Understanding spatial and temporal variability in cloud droplet concentration Robert Wood, University of Washington with Ryan Eastman, Daniel McCoy, Daniel Grosvenor (University of Washington); Matt Lebsock (JPL)

“Background” (minimum imposed) cloud droplet concentration influences aerosol indirect effects Quaas et al., AEROCOM (Atmos. Chem. Phys., 2009) Hoose et al. (GRL, 2009) Low N d background  strong Twomey effect High N d background  weaker Twomey effect Forcing [W m -2 ]  A  ln(N perturbed /N unpertubed ) LAND OCEAN

What controls CCN and cloud microphysical variability in the marine boundary layer? A simple CCN budget for the PBL E NTRAINMENT S URF. S OURCE P RECIP. S INK Assume nucleation/secondary processes unimportant Dry deposition is negligible (Georgi 1990) Sea-spray formulation (e.g. Clarke et al. 2006) Ignore advection Precipitation sink primarily from accretion process Equivalency of CCN and cloud drop conc. N d

Steady-state CCN budget Concentration relaxes to FT concentration N FT + wind speed dependent surface contribution dependent upon subsidence rate (D z i ) Precipitation sink controlled by precipitation rate at cloud base P CB. Use expression from Wood (2006). F REE T ROPOSPHERIC CCN P RECIP. S INK S EA -S PRAY P RODUCTION

Precipitation important in controlling gradient in cloud droplet concentration Wood et al. (J. Geophys. Res. 2012) Assume constant FT aerosol concentration Precipitation from CloudSat estimates from Lebsock and L’Ecuyer (2011) Observed surface winds Model N d gradients mostly driven by precipitation sinks

Precipitation is primary driver of geographical variability in mean N d away from coasts Wood et al. (J. Geophys. Res. 2012) Model reproduces significant amount of variance in N d over oceans  implications for interpretation of AOD vs r e relationships

Figure by R. Wood, SOCRATES White Paper (2014) Marked annual cycle of N d in low clouds over Southern Ocean Summer maximum likely biogenic (DMS, organics) In situ and satellite observations consistent Summertime albedo enhancement (Twomey) of  25% Strong seasonal cycle of aerosols and cloud droplet concentration N d over the Southern Ocean

CloudSat precipitation (2c-precip- column) Wintertime maximum for low cloud precipitation likely, but annual cycle not hugely strong (range: mm day -1 ) Outstanding retrieval problems over Southern Ocean Cold-topped clouds, radar echoes below 800 m altitude contaminated by ground clutter

Cannot rule out sinks Non dimensional precip sink Free-tropospheric CCN Surface CCN flux Entrainment rate Steady state CCN conc in MBL Steady state CCN/N d budget shows skill in predicting SE Pacific N d (Wood et al. 2012) Application to Southern Ocean challenging Poor FT CCN constraints Aforementioned issues with warm rain estimates Adapted from Wood et al. (2012) Key result is that S precip is  1-2 for mean drizzle rate of 1 mm day -1 MODIS/CloudSat observations

Seasonal cycles of N d Precipitation Droplet conc. r [ N d, R cb ] = r [ N d, R cb ] = Most subtropical stratocumulus regions exhibit significant seasonality in cloud droplet concentration that is anticorrelated with precipitation rate

Steady state CCN/N d model prediction Non dimensional precip sink Free-tropospheric CCN Surface CCN flux Entrainment rate Steady state CCN conc in MBL Adapted from Wood et al. (2012) Model predicted (N FT =125 cm -3 ) seasonality driven by precip only MODIS observed Southeastern Pacific (10-30 o S, o W) Seasonality of cloud droplet concentration over SE Pacific stratocumulus region can be explained largely by precipitation removal, which

Steady state CCN/N d model prediction Non dimensional precip sink Free-tropospheric CCN Surface CCN flux Entrainment rate Steady state CCN conc in MBL Adapted from Wood et al. (2012) Model predicted (N FT =125 cm -3 ) seasonality driven by precip only MODIS observed Southeastern Pacific (10-30 o S, o W) Seasonality of cloud droplet concentration over SE Pacific stratocumulus region can be explained largely by precipitation removal, which

Lagrangian framework Eastman and Wood (2015) 1:1 slope (r = 1   =  ) T=24 hr (24 hour trajectories) N d anomaly at t = 24 hr [cm -3 ] Note: linear slope  linear decay process (mean rate of decay of initial signal does not depend on the amplitude) N d anomaly at t = 0 [cm -3 ] Zero slope (r = 0   = 0)

Timescale for precipitation removal Values: K = 2.25 m 2 kg -1 (Wood 2006); z i = 1500 m; h = 350 m (typical values) (Wood 2006, J. Geophys. Res.) Cloud top height [km] CALIOP Cloud top height (Muhlbauer et al. 2014)

Take-home points Light precipitation from low clouds exerts major control on CCN and cloud droplet concentration Drives geographical variation of the annual mean N d away from coastal zones Drives N d seasonal cycle in some regions (e.g. SE Pacific, possibly S. Ocean) Lagrangian decorrelation timescale for N d (  30 hours) is substantially longer than for cloud cover Clouds decorrelate faster than the underlying aerosol that they ingest Decorrelation timescale comparable with timescale for precipitation removal but further Lagrangian framework analysis required to ascertain connection between precipitation and N d

Additional slides

Correcting solar zenith angle biases in MODIS- derived N d Grosvenor and Wood (Atmos. Chem. Phys. 2014)

Open cells drizzle harder, but more intermittently Muhlbauer et al. (2014)

What factors control the magnitude and uncertainty of the global first aerosol indirect effect ? Ghan et al. (J.Geophys. Res., 2013)

(a) Seasonal cycle; (b) contribution from natural, anthropogenic emissions and aerosol processes; (c) uncertainty ranges from different perturbed parameters Natural emissions contribute half of AIE uncertainty Volcanic and DMS produced SO 2 are major natural sources of uncertainty Anthropogenic SO2 key anth. Source Aerosol processes are not major sources of uncertainty in this analysis Carslaw et al. (Nature, 2013)