Estimation of surface carbon fluxes with an advanced data assimilation methodology

We perform every 6 h a simultaneous data assimilation of surface CO2 fluxes and atmospheric CO2 concentrations along with meteorological variables using the Local Ensemble Transform Kalman Filter (LETKF) within an Observing System Simulation Experiments framework. In this paper, we focus on the impact of advanced variance inflation methods and vertical localization of column CO2 data on the analysis of CO2. With both additive inflation and adaptive multiplicative inflation, we are able to obtain encouraging multiseasonal analyses of surface CO2 fluxes in addition to atmospheric CO2 and meteorological analyses. Furthermore, we examine strategies for vertical localization in the assimilation of simulated CO2 from GOSAT that has nearly uniform sensitivity from the surface to the upper troposphere. Since atmospheric CO2 is forced by surface fluxes, its short-term variability should be largest near the surface. We take advantage of this by updating observed changes only into the lower tropospheric CO2 rather than into the full column. This results in a more accurate analysis of CO2 in terms of both RMS error and spatial patterns. Assimilating synthetic CO2 ground-based observations and CO2 retrievals from GOSAT and AIRS with the enhanced LETKF, we obtain an accurate estimation of the evolving surface fluxes even in the absence of any a priori information. We also test the system with a longer assimilation window and find that a short window with an efficient treatment for wind uncertainty is beneficial to flux inversion. Since this study assumes a perfect forecast model, future research will explore the impact of model errors.