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中国科学院大学学报 ›› 2019, Vol. 36 ›› Issue (3): 331-337.DOI: 10.7523/j.issn.2095-6134.2019.03.006

• 环境科学与地理学 • 上一篇    下一篇

不同卫星反演的大气CO2浓度差异时空特征分析

吴长江1,2, 雷莉萍1, 曾招城3   

  1. 1. 中国科学院遥感与数字地球研究所 数字地球重点实验室, 北京 100094;
    2. 中国科学院大学资源与环境学院, 北京 100049;
    3. 美国加州理工学院地质与行星科学系, 美国加利福尼亚州帕萨迪纳市 CA91125
  • 收稿日期:2018-01-15 修回日期:2018-04-04 发布日期:2019-05-15
  • 通讯作者: 雷莉萍
  • 基金资助:
    国家重点研发计划项目(2016YFA0600303)资助

Spatio-temporal analysis of differences among atmospheric CO2 concentrations retrieved from different satellite observations

WU Changjiang1,2, LEI Liping1, ZENG Zhaocheng3   

  1. 1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
  • Received:2018-01-15 Revised:2018-04-04 Published:2019-05-15

摘要: 定量化分析不同遥感观测卫星所反演的大气CO2浓度之间的差异,对于利用卫星遥感数据准确揭示全球大气CO2浓度的时空变化特征具有重要的参考价值。利用CarbonTracker模型模拟的大气CO2廓线数据对SCIAMACHY、GOSAT和OCO-2卫星反演的大气CO2柱浓度数据进行校正,并通过计算卫星校正前后的差值分析不同卫星观测平台对大气廓线的响应差异。同时比较分析不同时空尺度的各卫星观测所刻画的大气CO2柱浓度变化的差异。实验结果表明,SCIAMACHY的差值明显大于其他2颗卫星,且在低纬度和高纬度区域分别显示(-0.25±0.15)×10-6和(-0.38±0.25)×10-6的浓度差异。消除这些差异后,3颗卫星的CO2柱浓度观测数据显示相似的季节变化、年变化特征以及相似的空间分布规律。研究结果表明,模型模拟数据可用来整合这3颗卫星由于观测仪器响应和时空尺度不同所引起的大气CO2柱浓度数据间的差异。

关键词: 大气CO2浓度, SCIAMACHY, GOSAT, OCO-2, 模型模拟

Abstract: Quantitative analysis of the differences among the CO2 concentrations retrieved from different satellites is important for understanding possibility of combining different satellites for observing the spatio-temporal variations in global atmospheric CO2 concentration. In this study, we investigated the differences among the atmospheric CO2 concentrations derived from SCIAMACHY, GOSAT, and OCO-2 by comparing with CO2 simulations from CarbonTracker. Firstly, the sensitivities of the three satellites to CO2 concentrations were quantified by adjusting the measurements based on the CO2 profiles from CarbonTracker. Secondly, the spatio-temporal patterns of XCO2 retrievals from the three satellites were further compared. The results show that SCIAMACHY shows averaged biases of (-0.25 ±0.15)×10-6 and (-0.38 ±0.25)×10-6 at high and low latitude regions, respectively, which are significantly larger than those showed by the other two satellites. Moreover, we found that, after removing these differences, the observations from the three satellites demonstrate similar seasonal and annual variations as well as similar spatial patterns. These results show that model simulations can be utilized to remove or reduce the differences among the XCO2 retrievals from different satellites.

Key words: atmospheric CO2 concentration, SCIAMACHY, GOSAT, OCO-2, model simulations

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