Post on 21-Dec-2015
Modeling COModeling CO22 and its and its sources and sinks with sources and sinks with
GEOS-ChemGEOS-Chem
Ray NassarRay Nassar11, Dylan B.A. Jones, Dylan B.A. Jones11, , Susan S. KulawikSusan S. Kulawik22 & Jing M. Chen & Jing M. Chen11
11University of Toronto, University of Toronto, 22JPL/CalTechJPL/CalTechGEOS-Chem Meeting, Harvard University, 2009 April 7-GEOS-Chem Meeting, Harvard University, 2009 April 7-
1010
GEOS-Chem CO2 Emissions
Fossil Fuels
Biofuel Biomass Burning
Yevich & Logan [2003], generic year annualGeneric seasonal or GFEDv2 monthly/8-day
Robert Andres (ORNL), generic, annual/monthly 1950-2005
*shown on different scales
GEOS-Chem CO2 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 BakerTakahashi et al. [1997], generic year annual
Often Turned Off
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-ChemGLOBALVIEW
Example of GEOS-Chem CO2 Distribution
Modified input.geos File
Defining Tagged CO2 Regions
Miller et al. (2007) Precision requirements for space-based XCO2
data, JGR
original method by Dylan Jones
New Method
Land
Ocean
Defining Tagged CO2 Regions
Numerical maps of land/ocean regions are
output to logfile
Satellite Measurements of CO2
AIRS
TES
SCIAMACHY
IASI
Active Sensing of CO2 Emissions over Nights Days and Seasons (ASCENDS) ~2016? GOSAT-II ?, MCAP ?, MEOS ? …..
GOSAT
TES Initial GuessTES RetrievalTES AverageCONTRAILMauna Loa
photo credit: Matt Rogers,Colorado State University
OCORebuild?
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
TES 16.5
Degrees of Freedom
TES CO2 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 CO2 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
ray.nassar@utoronto.ca