Modeling Ice Growth In Clouds

Transcription

1 Modeling Ice Growth In Clouds Uncertainties, Inconsistencies and New Approaches Perspective of Jerry Y. Harrington Pennsylvania State University With Special Thanks to: NSF, ASR, Dennis Lamb, Kara Sulia, Chengzhu Zhang, and Hugh Morrison Photos by D. Lamb

2 - What Are The Difficulties? - Ice Forms and Grows by a number of mechanisms Formation: Heterogeneous (solid aerosol, difficult to measure). Homogeneous (supercooled drops, solution important). Growth: Collection Processes (riming, aggregation depend on ice particle shape) Vapor Diffusion (complex vapor molecule incorporation in complex ways, producing intricate shapes. Important impacts on cloud mass.) All Very Uncertain. Focus on Vapor Growth Problem.

3 Why is the problem difficult? Wide Variety of Ice Crystal Habits!

4 Capacitance: Captures strong gradients as particle deviates from spheres/isometric

5 Real Crystal Simulation: Thick Plate Thin Plate Weaker Flux Stronger Flux

6 Mass-size relations Capacitance: C(c,a) = L f s (φ) Aspect ratio = constant Mass: m(l) = al b Contains entire crystal growth history Large variety possible Avramov and Harrington (2010)

7 Final Size C(c,a) = L f s (φ) Changing Aspect Ratio Faster Growth!!

8 20 minutes growth, liquid saturation Aspect ratio: constant Wrong shapevapor influence φ too small φ too large Lab Data Harrington and Sulia (2011)

9 Bergeron Process: Rapid growth of ice, drops evaporate Depends on habit Many small drops converted to a few large crystals => CAN GLACIATE CLOUD!

10 Mostly Liquid Ni ~ 0.1 l-1 rapid conversion of liquid to ice Mostly Ice Ni ~ 20 l-1 Increase Ice Concentration Increase ice concentrations Prenni et al. (2007)

11 SHEBA Data Prenni et al. (2007) Models Poor simulations of liquid during cold season.

12 Small Error Large Errors => Too little liquid Prenni et al. (2007)

13 - Mass-size: Large Water Path Ranges - Liquid Water Path [g m -3 ] Grow slow, fall fast Grow fast, fall slowly Ice Concentration [L -1 ] Avramov and Harrington (2010)

14 - What s the Problem with Mass-size? - Power depends on growth efficiencies: m(l) = al b but b ~ α c /α a Mass-size relations: Each crystal, different history Measured b, cloud average α a α c Growth Efficiency Temperature [C] Lamb and Scott (1974)

15 - Mass-size Inconsistency - Crystal Trajectories Each point in m = alb lifecycle of an entire crystal Inconsistent with dm/dt

16 - Can We Do Better? Chen and Lamb (1994) Habit Method α c α a c a Measured in the lab Depends on T only at liquid saturation Plates < 1.0 Columns > 1.0 Allows for a natural change in aspect ratio in time!!

17 20 minutes growth, liquid saturation Lab Data Habit Method: Black Solid and Dashed Lines Harrington and Sulia (2011)

18 - Can Be Turned Into Bulk Model - Used in Cloud/Forecast Models Predict average sizes only. Key Link: Theoretical method links power laws to growth history Harrington and Sulia (2011)

19 - Habits vs. Spheres - Kinematic Cloud Model T ~ -18 o C, N i = 10 L -1 Spheres: More liquid, spatially homogeneous Habits: Less liquid, elevated downdraft base. Sulia and Harrington (2011)

20 Method works well at liquid (high) saturation Mathematical snow Natural snow Based on Yokoyama (1993)

21 - But Not At Low Supersaturations - Efficiencies depend on mechanism/saturation Efficient growth Inefficient growth Lamb and Chen (1995)

22 Cirrus Uncinus Efficient Growth α = 1.0 Cirrocumulus Inefficient Growth α = Harrington et al. (2009)

23 New Theoretical Method: α c α a c a Predict α a and α c based on supersaturation Constant ρ v Oblate Spheroid

24 10 minutes growth Good Match At Low Ice Saturations And At High Ice Saturations Where Model Traditionally Works Well. Zhang and Harrington (2011)

