Evaluation of a data assimilation system for land surface models using CLM4.5

This showed that assimilating biomass and leaf area index observations decreased model error in C dynamics forecasts (29% using biomass observations and 40% using leaf area index observations) and that assimilation in combination shows greater improvement (51% using both observation streams). Assimilating real observations highlighted likely model structural errors and we implemented an adaptive model-variance-inflation technique to allow the model to track the observations.

Monthly and longer model forecasts using real observations were improved relative to forecasts without data assimilation. The reliable forecast lead-time varied by model pool and is dependent on how tightly the C pool is coupled to meteorologically driven processes. The EAKF and similar state data assimilation techniques could reduce errors in projections of the land C sink and provide more robust forecasts of C pools and land-atmosphere exchanges.