Aerosol-Cloud-Precipitation Interactions in warm clouds Graham Feingold NOAA Earth System Research Laboratory Boulder, Colorado USA Keck InsCtute for Space Studies Geoengineering Workshop May 2011 Acknowledgements • Keck InsCtute of Space Sciences • Present and former group members and colleagues – Hailong Wang, Huiwen Xue, Hongli Jiang, Adrian Hill, Armin Sorooshian – Ilan Koren, Phil Rasch, Bjorn Stevens, Patrick Chuang, Jen Small, Shouping Wang Aerosol Effects on Clouds Shiptracks (Courtesy, NASA) RiXs Stevens et al. 2005 Outline • Simple constructs that may have outlived their usefulness – Albedo effect – LifeCme effect – nth indirect effect • AnCcipated and unanCcipated responses • Correlaon vs. Causality • Self-organizing systems • Bistability and the role of Aerosol • Numerical Ship track experiments Some simple cloud physics • Clouds are dynamical enCCes and the characterisCcs of the cloud field depend on the meteorological regime – To first order, cloud fracCon and depth or LWP dictate albedo • More aerosol à more drops (almost always) – But drop size response is unpredictable d N ! Nd ! Na Drop concentraon, Ramanathan et al. 2001 Aerosol concentraon, Na Some simple cloud physics Small droplets don’t collide Collector drop radius easily with large droplets 2 2 K(x, y) ∝ (rx + ry )Vx −Vy E(x, y) Collected drop radius, µm Wallace and Hobbs 2006 E(x,y) = collecCon efficiency • Smaller drops are less likely to collide and coalesce; need ~ 20 µm radius drops to iniCate the process Some simple cloud physics Clean Polluted -3 N = 300 cm-3 Na = 50 cm a t=0 t=0 r=20 µm r=20 µm t=10 min t=10 min Drop radius, µm • Smaller drops are less likely to collide and coalesce; need ~ 20 µm radius drops to iniCate the process Spatial/Temporal scales • Involves complexity in both aerosol and clouds • Range of spaal scales – Aerosol parCcles 10s – 1000s nanometres – Cloud drops/ice parCcles: µm – cm – Cloud scales: ~ 10 m – 103 km 4:00 pm EDT, July 23, 2002 • Range of temporal scales – AcCvaon process (aerosolà droplet): < sec – Time to generate precipitaon ~ 20 min – Cloud systems: days • Coupled System – Scale interacCons 10 km 10 km 0 Some pitfalls Correlaon vs Causaon Most satellite studies show correla0ons between cloud and aerosol Increasingly, model reanalysis is being used to separate aerosol from meteorological influences Ice cream causes shark aacks Satellite remote-sensing Breon et al. 2002 Shark Attacks Shark Drop radius “slope”=0.05 – 0.08 Ice Cream Sales Aerosol Index Some pitfalls contd.. Remote sensing of aerosol-cloud interacCons from space is hard! - Adjacency - “Cloud contamina0on” - 3-D effects Photo credit: Internaonal Space Staon Some pitfalls contd.. Models are imperfect! - Range of scales is a huge challenge 4:00 pm EDT, July 23, 2002 - “All models are wrong: some are useful” George Box 10 km 10 km 0 What controls cloud albedo? 1/3 5/6 ! ~ Nd LWP A ~ ! (for low )" • Cloud OpCcal depth is 2.5 x more sensiCve to changes in LWP than changes in Nd • LWP has a much stronger control on cloud opCcal depth than Nd A useful metric Albedo Suscep0bility Nd =const Which clouds are most sensiCve to increases in Nd (vis-à-vis albedo)? dA/dNd dA A(1! A) 0.5 1 S = = A 0 dN 3N d LWP,... d ! Nd ! Na " 5 d ln LWP d lnk % S0!,LWP,... = S0! $1+ + ' # 2 d ln Nd d ln Nd & LWP response breadth response Platnick and Twomey 1994 Note: breadth k !1/ Feingold and Siebert 2008 Influences of aerosol on distribution breadth d lnk d ln Nd Not accounng for breadth effects Accoun)ng for breadth effects Non-precipitang clouds* Xue and Feingold, 2004 1/3 At constant LWP: τ /τ0 = (Nd / Nd0) • Non precipitang clouds: breadth effects reduce albedo suscepCbilty • Precipitang clouds: breadth effects enhance albedo suscepCbilty *For precipitang clouds see Feingold et al. JGR 1997 How does LWP respond to aerosol? Sign of the change is uncertain! " 5 d ln LWP % S0!,LWP,... = S0! $1+ +..' # 2 d ln Nd & Δ ln LWP β = Δ ln Nd Han et al. 2002: ISCPP Data Shiptracks shiptrack control re is smaller in ship tracks Cloud opCcal depth is # ~ constant re LWP tends to decrease # Reduced Increased " LWP LWP Figure 4. Average optical depths and droplet effective radii for contaminated (solid line) and uncontaminated (dashed line) pixels takenCoakley from 30-km and Walsh, 2002 track segments. The number of track segments, means and standard deviations of the distributions are given. Figure 8. " !ln# / !ln Re for the 30-km ship track segments in which the average change in droplet effective radius, uncontaminated " contaminated, was greater than 2 µm. " !ln# / !ln Re = 1 if the cloud liquid water amount remains constant. The number of segments, means, and estimates of two standard deviations for the ensemble means are given. Global low cloud satellite measurements and NCEP reanalysis r stability e Drop size decreases with aerosol Aerosol Index LWP LWP