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Global partitioning of NO x emissions using satellite observations. Lyatt Jaeglé University of Washington. Linda Steinberger University of Washington Randall Martin Dalhousie University Kelly Chance Harvard-Smithsonian Center for Astrophysics. Partitioned inventory. FF+BF. 3. BB.
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Global partitioning of NOx emissions using satellite observations Lyatt Jaeglé University of Washington Linda Steinberger University of Washington Randall Martin Dalhousie University Kelly ChanceHarvard-Smithsonian Center for Astrophysics
Partitioned inventory FF+BF 3 BB SOILS Jaeglé et al. [2004] Jaeglé et al. [2005] The Global Ozone Monitoring Experiment (GOME) Tropospheric NO2 columns GOME Top-down NOx inventory 1 2 Spectral fit Stratosphere AMF Inverse modeling with GEOS-CHEM Chance et al. [2000] Martin et al. [2002] Martin et al. [2003] Applied to GOME observations for year 2000 Use GEOS-CHEM as a priori NOx inventory: Anthropogenic emissions: GEIA scaled to 1998 Biofuel: Yevich & Logan [2003] Biomass burning 2000: Duncan et al. [2003] Soils: Yienger & Levy [1995]
Fuel Combustion 1. Spatial location of FF-dominated regions in a priori (>90%) 1 2 Biomass Burning 2. Spatiotemporal distribution of fires used to separate BB/soil VIRS/ATSR fire counts Soils No fires + background Algorithm for partitioning top-down NOx inventory GOME NOx emissions Algorithm tested using synthetic retrieval
Optimized inventory GOME (E) A priori (E’) A posteriori (E”) 1010atoms N cm-2 s-1 Combine top-down GOME emissions (E,err) with a priori emissions (E’,err’) weighted by relative errors optimized inventory: ln(E”) = ln(E) ln(err’)2 + ln(E’) ln(err)2 ln(err’)2 + ln(err)2 ln(err”)-2 = ln(err’)-2 + ln(err)-2
Fuel Combustion A priori A posteriori 1010atoms N cm-2 s-1 r = 0.96 (±80%) (±40%) United States Europe East Asia Line: A priori (FF+BF) A poster : 6.4 TgN/yr A priori : 6.3 TgN/yr 4.9 TgN/yr 4.9 TgN/yr 5.2 TgN/yr 4.8 TgN/yr A posteri. total Bars: A posteriori (FF+BF) • Aseasonal a posteriorifuel combustion emissions except for Europe and East Asia (wintertime heating) • China and India (4.4 and 1.7 TgN/yr) are 38% and 43% higher than Streets et al. [2003] inventory
GWEM Hoelzemann, ’05 5 TgN/yr Biomass Burning (2000) A priori A posteriori 1010atoms N cm-2 s-1 r = 0.85 (±200%) (±80%) SE Asia/India: 46% decrease N. Eq. Africa: 50% increase Line: A priori (BB) SE Asia/India N. Eq. Africa S. Eq. Africa A posteriori total Bars: A posteriori (BB) • Good agreement with BB seasonality from Duncan et al. [2003]
Largest soil emissions:seasonally dry tropical ecosystems +fertilized cropland ecosystems North Eq. Africa Soils Onset of rainy season: Pulsing of soil NOx! Soil emissions A posteriori (8.9 TgN/yr) 68% larger than a priori! r = 0.79 A priori A posteriori (±90%) (±200%)
Mid-latitudes soil emissions: 3.9 TgN/yr (a priori: 1.7 TgN/yr) United States Europe East Asia Soils Bars: a posteriori Lines: a priori • Summer mid-latitudes: soils account for ~50% of FF emissions! • East Asia (soils = 1 TgN/yr) consistent with inverse modeling study of Yuxuan Wang et al. [2004]
Summary • Fuel combustion emissions: 25.6 TgN/yr (±40%) within 10% of a priori emissions. • Biomass burning emissions: 5.8 TgN/yr (±80%) vs a priori 5.9 TgN/yr (±200%). Large differences: N. Eq. Africa + SE Asia/India. • Large soil emissions (8.9 vs 5.3 TgN/yr). Max during summer in NH and wet season in Tropics: • Role of N-fertilizers over croplands + rain-induced pulsing from semi-arid soils. Need to revisit Yienger & Levy? • Underestimate of soil contribution to background ozone?
Soil emissions over N. Eq. Africa GOME NO2: June 10-12 2000 Soils IDAF surface NO2 passive samplers Onset of rainy season: Pulsing of soil NOx! Jaeglé et al. [2004]
Annual GOME top-down NOx inventory: 2000 GOME GEOS-CHEM (a priori) NO2 columns 1015 molecules cm-2 Linear relationship between ENOx and NO2 NOx emissions 1010atoms N cm-2 s-1 Anthropogenic emissions: GEIA scaled to 1998 Biofuel: Yevich & Logan [2003] Biomass burning 2000: Duncan et al. [2003] Soils: Yienger & Levy [1995]
Fuel Combustion 1. Spatial location of FF-dominated regions in a priori (>90%) Biomass Burning 2. Spatiotemporal distribution of fires used to separate BB/soil VIRS/ATSR fire counts Soils No fires + background Algorithm for partitioning top-down NOx inventory GOME NOx emissions Algorithm tested using synthetic retrieval
Optimized inventory GOME (E) A priori (E’) A posteriori (E”) 1010atoms N cm-2 s-1 GEOS-CHEM