Department for Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.
European Commission, Joint Research Centre, Ispra, Italy.
Glob Chang Biol. 2022 Dec;28(24):7313-7326. doi: 10.1111/gcb.16397. Epub 2022 Sep 13.
Elevated atmospheric CO (eCO ) influences the carbon assimilation rate and stomatal conductance of plants, thereby affecting the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO effects in observational data of real-world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process-based land surface model QUINCY to simulate the leaf-level effects of increasing atmospheric CO concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO effects. The eCO effect on gross primary productivity (GPP) emerges at relatively low CO increase (∆[CO ] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO increase (∆[CO ] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO is detectable earlier for variables related to the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass-dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.
大气中 CO 浓度升高(eCO)会影响植物的碳同化速率和气孔导度,从而影响碳和水的全球循环。然而,在观测数据中检测到这些 eCO 的生理效应仍然具有挑战性,因为自然变化和混杂因素(例如变暖)可能会掩盖观测到的实际生态系统中 eCO 的影响。在这项研究中,我们旨在开发一种方法来检测与碳和水通量相关的各种变量中 CO 生理效应的出现。我们使用综合的基于过程的陆地表面模型 QUINCY 模拟生态系统中的观测设置,以模拟大气 CO 浓度升高对叶片水平的影响,以及其通过陆地碳和水循环在不同气候区和生物群落中传播一个世纪的影响。然后,我们开发了一种基于信噪比的统计方法来检测 eCO 效应的出现。eCO 对总初级生产力(GPP)的影响在相对较低的 CO 增加(∆[CO ]~20 ppm)时出现,此时叶面积指数相对较高。与 GPP 相比,导致蒸腾水通量减少(归一化为叶面积)的 eCO 效应仅在相对较高的 CO 增加(∆[CO ]>>40 ppm)时出现,这是由于对气候变率的高敏感性,因此信噪比较低。一般来说,当植物生产力不受气候限制时,与碳循环相关的变量对 eCO 的响应比水循环更早,并且在以森林为主的生态系统中比以草为主的生态系统更强。我们的结果为在原位通量测量中何时何地可以检测到生理 CO 效应、如何检测它们提供了一个步骤,并鼓励未来努力改善对陆地碳和水动态观测中这些效应的理解和量化。