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