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

represent anthropogenic aerosol effects on climate cannot be determined and described in a single manuscript. Liu et al. (2012) describe and evaluate two representations of the aerosol, one suitable for century climate simulations, the other more detailed. In this study we focus on the sensitivity of the estimated aerosol forcing to simplifications in the representation of the aerosol in the model, with an emphasis on distinguishing between radiative forcing mechanisms (direct, semidirect, indirect

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Kirsten Zickfeld, Michael Eby, Andrew J. Weaver, Kaitlin Alexander, Elisabeth Crespin, Neil R. Edwards, Alexey V. Eliseev, Georg Feulner, Thierry Fichefet, Chris E. Forest, Pierre Friedlingstein, Hugues Goosse, Philip B. Holden, Fortunat Joos, Michio Kawamiya, David Kicklighter, Hendrik Kienert, Katsumi Matsumoto, Igor I. Mokhov, Erwan Monier, Steffen M. Olsen, Jens O. P. Pedersen, Mahe Perrette, Gwenaëlle Philippon-Berthier, Andy Ridgwell, Adam Schlosser, Thomas Schneider Von Deimling, Gary Shaffer, Andrei Sokolov, Renato Spahni, Marco Steinacher, Kaoru Tachiiri, Kathy S. Tokos, Masakazu Yoshimori, Ning Zeng, and Fang Zhao

greenhouse gas and aerosol forcing linearly decreased from year-2005 values to zero by 2300. All model simulations are summarized in Table 1 . Table 1. Model experiments. 3. Results and discussion a. Historical simulation The performance of EMICs over the historical period is discussed in detail in Eby et al. (2013) . Here, we briefly summarize the main findings to allow the reader to put the EMICs' future projections and their uncertainty ranges into perspective. Over the twentieth century EMICs

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

, S. , and Coauthors , 2006 : How well do we understand and evaluate climate change feedback processes? J. Climate , 19 , 3445 – 3482 . Bretherton , C. S. , and S. Park , 2009 : A new moist turbulence parameterization in the Community Atmosphere Model . J. Climate , 22 , 3422 – 3448 . Cess , R. D. , and Coauthors , 1990 : Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models . J. Geophys. Res. , 95 , 16 601 – 16 615

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Keith Oleson

floor is pervious. The performance of the model has been evaluated against measured fluxes and temperatures from urban flux tower sites for short observation periods (less than a week) by Oleson et al. (2008a) and Oleson et al. (2010c) and reproduces known qualitative features of urban climatology, including UHIs ( Oleson et al. 2008b , 2010a ). Evaluation of the present day UHI and its latitudinal and seasonal variability has been performed by Oleson et al. (2008b) , Oleson et al. (2010a

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

. In this study, we evaluate the performance of a 20-yr run of CCSM4 in reproducing the primary characteristics of MJO, based on diagnostics established by the CL-MJOWG08. The CCSM4 model produces coherent, broadbanded, and energetic patterns in eastward-propagating intraseasonal zonal winds and OLR in the tropical Indian and Pacific Oceans that are generally consistent with MJO characteristics. Strong peaks occur in power spectra and coherence spectra with periods between 20–100 days and zonal

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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

–carbon feedbacks (e.g., Thornton et al. 2009 ; Arora and Montenegro 2011 ). These models allow us to estimate likely CO 2 levels that correspond to particular fossil fuel emission pathways and to better represent uncertainty in climate projections resulting from carbon cycle processes. Important challenges in this regard are evaluating ESM performance and understanding how biases in model structure influence future projections. Here, we developed several new approaches for evaluating the three

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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

and strength of the Beaufort Gyre, and the deep Arctic Ocean temperatures. A similar analysis focused on the Arctic atmospheric CCSM4 simulations is shown in de Boer et al. (2012) . The knowledge about CCSM4’s performance for several key variables of the Arctic climate will allow a critical evaluation of twenty-first-century simulations, as, for example, described in Vavrus et al. (2012) , and should also be a useful reference for other Arctic climate studies with the CCSM4. 2. Model a. CCSM4

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David M. Lawrence, Andrew G. Slater, and Sean C. Swenson

results, we identify priorities for further model improvement. 2. Description of models and simulations a. CCSM4 and CLM4 CCSM4 is a global climate model consisting of individual models of the atmosphere, land, ocean, and sea ice that are coupled together. An overview of CCSM4 and its performance relative to CCSM3 is provided in Gent et al. (2011) . Assessments of CCSM4 that are relevant to this study and that are included in the CCSM4 Journal of Climate Special Collection include assessments of

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Laura Landrum, Bette L. Otto-Bliesner, Eugene R. Wahl, Andrew Conley, Peter J. Lawrence, Nan Rosenbloom, and Haiyan Teng

threefold. First, the simulations would allow an evaluation of the capability of models to capture observed variability on multidecadal and longer time scales. Second, they would—through comparison to long CMIP5 “unforced” preindustrial control simulations—provide a means of assessing what portion of the variability is attributable to external forcing and what portion reflects purely internal variability. And third, the simulations would be useful in providing a longer-term perspective for detection and

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Markus Jochum, Alexandra Jahn, Synte Peacock, David A. Bailey, John T. Fasullo, Jennifer Kay, Samuel Levis, and Bette Otto-Bliesner

be the culprit, one would have to wonder if climate is sufficiently understood to assemble a GCM in the first place. Either way, it appears that reproducing the observed glacial–interglacial changes in ice volume and temperature represents a good test bed for evaluating the fidelity of some key model feedbacks relevant to climate projections. The potential causes for GCMs failing to reproduce inception are plentiful, ranging from numerics ( Vettoretti and Peltier 2003 ) on the GCMs side to

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