Mixed-Phase Arctic Cloud Experiment M-PACE Hans Verlinde With contributions from Jerry Harrington, Greg McFarquhar, Eugene Clothiaux, Scott Richardson,

Slides:

Advertisements

Similar presentations

Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space.

Advertisements

Integrated Profiling at the AMF

Allison Parker Remote Sensing of the Oceans and Atmosphere.

3D Radiative Transfer in Cloudy Atmospheres: Diffusion Approximation and Monte Carlo Simulation for Thermal Emission K. N. Liou, Y. Chen, and Y. Gu Department.

Earth System Science Teachers of the Deaf Workshop, August 2004 S.O.A.R. High Earth Observing Satellites.

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.

TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.

Cloud Physics Breakout Science Questions/Issues: Driving factors/relative importance of these factors in understanding precipitation formation needs to.

Page 1© Crown copyright The Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 Steve Abel First VOCALS Regional Experiment (REx) Preparatory.

Climate modeling Current state of climate knowledge – What does the historical data (temperature, CO 2, etc) tell us – What are trends in the current observational.

UNIVERSITY OF LEEDS School of Earth and Environment INSTITUTE FOR CLIMATE AND ATMOSPHERIC SCIENCE Arctic Summer Cloud-Ocean Study (ASCOS) Ian Brooks, Cathryn.

Use of a Small Unpiloted Aerial Vehicle for Remote Sensing in the Arctic – Potential and Limitations Jim Maslanik, Rationale.

ATS 351 Lecture 8 Satellites

Atmospheric structure from lidar and radar Jens Bösenberg 1.Motivation 2.Layer structure 3.Water vapour profiling 4.Turbulence structure 5.Cloud profiling.

The Atmospheric Radiation Measurement (ARM) Program: An Overview Robert G. Ellingson Department of Meteorology Florida State University Tallahassee, FL.

Observational approaches to understanding cloud microphysics.

Robert Wood, University of Washington many contributors VOCALS-REx Facility Status – NSF C-130.

Figure 2.10 IPCC Working Group I (2007) Clouds and Radiation Through a Soda Straw.

Atmosphere Sciences Instrumentation Lab Organized by David Delene - November 20, 2008 Overview of Instruments – 3:00 Clifford Hall 470 Lab Demonstrations.

Matthew Shupe Von Walden David Turner U. Colorado/NOAA-ESRL U. Idaho NOAA - NSSL New Cloud Observations at Summit, Greenland: Expanding the IASOA Network.

ARM Water Vapor Measurements for Validation and Analysis Jim Mather Technical Director for the ARM Climate Research Facility.

CALWATER2 Field Study of Air-Sea Interaction and AR dynamics in Midlatitude Pacific Storms Ship (NOAA Brown) Ship Field Duration – 30 days Time Window.

BAE 146 Aircraft RICO Schedule RICO Participating Scientists RICO Instrumentation Contact: Alan Blyth

Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.

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.

Page 1© Crown copyright 2004 Cirrus Measurements during the EAQUATE Campaign C. Lee, A.J. Baran, P.N. Francis, M.D. Glew, S.M. Newman and J.P. Taylor.

Clouds, Aerosols and Precipitation GRP Meeting August 2011 Susan C van den Heever Department of Atmospheric Science Colorado State University Fort Collins,

THE ARM UAV Program: Past, Present and Future Greg M. McFarquhar 1 and Will Bolton 2 1 University of Illinois, Urbana, IL 2 Sandia National Laboratories,

Water Cycle Breakout Session Attendees: June Wang, Julie Haggerty, Tammy Weckwerth, Steve Nesbitt, Carlos Welsh, Vivek, Kathy Sharpe, Brad Small Two objectives:

DOE’s Flagship Global Climate Change Program ARM Climate Research Facilities in Alaska The North Slope of Alaska Team at Sandia Labs/NM: Bernie Zak, Jeff.

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.

GV Sites in Canada David Hudak Paul Joe Environment Environnement Canada 2nd International GPM Ground Validation Workshop, September, Taipei, Taiwan.

Matthew Shupe, Ola Persson, Amy Solomon CIRES – Univ. of Colorado & NOAA/ESRL David Turner NOAA/NSSL Dynamical and Microphysical Characteristics and Interactions.

ARM Data Overview Chuck Long Jim Mather Tom Ackerman.

