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Title: Satellite Observations of Tropospheric Aerosol and CO From Biomass Burning


1
Satellite Observations of Tropospheric Aerosol
and CO From Biomass Burning
David Edwards, Louisa Emmons, Gabrielle Petron,
John Gille NCAR, Boulder CO, USA Didier
Hauglustaine, IPSL/LSCE, Paris, France Allen
Chu, Yoram Kaufman, NASA GSFC, Greenbelt MD, USA
2
Introduction
MOPITT Carbon Monoxide and MODIS Aerosol
3
The Terra/MOPITT Mission
Definition of trends distributions for
tropospheric CO is essential. A satellite-borne
CO sensor operating for extended periods could
help enormously (WMO, 1985)
  • The Measurement Of Pollution In The Troposphere
    mission is a joint CSA/NASA project launched on
    EOS Terra in Dec. 1999
  • MOPITT is a thermal and near-IR gas correlation
    radiometer designed to measures tropospheric CO
    profile CH4 column
  • Uses a nadir viewing cross-track scan, with a 22
    x 22 km2 pixel size, and 3 day global coverage

4
Sept. 2000
MODIS Aerosol
  • MODIS can distinguish between coarse and fine
    particles using multiple channels from the
    visible to the near IR
  • Coarse aerosol mode Mainly dust
    and sea salt
  • Reflects in the VIS NIR Appears red in the VIS
    due to absorption in the blue
  • Surface characterization is an issue over land
  • Fine aerosol mode Anthropogenic pollution
    and biomass burning
  • Reflects mainly in the VIS

Coarse mode
(AOD)
Fine Mode
5
Sept. 2000
MODIS
MODIS Aerosol MOPITT CO
  • Good correlation is obtained between MODIS fine
    mode AOD MOPITT CO
  • Both CO and fine mode aerosol result from
    anthropogenic combustion processes urban
    pollution, industrial emissions, biofuel use,
    biomass burning

Fine Mode AOD
MOPITT
CO Column
6
MODIS AOD Fine Mode
MOPITT CO Total Column
7
Biomass Burning
A Plume Tracing Case Study
8
EP/TOMS Observations of Biomass Burning Over
Southern Africa Sept. 25 2000
Aerosol and Fires
Tropospheric Ozone
Anne Thompson, NASA
9
Prescribed burn near Kruger National Park, South
Africa, September 7, 2000. Photo from the
Convair-580 (Flight 1834) during SAFARI
2000 by Peter Hobbs, UWA.
SeaWiFS true-color image acquired over Southern
Africa, September 4, 2000. Data acquired by the
Satellite Applications Center, Pretoria, South
Africa.
10
TMI 2100 h Sept. 4, 2000
Forward Trajectories Sept 1-13 2000, z03 km
11
Terra Observations of Biomass Burning Plume
Outflow, Sept. 1-5, 2000
MODIS Fine Mode AOD
MOPITT 700 hPa CO
12
Plume aging can be followed as the AOD/CO ratio
falls, indicating persistent CO but reduced
aerosol loadings due to the shorter lifetime
13
TRMM merged precipitation map Sept. 4, 2000
14
Providing global context to local measurements
Long-range transport of biomass burning products
October 1-15, 2001
  • MOPITT and ground-based FTIR CO total column
    measurements taken at Lauder, New Zealand.
  • The plume from biomass burning causes a peak in
    the measured CO in the usually clean air over New
    Zealand

FTIR data N. Pougatchev, NASA N. Jones, NIWA
Day from 1/1/2000
15
TRMM merged precipitation map Sept. 4, 2000
16
MOPITT CO Ratio 250 hPa/700 hPa Sept. 1-10, 2000
17
SAGE Aerosol, 8 - 13 km August - October, 1997 -
1999
Extinction x 1000 km-1
18
Effect of Aerosol on Ozone Production
  • Black carbon from biomass burning strongly
    absorbs solar radiation and can potentially
    reduce photolysis rates within biomass burning
    plumes
  • Reduction in photolysis can lead to a
    compensating offset to the expected increase in
    O3 production due to the higher concentrations of
    NOx, CO, and other hydrocarbons
  • Heterogeneous reactions on BC particles can also
    affect O3 concentrations and the uptake
    coefficient for O3 on BC particles remains
    uncertain

TUV photolysis rate calculation in a BC layer of
AOD at 340 nm of 0.5 in the lowest 3km of the
atmosphere
19
Interannual Variability
CO and Aerosol Distributions
20
MOPITT 700 hPa CO Zonal Variability 2000-2003
  • The peak of the Southern hemisphere biomass
    burning maximum occurs each year in
    September-October and has variable intensity
  • The Northern hemisphere winter maximum occurs in
    March-April
  • Apparent increase from 2000 to 2003
  • The 2002-3 winter maximum was particularly
    intense and started early

21
MOPITT CO Column September Mean
22
Russian 2002 Forest and Peat Fires
MODIS Fires Smoke, Sept.4 2002
FTIR CO column Zvenigorod-Moscow 2002
E. Grechko, Zvenigorod Research Station
High CO column values due to urban Days 0-200
Urban pollution Around day 250 Forest
and peat fires
23
Summer 2003 Fires in Portugal
MODIS Fires and Smoke
MOPITT 700 hPa CO
August 1-8, 2003
August 4, 2003
24
Emissions
Comparing CO and Aerosol
25
Correlations CO and Fine Mode AOD
South America, Sept. 1-5, 2000
Corr 0.65, Grad 2.3
Southern Africa, Sept. 1-5, 2000
Corr 0.82, Grad 3.5
CO and fine mode AOD correlations are strongest
for intense isolated plumes
26
Correlations CO and Fine Mode AOD
Europe/Russia, Sept. 1-10, 2000
Corr 0.16
27
Modeled CO distribution
2.
MOPITT Observed CO Distribution
3.
Tropospheric Chemistry and Transport
Inverse Modeling
1.
A priori Emissions
Optimized Emissions
28
Observed and Simulated CO Over Source Regions
Apr. 2000 - Mar. 2001
MOPITT data CO simulated with a priori sources CO
simulated with a posteriori sources
29
Summary
  • The combination of measurements from the new
    tropospheric
    satellite sensors will play an increasingly
    important role in explaining chemistry
    and transport processes in the lower atmosphere
  • MOPITT CO and MODIS fine particle measurements
    can be combined to examine the seasonal
    variability and transport of aerosols and trace
    gases in biomass burning plumes
  • African Sept. 2000 case study shows that biomass
    burning emissions under the influence of the
    prevailing meteorology provide for both low
    altitude intercontinental transport of pollution
    together with a mechanism for delivering emission
    products to the upper troposphere
  • Provides information about the impact of intense
    fire sources on global scale air quality
  • Several years of data allow interannual
    variability to be examined and we can begin to
    look for trends and potential climate effects
  • Satellite data provide a way to improve source
    emission estimates using top-down modeling
    approaches
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