1. Observational constraints on aerosol indirect effects and controlling processes Robert Wood, Atmospheric Sciences, University of Washington

2. 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.

3. Theoretical expression for AIE Response of cloud optical thickness  to change in some aerosol characteristic property A primary feedback (secondary)

4. (Mostly) regulating feedbacks in stratocumulus

5. Twomey’s hypothesis Increases in the number of aerosol particles will lead to increases in the concentration N d of cloud droplets For a given LWC, greater N d implies smaller droplets (since droplet radius r  {LWC/N d } 1/3 ) Greater N d  total surface area will increase (   N d r 2 h, so   N d 1/3 h 5/3 ) and clouds reflect more solar radiation d(ln  )/d(lnN d ) = 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 from Wood (2005) cloudbase drizzle rate [mm d -1 ] N d [cm -3 ]

7. Twomey Albrecht

8. Model estimates of the two major aerosol indirect effects (AIEs) Pincus and Baker (1994) – 1 st and 2 nd AIEs comparable GCMs (Lohmann and Feichter 2005) 1 st AIE: -0.5 to -1.9 W m -2 2 nd AIE: -0.3 to -1.4 W m -2 Large scale models problematic, so need observational and process model constraints

9. 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

10. 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

11. LES results Cloud droplet concentration [cm -3 ] LWP [g m -2 ] P 0 [mm d -1 ] w e [cm s -1 ] Impact of aerosols simulated by varying N d Increased N d  Reduced precipitation  increased TKE  increased entrainment w e Changes in w e can sometimes result in cloud thinning (reduced LWP) Also noted by Jiang et al. (2002) Ackerman et al. (2004)

12. Precipitation reduces TKE

13. short timescales long timescales short timescales

14. Different processes, different timescales R IE - ratio of secondary to primary AIEs short term thinning

15. Transient response of an equilibrated mixed layer PBL model to N d increases Ratio of 2nd to 1st AIE R IE (right) is a strong function of cloud base height More elevated cloud base heights z cb lead to Albrecht effects which partly cancel those due to Twomey effect Elevated z cb 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) 2 nd AIE cancels 1 st 2 nd AIE = 1 st 2 nd AIE = 0

16. 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 N d reduces sedimentation  pollution can lead to thinner clouds

17. 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)

18. 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)

19. 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) N d =10 cm -3 N d =60 cm -3 secondary circulation above MBL inversion rises at same rate in both regions

20. (Mostly) regulating feedbacks in stratocumulus

21. Response pathways Wood Albrecht Ackerman Wood Arnason and Greenfield (1972), Kogan and Martin (1995), Lee et al.(2009)

22. 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)

23. A proposal A limited area perturbation experiment to critically test hypotheses related to aerosol indirect effects Cost  $30M

25. 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 P CB  h 3.5 /N d 1.75 – treatment of evaporation below cloud to give surface precipitation

26. Effects of drizzle vs effects of sedimentation of cloud droplets GCSS DYCOMS-2 RF02 drizzling Sc case study Ackerman et al. (MWR, 2009) With drizzle, without sedimentation With drizzle and sedimentation Effect of drizzle Effect of sedimentation.....in this case, sedimentation dominates over drizzle impact on cloud LWP

27. Indirect effect ratio R IE 1st AIE 2nd AIE Define R IE = 2 nd AIE / 1 st AIE Relative strength of the Albrecht effect compared with Twomey For adiabatic cloud layers,   N d 1/3 LWP 5/6

28. 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) N d =N d,control –(perturb) N d =1.05  N d,control R IE is calculated – examine dependence of R IE upon forcings and parameterizations see Wood (2007), J. Atmos. Sci., 64, 2657- 2669.

29. Base case, N d,control =100 cm -3 For most forcing conditions 2 nd AIE > 1 st AIE R IE scales with surface precipitation in the control Little dependence of scaling upon forcing conditions

30. Base case, N d,control =200 cm -3 Lower values of R IE because surface precip. is lower Same R IE scaling with surface precip

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

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

33. Non-equilibrium response Timescale for 2 nd AIE is long – due to long z i adjustment timescale On short timescales R IE can be negative (noted in Ackerman, 2004) Important to understand timescales of aerosol evolution

34. Timescales But what is the timescale  N for evolution of N d ?  N =N d {dN d /dt} -1 –Coalescence scavenging (removal of CCN by coalescence of cloud/drizzle drops):  N  (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

35. 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 ≈ - dz CB /dt using moisture and energy budgets for MBL  is relative importance of entrainment drying compared with surface precipitation moistening

36. What determines  ? Ackerman showed RH FT important But cloudbase height dominates over wider range of phase space

37. Annual mean LCL 400-600 m over much of the subtropical and tropical oceans  cancellation of aerosol indirect effects?

38. 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 r driz =49  m r driz =65  m

39. 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

40. 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

41. Conclusions Relative strength of 2 nd AIE strongly dependent upon balance between precipitation suppression moistening and entrainment drying R IE reduced by ~50% by changing drizzle parameterization  need to understand climatology of precipitation and its dependency on LWP and N d Over timescales comparable with aerosol lifetime in the MBL, 1 st and 2 nd AIEs may cancel – implications for sensitivity of low clouds to aerosols Unlikely that current global models can capture the essential physics (evaporation/entrainment)

42. Shiptracks only seen in shallow boundary layers Durkee et al. (2000)

43. Track width as a function of age

44. CloudSat observes drizzle SE Pacific

46. The End

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

48. Re-examination of Klein and Hartmann data

49. Williams et al. (2006) Change in LTS (K) Low cloud amount in an ensemble of 2xCO 2 -control GCM simulations is poorly estimated using LTS’ (for which a general increase is predicted) Much better agreement with change in saturated stability (≈EIS’)

50. Precipitation parameterizations BASE: Comstock et al. (2004): P CB  h 3.5 /N d 1.75 Van Zanten et al. (2005): P CB  h 3 /N d Range typical in MBL clouds

51. Weak temperature gradient

52. Minimalist approach

53. No entrainment drying/warming Entrainment only allowed to influence z i leads to stronger LWP feedback

54. 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  /2

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

57. Sensitivity of cloud optical depth  to increasing N d in the MLM 1 st AIE 2 nd AIE constant LWP feedback on LWP For the MLM,   N d 1/3 LWP 5/6

58. Indirect effect ratio R IE 1st AIE 2nd AIE Define R IE = 2 nd AIE / 1 st AIE Relative strength of the Albrecht effect compared with Twomey

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

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

61. 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

62. Aerosol-cloud microphysical observations Observed cloud droplet concentration [cm -3 ] Predicted droplet concentration from aerosol spectrum [cm -3 ] From Twomey and Warner, J. Atmos. Sci., 24, 704-706, 1967 Aerosol concentration [cm -3 ] Cloud droplet concentration [cm -3 ] From Martin et al., J. Atmos. Sci., 51, 1823-1842, 1994 ….first measurements (1967) ….recent measurements (1994) + wind from land  wind from sea

63. Aerosol loading and cloud droplet radius…. ….35 years on from Breon et al. 2002, Science, 295, 834-838 6 8 10 12 14 Cloud droplet radius [micron] 0.0 0.1 0.2 0.3 0.4 Aerosol index

64. Remote sensing estimates from Feingold et al. 2003, GRL IE = dlnr e /dln  a  dln  /dlnN a = (dln  /dlnN d )(dlnN d /dlnN a ) (dlnN d /dlnN a )  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)