1. Ice Clouds in the Atmopshere
HomogeneousDeposition Condensation
Gravity Waves
Ice Crystals
Cirrus
Homogeneous IN
Stratus
Contact Immersion
Contact Immersion Condensation
Droplets
Convective updraft

2. Measurements of Ice Nuclei Concentrations

3. Instruments used to measure IN concentrations
Purpose: Measure the number of IN that can form for a range of water vapor supersaturation and temperature (“IN spectrum”) for a given freezing mode.
Supersaturation =
Saturation Ratio - 1
IN concentration
Supersaturation (%) =
100*(Saturation Ratio–1)
Supersaturation

4. IN Instruments: Basic Principles
Operation: Expose particle sample to a known water vapor supersaturation, and measure those that become droplets.
Desired range: 1% - 50% supersaturation (S)
Main challenges:
Generating a highly controlled level of supersaturation
Technique to invoke nucleation
Ice crystal detection
Need to differentiate from particles that do not become ice crystals.
Generally more difficult when you have dust/large non-ice particles (dust, ash).

5. IN Instruments: Generating Supersaturation
Thermal Gradient Instruments: Exploiting the nonlinear (concave) nature of the Clausius-Clapeyron Equation.
Hot plate
PoH2O
Saturation vapor
Pressure
wetted
surface
temperature
PH2O
supersaturation
Cool plate
Temperature
Generate a condition where water vapor concentration and temperature vary linearly between two points.
If both points are saturated, then supersaturation develops in between: this is how the CFDC and the ZINC/PINC works.

6. CFDC (CONTINUOUS FLOW DIFFUSION CHAMBER)
Developed by David C. Rogers
The chamber consist of two ice- coated concentric cylinders at different temperatures
In the last third of the chamber the outer cylinder are not ice-coated
Analyze deposition and immersion modes

7. ZINC (ZURICH ICE NUCLEATION CHAMBER)
CFDC type of chamber, which use a flat parallel plate design instead of a concentric cylinder geometry
Can reach 236 K with ice supersaturations of up to 50%
Analyze condensation and deposition mode
Portable version (PINC) also available commercially.

8. IMCA (IMMERSION MODE COOLING CHAMBER)
Analyze immersion mode
The Chamber is a combination of the IMCA and ZINC
First droplets are formed and then are freezed in the ZINC
The evaporation section has been removed to avoid the evaporation of droplets prior to detection.
An Ice optical detector is used to measure the frozen fraction of the droplets

9. CLINCH (COLLISION ICE NUCLEATION CHAMBER)
Uses ethanol in the cooling system
The droplets are injected in the top of the chamber, collide with the inlet particles and the flow through the chamber.
Chamber walls work at -20 C and have a thin homogeneous layer of ice to ensure the saturation with respect to ice.

10. COLD PLATE TECHNIQUE (CONTACT FREEZING)
Consist of a metallic surface coated with a thin layer of hydrophobic material
Two different ways to perform a contact freezing experiment:
Static cold plate
Dynamic cold plate
Many studies use variations of this method.

11. Ice Nucleation Spectra: very important
Nhet:
Empirical (e.g., Phillips et al. 2008)
Direct CNT (e.g., Hoose, et al. 2010)
Dispersion methods (a-pdf, soccer-ball, nucleation dispersion theory).
f(smax)=Nhet(smax)+Nhom(smax)
Freezing pulse or
Singular hypothesis
smax: Nucleation stops quickly after supersaturation is a maximum
Pf and Tchar: Freezing probability jumps from 0 to 1 in a narrow temperature range.
Stochastic understanding
Nucleation results from random fluctuations of water molecules which eventually generate a critical ice embryo.
Remember to mention missapplication of CNT

