
Ice Crystal Linear Growth Rates from −20° to −70°C: Confirmation from Wave Cloud Studies
Author(s) -
Martin J. Bailey,
John Hallett
Publication year - 2012
Publication title -
journal of the atmospheric sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.853
H-Index - 173
eISSN - 1520-0469
pISSN - 0022-4928
DOI - 10.1175/jas-d-11-035.1
Subject(s) - ice crystals , atmospheric sciences , supersaturation , aerosol , meteorology , growth rate , environmental science , climatology , materials science , physics , geology , thermodynamics , mathematics , geometry
As a result of recent comprehensive laboratory and field studies, many details have been clarified concerning atmospheric ice crystal habits below −20°C as a function of temperature, ice supersaturation, air pressure, and growth history. A predominance of complex shapes has been revealed that is not reflected in most models, with symmetric shapes often incorrectly emphasized. From the laboratory study, linear (maximum dimension), projected area, and volume growth rates of complex and simple habits have been measured under simulated atmospheric conditions for temperatures from −20° to −70°C. Presently, only a few in situ cases of measured ice crystal growth rates are available for comparison with laboratory results. Observations from the Interaction of Aerosol and Cold Clouds (INTACC) field study of a well-characterized wave cloud at −24°C are compared with the laboratory results using a simple method of habit averaging to derive a range of expected growth rates. Laboratory results are also compared with recently reported wave cloud results from the Ice in Clouds Experiment–Layer Clouds (ICE-L) study between −20° and −32°C, in addition to a much colder wave cloud at −65°C. Considerable agreement is observed in these cases, confirming the reliability of the laboratory measurements. This is the first of two companion papers that compare laboratory growth rates and characteristics with in situ measurements, confirming that the laboratory results effectively provide a predictive capability for cloud particle and particle ensemble growth.