Science Discipline Overview: Atmosphere (large-scale perspective)  How might large-scale atmospheric challenges add to the scientific arguments for MOSAIC?

Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:

RICO Modeling Studies Group interests RICO data in support of studies.

Group proposal Aerosol, Cloud, and Climate ( EAS 8802) April 24 th, 2006 Does Asian dust play a role as CCN? Gill-Ran Jeong, Lance Giles, Matthew Widlansky.

Matthew Shupe Ola Persson Paul Johnston Duane Hazen Clouds during ASCOS U. of Colorado and NOAA.

A NASA / NSF / NRL airborne field campaign focusing on atmospheric composition, chemistry, and climate over Southeast Asia. Programmatic Context, Issues.

1 Atmospheric profiling to better understand fog and low level cloud life cycle ARM/EU workshop on algorithms, May 2013 J. Delanoe (LATMOS), JC.

New observations of clouds, atmosphere, and precipitation at Summit, Greenland Matthew Shupe, Von Walden, David Turner Ryan Neely, Ben Castellani, Chris.

Boundary Layer Clouds.

Autonomous Polar Atmospheric Observations John J. Cassano University of Colorado.

Distribution of Liquid Water in Orographic Mixed-Phase Clouds Diana Thatcher Mentor: Linnea Avallone LASP REU 2011.

Preliminary modeling work simulating N-ICE snow distributions and the associated impacts on: 1)sea ice growth; 2)the formation of melt ponds; and 3)the.

Multidisciplinary drifting Observatory for the Study of Arctic Climate

The Multidisciplinary drifting Observatory

Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment, Part II: Multi-layered cloud GCSS.

Jetstream 31 (J31) in INTEX-B/MILAGRO. Campaign Context: In March 2006, INTEX-B/MILAGRO studied pollution from Mexico City and regional biomass burning,

Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.

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.

Satellite Indicators of Severe Weather. What Are The Relevant Scientific Questions And Objectives Related To This Topic? Preliminary considerations: Focus.

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.

SCM x330 Ocean Discovery through Technology Area F GE.

Toward Continuous Cloud Microphysics and Cloud Radiative Forcing Using Continuous ARM Data: TWP Darwin Analysis Goal: Characterize the physical properties.

Simulation of the Arctic Mixed-Phase Clouds

What are the causes of GCM biases in cloud, aerosol, and radiative properties over the Southern Ocean? How can the representation of different processes.

The Atmosphere during MOSAiC

The Water Cycle The continuous process of water evaporating, condensing, returning to the earth as precipitation, and returning to a water resource.

Atmosphere & Weather Review

NPOESS Airborne Sounder Testbed (NAST)

Microwave Remote Sensing

Group interests RICO data required

Group interests RICO data in support of studies

Presentation transcript:

Mixed-Phase Arctic Cloud Experiment M-PACE Hans Verlinde With contributions from Jerry Harrington, Greg McFarquhar, Eugene Clothiaux, Scott Richardson, and Chad Bahrmann

Arctic System Synthesis: Is the Arctic Headed Toward a New State? Talk delivered by Jonathan Overpeck for the Arctic System Science (ARCSS) Committee a the SEARCH Open Science Team Meeting, October 2003, Seattle. Slide Courtesy Jonathan Overpeck From ARCSS Big Sky Retreat Participants

Slide Courtesy Jonathan Overpeck From ARCSS Big Sky Retreat Participants

Role of clouds in the Arctic The following figure was taken from a recent NSF document on the hydrological cycle in the Arctic, detailing feedbacks among sea ice, precipitation, river runoff, and coastal oceans Vorosmarty et al. 2001

State of knowledge prior to SHEBA Cloud fractions > 70% in spring, summer and fall common Surface energy budget sensitive to cloud properties –Small changes in cloud fraction and/or effective radius of liquid produce ~40 W m -2 changes at surface (Curry et al. ‘93) –Changes in effective radius for ice produced ~80 W m -2 changes at surface (Harrington and Olsson, ‘01) Connection to other components –Modest changes in cloud properties produced ~3 m changes in equilibrium sea-ice thickness (Curry and Ebert,’90; Curry et al., ’93) In GCM –Changes in sea-ice impact cloudiness: cloud cover responds differently to reductions in sea-ice concentration depending on cloud and convection parameterizations (Royer et al., ’92)