12. Ice Nucleation Spectrum
Nhet:
Empirical (e.g., Phillips et al. 2008)
Direct CNT (e.g., Hoose, et al. 2010)
Dispersion methods (a-pdf, soccer-ball, nucleation dispersion theory).
f(smax)=Nhet(smax)+Nhom(smax)
Particle ice nucleation coefficient:
Area dispersion
Physically-based, externally-mixed approach.
Takes into account variability within the population
Mathematically simple
Only two parameters: mean contact angle and dispersion coefficient
Surface composition dispersion
Remember to mention missapplication of CNT 1
Barahona, ACP, 2012

13. IN Spectra
Deposition
Condensation
Immersion
Homogeneous
Active region
Dust
Homogeneous
Soot
No thresholds of artificial constrainsts
Nc(Si, T ) = Nc(Si, T )SOOT_COND + Nc(Si, T )DUST_DEP + Nc(Si, T )DUST_COND +…
The nucleation spectrum is obtained by linear combination of individual spectra.
Barahona et al., in preparation.

14. IN parameterizations from ambient data
IN concentration depends on aerosol concentration and supersaturation
Barahona, Rodriguez, and Nenes,JGR, 2010.

15. Putting all the pieces together

16. Putting it all together: types of ice clouds
Pure Ice
Ice/Water possible
Liquid water
Cloud type
Water phase

17. Focus on cirrus (high level, pure ice) clouds
Ice/Water possible
Liquid water
Cloud type
Water phase

18. Parameterizing Cirrus Formation for climate models
Soluble and insoluble
aerosol initial distribution
Expansion cooling and
ice supersaturation development
Heterogeneous IN freezing, droplets have formed
Crystal growth, fresh IN continue to freeze and deplete vapor
Homogeneous freezing of droplets
Ice Crystals 10
9.5 9
8.5 8
Cirrus w
Cirrus formation occurs when there is ice nucleation in the upper atmosphere. Observations suggests that two mechanisms are likely to contribute to cloud formation: Hom and Het and they are distinguished by differences in freezing T and RH.
The spontaneous freezing of droplets is called homogeneous. The moment and fraction of frozen droplets depends on RH and T. If a insoluble surface is present then it can help nucleation which is called heterogeneous.
Besides RH and T, het freezing depends on the nature of the material and the freezing mode: that is the interaction between solid-liquid and vapor phases at the moment of freezing.
All of this is happening in an environment that is changing RH, T, at some rate. Here are some of the typical conditions of cirrus formation.
Liquid droplets + Insoluble material
Conceptual steps:
RHi (%) 18

19. Source of strong nonlinearity: IN effects on Ice Crystal Concentration
Homogeneous and Heterogeneous
Homogeneous
Heterogeneous
Ice Crystal Concentration (cm-3)
“Limiting” IN concentration
Crystal number is very sensitive to the type of mechanism present.
Nlim represents the limiting concentration
Ice Nuclei Concentration (cm-3)
Barahona and Nenes, ACP, 2009a. 19

20. Source of strong nonlinearity: IN effects on Ice Crystal Concentration
Homogeneous and Heterogeneous
Homogeneous
Heterogeneous
Ice Crystal Concentration (cm-3)
“Maximum ” IN influence: Very large changes in cloud properties with a few IN
Crystal number is very sensitive to the type of mechanism present.
Nlim represents the limiting concentration
Ice Nuclei Concentration (cm-3)
Barahona and Nenes, ACP, 2009a. 20

21. Crystal production in cirrus: mechanism?
Outside cirrus Si > ≤ Si ≤ 1.4
Composition of nucleated ice-crystal residuals are shown for: homogeneous freezing was dominating nucleation (Center) heterogeneous nucleation was dominating (Right).
Composition of ice cloud residuals very different meaning
both heterogeneous & homogenoeus freezing occur in cirrus
models need to represent both mechanisms.

