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

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

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

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

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-1sr-1)

Resolution

Wavelengths

Horizontal: 333 m

Vertical: 30 m (λ = 532 nm) 60 m (λ = 1064 nm)

http://www-calipso.larc.nasa.gov/

CALIOP Data Products

0

22 )(),,~,( drTPrnrQNnr T

Q

r )(rn

P2T

n~

Total Attenuated Backscatter (km-1sr-1)

= radius

= complex index of refraction

= total number concentration

= lidar wavelength

= size distribution

= scattering efficiency

= scattering phase function

= two-way transmittanceTypical values: (km-1sr-1)

Aerosols: 10-3 to 10-2……..Clouds: 0.1 to 1

TN

CALIOP Data Products

Color Ratio

532

1064

Backscatter at 532 nm (km-1sr-1)

Col

or r

atio

Vaughan et al. (2005)

Typical values:

Clouds: ~1.0

Aerosols: 0.4 – 0.8

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

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. 2004 – Jan. 2005

Courtesy of Google Earth

Focus on nighttime data over ocean

Methodology

Alti

tude

1) Clouds between 0.5-2.0 km

2) Single layered

3) No clouds above ‘clear air’ profile

4) Horizontal distance to next cloud ≥ 3 km

Criteria:

Methodology

Alti

tude

1) From cloud top to cloud base altitudes

2) To ½ the distance to the next cloud

Store β':Satellite direction

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. ’09

333 m

30 m

MethodologyA

ltitu

de

Alti

tude

Distance to cloud edge

Averaging strategy

Number of samples:

1 2 3

½ dist. to cloud

MethodologyA

ltitu

de

Alti

tude

Distance to cloud edge

Averaging strategy

Number of samples:

1 2 3

MethodologyA

ltitu

de

Alti

tude

Distance to cloud edge

Averaging strategy

Number of samples:

1 2 3

Total Number of Samples

Distance to cloud edge (km )

Alt

itu

de

(k

m)

0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.990.5

1.0

1.5

2.0

10

100

1000

10000

100000

Median backscatter

λ= 532 nm

Distance to cloud edge (km )

Alt

itu

de

(k

m)

0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.990.5

1.0

1.5

2.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

10-3

(km -1sr-1)

Normalized median backscatter

λ= 532 nm

Distance to cloud edge (km )

Alt

itu

de

(k

m)

0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.990.5

1.0

1.5

2.0

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Integrated median backscatter

€

Δ γ1064 = 42 ± 2%%331532 Δγ

0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.991.0

1.5

2.0

2.5

3.0

Distance to cloud edge (km )

532 nm1,064 nm

10-3

(sr-1)

km

km T dzNnr0.2

5.0),,~,( γ

Median color ratio

Distance to cloud edge (km )

Alt

itu

de

(k

m)

0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.990.5

1.0

1.5

2.0

0.45

0.50

0.55

0.60

0.65

0.70

0.75

Layer averaged median color ratio

%515 Δ

0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.990.50

0.52

0.54

0.56

0.58

Distance to cloud edge (km )

Co

lor

rati

o

Theory vs. observations

%331532 Δγ

%2421064 Δγ

%515 Δ

When comparing 3 km from cloud edge to ~0.3 km to cloud edge…

How to explain?Observations

0

22 )( drTPrnrQ

km

kmdz

0.2

5.0 γ

What changes in aerosol properties can account for this?

Theory vs. observations

OPAC "m aritim e clean" aerosol size distribution

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000

0.01 0.1 1 10

Radius (μm )

dN

/dr

(cm

-3u

m-1

)

Total

Mode 1

Mode 2

Mode 3

Rj = median radius

σj = standard deviation

Nj = number concentration

Composition [Peter et al, 2008]:

r ≤ 0.2 μm, ammonium sulfate

r > 0.2 μm, sea salt

Log-normal Size distribution [Hess et al, 1998]:

Relative Humidity = 80%

3

1

2

log

/log

2

1exp

10lnlog2)(

j j

j

j

j Rr

r

Nrn

Parameters:

Far From Cloud Aerosol Distribution

Theory vs. observationsest fit to observations:

0.00001

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000

0.01 0.1 1 10

Radius (μm)

dn

(r)/

dr

(cm

-3μ

m-1

)

Far from cloudNear cloud

ΔRj = 34%

Δσj = −2%

ΔNj = −32%

ΔAOD532 = 16%

Prelim Observations from RICO100 – 200 m vs 1000 – 1100 m based on all RICO flights

PCASP

FSSP

Prelim Observations from RICO100 – 200 m vs 1000 – 1100 m

0.00001

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000

0.01 0.1 1 10

Radius (μm)

dn

(r)/

dr

(cm

-3μ

m-1

)

Far from cloudNear cloud

Potential ProcessesBest fit to observations: ΔRj = 34%, Δσj = −2%, ΔNj = −32%

Collision-coalescence: Increases Rj and σj, decreases Nj

Hygroscopic growth: Increases Rj and σj, leaving Nj unchanged

Precipitation scavenging: Decreases Rj, σj, and Nj

Other scavenging processes (nucleation, diffusion, impaction):

Increases Rj and σj, decreases Nj

No single process dominates based on observations

Theory vs. observations

Cloud contamination

Observed increase

(%)

N cloud droplets

(cm-3)

31 ± 3 0.018

42 ± 2 0.011

15 ± 5 0.007Δ

532γΔ

1064γΔ

Cloud contamination alone cannot explain the observations

€

n(r) = Ncr3e−br

Integrated median backscatter

0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.991.0

1.5

2.0

2.5

3.0

3.5

4.0

Distance to cloud edge (km )

532 nm, night1,064 nm, night532 nm, day1,064 nm, day

10-3

(sr-1)

Day vs Night

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)