环境科学
固碳
土壤碳
气候变化
植被(病理学)
生态系统
碳循环
降水
碳汇
土壤水分
大气科学
土壤科学
生态学
二氧化碳
地理
地质学
气象学
医学
病理
生物
作者
Rong Ge,Honglin He,Li Zhang,Xiaoli Ren,Mathew Williams,Guirui Yu,T. Luke Smallman,Tao Zhou,Pan Li,Zongqiang Xie,Silong Wang,Huimin Wang,Guoyi Zhou,Qi‐Bin Zhang,Anzhi Wang,Ze‐Xin Fan,Yiping Zhang,Weijun Shen,Huajun Yin,Luxiang Lin
摘要
Abstract The high uncertainty associated with the response of terrestrial carbon (C) cycle to climate is dominated by ecosystem C turnover time ( τ eco ). Although the relationship between τ eco and climate has been extensively studied, significant knowledge gaps remain regarding the differential climate sensitivities of turnover time in major biomass ( τ veg ) and soil ( τ soil ) pools, and their effects on vegetation and soil C sequestration under climate change are poorly understood. Here, we collected multiple time series observations on soil and vegetation C from permanent plots in 10 Chinese forests and used model‐data fusion to retrieve key C cycle process parameters that regulate τ soil and τ veg . Our analysis showed that τ veg and τ soil both decreased with increasing temperature and precipitation, and τ soil was more than twice as sensitive (1.27 years/°C, 1.70 years/100 mm) than τ veg (0.53 years/°C, 0.40 years/100 mm). The higher climate sensitivity of τ soil caused a more rapid decrease in τ soil than in τ veg with increasing temperature and precipitation, thereby significantly reducing the difference between τ soil and τ veg ( τ diff ) under warm and humid conditions. τ diff , an indicator of the balance between the soil C input and exit rate, was strongly responsible for the variation (more than 50%) in soil C sequestration. Therefore, a smaller τ diff under warm and humid conditions suggests a relatively lower contribution from soil C sequestration. This information has strong implications for understanding forest C‐climate feedback, predicting forest C sink distributions in soil and vegetation under climate change, and implementing C mitigation policies in forest plantations or soil conservation.
科研通智能强力驱动
Strongly Powered by AbleSci AI