22. Ice Parameterization Development Solving the parcel equations…
Global water vapor balance
Energy balance
Ice water vapor condensation
The solution of the problem can written in simple terms
The relation between pure homogeneous freezing and perturbed by ice nuclei fraction can be written as a function of two parameters. The IN concentration and the limiting IN concentration which looks like..
We put everything together
Ice crystal size distribution evolution = nucleation + growth
… lots of math and scaling…
Ice crystal growth
Barahona and Nenes, JGR, 2008; ACP, 2009ab

23. Solving the Equations
1- Only characteristic values are needed, i.e., the maximum supersaturation and ice crystal number; they are given by the root of the supersaturation balance:
Expansion cooling
Water vapor deposition -
2- To solve (1) the crystal size distribution must be known (but it depends both on supersaturation and time):
Using the nucleation spectrum we can simplify the balance
3- Assume that crystal size is controlled only by its residence time in the cloud (i.e., decouple nucleation and growth):
Barahona and Nenes, ACP, 2009b.

24. Solving the Equations 4- Introduce into the supersaturation balance:
After these transformations:
Supersaturation the only independent variable
The resulting expression is a convolution equation! (this type of expressions have been widely studied)
5 – Solve to find maximum supersaturation and the ice crystal concentration:
Using the nucleation spectrum we can simplify the balance
Barahona and Nenes, JGR, 2008; ACP, 2009a; ACP, 2009b.

25. Analytical Parameterization for Cirrus Ice Formation and Growth
The analytical solution of the parcel equations :
Ice
Crystal
Concentration
Barahona and Nenes, ACP, 2008,2009ab.
Simple and physically based. Completely theoretical and analytical (i.e., robust). Very fast!
Accounts for homogeneous and heterogeneous freezing
Works with a general definition of heterogeneous freezing:
Can take into account the contribution from several freezing modes and aerosol species (i.e., ranges of freezing thresholds).
Allows direct incorporation of theoretical and empirical data into large scale models. \

26. Parameterization Algorithm
Few lines of FORTRAN code. It is completely theoretical (i.e. rigorous and robust) and captures the (complex) physics of ice nucleation.
Suitable to study the aerosol indirect effect on climate from the modification of cirrus clouds.
Barahona and Nenes, ACP, 2008, 2009., JGR, 2008.

27. Cirrus parameterization evaluation: Compare Against Numerical Solution
Average error over a broad range of conditions: 5±12 %.
Orders of magnitude faster than the numerical solution
Any heterogeneous nucleation theory (or empirical observations) can be used.
Barahona and Nenes, ACP, 2009b.

28. Let’s get into some quantitative cirrus crystal number calculations
EXERCISE TIME !
Let’s get into some quantitative cirrus crystal number calculations

29. Putting all this together in a global model

30. Including the ice formation parameterization in a 3D global model
NASA GMI Chemical and Transport Model. Aerosol model: Liu et al. (2005).
Implementation:
Wind fields derived from GISS II’ GCM
Dust and black carbon as IN precursors
Cirrus allowed for T<235 K. Time step 1h, resolution 4°×°5
Dynamical forcing: Integrate over a Gaussian distribution of updraft velocities
σu =25 cm s-1
Gayet et al. (2006)
P(u)
u (cm s-1)

31. Description of Aerosol Freezing
Homogeneous (sulfate): Koop et al. (2000)
Heterogeneous (dust and black carbon): Nucleation Spectrum:
Aerosol size, chemical composition, surface properties
Dust and Black carbon freezing fraction as a function of supersaturation
There is still uncertainty in the form of NIN (Si );
we tested several definitions currently used in cloud studies. 31

32. Heterogeneous IN Spectra used in this Study
NIN (Si )
Description
Type
All IN
All dust and black carbon freeze at Si=130%
Theoretical
CNT-BN
All dust freezes at 130% and black carbon at water saturation (Si=150%-180%). Freezing fraction varies as predicted by CNT.Barahona and Nenes [2008].
Semi-Empirical
BKG
No explicit dependency in aerosol concentration, Phillips et al. [2007]
Empirical
PDA
Freezing fraction is scaled using total aerosol surface area, Phillips et al. [2008] 32

33. Heterogeneous IN Concentrations
P = 281 hPa
Empirical (RH only)
Empirical
IN concentration (cm-3)
-BN
Very different between distributions in each case.
AS BKG only depends on RH IN concentration is uniform
PDA and CNT show enhanced IN concentration in zones of high dust and bc concentrations
All IN shows very high IN concentration
IN conc. Is higher in the northern than in the southern hemisphere
Semi-Empirical
Theoretical
About two orders of magnitude difference in IN concentration
Barahona, Rodriguez, and Nenes,JGR, in press.

