A23L-06
Aerosol Types using Passive Remote Sensing: Global Distribution, Consistency Check, Total-Column Investigation and Translation into Composition Derived from Climate and Chemical Transport Model
Tuesday, 15 December 2015: 14:55
3006 (Moscone West)
Meloe S Kacenelenbogen1, Kyle William Dawson2, Matthew S Johnson3, Sharon P Burton4, Jens Redemann3, Otto P Hasekamp5, Johnathan W Hair4, Richard Anthony Ferrare4, Carolyn F Butler6, Brent N Holben7, Andreas Joel Beyersdorf4, Luke D Ziemba4, Karl D Froyd8, Jack E Dibb9, Taylor Shingler10, Armin Sorooshian10, Jose L Jimenez11, Pedro Campuzano Jost12 and Daniel J. Jacob13, (1)BAERI/NASA Ames Research Center, Moffett Field, CA, United States, (2)North Carolina State University Raleigh, Raleigh, NC, United States, (3)NASA Ames Research Center, Moffett Field, CA, United States, (4)NASA Langley Research Center, Hampton, VA, United States, (5)SRON Netherlands Institute for Space Research, Utrecht, Netherlands, (6)Science Systems and Applications Inc., Hampton, VA, United States, (7)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (8)NOAA Boulder, Boulder, CO, United States, (9)University of New Hampshire Main Campus, Durham, NH, United States, (10)University of Arizona, Tucson, AZ, United States, (11)University of Colorado at Boulder, Dept. of Chemistry and Biochemistry, Boulder, CO, United States, (12)University of Colorado Boulder, Boulder, CO, United States, (13)Harvard University, School of Engineering and Applied Sciences, Cambridge, MA, United States
Abstract:
To improve the predictions of aerosol composition in chemical transport models (CTMs) and global climate models (GCMs), we have developed an aerosol classification algorithm (called Specified Clustering and Mahalanobis Classification, SCMC) that assigns an aerosol type to multi-parameter retrievals by spaceborne, airborne or ground based passive remote sensing instruments [Russell et al., 2014]. The aerosol types identified by our scheme are pure dust, polluted dust, urban-industrial/developed economy, urban-industrial/developing economy, dark biomass smoke, light biomass smoke and pure marine. We apply the SCMC method to two different total-column datasets of aerosol optical properties: inversions from the ground-based AErosol RObotic NETwork (AERONET) and retrievals from the space-borne POLDER (Polarization and Directionality of Earth’s Reflectances) instrument. The POLDER retrievals that we use differ from the standard POLDER retrievals [Deuzé et al., 2001] as they make full use of multi-angle, multispectral polarimetric data [Hasekamp et al., 2011]. We analyze agreement in the aerosol types inferred from both AERONET and POLDER globally. Then, we investigate how our total-column “effective” SCMC aerosol types relate to different aerosol types within the column (i.e. either a mixture of different types within one layer in the vertical or the stacking of different aerosol types within the vertical column). For that, we compare AERONET-SCMC aerosol types to collocated NASA LaRC HSRL vertically resolved aerosol types [Burton et al., 2012] during the SEAC4RS and DISCOVER-AQ airborne field experiments, mostly over Texas in Aug-Sept 2013. Finally, in order to evaluate the GEOS-Chem CTM aerosol types, we translate each of our SCMC aerosol type into a unique distribution of GEOS-Chem aerosol composition (e.g. biomass burning, dust, sulfate, sea salt). We bridge the gap between remote sensing and model-inferred aerosol types by using multiple years of collocated AERONET retrievals and GEOS-Chem simulations globally
