Control of Cloud Droplet Concentration in Marine Stratocumulus Clouds - PowerPoint Presentation

Control of Cloud Droplet Concentration in Marine Stratocumulus Clouds Photograph: Tony Clarke, VOCALS REx flight RF07 Robert Wood University of Washington with help/data from Chris Terai , Dave Leon, Jeff Snider

Radiative impact of cloud droplet concentration variations George and Wood, Atmos. Chem. Phys., 2010 cloud droplet concentration N d albedo enhancement (fractional)

“Background” cloud droplet concentration critical for determining aerosol indirect effects Quaas et al., AEROCOM i ndirect effects intercomparison, Atmos. Chem. Phys., 2009Low Nd background  strong Twomey effect High N d background  weaker Twomey effect A  ln (Nperturbed/Nunpertubed) LAND OCEAN

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)

Extreme coupling between drizzle and CCN Southeast PacificDrizzle causes cloud morphology transitionsDepletes aerosols

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

Cloud droplet concentrations in marine stratiform low cloud over oceanLatham et al., Phil. Trans. Roy. Soc. (2011)The view from MODIS ....how can we explain this distribution? N d [cm -3 ]

Baker and Charlson modelCCN/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/N d values actually 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 -500

Baker and Charlson model Stable 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 ] 200 100 0 -100 -200 -300 -400 -500

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 NIGHT

Simple CCN budget in the MBL Model accounts for: EntrainmentSurface production (sea-salt)Coalescence scavengingDry deposition Model does not account for: New particle formation – significance still too uncertain to include Advection – more later

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 limit

Sea-spray flux parameterizations Courtesy of Ernie Lewis, Brookhaven National Laboratory u10=8 m s-1

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 distribution

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 dominates

Steady state (equilibrium) CCN concentration

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-Train

MODIS-estimated cloud droplet concentration N d, VOCALS Regional Experiment Data from Oct-Nov 2008

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 0.1

Conceptual model of background FT aerosol Clarke et al. ( J. Geophys. Res. 1998)

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)

Tomlinson et al., J. Geophys . Res. (2007) Observed MBL aerosol dry size distributions (SE Pacific) 0.08 m m 0.06

Precipitation over the VOCALS region CloudSat Attenuation and Z-R methodsVOCALS Wyoming Cloud Radar and in-situ cloud probes Very little drizzle near coast Significant drizzle at 85 o W WCR data courtesy Dave Leon

Predicted and observed N d, VOCALS Model increase in Nd toward coast is related to reduced drizzle and explains the majority of the observed increaseVery close to the coast (<5 o ) an additional CCN source is required Even at the heart of the Sc sheet (80 o W) coalescence scavenging halves the N d Results insensitive to sea-salt flux parameterization 17.5-22.5 o S

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 > 0.4 Use 2C-PRECIP-COLUMN and Z-R where 2C-PRECIP-COLUMN missing

Predicted and observed N d - histograms Minimum values imposed in GCMs

Mean precipitation rate (2C-PRECIP-COLUMN)

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 weak

Sea-salt source strength compared with entrainment from FT

Precipitation closure from Brenguier and Wood (2009) Precipitation rate dependent upon: cloud macrophysical properties (e.g. thickness, LWP); microphysical properties (e.g. droplet conc., CCN) precipitation rate at cloud base [mm/day]

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 effects

Effect of variable supersaturation Kaufman and Tanre 1994 Constant s Variable s

Range of observed and modeled CCN/droplet concentration in Baker and Charlson “drizzlepause” region where loss rates from drizzle are maximalBaker and Charlson source rates Baker and Charlson, Nature (1990)

Timescales to relax for N Entrainment: Surface: tsfc Precip: zi /(hKPCB) = 8x10^5/(3*2.25) = 1 day for PCB=1 mm day-1 t dep  z i/w dep - typically 30 days

Can dry deposition compete with coalescence scavenging? w dep = 0.002 to 0.03 cm s-1 (Georgi 1988)K = 2.25 m2 kg -1 (Wood 2006) For P CB = > 0.1 mm day -1 and h = 300 m= 3 to 30For precip rates > 0.1 mm day -1 , coalescence scavenging dominates

Examine MODIS Nd imagery – fingerprinting of entrainment sources vs MBL sources.