Planet Formation Topic: Viscous accretion disks Lecture by: C.P. Dullemond.
Topic 5: Precipitation Formation · Topic theme The aim of Topic 5 is to examine ice processes...
Transcript of Topic 5: Precipitation Formation · Topic theme The aim of Topic 5 is to examine ice processes...
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Topic 5:Precipitation Formation
Co leaders: Paul Field, Andy Heymsfield, Jerry Straka
Contributors:Sara Pousse-Nottelmann; Alexandria Johnson; Andrea Flossmann;;
Charmain Franklin; Irina Gorodetskaya; Ismail Gultepe; Jonny Crosier; Martina Krämer; Paul Lawson; Ulrike Lohmann; Wolfram
Wobrock
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Topic themeThe aim of Topic 5 is to examine ice processes involved in precipitation formation.
The objectives of the group are to:
• Assess what is known about each process
• Identify what we know less about
• Suggest ways to close these gaps through:•identifying methodologies for measuring these processes (including insitu and remote sensing platforms).
•identifying required observations
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Recap – previous BAMS article• What are the respective roles of homogeneous and heterogeneous
nucleation under different ambient conditions?• What is the relationship between IN and ice crystal concentration?• When do secondary ice formation processes become important,
what are the mechanisms for secondary ice formation, and on what do these processes depend?
• What are the freezing mechanisms below205 K?• What are the optical properties of ice crystals as a function of habit?• What are the sedimentation velocities of ice crystals as a function of
habit?• What are the spatial scales of cirrus cloud inhomogeneities?• What is the value of the accommodation coefficient for ice and does
it vary with temperature and humidity?
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Processes Just consider processes involving ice
• Ice nucleation – [massive subject area – also See TOPIC 1
• Diffusional growth/sublimation/evaporation/melting • Aggregation• Secondary ice production (splintering/breakup) (also see
TOPIC3) • Riming/graupel/hail formation• Sedimentation: fallspeed, mass
• Aerosol-cloud interactions: Inadvertent Weather Modification?
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Ice nucleation[Consider conditions warmer than T=-35C – see topic 1 for colder]What we do know:At T>-35C, immersion freezing is dominant process (Lab, field work
e.g. ICE-L, AIDA, CFD results)Dust (size>0.5microns) correlates well with IN concentrations
suggesting that dust is a good IN (DeMott et al. 2010)Lab tests have isolated Feldspar as the active component in dust
(Atkinson et al. 2013)
What we know less about:Are other aerosol types are important (e.g. bio, soot) ?Do we need to worry about time dependence (e.g. Vali)?
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Diffusional growth/sublimation/melting
What do we know:• Use electrostatic analogy• In the last 3 years – models developed that use different habits
(Harrington)• Estimates of capacitance from modelling of mass flux to particle
(Westbrook et al. 2008) and using aircraft observations (Field et al 2008)
• Earlier lab estimates from Bailey+Hallett.
What we know less about• No new estimates of ventilation parameters• Few estimates of capacitance of complex shapes
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Wave clouds:Heymsfield et al. 2011
Use of wave clouds as naturallaboratories
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Westbrook and Heymsfield 2011– testing of capacitance and ventilation
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Aggregation
What we do know: Stochastic Collection equation
Bulk representation - Ea
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Aggregation
Need to know:• PSD, V, K(I,j)
What we know less about:• K(i,j) – what is its form? Is sweep out
kernel adequate model?• What is aggregation efficiency, Ea, as
function of T, E-field ,etc?
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Courtesy: Richard Cotton
Obtaining ice particle model parameters with in-situ aircraft observations
•Lagrangian spiral descent •Drift with horizontal wind•Descend with mean speed of ice crystals•Repeatedly sample same ice crystals as they aggregate
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Use modelling to interpret results and estimate Ea.e.g. Passarelli 1978, Mitchell 1988, Field et al. 2006.
Ice saturation conditionsOnly aggregation process is acting to change PSD
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-The Manchester Ice Cloud Chamber (MICC) is a fall-tube 10 m tall and 1 m in diameter.
CPI1
CPI2
Generate ice at the top
Combine observations from CPI1, 2 with modelling to estimate Ea
Connolly et al 2012
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Secondary multiplicationFrom AMS Glossary
Hallett–Mossop process(Also called rime splintering.) One of the mechanisms thought to be responsible for secondary ice production, when ice crystal concentrations in clouds well in excess (x 10 000) of the ice nucleus concentration are found.
Ice particles are produced in the range of temperature -3°to -8°C (with a maximum at -4°C) as graupel grows by accretion provided the cloud droplet spectrum contains appropriate numbers of droplets smaller than 12 μm and greater than 25 μm. About 50 splinters are produced per milligram of accreted ice.
