U N I V E R S I T Y O F W A S H I N G T O N S C H O O L O F N U R S I N G 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

U N I V E R S I T Y O F W A S H I N G T O N S C H O O L O F N U R S I N G Tropospheric NO 2 columns Top-down NO x inventory Chance et al. [2000] Martin et al. [2002] Martin et al. [2003] GOME The Global Ozone Monitoring Experiment (GOME) Applied to GOME observations for year 2000 Use GEOS-CHEM as a priori NO x inventory: 12 Jaeglé et al. [2004] Jaeglé et al. [2005] Partitioned inventory FF+BF BB SOILS 3 Spectral fit Stratosphere AMF Inverse modeling with GEOS-CHEM Anthropogenic emissions: GEIA scaled to 1998 Biofuel: Yevich & Logan [2003] Biomass burning 2000: Duncan et al. [2003] Soils: Yienger & Levy [1995]

Algorithm for partitioning top-down NO x inventory Algorithm tested using synthetic retrieval GOME NO x emissions Fuel Combustion 1. Spatial location of FF- dominated regions in a priori (>90%) 1 Biomass Burning 2. Spatiotemporal distribution of fires used to separate BB/soil VIRS/ATSR fire counts Soils No fires + background 2

Combine top-down GOME emissions (E,err) with a priori emissions (E’,err’) weighted by relative errors  optimized inventory: Optimized inventory GOME (E) A priori (E’) A posteriori (E”) 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) atoms N cm -2 s -1

Fuel Combustion A priori A posteriori (±80%) r = 0.96 (±40%)  Aseasonal a posteriori fuel 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 United States Europe East Asia atoms N cm -2 s -1 Bars: A posteriori (FF+BF) Line: A priori (FF+BF) A poster : 6.4 TgN/yr A priori : 6.3 TgN/yr 4.9 TgN/yr 5.2 TgN/yr 4.8 TgN/yr A posteri. total

Biomass Burning (2000) A priori A posteriori  Good agreement with BB seasonality from Duncan et al. [2003] (±200%) r = 0.85 (±80%) SE Asia/India N. Eq. Africa S. Eq. Africa N. Eq. Africa: 50% increase SE Asia/India: 46% decrease GWEM Hoelzemann, ’05 5 TgN/yr Line: A priori (BB) Bars: A posteriori (BB) atoms N cm -2 s -1 A posteriori total

Soil emissions A posteriori (8.9 TgN/yr) 68% larger than a priori! A priori A posteriori Largest soil emissions: seasonally dry tropical ecosystems (±200%) (±90%) + fertilized cropland ecosystems r = 0.79 Soils Onset of rainy season: Pulsing of soil NO x ! North Eq. Africa

Mid-latitudes soil emissions: 3.9 TgN/yr (a priori: 1.7 TgN/yr)  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] United States Europe East Asia Bars: a posteriori Lines: a priori Soils

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 Onset of rainy season: Pulsing of soil NO x ! GOME NO 2 : June IDAF surface NO 2 passive samplers Jaeglé et al. [2004] Soils

Annual GOME top-down NO x inventory: 2000 NO x emissions atoms N cm -2 s -1 NO 2 columns GOMEGEOS-CHEM (a priori) molecules cm -2 Anthropogenic emissions: GEIA scaled to 1998 Biofuel: Yevich & Logan [2003] Biomass burning 2000: Duncan et al. [2003] Soils: Yienger & Levy [1995] Linear relationship between E NOx and  NO2

Algorithm for partitioning top-down NO x inventory Algorithm tested using synthetic retrieval GOME NO x emissions 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

Optimized inventory GOME (E) A priori (E’) A posteriori (E”) atoms N cm -2 s -1 GEOS-CHEM