decreases with aerosol Aerosol Index Cloud Albedo Albedo ~ constant (compeCng effects) Aerosol Index Matsui et al. 2006 D15205 LEBSOCK ET AL.: AEROSOL INDIRECT EFFECTS D15205 the albedo enhancement is indeed diminished in the lower static stability regime presumably because the LWP de- crease is larger in these environments than in the high stability regime. This independent result adds credence to the observations that clouds in unstable environments tend to suffer decreases in LWP not observed in stable environ- ments. The analysis has been performed with both the CERES TOA cloudy sky albedo and a cloud top albedo derived using the MODIS cloud products (t, re)andthetwo stream approximation. Both estimates of cloud albedo lead to the same conclusions, therefore the following results show the CERES albedo because it provides an independent estimate of this quantity. Figure 4 demonstrates that the two products are well correlated. The high bias of the MODIS estimate results from the sensor resolution differences and the fact that MODIS is a cloud top estimate as opposed to Figure 4. Comparison of the CERES TOA albedo with the TOA estimate of CERES. the cloud top albedo derived from the MODIS cloud [19]Althoughthepreviousresultssuggestthatthecon- products using a two stream radiative transfer model. stant liquid water path approximation is not valid on the global scale, it is still of interest to examine the constant MODIS cloud properties and the CloudSat rain flag and liquid water path case. To do this cloud properties are may contain some clear sky. In fact, on average, the CERES examined as a function of LWP. For example, the CERES pixels used in this analysis contain 19.2% clear sky. To cloud albedo is plotted against LWP for four stability and account for this fact and to eliminate any relationship aerosol regimes in Figure 5A. This plot demonstrates that between cloud fraction and AI the cloudy sky albedo is for a constant LWP the increase in albedo is greater for high derived from the TOA flux. First dividing the observed LWP clouds than it is for low LWP clouds. This result might upwelling TOA flux by the calculated incident TOA flux be slightly counter-intuitive, as one would expect a satura- derives the all sky albedo. Second, the clear sky component tion of the cloud albedo for high LWP. An explanation for of the flux is removed using the CERES estimate of clear this phenomenon can be found in Figure 5B, which shows sky area within the CERES field of view via, re as a function of LWP for the same stability and aerosol regimes as in Figure 5A. The sensitivity of the effective radius to AI increases with LWP and this in turn results in aall 1 f aclr acld À ðÞÀ ; 4 an increased sensitivity of the optical depth to AI. It can be ¼ f ð Þ predicted that the sensitivity of re to aerosol should increase with increasing LWP by considering the LWP of a vertically where f is the MODIS cloud fraction within the CERES homogenous cloud with a mean volume radius rv,number footprint and a is the albedo. concentration N,andthicknessH, [18]ThetrendoftheCEREScloudyskyalbedowithAI is given in Figure 1E and again the magnitude of the trends 4 3 are shown in Table 2. These independent estimates of LWP pr rv NH: 5 ¼ 3 l ð Þ albedo agree wellMeteorological with the previous results, indicating context that for aerosol influences Figure 5. Trends ofα the = A = albedo (A) CERES cloudy sky albedo and (B) MODIS re with the AMSR-E LWP. The delineation between cleancld and dirty is given by AI = 0.1. The stable curve represents all LTSS between 18 and 21 K. The unstable curve represents all LTSS between 12 and 15 K. Lebsock et al. 2008 A-Train data 6of12 Modeling of Aerosol effects on Stratocumulus in LES Models help determine causality PolluCon elevates cloud tops LWP decreases with increasing aerosol: Evapora0on-entrainment feedback polluted clean height 100/cc 1000/cc Bulk microphysics LWC Also, Xue and Feingold 2006 Wang, Wang, Feingold, 2003 for cumulus Modeling of Aerosol effects on Stratocumulus in LES Relavely moist above inversion Very dry above inversion dLWP >0 ? dNd <0 Humidity of air above stratocumulus is important Ackerman et al. 2004 Modeling of Aerosol effects on Stratocumulus in LES Fine resolu)on FIRE-I Scu, non precipitang Low aerosol - Coarse resoluon has lower LWP High aerosol - Reducon in LWP with Δx = Δy = 20 m; Δz = 10 m increasing aerosol Coarse resoluon - Effect of representa0on of Low aerosol mixing (homogeneous vs. inhomogeneous; black vs grey) is small High aerosol Δx = Δy = 40 m; Δz = 20 m Hill et al. 2009 Cumulus Clouds Cumulus Clouds Height, m 12 km 12 km Houston, GoMACCS 2006: Mike Hubbel adiabac • Large variaon in cloud depth • Significant entrainment • * Subadiabac liquid water Height, m • Cloud FracCon ~ 10% • Frequent in trade wind regime Adiabac rao • Precipitaon common *Warner et al.