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Monitoring carbon dioxide from space: Retrieval algorithm and flux inversion based on GOSAT data and using CarbonTracker-China

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Abstract

Monitoring atmospheric carbon dioxide (CO2) from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of “The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change: Carbon Budget and Relevant Issues”. The carbon flux inversion system is composed of two separate parts: the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS), and CarbonTracker-China (CT-China), developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite (GOSAT) measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO2 (column-averaged CO2 dry-air mole fraction) is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO2 product is used in carbon flux estimation by CT-China. The net ecosystem CO2 exchange is −0.34 Pg C yr−1 (±0.08 Pg C yr−1), with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.

摘要

基于高光谱分辨率短波红外卫星观测可以获取高精度的全球大气CO2浓度资料, 有效提高碳通量的计算精度, 从而降低温室气体排放在气候变化研究中的不确定性. 本文介绍在中国科学院“应对气候变化的碳收支认证及相关问题”战略性科技先导专项的支持下建立的碳通量计算系统, 实现了从卫星观测到碳通量计算. 该系统由两部分构成: 中国科学院大气物理研究所研发的基于卫星观测的大气CO2浓度反演算法(The Institute of Atmospheric PhysicsCarbon Dioxide Retrieval Algorithm for Satellite Remote Sensing, IAPCAS)和中国科学院地理科学与资源研究所研发的CarbonTracker-China(CT-China)碳同化系统. 本研究使用日本Greenhouse gases Observing SATellite(GOSAT)卫星观测资料进行大气CO2浓度反演和碳通量计算. 为提高IAPCAS反演产品的质量, 研发了一种基于观测参数和反演参数的质量控制方法, 用于数据筛选和偏差订正等反演产品的优化, 最终25%–30%被认为是高质量产品, 可供数据分析和碳通量反演使用. 结合地表覆盖类型和人口密度, 本研究分析了大气CO2浓度的季节变化特征. 使用IAPCAS的反演产品, 应用CT-China开展了中国地区碳通量的计算实验, 结果表明净生态系统CO2交换量为−0.34 Pg C yr−1 (±0.08 Pg C yr−1). 理论上讲, 与仅使用地基观测相比, 使用卫星资料的碳通量计算可以有效降低85%的不确定性.

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Acknowledgements

The authors would like to thank the GOSAT project and TCCON for providing the observational data. This work was funded by the Strategic Priority Research Program—Climate Change: Carbon Budget and Relevant Issues (Grant No. XDA05040200), the National Key Research and Development Program of China (Grant No. 2016YFA0600203), the National Natural Science Foundation of China (Grant Nos. 41375035 and 31500402), and the Chinese Academy of Sciences Strategic Priority Program on Space Science (Grant No. XDA04077300).

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Authors and Affiliations

  1. Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Dongxu Yang, Yi Liu, Zhaonan Cai & Daren Lü

  2. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Huifang Zhang & Baozhang Chen

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Baozhang Chen

  4. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, 210023, China

    Baozhang Chen

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  1. Dongxu Yang
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Correspondence to Yi Liu.

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Yang, D., Zhang, H., Liu, Y. et al. Monitoring carbon dioxide from space: Retrieval algorithm and flux inversion based on GOSAT data and using CarbonTracker-China. Adv. Atmos. Sci. 34, 965–976 (2017). https://doi.org/10.1007/s00376-017-6221-4

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  • DOI: https://doi.org/10.1007/s00376-017-6221-4

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