Photograph Tony Clarke VOCALS REx flight RF07 Robert Wood University of Washington with helpdata from Chris Terai Dave Leon Jeff Snider Radiative impact of cloud droplet concentration variations ID: 700829
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Slide1
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 SniderSlide2
Radiative impact of cloud droplet concentration variations
George and Wood,
Atmos. Chem. Phys., 2010
cloud droplet
concentration
N
d
albedo
enhancement
(fractional)Slide3
“Background” cloud droplet concentration critical for determining aerosol indirect effects
Quaas et al., AEROCOM i
ndirect effects intercomparison, Atmos. Chem. Phys., 2009Low Nd background
strong
Twomey
effect
High
N
d background
weaker
Twomey
effect
A
ln
(Nperturbed/Nunpertubed)
LAND
OCEANSlide4
Aerosol (d>0.1 m
m) vs cloud droplet concentration (VOCALS, SE Pacific)
1:1 line
see also...
Twomey
and Warner (1967) Martin et al. (1994)Slide5
Extreme coupling between drizzle and CCN
Southeast PacificDrizzle causes cloud morphology transitionsDepletes aerosolsSlide6
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
CF
liq
>0.6 in daily L3 Slide7
Cloud droplet concentrations in marine stratiform
low cloud over oceanLatham et al., Phil. Trans. Roy. Soc.
(2011)The view from MODIS
....how can we explain this distribution?
N
d
[cm
-3
]Slide8
Baker and Charlson
modelCCN/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
-500Slide9
Baker and Charlson model
Stable region A exists because CCN loss rates due to drizzle increase strongly with CCN concentrationIn 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
-500Slide10
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 NIGHTSlide11
Simple CCN budget in the MBL
Model accounts for:
EntrainmentSurface production (sea-salt)Coalescence scavengingDry deposition
Model does not account for:
New particle formation – significance still too uncertain to include
Advection – more laterSlide12
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 limitSlide13
Sea-spray flux parameterizations
Courtesy of Ernie Lewis, Brookhaven National Laboratory
u10=8 m s-1Slide14
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 distributionSlide15
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 PCB = > 0.1 mm day-1 and h = 300 m
= 3 to 30
For
precip
rates
>
0.1 mm day
-1
, coalescence scavenging dominatesSlide16
Steady state (equilibrium) CCN concentrationSlide17
Variable
Source
Details
N
FT
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 mean
U
10
Quikscat
/Reanalysis
-
P
CB
CloudSat
PRECIP-2C-COLUMN,
Haynes et
al. (2009) & Z-based retrieval
h
MODIS
LWP, adiabatic assumption
z
i
CALIPSO
or
MODIS
or COSMIC
MODIS
T
top
, CALIPSO
z
top
, COSMIC
hydrolapse
Observable constraints from A-TrainSlide18
MODIS-estimated cloud droplet concentration N
d, VOCALS Regional Experiment
Data from Oct-Nov 2008Slide19
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
0.1Slide20
Conceptual model of background FT aerosol
Clarke et al. (
J. Geophys. Res. 1998)Slide21
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)Slide22
Tomlinson et al.,
J. Geophys
. Res. (2007)
Observed MBL aerosol dry size distributions (SE
Pacific)
0.08
m
m
0.06Slide23
Precipitation over the VOCALS region
CloudSat Attenuation and Z-R methodsVOCALS Wyoming Cloud Radar and in-situ cloud probes
Very little drizzle near coast
Significant drizzle at 85
o
W
WCR
data courtesy Dave LeonSlide24
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 increaseVery 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
SSlide25
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 missingSlide26
Predicted and observed
N
d
- histograms
Minimum values
imposed in GCMsSlide27
Mean precipitation rate (2C-PRECIP-COLUMN)Slide28
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 weakSlide29
Sea-salt source strength compared with entrainment from FTSlide30
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]Slide31
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 effectsSlide32Slide33
Effect of variable supersaturation
Kaufman and Tanre 1994
Constant s
Variable
s
Slide34
Range of observed and modeled CCN/droplet concentration in Baker and
Charlson “drizzlepause” region where loss rates from drizzle are maximalBaker and Charlson source rates
Baker and Charlson, Nature (1990)Slide35
Timescales to relax for N
Entrainment: Surface: tsfc Precip: z
i/(hKPCB) = 8x10^5/(3*2.25) = 1 day for PCB=1 mm day-1 t
dep
z
i/w
dep
- typically 30 daysSlide36
Can dry deposition compete with coalescence scavenging?
w
dep = 0.002 to 0.03 cm s-1 (Georgi 1988)K = 2.25 m2
kg
-1
(Wood 2006)
For
P
CB
= > 0.1 mm day
-1
and h
= 300 m= 3 to 30For precip
rates
>
0.1 mm day
-1
,
coalescence scavenging dominatesSlide37
Examine MODIS
Nd imagery – fingerprinting of entrainment sources vs MBL sources.Slide38