利用卫星遥感探测大气二氧化碳(CO2)浓度,相比传统的地面观测方法,具有稳定、连续、大尺度观测等诸多优点,能更好地获得全球CO2的时空分布与变化特征。随着卫星遥感技术的发展,一系列具备大气CO2探测能力的卫星相继发射升空,大气制图扫描成像吸收光谱仪(SCIAMACHY)、温室气体观测卫星(GOSAT)、大气红外探测仪(AIRS)等卫星传感器,已经获得了多年的全球CO2浓度分布产品。对这些卫星资料进行对比分析,并与本底观测数据进行全球范围的长时间序列的对比验证。研究显示,3种CO2卫星遥感产品中,SCIAMACHY数据系统性略高于本底数据,且观测范围局限性较大;GOSAT数据稳定性较好,但系统误差较大,平均低于本底数据近9ppmv;AIRS数据产品相比前两者优势突出,单月全球覆盖率达到90%左右,与本底观测数据平均误差小于2ppmv(0.5%),相关系数达到0.9以上,能够较好地反映全球大气CO2浓度的时空特征。卫星遥感产品与本底观测资料显示,全球CO2浓度空间分布呈现出明显的纬度分布规律与海陆分布规律,时间变化规律方面则表现出明显的季节性周期变化。
Atmospheric concentration of carbon dioxide (CO2), which is one of the most important anthropogenic greenhouse gases, has increased significantly since the beginning of the industrial revolution. It is expected that further increase of CO2 will definitely result in a warmer climate with adverse consequences including rising sea levels and increasing extreme weather conditions. A reliable prediction requires an accurate understanding of the sources and sinks of the greenhouse gases. Compared to traditional methods based on ground-based observations, an approach that uses satellite remote sensing to detect atmospheric CO2 concentration has many other advantages such as stability, continuity, large-scale, as well as easily getting global spatial and temporal distribution of CO2. As the technology of satellite remote sensing is growing rapidly, a series of satellites, launched for detecting atmospheric CO2 concentration, including SCIAMACHY, GOSAT and AIRS, have been collecting a large amount of global CO2 concentration distribution data for many years. This paper gives analyses by comparing those satellite data among themselves in some parameter indexes and conducts validation by comparing those remote sensing data to the long-term global ground-based observation records. The results indicate that, among those three CO2 satellite remote sensing products, the SCIAMACHY data is systemically slightly higher than ground-based observations and limited in coverage, the GOSAT data is predominant in stability but inferior in systematic errors which is nearly 9ppmv on average lower than ground-based data, and the AIRS data, which is better than the aforementioned two satellites in both coverage and accuracy, whose monthly global coverage is up to 90%, the average error is less than 2ppmv(0.5%) from the ground-based observations and the correlation coefficient is greater than 0.9, can be able to better reflect the global distributions of atmospheric CO2 concentration. An investigation from satellite and ground-based observations shows that the global spatial distribution of CO2 concentration represent significant latitude distribution and land-sea distribution, and there is a significant seasonal variation in CO2 concentration that illustrates a peak most likely in April and a valley in August.
[1] Intergovernmental Panel on Climate Change (IPCC). Climate change 2007-Synthesis report[R]. Cambridge, UK: Cambridge University Press, 2007.
[2] BousquetP, Peylin P, Ciais P, et al. Regional changes in carbon dioxide fluxes of land and oceans since 1980[J]. Science, 2000, 290(5495): 1342-1346.
[3] 程洁, 柳钦火, 李小文. 星载高光谱红外传感器反演大气痕量气体综述[J]. 遥感信息, 2007(2): 90-97.
[4] Schneising O, Buchwitz M, Reuter M, et al. Long-term analysis of carbon dioxide and methane column-averaged mole fractions retrieved from SCIAMACHY[J]. Atmospheric Chemistry and Physics, 2011, 11(6): 2863-2880.
[5] Fan S, Gloor M, Mahlman J, et al. A large terrestrial carbon sink in North America implied by atmospheric and Oceanic Carbon Dioxide Data and Models [J]. Science, 1998, 282(5388): 442-446.
[6] Bovensmann H, Burrows J, Buchwitz M, et al. SCIAMACHY: Mission Objectives and Measurement Modes [J]. Journal of the Atmospheric Sciences, 1999, 56(2):127-150.
[7] Aumann H, Chahine M, Gautier C, et al. AIRS/AMSU/HSB on the Aqua Mission: Design, Science, Objectives, Data Products and Processing Systems [J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(2):253-264.
[8] Yokota T, Yoshida Y, Eguchi N, et al. Global Concentrations of CO2 and CH4 Retrieved from GOSAT: First Preliminary Results [J], SOLA, 2009, 5: 160-163.
[9] Crisp D, Johnson C. The Orbiting Carbon Observatory Mission [J]. Acta Astronautica, 2005, 56:193-197.
[10] 石广玉, 戴铁, 徐娜. 卫星遥感探测大气CO2浓度研究最新进展[J]. 地球科学进展, 2010, 25(1): 7-13.
[11] McNally A, Watts P. A Cloud Detection Algorithm for High-Spectral-Resolution Infrared Sounders [J]. Quarterly Journal of the Royal Meteorological Society, 2003, 129(595): 3411-3423.
[12] Christi M, Stephens G. Retrieving Profiles of Atmospheric in Clear Sky and in the Presence of Thin Cloud Using Spectroscopy from the Near and Thermal Infrared: A Preliminary Case Study [J]. Journal of geophysical research, 2004, 109(D4).
[13] Maddy E, Barnet C, Goldberg M, et al. CO2 retrievals from the atmospheric infrared sounder: Methodology and validation[J]. Journal of Geophysical Research, 2008, 113(D11).
[14] Enting I, Pierman G, Heimenn M. Average global distribution of CO2[C]. // Heimenn M (eds.). The Global Carbon Cycle. New York: Springer Verlag, 1993.
[15] 白文广, 张兴赢, 张鹏. 卫星遥感监测中国地区对流层二氧化碳时空变化特征分析[J]. 科学通报, 2010, 55: 2953-2960.
[16] Zimov S, Davidov S, Voropaev Y, et al. Siberian CO2 efflux in winter as a CO2 source and cause of seasonality in atmospheric CO2[J]. Climatic Change, 1996, 33: 111-120.
[17] Schaefer K, Tinglun Z, Bruhwiler L, et al. Amount and timing of permafrost carbon release in response to climate warming[J]. Tellus, 2011(2), 63: 165-180.
[18] Tiwari Y, Gloor M, Engelen R, et al. Comparing CO2 retrieved from atmospheric infrared sounder with model predictions: Implications for constraining surface fluxes and lower-to-upper troposphere transport[J]. Journal of Geophysical Research, 2006, 111(D17).
