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Robert Wood, Kuan-Ting O, Christopher S. Bretherton, Johannes Mohrmann, Bruce. A. Albrecht, Paquita Zuidema, Virendra Ghate, Chris Schwartz, Ed Eloranta, Susanne Glienke, Raymond A. Shaw, Jacob Fugal, and Patrick Minnis

concentration N d , which scales as dA / dN d = A (1 − A )/3 N d ( Platnick and Twomey 1994 ) and thus is only about one-third lower for A = 0.2 compared with A = 0.5. On the other hand, the extremely low N d in UCL clouds implies a very high albedo susceptibility (Twomey effect). Marine low clouds shoulder a large fraction of the global indirect aerosol forcing in climate models (e.g., Kooperman et al. 2012 ; Carslaw et al. 2013 ; Lee et al. 2016 ), so it is important to understand whether

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Bruce Albrecht, Virendra Ghate, Johannes Mohrmann, Robert Wood, Paquita Zuidema, Christopher Bretherton, Christian Schwartz, Edwin Eloranta, Susanne Glienke, Shaunna Donaher, Mampi Sarkar, Jeremy McGibbon, Alison D. Nugent, Raymond A. Shaw, Jacob Fugal, Patrick Minnis, Robindra Paliknoda, Louis Lussier, Jorgen Jensen, J. Vivekanandan, Scott Ellis, Peisang Tsai, Robert Rilling, Julie Haggerty, Teresa Campos, Meghan Stell, Michael Reeves, Stuart Beaton, John Allison, Gregory Stossmeister, Samuel Hall, and Sebastian Schmidt

the fair-weather cumulus regimes within the subtropical easterlies over the northern Pacific. Examine the cloud microphysical properties and processes as a function of boundary layer depth, toward assessing the relative contributions of internal processes (e.g., entrainment, turbulence, and drizzle) and external forcing (e.g., sea surface temperature, winds, and subsidence) to boundary layer cloud system evolution. Evaluate the relative importance of boundary layer deepening and precipitation

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Johannes Mohrmann, Christopher S. Bretherton, Isabel L. McCoy, Jeremy McGibbon, Robert Wood, Virendra Ghate, Bruce Albrecht, Mampi Sarkar, Paquita Zuidema, and Rabindra Palikonda

remains cloudier. The two cases differ in initial MBL depth, decoupling, microphysics, and large-scale forcings. Note that the agreement between outbound flight number and case number (L06 coming from RF06, L10 from RF10) is coincidental. b. Satellite and reanalysis data In addition to the GV measurements, satellite and reanalysis data are used. Where possible, these are compared to aircraft observations for additional validation. The satellite data consists of cloud property fields generated from

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M. Christian Schwartz, Virendra P. Ghate, Bruce. A. Albrecht, Paquita Zuidema, Maria P. Cadeddu, Jothiram Vivekanandan, Scott M. Ellis, Pei Tsai, Edwin W. Eloranta, Johannes Mohrmann, Robert Wood, and Christopher S. Bretherton

1. Introduction A physically reasonable treatment of low clouds within climate models is required for the realistic modeling of the climate’s sensitivity to greenhouse gas forcing (e.g., Wetherald and Manabe 1988 ; Tiedtke 1993 ; Stephens 2005 ). Furthermore, low clouds account for a great deal of intermodel variability in cloud feedback factors ( Bony and Dufresne 2005 ; Zhang et al. 2013 ). One persistent difficulty with modeling marine boundary layer (MBL) clouds is accurately capturing

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Kuan-Ting O, Robert Wood, and Christopher S. Bretherton

(CSET) project (AGS-1445813). We thank Marcia Baker and Jørgen Jensen for helpful discussion. REFERENCES Ackerman , A. S. , M. P. Kirkpatrick , D. E. Stevens , and O. B. Toon , 2004 : The impact of humidity above stratiform clouds on indirect aerosol climate forcing . Nature , 432 , 1014 – 1017 , . 10.1038/nature03174 Albrecht , B. A. , 1989 : Aerosols, cloud microphysics, and fractional cloudiness . Science , 245 , 1227 – 1230 , https

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Christopher S. Bretherton, Isabel L. McCoy, Johannes Mohrmann, Robert Wood, Virendra Ghate, Andrew Gettelman, Charles G. Bardeen, Bruce A. Albrecht, and Paquita Zuidema

bins, was positively correlated with estimated inversion strength (EIS) with a regression slope close to past climatology. After controlling for EIS, the low cloud cover was not well correlated with cloud droplet concentration, which suggests that the Sc–Cu transition is not strongly controlled by aerosol processes. This contrasts with some recent idealized large-eddy simulation studies (e.g., Yamaguchi et al. 2017 ), but is consistent with another such study using data-constrained forcings and

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Jenny V. Turton, Thomas Mölg, and Dirk Van As

on the location, altitude, and observational period for the four AWS datasets used. T , air temperature; RH, relative humidity; WS, wind speed; WD, wind direction; P , air pressure; SW, shortwave radiation; LW, longwave radiation; CC, cloud cover; and TSK, skin temperature. A plus sign indicates that wind direction data were recorded by the logger, but the reference direction was not recorded. Therefore, wind direction data cannot be used with accuracy. An asterisk indicates that there is a gap

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