The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions.

土壤有机碳 (SOC) 的储存和循环受多种共变因素的控制，包括气候、植物生产力、土壤特性和干扰历史。然而，尚不清楚这些因素中哪些是全球和生物群落内观察到的 SOC 储量的主要预测因子，以及这些预测因子的作用如何在观察和基于过程的模型之间变化。在这里，我们使用全球观测和一组土壤生物地球化学模型来量化关键状态因素（即年平均温度、净初级生产力和土壤矿物学）在解释 SOC 储量的生物群落到全球尺度变化方面的紧急重要性。我们使用机器学习方法来解开协变量的作用并阐明个体与 SOC 的关系，而不强加预期的关系先验. 虽然我们在观察和模型中观察到 SOC 与协变量之间的定性相似关系，但非线性的幅度和程度在模型和观察之间存在很大差异。模型似乎过分强调温度和初级生产力的重要性（特别是分别在森林和草本生物群落中），而观察表明土壤矿物质的相对重要性更大。这种不匹配在全球范围内也很明显。然而，我们观察到特定生物群落的观测值和模型输出之间的一致性——即温带落叶林和草原，它们分别显示出 SOC 储量与温度和生产力之间更强的关系。这种方法突出了具有最大不确定性和与观测不匹配的生物群落，以进行有针对性的模型改进。