Horizontal Distribution of Ice and Water in Arctic Stratus Clouds During MPACE Michael Poellot, David Brown – University of North Dakota Greg McFarquhar,

Slides:



Advertisements
Similar presentations
Numerical Weather Prediction Parametrization of Diabatic Processes Cloud Parametrization 2: Cloud Cover Richard Forbes and Adrian Tompkins
Advertisements

Radar/lidar observations of boundary layer clouds
Robin Hogan & Anthony Illingworth Department of Meteorology University of Reading UK Parameterizing ice cloud inhomogeneity and the overlap of inhomogeneities.
Clouds and radiation in a LSM L=50km. Veldhoven, Stratocumulus cloud albedo: example cloud layer depth = 400 m effective cloud droplet.
BBOS meeting on Boundary Layers and Turbulence, 7 November 2008 De Roode, S. R. and A. Los, QJRMS, Corresponding paper available from
Veldhoven, Large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity Stephan de Roode (1,2) & Alexander Los (2)
Marc Schröder et al., FUB BBC2 Workshop, De Bilt, 10.´04 Problems related to absorption dependent retrievals and their validation Marc Schröder 1, Rene.
3D Radiative Transfer in Cloudy Atmospheres: Diffusion Approximation and Monte Carlo Simulation for Thermal Emission K. N. Liou, Y. Chen, and Y. Gu Department.
Lidar-Based Microphysical Retrievals During M-PACE Gijs de Boer Edwin Eloranta The University of Wisconsin - Madison ARM CPMWG Meeting, October 31, 2006.
Steven Siems 1 and Greg McFarquhar 2 1 Monash University, Melbourne, VIC, Australia 2 University of Illinois, Urbana, IL, USA Steven Siems 1 and Greg McFarquhar.
Evaluation of ECHAM5 General Circulation Model using ISCCP simulator Swati Gehlot & Johannes Quaas Max-Planck-Institut für Meteorologie Hamburg, Germany.
By : Kerwyn Texeira. Outline Definitions Introduction Model Description Model Evaluation The effect of dust nuclei on cloud coverage Conclusion Questions.
Initial testing of longwave parameterizations for broken water cloud fields - accounting for transmission Ezra E. Takara and Robert G. Ellingson Department.
Initial 3D isotropic fractal field An initial fractal cloud-like field can be generated by essentially performing an inverse 3D Fourier Transform on the.
Profiling Clouds with Satellite Imager Data and Potential Applications William L. Smith Jr. 1, Douglas A. Spangenberg 2, Cecilia Fleeger 2, Patrick Minnis.
Cloud Biases in CMIP5 using MISR and ISCCP simulators B. Hillman*, R. Marchand*, A. Bodas-Salcedo, J. Cole, J.-C. Golaz, and J. E. Kay *University of Washington,
Photo by Dave Fratello. Focus To evaluate CAM5/CARMA at 1x1 degree resolution with aircraft observations. - Improve cirrus cloud representation in the.
- 1 - A Simple Parameterization For Mid-latitude Cirrus Cloud Ice Particle Size Spectra And Ice Sedimentation Rates David L. Mitchell Desert Research Institute,
The Arctic Climate Paquita Zuidema, RSMAS/MPO, MSC 118, Feb, 29, 2008.
Acknowledgments This research was supported by the DOE Atmospheric Radiation Measurements Program (ARM) and by the PNNL Directed Research and Development.
Validation of a Satellite Retrieval of Tropospheric Nitrogen Dioxide Randall Martin Dalhousie University Smithsonian Astrophysical Observatory Daniel Jacob.
Characterization of Arctic Mixed-Phase Cloudy Boundary Layers with the Adiabatic Assumption Paquita Zuidema*, Janet Intrieri, Sergey Matrosov, Matthew.
Determination of the optical thickness and effective radius from reflected solar radiation measurements David Painemal MPO531.
Radiative Properties of Eastern Pacific Stratocumulus Clouds Zack Pecenak Evan Greer Changfu Li.
Case Study Example 29 August 2008 From the Cloud Radar Perspective 1)Low-level mixed- phase stratocumulus (ice falling from liquid cloud layer) 2)Brief.
The Microwave Temperature Profiler (MTP) on START-08 MJ Mahoney - JPL/Caltech Julie Haggerty - NCAR Jan 9, 2008.
Matthew Shupe, Ola Persson, Amy Solomon CIRES – Univ. of Colorado & NOAA/ESRL David Turner NOAA/NSSL Dynamical and Microphysical Characteristics and Interactions.
Estimation of Cloud and Precipitation From Warm Clouds in Support of the ABI: A Pre-launch Study with A-Train Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro,
Aerosol-Cloud Interactions and Radiative Forcing: Modeling and Observations Graham Feingold 1, K. S. Schmidt 2, H. Jiang 3, P. Zuidema 4, H. Xue 5, P.
