Simulation of deposition nucleation using ice nucleation parametrizations and comparison of model results to measured cirrus cloud properties

Kasper Juurikkala,Ari Laaksonen,André Welti

crossref(2023)

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摘要
<p>Ice nucleation in the upper troposphere occurs either homogeneously or heterogeneously. For heterogeneous nucleation, mineral dust is known as an efficient ice nucleating particle (INP) or an aerosol that has the ability to nucleate ice. The global abundance of these mineral particles is poorly understood and thus it often limits accurate representation in model studies. The aim of this work is to simulate ice nucleation with deposition nucleation and compare the results to in-situ measurements.<br />The simulations are run with large-eddy model UCLALES-SALSA by applying multiple existing deposition nucleation parametrizations for mineral dust. These parametrizations are either based on laboratory measurements or classical nucleation theory. For the simulation setup, ECMWF reanalysis and campaign data from NASA MACPEX are used to create suitable conditions for in-situ cirrus formation. The simulations are based on the 16th of April 2011 science flight which was flown into a synoptic cirrus that formed over Northern Mexico to the Gulf of Mexico.<br />The simulated ice with all used parametrizations on average produced concentrations within two orders of magnitude of what was measured with onboard instruments. The main limiting factor for ice number concentration in the simulations is mineral dust concentration since every ice crystal formed only on mineral dust particles. The ice number concentration in measurements exceeded the mineral dust concentration which indicates that other INPs or freezing mechanisms might be involved in this scenario.<br />Further simulations are required to grasp a better understanding of the role of mineral dust in the cirrus cloud formation.</p> <p>&#160;</p> <p>Acknowledgements</p> <p>This work was supported by the Academy of Finland Flagship ACCC (grant no. 337552) and<br />MEDICEN project (grant no. 345125).</p>
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