基于碳同位素观测的大气CO2反演法的优化研究

Atmospheric carbon dioxide (CO2) inversion, as one of the “top-down” approaches, is an important way to trace spatialtemporal variations of net CO2 exchange across the earth's surface. This method was designed to estimate CO2 flux by using Bayesian synthesis technique to minmize the difference between the simulated and observed CO2 concentrations. Now atmospheric inversion has been well developed and successfully applied to estimate global and regional carbon fluxes. However, estimating CO2 surface fluxes with inversion modeling remains challenging, and the inverted CO2 fluxes still exhibit a large uncertainty partly because of lack of surface CO2 observations. Currently observations of δ13C provide an additional constraint in a global inversion of the surface CO2 fluxes. In this study, we'll improve the atmospheric inversion model of CarbonTracker–China,CAS by jointly assimilating atmospheric δ13C measurements and CO2 concentration observations to constrain land surface CO2 estimations. Full evaluation with independent δ13C and CO2 measurements will be performed on the improved model to check the accuracy of modeled δ13C and CO2 concentration. And also, cross-validation on model-estimated CO2 flux will be carried out to verify model robustness and contribution of the atmospheric δ13C. After model evaluation, this improved model will be applied to presenting an estimate of the weekly net ecosystem exchange of CO2 (NEE) over China.

大气CO2反演法是一种“自上而下（ Top-down ）”的碳源汇估算方法，它通过大气CO2浓度模拟值和观测值的差异来调整、优化碳源汇估算值。该方法现已成为碳循环研究的重要方法之一。受CO2观测站点数量不足，大气CO2反演法模拟结果的精度、时间和空间分辨都比较低。鉴于当前研究的不足，我们拟将碳同位素观测数据δ13C引入到大气CO2反演模型中，拓展大气CO2反演法的观测数据类型、增加新的观测数据制约条件，形成一套基于碳同位素δ13C和CO2浓度观测的碳源汇估算系统，实现大气CO2反演法的优化，以期推动我国基于碳同位素观测的大气CO2反演法研究领域的发展。将这种改进后的方法应用于中国区域，生成一套高精度的中国陆地生态系统碳源汇时空数据产品，并通过观测、交叉检验法来验证产品的精度、数据的不确定性。

CO2站点观测数量不足是制约大气反演法碳源汇精确估算的主要因素之一。本研究针对大气反演法观测数据不足、模拟结果精度低等问题，采用引入碳同位素δ13C观测来拓展观测数据种类、增加观测数据的研究思路，研究基于碳同位素δ13C和CO2浓度多源观测的大气CO2反演法的优化方法，开展CO2通量的模拟、验证和对比研究，重点探讨碳同位素对碳通量反演结果的影响。在此基础上，本研究以大气CO2反演模型CarbonTrcaker-China，CAS（简称：CT-China）为蓝本，在CT-China上发展了一套基于碳同位素δ13C和CO2浓度观测的碳源汇估算系统，模拟和分析全球和中国的陆地碳汇时空分布特征。其模拟结果表明：全球陆地和海洋均为大气碳汇区，分别吸收了3.83±2.75Pg和2.13±0.89 Pg C/yr碳，其中，陆地的北美温带东部、欧洲北部以及欧亚大陆寒冷温带过渡区为强碳汇区，而南美热带、北非为碳源区。我国大部分地区为碳汇区。研究还发现，13CO2观测数据的加入，极大的改变了全球碳源汇时空分布，特别是对全球海洋和陆地碳通量进行了重新分割，使其全球陆地碳汇估算从4.67±2.72PgC/yr降低为3.83±2.75PgC/yr，而海洋碳汇估算从1.88±0.85PgC/yr增加为2.13±0.89PgC/yr。同时，加入13CO2观测数据后，中国陆地碳汇的估算从0.29±0.16PgC/yr增加为0.39±0.15PgC/yr。该研究进一步揭示了13C观测对于提高碳源汇估测结果及陆地-海洋碳通量分割精度具有重要科学意义。