涡度相关法
生物圈
焊剂(冶金)
环境科学
大气科学
碳循环
协方差
CMAQ
碳通量
碳纤维
气候学
气象学
物理
化学
臭氧
生态系统
地质学
统计
材料科学
数学
生态学
有机化学
天文
复合数
复合材料
生物
作者
Zhen Peng,Xingxia Kou,Shaoying Wang,Yu Zhang,Meigen Zhang,Fei Hu,Shiguang Miao,Junxia Dou
摘要
Abstract Top‐down methods commonly use atmospheric CO 2 concentration observations to constrain carbon source and sinks. Despite the increase in spaceborne and ground‐based concentration measurements, atmospheric inversions are usually limited by uncertainties in chemical transport models (CTMs) when relating fluxes to observed CO 2 mole fractions. CO 2 eddy covariance (EC) flux measurements have been widely used to directly measure CO 2 fluxes over various ecosystems, but they have rarely been used as constraints in top‐down estimations. In this study, we focused on the development of a novel fluxes assimilation scheme through direct flux observations within an Ensemble Square Root Filter assimilation framework. The assimilation scheme avoided some of complexities of concentration observation assimilations. The methodology was primarily applied to typical regions in west China, taking advantage of eight long‐term ecosystem EC sites. Moreover, four sets of assimilation experiments were designed to quantify the impacts of observational constraints by flux and concentration measurements. Generally, results indicate that the monthly and hourly statistics of the a posteriori fluxes constrained by flux observations agreed well with flux measurements, demonstrating reasonable performance in seasonal and diurnal variations. Specifically, assimilation results demonstrated the advantage of a posteriori estimates inferred from flux measurements during growing season, as compared to results inferred from concentrations, while some limitation still exists in monthly budget estimates. Nevertheless, it is important to note that current results are only a mathematical optimum. CO 2 biospheric fluxes can be estimated more reliably and robustly at the regional scale given considerably more flux observations for efficient constraint.
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