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

1. Introduction Anthropogenic aerosol is thought to play an important role in driving climate change, but its role is so complex that uncertainty in estimates of radiative forcing of climate change is dominated by uncertainty associated with forcing by anthropogenic aerosol ( Forster et al. 2007 ). This complexity arises because anthropogenic aerosol alters the planetary energy balance through a variety of mechanisms operating across a wide range of spatial scales: direct effects ( Haywood and

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

1. Introduction The earth’s climate system is being perturbed by anthropogenic radiative forcing. In addition to direct radiative forcing of the system (e.g., from anthropogenic greenhouse gases), the responses to radiative forcing (surface and atmospheric temperature changes) cause feedbacks within the system that amplify or damp the changes ( Schneider 1972 ; Cess et al. 1990 ; Bony et al. 2006 ). Increases in temperature allow the specific humidity to increase, which increases the

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Esther C. Brady, Bette L. Otto-Bliesner, Jennifer E. Kay, and Nan Rosenbloom

versions of the models [e.g., for the Community Climate System Model (CCSM), see Shin et al. (2003) and Otto-Bliesner et al. (2003 , 2006 ); for PMIP models, see Table 1 and Otto-Bliesner et al. (2009) ]. Using the same version for both past and future climate change experiments should enhance progress in addressing some of the outstanding scientific questions of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). Table 1. Summary of forcings, boundary conditions

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Gerald A. Meehl, Warren M. Washington, Julie M. Arblaster, Aixue Hu, Haiyan Teng, Claudia Tebaldi, Benjamin N. Sanderson, Jean-Francois Lamarque, Andrew Conley, Warren G. Strand, and James B. White III

these sets of experiments. In particular, the focus will be on the climate system response to various external forcings, both natural (volcanoes and solar) and anthropogenic [greenhouse gases (GHGs), ozone, land use, sulfate aerosols and carbon (both primary organic and black)] aerosols. Where appropriate, comparisons will be made to previous versions of the model, particularly CCSM3, to show where and how the CCSM4 simulations differ from CCSM3. Results from the climate change projections from CCSM

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

1. Motivation and research questions Arctic amplification, broadly defined as greater-than-global Arctic warming in response to external forcing and/or internal climate variability, is ubiquitous in climate models and observations ( Manabe and Stouffer 1980 ; Miller et al. 2010 ; Serreze and Barry 2011 ). Despite a long and rich history of numerical model experiments and observational analysis, the relative importance of processes controlling Arctic amplification is still subject to debate

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C. M. Bitz, K. M. Shell, P. R. Gent, D. A. Bailey, G. Danabasoglu, K. C. Armour, M. M. Holland, and J. T. Kiehl

1. Introduction Equilibrium climate sensitivity (ECS) is an often used metric to evaluate the climate response to a perturbation in the radiative forcing. It is specifically defined as the equilibrium change in global mean surface air temperature that results from doubling the concentration of carbon dioxide (CO 2 ) in the atmosphere ( IPCC 1990 ). In this study we investigate how the new Community Climate System Model, version 4 (CCSM4) responds to doubling CO 2 compared to the previous

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Gerald A. Meehl, Warren M. Washington, Julie M. Arblaster, Aixue Hu, Haiyan Teng, Jennifer E. Kay, Andrew Gettelman, David M. Lawrence, Benjamin M. Sanderson, and Warren G. Strand

, and volcanic aerosols are held constant at year 2005 values. The latter could of course produce somewhat of an overestimate of projected warming since there will be some volcanic eruptions during the twenty-first century. But because the location, timing, and magnitude of future volcanic eruptions are inherently unpredictable, their effects are not included. But in relative terms, this is likely not a big effect given the uncertainty of other future forcings. In addition to these simulations, we

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Stephen J. Vavrus, Marika M. Holland, Alexandra Jahn, David A. Bailey, and Benjamin A. Blazey

of 2007–10 having the four lowest ice extents on record ( Perovich et al. 2010 ). Because these kinds of observed changes are consistent with the Arctic’s response to greenhouse forcing projected by climate models, there is widespread concern that the recent behavior is a harbinger of more transformative regional climate change this century that may reverberate globally. The Arctic is considered to be the most climatically sensitive area in the world ( Solomon et al. 2007 ), and there is both

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

Cubasch 2007 ) have typically been able to simulate a connection between orbital forcing, temperature, and snow volume. So far, however, fully coupled, nonflux-corrected primitive equation general circulation models (GCMs) have failed to reproduce glacial inception, the cooling and increase in snow and ice cover that leads from the warm interglacials to the cold glacial periods (e.g., Vettoretti and Peltier 2003 ; Born et al. 2010 ; Jochum et al. 2010 , hereafter JPML ). Milankovitch (1941

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William H. Lipscomb, Jeremy G. Fyke, Miren Vizcaíno, William J. Sacks, Jon Wolfe, Mariana Vertenstein, Anthony Craig, Erik Kluzek, and David M. Lawrence

. 2007 ) projected 0.18 to 0.59 m of sea level rise by 2100 but excluded ice sheet dynamical feedbacks because existing ice sheet models were deemed inadequate. Semiempirical models (e.g., Rahmstorf 2010 ; Jevrejeva et al. 2010 ) typically estimate twenty-first-century sea level rise of ~0.5 to 2.0 m, based on the assumption that the rate of SLR is linearly proportional to changes in global-mean temperature or radiative forcing. The differences between process-based and semiempirical projections

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