1. Using MODIS fire count data as an interim solution for estimating biomass burning emission of aerosols and trace gases Mian Chin, Tom Kucsera, Louis Giglio, Thomas Diehl NASA Goddard Space Flight Center, U.S.A.

2. Emission, emission, emission Emission is one of the most important factors that determines the amount of aerosols and trace gases in the atmosphere The quality of global model simulations critically depends on the accuracy of emissions used in the model

3. Emissions in the GOCART model for aerosol simulations (1) Fossil fuel/biofuel consumptions: Emit SO 2, BC, OC We currently use the IPCC 2000 emissions, based on energy use, population density, and technology We assume these emissions are relatively constant with some seasonal variations Volcanic/biogenic emissions: Volcanic emission of SO 2 based on the global volcanism database and TOMS SO 2 index Ocean emission of DMS from ocean using empirical relationship between the winds and DMS seawater concentrations Biogenic emission of OC based on global inventory

4. Emissions in the GOCART model for aerosol simulations (2) Dust and sea-salt emissions: We use empirical relationships between emission and meteorological conditions Dust emission is a function of surface type, surface wetness, and wind speed Sea-salt emissions is a function of wind speed Biomass burning emissions: We currently use the monthly averaged emission data based estimated based on the TRMM and ATSR fire data, MODIS burned area estimates, and dry mass burned (van der Werf et al., 2003, 2005) No daily variations is given

5. Challenges in estimating biomass burning emissions Biomass burning emission is highly variable with space and time It is difficult to use a “climatology” to model the biomass burning emission for a particular region at a certain time Only satellite data can provide global coverage of fire monitoring at real time, but converting the satellite fire data to biomass burning emission takes considerable efforts, making near real time simulation impossible These products are only available for monthly average which are not adequate for fires that last just a fraction of a month

6. Using MODIS fire counts for daily & near-real-time fire emission Here we explore the possibility of using MODIS fire counts (at 1-km 2 pixel resolution) to model daily biomass burning emissions of aerosols and trace gases as an “interim” solution This methods can be used in aerosol forecast for mission support, in which the near real time fire counts can be incorporated into the model

7. Mass of tracer i (M i ) emitted from fire: M i = A ∙ B ∙ C ∙ E i A = Area burned B = Biomass density (or fuel load) C = Completeness of burning (or burning efficiency) E i = Emission factor of tracer i Emission of aerosols and trace gases from fire Dry mass burned

8. Area burned (A) This is probably the most difficult quantity to determine on daily bases Currently we assume that each 1-km 2 MODIS fire pixel is filled with fire, such that the burned area within a model gridbox (1.25ºlong x 1ºlat or 2.5ºx2º) = total number of 1-km 2 fire pixel within the box Terra-MODIS fire counts 20040701

9. Biomass density (B) and Completeness of burning (C) Based on Hoelzemann et al., JGR 2004

10. Emission factors for tracers (E i ) Ecosystem- dependent Burning stage- dependent Also depending on temperature, moisture, etc. Large uncertainties Savana/ Grassland Tropical Forest Extratropical forest Biofuel Agriculture Residual BC 0.48±0.180.66±0.310.56±0.190.59±0.370.69±0.13 OC 3.4±1.45.2±1.58.6 – 9.74.0±1.23.3 SO 2 0.35±0.160.57±0.231.00.27±0.30.4 CO 65±20104±20107±3778±3192±84 CO 2 1613±951580±901569±1311550±951515±177 Emission factors (g tracer / kg dry matter) for selected tracers from Andreae and Merlet, GBC 2001:

11. Example: BC biomass burning emission used in the GOCART model BC biomass burning emission July 1 2004 (M BC = A∙B∙C∙E BC )

12. GOCART model simulation of aerosols Example: Total aerosol optical thickness at 550 nm, July 1 2004 (including biomass burning, anthropogenic, dust, and sea-salt emissions) Comprehensive evaluation with satellite and other data are in progress

13. GOCART model simulation of aerosols Total aerosol optical thickness at 550 nm, July 2004 (including biomass burning, anthropogenic, dust, and sea-salt emissions) Comprehensive evaluation with satellite and other data are in progress GOCARTMODIS

14. Comparison with AEORNET AOT over North America during INTEX-A AERONET Total Sulfate Dust OC BC Sea-salt

15. Future plan Using MODIS fire counts for daily emissions: Better estimates of area burned: Using the relationship between Terra-MODIS fire counts and area burned at different regions (Giglio et al., 2005) Using combined Terra- and Aqua-MODIS fire counts Better estimates of seasonal variations of dry mass burned: Linking MODIS fire counts to the monthly averaged dry mass burned estimates (van der Werf et al., 2005): Using aerosol emission derived from MODIS fire radiative energy and aerosol optical depth (Ichoku and Kaufman)

16. Acknowledgment MODIS fire team for fire counts data MODIS aerosol team for providing aerosol data (special thanks to Rob Levy) AERONET team Funding from NASA EOS

17. GOCART model simulation of aerosols (M i = A∙B∙C∙E i ) BC biomass burning emission July 2004 Example: MODIS fire counts and BC biomass burning emission, July 1 2004 MODIS (Terra) fire counts July 2004