Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion

Zhenggang Du, Xuhui Zhou, Junjiong Shao, Guirui Yu, Huimin Wang, Deping Zhai, Jianyang Xia, Yiqi Luo

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)548-565
Number of pages18
JournalJournal of Advances in Modeling Earth Systems
Volume9
Issue number1
DOIs
StatePublished - Mar 1 2017
Externally publishedYes

Keywords

  • Shannon information index
  • carbon-nitrogen coupling model
  • data assimilation
  • relative information
  • uncertainty

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • General Earth and Planetary Sciences

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