FY 2011 Third Quarter ReportEstimate of Historical Aerosol Direct and Indirect Effects
Observational constraints on aerosol indirect effects and controlling processes Robert Wood,...
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Transcript of Observational constraints on aerosol indirect effects and controlling processes Robert Wood,...
Observational constraints on aerosol indirect effects and controlling
processes
Robert Wood, Atmospheric Sciences,
University of Washington
Motivation• Aerosol impacts on clouds are not simply explained by
Twomey’s arguments– Changes in macrophysical cloud properties produce radiative
impacts of same order as those from Twomey (e.g. Lohmann and Feichter 2005, Isaksen et al. 2009)
• Conceptually, it is useful to divide AIEs into two types: – primary or quasi-instantaneous effects (e.g. Twomey effect,
dispersion effect);– effects that require an understanding of the system feedbacks
on timescales comparable to or longer than the cloud element lifetime.
Theoretical expression for AIE
• Response of cloud optical thickness t to change in some aerosol characteristic property A
primary feedback
(secondary)
(Mostly) regulating feedbacks in stratocumulus
Twomey’s hypothesis • Increases in the number of aerosol particles will
lead to increases in the concentration Nd of cloud droplets
• For a given LWC, greater Nd implies smaller droplets (since droplet radius r {LWC/Nd}1/3)
• Greater Nd total surface area will increase (t Nd r2h, so t Nd
1/3h5/3) and clouds reflect more solar radiation
• d(lnt)/d(lnNd) = 1/3
Albrecht’s hypothesis (1989)• A greater concentration of smaller drops (Twomey)
suppresses precipitation because the coalescence efficiency of cloud droplets increases strongly with droplet size.
• Reduced precipitation leads to increased cloud thickness, liquid water content, coverage more reflective clouds
from Wood (2005)
cloud
base
driz
zle ra
te [m
m d
-1]
Nd
[cm-3]
Twomey
Albrecht
Model estimates of the two major aerosol indirect effects (AIEs)
• Pincus and Baker (1994) – 1st and 2nd AIEs comparable
• GCMs (Lohmann and Feichter 2005) 1st AIE: -0.5 to -1.9 W m-2
2nd AIE: -0.3 to -1.4 W m-2
Large scale models problematic, so need observational and process
model constraints
Shiptrack surprises!
• Liquid water content in shiptracks is typically reduced compared with surrounding cloud
• Clear refutation of Albrecht’s hypothesis
courtesy Jim Coakley, see Coakley and Walsh (2002)
3.7 m
Precipitation suppression in ship tracks
Christensen and Stephens (submitted JGR)
• First observed in MAST (Ferek et al. 2000,
JAS)
CloudSat brings new perspective:
• Marked suppression of drizzle in most
shiptracks in closed cellular Sc.
• Enhancement of precipitation in some
open cell cases
• Poor data below 800 m
• Reliable statistics?
CloudSat
LES results
Cloud droplet concentration [cm-3]
LWP [g m-2]
P0
[mm d-1]
we
[cm s-1]
• Impact of aerosols simulated by varying Nd
• Increased Nd Reduced precipitation increased TKE increased entrainment we
• Changes in we can sometimes result in cloud thinning (reduced LWP)
• Also noted by Jiang et al. (2002)
Ackerman et al. (2004)
Precipitation reduces TKE
short
timescales
long
timescales
short
timescales
Different processes, different timescales
RIE
- ratio of secondary to primary AIEs
short term thinning
Transient response of an equilibrated mixed layer PBL model to Nd increases
• Ratio of 2nd to 1st AIE RIE (right) is a strong function of
cloud base height• More elevated cloud base heights zcb lead to Albrecht effects which partly cancel
those due to Twomey effect• Elevated zcb associated with
dry FT and less surface drizzle, consistent with LES results, but with a far less
sophisticated model hope for the representation in
climate models Wood (J. Atmos. Sci., 2007),
also seen in LES (Chen et al. 2011)
2nd AIE cancels 1st
2nd AIE = 1st
2nd AIE = 0
Sedimentation of cloud droplets
Bretherton, Blossey and Uchida, GRL, 2007
Cloud droplet sedimentation removes water from the (10 m thick) entrainment interface, lowers LWC there, reduces
evaporative cooling, and suppresses entrainment, resulting in thicker clouds
Since increased Nd
reduces sedimentation pollution can lead to thinner clouds
Entrainment enhancement raises inversion/cloud top height....in some cases
Christensen and Stephens, J. Geophys. Res. (2011)
