1. Aerosol-Cloud Interaction from Passive Satellite Remote Sensing: Accomplishments and Missing Pieces
Lorraine A. Remer
JCET UMBC
Astronaut photograph from Apollo 8 , December 22, Provided by NASA Earth Observatory

2. Angular distribution of scattered light is wavelength dependent
Light scattering by small particles
Incident light is scattered in preferential directions, and may change orientation of the wave (polarization)

3. Scattered radiation has a
Measured intensity
Spectral signature
Angular signature
Polarization signature
These depend on the characteristics of the particles:
Loading/Amount
Size
Shape
Composition
Remote sensing inverts the measured signatures to obtain the characteristics of the particles.

4. Aerosol Remote Sensing
AVHRR measured Intensity
with some spectral
MODIS measures intensity
with better spectral
MISR measures intensity with angular signature and some spectral
POLDER measures intensity and polarization with angular spectral signature, limited by accuracy and pixel spatial resolution
Retrieved loading and
some size
Retrieves loading and better size, with some absorption
Retrieves loading, shape, size and some absorption and height
Retrieves loading and array of particle properties

5. Passive remote sensing of cloud microphysics
COT
reff
Nakajima and King (1990)

6. Remote Sensing of Aerosol-Cloud Interaction
More aerosol =
More CCN =
More cloud
droplets =
Brighter cloud
Also, same amount of available water, implies smaller droplets
Twomey, 1974, 1977

7. r3.75 reff r0.64 COT AOT Aerosol-cloud associations
10s of thousands of clouds, some enveloped in smoke in the Amazon
r3.75
reff
As aerosol increases, cloud droplets decrease, but so does cloud brightness
r0.64
COT
Kaufman and Nakajima
(1993)
Apparent cloud free reflectance

8. Aerosol-Cloud associations
Cloud fraction
Cloud droplet effective radius
Longitude
Longitude
Shallow clouds in the Atlantic
Kaufman et al. (2005)

9. Aerosol-cloud associations
Convective clouds in the Atlantic
Cloud top pressure
AOD
Cloud fraction
Cloud top pressure
Koren et al. (2005)
reff
Water cloud COT

10. Aerosol-cloud associations
Cloud fraction
AOD
Clouds and smoke in the Amazon
Koren et al. (2004)

11. Aerosol-cloud associations
Later Feingold’s model showed that suppression of cloud coverage could be explained by reducing surface fluxes from the vegetation. Feingold et al. 2005
Cloud fraction
AOD
Clouds and smoke in the Amazon
Koren et al. (2004)

12. Aerosol-cloud associations
AOD
Cloud top pressure
Cloud fraction
Analysis of MODIS products over the Amazon
Koren et al. (2008)

13. Analysis of MODIS products over the Amazon
Cloud fraction
Cloud top pressure
AOD
AOD
Microphysics dominates at low AOD, then saturates
Radiative effects get stronger as AOD increases
Koren et al. (2008)

14. Associations with aerosol optical depth
Cloud brightness, some times
Smaller droplets
Taller clouds OR suppressed
Greater cloud coverage OR less
5. Greatest microphysical effects in cleanest environments
6. Greatest radiative effects in heaviest aerosol loading
Everything shown…. Uses remote sensing products based on spectral signature only

15. Why these correlations?
Retrieval artifacts in either aerosol or cloud products, or both.
Aerosols and clouds are jointly driven by meteorology, and therefore correlated to each other.
3. Aerosols are affecting clouds, or vice-versa, physically.

16. Possibility that associations are artifacts
Below, using GOCART model for aerosol
Associations are
Found in lidar
Found in AERONET analyses
Found using GOCART for aerosol
Blue =
low AOT
Koren et al. (2010)

17. Possibility that associations are meteorology
Cloud top pressure
correlation
Cloud fraction
correlation
correlation
AOD
Koren et al. (2010)

18. Clouds and aerosols both strongly correlated to meteorological variables, but different variables
Cloud top pressure
correlation
Cloud fraction
correlation
correlation
AOD
Koren et al. (2010)

19. Possibility that associations are meteorology
Koren et al. (2010)

20. Are we done with passive remote sensing of aerosol-cloud interaction?
So, is it a done deal?
Are we done with passive remote sensing of
aerosol-cloud interaction?
Far from it.
Associations are suggestive of interaction, but do not determine causality.
AOT is not CCN, nor even number concentration.
Cloud r_eff is not size distribution.
Using re-analyses to constrain meteorology is at the wrong scale.
All remote sensing products are too coarse in spatial scale.
Cloud top pressure from an ensemble is not the same as vertical profiles.
Cloud fields create a “twilight zone” or continuum between aerosol and cloud droplets.

21. Modeling helps to provide insight into processes
Plotted are planer views of LWP in the model
Clean
Polluted
Here, adding aerosols acts to decrease LWP associated with weak clouds (twilight zone)
But strengthens the stronger convective elements.
Koren et al.

22. From Danny Rosenfeld:
The number of activated CCN (Na) for a given super-saturation (S).
S is calculated from the knowledge of Na and Wb (Cloud base updraft). S = C(T,P)Wb3/4Na-1/2
Danny shows that both Na and Wb can be retrieved from high resolution (375 m) NPP/VIIRS
satellite data, and validated against the SGP measurements.
Daniel Rosenfeld

23. 30 m resolution pixels yields insight into cloud fields
From Robert Levy

24. Finer resolution thermal; smarter choices of wavelengths
Martins et al. (2011)

25. Polarization allows retrieval of cloud droplet size distribution
Martins, personal communication
Breon and Goloub, 1998

26. Missing pieces
The need to focus on processes and not statistics
The need to intertwine modeling and observations
in aerosol remote sensing….
Multi-angle, multi-wavelength polarimeter
Lidar (HSRL) for 3D characterization
retrievals over snow/ice
in cloud remote sensing…
finer resolution, especially in thermal
Better constraints on ice in clouds
Passive/active observations together
3D RT retrievals
High capability geosynchronous observations

27. Intro to remote sensing
Remote sensing of aerosol history
- AVHRR, TOMS
- EOS era (MODIS DT Remer 2005; Levy 2014; MISR
- quantifying absorption (Satheesh, Wells, Zhu)
- retrievals over bright surfaces (OMI, MISR, Deep Blue)
- retrievals over clouds (torres 2012, Waquet 2009; Jethva 2013)
Remote sensing of clouds history
- Nakajima and King
- EOS era
Remote sensing of aerosol-cloud interaction
The need to focus on processes and not statistics
The need to intertwine modeling and observations
The missing pieces:
- in aerosol remote sensing…. Multi-angle, multi-wavelength polarimeter
lidar fpr 3D characterization
retrievals over snow/ice
- in cloud remote sensing… finer resolution, especially in thermal
Multi-angle, multi-wavelength polarimeter
Better constraints on ice
3D RT retrievals
- separating processes in the twilight zone…. Fine resolution polarimetry
- observational/modeling synthesis that focuses on processes