1. GEOS-5 Simulations of Aerosol Index and Aerosol Absorption Optical Depth with Comparison to OMI retrievals. V. Buchard, A. da Silva, P. Colarco, R. Spurr GMAO meeting – 03/31/2011

2. Aerosol transport module GOCART implemented in the GEOS-5 climate model. – Driven by assimilated meteorological fields, radiative feedbacks – Simulates 5 aerosol types : ( dust, sea salt, black and organic carbon and sulfate) – Horizontal resolution : 0.5°x 0.65° – 72 vertical levels from surface to 85 km Aerosol Mass Concentration : for each grid cell for each layer Assimilation of MODIS/ MISR aerosol data 1) AEROSOL MODEL : Mie calculations Aerosol Mass Concentration AOD Updates

3. 2) Vertical profiles of optical properties at OMI lat/long: AOD, SSA, g, P(θ) at OMI lat/long and λ for each layer Mie calculations 354 nm 388 nm 471 nm Vertical profiles of AOD, SSA and g at OMI λ Aerosol Mass Concentration : for each layer geolocation at OMI lat/long OMI (Ozone Monitoring Instrument)/ AURA

4. VLIDORT Radiative transfer code : – Discrete Ordinate Method – Scalar or Vector mode – Several options for surface specification (Lambertian surface used here) AOD, SSA, g, P(θ) at OMI lat/long and λ for each layer Input parameters from OMI file : - Geometry angles : Solar Zenith Angle Relative Azimuth Angle Sensor Zenith Angle - Surface albedo at 3 λ Model Pressure & Temperature 3) Radiative transfer calculation : TOA radiances Aerosol Index

5. Qualitative indicator of the presence of absorbing aerosol ( AI > 0 ). AI is derived from the change in the spectral dependence of the back- scattered UV radiances induced by aerosols relative to the Rayleigh scattering between 354 and 388 nm (Torres et al., 2007) : AI depends on : - aerosol concentration - aerosol layer height - aerosol optical properties

6. ⇒ Globally, good agreement but : A 388 nm  Overestimation of the modeled radiances over land,  Underestimation of the modeled radiances over ocean in the southern hemisphere. 388 nm GEOS-5 Free running modelOMI Difference OMI – GEOS-5 GEOS-5 with assimilation of MODIS/MISR AOD 388 nm

7. A 354 & 471 nm ⇒ Same conclusions as 388nm 354 nm 471 nm GEOS-5 Free running model OMI Difference OMI – GEOS-5

8. ⇒ GEOS-5 simulated AI captures major features, but is not perfect…  too much dust in Northwest Africa,  not enough dust in Arabia Peninsula,  not enough biomass burning (Southwest Africa). => Assimilation of AOD from MODIS/MISR have small impact on AI. GEOS-5 Free running modelOMI Difference OMI – GEOS-5 GEOS-5 with assimilation of MODIS/MISR AOD Difference OMI – GEOS-5

9.  OMI and modeled - AAOD capture both : African dust Biomass plumes in Southwest Africa Dust in Arabia Peninsula A 388 nm  AI – AAOD : Biomass plume too low In Africa : dust plume or concentration too high in the model ? In Arabia Peninsula : the model places the dust plumes too low ? OMI - AAOD OMI - AI GEOS-5 - AAOD GEOS-5 - AI  Model has some absorption in China, not observed by OMI

11. In Africa  Modeled dust plume height seems to be well placed Modeled Mass Mixing ratio for Dust

12. In Arabia Peninsula Modeled Mass Mixing ratio for Dust  Large amount of aerosol close to the surface

13. Conclusions : VLIDORT simulated Radiances and AI agree well with OMI. Altitude of the aerosol layer can explain differences in AI (ex : Arabia Peninsula). Plume height in the model or Planetary Boundary Layer Process ?. MODIS/MISR AOD assimilation have marginal impact on the comparison between AI products (MISR/MODIS do not contain absorption information). Future work : Recompute the AI with the new model (New Biomass emissions, optical tables) Include clouds in the radiative transfer calculation and assessing their impact on AI. Inclusion of water leaving radiances using MODIS assimilated chlorophyll dataset (from Watson Gregg).