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

Abstract

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.

Uncertainty propagation in a global biogeochemical model driven by leaf area data

“…However, the high northern latitudes rather than the tropical regions have the most considerable discrepancies in simulated element storage due to the uncertainty of permafrost processes and the difficulty of spinning the model to equilibrium (Thornton and Rosenbloom, 2005;Xia et al, 2012). The results of the uncertainty pattern in element storage are consistent with that emerged in the current generation of Earth System Models (Arora et al, 2013;Friedlingstein et al, 2014;Zhou et al, 2021;Wei et al, 2022b). The contrast spatial distribution of uncertainty between satellite-derived LAI data and the modeled C-N-P storages indicates a nonlinear propagation of leaf area uncertainty in the biogeochemical models.…”

Across‐model spread and shrinking in predicting peatland carbon dynamics under global change

“…Earth system models (ESMs) is a primary tool for predicting the global carbon (C) cycle and informing climate change mitigation policies (IPCC, 2021). However, ESMs have yielded large across-model spread in the predicted global C cycle, which primarily arises from the land C component, as shown in the fourth to sixth assessment reports of the Intergovernmental Panel on Climate Change (IPCC; Ciais et al, 2013;Friedlingstein et al, 2006;J. Zhou et al, 2021).…”

“…This common structure makes it possible to unify the land C models in a matrix form, by accommodating any number of pools, and by folding all C cycling processes into the terms of the matrix equation related to C input, C allocation into different plant organs, C turnover rate and its environmental modifier, and C transfers among pools (Luo et al, 2016). This unification enables an analytical analysis of sources of model uncertainty or across-model spread using a traceability framework in a hierarchal way (Luo et al, 2017;Xia et al, 2013;J. Zhou et al, 2021;S.…”

Function Evaluation and Optimal Strategies of Three Types of Space in Cross-provincial Areas from the Perspective of High-Quality Development: A Case Study of Yangtze River Delta, China

“…Also, multi-source data such as remote sensing images, geographical conditions and Internet POI have also been comprehensively used [23,24]. In addition, some scholars established the index system and determined the evaluation scheme according to the status quo of the research object, such as the land suitability evaluation [25,26], suitability evaluation of a crop planting area serving agriculture [27], the suitability evaluation of a specific spatial function including ecosystem, agriculture, landscape and other fields [28], and the construction of evaluation index system of comprehensive land development suitability [29,30]. Because the scientific logic of the suitability evaluation has always been the focus of academic attention and questioning, in the future, the spatial suitability evaluation should not only improve the application of data and methods, but also clarify the evaluation logic problems, meeting the scientific and operational requirements for spatial planning and optimization practice [31].…”