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Vanda Salgueiro, Maria João Costa, Ana Maria Silva, and Daniele Bortoli

and at the top of the atmosphere (TOA) ( Dong et al. 2006 ). A way of quantifying the cloud radiation effects at the surface and at the TOA is the cloud radiative forcing (CRF), which is defined as an instantaneous change in net total radiation (SW plus LW; in W m −2 ) obtained under cloudy conditions and its clear-sky counterpart; CRF can produce a cooling (negative CRF) or a warming (positive CRF) effect on the earth–atmosphere system. CRF has been a research topic over the last decades because

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Nathaniel B. Miller, Matthew D. Shupe, Christopher J. Cox, Von P. Walden, David D. Turner, and Konrad Steffen

the net radiative flux at the surface ( Walsh and Chapman 1998 ), thereby impacting the surface energy budget. The shortwave and longwave radiative effect of clouds, or cloud radiative forcing (CRF), can be quantified by comparing the actual surface radiative flux to the flux during an equivalent clear-sky scene. In general, Arctic clouds have a warming effect on the surface, except for a period in the summer when the sun is highest and surface albedo is lowest ( Curry and Ebert 1992 ; Intrieri

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Clark Weaver, Jay Herman, Gordon Labow, David Larko, and L.-K. Huang

1. Introduction Cloud radiative feedback quantifies an aspect of how clouds respond to a warming climate, specifically, the change in the top-of-atmosphere (TOA) reflected radiative flux from changes in cloud amount or morphology ( R cloud ) per degree temperature change at the earth’s surface. Here R cloud is called the cloud radiative forcing (watts per meter squared). Currently, the longest observational data record for shortwave (SW) R cloud is from the International Satellite Cloud

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Jian Yuan, Dennis L. Hartmann, and Robert Wood

called cloud radiative forcing (CRF), may respond to external influences on the climate system and thereby constitute a substantial climate feedback (e.g., Schneider 1972 ; Cess et al. 1996 ). The tropical climate system response to an external perturbation is an important outstanding problem, and cloud feedback still stands as a large source of uncertainty in predicting future climate ( Cess et al. 2001b ; Stephens 2005 ; Solomon et al. 2007 ). Clouds respond both to large-scale dynamical

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Leighton A. Regayre, Kirsty J. Pringle, Lindsay A. Lee, Alexandru Rap, Jo Browse, Graham W. Mann, Carly L. Reddington, Ken S. Carslaw, Ben B. B. Booth, and Matthew T. Woodhouse

1. Introduction Aerosols affect Earth’s climate by absorbing and scattering solar and terrestrial radiation ( Twomey 1977 ; Boucher et al. 2013 ). The cloud albedo effect (CAE) ( Boucher et al. 2013 ), characterized by a decrease in cloud drop effective radius that results from an increase in cloud droplet number concentration for a given amount of liquid water ( Twomey 1977 ), is the largest component of the aerosol–cloud interaction. Uncertainty in the magnitude of CAE forcing remains the

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Hiroki Ichikawa, Hirohiko Masunaga, Yoko Tsushima, and Hiroshi Kanzawa

1. Introduction The radiative effect of clouds, often called cloud radiative forcing (CRF), associated with convective activity largely controls the radiative balance–imbalance at the top of the atmosphere (TOA) over the tropics through the horizontal extension of high clouds that accompany deep convection ( Ramanathan and Collins 1991 ; Lindzen et al. 2001 ; Hartmann et al. 2001 ). The response of CRF associated with convective activity to an imposed climate perturbation is thus fundamental

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Simone Lolli, James R. Campbell, Jasper R. Lewis, Yu Gu, Jared W. Marquis, Boon Ning Chew, Soo-Chin Liew, Santo V. Salinas, and Ellsworth J. Welton

1. Motivation Campbell et al. (2016) isolate top-of-atmosphere (TOA) net cirrus cloud radiative forcing (CRF) properties for a continuous 1-yr, single-layer cloud dataset developed from NASA ground-based Micro-Pulse Lidar Network (MPLNET; ) ( Welton et al. 2001 ; Campbell et al. 2002 ; Lolli et al. 2013 ) observations collected at Greenbelt, Maryland [38.99°N, 76.84°W; 50 m above mean sea level (MSL)]. They estimate that cirrus clouds exert an absolute net

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Byung-Ju Sohn, Johannes Schmetz, Rolf Stuhlmann, and Joo-Young Lee

, the term A c ( L * f − L * c ) in Eq. (1) can be referred to as the cloud radiative forcing (CRF). In a satellite approach, CRF is generally determined by differencing the observed total flux from the clear-sky flux (e.g., Ramanathan et al. 1989 ). Thus, LW cloud radiative forcing [CRF( L )] is where L * is a direct measurement by the radiometer at the TOA while L * f is estimated from the composite of clear-sky pixels, which for longwave infrared measurements must be away from the cloudy

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James R. Campbell, Erica K. Dolinar, Simone Lolli, Gilberto J. Fochesatto, Yu Gu, Jasper R. Lewis, Jared W. Marquis, Theodore M. McHardy, David R. Ryglicki, and Ellsworth J. Welton

1. Introduction Campbell et al. (2016 , hereinafter C16 ) and Lolli et al. (2017 , hereinafter L17 ) describe multiyear ground-based NASA Micro-Pulse Lidar Network (MPLNET, 532 nm; Welton et al. 2001 ; Campbell et al. 2002 ) measurements of cirrus clouds and corresponding estimates of their net daytime top-of-the-atmosphere (TOA) cloud radiative forcing (CRF; i.e., the difference in TOA solar and infrared radiation budgets estimated in the presence of cloud versus that of the corresponding

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Timothy Andrews and Mark A. Ringer

even reverse, potentially dangerous climate change ( Bouttes et al. 2013 ). For example, are feedback processes that influence the global radiation balance reversible? b. Effective radiative forcing and adjustments A recent development has been the realization that some aspects of cloud changes, when driven by increased CO 2 , are better thought of as a rapid atmospheric adjustment to be included in the external perturbation F , rather than a surface temperature mediated feedback [see Andrews et

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