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Mampi Sarkar, Paquita Zuidema, Bruce Albrecht, Virendra Ghate, Jorgen Jensen, Johannes Mohrmann, and Robert Wood

reducing inversion strength toward the equator. The main reason for this exercise is simply to develop an intuition of how the subcloud evaporation evolves, and in particular if it increases or decreases over the course of a Lagrangian trajectory, based on mean observed conditions. e. Selection of Lagrangian trajectories Eleven boundary layer modules (out of a total of 54 modules) from three flight pairs (out of seven) satisfy our selection criteria for SCT and do not include high clouds. These are

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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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Johna E. Rudzin, Lynn K. Shay, and Benjamin Jaimes de la Cruz

values that are within the WCE regime ( Table 3 ), indicating the PLUME has similar warm, moist boundary layer conditions that we expect over a deep thermal ocean regime. Values within the PLUME are also comparable to those estimated when Hurricane Earl (2010) underwent intensification ( ~360 K; Jaimes et al. 2015 ), those inside 2 Rmax when Hurricane Edouard (2014) underwent intensification ( ~355–365 K; Zhang et al. 2017 ), and those within the inner core of a moderately sheared (7.5 m s −1

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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

using the menu to select the fields displayed with the flight track information. In addition, the field catalog can be used to show near-real-time displays of time–height plots of the HCR and HSRL returns. Other data collected can be plotted and displayed in real time or after flights using EOL’s Aeros visualization software. OBSERVATIONAL HIGHLIGHTS. A wide range of boundary layer structures and aerosol, cloud, and precipitation conditions were observed during the CSET missions that captured the

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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

boundary conditions ( McGibbon and Bretherton 2017 ). The CSET lidar–radar cloud fraction compared well with GOES satellite retrievals of cloud fraction, but the GOES retrievals of droplet concentration for CSET were often biased low, especially in the cumulus regime. This issue merits further investigation. In almost all flights and regimes, the thickest clouds were precipitating, but the areal coverage of column-maximum radar echoes exceeding 0 dB Z was only about 5% in both Cu and Sc regimes, with

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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

1. Introduction Marine boundary layer (MBL) clouds are important contributors to Earth’s energy budget, due to their extensive spatial coverage, high albedo, and relatively warm cloud-top temperature ( Hartmann et al. 1992 ). As a result, these clouds have long been subject to observational and modeling scrutiny ( Albrecht et al. 1988 ; Stevens et al. 2005 ; Wood, 2012 ). An important climatological feature is the subtropical stratocumulus (Sc) to trade cumulus (Cu) transition (SCT), where

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

closest grid point to the coordinates of the AWSs. 3. Results a. Reanalysis–observations comparison To gain information on the longer-term conditions and climatology of the lower atmospheric boundary layer over 79N, we must ensure that the reanalysis data are representative of the real conditions. Here, we compare the reanalysis products with 79N AWS data ( Table 2 ). ERA-I and MERRA2 have relatively small mean biases for annual average temperature (e.g., 0.8°C bias for air temperature at AWS 9602 and

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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

1. Introduction Assessment of the physical factors controlling the coverage and albedo of marine boundary layer (MBL) clouds remains a pressing challenge. The speed at which the stratocumulus (Sc)-to-cumulus (Cu) transition (SCT) occurs in air masses downstream of the eastern subtropical ocean basins determines the albedo of the tropics, and similar cloud transitions in postfrontal air masses are important for determining midlatitude storm-track albedo. These transitions in cloudiness are

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Patrik Benáček and Máté Mile

inaccuracies of the NWP model like parameterization, spatial and temporal discretization, the imperfect use of boundary conditions, and unrepresented physical and dynamical processes ( Lahoz et al. 2010 ; Torn and Davis 2012 ; Romine et al. 2013 ). The observation biases involve instrument error (e.g., poor calibration), approximations in forward operator and data processing such as the radiative transfer model or cloud detection ( Auligné et al. 2007 ). This creates a complex bias depending on, for

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

1. Introduction The number concentration of water droplets and cloud condensation nuclei (CCN) continue to be variables of importance in marine boundary layer (MBL) clouds because they help determine cloud albedo (i.e., first indirect effect; Twomey 1977 ) and its perturbation due to aerosol can modulate the ability to form precipitation ( Albrecht 1989 ). The is limited by through the process of cloud droplet activation in which CCN serve as nuclei for cloud droplets. Changes in

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