1. Retrieving sources of fine aerosols from MODIS/AERONET observations by inverting GOCART model INVERSION: Oleg Dubovik 1 Tatyana Lapyonok 1 Tatyana Lapyonok 1 INSPIRATION : Yoram Kaufman 1, GOCART: Mian Chin 1 Paul Ginoux 2 Paul Ginoux 2 MODIS : Yoram Kaufman 1, Lorraine Remer 1 Lorraine Remer 1 Rob Levy 1 Rob Levy 1 AERONET: Brent Holben 1 1- NASA/GSFC, Greenbelt, MD, USA 2 - NOAA/GFDL, Princeton, NJ, USA

2. Outlines:  General idea  Formulating inversion of aerosol transport of adjoint operator  Applying the approach for MODIS-GOCART inversion MODIS-GOCART inversion  Numerical tests  Inverting actual observations  Conclusions

3. General Idea: GOCART : Global aerosol simulations - assimilated meteorology - advection and convection - removal processes Main “Uncertainty”: aerosol sources MODIS : Global observations of ambient aerosol AERONET : Semi-Global accurate observations of aerosol Synergy of Observation and Modeling: Retrieving sources (location and strength) providing best agreement between observations of MODIS /AERONET and GOCART simulations

4. Aerosol Transport: Basic relationship: -mass -emission Transport processes: (advection, diffusion, dry deposition, cloud convection, wet removal, etc.) Matrix approximation: Integral form :

5. Transport Inversion: Aerosol Mass Transport Operator Source - Aerosol Emission Measured Aerosol Mass Least Square Method:

6. Dimension of the Problem: Longitude Points (~144) X Latitude Points (~91) X Vertical Layers (~30) X Time steps (~12 days) = ~ 5,000,000 Longitude Points (~144) X Latitude Points (~91) X Time steps (~12 days) = ~ 200,000 5,000,000 X 200,000

7. Steepest Descent Method: for p , steepest descent method is equivalent to LSM It converges as: -has predominant diagonal due local character of aerosol transport

8. Inversion using adjoint of Transport: For Transport: -reversed order of time integration -reversed basic processes (e.g., advection  backward advection, falling  uplifting, updraft  falling) - Adjoint (“Transpose”) Transport

9. Characteristics of Inversion using adjoint of Transport: Advantage: allows inversion with time and space resolution of forward model (for GOCART: spatial resolution 2 0 x 2.5 0, time resolution 20 min) Disadvantage: requires iterations (the higher resolution the larger number of iterations) (we are searching for possibilities to accelerate convergence, e.g. by using conjugated gradients)

10. Conversion of MODIS measurements into aerosol mass Over Ocean: Over Ocean: 470; 550; 660; 870; 1200; 1600; 2100 nm Over Land: Over Land: 470; 550; 660 nm There is informationonly about sizes There is information only about sizes. No information about composition. We use :  fine (550) only  fine ≈  total (  total -  coarse )/(  fine -  coarse ) (to be improved)

11. General assumptions in the inversion - MODIS  is correct (have only random errors) - MODIS  is correct (have only random errors) - transport of GOCART is absolutely correct (i.e., meteorological fields and all transport processes are correct) - emission in GOCART is restricted to near surface layer and assumed to be constant layer and assumed to be constant during 24 hours during 24 hours - time differences in MODIS  accounted (!!!) - MODIS resolution is scaled down to GOCART resolution GOCART resolution

12. GOCART Model Global Ozone Chemistry Aerosol Radiation and Transport model An atmospheric process model using assimilated meteorological fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS) Including major types of aerosols, sulfate, dust, BC, OC, and sea-salt, from both anthropogenic and natural sources Calculating aerosol composition, 4-D distributions, optical thickness, radiative forcing we use only one aerosol we use only mass 4-D distributions

