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David M. Romps

1. Introduction The lifting condensation level (LCL) is the height at which an air parcel would saturate if lifted adiabatically. The LCL is a key concept in the prediction of cloud cover (e.g., Wetzel 1990 ), the parameterization of convection and precipitation in weather and climate models (e.g., Emanuel and Živković-Rothman 1999 ), and the interpretation of atmospheric dynamics on other planets (e.g., Atreya et al. 2006 ). Over the past 180 years, many explicit, analytic expressions have

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Alessandra S. Lanotte
,
Agnese Seminara
, and
Federico Toschi

1. Introduction The growth of droplets by condensation is a long-standing problem of cloud physics ( Pruppacher and Klett 1997 ), meteorology (see, e.g., Houghton et al. 2001 ), medicine ( Martonen 2000 ), and engineering ( Zhao et al. 1999 ). A fundamental understanding of key issues, such as the turbulent mixing inside clouds or the interaction of turbulence with microphysics, is important for a variety of applications (e.g., the parameterization of small scales in large-scale models, the

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Paul A. O’Gorman
and
Tapio Schneider

, according to Galewsky et al., has last been saturated at points lying poleward and upward approximately on the isentrope going through the dry region of the subtropics. This indicates that, as suggested by Kelly et al. (1991) and Yang and Pierrehumbert (1994) , subtropical air parcels are carried poleward and upward by large-scale eddies, cool adiabatically with attendant condensation and drying along their trajectories, and return to the subtropics drier than they were originally. At the same time

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Stephen M. Saleeby
,
William R. Cotton
,
Douglas Lowenthal
,
Randolph D. Borys
, and
Melanie A. Wetzel

where subsidence allows sublimation, a reduction in total surface snowfall, and the disappearance of the “feeder” cloud ( Rauber et al. 1986a , b ). Characteristics of the cloud droplet spectra in the orographic cloud are related to riming efficiency. Aerosols, primarily those with the composition to act as cloud condensation nuclei (CCN), that enter a cloud typical of winter orographic systems in the Park Range can influence the resulting cloud droplet number concentration (CDNC) and droplet size

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Roberto Paoli
and
Karim Shariff

1. Introduction a. Motivation This work concerns the role of local finescale turbulence in determining the statistics of size distribution of water droplets in clouds [see Vaillancourt and Yau (2000) and Shaw (2003) for a review]. By “turbulence” we mean fluctuations of vapor concentration, temperature, and velocity induced by eddies. Far from passively depending on the turbulence for their condensational growth and motion, droplets affect the turbulence by changing the vapor field through

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A. Jaramillo
,
O. J. Mesa
, and
D. J. Raymond

1. Introduction Makarieva and Gorshkov (2007) proposed a new hypothesis where a “previously unstudied force” associated with condensation is the driver of low-level circulations, explaining phenomena like cyclones, monsoon circulations, and even the Hadley circulation (e.g., Makarieva and Gorshkov 2007 , 2009a , b , c , 2010 ; Gorshkov et al. 2012 ; Makarieva et al. 2013 , 2014 , 2015 , 2017 ). This hypothesis, called by its authors condensation-induced atmospheric dynamics [the

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A. M. Makarieva
,
V. G. Gorshkov
,
A. D. Nobre
,
A. V. Nefiodov
,
D. Sheil
,
P. Nobre
, and
B.-L. Li

1. Introduction Jaramillo et al. (2018) critiqued our theory of condensation-induced atmospheric dynamics (CIAD). CIAD results from the difference between evaporation and condensation. While most evaporation occurs at Earth’s surface, and is a slow, widely distributed process, condensation in contrast occurs within the atmospheric volume and, depending on vertical air velocity, can be orders of magnitude more rapid than evaporation. In simplified form, water vapor with partial pressure p υ

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Wojciech W. Grabowski
and
Hugh Morrison

1. Introduction Fan and Khain (2021 , subsequently FK21 ) comment on our recent paper, Grabowski and Morrison (2020 , subsequently GM20 ), that presents numerical simulations addressing the impact of ultrafine cloud condensation nuclei (CCN) on deep convection. GM20 follows our previous studies that investigate the proposed convective invigoration, such as Grabowski (2015 , G15 hereafter) and Grabowski and Morrison (2016) . The overall spirit of FK21 is that invigoration has been

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Jørgen B. Jensen
and
Alison D. Nugent

entrainment ( Baker and Latham 1979 ; Jonas 1996 ), turbulent enhancement to gravitational coalescence (e.g., Franklin et al. 2005 ; Wang et al. 2006 ), and radiative effects (e.g., Roach 1976 ; Lebo et al. 2008 ). In contrast, the condensation process leading up to the onset of coalescence has been accepted as being reasonably well described Pruppacher and Klett (1997) , at least in the absence of entrainment (e.g., Baker and Latham 1979 ). Yet, in this manuscript we argue that many cloud process

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Kyo-Sun Sunny Lim
and
Song-You Hong

though it requires more computational time than the single-moment approach. The double-moment microphysics scheme also requires cloud condensation nuclei (CCN) information when the CCN number concentration is predicted. Studies have shown the superiority of the double-moment approach in simulating precipitating convective clouds even though the strength of these double-moment schemes relies on the accuracy of the representation of several microphysical processes ( Cohard and Pinty 2000b ; Lee et al

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