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Modeling CO 2 and its sources and sinks with GEOS-Chem

Modeling CO 2 and its sources and sinks with GEOS-Chem. Ray Nassar 1 , Dylan B.A. Jones 1 , Susan S. Kulawik 2 & Jing M. Chen 1 1 University of Toronto, 2 JPL/CalTech GEOS-Chem Meeting, Harvard University, 2009 April 7-10. GEOS-Chem CO 2 Emissions. Biofuel. Biomass Burning.

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Modeling CO 2 and its sources and sinks with GEOS-Chem

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  1. Modeling CO2 and its sources and sinks with GEOS-Chem Ray Nassar1, Dylan B.A. Jones1, Susan S. Kulawik2 & Jing M. Chen1 1University of Toronto, 2JPL/CalTech GEOS-Chem Meeting, Harvard University, 2009 April 7-10

  2. GEOS-Chem CO2 Emissions Biofuel Biomass Burning Generic seasonal or GFEDv2 monthly/8-day Yevich & Logan [2003], generic year annual Fossil Fuels *shown on different scales Robert Andres (ORNL), generic, annual/monthly 1950-2005

  3. GEOS-Chem CO2 Surface Exchange Carnegie-Ames-Stanford-Approach (CASA) model daily Net Ecosystem Production (NEP) for 2000 “Balanced Biosphere” Ocean Exchange Net Terrestrial Exchange Often Turned Off Takahashi et al. [1997], generic year annual TransCom 3, 2000 annual, from David Baker

  4. Evaluation with GLOBALVIEW-CO2 Reference: GLOBALVIEW-CO2: Cooperative Atmospheric Data Integration Project - Carbon Dioxide, available via anonymous FTP to ftp.cmdl.noaa.gov, path: ccg/co2/GLOBALVIEW, [2008]. Mauna Loa GEOS-Chem GLOBALVIEW Example of GEOS-Chem CO2 Distribution

  5. Modified input.geos File

  6. Defining Tagged CO2 Regions New Method original method by Dylan Jones Land Miller et al. (2007) Precision requirements for space-based XCO2 data, JGR Ocean

  7. Defining Tagged CO2 Regions Numerical maps of land/ocean regions are output to logfile

  8. Satellite Measurements of CO2 AIRS SCIAMACHY IASI OCO GOSAT TES Rebuild? TES Initial Guess TES Retrieval TES Average CONTRAIL Mauna Loa photo credit: Matt Rogers, Colorado State University Active Sensing of CO2 Emissions over Nights Days and Seasons (ASCENDS) ~2016? GOSAT-II ?, MCAP ?, MEOS ? …..

  9. Preliminary Pseudo-data Inversions • Pseudo-data inversion or Observing System Simulation Experiment (OSSE) • GEOS-Chem model run (GEOS-4 2ºx2.5º) for 2005 is designated as “Truth” • Sampled model at 76 GLOBALVIEW sites 48 times throughout year and at 96 TES 20ºx30º monthly-averaged boxes (applied noise) • Assumed GLOBALVIEW precisions: 0.3% (typical) and 0.03% (high precision) • TES precision from a representative retrieval: ~1 ppm for 20ºx30º monthly average over water (but bias must be characterized) 14 land regions (combustion + biospheric exchange) + ROW (oceans & ice) = 29 elements • Assumed a priori flux uncertainties: 100% for terrestrial biosphere regions, 30% for combustion (fossil fuel + biofuel + biomass burning) and 30% for ROW

  10. TES and GLOBALVIEW OSSE Results Degrees of Freedom TES CO2 data generally provide a posteriori flux estimates closer to the “Truth” and with lower a posteriori errors than GLOBALVIEW

  11. Future Work • Forward simulations with monthly fossil fuel emissions • OSSEs using new regions 28 land regions based on AVHRR 1°x1° veg types 11 TransCom ocean regions • Compare separate inversions with real TES and GLOBALVIEW data • OSSEs and real inversions combining TES and GLOBALVIEW data • GOSAT data or other satellite observations • Eventually work with GEOS-Chem CO2 adjoint? ray.nassar@utoronto.ca Acknowledgements: Funding at U of T was provided by the Natural Science & Engineering Research Council (NSERC) of Canada and funding at JPL/CalTech was provided under contract to NASA

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