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A. E. Dessler

1. Introduction Feedbacks change the top-of-atmosphere (TOA) net energy balance in response to a change in surface temperature, thereby altering the warming required to reestablish equilibrium. In fact, it turns out that feedbacks, rather than direct heating from greenhouse gases, are responsible for the majority of the warming we expect over the coming century. Because of this, validating the representation of feedbacks in global climate models (GCMs) is of great interest to the scientific

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Zhengyu Liu and Na Wen

1. Introduction One of the most challenging aspects of the climate system is the interaction between the atmosphere and its lower boundary ocean and land. [Hereafter, the lower boundary will be referred to as the sea surface temperature (SST).] It is well known that the atmosphere exerts a strong control on SST variability. It is also known that the SST variability, in turn, can generate significant feedback response in the atmosphere through local and remote processes. This full climate

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J. M. Gregory, C. D. Jones, P. Cadule, and P. Friedlingstein

resolution typically of 2°–3°. Despite the complexity of the system, model results indicate that its global-mean behavior can be described in rather simple terms as linear responses and feedbacks. The resulting conceptual framework is useful for comparison of the models with one another and the real world. In recent years, increasing attention has been paid to the possible responses of terrestrial ecosystems and ocean biogeochemistry to increasing atmospheric CO 2 concentration and the consequent

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Gerard H. Roe and Marcia B. Baker

1. Introduction Early efforts to represent the earth’s climate with energy balance models (EBMs) uncovered the disconcerting possibility that a relatively small decrease in the solar output might lead to a catastrophic global glaciation—the result of a runaway ice albedo feedback (e.g., Budyko 1969 ; North 1975 ; Lindzen and Farrell 1977 ). Although the issue remains controversial (e.g., Kerr 2000 ; Fairchild and Kennedy 2007 ; Allen and Etienne 2008 ), assorted lines of geological

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Ping Zhu, James J. Hack, and Jeffrey T. Kiehl

1. Introduction It has long been recognized that the radiative effects of clouds play an important role in regulating the earth’s energy budget and modulating anthropogenic climate changes. Although cloud–climate feedbacks have been intensely studied in the past decades, the sign of net cloud feedback is still a matter of uncertainty as concluded by the Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC; Houghton et al. 2001 ). Understanding the mechanisms of

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Kirsten Zickfeld, Michael Eby, H. Damon Matthews, Andreas Schmittner, and Andrew J. Weaver

atmospheric CO 2 and the response to climate change. The current generation of coupled climate–carbon cycle models simulates increases in carbon uptake in response to elevated CO 2 levels ( Friedlingstein et al. 2006 ; Plattner et al. 2008 ; Gregory et al. 2009 ; Boer and Arora 2009 ). This response slows the rate of atmospheric CO 2 increase and hence results in a negative feedback. This feedback will in the following be referred to as the “concentration–carbon cycle” feedback ( Boer and Arora 2009

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Chen Zhou, Mark D. Zelinka, Andrew E. Dessler, and Ping Yang

, resulting in a feedback to the global climate system. Uncertainty in the magnitude of this cloud feedback is one of the primary contributors to the large spread of climate sensitivity estimated by general circulation models (GCMs; e.g., Dufresne and Bony 2008 ). Therefore, improving our understanding of the cloud feedback is an important step for improving our confidence in predictions of future climate change. Many previous studies have examined the cloud feedback in GCMs in response to long

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Guosen Chen

development of the system. The temperature feedback, manifested in the temperature tendency term, dominates the thermodynamics in these theories, while the moisture feedback, manifested in the moisture tendency term, was often neglected by assuming the balance between low-level moisture convergence and precipitation [except Emanuel (1987) , who used moisture entropy equation that incorporated both temperature and moisture tendencies]. The MJO modes in these early theories often have faster propagation

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Brian J. Soden, Isaac M. Held, Robert Colman, Karen M. Shell, Jeffrey T. Kiehl, and Christine A. Shields

1. Introduction Climate models exhibit a range of sensitivities in response to increased greenhouse gases due to differences in feedback processes that amplify or damp the initial radiative perturbation ( Cubasch and Cess 1990 ). Analyzing these feedbacks is therefore of central importance to our understanding of climate sensitivity. However, the analysis of feedbacks in GCMs has a potential for ambiguity not present in the analysis of simple steady-state models, owing to the time dependence

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A. Gettelman and Q. Fu

1. Introduction The largest uncertainty in predicting the future state of the atmosphere lies in properly estimating the internal changes to the climate system in response to a radiative perturbation ( Cess et al. 1989 ; Cess 2005 ). The impact of these internal changes, commonly called feedbacks on the climate system, can be as large as the primary forcing signal. Perhaps the most important feedback in the earth’s climate system is the climate feedback due to upper-tropospheric water vapor (H

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