Regional-scale geostatistical inverse modeling of North American CO<sub>2</sub> fluxes: a synthetic data study

Abstract

Abstract.A series of synthetic data experiments is performed to investigate the ability of a regional atmospheric inversion to estimate grid-scale CO 2 fluxes during the growing season over North America. The inversions are performed within a geostatistical framework without the use of any prior flux estimates or auxiliary variables, in order to focus on the atmospheric constraint provided by the nine towers collecting continuous, calibrated CO 2 measurements in 2004. Using synthetic measurements and their associated concentration footprints, flux and model-data mismatch covariance parameters are first optimized, and then fluxes and their uncertainties are estimated at three different temporal resolutions. These temporal resolutions, which include a four-day average, a four-day-average diurnal cycle with 3-hourly increments, and 3-hourly fluxes, are chosen to help assess the impact of temporal aggregation errors on the estimated fluxes and covariance parameters. Estimating fluxes at a temporal resolution that can adjust the diurnal variability is found to be critical both for recovering covariance parameters directly from the atmospheric data, and for inferring accurate ecoregion-scale fluxes. Accounting for both spatial and temporal a priori covariance in the flux distribution is also found to be necessary for recovering accurate a posteriori uncertainty bounds on the estimated fluxes. Overall, the results suggest that even a fairly sparse network of 9 towers collecting continuous CO 2 measurements across the continent, used with no auxiliary information or prior estimates of the flux Correspondence to: A. M. Michalak (amichala@umich.edu) distribution in time or space, can be used to infer relatively accurate monthly ecoregion scale CO 2 surface fluxes over North America within estimated uncertainty bounds. Simulated random transport error is shown to decrease the quality of flux estimates in under-constrained areas at the ecoregion scale, although the uncertainty bounds remain realistic. While these synthetic data inversions do not consider all potential issues associated with using actual measurement data, e.g. systematic transport errors or problems with the boundary conditions, they help to highlight the impact of inversion setup choices, and help to provide a baseline set of CO 2 fluxes for comparison with estimates from future real-data inversions.

A systematic approach for comparing modeled biospheric carbon fluxes across regional scales

“…Geostatistical approaches have been applied to many fields in the Earth and environmental sciences, including geology, ecology, and hydrology to study spatial phenomena (e.g., Chiles and Delfiner, 1999;Webster and Oliver, 2007). In studying land-atmosphere carbon dynamics, geostatistical inversion modeling has been used to estimate the spatial distribution of carbon sources and sinks both globally, and regionally over North America (e.g., Michalak et al, 2004;Gourdji et al, , 2010Mueller et al, 2008). Geostatistical regression approaches, such as the one described here, have been used regionally to evaluate the relationship between environmental variables (e.g., Erickson et al, 2005), and have recently been applied to evaluate the potential controls on carbon uptake and release at flux tower sites in North America (NA) (e.g., Mueller et al, 2010;Yadav et al, 2010).…”

Comparing Lagrangian and Eulerian models for CO<sub>2</sub> transport – a step towards Bayesian inverse modeling using WRF/STILT-VPRM

“…The model has been used extensively in regional simulations and inversion studies for different greenhouse gases (Lin et al, 2003(Lin et al, , 2004Gerbig et al, 2003b;Miller et al, 2008;Gourdji et al, 2010;Göckede et al, 2010). A brief description of STILT is given as follows.…”

A global coupled Eulerian-Lagrangian model and 1 × 1 km CO<sub>2</sub> surface flux dataset for high-resolution atmospheric CO<sub>2</sub> transport simulations

“…Several studies have reported the advantages of using coupled Eulerian-Lagrangian models, and some have used the WRF-STILT modelling system (Nehrkorn et al, 2010) to estimate the CO 2 surface fluxes over North America (Gourdji et al, 2010). Rigby et al (2011) and Koyama et al (2009) outlined a method for combining information on the emissions-mole fraction sensitivity from Eulerian and Lagrangian chemical transport models, for use in estimating emissions at a global scale.…”