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。
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.
[1] Schneising O, Heymann J, Buchwitz M, et al. Anthropogenic carbon dioxide source areas observed from space:assessment of regional enhancements and trends[J]. Atmospheric Chemistry & Physics, 2013, 13(5):2445-2454.
[2] 刘毅, 杨东旭, 蔡兆男. 中国碳卫星大气CO2反演方法:GOSAT数据初步应用[J]. 科学通报, 2013, 58(11):996-999.
[3] Hakkarainen J, Ialongo I, Tamminen J. Direct space-based observations of anthropogenic CO2 emission areas from OCO-2[J]. Geophysical Research Letters, 2016, 43(21):400-406.
[4] Janardanan R, Maksyutov S, Oda T, et al. Comparing GOSAT observations of localized CO2 enhancements by large emitters with inventory-based estimates[J]. Geophysical Research Letters, 2016, 43(7):3486-3493.
[5] 布然, 雷莉萍, 郭丽洁,等. 大气CO2浓度时空变化卫星遥感监测的应用潜力分析[J]. 遥感学报, 2015, 19(1):34-45.
[6] He Z, Zeng Z C, Lei L, et al. A data-driven assessment of biosphere-atmosphere interaction impact on seasonal cycle patterns of XCO2 using GOSAT and MODIS observations[J]. Remote Sensing, 2017, 9(3):251-272.
[7] Wunch D, Wennberg P O, Osterman G, et al. Comparisons of the orbiting carbon observatory-2(OCO-2) XCO2 measurements with TCCON[J]. Atmospheric Measurement Techniques, 2017, 10(6):2209-2238.
[8] Kulawik S, Wunch D, O'Dell C, et al. Consistent evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON[J]. Atmospheric Measurement Techniques, 2015, 8(6):6217-6277.
[9] Connor B J, Rodgers C D. Intercomparison of remote sounding instruments[J]. Journal of Geophysical Research Atmospheres, 2003, 108(D3):2152-2206.
[10] Jing Y, Shi J, Wang T, et al. Mapping global atmospheric CO2 concentration at high spatiotemporal resolution[J]. Atmosphere, 2014, 5(4):870-888.
[11] Butz A, Deutscher N M. The greenhouse gas climate change initiative (GHG-CCI):comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO2 and CH4[J]. Atmospheric MeasurementTechniques Discussions, 2014, 6(5):8679-8741.
[12] O'Dell C W, Connor B, Bosch H, et al. The ACOS CO2 retrieval algorithm-Part 1:Description and validation against synthetic observations[J]. Atmospheric Measurement Techniques, 2012, 4(1):99-121.
[13] Worden J R, Doran G, Kulawik S, et al. Evaluation and attribution of OCO-2 XCO2 uncertainties[J]. Atmospheric Measurement Techniques, 2017, 10(7):1-28.
[14] Reuter M, Bovensmann H, Buchwitz M, et al. Retrieval of atmospheric CO2 with enhanced accuracy and precision from SCIAMACHY:validation with FTS measurements and comparison with model results[J]. Journal of Geophysical Research Atmospheres, 2011, 116(D4):220-237.
[15] Peters W, Jacobson A R, Sweeney C, et al. An atmospheric perspective on North American carbon dioxide exchange:CarbonTracker[J]. Proc Natl Acad Sci USA, 2007, 104(48):18925-18930.
[16] Rodgers C D. Inverse methods for atmospheric sounding[M]. London:World Scientific, 2000:187-196.
[17] Gurney K R, Law R M, Denning A S, et al. Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models[J]. Nature, 2002, 415(6872):626-630.
[18] Law R M, Rayner P J, Steele L P, et al. Using high temporal frequency data for CO2 inversions[J]. Global Biogeochemical Cycles, 2002, 16(4):1053-1070.
[19] Chevallier F, Broquet G, Pierangelo C, et al. Probabilistic global maps of the CO2 column at daily and monthly scales from sparse satellite measurements[J]. Journal of Geophysical Research Atmospheres, 2017, 122(14):7614-7629.
[20] Han G, Ma X, Liang A, et al. Performance evaluation for China's planned CO2-IPDA[J]. Remote Sensing, 2017, 9(8):768-789.
