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The spatial variability of aerosol properties in the vicinity of trade wind cumuli over the Tropical Western Atlantic observed from RICO aircrafts and.

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Presentation on theme: "The spatial variability of aerosol properties in the vicinity of trade wind cumuli over the Tropical Western Atlantic observed from RICO aircrafts and."— Presentation transcript:

1 The spatial variability of aerosol properties in the vicinity of trade wind cumuli over the Tropical Western Atlantic observed from RICO aircrafts and CALIOP Larry Di Girolamo Jason Tackett Marile Colon-Robles Bob Rauber Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign

2 Motivation Aerosols modify cloud properties through a variety of “indirect effects.” Many modeling studies into aerosol indirect effects usually prescribe horizontally homogeneous aerosol properties Courtesy of S. Tripathi

3 Motivation Clouds modify aerosol properties through a variety of chemical and dynamical processes. The cloud processing and detrainment of aerosols, coupled with humidity haloes, implies that aerosol properties in the near- cloud environment are different than the far cloud environment. Where’s the observations? Adapted from Hegg (2001) Cloud processing

4 Where’s the observations? Passive satellite sensors have been hampered by 3-D radiative cloud- adjacency effects (e.g. Wen et al. (2007), Yang and Di Girolamo (2008)). Aircraft in situ observations have been hampered by inadequate sampling lengths to provide “near-cloud” aerosol spectra. Sun-photometers are also subjected 3-D radiative cloud-adjacency effects… but Koren et al. (2007) and Redemann et al. (2009) both observed an AOD increase of ~10 – 13% near cloud. Lidar are also hampered by 3-D radiative cloud-adjacency effects when operated during the day… but Su et al. (2008) observed an AOD increase of ~ 8 – 17% near cloud using the HSRL. Tackett and Di Girolamo (GRL 2009 submitted) using CALIPSO

5 CALIOP : Cloud Aerosol LIdar with Orthogonal Polarization λ = 532 nm and 1064 nm The CALIOP Instrument Backscatter : Fraction of radiance scattered in backward direction (km -1 sr -1 ) Resolution Wavelengths Horizontal: 333 m Vertical: 30 m (λ = 532 nm) 60 m (λ = 1064 nm)

6 CALIOP Data Products Total Attenuated Backscatter (km -1 sr -1 ) = radius = complex index of refraction = total number concentration = lidar wavelength = size distribution = scattering efficiency = scattering phase function = two-way transmittance Typical values: (km -1 sr -1 ) Aerosols: to ……..Clouds: 0.1 to 1

7 CALIOP Data Products Color Ratio Backscatter at 532 nm (km -1 sr -1 ) Color ratio Vaughan et al. (2005) Typical values: Clouds: ~1.0 Aerosols: 0.4 – 0.8

8 CALIOP Data Products  ’   ’   ’  (1/3 km) Cloud Layer Product for cloud masking (1/3 and 5 km) CALIOP Aerosol Product (5 km) is NOT used

9 Region & Time of Interest ~2100 km ~2700 km ~3000 km RICO: Rain In Cumulus over the Ocean Focus is on the Caribbean in winter RICO Field Campaign Dec – Jan Courtesy of Google Earth Focus on nighttime data over ocean

10 Methodology Altitude 1)Clouds between km 2)Single layered 3)No clouds above ‘clear air’ profile 4)Horizontal distance to next cloud ≥ 3 km Criteria:

11 Methodology Altitude 1)From cloud top to cloud base altitudes 2)To ½ the distance to the next cloud Store β' : Satellite direction β'β' Dist. from cloud Total meeting criteria: 26,833 clouds 34,371 cloud edges Dec. ’06 – Feb. ’07 & Dec. ’07 – Feb. ’08 Dec. ’08 – Feb. ’ m 30 m

12 Methodology Altitude Distance to cloud edge Averaging strategy Number of samples: 1 23 ½ dist. to cloud

13 Methodology Altitude Distance to cloud edge Averaging strategy Number of samples: 1 23

14 Methodology Altitude Distance to cloud edge Averaging strategy Number of samples: 1 23

15 Total Number of Samples

16 Median backscatter λ= 532 nm

17 Normalized median backscatter λ= 532 nm

18 Integrated median backscatter

19 Median color ratio

20 Layer averaged median color ratio

21 Theory vs. observations When comparing 3 km from cloud edge to ~0.3 km to cloud edge… How to explain?Observations What changes in aerosol properties can account for this?

22 Theory vs. observations R j = median radius σ j = standard deviation N j = number concentration Composition [Peter et al, 2008]: r ≤ 0.2 μm, ammonium sulfate r > 0.2 μ m, sea salt Log-normal Size distribution [Hess et al, 1998]: Relative Humidity = 80% Parameters: Far From Cloud Aerosol Distribution

23 Theory vs. observations est fit to observations: ΔR j = 34% Δσ j = −2% ΔN j = −32% ΔAOD 532 = 16%

24 Prelim Observations from RICO 100 – 200 m vs 1000 – 1100 m based on all RICO flights PCASP FSSP

25 Prelim Observations from RICO 100 – 200 m vs 1000 – 1100 m

26 Potential Processes Best fit to observations: ΔR j = 34%, Δσ j = −2%, ΔN j = −32%  Collision-coalescence: Increases R j and σ j, decreases N j  Hygroscopic growth: Increases R j and σ j, leaving N j unchanged  Precipitation scavenging: Decreases R j, σ j, and N j  Other scavenging processes (nucleation, diffusion, impaction): Increases R j and σ j, decreases N j No single process dominates based on observations

27 Theory vs. observations Cloud contamination Observed increase (%) N cloud droplets (cm -3 ) 31 ± ± ± Cloud contamination alone cannot explain the observations

28 Integrated median backscatter Day vs Night

29 Conclusion  Systematic increase in backscatter near cloud edge  Layer integrated backscatter increased by ~31% at λ = 532 nm and ~42% at λ = 1064 nm  Layer averaged color ratio increased by ~15%  An increase in aerosol sizes and a decrease in number concentration near cloud edge best explains the observations (ongoing RICO aircraft analysis)  The method and results are amenable for evaluating models How does lidar backscatter in the vicinity of clouds compare to far from clouds?  Greatest enhancement at cloud base and top Tackett and Di Girolamo (GRL submitted)


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