1. Limits to Aerosol Indirect Effects in marine low clouds
Robert Wood, Atmospheric Sciences,
University of Washington

2. Motivation
Aerosols can potentially influence earth’s radiation budget both by direct interaction with sunlight (aerosol direct effect), and also by altering cloud radiative properties (aerosol indirect effects, AIEs)
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.

3. IPCC, 2007

4. Theoretical expression for AIE
Response of cloud optical thickness t to change in some aerosol characteristic property A
Generally, because AIEs must be dominated by warm clouds and ice clouds formed by homogeneous freezing, the property most relevant to the problem is the cloud condensation nucleus concentration (CCN).
Aerosol size and composition effects can also play a role
primary feedback

5. 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 ( Nd r2h, so   Nd1/3h5/3) and clouds reflect more solar radiation
d(ln)/d(lnNd) = 1/3

6. 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
cloudbase drizzle rate [mm d-1]
Nd [cm-3]
from Wood (2005)

7. Twomey
Albrecht

8. Significant aerosol-climate effects (IPCC 2007)

9. 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: to -1.9 W m-2
2nd AIE: -0.3 to -1.4 W m-2
Limited investigation of factors that control the relative importance of the two AIEs

10. courtesy Jim Coakley, see Coakley and Walsh (2002)
Shiptrack surprises!
3.7 m
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)

11. Drizzle suppression in shiptracks
Liquid water content [g m-3]
cloud
drizzle
Drizzle is frequently found to be suppressed in shiptracks
So what’s wrong with Albrecht’s hypothesis?
Ferek et al. 2000

12. Cloud droplet concentration [cm-3]
LES results
Ackerman et al. (2004)
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)
LWP [g m-2]
P0 [mm d-1]
we [cm s-1]
Cloud droplet concentration [cm-3]

13. Precipitation reduces TKE

14. Mixed layer model (MLM) approach
Mixed layer model (Lilly 1968)
– convective-radiative framework for understanding stratiform boundary layer clouds and their dependence upon meteorological forcings
from Stevens et al. (2003)

15. 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

16. Indirect effect ratio RIE
1st AIE nd AIE
For adiabatic cloud layers,   Nd1/3 LWP5/6
Define RIE = 2ndAIE / 1st AIE
Relative strength of the Albrecht effect compared with Twomey

17. Suite of simulations Surface divergence [10-6 s-1]: {2, 3, 4}
see Wood (2007), J. Atmos. Sci., 64,
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=1.05Nd,control
RIE is calculated – examine dependence of RIE upon forcings and parameterizations

18. 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

19. Base case, Nd,control=200 cm-3
Lower values of RIE because surface precip. is lower
Same RIE scaling with surface precip

20. Different drizzle parameterizations
BASE (Comstock)
VanZanten et al. (2005)

21. Fixed entrainment
Only surface moisture/energy budget important (Albrecht effect)
Entrainment important in determining the nature of the feedback response

22. 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

23. Timescales But what is the timescale N for evolution of Nd?
N=Nd {dNd/dt}-1
Coalescence scavenging (removal of CCN by coalescence of cloud/drizzle drops): N  (cloudbase precip rate)-1 ≈ days
Advection timescale – typically 1-2 days

24. short
timescales
short
timescales
long
timescales

25. Timescales

26. 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

27. What determines  ? Ackerman showed RHFT important
But cloudbase height dominates over wider range of phase space

28. Annual mean LCL m over much of the subtropical and tropical oceans  cancellation of aerosol indirect effects?

29. SEP stratocumulus in GCMs
Poor representation of the vertical structure of stratocumulus-topped boundary layers
– surface moisture budget is completely out to lunch
Bretherton et al. 2004, BAMS

30. Caldwell and Bretherton (2008), LES
It’s not just drizzle….
Caldwell and Bretherton (2008), LES
Ackerman et al. (2008), GCSS Case Study of 14 LES finds that including droplet sedimentation (reducing Nd) increases LWP in all cases

31. Analogies in trade cumulus clouds
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
Xue and Feingold (2006) CF
LWP
aerosol concentration [cm-3]

32. 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)

34. CloudSat observes drizzle
SE Pacific

35. 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

37. The End

38. Low clouds in climate models - change in low cloud amount for 2CO2
GFDL
Low clouds in climate models - change in low cloud amount for 2CO2
CCM
model number
from Stephens (2005)

39. Re-examination of Klein and Hartmann data

40. 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’)
Williams et al. (2006)

41. Precipitation parameterizations
Range typical in MBL clouds
BASE: Comstock et al. (2004): PCBh3.5/Nd1.75
Van Zanten et al. (2005): PCBh3/Nd

42. Weak temperature gradient

43. Minimalist approach

44. No entrainment drying/warming
Entrainment only allowed to influence zi
leads to stronger LWP feedback

45. Doubling and halving entrainment efficiency
 2
 /2
Enhanced entrainment counteracts (by drying) the increased LWP caused by reduced precip. efficiency…
….but data fall on same curve

47. Cloud feedbacks remain the largest uncertainty in the prediction of future climate change
from Cess et al. 1989, 1996

48. Sensitivity of cloud optical depth  to increasing Nd in the MLM
1st AIE nd AIE
constant LWP feedback on LWP
For the MLM,   Nd1/3 LWP5/6

49. Indirect effect ratio RIE
1st AIE nd AIE
Define RIE = 2ndAIE / 1st AIE
Relative strength of the Albrecht effect compared with Twomey

50. Relationship between LTS and EIS is not unique
For a given value of LTS, EIS decreases with surface (or 700 hPa) temperature

51. (2) Aerosols and low clouds
Chiquicamata, Chile
Wood et al. (2006)
Cloud droplet concentration

52. 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

53. Aerosol-cloud microphysical observations
….first measurements (1967)
Aerosol-cloud microphysical observations
+ wind from land
 wind from sea
….recent measurements (1994)
Observed cloud droplet concentration [cm-3]
Cloud droplet concentration [cm-3]
Predicted droplet concentration
from aerosol spectrum [cm-3]
From Twomey and Warner,
J. Atmos. Sci., 24, , 1967
Aerosol concentration [cm-3]
From Martin et al.,
J. Atmos. Sci., 51, , 1994

54. Aerosol loading and cloud droplet radius…. ….35 years on
Aerosol index
from Breon et al. 2002, Science, 295,
Cloud droplet radius [micron]

55. Remote sensing estimates
IE = dlnre/dlna  dln/dlnNa = (dln/dlnNd)(dlnNd/dlnNa)
(dlnNd/dlnNa)  0.5 to 0.7
Expect IE =1/3(0.5 to 0.7)  0.17 to 0.23
IE (Observations)
(Feingold et al.)
0.085 (Breon, ocean)
0.04 (Breon, land)
0.06 (Nakajima et al., ocean)
from Feingold et al. 2003, GRL