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.

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Presentation transcript:

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

GEOS-Chem CO 2 Surface Exchange “Balanced Biosphere” Ocean Exchange Net Terrestrial Exchange Carnegie-Ames- Stanford-Approach (CASA) model daily Net Ecosystem Production (NEP) for 2000 TransCom 3, 2000 annual, from David Baker Takahashi et al. [1997], generic year annual Often Turned Off

Evaluation with GLOBALVIEW-CO 2 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 CO 2 Distribution

Modified input.geos File

Defining Tagged CO 2 Regions Miller et al. (2007) Precision requirements for space-based X CO 2 data, JGR original method by Dylan Jones New Method Land Ocean

Defining Tagged CO 2 Regions Numerical maps of land/ocean regions are output to logfile

Satellite Measurements of CO 2 AIRS TES SCIAMACHY IASI Active Sensing of CO 2 Emissions over Nights Days and Seasons (ASCENDS) ~2016? GOSAT-II ?, MCAP ?, MEOS ? ….. GOSAT TES Initial Guess TES Retrieval TES Average CONTRAIL Mauna Loa photo credit: Matt Rogers, Colorado State University OCO Rebuild?

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) Assumed a priori flux uncertainties: 100% for terrestrial biosphere regions, 30% for combustion (fossil fuel + biofuel + biomass burning) and 30% for ROW 14 land regions (combustion + biospheric exchange) + ROW (oceans & ice) = 29 elements

TES and GLOBALVIEW OSSE Results GLOBALVIEW 0.3% 6.7 GLOBALVIEW 0.03%13.4 TES16.5 Degrees of Freedom TES CO 2 data generally provide a posteriori flux estimates closer to the “Truth” and with lower a posteriori errors than GLOBALVIEW

Forward simulations with monthly fossil fuel emissions OSSEs using new regions Future Work 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 CO 2 adjoint? 28 land regions based on AVHRR 1°x1° veg types 11 TransCom ocean regions 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