.....also Taylor and Ackerman, QJRMS, 1999 (MAST Sanko Peace case study)
Marked deepening appears to occur only in previously collapsed MBLs
surrounding cloud remains
radiatively active
little or radiatively inactive surrounding cloud
Christensen and Stephens, J. Geophys. Res. (2011)
LES modeling of open-
closed cell boundary
Berner et al. (2011, ACP), and Bretherton et al. (2010, JAMES)
Wang and Feingold (2009), Wang et al. (2010)
Nd
=10 cm-3Nd
=60 cm-3
secondary circulation above MBL
inversion rises at same
rate in both regions
(Mostly) regulating feedbacks in stratocumulus
Response pathways
Wood
Albrecht
Ackerman
Wood
Arnason and Greenfield (1972), Kogan and Martin (1995), Lee et al.(2009)
Prospects• Turbulence, entrainment and drizzle absolutely
central to how STBL clouds respond to aerosol injections– Well-defined and testable hypotheses exist for how the
STBL should respond– Process models becoming capable of handling all relevant
aerosol-cloud interactions• New in-situ and spaceborne measurements offer
tremendous possibilities to go beyond previous studies (e.g. airborne doppler radar and lidar turbulence/drizzle)
A proposal
• A limited area perturbation experiment to critically test hypotheses related to aerosol indirect effects
• Cost $30M
Mixed layer model• LW/SW radiation and bulk surface flux (LHF/SHF)
parameterizations
• Entrainment closure (Turton and Nicholls 1986)
• Precipitation: For standard runs use formulation derived from shipborne radar observations in SE Pacific stratocumulus (Comstock et al. 2004).
– cloud base precipitation PCB h3.5/Nd1.75
– treatment of evaporation below cloud to give surface precipitation
Effects of drizzle vs effects of sedimentation of cloud droplets GCSS DYCOMS-2 RF02 drizzling Sc case study
Ackerman et al. (MWR, 2009)
With
driz
zle,
with
out s
edim
enta
tion
With
driz
zle
and
sedi
men
tatio
nEffect of drizzle Effect of sedimentation
.....in this case, sedimentation dominates over drizzle impact
on cloud LWP
Indirect effect ratio RIE
1st AIE 2nd AIE
Define RIE
= 2ndAIE / 1st AIE
Relative strength of the Albrecht effect compared with Twomey
For adiabatic cloud layers, t Nd
1/3 LWP5/6
Suite of simulations
• Surface divergence [10-6 s-1]: {2, 3, 4}• Sea Surface Temp. [K]: {288, 292, 296}• Moisture above MBL [g kg-1]: {1, 3, 6}
• 700 hPa potential temperature set to 312 K• No advective terms• MLM is run to equilibrium twice:
– (control) Nd=Nd,control
– (perturb) Nd=1.05Nd,control
• RIE is calculated – examine dependence of RIE upon forcings and parameterizations
see Wood (2007), J. Atmos. Sci., 64, 2657-2669.
Base case, Nd,control=100 cm-3
• For most forcing conditions 2nd AIE > 1st AIE
• RIE scales with surface precipitation in the control
• Little dependence of scaling upon forcing conditions
Base case, Nd,control=200 cm-3
• Lower values of RIE because surface precip. is lower
• Same RIE scaling with surface precip
Different drizzle parameterizationsBASE (Comstock)
VanZanten et al. (2005)
Fixed entrainment
• Only surface moisture/energy budget important (Albrecht effect)
• Entrainment important in determining the nature of the feedback response
Non-equilibrium response
• Timescale for 2nd AIE is long – due to long zi adjustment timescale
• On short timescales RIE can be negative (noted in Ackerman, 2004)
• Important to understand timescales of aerosol evolution
Timescales
But what is the timescale tN for evolution of Nd?
tN=Nd {dNd/dt}-1
– Coalescence scavenging (removal of CCN by coalescence of cloud/drizzle drops): tN (cloudbase precip rate)-1 ≈ 0.5-1 days
• Coalescence scavenging timescale is comparable to entrainment drying timescale aerosol perturbation is largely removed before MBL reaches equilibrium
Short timescale cloud response• Cloud base height determined by a balance between surface
precipitation moistening (P) and entrainment drying (E)
• Derive expression for cloud thickness change dh/dt ≈ - dzCB/dt using moisture and energy budgets for MBL
is relative importance of entrainment drying compared with
surface precipitation moistening
What determines ?• Ackerman showed RHFT important
• But cloudbase height dominates over wider range of phase space
Annual mean LCL 400-600 m over much of the subtropical and tropical oceans
cancellation of aerosol indirect effects?