25 Main Points Ice vapor growth critical for cloud phase evolution, structure, and even dynamics. Classic ice vapor growth uses spheres or mass-size relations Spheres growth too slow Mass-size Contains entire crystal growth history, inconsistent. Wrong exponent as temperatures change Produces large scatter in predicted water paths Habit prediction possible, developed for cloud models, based in laboratory-determined crystal growth properties. Unified with lab data at low ice supersaturations. Methods being tested, and implemented into cloud models. Photos by D. Lamb

26 Matches exact hexagonal growth model Zhang and Harrington (2011)

27 - Mixed-Phase: Habit Impacts - Different habits: Lower ice concentrations for faster growing habits l l -1 Avramov and Harrington (2010)

28 - Mass-size vs. Habit Evolution Models - Mass-size, Spheres: Cannot capture IWC evolution has habits/aspect ratios change Harrington and Sulia (2011)

29 dm/dt = 4πD v C(c,a)[ρ far -ρ sfc ] C(c,a) = Lf s (φ) Capacitance, C = effective size L = Maximum Dimension f s (φ) = accounts for shape (constant in models) Advantage: Easy to use..but must relate mass and size somehow!!

30 Modeling: One Link to The Real World! Causal connections between physical processes Models only as good as the theories/data used to test and derive them! In-situ data: microphysics structure, dynamics Detailed growth of individual particles; test theories.

31 Capture 3 processes: (1) Vapor diffusion: Vapor transport through space (2) Molecular incorporation: Different on basal and prisim faces. Controlled by growth efficiency (αb, αp) (3) Habit Evolution: Depends primarily on T, si.

32 - Parcel Model: Habit Evolution - Equivalent Density Spheres underpredict ice growth rates Slower glaciation, more liquid Sulia et al. (2010)

33 Lagrangian: Predict individual sizes and mass Bulk Models: Predict total # and mass (IWC); perhaps mean size Eulerian bin: Predict # and mass in size ranges Eulerian & Bulk Used In Cloud Models Must Track One Representative Size!

34 What we model Real Crystals Prolate Oblate (1) Use Spheroids (capture primary habit: a and c axes) (2) Mass Diffusion to Crystal (Capacitance Model) (3) Redistribute Mass Along a and c axes (Crystal Growth Model)

35 Multi-stage Nature of Vapor Deposition What happens when layer has grown out? Surface4.cdr

36 Possible Structures of Singular Faces Smooth Energy ratio Rough From Markov (2003)

37 Kuroda and Lacmann (1982) Role of surface kinetics in habit formation Phase changes on surface Blend of mechanisms: I V-QL-S during multilayer adsorption II Adhesive growth on rough surface III 2-D nucleation on smooth surface Quasi-liquid layer Basal face Prism face Plate Column Plate Column T Primary habit

38 Forming Layers on Singular Faces (1) Monomolecular 2-D islands (2) Multilayer islands From Markov (2003)

39 Burton, Cabrera and Frank (1951) General expectation A complete theory of the growth of defective crystals (at low supersaturations) Spiral steps in growth of ICE from the melt Burton et al. (1951, Phil. Trans. Roy. Soc. London, Series A, 243,

40 From Markov (2003) Formation of a Spiral Growth Pyramid

41 Vertical-flow Levitation System for MASS Growth Measurements Relative mass related to spring-point frequencies: LevitationChamber_vert.cdr

42 Cirrus-like Ice Particle Subsaturated Supersaturated Only 6 out of every 1000 molecules involved in vapor deposition to small ice particles! Particle-average mass accommodation coefficient

43 Modeling the Growth of a 3-D Snow Crystal Do we need physics anymore? Gravner and Griffeath (2008)

44 The Arctic climate is changing! => Retreating sea ice Minimum extent is lower than any climate model prediction Clouds may be important! Stroeve et al. (2007)

45 Growth efficiency can impact cirrus: => Inefficient growth: Higher ice concentrations and stronger vertical motions Growth Efficiency (α) => Efficient growth: Smaller ice concentrations and weaker vertical motions. Growth Efficiency (α)

46 - Parcel Model: Habit Evolution - Bulk habit model captures evolution of mass and habit (mean a and c axes) Accurate despite changing temperature and saturation, which changes habit growth. Sulia et al. (2010)

47 - Can We Do Better? Chen and Lamb (1994) Habit Method α c α a c a