The effect of nutrient availability on plant growth and the terrestrial carbon sink under climate change and elevated CO₂ remains one of the main uncertainties of the terrestrial carbon cycle. This is partially due to the difficulty of assessing nutrient limitation at large scales over long periods of time. Consistent declines in leaf nitrogen (N) content and leaf δ¹⁵N have been used to suggest that nitrogen limitation has increased in recent decades, most likely due to the concurrent increase in atmospheric CO₂. However, such data sets are often not straightforward to interpret due to the complex factors that contribute to the spatial and temporal variation in leaf N and isotope concentration. We use the land surface model (LSM) QUINCY, which has the unique capacity to represent N isotopic processes, in conjunction with two large data sets of foliar N and N isotope content. We run the model with different scenarios to test whether foliar δ¹⁵N isotopic data can be used to infer large-scale N limitation and if the observed trends are caused by increasing atmospheric CO₂, changes in climate or changes in sources and magnitude of anthropogenic N deposition. We show that while the model can capture the observed change in leaf N content and predict widespread increases in N limitation, it does not capture the pronounced, but very spatially heterogeneous, decrease in foliar δ¹⁵N observed in the data across the globe. The addition of an observation-based temporal trend in isotopic composition of N deposition leads to a more pronounced decrease in simulated leaf δ¹⁵N. Our results show that leaf δ¹⁵N observations cannot, on their own, be used to assess global-scale N limitation and that using such a data set in conjunction with an LSM can reveal the drivers behind the observed patterns.

中文翻译：

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来自叶面δ15N数据和地表模型的长期生态系统氮限制

在气候变化和 CO ₂升高的情况下，养分有效性对植物生长和陆地碳汇的影响仍然是陆地碳循环的主要不确定因素之一。这部分是由于难以在长时间内大规模评估营养限制。叶片氮 (N) 含量和叶片 δ ¹⁵ N 的持续下降已被用于表明近几十年来氮限制有所增加，很可能是由于大气 CO ₂的同时增加. 然而，由于导致叶片 N 和同位素浓度的空间和时间变化的复杂因素，这些数据集通常不容易解释。我们使用地表模型 (LSM) QUINCY，它具有表示 N 同位素过程的独特能力，并结合叶面 N 和 N 同位素含量的两个大型数据集。我们在不同场景下运行模型，以测试叶面 δ ¹⁵ N 同位素数据是否可用于推断大规模 N 限制，以及观察到的趋势是否是由大气 CO ₂增加引起的, 气候变化或人为 N 沉积的来源和幅度的变化。我们表明，虽然该模型可以捕捉观察到的叶片 N 含量的变化并预测 N 限制的广泛增加，但它并没有捕捉到在全球数据中观察到的叶 δ ¹⁵ N 的显着降低，但在空间上非常不均匀。在 N 沉积的同位素组成中添加基于观测的时间趋势导致模拟叶片 δ ¹⁵ N更显着下降。我们的结果表明，叶片 δ ¹⁵ N 观测值本身不能用于评估全球尺度N 限制以及将此类数据集与 LSM 结合使用可以揭示观察到的模式背后的驱动因素。