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.
HE Qian, YU Tao, CHENG Tianhai, GU Xingfa, XIE Donghai, WU Yu
. Atmospheric Carbon Dioxide Satellite Remote Sensing Retrieval Accuracy Inspection and Spatio-temporal Characteristics Analysis[J]. Journal of Geo-information Science, 2012
, 14(2)
: 250
-257
.
DOI: 10.3724/SP.J.1047.2012.00250
[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).
