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

at cooler temperatures, causing warming. Low clouds cool and high clouds warm, with the balance of effects being a net cooling ( Stephens 2005 ). Changes to cloud amount, location, and radiative properties (e.g., optical depth) can exert feedbacks on the system. Any of these feedbacks may significantly alter the magnitude of the response to radiative forcing. The water vapor feedback, for example, is large and positive ( Held and Soden 2000 ). While it is straightforward to calculate the direct

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

. But Ghan and Easter (2006) showed that for the purpose of characterizing aerosol radiative forcing it is sufficient to distinguish between interstitial aerosol and particles attached to cloud droplets, without treating transport of cloudborne particles. Given a representation of the aerosol size distribution, mixing state, attachment state, composition, and internal structure, the next question is which processes in the aerosol life cycle need to be represented to accurately simulate the

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J. E. Kay, B. R. Hillman, S. A. Klein, Y. Zhang, B. Medeiros, R. Pincus, A. Gettelman, B. Eaton, J. Boyle, R. Marchand, and T. P. Ackerman

geographic region (e.g., B11 ). Section 2 describes our methodology including a description of the COSP implementation in CAM, the CAM runs analyzed, and the cloud evaluation strategy. Section 3 contains the results. We identify compensating errors between cloud fraction and cloud optical properties that are not apparent from the radiative fluxes or cloud forcing alone. We also find that, while it has deficiencies, the most recent CAM version, CAM5 ( Neale et al. 2011a ), has significantly reduced

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

equilibrium Arctic response to 2 × CO 2 differences between the coupled model experiments? We find that the atmospheric model physics is more important than the complexity of the ocean model (slab ocean model versus full-depth ocean model) for explaining Arctic surface warming and amplification differences. In particular, the 2 × CO 2 forcing and the influence of Arctic clouds on shortwave feedbacks explain the largest identified greenhouse warming differences. 2. Coupled climate model experiments

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

omitting the clouds from the radiation calculation)—more than 4 times the original signal. The snow/ice–albedo feedback is then calculated as 6.7 W m −2 (8.6–1.9 W m −2 ). Interestingly, the low cloud cover is smaller in OP115 than in CONT, reducing the difference in total TOA shortwave radiation by 3.1 to 5.5 W m −2 (green line). Summing up, an initial forcing of 1.9 W m −2 north of 60°N, is amplified through the snow–ice–albedo feedback by 6.7 W m −2 , and damped through a negative cloud feedback

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Kevin Raeder, Jeffrey L. Anderson, Nancy Collins, Timothy J. Hoar, Jennifer E. Kay, Peter H. Lauritzen, and Robert Pincus

example, tracers, which must be nonnegative, or cloud fraction, which must remain between 0 and 1. The spread inflation algorithm can push the values of these variables outside of their ranges for some ensemble members. In many cases the damage from this is minimal because CAM will reset them to the proper range when it starts, but if such variables are the primary interest of the assimilation ( Pincus et al. 2011 ), DART includes a rank histogram assimilation algorithm, which can accommodate such

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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

.5 also had a double ITCZ so that just increasing the atmospheric resolution may not eliminate the double ITCZ; further parameterization improvements are almost certainly required. The CCSM4 still has biases compared to observations in the latitudinal distribution of both the shortwave and longwave cloud forcing (not shown). Unfortunately, these biases do not get smaller when the higher horizontal resolution of 0.5° is used in the atmospheric component because the cloud distribution in CCSM4 is not

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

response) is usually taken to be “feedback,” and the former is included in the “forcing” ( Gregory and Webb 2008 ). We have performed separate calculations with SST changes only (feedback or slow response) to verify that, for cloud feedbacks, almost all of the response is due to the SST change alone. The net (LW + SW) ΔCRE changes by 1.14 W m −2 for doubling CO 2 and changing SST together and by 1.21 W m −2 for changing SST only (feedback), whereas the CO 2 -only (fast response) change is −0.02 W m

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

fraction, and cloud radiative forcing The application of the new convection changes, the inclusion of freeze-drying processes, and the improved equilibrium cloud state all lead to changes in the latitudinal distribution of cloud fraction and associated cloud radiative forcings. Figure 6 shows significant decreases in both total and low cloud fraction from CAM3 to CAM4. The most significant decreases are over Northern Hemisphere mid- to high-latitude regions in the transition from CAM3-DIL to CAM3

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

of temperature, sea ice, precipitation, cloud amount, sea level pressure, and the upper ocean, while leaving most of the assessment of CCSM4’s simulated present-day Arctic climate for two related papers ( de Boer et al. 2012 ; Jahn et al. 2012 ). We also only consider the model response to the strongest greenhouse forcing among the representative concentration pathways (RCPs) being used in the new Coupled Model Intercomparison Project, version 5 (CMIP5). This high-end scenario is called RCP8

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