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Topic #6: Ice/liquid mass partitioning in mixed phase cloud. Co-leaders Greg McFarquhar (in-situ) Johannes Bühl (remote sensing). Participants In-situ: Bundke, Esposito, Hamilton, Henneberger Johnson, Jourdan, Krämer, Lawson, Meyer, Minikin, Schwarzenboeck, Ulanowski
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Topic #6: Ice/liquid mass partitioning in mixed phase cloud Co-leaders Greg McFarquhar (in-situ) Johannes Bühl (remote sensing) Participants In-situ: Bundke, Esposito, Hamilton, Henneberger Johnson, Jourdan, Krämer, Lawson, Meyer, Minikin, Schwarzenboeck, Ulanowski Remote sensing: Alexander, Bieligk, Kommpula, Maahn, Wang Modeling: Flossman, Lohmann
1. General Description of Topic Theme and Objectives of the Topic Working Group • From Baumgardner et al. (2012) • What is the definition of a mixed-phase cloud? • What is minimum ratio of LWC/IWC required to identify cloud as mixed-phase • What are spatial scales of mixing between liquid & ice and how do they vary with height & meteorological conditions? • How are liquid & ice partitioned with respect to particle sizes (e.g., are all small particles liquid & all large particles ice) • How can small particles be distinguished from supercooled droplets & do frozen drops evolve in shape according to condition?
2. Brief Status of Topics • 2.1 In-Situ • Review of material from 2010 workshop in attached slides • 2.2 Remote Sensing • See following example • 2.3 Modeling • Need info
Remote Sensing – Example Case • Liquid vs. ice mass partitioning possible by combination of remote sensing and in-situ measurements • Cloud radars (like the Mira36) are most not sensitive to detect falling ice particles • Lidars are best in detecting liquid (sub)layers • Lidar/Radar depolarization and terminal fall speed can be used to further classify detected particles • LWC and IWC as products of CLOUDNET • LWC: Scaled adiabatic Method (assisted by Radiometer) • IWC: Parametrization from In-Situ Measurements (Hogan 2006)
Remote Sensing – Example Case Lidar Radar Signal Depolarization Fall Velocity
Remote Sensing – Example Case Liquid Water Content [kg/m^3] (CLOUDNET Scaled Adiabatic) Ice Water Content [kg/m^3] (Parametrization of Hogan et. al., 2006)
3. Progress in Last 3 years • 3.1 In-Situ • McFarquhar et al. (2013) analysis of CPI images of small particles in mixed-phase clouds • Aerosol effects on mixed-phase clouds during ISDAC (Jackson et al. 2012) • Tethered balloon observations (Lawson et al. 2011; Sikand et al. 2013) • Dependence of vertical profiles on meteorology (e.g., shallow vs. synoptic clouds, Noh et al. 2013) • 3.2 Remote Sensing • Dual polarization radar to discriminate phase (Plummer et al. 2010) • Radar doppler spectra (Luke et al. 2010; Verlinde et al. 2013) to detect supercooled water • Liao and Meneghini (2013) dielectric constants computed for oblate and prolate spheroids • 3.3 Modeling • Studies of aerosol effects on models of mixed-phase clouds (Morrison et al. 2010; Zubler et al. 2011) • Influence of ice habit on glaciation and evolution (Sulia & Harrington 2011; Avramov and Harrington 2010) • Impact of aerosols on global modeling of mixed clouds (Storelvmo et al. 2011)
4. Remaining Unknowns and Uncertainties • 4.1 In-Situ • Discrimination between water/ice for smallest hydrometeors still difficult • Elimination of shattered artifacts from probes still requires care and caution • Fine spatial resolution observations required to determine spatial scale of mixing • Observations in greater range of aerosol concentrations/compositions & meteorology • 4.2 Remote Sensing • Definition of “mixed-phase cloud” still unclear • Estimation of IWC/TWC or IWP/TWP should be taken into account • 4.3 Modeling