Liquid and Ice Cloud Microphysics in the CSU General Circulation Model. Part 1: Model Description and Simulated Microphysical Processes

Fowler, Laura D.; Randall, David A.; Rutledge, Steven A.

doi:10.1175/1520-0442(1996)009<0489:laicmi>2.0.co;2

Cited by 256 publications

(155 citation statements)

References 51 publications

Supporting

Mentioning

154

Contrasting

Order By: Relevance

“…The q 6 spatial profile is defined as q 6 the number of water molecules, and the local q 6 of molecule i, respectively. For ρ HC (z) and ρ DDC (z) calculations, thicker 0.2-nm bins are used, and each cage's contribution is equally distributed among the bins that it intersects (see SI Text for details).…”

Section: Methodsmentioning

confidence: 99%

“…Indeed, the radiative properties (4) and precipitation propensity (5) of a cloud are both determined by the amount of ice that it harbors. Not surprisingly, the liquid fraction of clouds is an extremely important input parameter in meteorological models (6). Freezing is a first-order phase transition and proceeds through a mechanism known as nucleation and growth.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Computational investigation of surface freezing in a molecular model of water

Haji-Akbari

Debenedetti

2017

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Water freezes in a wide variety of low-temperature environments, from meteors and atmospheric clouds to soil and biological cells. In nature, ice usually nucleates at or near interfaces, because homogenous nucleation in the bulk can only be observed at deep supercoolings. Although the effect of proximal surfaces on freezing has been extensively studied, major gaps in understanding remain regarding freezing near vapor-liquid interfaces, with earlier experimental studies being mostly inconclusive. The question of how a vapor-liquid interface affects freezing in its vicinity is therefore still a major open question in ice physics. Here, we address this question computationally by using the forward-flux sampling algorithm to compute the nucleation rate in a freestanding nanofilm of supercooled water. We use the TIP4P/ice force field, one of the best existing molecular models of water, and observe that the nucleation rate in the film increases by seven orders of magnitude with respect to bulk at the same temperature. By analyzing the nucleation pathway, we conclude that freezing in the film initiates not at the surface, but within an interior region where the formation of doublediamond cages (DDCs) is favored in comparison with the bulk. This, in turn, facilitates freezing by favoring the formation of nuclei rich in cubic ice, which, as demonstrated by us earlier, are more likely to grow and overcome the nucleation barrier. The films considered here are ultrathin because their interior regions are not truly bulk-like, due to their subtle structural differences with the bulk.ice | nucleation | molecular simulations | surface freezing | statistical mechanics F reezing of water into ice is ubiquitous in nature and can occur in a wide range of environments, from biological cells (1) and soil (2) to meteors (3) and atmospheric clouds (4). Ice formation is particularly important in the atmospheric processes that exert a major influence on our weather and climate. Indeed, the radiative properties (4) and precipitation propensity (5) of a cloud are both determined by the amount of ice that it harbors. Not surprisingly, the liquid fraction of clouds is an extremely important input parameter in meteorological models (6). Freezing is a first-order phase transition and proceeds through a mechanism known as nucleation and growth. In mixed-phase clouds, which are responsible for the bulk of land surface precipitation (7), freezing is usually nucleationlimited, and individual freezing events are rare. Predicting the timing of such rare freezing events is important in modeling cloud microphysics. However, nucleation rates, which are volume-or area-normalized inverse nucleation times, can only be measured experimentally over a narrow range of temperatures and pressures (8), and extrapolations to other temperatures are nontrivial (9). In nature, homogeneous nucleation of ice is only likely at very low temperatures, and the majority of freezing events in mixed-phase clouds occur heterogeneously, at interfaces provided by external insol...

show abstract

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Computational investigation of surface freezing in a molecular model of water

Haji-Akbari

Debenedetti

2017

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…This heating exerts a dominant influence on the large-scale, "Hadley" circulation of the atmosphere (Randall et al 1989) as well as on deep convective cloud systems (Grabowski et al 2000). Other processes are also affected by the vertical distribution of heating (e.g., Liang and Wang 1997), notably through the connections between convection and precipitation (e.g., Fowler and Randall 1994;Parsons et al 2000).…”

Section: The Nature Of the Cloudsat Sciencementioning

confidence: 99%

The Cloudsat Mission and the a-Train

Stephens

Vane

Boain

et al. 2002

Bull. Amer. Meteor. Soc.

1,938

968

View full text Add to dashboard Cite

“…The large-scale cloud microphysical scheme is the one developed by Fowler et al (1996), with cumulus detrainment as a source of cloud water and/or ice. The radiation is the Stephens parameterization.…”

Section: Figurementioning

confidence: 99%