Photograph Tony Clarke VOCALS REx flight RF07 Robert Wood University of Washington with helpdata from Dan Grosvenor Daniel McCoy Chris Terai Matt Lebsock Dave Leon Jeff Snider Tony Clarke ID: 163061
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Slide1
Factors controlling cloud droplet concentration in low clouds over the oceans
Photograph: Tony Clarke, VOCALS
REx flight RF07
Robert Wood
University of Washington
with help/data from Dan Grosvenor, Daniel McCoy, Chris
Terai
, Matt
Lebsock
, Dave Leon, Jeff Snider, Tony ClarkeSlide2
Radiative impact of geographical variations in cloud droplet concentration
George and Wood,
Atmos. Chem. Phys., 2010
cloud droplet
concentration
N
d
albedo
enhancement
(fractional)Slide3
“Background” (minimum imposed) cloud droplet concentration influences aerosol indirect effects
Quaas
et al., AEROCOM (Atmos. Chem. Phys., 2009) Hoose et al. (GRL, 2009)
Low
N
d
background
strong
Twomey
effect
High
N
d
background weaker Twomey effect
A ln(Nperturbed/Nunpertubed)
LANDOCEAN
Forcing [W m
-2]
0 10 20 30 40 Slide4
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 transitions and depletes 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. (2012)The view from MODIS
....how can we explain this distribution?
N
d
[cm
-3
]
Minimum values
imposed in GCMs
(instantaneous mean
over 1x1
o
boxes)Slide8
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 mostly 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
-300-400-500Baker and Charlson modelSlide9
Baker and Charlson
modelStable 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]
2001000-100-200-300-400-500Slide10
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 scavenging
Dry deposition
Model does not account for:
New particle formation – significance still too uncertain to include
AdvectionSlide12
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
Remote SE Pacific FT CCN spectrum very similar with that at Mauna LoaSlide21
Conceptual model of background FT aerosol
Clarke et al. (
J. Geophys. Res. 1998)Slide22
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)Slide23
Precipitation and FT CCN over the VOCALS regionSlide24
Predicted and observed N
d, VOCALS17.5-22.5oS
Model increase in
N
d
toward coast is related to
reduced drizzle
and explains the majority of the observed increase
Very close to the coast (<5
o
) an additional CCN source is requiredEven at the heart of the Sc sheet (80oW) coalescence scavenging halves the NdResults relatively insensitive to sea-salt flux parameterizationSlide25
Sensitivity to sea-salt parameterizationSlide26
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 > 0.4Slide27Slide28
Predicted and observed
N
d
- histograms
Minimum values
imposed in GCMs
Histograms are monthly means for 1x1
o
boxesSlide29
Predicted and observed
N
d
- histogramsSlide30
Predicted and observed
N
d
- histogramsSlide31Slide32
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 weakSlide33
Sea-salt source strength compared with entrainment from FTSlide34
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 effectsSlide35
Southern Ocean Annual Cycle
Wood et al. (2013)
Marked annual cycle of Nd in low clouds over Southern Ocean
Summer maximum likely biogenic (DMS?)
In-situ and satellite observations consistent
Summer maximum (70 cm
-3
); Winter minimum (35 cm
-3
)
Twomey
effect 25%
Not well captured in models (e.g. CAM5) Slide36
Annual cycle of cloud droplet concentration
MODIS
CAM 5
NH Springtime maximum captured in CAM 5
SH Subtropical cycle reasonably well-captured
Southern Ocean annual cycle not well-capturedSlide37
Monthly
Nd anomaly(monthly mean – annual mean)Slide38
CloudSat precipitation
Wintertime maximum for low cloud precipitation likely, but annual cyle not particularly strong (range: 1.2-1.5 mm day-1)Outstanding retrieval problems
over Southern OceanCold-topped clouds