Abstract
Abstract. Inverse modeling methods are now commonly used for estimating surface fluxes of carbon dioxide, using atmospheric mass fraction measurements combined with a numerical atmospheric transport model. The geostatistical approach to flux estimation takes advantage of the spatial and/or temporal correlation in fluxes and does not require prior flux estimates. In this work, a previously-developed, computationally-efficient, fixed-lag Kalman smoother is adapted for application with a geostatistical approach to atmospheric inversions. This method makes it feasible to perform multi-year geostatistical inversions, at fine resolutions, and with large amounts of data. The new method is applied to the recovery of global gridscale carbon dioxide fluxes for 1997 to 2001 using pseudodata representative of a subset of the NOAA-ESRL Cooperative Air Sampling Network.
Highlights
Inverse modeling methods are commonly used for estimating surface fluxes of carbon dioxide, using atmospheric mass fraction measurements combined with a numerical atmospheric transport model
The main goal of the proposed approach is to decrease the computational cost associated with solving large-scale geostatistical inverse problems aimed at constraining budgets of atmospheric trace gases, while providing a best estimate and estimated uncertainty equivalent to those obtained using a batch inversion, where all fluxes are estimated using all available measurements
The tools developed in this paper decrease the computational costs associated with the solution of a geostatistical inverse problem aimed at estimating fluxes of atmospheric trace gases
Summary
Inverse modeling methods are commonly used for estimating surface fluxes of carbon dioxide, using atmospheric mass fraction measurements combined with a numerical atmospheric transport model. One solution to the second of these problems was recently proposed by Bruhwiler et al (2005) in the form of a fixed-lag Kalman smoother (FLKS) that steps through an inversion in multiple steps while conserving information about the covariance between sequential sets of fluxes. This method builds upon the time-stepping approach presented in Law (2004), and dramatically increases the computational efficiency of inversions, while providing uncertainty estimates almost identical to those obtained using batch inversions. Other recently proposed numerical tools based on variational approaches (e.g. Chevallier et al, 2005; Baker et al, 2006) and ensemble methods (e.g. Peters et al, 2005; Zupanski et al, 2007) can solve large inverse problems, but are not designed to provide full information on flux uncertainties and their covariances
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