A traceability analysis system for model evaluation on land carbon dynamics: design and applications

• Published in: Ecological processes Vol. 10; no. 1; p. 12
• Main Authors: Zhou, Jian, Xia, Jianyang, Wei, Ning, Liu, Yufu, Bian, Chenyu, Bai, Yuqi, Luo, Yiqi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 29.01.2021; Springer Nature B.V; SpringerOpen
• Subjects: Analysis; Carbon; Carbon cycle; carbon sequestration; Cloud computing; CMIP6; Dynamics; Earth and Environmental Science; ecosystems; Environment; Evaluation; gross primary productivity; Intercomparison; land carbon cycle; model evaluation; model uncertainty; model validation; Primary production; Residence time; Residence time distribution; Simulation; traceability; traceability analysis; Tracking; Uncertainty; Uncertainty analysis; CMIP6; land carbon cycle; model evaluation; traceability analysis; uncertainty
• DOI: 10.1186/s13717-021-00281-w
• ISSN: 2192-1709

Background An increasing number of ecological processes have been incorporated into Earth system models. However, model evaluations usually lag behind the fast development of models, leading to a pervasive simulation uncertainty in key ecological processes, especially the terrestrial carbon (C) cycle. Traceability analysis provides a theoretical basis for tracking and quantifying the structural uncertainty of simulated C storage in models. Thus, a new tool of model evaluation based on the traceability analysis is urgently needed to efficiently diagnose the sources of inter-model variations on the terrestrial C cycle in Earth system models. Methods A new cloud-based model evaluation platform, i.e., the online traceability analysis system for model evaluation (TraceME v1.0), was established. The TraceME was applied to analyze the uncertainties of seven models from the Coupled Model Intercomparison Project (CMIP6). Results The TraceME can effectively diagnose the key sources of different land C dynamics among CMIIP6 models. For example, the analyses based on TraceME showed that the estimation of global land C storage varied about 2.4 folds across the seven CMIP6 models. Among all models, IPSL-CM6A-LR simulated the lowest land C storage, which mainly resulted from its shortest baseline C residence time. Over the historical period of 1850–2014, gross primary productivity and baseline C residence time were the major uncertainty contributors to the inter-model variation in ecosystem C storage in most land grid cells. Conclusion TraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation.