1. Control of Cloud Droplet Concentration in Marine Stratocumulus Clouds Photograph: Tony Clarke, VOCALS REx flight RF07 Robert Wood University of Washington with help/data from Chris Terai, Dave Leon, Jeff Snider Robert Wood University of Washington with help/data from Chris Terai, Dave Leon, Jeff Snider

2. Radiative impact of cloud droplet concentration variations George and Wood, Atmos. Chem. Phys., 2010 cloud droplet concentration N d albedo enhancement (fractional)

3. “Background” cloud droplet concentration critical for determining aerosol indirect effects Quaas et al., AEROCOM indirect effects intercomparison, Atmos. Chem. Phys., 2009 Low N d background  strong Twomey effect High N d background  weaker Twomey effect  A  ln(N perturbed /N unpertubed ) LAND OCEAN

4. Aerosol (d>0.1  m) vs cloud droplet concentration (VOCALS, SE Pacific) 1:1 line see also... Twomey and Warner (1967) Martin et al. (1994)

5. Extreme coupling between drizzle and CCN Southeast Pacific Drizzle causes cloud morphology transitions Depletes aerosols

6. MODIS-estimated mean cloud droplet concentration N d Use method of Boers and Mitchell (1996), applied by Bennartz (2007) Screen to remove heterogeneous clouds by insisting on CF liq >0.6 in daily L3

7. Cloud droplet concentrations in marine stratiform low cloud over ocean Latham et al., Phil. Trans. Roy. Soc. (2011) The view from MODIS....how can we explain this distribution? N d [cm -3 ]

8. Baker and Charlson model CCN/cloud droplet concentration budget with sources (specified) and sinks due to drizzle (for weak source, i.e. low CCN conc.) and aerosol coagulation (strong source, i.e. high CCN conc.) Stable regimes generated at point A (drizzle) and B (coagulation) Observed marine CCN/N d values actually fall in unstable regime! Baker and Charlson, Nature (1990) observations 10 100 1000 CCN concentration [cm -3 ] CCN loss rate [cm -3 day -1 ] 200 100 0 -100 -200 -300 -400 -500

9. Baker and Charlson model Stable region A exists because CCN loss rates due to drizzle increase strongly with CCN concentration – In the real world this is probably not the case, and loss rates are  constant with CCN conc. However, the idea of a simple CCN budget model is alluring Baker and Charlson, Nature (1990) observations 10 100 1000 CCN concentration [cm -3 ] CCN loss rate [cm -3 day -1 ] 200 100 0 -100 -200 -300 -400 -500

10. Prevalence of drizzle from low clouds Leon et al., J. Geophys. Res. (2008) Drizzle occurrence = fraction of low clouds (1-4 km tops) for which Z max > -15 dBZ DAY NIGHT

11. Simple CCN budget in the MBL Model accounts for: Entrainment Surface production (sea-salt) Coalescence scavenging Dry deposition Model does not account for: New particle formation – significance still too uncertain to include Advection – more later

12. Production terms in CCN budget FT Aerosol concentration MBL depth Entrainment rate Wind speed at 10 m Sea-salt parameterization-dependent constant We use Clarke et al. (J. Geophys. Res., 2007) at 0.4% supersaturation to represent an upper limit

13. Sea-spray flux parameterizations Courtesy of Ernie Lewis, Brookhaven National Laboratory u 10 =8 m s -1

14. Loss terms in CCN budget: (1) Coalescence scavenging Precip. rate at cloud base MBL depth Constant cloud thickness Wood, J. Geophys. Res., 2006 Comparison against results from stochastic collection equation (SCE) applied to observed size distribution

15. Loss terms in CCN budget: (2) Dry deposition Deposition velocity w dep = 0.002 to 0.03 cm s -1 (Georgi 1988) K = 2.25 m 2 kg -1 (Wood 2006) For P CB = > 0.1 mm day -1 and h = 300 m = 3 to 30 For precip rates > 0.1 mm day -1, coalescence scavenging dominates

16. Steady state (equilibrium) CCN concentration

17. VariableSourceDetails N FT Weber and McMurry (1996) & VOCALS in-situ observations (next slide) 150-200 cm -3 active at 0.4% SS in remote FT DERA-40 Reanalysisdivergent regions in monthly mean U 10 Quikscat/Reanalysis- P CB CloudSatPRECIP-2C-COLUMN, Haynes et al. (2009) & Z-based retrieval hMODISLWP, adiabatic assumption zizi CALIPSO or MODIS or COSMIC MODIS T top, CALIPSO z top, COSMIC hydrolapse Observable constraints from A-Train

