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Comparison of the TOMS AI and Model Results for Biomass Burning

Comparison of the TOMS AI and Model Results for Biomass Burning. Sophia Y. Zhang Joyce E. Penner University of Michigan, Ann Arbor, Michigan. Motivation. Biomass carbon aerosols are major absorbing aerosols in the troposphere. Biomass emissions and atmospheric loadings remain uncertain.

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Comparison of the TOMS AI and Model Results for Biomass Burning

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  1. Comparison of the TOMS AI and Model Results for Biomass Burning Sophia Y. Zhang Joyce E. Penner University of Michigan, Ann Arbor, Michigan

  2. Motivation • Biomass carbon aerosols are major absorbing aerosols in the troposphere. • Biomass emissions and atmospheric loadings remain uncertain. • The TOMS AI has both good spatial and temporal coverage for absorbing aerosols.

  3. Methods outline • Model biomass carbon aerosol spatial distributions for current biomass carbon emissions. • Model the aerosol index. • Compare model results with the TOMS AI. • Infer the biomass burning emissions using inverse model and the TOMS AI.

  4. Modeling the aerosol index modeled AI radiative transfer model biomass aerosol concentration aerosol optical properties transport model aerosol size distribution biomass burning emissions

  5. Input Emissions: Dust emissions [Ginoux et al. 2001] Biomass emissions [Liousse et al. 1996]

  6. Transport model The IMPACT model with 2 o lat x 2.5 o long and 46 vertical layers. DAO 1997 meteorological data. Radiative transfer model • The Herman and Browning code [Herman and Browning 1965]

  7. Aerosol size distribution and refractive index *assume black carbon is 10% of total mass

  8. Aerosol Index • AI is defined as: DN=-100 {log10[(I340/I380)model] -log10[(I340/I380)Rayleigh] (I380)model = (I380)Rayleigh • AI approximately linearly proportional to aerosol optical thickness. • AI depend on the residence height of aerosols. It cannot detect aerosols close to surface.

  9. Seasonal variation of aerosol optical thickness and TOMS AI Dashed lines are optical thickness for biomass carbon. TOMS AI AOD Sahara Desert: 15 o – 30 o N, 10 o W – 30 o E Sahelian region: 0 o – 13 o N, 20 o W – 20 o E Southern Africa: 5 o – 25 o S, 5 o – 30 o E South America: 5 o – 25 o S, 45 o – 65 o W

  10. JAN APR Biomass black carbon column burden (mg/m2)

  11. JUL OCT Biomass black carbon column burden (mg/m2)

  12. Biomass black carbon concentration (ng/m3) zonal Average JAN APR

  13. Biomass black carbon concentration (ng/m3) zonal Average JUL OCT

  14. TOMS AI Modeled AI TOMS AI and modeled AI in January 1997

  15. TOMS AI and modeled AI in April 1997 TOMS AI Modeled AI

  16. TOMS AI and modeled AI in July 1997 TOMS AI Modeled AI

  17. TOMS AI and modeled AI in October 1997 TOMS AI Modeled AI

  18. Conclusion • In January, biomass burning near the coast of Gulf Guinea is not strong enough. There is an underestimate of absorbing aerosols over the ocean. • In April, biomass burning in Africa is weak both in the TOMS AI and model results. However, the AI in northern India is larger than the observations.

  19. Conclusion • In July, modeled AI in southern Africa is much weaker than the observations. This can be caused by a weaker biomass burning emissions or the failure of model to produce the correct vertical profile for biomass black aerosols. • In October,the strong biomass burning in Indonesia is missing both in the column burning and modeled AI. The biomass burning in northern South America seems to be too weak.

  20. Future work • Inverse model using the results of modeled AI and TOMS AI will optimize the differences between the observation and model results. • The a posteriori biomass burning emission will be available soon.

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