Global carbon assimilation system using a local ensemble Kalman filter with multiple ecosystem models

Abstract

Abstract In this paper, a global carbon assimilation system (GCAS) is developed for optimizing the global land surface carbon flux at 1° resolution using multiple ecosystem models. In GCAS, three ecosystem models, Boreal Ecosystem Productivity Simulator, Carnegie‐Ames‐Stanford Approach, and Community Atmosphere Biosphere Land Exchange, produce the prior fluxes, and an atmospheric transport model, Model for OZone And Related chemical Tracers, is used to calculate atmospheric CO 2 concentrations resulting from these prior fluxes. A local ensemble Kalman filter is developed to assimilate atmospheric CO 2 data observed at 92 stations to optimize the carbon flux for six land regions, and the Bayesian model averaging method is implemented in GCAS to calculate the weighted average of the optimized fluxes based on individual ecosystem models. The weights for the models are found according to the closeness of their forecasted CO 2 concentration to observation. Results of this study show that the model weights vary in time and space, allowing for an optimum utilization of different strengths of different ecosystem models. It is also demonstrated that spatial localization is an effective technique to avoid spurious optimization results for regions that are not well constrained by the atmospheric data. Based on the multimodel optimized flux from GCAS, we found that the average global terrestrial carbon sink over the 2002–2008 period is 2.97 ± 1.1 PgC yr −1 , and the sinks are 0.88 ± 0.52, 0.27 ± 0.33, 0.67 ± 0.39, 0.90 ± 0.68, 0.21 ± 0.31, and 0.04 ± 0.08 PgC yr −1 for the North America, South America, Africa, Eurasia, Tropical Asia, and Australia, respectively. This multimodel GCAS can be used to improve global carbon cycle estimation.