34. Comparison against Homogeneous Freezing
P = 281 hPa
Only heterogeneous
Homogeneous freezing dominant
Homogeneous
“Weak" competition
“Strong" competition
Heterogeneous
Empirical (RH only)
Empirical
Very slight and uniform reduction when using BKG.
Same in PDA but localized strong effects (almost %) in high dust.
CNT shows strong reduction in most of the globe, being stronger in combined dust and bc.
All IN is only heterogeneous. Higher concentrations than in homogeneous freezing.
Semi-Empirical
Theoretical
Strong competition
At least a factor of 10 variation in global mean ice crystal concentration. Most significant in Northern Hemisphere
Barahona, Rodriguez, and Nenes,JGR, in press.

35. Focus on mid-level, mixed phase clouds
Pure Ice
Ice/Water possible
Liquid water
Cloud type
Water phase

36. IN vs CCN: implications for clouds
Ice nuclei (IN) are far less abundant in the atmosphere than cloud condensation nuclei (CCN). (Typical CCN and IN concentrations: 100 cm-3 and 0.01 cm-3)
Hence, in an ice cloud, cloud water is typically distributed on fewer cloud particles than in a liquid cloud.
Consequently, the ice crystals
are larger than the cloud droplets
and therefore more likely to
fall out as precipitation

37. Bergeron-Findeisen process
Critical for the microphysical evolution of mixed-phase clouds
Once freezing starts, the complete glaciation of a mixed
phase cloud can be rapid.
Ice crystals near droplets cause:
Liquid drops to evaporate
Vapor deposits on ice
Process continues until liquid water completely gone.
General particle size increases considerably from BF process because IN << CCN
Latent heat release important for dynamical evolution of clouds. Shifts in particle size affects radiation and precipitation of cloud.

38. Bergeron-Findeisen process
Critical for the microphysical evolution of mixed-phase clouds
Once freezing starts, the complete glaciation of a mixed
phase cloud can be rapid
Ice crystals near droplets cause:
Liquid drops to evaporate
Vapor deposits on ice
Process continues until liquid water completely gone.
General particle size increases considerably from BF process because IN << CCN
Water phase
diagram
BF happens because the vapor pressure over liquid water is HIGHER than the v.pressure over ice, so mass transfers between them

39. Precipitation evolution in cold clouds
Freezing: Supercooled cloud droplets freeze when the temperature is low enough or the ice nuclei are efficient enough.
Diffusional growth: Cold clouds are usually saturated with respect to water and supersaturated with respect to ice => Diffusional growth is very efficient in cold clouds
Riming: In cold clouds with large LWC (e.g., convective
clouds), freezing of supercooled droplets on falling ice
crystals is a major growth mechanism
Aggregation: Further growth by collisions between ice crystals that stick together, especially if the temperature is relatively high.
Ice multiplication: Effective between -3°C and -8°C (Hallett-Mossop mechanism)

40. Aerosol-Cloud-Dynamics Interactions

41. Aerosol effects on ice clouds and climate
Enhanced competition with homogeneous freezing
Enhanced/decreased cirrus
Early cloud glaciation
Increased precipitation
Early cloud glaciation Convective invigoration
Courtesy: D.Barahona

42. Aerosol-Precipitation Feedbacks
Aerosols reduce drizzle.
More water reaches the freezing level.
More latent heat is released during freezing.
Convective envigoration.
Dynamical feedbacks from aerosol effects can change cloud
structure/precipitation patterns (cloud feedback?).

43. Convective invigoration is everywhere
Koren et al., Nature Geosci. (2012).
“Invigoration of clouds and the intensification of rain rates is a preferred response to an increase in aerosol concentration.”