Carbon cycle research through observation or modeling has uncertainties inevitably. While numerous modeling studies have included uncertainty considerations, uncertainties are rarely quantified and traced to specific sources, especially at the regional scale.

Model-data fusion (MDF) technique provides a powerful tool to effectively reduce and quantify the uncertainties in model parameters through utilizing multi-source observation data. And then, the spatiotemporal variations of terrestrial ecosystem carbon cycle can be revealed more accurately, combining with uncertainty quantification and partitioning methods. 

To quantify uncertainty and determine the main sources of uncertainty in carbon cycle research, Prof. HE Honglin from Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences  and his colleagues established an uncertainty analysis methodological framework based on MDF technique. The uncertainty in model parameters and outputs can be evaluated reasonably, guiding and bridging future field observation and ecosystem modeling.

They chose Qianyanzhou (QYZ) subtropical coniferous plantation in China as typical ecosystem and Tibetan alpine grasslands as typical region to perform site and regional scale uncertainty analysis using the proposed framework. The uncertainties in the carbon fluxes of QYZ forest and Tibetan alpine grasslands were analyzed systematically and thoroughly, and the main uncertainty sources were found, which laid the foundation for reducing uncertainty in the future. 

The studies have been published in Journal of Geophysical Research: Biogeosciences.

1. Ren, X. L., H. L. He*, D. J. P. Moore, L. Zhang, M. Liu, F. Li, G. R. Yu, and H. M. Wang, Uncertainty analysis of modeled carbon and water fluxes in a subtropical coniferous plantation, Journal of Geophysical Research: Biogeosciences, (2013)118(4), 1674-1688.

2. He H. L., M. Liu, X. M. Xiao, X. L. Ren, L. Zhang, X. M. Sun, Y. H. Yang, Y. N. Li, L. Zhao, P. L. Shi, M. Y. Du, Y. M. Ma, M. G. Ma, Y. Zhang, and G. R. Yu*, Large-scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands, Journal of Geophysical Research: Biogeosciences, (2014)119, doi:10.1002/2013JG002449.