Key Laboratory of Ecosystem Network Observation and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; School of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA.
Sci Total Environ. 2022 Mar 25;814:152786. doi: 10.1016/j.scitotenv.2021.152786. Epub 2022 Jan 4.
Understanding gross primary productivity (GPP) response to precipitation (PPT) changes is essential for predicting land carbon uptake under increasing PPT variability and extremes. Previous studies found that ecosystem GPP may have an asymmetric response to PPT changes, leading to the inconsistency of GPP gains in wet years compared to GPP declines in dry years. However, it is unclear how the asymmetric responses vary among vegetation types and under different PPT variabilities. This study evaluated the global patterns of asymmetries of GPP response to different PPT changes using two state-of-science global GPP datasets. The result shows that under mild PPT changes (|ΔPPT| ≤ 25%), grasslands, savannas, shrublands, and tundra show positive asymmetric responses (i.e., larger GPP gains in wet years than GPP losses in dry years), while other vegetation types show negative asymmetric responses (i.e., larger GPP losses in dry years than GPP gains in wet years). Conversely, all vegetation types show negative GPP asymmetric responses to moderate (25% < |ΔPPT| ≤ 50%) and extreme (|ΔPPT| > 50%) PPT changes. Thus, we propose a new non-linear asymmetric GPP-PPT model that incorporates three modes with regards to vegetation types. Meanwhile, we found that the spatial patterns of asymmetry were mainly driven by PPT amount and variability. Stronger and negative asymmetries were found in areas with smaller PPT amount and variability, while positive asymmetries were found in areas with higher PPT variability. These findings promote our understanding of carbon dynamics under increased PPT variability and extremes and provide new insights for land models to better predict future carbon uptake and its feedback to climate change.
理解总初级生产力（GPP）对降水（PPT）变化的响应对于预测在不断增加的 PPT 变异性和极端情况下陆地碳吸收至关重要。先前的研究发现，生态系统的 GPP 可能对 PPT 变化呈不对称响应，导致湿润年份的 GPP 增加与干旱年份的 GPP 减少不一致。然而，尚不清楚不对称响应如何因植被类型和不同 PPT 变异性而异。本研究使用两个最先进的全球 GPP 数据集评估了 GPP 对不同 PPT 变化的不对称响应的全球模式。结果表明，在轻度 PPT 变化（|ΔPPT|≤25%）下，草原、稀树草原、灌木林和苔原表现出正不对称响应（即湿润年份的 GPP 增加大于干旱年份的 GPP 减少），而其他植被类型表现出负不对称响应（即干旱年份的 GPP 减少大于湿润年份的 GPP 增加）。相反，所有植被类型对中度（25%<|ΔPPT|≤50%）和极端（|ΔPPT|>50%）PPT 变化都表现出负的 GPP 不对称响应。因此，我们提出了一个新的非线性不对称 GPP-PPT 模型，该模型考虑了植被类型的三种模式。同时，我们发现不对称的空间模式主要受 PPT 量和变异性的驱动。在 PPT 量和变异性较小的地区发现了更强和负的不对称性，而在 PPT 变异性较高的地区发现了正的不对称性。这些发现促进了我们对增加的 PPT 变异性和极端条件下碳动态的理解，并为土地模型提供了新的见解，以更好地预测未来的碳吸收及其对气候变化的反馈。