Where do we stand after SHEBA/FIRE III? - low-level clouds are predominantly mixed-phase and long-lived, with characteristics distinctly different from lower latitude clouds - impact on surface energy budget depend on characteristics of clouds - role of aerosol in cloud characteristics (CCN/IFN) Where do we need to go? - need a field experiment that targets mixed-phase clouds in the Arctic - corroborate & improve cloud models (process studies link to remote sensing) - data base needed for remote sensing retrieval validation Courtesy Janet Intrieri

Scientific Questions Hans Verlinde, Jerry Harrington M-PACE Scientists and Greg McFarquhar UAV Mission Scientist How are mixed-phase cloud microphysics, radiation, and cloud dynamics linked? Local Effects: Mixed-phase microphysics (growth, spatial and temporal distributions) and the subsequent direct and indirect links to radiation and cloud turbulence. How well do cloud models capture these processes? Large-Scale Effects: How important is large-scale convergence/divergence of heat and moisture to mixed-phase longevity (vs. local processes)? Connections to Observations: Can we use radar/lidar retrievals (which provide measures of cloud properties and turbulence) in a synergistic fashion with cloud models to improve retrievals?

Specific Objectives Horizontal structure and variability of the cloud microphysics and dynamics. Vertical profiles of microphysics, particularly over the ground based remote sensing sites. Coincident radiance/irradiance data above/below cloud layers with in situ microphysical data. Impacts of multiple cloud layers on cloud characteristics and measurements. Scattering-phase function of different clouds types. Water vapor profiles in clear and cloudy conditions. Clear sky emissivity. Atmospheric structure at corners of grid box during cloudy events.

Observing facilities in place: 1.DOE-ARM ground base observing sites at Barrow and Atqasuk supplemented with de-pol. lidar (Sassen) 2.Oliktok Point – PNNL Atmospheric Remote Sensing Lab. (PARSL), scanning HIS. 3.Enhanced radiosonde releases at Barrow, Atqasuk, Oliktok, and Toolik Lake. 4.In-situ aircraft -- UND Citation (microphysical inst.), based in Deadhorse CSU Continuous Flow Diffusion IN counter NCAR CCN Counter DMT Cloud Spectrometer and Impactor SPEC Cloud Particle Imager 5.Remote sensing aircraft – DOE UAV (Proteus) with lidar, cloud radar, and in-situ microphysical inst. Based in Fairbanks. Potential collaborations: 1.Aerosonde (NSF) 2.CIN on Citation (NSF) Experimental Design

Citation Instrumentation State parameters: 2 temperature probes, pressure Cooled mirror (EG&G) and laser hygrometer for dew point Rosemont ice detector for supercooled water CSIRO King probe and Nevzorov Probe for LWC CSI and Nevzorov Probe for total condensed water FSSP 100 for cloud droplet spectrum 2D-C, 2D-P or HVPS, CPI for particle imaging Cloud integration nephelometer NCAR CCN and CSU CFDN IN counter

Proteus Instrumentation Active remote sensing: –Millimeter wave cloud radar –Cloud detecting lidar Passive Remote sensing –Spectral radiance package –Broadband radiometers –Solar spectral flux radiometers –Scanning high-resolution interferometer sounder –Diffuse field camera In Situ: –State parameter package –Cloud, aerosol and precipitation spectrometer (CAPS) –Cloud integrating Nephelometer –Video ice particle sampler –Nevzorov Probe

Wind finding technique 2 Vaisala RS90 sensors (P, T, q) Piezoelectric Plate – to detect icing KT11 pyrometer (Tsfc down to -40 C) Olympus 3030Z Digital Camera GPS (location and altitude) Aerosonde Payloads Wind finding techniqueWind finding technique Iridium satellite phone (over the horizon comms)Iridium satellite phone (over the horizon comms) Video camera with thermal and vis channelsVideo camera with thermal and vis channels HHPC-6 large particle counterHHPC-6 large particle counter VIPS (Video Ice Particle Sampler)VIPS (Video Ice Particle Sampler)

Experimental Layout

OliktokBarrow

Relevant Aerosonde Missions Long-duration profiling (slant, box, spiral drift) Cloud/aerosol dwell sampling and profiling Characterization of mesoscale variability in BL structure. Cloud field or surface recon with vis or thermal video camera. Lagrangian cloud-tracking