13. Use of the GOCART model in the inversion Included into inversion: Transport (advection, convection, BL mixing)Transport (advection, convection, BL mixing) Dry deposition and settlingDry deposition and settling Wet depositionWet deposition NOT Included in the inversion: Emissions of aerosols and their precursorsEmissions of aerosols and their precursors Chemistry (gas-to-particle conversion)Chemistry (gas-to-particle conversion) Hygroscopic growth and size distributionsHygroscopic growth and size distributions Included into inversion: Transport (advection, convection, BL mixing)Transport (advection, convection, BL mixing) Dry deposition and settlingDry deposition and settling Wet depositionWet deposition NOT Included in the inversion: Emissions of aerosols and their precursorsEmissions of aerosols and their precursors Chemistry (gas-to-particle conversion)Chemistry (gas-to-particle conversion) Hygroscopic growth and size distributionsHygroscopic growth and size distributions

14. Assumed emission Initial guess Retrieved (40 iterations) Emission Sources Testing of inversion

15. Modeled Simulated from initial guess Retrieved Testing of inversion Optical Thickness

16. Testing of inversion Assumed emission Initial guess Retrieved (40 iterations) Emission Sources

17. Testing of inversion Modeled Simulated from initial guess Retrieved Optical Thickness

18. Combining MODIS and AERONET MODIS Observations AERONET Observations MODIS and AERONET Observations Optical Thickness

19. GOCART average emission August 2000 Sulfates BC + OC Sulfates + BC +OC Emission Sources

20. Dust + Sea Salt Sea Salt Dust GOCART average emission August 2000

21. Emission Sources 9 day average, retrieval is NOT constrained to the land GOCART Emission: Sulfates + BC +OC Retrieved Emission (40 iterations)

22. Optical Thickness 9 day average, Optical Thickness retrieval is NOT CONSTRAINED to the land Observations from Retrieved emission MODIS+AERONET Observations

23. Retrieved Emission (40 iterations) GOCART Emission: Sulfates + BC +OC Emission Sources 9 day average, Optical Thickness retrieval IS CONSTRAINED to the land

24. Observations from Retrieved emission MODIS+AERONET Observations 9 day average, Optical Thickness retrieval IS CONSTRAINED to the land Optical Thickness

25. CARBON EMISSION ( g C / m2 /month) Combination of satellite hotspots (TRMM and ATSR), satellite burned area (MODIS), and a biogeochemical model (CASA) Guido van der Werf Jim Collatz

26. GOCART Emission: Sulfates + BC +OC Retrieved Emission (40 iterations) Combination of satellite hotspots (TRMM and ATSR), satellite burned area (MODIS), and a biogeochemical model (CASA) Guido van der Werf Jim Collatz Emission Sources

27. Single day,retrieval NOT CONSTRAINED to the land GOCART BC Emission: Observations modeled with Initial guess Retrieved emission (40 iterations) Emission Sources

28. Single day,retrieval NOT CONSTRAINED to the land MODIS Observations modeled from initial guess Observations modeled from retrieved sources Optical Thickness

29. Single day,retrieval CONSTRAINED to the land GOCART BC Emission: Initial guess Retrieved Emission (40 iterations) Emission Sources

30. Single day,retrieval CONSTRAINED to the land MODIS Observations modeled from initial guess Observations modeled from retrieved sources Optical Thickness

31. Potential error sources in the inversion MODIS: -standard retrieval errors -unscreened cloud contamination -Transport of GOCART: -uncertainties in meteorological fields (3D winds, rain fields, etc.); -uncertainties in 3D clouds (is cloud cover consistent with MODIS???); -uncertainties in process modeling and numerical instability - Inversion by itself: -all assumptions have limited accuracy; -number of iterations is limited; -numerical instability (additional to forward model); -Issues in general assumptions (existence of derivatives, etc.)

32. Results:  Adjoint inversion of GOCART has been developed for retrieving aerosol sources from MODIS  The method was successfully applied for retrieval sources from 9 days of MODIS data Plans:  Check for possible numerical improvements  Include course aerosol into retrieval  Find a way of making our results useful for GOCART (?)  Apply the method to longer record of data