Technical Note: Adapting a fixed-lag Kalman smoother to a geostatistical atmospheric inversion framework

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.