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Daniel T. McCoy, Dennis L. Hartmann, and Daniel P. Grosvenor

–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) ( Wu et al. 2009 ), an excellent choice for accurately diagnosing low cloud amount and top height. Given these issues we have chosen to use MISR to retrieve cloud fraction, optical depth, and height, particularly because low clouds dominate over the Southern Ocean. Even if the MODIS CTH–OD histogram could be brought into agreement with the retrievals performed by instruments that do not discard cloud edges, there are still

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Wuyin Lin, Minghua Zhang, and Norman G. Loeb

. Kubar , T. L. , D. L. Hartmann , and R. Wood , 2009 : Understanding the importance of microphysics and macrophysics for warm rain in marine low clouds. Part I: Satellite observations. J. Atmos. Sci. , 66 , 2953 – 2972 . Leon , D. C. , Z. Wang , and D. Liu , 2008 : Climatology of drizzle in marine boundary layer clouds based on 1 year of data from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Stellite Observations (CALIPSO). J. Geophys. Res. , 113 , D00A14 . doi

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Yunying Li and Minghua Zhang

to those of other cloud types. It also studies the impact of Cu on large-scale circulation in a model sensitivity experiment. Section 5 contains a summary. 2. Data and model experiment design a. Data The Cu in the products of CloudSat ( Stephens et al. 2008 ) and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) ( Winker et al. 2009 ) is defined as isolated clouds with base height at 0–3 km above the ground, horizontal scale of 1–10 km, vertical extent of

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Casey J. Wall, Dennis L. Hartmann, and Po-Lun Ma

flying in the A-Train constellation ( Stephens et al. 2002 ). The CERES ( Wielicki et al. 1996 ) instrument retrieves top-of-atmosphere radiative fluxes ( Loeb et al. 2005 ). The Cloud Profiling Radar (CPR; Stephens et al. 2008 ) on board the CloudSat satellite and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP; Winker et al. 2007 ) on board the Cloud-Aerosol lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) satellite are both active instruments that retrieve the

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David Painemal, Kuan-Man Xu, Anning Cheng, Patrick Minnis, and Rabindra Palikonda

first 9 yr and 3 months of the integration. Good agreement between 1-yr (year 10) and 4-yr (years 6–9) averaged results of the MMF integration reported in Xu and Cheng (2013a) , with low-cloud fraction correlations exceeding 0.99 and a bias of only 0.2%, demonstrates that the 1-yr simulations presented in this study are representative of multiyear simulations. b. Satellite observations The satellite dataset includes cloud retrievals from sun-synchronous Cloud–Aerosol Lidar and Infrared Pathfinder

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Anne Sledd and Tristan L’Ecuyer

. Climate , 30 , 4477 – 4495 , . 10.1175/JCLI-D-16-0666.1 Morrison , A. , J. E. Kay , H. Chepfer , R. Guzman , and V. Yettella , 2018 : Isolating the liquid cloud response to recent Arctic sea ice variability using spaceborne lidar observations . J. Geophys. Res. Atmos. , 123 , 473 – 490 , . 10.1002/2017JD027248 Morrison , A. , J. E. Kay , W. Frey , H. Chepfer , and R. Guzman , 2019 : Cloud

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John J. D’Alessandro, Minghui Diao, Chenglai Wu, Xiaohong Liu, Jorgen B. Jensen, and Britton B. Stephens

; Hu et al. 2009 ; Chylek et al. 2006 ; Naud et al. 2006 ; Riedi et al. 2010 ). For example, the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) cloud phase identification is mostly affected by the cloud top ( Cesana et al. 2016 ); the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top phase product has limited accuracy from −25° to −5°C ( Morrison et al. 2011 ); the Polarization and Directionality of Earth Reflectances (POLDER) spaceborne instrument

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Brian J. Soden and John R. Lanzante

was used by Soden and Bretherton (1994) and Soden et al. (1994) to compare GCMand Raman lidar profiles of moisture with GOES observations of T6.?. One of the limitations with the radiosonde dataset isthat, for certain locations, moisture is not reported atall levels in the upper troposphere, while accurate radiative transfer calculations require moisture profilesup to 100 mb. In situations where relative humidity inthe upper troposphere is not fully reported, the moistureprofiles are completed

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Ryan Eastman and Stephen G. Warren

confirmed in more recent surface observations by Hahn et al. (1995 , their Fig. 13a). Wang and Key (2005 , their Fig. 2) compared Arctic cloud climatologies from surface observations, TOVS Path P, and AVHRR satellite data for areas north of 80°N. All three agree that there is a summertime maximum in Arctic cloud cover and a minimum in April. Using SHEBA lidar and radar, Intrieri et al. (2002) showed that cloud cover occurred most frequently in September and least frequently during February. The

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Scott N. Williamson, David S. Hik, John A. Gamon, Jeffrey L. Kavanaugh, and Saewan Koh

the identification of cloud contamination, including using meteorological stations in a coordinated approach to produce validated satellite data, should be employed. Further work is required to identify the type and degree of cloud contamination in MODIS land surface temperature. A reevaluation of the MODIS cloud mask could be completed using the technique outlined in this paper in conjunction with more precise observations, such as lidar, to further refine spectral thresholds for cloud

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