Multisensor approaches for understanding connections between aerosols, shallow clouds and precipitation. Robert Wood University of Washington

Transcription

1 Multisensor approaches for understanding connections between aerosols, shallow clouds and precipitation Robert Wood University of Washington

2 Themes Multi-sensor constraints on the CCN budget in the marine boundary layer Constraining controls on CCN over the SE Pacific Low CCN events over the eastern North Atlantic The importance of the time domain

3 Increasing focus on aerosol-cloud interactions in marine low cloud studies

4 The Twomey effect Twomey s expression for cloud optical thickness : Cloud droplet concentration d / / h Use to estimate the rate of change of albedo with N d (termed the albedo susceptibility, Platnick and Twomey 1994), to obtain: d = (1 ) 3 d Liquid water content Cloud thickness Clouds with low N d are most susceptible to an increase in N d Need to understand what controls natural N d to quantify forcing from anthropogenic aerosol-cloud interactions (see also Carslaw et al. 2013, Ghan et al. 2013) Platnick, S., and S. Twomey, 1994: Determining the Susceptibility of Cloud Albedo to Changes in Droplet Concentration with the Advanced Very High Resolution Radiometer. Journal of Applied Meteorology 33,

5

6 What controls CCN and cloud microphysical variability in the marine boundary layer? A simple CCN budget for the PBL ENTRAINMENT SURF. SOURCE PRECIP. SINK 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 Wood et al. (2012, J. Geophys. Res.)

7 Steady-state CCN budget FREE TROPOSPHERIC CCN SEA-SPRAY PRODUCTION PRECIP. SINK 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).

8 Coalescence scavenging: how it works Cloud droplets [r 10 mm] Fall speed [m s -1 ] Coalescence events combine CCN Droplet radius [mm] Activation Drizzle drop [r 100 mm, formed from The coalescence of 1000 Cloud droplets] Fallout and evaporation Cloud condensation nuclei (CCN, r 0.1 mm) single (large) CCN loss of CCN number but not mass

9 Precipitation important in controlling N d gradient Aircraft-measured FT aerosol concentrations Precipitation from CloudSat radar estimates from Lebsock and L Ecuyer (2011) Observed surface winds (QuikSCAT) Model N d gradients mostly driven by precipitation sinks MODIS N d Wood et al. (J. Geophys. Res. 2012)

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

11 Precipitation sink drives seasonality in N d Southeastern Pacific (10-30 o S, o W) MODIS observed Model predicted (N FT =125 cm -3 ) seasonality driven by precip only Free-tropospheric CCN Steady state CCN conc in MBL Surface CCN flux = Non dimensional precip sink Entrainment rate Adapted from Wood et al. (2012) Steady state CCN/N d budget shows skill in predicting SE Pacific N d assuming seasonally invariant FT aerosols. Application to other regions challenging Unknown FT CCN seasonality constraints CALIPSO lidar may be able to constrain Problems with mixed phase precipitation

12 Steady state CCN/N d model prediction Southeastern Pacific (10-30 o S, o W) MODIS observed Model predicted (N FT =125 cm -3 ) seasonality driven by precip only Free-tropospheric CCN Steady state CCN conc in MBL Surface CCN flux = Non dimensional precip sink Entrainment rate Adapted from Wood et al. (2012) Steady state CCN/N d budget shows skill in predicting SE Pacific N d assuming seasonally invariant FT aerosols. Application to other regions challenging Unknown FT CCN seasonality constraints Problems with mixed phase precipitation

13 Origins of low CCN events over the eastern North Atlantic Identify low CCN events at 6hr mean CCN (SS=0.1%) below 20 cm -3 Weak southerly flow associated with most events Favored during winter Link with marine cold air outbreaks Climatology Wind Roses Low CCN events Case, Dec Dec 2009, 18 UTC 15 Dec 2009, 12 UTC 17 Dec 2009, 0 UTC Stemmler et al. (2015) Sea level pressure [hpa] Cold air outbreak index (Kolstad) [K]

14 Low CCN events created several days upstream of Graciosa N d [cm -3 ] Cloud droplet concentration MODIS data composited on ensemble of trajectories Associated with with high LWP clouds 2-4 days before arrival LWP [g m -2 ] Liquid water path high LWP 2-4 days Before arrival Low CCN events Reduced N d persists until Graciosa days before arrival at Graciosa Stemmler et al. (2015)

15 Coalescence scavenging rate of cloud droplets depends primarily on LWP Coalescence scavenging rate d, where is the precip. rate (at cloud base), so d not strongly dependent upon but is strong function of LWP Wood (J. Geophys. Res., 2006)

16 Low CCN events associated with cold air outbreak Azores

17 Lagrangian framework for Sc to Cu transition Treat anomalies of cloud cover as a red noise process [ +, ( )] = / where is the Lagrangian decorrelation timescale. Slope of initial (t = 0) vs final (t = T = 24 hr) anomalies (right) provides the value of r, then f c anomaly at t = 24 hr [%] Note: linear slope linear decay process (mean rate of decay of initial signal does not depend on the amplitude) T=24 hr (24 hour trajectories) Zero slope (r = 0 = 0) = / ln 20 hours We can explore how the cloud cover change depends upon various factors f c anomaly at t = 0 [%] Eastman et al. (2015)

18 Lagrangian cloud cover changes Examine 24 hr cloud cover changes for 60,000 PBL trajectories over subtropical Eastern oceans PBL (cloud top) height most important for determining breakup Sparse/light precipitation does not make a difference [top panel] Cloud cover change [compared with climatological transition] Non-precipitating Frequency of precipitation Precipitating Precipitating For shallow PBLs, more Non-precipitating frequent/heavier precipitation appears to Frequency of precipitation suppress cloudiness decreases; opposite might be the case for deeper clouds [bottom panel] Eastman et al. (2015)

19 Summary Multi-sensor observations (aircraft, satellite, ground based) need to be combined to better understand interactions between clouds, precipitation and aerosols Time-domain observations (Lagrangian approach) can be used to understand multi-day air mass histories and their impact on the CCN budget in the marine boundary layer

20