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This study presents a Geostatistical Inversion Model (GIM) that estimates global CO2 fluxes from 1997 to 2001 using a data-driven approach that eliminates explicit prior estimates. It leverages spatial autocorrelation in flux distribution and incorporates auxiliary variables similarly to multi-linear regression. The model utilizes CO2 flask data from the NOAA-ESRL network and the TM3 atmospheric transport model to produce global monthly CO2 flux estimates at a resolution of 3.75°x5°. Additionally, it includes North American flux estimates using advanced atmospheric measurement techniques.
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Biospheric Models as Priors Deborah Huntzinger, U. Michigan
Synthesis Bayesian Inversion Inversion Carbon Budget
Geostatistical Inversion Model (GIM) Inversion Carbon Budget
Geostatistical Inversion Model (GIM) Inversion Carbon Budget
Geostatistical Inversion Model (GIM) Deterministic component Stochastic component • 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:
Global GIM CO2 Flux Estimation Estimate global monthly CO2 fluxes at 3.75°x5° for 1997 to 2001 using: • CO2 flask data from NOAA-ESRL network • TM3 atmospheric transport model • Auxiliary environmental data S. Gourdji January 2000 Gourdji, Mueller, Schaefer, Michalak (JGR 2008) Mueller, Gourdji, Michalak (JGR 2008)
Global GIM CO2 Flux Estimation Mueller et al. (JGR, 2008)
North American GIM CO2 Flux Estimates T. Nehrkorn 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) Sharon Gourdji, U. Michigan
North American GIM CO2 Flux Estimates Sharon Gourdji, U. Michigan
North American GIM CO2 Flux Estimates Sharon Gourdji, U. Michigan