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The tropopause, as a transition layer between the troposphere and the stratosphere, has a significant influence on the inversion of trace gas concentration. Theoretical analysis of the influence of tropopause on the vertical distribution of atmospheric molecular content, combined with Lhasa observation data, is presented, and the quantitative analysis of the influence of tropopause on the inversion of column-averaged dry air mole fractions (DMFs) is given as well. The comparison results show that the tropopause height has a great influence on the inversion results. First, its height variation has a little effect on XH2O, but it has a great influence on XCO2, XCH4 and XCO. The XCO2 and XCH4 have positive correlation with tropopause height variation, but for XCO, negative correlation with the tropopause height variation is observed. The correlation coefficient of XCO2, XCH4 and XCO are 0.998, 0.78 and 0.994, respectively. When the tropopause height is varied by 3 km, XCO2, XCH4 and XCO are varied by 8.64%、0.0354% and 0.0488%, respectively. The column-averaged dry air mole water vapor, carbon dioxide, carbon monoxide and methane in Lhasa are observed based on ground-based Fourier transform infrared spectrometer EM27/SUN. The time series of XH2O, XCO2, XCH4 and XCO in a period from August 6 to August 16, 2018 in Lhasa were obtained. The main achievements are as follows. In the observation period, the daily average value of XH2O, XCO2, XCH4 and XCO vary between 3432 and 4287 ppmv, 406.1 and 408.2 ppmv, 1.673 and 1.720 ppmv, and 0.082 and 0.095 ppmv, respectively. The average value of XH2O, XCO2, XCH4 and XCO are 3919.70, 406.887, 1.689, and 0.091 ppmv, res[ectively. Comparison between XCO2 and XCH4 time series shows that XCO2 and XCH4 time series have similar daily trends, the correlation coefficient between XCO2 and XCH4 time serires is higher than 0.5. In particular, the correlation coefficient reached about 0.86 on August 7, 8, 13, 2018. High correlation coefficient indicates that CO2 and CH4 molecules come from the same source. Compared with the WACCM simulation values, the XCO2 and XCH4 of the ground-based observations are small. The observation results can provide reference and first-hand direct observation data for the study of the temporal and spatial distribution of greenhouse gases in the temperate zone of the plateau in China.
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Keywords:
- Fourier transform infrared spectroscopy /
- greenhouse gases /
- column-averaged dry air mole fractions /
- tropopause
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图 1 (a) 观测站点位置(拉萨市气象局); (b) 观测设备(傅里叶变换光谱仪, EM27/SUN)
Figure 1. (a) Observing site (Lhasa meteorological bureau); (b) FTIR spectrometer (EM27/SUN).
图 2 不同对流层顶高度下的H2O (a), HDO (b), CO2 (c), CH4 (d), CO (e), N2O (f), HF (g)及O2 (h)廓线
Figure 2. The profiles of H2O (a), HDO (b), CO2 (c), CH4 (d), CO (e), N2O (f), HF (g) and O2 (h) at different heights of top troposphere
图 3 XH2O (a), (e), XCO2 (b), (f), XCH4 (c), (g)及XCO (d), (h)日变化随对流层顶高度的变化
Figure 3. The diurnal variation of XH2O (a), (e), XCO2 (b), (f), XCH4 (c), (g) and XCO (d), (h) with tropopause height.
图 4 (a) CO2, (b) CH4及(c) CO平均摩尔分数与对流层顶的线性拟合
Figure 4. Linear fit of the average mole fraction of CO2 (a), CH4 (b) and CO (c) to the top of the troposphere.
图 5 XH2O (a), XCO2 (b), XCH4 (c), XCO (d)的日平均序列
Figure 5. Time series of XH2O (a), XCO2 (b), XCH4 (c)and XCO (d).
图 6 2018年8月7日、8日 XCO2 (a), (b), XCH4 (c), (d)时间序列
Figure 6. Time series of XCO2 (a), (b)and XCH4 (c), (d)at August 7, 8, 2018.
图 7 XCO2与XCH4相关性 (a) 2018-08-6; (b) 2018-08-7; (c) 2018-08-8; (d) 2018-08-10; (e) 2018-08-12; (f) 2018-08-13; (g) 2018-08-14; (h) 2018-08-16
Figure 7. The correlation between XCO2 and XCH4: (a) August 6, 2018; (b) August 7, 2018; (c) August 8, 2018; (d) August 10, 2018
图 8 2018年8月6日—16日72 h后向轨迹图 (a) 2018年8月4—6日; (b) 2018年8月7—9日; (c) 2018年8月10—12日; (d) 2018年8月14—16日
Figure 8. 72-hour back trajectories of Lhasa during August 6—16, 2018: (a) August 4-6, 2018; (b) August 7-9, 2018; (c) August 10-12, 2018; (d) August 14-16, 2018
图 9 地基观测XCO2 (a), XCH4 (b)日平均值与WACCM数据对比
Figure 9. Comparison of XCO2 (a) and XCH4 (b) based on ground-based observations and WACCM data.
