. Climate , 25 , 2676 – 2695 . Donohoe , A. , and D. S. Battisti , 2011 : Atmospheric and surface contributions to planetary albedo and their relationship to the total meridional energy transport . J. Climate , 24 , 4401 – 4417 . Gent , P. R. , and Coauthors , 2011 : The Community Climate System Model, version 4 . J

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Samuel Levis, Gordon B. Bonan, Erik Kluzek, Peter E. Thornton, Andrew Jones, William J. Sacks, and Christopher J. Kucharik

1. Introduction Past studies indicate that managed and unmanaged terrestrial ecosystems interact with the atmosphere and other components of the earth system through a variety of biogeophysical and biogeochemical processes and characteristics. Levis (2010) reviews this topic. In the present study we consider such effects by simulating certain managed ecosystems. Managed ecosystems add to simulations of the earth system the uncertainty of human interference. Numerous climate-modeling studies

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

, and conclusions are in section 7 . 2. Methodology We apply radiative kernels calculated offline to the climate response in doubled CO 2 experiments with atmospheric GCMs coupled to slab ocean models (SOMs). In CESM, SOM experiments yield results very similar to atmospheric models coupled to a full dynamic ocean ( Bitz et al. 2012 ). For feedbacks attributed to atmospheric physical parameterizations, the same feedbacks found in the SOM runs can be diagnosed with stand-alone atmosphere model SST

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

1. Introduction The coupling between the atmosphere and ocean is a major player in the earth’s climate system and governor of climate change. The former has a limited capacity to store water and heat but is connected to the ocean, which is effectively an infinite reservoir of water and has more heat capacity in only its upper few meters than exists in the entire atmosphere. The direct coupling of the planetary boundary layers (PBLs) is accomplished through the air–sea fluxes. In nature, the

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Jenny Lindvall, Gunilla Svensson, and Cecile Hannay

biogeochemical systems are mainly through near-surface variables. Thus, it is of interest to evaluate them in order to assess biases and possible model deficiencies. Few studies that evaluate the performance of planetary boundary layer (PBL) parameters in GCMs are found in the literature. Some very early studies include Boer et al. (1991) and Randall et al. (1992) . At the time of these intercomparisons, most models did not resolve the diurnal variation in solar insolation and only had a few vertical grid

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

simulations will be compared to the Atmospheric Model Intercomparison Project (AMIP)-type Community Atmosphere Model, version 4 (CAM4) atmosphere-only runs to show how coupling changes the monsoon simulations. Additionally, comparisons will be made where appropriate to the previous generation of this model (CCSM3) to document any changes or improvements to the monsoon simulations. The monsoons in CCSM3 were previously described by Meehl et al. (2006) and can also be compared to monsoon simulations in a

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

) found sensitivity in a single GCM was altered by changes to ice microphysics in the Southern Hemisphere storm track. Soden and Vecchi (2011) looked at the spatial distribution of feedbacks in a multimodel ensemble, and Taylor et al. (2011a) decomposed feedbacks spatially in a single GCM. Taylor et al. (2011b) examined the seasonality of feedbacks. This study uses experiments from an atmosphere–ocean GCM to explore which feedbacks determine climate sensitivity and what processes affect these

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

2 models had MJO variance comparable to observations but that many other MJO features were lacking realism even in these models. Kim et al. (2009) studied a recent set of global models and noted that only two of them, the super-parameterized Community Atmosphere Model (SPCAM) and ECHAM4/Ocean Isopycnal Model (OPYC), yielded a respectable representation of MJO. The aforementioned multimodel studies attempted to provide insight into what is important for MJO simulation by comparing the different

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Synte Peacock

The general circulation model used in this study is the most recent version of CCSM4, which consists of active atmosphere, ocean, land, and sea ice components. A preindustrial control simulation with fixed CO 2 (284.7 ppm), fixed incoming solar radiation at the top of the atmosphere (1360.9 W m −2 ), and prescribed aerosols (black and organic carbon, sulfate, dust, and sea salt) was run for 1300 years to achieve an equilibrium climate and initial conditions for the twentieth-century simulations

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