Hogan et al 2002
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Secondary ice productionExtension to riming snow (Hogan et al. 2002, Crosier et al. 2011 ,
Crawford et al. 2012 )
Need laboratory confirmation
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Secondary ice productionNew lab work by Knight at -5C“There appears to be an ice multiplication mechanism
in these conditions that does not involve riming.”
Recent studies have looked at crystal fragmentation using aircraft data (Schwarzenboeck et al. 2009, earlier proposal from Vardiman)
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Graupel/Hail production
If latent heating during riming leads to T~0C then wet growth (droplets flow before freezing, air bubbles escape, ice is clear), otherwise dry growth (air bubbles included, ice is translucent
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Exponential size distribution parameters based on surface measurements
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SedimentationWhat we know• Is a control on the amount of ice in the atmosphere –
important for radiation, precip and water vapour.• Represented by v-D power laws based on direct
observation and/or Best number-Reynolds number parametrization (e.g. Mitchell and Heymsfield)
• Variable (+- factor of 2? for same size across many powerlaws )
What we know less about• How to include variability of v-D in models (do we need
to?)
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Irina Gorodetskaya
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Sedimentation
Protat and Williams 20110.1m/s residual in method: less than variability
Should this variability be represented?
Szyrmer and Zawadzki 2010Disdrometer
Heymsfield and Westbrook Used tank and lab data to combine drag data with the Best-Re number method of estimating Vt (2010)
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PSDWhat we do know• PSD has an exponential tail• Scalable (possibly due to aggregation controlling its
evolution)• Past measurements affected by shattering
What we know less about• What does the small end of the PSD look like? Where
should the mode be centred?• Graupel/hail size distributions in cloud• how do ice number concentrations relate to IN
concentrations? How does secondary ice affect this?
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Recent Developments in Knowledge of ICE PSDs
• New Probes, etc– CDP– SID‐2 and SID‐3– HOLODEC– 2D‐S, 3VCPI– CIP Grey– HVPS‐3– Probe Tips
• Characterizing Probe Response– Ice Shattering in Wind Tunnels, Observations (Korolev, Lawson)– Ice scattering models
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PSD
Recent papers:• Tian et al (2010 JAS) suggests that lognormal
distributions are best – challenging the exponential tail idea.
• Woods et al (2008 JAS) grouped PSD according to habit. Exponential fit parameters a function of T and habit
• Mitchell et al (2010 JAS) combine remote sensing with PSD representations to retrieve the sub-100 microns size range of the PSD.
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Impact of PSD shape on the far-infraredspectrum (Baran, 2007 QJRMS 133, pgs
1425-1437)Theoretical BT differences between shapes of the PSD but Deis the same
De=8 µm
De=48 µm
De=48 µm
TAFTS simulations: CIRCREX field campaign fall of 2013 spring 2014
33 25 20 17 um
100 67 50 40 um
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New Observations• DOE ARM Projects (ISDAC)• NASA Cirrus Studies (MACPEX)• NASA GPM Studies• Hurricanes (GRIP, PREDICT)• UK Met Office• Balloon‐Borne Launches (Sweden)• Laboratory (AIDA)• Contrail, Hole Punch Obs.
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Mass-sizeWhat do we know:• m-D power law representation• Aggregrates mass ~D2 - insensitive to monomer habit (Westbrook et al)Estimate mass-size relationship through:Direct measurement of individual particles in lab or captured outsideEquating bulk ice water measurement from CVI (e.g. Heymsfield et al . 2010)
or Nevzorov probe with integrated size distribution (e.g. Cotton et al. 2010)
• m-D relations highly variable
What we know less about:
• How to implement variability into models. Do we need to – or is a mean representation sufficient?
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Improving the representation of precipitation processes
Requirement:Ice particle properties (m, v, Cap, vent) AND their variability as a function of spatial scale
Solution:Improved sensitivity and range of bulk ice water probes.Additional statistics to provide spatial coherenceDevelop cloud microphysics representations that can use variability
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Requirement:Validation of PSD representation (for ice and graupel/hail)
Solution:Multi-sensor comparisons with same volume of ice cloud (e.g. Radar, lidar, microwave)Measurements of insitu graupel distributions
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Requirement:Determine collection efficiencies for ice aggregation and graupel/hail formation
Solution:New laboratory workMore insitu Lagrangian flight studies
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Requirement:Improved Ice nucleation treatment in models
Solution:Continued laboratory and field workDetermine whether time dependence needs to be representedExplore usefulness of including prognostic IN in models