High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget Braun, S. A., 2006: High-Resolution Simulation of Hurricane Bonnie (1998).
Marc Schröder, FUB Tutorial, De Bilt, 10.´04 Photon path length distributions and detailed microphysical parameterisations Marc Schröder Institut für Weltraumwissenschaften,
Frank J. LaFontaine 1, Robbie E. Hood 2, Courtney D. Radley 3, Daniel J. Cecil 4, and Gerald Heymsfield 5 1 Raytheon Information Solutions, Huntsville,
Identifying 3D Radiative Cloud Effects Using MODIS Visible Reflectance Measurements Amanda Gumber Department of Atmospherics and Oceanic Sciences/CIMSS.
AEROSOL & CLIMATE ( IN THE ARCTIC) Pamela Lehr METEO 6030 Spring 2006
Matthew Shupe Ola Persson Paul Johnston Duane Hazen Clouds during ASCOS U. of Colorado and NOAA.
April Hansen et al. [1997] proposed that absorbing aerosol may reduce cloudiness by modifying the heating rate profiles of the atmosphere. Absorbing.
Simple CCN budget in the MBL Model accounts for: Entrainment Surface production (sea-salt) Coalescence scavenging Dry deposition Model does not account.
Using Ship Tracks to Characterize the Effects of Haze on Cloud Properties Matthew W. Christensen, James A. Coakley, Jr., Matthew S. Segrin, William R.
Observed & Simulated Profiles of Cloud Occurrence by Atmospheric State A Comparison of Observed Profiles of Cloud Occurrence with Multiscale Modeling Framework.
Towards a Characterization of Arctic Mixed-Phase Clouds Matthew D. Shupe a, Pavlos Kollias b, Ed Luke b a Cooperative Institute for Research in Environmental.
High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget SCOTT A. BRAUN J. Atmos. Sci., 63,
Representation of Subgrid Cloud-Radiation Interaction and its Impact on Global Climate Simulations Xinzhong Liang (Illinois State Water Survey, UIUC )
Zhibo (zippo) Zhang 03/29/2010 ESSIC
Modelling and observations of droplet growth in clouds A Coals 1, A M Blyth 1, J-L Brenguier 2, A M Gadian 1 and W W Grabowski 3 Understanding the detailed.
Radiative Influences on Glaciation Time-Scales in Mixed-Phase Clouds Zachary Lebo, Nathanial Johnson, and Jerry Harrington Penn State University Acknowledgements:
Point Comparison in the Arctic (Barrow N, 156.6W ) Part I - Assessing Satellite (and surface) Capabilities for Determining Cloud Fraction, Cloud.
Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment, Part II: Multi-layered cloud GCSS.
Stratiform Precipitation Fred Carr COMAP NWP Symposium Monday, 13 December 1999.
Horizontal Variability In Microphysical Properties of Mixed-Phase Arctic Clouds David Brown, Michael Poellot – University of North Dakota Clouds are strong.
Investigation of Microphysical Parameterizations of Snow and Ice in Arctic Clouds During M-PACE through Model- Observation Comparisons Amy Solomon 12 In.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
Update on progress with the implementation of a new two-moment microphysics scheme: Model description and single-column tests Hugh Morrison, Andrew Gettelman,
TOMS Ozone Retrieval Sensitivity to Assumption of Lambertian Cloud Surface Part 2. In-cloud Multiple Scattering Xiong Liu, 1 Mike Newchurch, 1,2 Robert.
SHEBA model intercomparison of weakly-forced Arctic mixed-phase stratus Hugh Morrison National Center for Atmospheric Research Thanks to Paquita Zuidema.
The Lifecyle of a Springtime Arctic Mixed-Phase Cloudy Boundary Layer observed during SHEBA Paquita Zuidema University of Colorado/ NOAA Environmental.
- 1 - Satellite Remote Sensing of Small Ice Crystal Concentrations in Cirrus Clouds David L. Mitchell Desert Research Institute, Reno, Nevada Robert P.
Effect of the Variability of the Radiative Properties of Light Absorbing Particles (LAC) on the Aerosol Direct Forcing in the ACE Asia Region R.W. Bergstrom.
MODIS, AIRS, and Midlevel Cloud Phase Shaima Nasiri CIMSS/SSEC, UW-Madison Brian Kahn Jet Propulsion Laboratory MURI Hyperspectral Workshop 7-9 June, 2005.
(Very) Last updates of the Liu-Penner parametrization in Hirlam. (and of the KF -scheme) Karl-Ivar-Ivarsson, Aladin/ Hirlam all staff meeting Norrköping.
Radiative-Convective Model. Overview of Model: Convection The convection scheme of Emanuel and Živkovic-Rothman (1999) uses a buoyancy sorting algorithm.
What Are the Implications of Optical Closure Using Measurements from the Two Column Aerosol Project? J.D. Fast 1, L.K. Berg 1, E. Kassianov 1, D. Chand.
