Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion. (25th February 2017) - Du, Zhenggang; Zhou, Xuhui; Shao, Junjiong; Yu, Guirui; Wang, Huimin; Zhai, Deping; Xia, Jianyang; Luo, Yiqi
Abstract: Substantial efforts have recently been made toward integrating more processes to improve ecosystem model performances. However, model uncertainties caused by new processes and/or data sets remain largely unclear. In this study, we explore uncertainties resulting from additional nitrogen (N) data and processes in a terrestrial ecosystem (TECO) model framework using a data assimilation system. Three assimilation experiments were conducted with TECO‐C‐C (carbon (C)‐only model), TECO‐CN‐C (TECO‐CN coupled model with only C measurements as assimilating data), and TECO‐CN‐CN (TECO‐CN model with both C and N measurements). Our results showed that additional N data had greater effects on ecosystem C storage (+68% and +55%) compared with added N processes (+32% and −45%) at the end of the experimental period (2009) and the long‐term prediction (2100), respectively. The uncertainties mainly resulted from woody biomass (relative information contributions are +50.4% and +36.6%) and slow soil organic matter pool (+30.6% and −37.7%) at the end of the experimental period and the long‐term prediction, respectively. During the experimental period, the additional N processes affected C dynamics mainly through process‐induced disequilibrium in the initial value of C pools. However, in the long‐term prediction period, the N data and processes jointly influenced the simulated C dynamics by adjusting the posterior probability density functions of key parameters. These results suggest that additional measurements of slow processes are pivotal to improving model predictions. Quantifying the uncertainty of the additional N data and processes can help us explore the terrestrial C‐N coupling in ecosystem models and highlight critical observational needs for future studies. Key Points: Bayesian probabilistic inversion and Shannon information index are used to quantify relative information contributions in a terrestrial ecosystem model framework The additional data and processes of nitrogen module have the different weights in affecting ecosystem carbon dynamics The nitrogen data have greater effects on total ecosystem C storage compared with added nitrogen processes