Sensitive to size of drizzle drops
• Reducing mean radius of drizzle drops leads to more evaporation, and different ratio of surface moistening and entrainment drying
• Representation of evaporation critical
rdriz
=49 m
rdriz
=65 m
SEP stratocumulus in GCMs
Poor representation of the vertical structure of stratocumulus-topped boundary layers
– surface moisture budget is completely out to lunchBretherton et al. 2004, BAMS
Analogies in trade cumulus clouds
aerosol concentration [cm-3]
CF
LWP
Xue and Feingold (2006)• LES model of trade Cu
• Both cloud fraction (CF) and LWP decrease with increasing CCN conc.
• Effect attributed to more rapid evaporation of smaller cloud droplets (higher N) during entrainment events resulting in more rapid cloud dissipation
Conclusions• Relative strength of 2nd AIE strongly dependent upon
balance between precipitation suppression moistening and entrainment drying
• RIE reduced by ~50% by changing drizzle parameterization need to understand climatology of precipitation and its dependency on LWP and Nd
• Over timescales comparable with aerosol lifetime in the MBL, 1st and 2nd AIEs may cancel – implications for sensitivity of low clouds to aerosols
• Unlikely that current global models can capture the essential physics (evaporation/entrainment)
Shiptracks only seen in
shallow boundary
layers
Durkee et al. (2000)
Durkee et al.
(2000)
Track width as a
function of age
CloudSat observes drizzle
SE Pacific
The End
Low clouds in climate models
- change in low cloud amount for
2CO2
from Stephens (2005)
GFDL
CCM
model number
Re-examination of Klein and Hartmann data
Williams et al. (2006)
Change in LTS (K)
Low cloud amount in an ensemble of 2xCO2-control GCM simulations is poorly estimated using LTS’ (for
which a general increase is predicted)
Much better agreement with change in saturated stability
(≈EIS’)
Precipitation parameterizations
BASE: Comstock et al. (2004): PCBh3.5/Nd1.75
Van Zanten et al. (2005): PCBh3/Nd
Range typical in MBL clouds
Weak temperature gradient
Minimalist approach
No entrainment drying/warming
• Entrainment only allowed to influence zi
• leads to stronger LWP feedback
Doubling and halving entrainment efficiency
Enhanced entrainment counteracts (by drying) the increased LWP caused by reduced precip. efficiency…
….but data fall on same curve
2
/2
Cloud feedbacks remain the largest uncertainty in the
prediction of future climate change
from Cess et al. 1989, 1996
Sensitivity of cloud optical depth t to increasing Nd in the MLM
1st AIE 2nd AIE constant LWP feedback on LWP
For the MLM, t Nd
1/3 LWP5/6
Indirect effect ratio RIE
1st AIE 2nd AIE
Define RIE
= 2ndAIE / 1st AIE
Relative strength of the Albrecht effect compared with Twomey
• Relationship between LTS and EIS is not unique
• For a given value of LTS, EIS decreases with surface (or 700 hPa) temperature
(2) Aerosols and low clouds
Cloud droplet concentrationWood et al. (2006)
Chiquicamata, Chile
Primary effect (Twomey)• Seminal papers in 1974, 1977 hypothesizing an
important potential brightening of clouds subject to increased aerosol concentration
• Importance of fixed cloud macrophysical properties (LWP) – difficult to test in practice
Aerosol-cloud microphysical observations
Obse
rved
clo
ud d
ropl
et c
once
ntra
tion
[cm
-3]
Predicted droplet concentrationfrom aerosol spectrum [cm-3]
From Twomey and Warner, J. Atmos. Sci., 24, 704-706, 1967
Aerosol concentration [cm-3]
Clou
d dr
ople
t con
cent
ratio
n [c
m-3
]
From Martin et al., J. Atmos. Sci., 51, 1823-1842, 1994
….first measurements (1967)
….recent measurements (1994)
+ wind from land wind from sea
Aerosol loading and
cloud dropletradius….
….35 years on
from Breon et al. 2002, Science, 295,
834-838 6 8 10 12 14Cloud droplet radius [micron]
0.0 0.1 0.2 0.3 0.4 Aerosol index
Remote sensing estimates
from Feingold et al. 2003, GRL
IE = dlnre/dlnt
a dlnt/dlnN
a = (dlnt/dlnN
d)(dlnN
d/dlnN
a)
(dlnNd
/dlnNa) 0.5 to 0.7
Expect IE =1/3(0.5 to 0.7) 0.17 to 0.23
IE (Observations)
0.02-0.16 (Feingold et al.)
0.085 (Breon, ocean)
0.04 (Breon, land)
0.06 (Nakajima et al., ocean)