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Determining emissions of environmentally important gases using data from aircraft and satellites

Determining emissions of environmentally important gases using data from aircraft and satellites. Paul Palmer. with Dorian Abbot, Arlene Fiore, Colette Heald, Daniel Jacob, Dylan Jones, Jennifer Logan, Loretta Mickley, Bob Yantosca Harvard University Randall Martin, Kelly Chance, Thomas Kurosu

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Determining emissions of environmentally important gases using data from aircraft and satellites

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  1. Determining emissions of environmentally important gases using data from aircraft and satellites Paul Palmer withDorian Abbot, Arlene Fiore, Colette Heald, Daniel Jacob, Dylan Jones, Jennifer Logan, Loretta Mickley, Bob Yantosca Harvard University Randall Martin, Kelly Chance, Thomas Kurosu Harvard-Smithsonian Glen Sachse NASA Langley;Don Blake UCI; David Streets Argonne National Laboratory; Henry Fuelberg, Chris Kiley FSU http://www.people.fas.harvard.edu/~ppalmer

  2. Top-down and bottom-up emission inventories GOME, MOPITT, SCIAMACHY TES, OMI Global 3d chemistry transport model

  3. CO inverse modeling • Product of incomplete combustion; main sink is OH • Lifetime ~1-3 months • Relative abundance of observations CMDL network for CO and CO2 • Previous studies found a discrepancy between Asian emission inventories and observations

  4. Limitation of remote data for inverse model calculations RH + OH … CO Direct & indirect emissions Many 100s km 10s km CMDL site 1000s km Increasing model transport error

  5. TRACE-P data can improve level of disaggregation of continental emissions Feb – April 2001 Main transport processes: • DEEP CONVECTION • OROGRAPHIC LIFTING • FRONTAL LIFTING warm air cold air cold front

  6. Modeling Overview Forward model (GEOS-CHEM) Emissions x P3B, DC8 observations y BF y = Kxa +  FF BB DACOM (Sachse) Inverse model xs = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa) SS = (KTSy-1K + Sa-1)-1

  7. TRACE-P CO Emissions Inventories Biomass burning: Variability from observed daily firecount data (AVHRR) (Heald/Logan) Anthropogenic emissions for Y2K1 (fuel consumption) (Streets)

  8. Tagged model CO simulation for TRACE-P Global 3D CTM 2x2.5 deg resolution China Japan Korea Southeast Asia Rest of World [OH] from full-chemistry model (CH3CCl3 = 6.3 years)

  9. A priori emissions have a large negative bias in the boundary layer GEOS-CHEM Observation CO [ppb] A priori Lat [deg]

  10. Inverse Model (a.k.a. Weighted linear least-squares) • xs = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa) • SS = (KTSy-1K + Sa-1)-1 • x = state vector (emissions) • y = observation vector (TRACE-P CO, ppb) • Choice of state vector… • Aggregate anthropogenic emissions • Aggregate Korea/Japan Gain matrix

  11. GEOS-CHEM Error specification is crucial • Emission uncertainties for AsiaSa : • Anthropogenic (D. Streets): China (78%), Japan (17%), Southeast Asia (100%), Korea (42%) • Biomass burning: 50%; Chemistry (largely CH4): 25% • Observation uncertainty Sy: • Measurement accuracy (1%) Representation (14ppb or 25%): Model errors… TRACE-P Estimated: 1 sigma value about mean observed 2x2.5 value GEOS-CHEM 2x2.5 cell

  12. MODEL ERROR All latitudes Altitude [km] Model error: (y*RRE)2 ~38ppb (>70% of total observation error) (measured-model) /measured RRE Mean bias

  13. Our best estimate is insensitive to inverse model assumptions 1-sigma uncertainty China (anthropogenic) China (BB) Southeast Asia Korea + Japan Rest of World A priori A posteriori

  14. A posteriori emissions improve agreement with observations GEOS-CHEM Observation CO [ppb] A priori A posteriori Lat [deg]

  15. MOPITT shows low CO columns over Southeast Asia during TRACE-P MOPITT GEOS-CHEM [1018 molec cm-2] MOPITT – GEOS-CHEM Largest difference c/o Heald, Emmons, Gille [1018 molec cm-2]

  16. Next steps with CO… • Multi-species inversion will bring additional information: • CH3CN will bring information about biomass burning • CO2 used to disaggregate emissions from Korea and Japan (CO2/CO)

  17. Can calculate emissions of anthropogenic halocarbon X given the X:CO slope and CO emissions 2 km Direct & indirect emissions Fresh emissions CO, species with   CO, +many other species Asian continent Western Pacific Blake group: CH3CCl3,CCl4,Halons 1211, 1301, 2402, CFCs11, 12, 113, 114, 115

  18. Back-trajectories of top 5% of observed values indicate local sources Only a strong local source Proxy for OH

  19. CO:CH3CCl3 relationships  = value above “background”

  20. Large global & regional implications Eastern Asia estimates • CH3CCl3,CCl4,CFCs 11 & 12): • represents >80% of East Asia ODP (70% of total global ODP) • 103.1 ODP Gg/yr (East Asia) •  East Asia ODP = 70% •  Global ODP = 20% 3.0 Previous work This work 2.3 1.4 Gg/yr 0.9 CCl4 CH3CCl3 CFC-12 CFC-11 • Methodology has the potential to monitor magnitude and trends of emissions of a wide range of environmentally important gases

  21. Satellite data will become integral to the study of tropospheric chemistry in the next decade N = NadirL = Limb

  22. Global Ozone Monitoring Experiment • Nadir-viewing SBUV instrument • Pixel 320 x 40 km2 • 10.30 am cross-equator time • Global coverage in 3 days • HCHO slant columns fitted: 337-356nm • - fitting uncertainty 4 x 1015 molec cm-2 Isoprene Biomass Burning HCHO JULY 1997

  23. Isoprene dominates HCHO production over US during summer North Atlantic Regional Experiment 1997 Southern Oxidant Study 1995 measurements GEOS-CHEM model Altitude [km] Altitude [km] Defined background CH4 + OH [ppb] Continental outflow Surface source (mostly isoprene+OH)

  24. Using HCHO Columns to Map Isoprene Emissions kHCHOHCHO EISOP = ___________ Yield ISOPHCHO Displacement/smearing length scale 10-100 km hours hours HCHO h, OH OH isoprene

  25. HCHO columns – July 1996 BIOGENIC ISOPRENE IS THE MAIN SOURCE OF HCHO IN U.S. IN SUMMER GOME GEOS-CHEM [1016molec cm-2] GEIA isoprene emissions Model:Observed HCHO columns r2 = 0.7 n = 756 Bias = 11% [1012 atoms C cm-2 s-1]

  26. GOME isoprene emissions (July 1996) agree with surface measurements r2 = 0.77 Bias -12% 0 5 [1012 atom C cm-2 s-1]

  27. SEASONAL VARIABILITY IN GOME HCHO COLUMNS GOME GEOS-CHEM GOME GEOS-CHEM MAR JUL APR AUG MAY SEP r>0.75 bias~20% JUN OCT 0 1016 molecules cm-2 2.5

  28. INTERANNUAL VARIABILITY IN GOME HCHO COLUMNS (1995-2001) August Monthly Means & Temperature Anomaly GOME GOME T T 2.5 95 99 2 1016 molecules cm-2 96 °C 00 -2 97 01 0 •Interannual Variability ~30% 98

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