Inverse Modeling of CO Emissions Results for Biomass Burning

1. Multi-year inversion of CO sources using MOPITT data Inverse Modeling of CO EmissionsResults for Biomass Burning Gabrielle Pétron National Center for Atmospheric Research gap@ucar.edu Land clearing fires in the Kalimantan region of the island of Borneo, Indonesia NASA photo, 09/18/91 IGBP-QUEST Fire Fast Track Initiative Workshop

2. NO2 +UV O3 + NO CO + OH + O2  CO2 + HO2 COV CO NOx NOx Role of CO in the troposphere Houston • Oxidation capacity of the atmosphere • Precursor of tropospheric ozone • Indoor and urban pollutant Santiago http://eces.org/archive/gallery/airgfx IGBP-QUEST Fire Fast Track Initiative Workshop

3. CO emissions • Known : nature of sources • combustion of C matter, chemical production... • Uncertain : sources quite variable (x,t) • intensities • location • timing, seasonality, interannual variations • splitting : fossil fuel/biofuel.... • Tools to study CO budget: • emissions inventories • observations • models IGBP-QUEST Fire Fast Track Initiative Workshop

4. Sources & Sinks of CO TgCO/yr • Fossil fuel : 300-600 Biomass burning: 300-900 (forests, savannas, agric. waste burning, fuel wood use) • Vegetation : 50-200 • Oceans : 6- 30 • Methane oxidation: 400-1000 • HCNM oxidation : 300-1000 TOTAL Source = 1400 – 3700 TgCO/yr • Photochemical sink : 1400-2600 • Surface deposition: 150-500 TOTAL Sink = 1550 – 3100 TgCO/yr IGBP-QUEST Fire Fast Track Initiative Workshop

5. Tools to study CO budget • Observations • local,regional,global • in situ/remote sensing • continuous, campaign • Models • city-scale,regional,global • transport/chemistry (on/offline) IGBP-QUEST Fire Fast Track Initiative Workshop

6. July 2000 Total column of CO : MOZART2 (top) and MOPITT (bottom) underestimation of biomass burning emissions IGBP-QUEST Fire Fast Track Initiative Workshop

7. Motivations for Inverse Modeling and Data Assimilation • Quality and expansion of the Observing Systems • surface stations networks • remote sensing • intensive regional campaign • Progress in Modeling • computation capacity • representativeness of complex CTMs • improved parameterizations • chemistry (more species, trace gases and particles) • transport (esp. analyzed meteorological fields) IGBP-QUEST Fire Fast Track Initiative Workshop

8. MOPITT/MOZART CO at 850 hPa A priori biomass burning (WF) monthly emissions in MOZART: scaling of MODIS fire counts Phase 1 : April 2000 to April 2001 Phase 2 : September 2001 to December 2003 IGBP-QUEST Fire Fast Track Initiative Workshop

9. MOZART Modeled CO MOPITT Observed CO Atmospheric CTM INVERSE MODELING A Priori Emission Inventory Optimized Emission Inventory POET CO emissions + a priori WF Optimized CO emissions Figure 2 Figure 3 Multi-year CO inversionMOZART CTM MOPITT satellite dataApril 2000-December 2003 3 2 The discrepancies between the observed and the modeled CO distributions can be used to optimize poorly known parameters of the model – here, CO anthropogenic emissions. 1 4 IGBP-QUEST Fire Fast Track Initiative Workshop

10. Fossil Fuel Use Biofuel Use Wildland Fires CO FF BF WF optimized emissions for four latitudinal bands IGBP-QUEST Fire Fast Track Initiative Workshop

11. a priori inventory based on MODIS fire counts CO WF Emissions inversion results North Asia: high emissions in 2002 and 2003 South East Asia: high emissions in 2003 IGBP-QUEST Fire Fast Track Initiative Workshop

12. CO WF emissions in boreal regions van der Werf (MODIS fc, CASA model,...) Ito and Penner (GBA 2000,...): artifact ~no fire detection at high latitude fire counts: sampling bias < polar orbit satellite only sees a fraction of the fires yet useful to detect fires in dense tropical forest burnt area: integrated fire activity over 10 days problem for both approaches: what is the fraction of pixel burnt emission factors: “large” uncertainties (flaming/smoldering, ...) IGBP-QUEST Fire Fast Track Initiative Workshop

13. CO WF emissions in Africa no inversion: January to March 2000 & May to Aug 2001 • >0o: fairly good a priori emissions • 0-10oS: emissions do not decrease as fast as fire counts regional total • < 10oS : clear a priori under-estimation IGBP-QUEST Fire Fast Track Initiative Workshop

14. Conclusions • Inversion results: • Results quite robust/ inversion parameters (paper to be submitted) • WF emissions are not proportional to fire counts • Emission Modeling: • Intercomparison with WF top-down inventories • assess parameterization • Emission Inference: • Requires to use several fire products AND chemical data IGBP-QUEST Fire Fast Track Initiative Workshop