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.

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

Emissions in the GOCART model for aerosol simulations (1)

Fossil fuel/biofuel consumptions: Emit SO2, 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 SO2

based on the global volcanism database and TOMS SO2 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

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

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

Using MODIS fire counts for daily & near-real-time fire emission

Here we explore the possibility of using MODIS fire counts (at 1-km2 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

Mass of tracer i (Mi) emitted from fire:

Mi = A ∙ B ∙ C ∙ Ei

A = Area burnedB = Biomass density (or fuel load)C = Completeness of burning (or burning efficiency)Ei = Emission factor of tracer i

Emission of aerosols and trace gases from fire

Dry mass burned

Area burned (A)

This is probably the most difficult quantity to determine on daily bases

Currently we assume that each 1-km2 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-km2 fire pixel within the box

Terra-MODIS fire counts 20040701

Biomass density (B) and Completeness of burning (C)

Based on Hoelzemann et al., JGR 2004

Emission factors for tracers (Ei)

Ecosystem-dependent

Burning stage-dependent

Also depending on temperature, moisture, etc.

Large uncertainties

Savana/ Grassland

Tropical Forest

Extratropical forest Biofuel Agriculture

Residual

BC0.48±0.18

0.66±0.31

0.56±0.19

0.59±0.37

0.69±0.13

OC 3.4±1.4 5.2±1.58.6 – 9.7

4.0±1.2 3.3

SO20.35±0.16

0.57±0.23

1.0 0.27±0.3 0.4

CO 65±20 104±20 107±37 78±31 92±84

CO2 1613±95 1580±90 1569±131 1550±95 1515±177

Emission factors (g tracer / kg dry matter) for selected tracers from Andreae and Merlet, GBC 2001:

Example: BC biomass burning emission used in the GOCART model

BC biomass burning emission July 1 2004

(MBC = A∙B∙C∙EBC )

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

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

GOCART MODIS

Comparison with AEORNET AOT over North America during INTEX-A

AERONET

Total

Sulfate

Dust

OC

BC

Sea-salt

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)

monthly

monthlydailydaily firecounts

DMfirecountsDM ×=

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

GOCART model simulation of aerosols (Mi = A∙B∙C∙Ei)

BC biomass burning emission July 2004

Example: MODIS fire counts and BC biomass burning emission, July 1 2004

MODIS (Terra) fire counts July 2004