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. 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). 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. TraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation.

中文翻译：

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用于土地碳动力学模型评估的可追溯性分析系统：设计和应用

越来越多的生态过程已被纳入地球系统模型。但是，模型评估通常落后于模型的快速开发，从而导致关键生态过程（尤其是陆地碳（C）循环）普遍存在模拟不确定性。可追溯性分析为跟踪和量化模型中模拟C存储的结构不确定性提供了理论基础。因此，迫切需要一种基于可追溯性分析的模型评估新工具，以有效地诊断地球系统模型中地面C周期的模型间变化源。建立了一个新的基于云的模型评估平台，即用于模型评估的在线可追溯性分析系统（TraceME v1.0）。使用TraceME来分析耦合模型比较项目（CMIP6）的七个模型的不确定性。TraceME可以有效地诊断CMIIP6模型中不同土地碳动态的关键来源。例如，基于TraceME的分析表明，在七个CMIP6模型中，全球土地C储量的估计值变化了约2.4倍。在所有模型中，IPSL-CM6A-LR模拟了最低的土地碳储存，这主要是由于其最短的基线碳停留时间所致。在1850-2014年的历史时期内，大多数陆地网格单元中的总初级生产力和基线C停留时间是造成模型间生态系统C储量变化的主要不确定因素。