Terrestrial carbon (C)-climate feedbacks depend strongly on how soil organic matter (SOM) decomposition responds to temperature. This dependency is often represented in land models by the parameter Q 10 , which quantifies the relative increase of microbial soil respiration per 10 °C temperature increase. Many studies have conducted paired laboratory soil incubations and inferred “active” and “slow” pool Q 10 values by fitting linear two-pool models to measured respiration time series. Using a recently published incubation study (Qin et al. in Sci Adv 5(7):eaau1218, 2019) as an example, here we first show that the very high parametric equifinality of the linear two-pool models may render such incubation-based Q 10 estimates unreliable. In particular, we show that, accompanied by the uncertain initial active pool size, the slow pool Q 10 can span a very wide range, including values as high as 100, although all parameter combinations are producing almost equally good model fit with respect to the observations. This result is robust whether or not interactions between the active and slow pools are considered (typically these interactions are not considered when interpreting incubation data, but are part of the predictive soil carbon models). This very large parametric equifinality in the context of interpreting incubation data is consistent with the poor temporal extrapolation capability of linear multi-pool models identified in recent studies. Next, using a microbe-explicit SOM model (RESOM), we show that the inferred two pools and their associated parameters (e.g., Q 10 ) could be artificial constructs and are therefore unreliable concepts for integration into predictive models. We finally discuss uncertainties in applying linear two-pool (or more generally multiple-pool) models to estimate SOM decomposition parameters such as temperature sensitivities from laboratory incubations. We also propose new observations and model structures that could enable better process understanding and more robust predictive capabilities of soil carbon dynamics.

中文翻译：

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线性双池模型不足以从孵化推断土壤有机质分解温度敏感性

陆地碳 (C) 气候反馈强烈依赖于土壤有机质 (SOM) 分解对温度的反应。这种依赖性在土地模型中通常由参数 Q 10 表示，该参数量化了每 10 °C 温度升高微生物土壤呼吸的相对增加。许多研究进行了配对实验室土壤孵化，并通过将线性双池模型拟合到测量的呼吸时间序列来推断“活跃”和“慢速”池 Q 10 值。以最近发表的孵化研究（Qin et al. in Sci Adv 5(7):eaau1218, 2019）为例，在这里我们首先表明线性双池模型的非常高的参数等价性可能会呈现这种基于孵化的Q 10 估计不可靠。特别是，我们表明，伴随着不确定的初始活动池大小，慢池 Q 10 可以跨越非常广泛的范围，包括高达 100 的值，尽管所有参数组合都产生了几乎同样好的模型拟合观测值。无论是否考虑活动池和慢池之间的相互作用，该结果都是可靠的（通常在解释孵化数据时不考虑这些相互作用，但它们是预测土壤碳模型的一部分）。在解释孵化数据的背景下，这种非常大的参数等价性与最近研究中发现的线性多池模型的时间外推能力较差是一致的。接下来，使用微生物显式 SOM 模型 (RESOM)，我们展示了推断的两个池及其相关参数（例如，Q 10 ) 可能是人工构造，因此对于集成到预测模型中是不可靠的概念。我们最后讨论了应用线性双池（或更普遍的多池）模型来估计 SOM 分解参数（如实验室孵化的温度敏感性）的不确定性。我们还提出了新的观察和模型结构，可以更好地理解土壤碳动力学的过程和更强大的预测能力。