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Statistics of Ice Nucleation Conditions in Mixed-Phase Clouds

Subject Area Atmospheric Science
Term from 2011 to 2019
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 170852269
 
Heterogeneous Ice nucleation occurs on specific aerosol particles with a temperature- and humidity-dependent efficiency, which can be well characterized in laboratory experiments and parameterized for the description of primary ice formation in models on various scales. Given these efficiencies, under which conditions and how often does ice nucleation actually occur in typical mixed-phase douds? This question will be addressed with the new model ICON-LES (the large eddy simulation version of the icosahedral non-hydrostatic model currently being developed by the German Weather Service and the Max-Planck Institute for Meteorology, Hamburg), which contains a double-moment cloud scheme and will be extended by aerosol-specific ice nucleation parameterizations. In addition, a simple treatment of in-droplet aerosol will be included in order to distinguish between potential immersion INPs and interstitial INPs, which can initiate deposition or contact ice nucleation. In idealized simulations of different mixed-phase cloud types commonly occurring in Europe (winter fog, altostratus, orographic clouds, deep convection), using climatological aerosol fields, the occurrence of immersion, deposition and contact ice nucleation and INP number concentrations will be statistically evaluated and compared to the ranges of sensitivity ofthe INUIT laboratory and field instruments. Furthermore, the preferred locations of these processes within the clouds (e.g. contact freezing in entrainment regions at cloud edges) will be analyzed and quantified. The case study setup ofthe deep convective cloud will be coordinated with RP3 so that a comparison ofthe bin- and double-moment microphysics treatments is possible. Finally, this project together with VJP-F and WP-L will conduct an INP closure study for the measurements during the Mediterranean campaign and will address the question which parameters lead to the largest uncertainties in the fonward modeling of INPs.
DFG Programme Research Units
 
 

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