Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first‐order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high‐quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics‐function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

中文翻译：

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数据同化后结构不同的模型模拟全球土壤有机碳的收敛性

当前的生物地球化学模型产生的碳-气候反馈预测具有很大的不确定性，这通常归因于它们在模拟全球土壤有机碳（SOC）动态时的结构差异。然而，量化强度并代表不同土壤碳循环过程特性的模型参数值的选择也可能导致模型模拟的不确定性。在这里，我们展示了使用常见观测数据在减少全球 SOC 存储估计中的模型不确定性方面的关键作用。两个结构不同的模型具有独特的碳库、分解动力学和碳转移路径，用其惯用的参数值模拟相反的全球 SOC 分布，但在使用数据同化方法接受同一全球 SOC 数据库的通知后，收敛到相似的结果。收敛的空间 SOC 模拟源自这两个模型在数据同化后对碳转移效率、基线分解率以及环境对碳通量的影响等关键模型组件的相似模拟。此外，数据同化结果表明，使用遵循一阶或 Michaelis-Menten 动力学的模型在全球范围内对 SOC 进行同样有效的模拟。然而，需要更广泛的数据以及高质量的控制和保证来进一步约束 SOC 动力学模拟并减少不受约束的参数。新的数据集，例如微生物基因组学与功能关系，也可能提出在未来模型开发中需要考虑的新结构。总的来说，我们的结果强调了观测数据在为模型开发和约束模型预测提供信息方面的重要性。