Estimation of surface carbon fluxes with an advanced data assimilation methodology

Ji-Sun Kang, University of Maryland, jskang@atmos.umd.edu (Presenter)
Eugenia Kalnay, University of Maryland, ekalnay@atmos.umd.edu
Takemasa Miyoshi, University of Maryland, miyoshi@atmos.umd.edu
Junjie Liu, JPL, junjie.liu@jpl.nasa.gov
Fung Inez, University of California, Berkeley, ifung@berkeley.edu

We perform every 6 hours 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 (OSSEs) framework. Surface CO2 fluxes are not observed, but estimated as if they were “parameters”, by augmenting the state vector of atmospheric CO2 concentrations with the surface fluxes, and using the ensemble Kalman filter to estimate the multivariate error covariance. This methodology is compared with the widely used Carbon Tracker approach also based on Ensemble Kalman Filter. The “localization of variables” method has been shown to reduce sampling errors in the CO2 LETKF multivariate analysis system. We further focus on the impact of advanced inflation methods and vertical localization of column CO2 data on the analysis of CO2 variables. 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. By contrast, the analysis performed with a standard fixed multiplicative inflation results is not able to follow the seasonal evolution of surface CO2 fluxes. In addition, we examine strategies for vertical localization in the assimilation of simulated CO2 from GOSAT (or OCO-2) that have 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 layer. We take advantage of this by updating only the lower tropospheric CO2, rather than the full column. This results in a more accurate analysis of CO2 in terms of both RMS error and spatial patterns. Assimilating simulated CO2 ground-based observations and CO2 retrievals from GOSAT and AIRS with the enhanced LETKF, we obtain a rather accurate estimation of the evolving surface fluxes even in the absence of any a priori information.

Presentation Type:  Poster

Session:  Coupled Processes at Land-Atmosphere-Ocean Interfaces   (Mon 4:00 PM)

Associated Project(s): 

• Liu, Junjie: An integrated carbon data assimilation system to advance understanding and predictions of the carbon cycle ...details

Poster Location ID: 43