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Aerosol-cloud-precipitation interactions in warm clouds: is the tail wagging the dog?. or Control of cloud droplet concentration in marine stratocumulus clouds. Robert Wood University of Washington with Rhea George, Chris Terai , Dave Leon, Jeff Snider.

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aerosol cloud precipitation interactions in warm clouds is the tail wagging the dog

Aerosol-cloud-precipitation interactions in warm clouds: is the tail wagging the dog?

or

Control of cloud droplet concentration in marine stratocumulus clouds

Robert Wood

University of Washington

with Rhea George, Chris Terai, Dave Leon, Jeff Snider

Photograph: Tony Clarke, VOCALS REx flight RF07

radiative impact of cloud droplet concentration variations
Radiative impact of cloud droplet concentration variations

Satellite-derived cloud droplet

concentration Nd

albedo enhancement

(fractional)

low level wind

George and Wood, Atmos. Chem. Phys., 2010

background cloud droplet concentration critical for determining aerosol indirect effects
“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

aerosol d 0 1 m m vs cloud droplet concentration vocals se pacific

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)

extreme coupling between drizzle and ccn
Extreme coupling between drizzle and CCN
  • Southeast Pacific
  • Drizzle causes cloud morphological transitions
  • Depletes aerosols

Wood et al. (J. Geophys. Res., 2008)

modis estimated mean cloud droplet concentration n d
MODIS-estimated mean cloud droplet concentrationNd

Can we explain these patterns?

  • 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
prevalence of drizzle from low clouds
Prevalence of drizzle from low clouds

DAY NIGHT

Leon et al., J. Geophys. Res. (2008)

Drizzle occurrence = fraction of low clouds (1-4 km tops) for which Zmax> -15 dBZ

simple ccn budget in the mbl
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
production terms in ccn budget
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

sea spray flux parameterizations
Sea-spray flux parameterizations

u10=8 m s-1

Courtesy of Ernie Lewis, Brookhaven National Laboratory

loss terms in ccn budget 1 coalescence scavenging
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

loss terms in ccn budget 2 dry deposition
Loss terms in CCN budget: (2) Dry deposition

Deposition velocity

wdep= 0.002 to 0.03 cm s-1 (Georgi 1988)

K = 2.25 m2kg-1 (Wood 2006)

For PCB = > 0.1 mm day-1and h = 300 m

= 3 to 30

For precip rates > 0.1 mm day-1, coalescence scavenging dominates

modis estimated cloud droplet concentration n d vocals regional experiment
MODIS-estimated cloud droplet concentration Nd, VOCALS Regional Experiment
  • Data from Oct-Nov 2008
  • Sampling along 20oS across strong microphysical gradient
free tropospheric ccn source
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%

conceptual model of background ft aerosol
Conceptual model of background FT aerosol

Clarke et al. (J. Geophys. Res. 1998)

self preserving aerosol size distributions
Self-preserving aerosol size distributions
  • after Friedlander, explored by Raes:

Fixed supersaturation: 0.8% 0.4% 0.2%

Variable

supersat. 0.3  0.2%

Kaufman and

Tanre (Nature 1994)

Raes et al., J. Geophys. Res. (1995)

slide21

Observed MBL aerosol dry size distributions (SE Pacific)

0.08 mm

0.06

Tomlinson et al., J. Geophys. Res. (2007)

precipitation over the vocals region
Precipitation over the VOCALS region
  • CloudSatAttenuation 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

predicted and observed n d vocals
Predicted and observed Nd, VOCALS
  • Model increase in Ndtoward coast is related to reduced drizzleand 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

17.5-22.5oS

predicted and observed n d
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
slide25

Predicted and observed Nd- histograms

Minimum values

imposed in GCMs

reduction of n d from precipitation sink
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

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

precipitation closure
Precipitation closure

from Brenguier and Wood (2009)

  • Precipitation rate dependent upon:
    • cloud macrophysicalproperties (e.g. thickness, LWP);
    • microphysical properties (e.g. droplet conc., CCN)

precipitation rate at cloud base [mm/day]

conclusions
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.
  • Significant fraction of the variability in Nd across regions of extensive low clouds is likely related to precipitation sinks rather than source variability => there is a tail wagging part of the dog!
  • It may be difficult to separate the chicken from the egg in correlative studies suggesting inverse dependence of precipitation rate on cloud droplet concentration
a proposal
A proposal
  • A limited area perturbation experiment to critically test hypotheses related to aerosol indirect effects
  • Cost $30M
precipitation susceptibility
Precipitation susceptibility
  • Construct from Feingold and Siebert (2009) can be used to examine aerosol influences on precipitation in both models and observations

S = -(dlnRCB/dlnNa)LWP,h

  • S decreases strongly with cloud thickness
  • Consistent with increasing importance of accretion in thicker clouds
  • Consistent with results from A-Train (Kubar et al. 2009, Wood et al. 2009)

Data from stratocumulus over the SE Pacific, Terai and Wood (Geophys. Res. Lett., 2011)

effect of variable supersaturation
Effect of variable supersaturation

Constant s

  • Kaufman and Tanre 1994

Variable s

slide35

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)

slide36

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

can dry deposition compete with coalescence scavenging
Can dry deposition compete with coalescence scavenging?

wdep= 0.002 to 0.03 cm s-1 (Georgi 1988)

K = 2.25 m2kg-1 (Wood 2006)

For PCB = > 0.1 mm day-1and h = 300 m

= 3 to 30

For precip rates > 0.1 mm day-1,

coalescence scavenging dominates

cloud droplet concentrations in marine stratiform low cloud over ocean
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)

baker and charlson model
Baker and Charlson model

200

100

0

-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]

10 100 1000

CCN concentration [cm-3]

Baker and Charlson, Nature (1990)

baker and charlson model1
Baker and Charlson model

200

100

0

-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]

10 100 1000

CCN concentration [cm-3]

Baker and Charlson, Nature (1990)