Biospheric Models as Priors

Deborah Huntzinger, U. Michigan

Synthesis Bayesian Inversion

InversionCarbon Budget

InversionCarbon Budget

Geostatistical Inversion Model (GIM)

InversionCarbon Budget

Geostatistical Inversion Model (GIM)

Data-driven approach eliminating use of explicit prior estimates

Takes advantage of spatial autocorrelation in flux distribution

Incorporates auxiliary variables related to flux processes in a manner analogous to multi-linear regression

Objective function and flux estimates:

1 1,

1 1( ) ( ) ( ) ( )2 2

T TL − −= − − + − −s â y Hs R y Hs s Xâ Q s Xâ

Deterministiccomponent

Stochasticcomponent

Geostatistical Inversion Model (GIM)

Global GIM CO2 Flux Estimation

Xβ - Gross Domestic Product DensityXβ - Population DensityXβ - Leaf Area IndexXβ - Fraction of Photosynthetically Active Radiation (fPAR)Xβ - % Shrub CoverXβ - Land and Ocean ConstantsStochastic ComponentFINAL BEST ESTIMATES

S. Gourdji

January 2000

Gourdji, Mueller, Schaefer, Michalak (JGR 2008)Mueller, Gourdji, Michalak (JGR 2008)

Estimate global monthly CO2 fluxes at 3.75°x5° for 1997 to 2001 using:CO2 flask data from NOAA-ESRL networkTM3 atmospheric transport modelAuxiliary environmental data

Mueller et al. (JGR, 2008)

Global GIM CO2 Flux Estimation

1°x1° North American fluxes estimated for 2004 and 2006 using continuous & weekly flask atmospheric measurements, a Lagrangian atmospheric particle-tracking model (STILT), and high-resolution meteorology (WRF)

T. Nehrkorn

North American GIM CO2 Flux Estimates

Sharon Gourdji, U. Michigan

North American GIM CO2 Flux Estimates

Sharon Gourdji, U. Michigan

North American GIM CO2 Flux Estimates

Sharon Gourdji, U. Michigan

GIM Scalability