Max Planck Institute for Biogeochemistry, Jena, Germany.
The Finnish Meteorological Institute, Helsinki, Finland.
Glob Chang Biol. 2022 Jan;28(2):493-508. doi: 10.1111/gcb.15933. Epub 2021 Oct 24.
The effect of nutrient availability on plant growth and the terrestrial carbon sink under climate change and elevated CO 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 δ N have been used to suggest that nitrogen limitation has increased in recent decades, most likely due to the concurrent increase in atmospheric CO . 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 δ N isotopic data can be used to infer large-scale N limitation and if the observed trends are caused by increasing atmospheric CO , 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 δ N 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 δ N. Our results show that leaf δ N 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.
养分可利用性对植物生长和气候变化及大气 CO 升高背景下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。这在一定程度上是由于难以在长时间内评估大范围内的养分限制。叶片氮（N）含量和叶片 δN 的持续下降被用来表明，氮限制在最近几十年有所增加，这很可能是由于大气 CO 浓度的同时增加所致。然而，由于导致叶片 N 和同位素浓度时空变化的复杂因素，此类数据集往往难以解释。我们使用具有代表 N 同位素过程独特能力的陆地表面模型（LSM）QUINCY，结合两个大型叶片 N 和 N 同位素含量数据集。我们使用不同的情景运行模型，以检验叶片 δN 同位素数据是否可用于推断大尺度 N 限制，以及观察到的趋势是否是由大气 CO 增加、气候变化或人为 N 沉降源和强度的变化引起的。我们表明，虽然模型可以捕捉到叶片 N 含量的观察到的变化，并预测 N 限制的广泛增加，但它不能捕捉到全球范围内数据中观察到的叶片 δN 明显但非常空间异质的下降。在基于观测的 N 沉降同位素组成的时间趋势的基础上增加，会导致模拟叶片 δN 更明显的下降。我们的结果表明，单独的叶片 δN 观测值不能用于评估全球范围内的 N 限制，并且结合 LSM 使用这样的数据集可以揭示观察到的模式背后的驱动因素。
