Aerosol-Cloud Interaction from Passive Satellite Remote Sensing: Accomplishments and Missing Pieces   Lorraine A. Remer JCET UMBC Astronaut photograph from Apollo 8 , December 22, 1968. Provided by NASA Earth Observatory

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)

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.

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

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

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

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

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

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

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

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)

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

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)

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

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.

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)

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

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)

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

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.

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.

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

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

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

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

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

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