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

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.

IPCC, 2007

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

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

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)

Twomey Albrecht

Significant aerosol-climate effects (IPCC 2007)

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 Limited investigation of factors that control the relative importance of the two AIEs

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)

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

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]

Precipitation reduces TKE

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)

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

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

Suite of simulations Surface divergence [10-6 s-1]: {2, 3, 4} see Wood (2007), J. Atmos. Sci., 64, 2657-2669. 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

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 parameterizations BASE (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 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 ≈ 0.5-1 days Advection timescale – typically 1-2 days

short timescales short timescales long timescales

Timescales

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?

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

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

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]

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)

CloudSat observes drizzle SE Pacific

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

The End

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)

Re-examination of Klein and Hartmann data

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)

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

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

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

Sensitivity of cloud optical depth  to increasing Nd in the MLM 1st AIE 2nd AIE constant LWP feedback on LWP For the MLM,   Nd1/3 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 Chiquicamata, Chile Wood et al. (2006) Cloud droplet concentration

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 ….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, 704-706, 1967 Aerosol concentration [cm-3] From Martin et al., J. Atmos. Sci., 51, 1823-1842, 1994

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

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) 0.02-0.16 (Feingold et al.) 0.085 (Breon, ocean) 0.04 (Breon, land) 0.06 (Nakajima et al., ocean) from Feingold et al. 2003, GRL