表 1 反演波段
Table 1. Inversion band.
气体种类 反演波段 干扰分子 H2O 8353.4—8463.1 CH4 CO2 6173.0—6390.0 H2O, HDO, CH4 CH4 5897.0—6145.0 H2O CO 4208.7—4318.8 CH4, H2O, HDO O2 7765.0—8005.0 H2O, HF, CO2 
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[1] 王鑫, 吕达仁 2007 自然科学进展 17 913
Google Scholar
Wang X, Lu D R 2007 Prog. Nat. Sci. 17 913
Google Scholar
[2] 洪健昌, 郭建平, 杜军, 王鹏祥 2016 气象学报 74 827
Hong J C, Guo J P, Du J, Wang P X 2016 Acta Meteorol. Sin. 74 827
[3] 周顺武, 杨双艳, 张人禾, 马振峰 2010 大气科学学报 33 307
Google Scholar
Zhou S W, Yang S Y, Zhang R H, Ma Z F 2010 Trans. Atmos. Sci. 33 307
Google Scholar
[4] 田红瑛, 田文寿, 雒佳丽, 张杰, 杨琴, 黄倩 2014 高原气象 33 1
Google Scholar
Tian H Y, Tian W S, Luo J L, Zhang J, Yang Q, Huang Q 2014 Plateau Meteorol. 33 1
Google Scholar
[5] 王敏仲, 魏文寿, 何清, 杨莲梅, 程玉景 2012 高原气象 31 1203
Wang M Z, Wei W S, He Q, Yang L M, Cheng Y J 2012 Plateau Meteorol. 31 1203
[6] 杨双艳, 周顺武, 张人禾, 吴萍, 李慧, 马振峰 2012 大气科学学报 35 438
Google Scholar
Yang S Y, Zhou S W, Zhang R H, Wu P, Li H, Ma Z F 2012 Trans. Atmos. Sci. 35 438
Google Scholar
[7] 周秀骥, 李维亮, 陈隆勋, 刘煜 2004 气象学报 62 513
Zhou X J, Li W L, Chen L X, Liu Y 2004 Acta Meteorol. Sin. 62 513
[8] 薛志航, 邓创, 孙一 2018 成都信息工程大学学报 33 464
Xue Z H, Deng C, Sun Y 2018 J. Chengdu Univ. Inf. Technol. 33 464
[9] Guo D, Su Y C, Shi C H, Xu J J, Powell A M 2015 J. Atmos Sol. Terr. Phys. 130 127
[10] Wunch D, Toon G C, Wennberg P O 2010 Atmos. Meas. Tech. 8 2
[11] Hase F, Hannigan J W, Coffey M T, Goldman A, Hopfner M, Jones N B, Rinsland C P, Wood S W 2004 J. Quant. Spectrosc. Radiat. Transfer 87 25
Google Scholar
[12] 单昌功, 王薇, 刘诚, 徐兴伟, 孙友文, 田园, 刘文清 2017 66 220204
Google Scholar
Shan C G, Wang W, Liu C, Xu X W, Sun Y W, Tian Y, Liu W Q 2017 Acta Phys. Sin. 66 220204
Google Scholar
[13] 田园, 孙友文, 谢品华, 刘诚, 刘文清, 刘建国, 李昂, 胡仁志, 王薇, 曾议 2015 64 070704
Google Scholar
Tian Y, Sun Y W, Xie P H, Liu C, Liu W Q, Liu J G, Li A, Hu R Z, Wang W, Zeng Y 2015 Acta Phys. Sin. 64 070704
Google Scholar
[14] 单昌功, 刘诚, 王薇, 孙友文, 刘文清, 田园, 杨维 2017 光谱学与光谱分析 37 1997
Shan C G, Liu C, Wang W, Sun Y W, Liu W Q, Tian Y, Yang W 2017 Spectrosc. Spect. Anal. 37 1997
[15] Kiel M, Hase F, Blumenstock T, Kirner O 2016 Atmos. Meas. Tech. 9 2223
Google Scholar
[16] Hase F, Frey M, Blumenstock T, Groß J, Kiel M, Mengistu Tsidu G, Schäfer K, Sha M K, Orphal J 2015 Atmos. Meas. Tech. 8 3059
Google Scholar
[17] Wunch D, Toon G C, Wennberg P Q, et al. 2010 Atmos. Meas. Tech. 3 1351
Google Scholar
[18] Frey M, Hase F, Blumenstock T, Gross J, Kiel M, Tsidu G M, Schafer K, Sha M K, Orphal J 2015 Atmos. Meas. Tech. 8 3047
Google Scholar
[19] Hase F, Drouin B J, Roehl C M, et al. 2013 Atmos. Meas. Tech. 6 3527
Google Scholar
[20] Zhou Min Q, Langerock B, Wells K C, et al. 2019 Atmos. Meas. Tech. 12 1393
Google Scholar
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