Toward Continuous Cloud Microphysics and Cloud Radiative Forcing Using Continuous ARM Data: TWP Darwin Analysis Goal: Characterize the physical properties.
Multi-Layer Arctic Mixed-Phase Clouds Simulated by a Cloud-Resolving Model: Comparison with ARM Observations and Sensitivity Experiments Yali Luo State.
LOGNORMAL DATA ASSIMILATION: THEORY AND APPLICATIONS
Investigating Cloud Inhomogeneity using CRM simulations.
Simulation of the Arctic Mixed-Phase Clouds
Precipitation driving of droplet concentration variability in marine low clouds A simple steady-state budget model for cloud condensation nuclei, driven.
Han, J. , W. Wang, Y. C. Kwon, S. -Y. Hong, V. Tallapragada, and F
Application of Stochastic Techniques to the ARM Cloud-Radiation Parameterization Problem Dana Veron, Jaclyn Secora, Mike Foster, Christopher Weaver, and.
Fig. 1 Fractional coverage of the mapping method used in this study.
Presentation transcript:

Horizontal Distribution of Ice and Water in Arctic Stratus Clouds During MPACE Michael Poellot, David Brown – University of North Dakota Greg McFarquhar, Gong Zhang – University of Illinois Urbana-Champaign Introduction Radiative properties of clouds are strongly tied to optical depth and phase. Studies have shown that the cloud phase regions are not uniformly distributed (Lawson et al., 2001) and that using a model parameterization with an average phase fraction can lead to significant errors in predicted radiative budgets (Cahalan et al., 1994). Therefore, sub-grid scale variability must be accurately parameterized to get the radiative budget correct and so knowledge of the distribution of ice and water phases is essential. Technique In situ measurements of cloud microphysical properties were made using the University of North Dakota Citation aircraft during the Mixed-Phase Arctic Cloud Experiment (MPACE) project. This data set has been processed by the University of Illinois to produce time series of 10-second averages of microphysical parameters, including cloud phase and condensate amount (McFarquhar et al., 2007). MPACE missions where the Citation performed extended horizontal sampling of stratiform cloud conditions were selected for this study. Clustering of cloud phase was determined by binning contiguous occurrences of like phase during horizontal sampling legs. Samples in precipitation below the lowest layer were not included. Assuming a constant sampling speed, the phase cluster time periods can be converted into distance, e.g., 3 samples x 10 sec x 90 m s -1 = 2.7 km. Summary Clouds during the MPACE period were dominated by mixed phase. There were substantial differences in distribution of phase between single and multi-layer cloud cases, which appears to be related to the large scale forcing and airmass trajectory. Multi-layer systems were quite heterogeneous with significant regions of ice phase and relatively low liquid water paths. The lack of ice-only phase in single layer clouds indicates that use of the plane-parallel assumption may be appropriate in this case. References Lawson, R., B. A. Baker, C. G. Schmitt, and T. L. Jensen, 2001: An overview of microphysical properties of Arctic clouds observed in May and July 1998 during FIRE ACE. J. Geophys. Res., 106, – Cahalan, R. F., W. Ridgeway, W. J. Wiscombe, T. L. Bell, and J. B. Snider, 1994: The albedo of fractal stratocumulus clouds. J. Atmos. Sci., 51, 2434–2455. McFarquhar, G.M., G. Zhang, M.R. Poellot, G.L. Kok, R. McCoy, T. Tooman, and A.J. Heymsfield, 2007: Ice properties of single layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment (MPACE). Part I: Observations. J. Geophys. Res., 112, D24202, doi: /2007JD Discussion Multi-layer clouds were sampled on Oct. 5, 6 and 8 and single-layer on Oct. 8 and 10. Fig. 1 shows phase partitioning by mission, and phase distribution for Oct. 6 and Oct. 9 is shown in Fig. 2. Back trajectories for these two flights are shown in Figs. 3. The ice phase dominated 2 of 3 multi-layer cases, occurring throughout the depth of the cloud, and was absent in the single-layer case. Liquid water paths ranged from g m -2 on Oct. 9 and only 6-60 g m -2 on Oct. 6. Phase clusters tended to be smaller for the multi-layer cases (Fig 4.), although there was one large region of ice. The single layer clouds were nearly homogeneous in phase (Fig. 5), with cluster size limited by sample segment length. Figure 3. Backwards trajectories of cloudy air masses originating at Barrow, Alaska for Oct. 6 (left) and Oct. 9 (right). The red, blue, and green lines on Oct. 6 represent the first cloud layer, second cloud layer, and above the second cloud layer, respectively. For Oct. 9 they represent below, in, and above the single cloud layer. Figure 1. Phase partitioning Figure 2. Phase occurrence by height. 1=ice, 2=mixed, 3=water Figure 4. Phase cluster size (left) and length of sampling segments for multi-layer cases. Figure 5. Same as Fig. 4, for single-layer cases.