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

Measurements of Ice Nuclei Concentrations

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

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).

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.

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

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.

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

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.

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.

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

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

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.

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

Putting all the pieces together

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

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

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 100 110 120 130 140 150 160 Conceptual steps: RHi (%) 18

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

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

Crystal production in cirrus: mechanism? Outside cirrus Si > 1.4 1.0 ≤ 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.

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

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.

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.

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. \

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.

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.

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

Putting all this together in a global model

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)

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

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

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.

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 40-50 %) 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.

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

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

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.

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

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)

Aerosol-Cloud-Dynamics Interactions

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

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?).

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.”