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