Jen-Ping Chen Department of Atmospheric Sciences National Taiwan University NCU Seminar 2010/11/30

2 IPCC 2007 AR4 drizzle suppression, increased LWC Twomey, S., Pollution and the planetary albedo, Atmospheric Environment, 8, , Increased CDNC

Drop # vs. aerosol #. [Ramanathan et al. 2001] ppt vs. AOD. [ TMI ; Lin et al. 2006] H: cloud depth N act : cloud drop # dq/dt: drizzle rate high CWF low CWF Rainfall freq. vs. CN [ARM data; Li & Niu 2008] Drizzle rate vs. 1/N. [Pawlowska & Brenguir 2003]

BINNED vs. BULK

statistical analyses detailed model basic physical equations bulkwater equation Traditional (Kessler-type) parameterization scheme empirical solution analytical solution basic physical equations bulkwater equation assumed size dist. simplified kernel Physical-statistical parameterization scheme (Chen & Liu 2004)

water solute shape Chen and Lamb (1994, 1999) N C, Q C N R, Q R Multi-component particle framework

CCN GCCN IN

Number  Size Aerosol effect on cloud and precipitation −Any differences between continental, maritime and polluted clouds? −Still assuming E(r 1,r 2 ) = constant in a 2-moment scheme? −No explicit ice nuclei? Cloud radiative effects −1 st, 2 nd, 3 rd … indirect effects Effect of cloud on aerosol −Aerosol scavenging −Aerosol recycling −Cloud chemistry

Table 1: Bulk processes and r 2 of fitting

10 log d N / dlog r log r S max K ö hler curve SS r cut rain embryo 10  m nuclei mode accumulation mode coarse mode

(Clark 1974) Solution: embedded Lagrangian parcel Adiabatic cooling forced by grid updraft. Cheng et al. (2007) showed  20% more cloud drops with it than the Eulerian approach

Kessler Type  Assumed size distribution function  Generation functions: theoretically and empirically derived  Saturation adjustment  No number concentration  Analytical solutions often do not exist  must simplify the kernels Physical-Statistical  Does not assume size distribution functions  Generation functions: statistical fitting of results from a detailed mode  Accuracy approach detailed model and computational more efficient Scheme (# moments) Detailed (n-moment) Kessler (1-moment) Lee (1-moment) PS (2-moment) CPU

cloud water content rainwater content 1.Increase cloud drop # concentration, reduce cloud drop size 2.Decrease rainwater content, increase cloud water content Warm cloud effects:

16 negligible GCCN 50 per liter GCCN

17 negligible GCCN 50 per liter GCCN  anti-Twomey’s 1 st & 2 nd indirect effects

cloud ice snowgraupel ice nuclei raindrop S CWC BF ECEC ECEC RWC S: saturation ratio  CWC: cloud water content  RWC: rainwater content  BF: Bergeron-Findeisen process  E C : collision efficiency  CWC red: positive influence blue: negative influence ECEC CWC cloud drop

S S w =1 S i =1  S w ~ 1%  S i ~ 10% depressenhance deposition nucleation condensation for constant CWL

        Freezing nucleation rate  LWC #/cm -3 cloud drops

22 for constant CWL

Inertia effect r1r1 r2r2

Set A: Clean continental background Average continental Urban Set B: Average continental /100 Average continental /10 Average continental Average continental *10 Average continental *100

Accumulated Rainfall Summer convection Cold front (weak)

initiation cloud ice vapor deposition 2003/05/16

snow rimingmelting vapor deposition initiation 2003/05/16

riminggraupel/hail melting vapor deposition initiation 2003/05/16

rain accretioncondensation cold-initiationwarm-initiation 2003/05/16

CN concentration cold+warm rain graupel /hail snow warm rain EcEc nuc (r c ) BF EcEc snow LWC warm raingraupel/hail snow CN concentration

Hoose et al. (2010)

IN type: bacteria soil dust soot Concentrations variation: 0.04 L L -1 4 L L L L -1 typical continental cloud seeding clean, maritime

Properties of ice nuclei: (1) r N particle radius (2)  g # activation energy (3) m wetting coefficient or  contact angle Nucleation thermodynamic parameters -- determined from laboratory data A,  g g : ambient parameters f: geometric factor function of ambient parameter and wetting coefficient (Chen et al. 2008)

Cloud Ice initiationdeposition

Snow initiationdeposition melting riming

Graupel/Hail initiationdeposition meltingriming

IN concentration cold rain production snow graupel /hail snow graupel/hail cloud ice BF UU snow E c, U 

surface raincold-rain

 physical responses to increasing CN A. suppresses warm-rain formation B. reduce ice nucleation in terms of cloud ice mass (size) C. enhance Wagner-Bergeron-Findeisen process (except in convective core)  enhance snow and graupel initiation D. Reduce riming efficiency reversal concentration for cold-rain formation minimal snow production (B,C) optimal graupel/hail production (C,D) warm rainsnowgraupel Overall results depends on cloud types and lifetime (e.g. convective versus stratiform)

 physical responses to increasing IN enhance ice nucleation and cloud ice formation. Increase snow initiation; reduce W-B-F growth due to consumption by cloud ice and competition among snow; reduce snow size and fall speed. Increase graupel initiation; reduce riming due to reduced size of and competition among graupeln; reduce fall speed snow graupel reversal IN concentration for cold-rain formation decreases when IN concentration is lowered or elevated implication to the ineffectiveness of cold-cloud seeding overall results may depend on cloud types and lifetime, and the strength of warm rain production.

Evolution of deep convective clouds developing in the pristine (top) and polluted (bottom) atmosphere. [Rosenfeld et al.2008] Latent heat effect

Vertical Velocity Spectrum

nucleation ↓ (r c ↓, S↓) deposition ↓ (S↓, BF ↓ ) riming ↑ ↓ (LWC↑, E c ↓) latent heating ↑ (LWC↑) nucleation ↑ ↓ (r c ↓, S ↑ ) deposition ↑ (S ↑, r c ↓, BF↑) riming ↑ ↓ (LWC↑, E c ↓) latent heating ↑ (BF↑) evaporation↑ (r R ↓) outflow anvilconvective core cold pool

(Teller and Levin 2006)