18. MODIS-estimated cloud droplet concentration N d, VOCALS Regional Experiment Data from Oct-Nov 2008

19. Free tropospheric CCN source S = 0.9% S = 0.25% Data from VOCALS (Jeff Snider) Continentally- influenced FT Remote “background” FT Weber and McMurry (FT, Hawaii) S=0.9% 0.5 0.25 0.1

20. Conceptual model of background FT aerosol Clarke et al. (J. Geophys. Res. 1998)

21. Self-preserving aerosol size distributions after Friedlander, explored by Raes: Raes et al., J. Geophys. Res. (1995) Fixed supersaturation: 0.8% 0.4% 0.2% Variable supersat. 0.3  0.2% Kaufman and Tanre (Nature 1994)

22. Tomlinson et al., J. Geophys. Res. (2007) Observed MBL aerosol dry size distributions (SE Pacific) 0.08  m 0.06

23. Precipitation over the VOCALS region CloudSat Attenuation and Z-R methods VOCALS Wyoming Cloud Radar and in-situ cloud probes Very little drizzle near coast Significant drizzle at 85 o W WCR data courtesy Dave Leon

24. Predicted and observed N d, VOCALS Model increase in N d toward coast is related to reduced drizzle and explains the majority of the observed increase Very close to the coast (<5 o ) an additional CCN source is required Even at the heart of the Sc sheet (80 o W) coalescence scavenging halves the N d Results insensitive to sea-salt flux parameterization 17.5-22.5 o S

25. Predicted and observed N d Monthly climatological means (2000-2009 for MODIS, 2006-2009 for CloudSat) Derive mean for locations where there are >3 months for which there is: (1) positive large scale div. (2) mean cloud top height <4 km (3) MODIS liquid cloud fraction > 0.4 Use 2C-PRECIP-COLUMN and Z-R where 2C-PRECIP- COLUMN missing

26. Predicted and observed N d - histograms Minimum values imposed in GCMs

27. Mean precipitation rate (2C-PRECIP-COLUMN)

28. Reduction of N d from precipitation sink 0 10 20 50 100 150 200 300 500 1000 2000 % Precipitation from midlatitude low clouds reduces N d by a factor of 5 In coastal subtropical Sc regions, precip sink is weak

29. Sea-salt source strength compared with entrainment from FT

30. Precipitation closure from Brenguier and Wood (2009) Precipitation rate dependent upon: cloud macrophysical properties (e.g. thickness, LWP); microphysical properties (e.g. droplet conc., CCN) precipitation rate at cloud base [mm/day]

31. Conclusions Simple CCN budget model, constrained with precipitation rate estimates from CloudSat predicts MODIS-observed cloud droplet concentrations in regions of persistent low level clouds with some skill. Entrainment of constant “self-preserving” aerosols from FT (and sea-salt in regions of stronger mean winds) can provide sufficient CCN to supply MBL. No need for internal MBL source (e.g. from DMS). Significant fraction of the variability in N d across regions of extensive low clouds is likely related to drizzle sinks rather than source variability => implications for aerosol indirect effects

33. Effect of variable supersaturation Kaufman and Tanre 1994 Constant  Variable 

34. Range of observed and modeled CCN/droplet concentration in Baker and Charlson “drizzlepause” region where loss rates from drizzle are maximal Baker and Charlson source rates Baker and Charlson, Nature (1990)

35. Timescales to relax for N Entrainment: Surface:  sfc  Precip: z i /(hKP CB ) = 8x10^5/(3*2.25) = 1 day for P CB =1 mm day -1  dep  z i /w dep - typically 30 days

36. Can dry deposition compete with coalescence scavenging? w dep = 0.002 to 0.03 cm s -1 (Georgi 1988) K = 2.25 m 2 kg -1 (Wood 2006) For P CB = > 0.1 mm day -1 and h = 300 m = 3 to 30 For precip rates > 0.1 mm day -1, coalescence scavenging dominates

37. Examine MODIS Nd imagery – fingerprinting of entrainment sources vs MBL sources.