control of cloud droplet concentration in marine stratocumulus clouds photograph: tony clarke,...

Control of Cloud Droplet Concentration in Marine Stratocumulus Clouds

Photograph: Tony Clarke, VOCALS REx flight RF07

Robert WoodUniversity of Washington

“Background” cloud droplet concentration critical for determining aerosol indirect effects

Quaas et al., AEROCOM indirect effects intercomparison, Atmos. Chem. Phys., 2009

Low Nd background strong Twomey effectHigh Nd background weaker Twomey effect

A ln(Nperturbed/Nunpertubed)

LANDOCEAN

Extreme coupling between drizzle and CCN• Southeast Pacific• Drizzle causes cloud

morphology transitions

• Depletes aerosols

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

Cloud droplet concentrations in marine stratiform low cloud over ocean

Latham et al., Phil. Trans. Roy. Soc. (2011)

The view from MODIS

• Observed CCN/droplet concentration occurs in Baker and Charlson (B&C) “drizzlepause” region where CCN loss rates from drizzle are maximal, does not occur where CCN concentrations are stable (point A)

• Clouds in B&C too thick generate drizzle too readily

• Drizzle parameterization too “thresholdy” (precip suddenly cuts off when

Baker and Charlson, Nature (1990)ob

serv

ation

s

10 100 1000CCN concentration [cm-3]

CCN

loss

rate

[cm

-3 d

ay-1

]

200

100

0

-100

-200

-300

-400

-500

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 Zmax> -15 dBZ

DAY NIGHT

Simple CCN budget in the MBL

Model accounts for:• Entrainment• Surface production (sea-salt)• Coalescence scavenging• Dry depositionModel does not account for:• New particle formation – significance still too uncertain to

include• Advection – more later

Production terms in CCN budgetFT Aerosol concentration

MBL depth

Entrainment rate

Wind speed at 10 mSea-salt

parameterization-dependentconstant

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

Loss terms in CCN budget: (2) Dry deposition

Deposition velocity

wdep = 0.002 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

Steady state (equilibrium) CCN concentration

Variable Source DetailsNFT Weber and McMurry

(1996) & VOCALS in-situ observations (next slide)

150-200 cm-3 active at 0.4% SS in remote FT

D ERA-40 Reanalysis divergent regions in monthly meanU10 Quikscat/Reanalysis -PCB CloudSat PRECIP-2C-COLUMN, Haynes et al.

(2009) & Z-based retrieval

h MODIS LWP, adiabatic assumptionzi CALIPSO or MODIS or

COSMICMODIS Ttop, CALIPSO ztop, COSMIC

hydrolapse

Observable constraints from A-Train

MODIS-estimated cloud droplet concentration Nd, VOCALS Regional Experiment

• Use method of Boers and Mitchell (1996) • Applied to MODIS data by Bennartz (2007)

Data from Oct-Nov 2008

Free tropospheric CCN source

S = 0.9%

S = 0.25%

Data from VOCALS (Jeff Snider)

Continentally-influenced FTRemote “background” FT

Weber and McMurry (FT, Hawaii)

S=0.9%

0.5

0.25

0.1

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 85oW

WCR data courtesy Dave Leon

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

Predicted and observed Nd

• 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

Predicted and observed Nd - histograms

Minimum valuesimposed in GCMs

Mean precipitation rate (2C-PRECIP-COLUMN)

Reduction of Nd from precipitation sink

0 10 20 50 100 150 200 300 500 1000 2000 %

• Precipitation from midlatitude low clouds reduces Nd by a factor of 5• In coastal subtropical Sc regions, precip sink is weak

Sea-salt source strength compared with entrainment from FT

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)

prec

ipita

tion

rate

at c

loud

bas

e [m

m/d

ay]

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

• 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)

• 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

4.310/ UNzi

Can dry deposition compete with coalescence scavenging?

wdep = 0.002 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

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