Interannual variation of terrestrial carbon cycle: Issues and perspectives

碳循环 环境科学 生态系统 大气科学 气候变化 水循环 碳通量 陆地生态系统 大气碳循环 温室气体 碳汇 气候学 土壤碳 生态学 生物 土壤科学 土壤水分 地质学
作者
Shilong Piao,Xuhui Wang,Kai Wang,Xiangyi Li,Ana Bastos,Josep G. Canadell,Philippe Ciais,Pierre Friedlingstein,Stephen Sitch
出处
期刊:Global Change Biology [Wiley]
卷期号:26 (1): 300-318 被引量:366
标识
DOI:10.1111/gcb.14884
摘要

With accumulation of carbon cycle observations and model developments over the past decades, exploring interannual variation (IAV) of terrestrial carbon cycle offers the opportunity to better understand climate-carbon cycle relationships. However, despite growing research interest, uncertainties remain on some fundamental issues, such as the contributions of different regions, constituent fluxes and climatic factors to carbon cycle IAV. Here we overviewed the literature on carbon cycle IAV about current understanding of these issues. Observations and models of the carbon cycle unanimously show the dominance of tropical land ecosystems to the signal of global carbon cycle IAV, where tropical semiarid ecosystems contribute as much as the combination of all other tropical ecosystems. Vegetation photosynthesis contributes more than ecosystem respiration to IAV of the global net land carbon flux, but large uncertainties remain on the contribution of fires and other disturbance fluxes. Climatic variations are the major drivers to the IAV of net land carbon flux. Although debate remains on whether the dominant driver is temperature or moisture variability, their interaction,that is, the dependence of carbon cycle sensitivity to temperature on moisture conditions, is emerging as key regulators of the carbon cycle IAV. On timescales from the interannual to the centennial, global carbon cycle variability will be increasingly contributed by northern land ecosystems and oceans. Therefore, both improving Earth system models (ESMs) with the progressive understanding on the fast processes manifested at interannual timescale and expanding carbon cycle observations at broader spatial and longer temporal scales are critical to better prediction on evolution of the carbon-climate system.
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