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

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

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

4. Aerosol (d>0.1 mm) 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 Nd
Use method of Boers and Mitchell (1996), applied by Bennartz (2007)
Screen to remove heterogeneous clouds by insisting on CFliq>0.6 in daily L3

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

8. Baker and Charlson model
200
100
-100
-200
-300
-400
-500
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/Nd values actually fall in unstable regime!
observations
CCN loss rate [cm-3 day-1]
CCN concentration [cm-3]
Baker and Charlson, Nature (1990)

9. Baker and Charlson model
200
100
-100
-200
-300
-400
-500
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
observations
CCN loss rate [cm-3 day-1]
CCN concentration [cm-3]
Baker and Charlson, Nature (1990)

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

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

13. Sea-spray flux parameterizations
u10=8 m s-1
Courtesy of Ernie Lewis, Brookhaven National Laboratory

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

15. Loss terms in CCN budget: (2) Dry deposition
Deposition velocity
wdep = to 0.03 cm s-1 (Georgi 1988)
K = 2.25 m2 kg-1 (Wood 2006)
For PCB = > 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. Observable constraints from A-Train
Variable
Source
Details
NFT
Weber and McMurry (1996) & VOCALS in-situ observations (next slide)
cm-3 active at 0.4% SS in remote FT D
ERA-40 Reanalysis
divergent regions in monthly mean
U10
Quikscat/Reanalysis -
PCB
CloudSat
PRECIP-2C-COLUMN, Haynes et al. (2009) & Z-based retrieval h
MODIS
LWP, adiabatic assumption zi
CALIPSO or MODIS or COSMIC
MODIS Ttop, CALIPSO ztop, COSMIC hydrolapse

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

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

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:
Fixed supersaturation: % 0.4% 0.2%
Variable
supersat  0.2%
Kaufman and
Tanre (Nature 1994)
Raes et al., J. Geophys. Res. (1995)

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

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

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

25. Predicted and observed Nd
Monthly climatological means ( for MODIS, 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 Nd - histograms
Minimum values
imposed in GCMs

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

28. Reduction of Nd from precipitation sink
Precipitation from midlatitude low clouds reduces Nd 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
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]
from Brenguier and Wood (2009)

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 Nd 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
Constant s
Kaufman and Tanre 1994
Variable s

34. Baker and Charlson source rates
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: tsfc 
Precip: zi/(hKPCB) = 8x10^5/(3*2.25) = 1 day for PCB=1 mm day-1
tdep  zi/wdep - typically 30 days

36. Can dry deposition compete with coalescence scavenging?
wdep = to 0.03 cm s-1 (Georgi 1988)
K = 2.25 m2 kg-1 (Wood 2006)
For PCB = > 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.