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Matthew D. Shupe, Pavlos Kollias, Sergey Y. Matrosov, and Timothy L. Schneider

. Atmos. Sci , 48 , 1005 – 1023 . 10.1175/1520-0469(1991)048<1005:AEFTEO>2.0.CO;2 Sassen, K. , DeMott P. J. , Prospero J. M. , and Poellot M. R. , 2003 : Saharan dust storms and indirect aerosol effects on clouds: CRYSTAL-FACE results. Geophys. Res. Lett., 30, 1633, doi: 10.1029/2003GL017371 . Shupe, M. D. , and Intrieri J. M. , 2004 : Cloud radiative forcing or the Arctic surface: The influence of cloud properties, surface albedo, and solar zenith angle. J. Climate , 17 , 616

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Bryan A. Baum and Qing Trepte

. V. Kliche, J. Chou, and R. M. Welch, 1996: First estimates of the radiative forcing of aerosols generated from biomass burning using satellite data. J. Geophys. Res., 101, 21 265–21 273. 10.1029/96JD02161 Davis, P., L. L. Stowe, and E. P. McClain, 1993: Development of a cloud layer detection algorithm for the clouds from AVHRR (CLAVR) Phase II Code. Proc. SPIE Symp. . 10.1117/12.154904 Gesell, G., 1989: An algorithm for snow and ice detection using AVHRR data: An extension to the APOLLO

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Masanori Saito and Hironobu Iwabuchi

1. Introduction Clouds cover more than 60% of the globe, and as a result they have a large impact on radiative energy transfer in the earth–atmosphere system and the hydrological cycle ( Rossow and Schiffer 1991 ; Stephens 2005 ). However, cloud feedback on climate associated with cloud macroscopic characteristics is not well known quantitatively, thereby causing large uncertainties in climate models ( IPCC 2007 ; Probst et al. 2012 ). Large-scale cloud coverage data and cloud optical

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Leonhard Scheck, Martin Weissmann, and Bernhard Mayer

reflectances . J. Atmos. Oceanic Technol. , 31 , 1216 – 1233 , https://doi.org/10.1175/JTECH-D-13-00116.1 . 10.1175/JTECH-D-13-00116.1 Marquart , S. , and B. Mayer , 2002 : Towards a reliable GCM estimation of contrail radiative forcing . Geophys. Res. Lett. , 29 , 1179 , https://doi.org/10.1029/2001GL014075 . 10.1029/2001GL014075 Martin , G. M. , D. W. Johnson , and A. Spice , 1994 : The measurement and parameterization of effective radius of droplets in warm stratocumulus clouds

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Yonggang Wang and Bart Geerts

regard the cold spike as an error, related to the instrument or the platform; the second explanation regards the cold spike as physically real. Regarding the first explanation, if the cloud exit cold spike is due to changes in aircraft altitude, then the aircraft should, on average, quickly climb upon exiting a cumulus and then gradually recover to its assigned altitude. The brief, rapid ascent could be due to persistent downdrafts in the sampled cumuli, forcing the (auto)pilot into climb mode within

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Seung-Hee Ham, Seiji Kato, and Fred G. Rose

. Várnai , G. Wen , and R. F. Cahalan , 2006 : Impact of three-dimensional radiative effects on satellite retrievals of cloud droplet sizes . J. Geophys. Res. , 111 , D09207 , https://doi.org/10.1029/2005JD006686 . McClatchey , R. A. , R. W. Fenn , J. E. A. Selby , F. E. Volz , and J. S. Garing , 1972 : Optical properties of the atmosphere. Air Force Cambridge Research Laboratory Environmental Research Paper 411, 3rd ed., 110 pp., https://apps.dtic.mil/dtic/tr/fulltext/u2

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R. Paul Lawson, Knut Stamnes, Jakob Stamnes, Pat Zmarzly, Jeff Koskuliks, Chris Roden, Qixu Mo, Michael Carrithers, and Geoffrey L. Bland

net radiation is the largest component of the surface energy budget. Also, unlike any other region on the globe, clouds exert a positive net radiative forcing at the surface resulting from a combination of high surface albedo and strong surface inversions ( Intrieri et al. 2002b ). A proper treatment of clouds in the polar regions is a prerequisite for reliable estimates of climate forcing, the onset of snowmelt, the rate of snow/ice ablation, the length of the melt season ( Zhang et al. 1997

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Seiji Kato, Fred G. Rose, and Thomas P. Charlock

1. Introduction The reflected shortwave irradiances from clouds vary because of temporal and spatial variations of cloud optical properties. In addition, the resolution of instruments measuring radiation is small enough to detect the variations but not large enough to average them out. For climate purposes, our interest is on an average albedo, transmittance, and absorptance over a finite area or time. Because the albedo and transmittance are not linearly related to cloud optical thickness

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Eric S. Maddy, Thomas S. King, Haibing Sun, Walter W. Wolf, Christopher D. Barnet, Andrew Heidinger, Zhaohui Cheng, Mitchell D. Goldberg, Antonia Gambacorta, Chen Zhang, and Kexin Zhang

generally used to derive sea surface temperature and other surface properties. High spatial resolution AVHRR measurements collocated within the IASI spatial footprints therefore ideally enable the detection and removal of the spectral fingerprint of clouds from IASI spectra. Fig . 1. IASI spectrum for Air Force Geophysics Laboratory (AFGL) U.S. Standard Tropical Atmosphere, 1976 (black) and overlaid AVHRR SRFs (red) for AVHRR channels 4 and 5. Collocation between IASI and AVHRR uses an algorithm

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Maki Hirakata, Hajime Okamoto, Yuichiro Hagihara, Tadahiro Hayasaka, and Riko Oki

2008 ). These differences in cloud parameterization cause a wide spread in the model results ( Ho et al. 1998 ; Tsushima et al. 2006 ). Unfortunately, most models also assume that ice crystals are oriented randomly ( Takano et al. 1992 ), in spite of the reported influences of cloud particle shape on radiative forcing ( Takano and Liou 1989 ; Wendisch et al. 2005 ). This variation leads to uncertainties in GCM outputs, to some extent. Detailed cloud observations are needed for a more

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