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The temporal and spatial variation of CO2 column concentration over China from 2003 to 2018 based on multi-source satellite remote sensing data

  • role： First author第一作者
  • Affiliation：

    College of Harbour and Coastal Engineering, Jimei University, Xiamen 361021, China

    National Geographic Conditions Monitoring Research Center, Jimei University, Xiamen 361021, China

  • Email：zyxiao@jmu.edu.cn
  • Introduction：肖钟湧，研究方向为大气环境遥感。E-mail：zyxiao@jmu.edu.cn
XIAO Zhongyong12,  
  • Affiliation：

    College of Harbour and Coastal Engineering, Jimei University, Xiamen 361021, China

    National Geographic Conditions Monitoring Research Center, Jimei University, Xiamen 361021, China

CHEN Yingfeng12,  
  • Affiliation：

    College of Harbour and Coastal Engineering, Jimei University, Xiamen 361021, China

    National Geographic Conditions Monitoring Research Center, Jimei University, Xiamen 361021, China

LIN Xiaofeng12,  
  • Affiliation：

    College of Harbour and Coastal Engineering, Jimei University, Xiamen 361021, China

    National Geographic Conditions Monitoring Research Center, Jimei University, Xiamen 361021, China

LIU Shanhong12,  
  • Affiliation：

    College of Harbour and Coastal Engineering, Jimei University, Xiamen 361021, China

    National Geographic Conditions Monitoring Research Center, Jimei University, Xiamen 361021, China

XIE Jinghan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Harbour and Coastal Engineering, Jimei University, Xiamen 361021, China

  • Email：xxq187@126.com
  • Introduction：谢先全，研究方向为GIS及遥感应用。E-mail：xxq187@126.com
XIE Xianquan1*

resumen

This paper used CO2 products inversed from Scanning Imaging Absorption spectrometer for Atmospheric CartograpHY (SCIAMACHY) and Greenhouse Gases Observing Satellite Fourier Transformation Spectrometer (GOSAT FTS), and the linear regression analysis was used to validate the remote sensing data with CO2 concentration data observed at the Waliguan station, then the remote sensing data were corrected. The superposition model of linear and sine function was further used to analyze the temporal trend and periodicity of regional mean CO2 over China from 2003 to 2018, and the CO2 was synthesized at different temporal scales to study the spatial distribution characteristics. Finally, the influencing factors on these temporal and spatial characteristics were analyzed. The results showed that SCIAMACHY and GOSAT CO2 retrievals agree well in general with ground observation CO2, but the satellite remote sensing has obvious systematic errors. The differences between GOSAT and WDCGG CO2 were larger than those between SCIAMACHY and WDCGG CO2, with an average deviation of -3.89 ppm and 1.00 ppm, respectively. The SCIAMACHY CO2 was overestimated at smaller value and underestimated at the larger value. In terms of seasonal variation, the regional monthly mean value of CO2 column concentration appeared a cyclical variation at 12 months and gradually increase with temporal processing. The annual regional average CO2 column concentration in 2003 and 2018 were 374.4 ppm and 413.7 ppm, respectively. The CO2 increased 39.3 ppm (about 10.51%) from 2003 to 2018, with the average annual growth rate of 0.59%. The monthly variation of CO2 column concentration showed significant temporal and spatial differences characterizing by a sinusoidal fluctuation, with the minimum and maximum values appeared in August and April with 407.7 ppm and 416.3 ppm in 2018, respectively. The periodic characteristics of CO2 column concentration are primarily affected by the vegetation growth cycle of terrestrial ecosystems, various chemical processes in the soil, and anthropogenic emissions. The multi-yearly average CO2 column concentration values from 2003 to 2018 varied between 388 and 398 ppm, with the standard deviation ranged from 10 ppm to 15 ppm. Additionally, the high values of CO2 column concentration mainly appeared in the subtropical and temperate regions in the eastern China. The annual average CO2 column concentration in 2018 is up to 417.9 ppm. The lowest value was in northern Inner Mongolia of about 409.5 ppm in 2018. The CO2 column concentration in the entire region of China showed increased significantly from 2003 to 2018, but the growth rate has obvious heterogeneity in space. The increase values from 2003 to 2018 were between 31.0 ppm and 45.4 ppm. Furthermore, the growth rate had obvious heterogeneity in space, which was in range of 8.9%—12.2% in space. There was an obvious shed line for the growth rate of CO2 column concentration, which was from northeast to southwest, coinciding with the geographical dividing line of China (Mohe-Tengchong Line). The largest growth rate occurred at the junction of Liaoning and Jilin Province, with the value of 12.2%. The region with the smaller growth appeared in Central China, with the lowest growth rate of about 8.9%.

palabra clave

SCIAMACHY;GOSAT;CO2 column concentration;remote sensing;Variation coefficient

References

  1. 1.
    Bai W G, Zhang X Y and Zhang P. 2010. Temporal and spatial distribution of tropospheric CO2 over China based on satellite observation. Chinese Science Bulletin, 55(30): 3612-3618
  2. 2.
    Barkley M P, Monks P S, Hewitt A J, Machida T, Desai A, Vinnichenko N, Nakazawa T, Arshinov M Y, Fedoseev N and Watai T. 2007. Assessing the near surface sensitivity of SCIAMACHY atmospheric CO2 retrieved using (FSI) WFM-DOAS. Atmospheric Chemistry and Physics, 7(13): 3597-3619
  3. 3.
    Bu R, Lei L P, Guo L J, Liu D and Zeng Z C. 2015. Temporal and spatial potential applications of satellite remote sensing of atmospheric CO2 concentration monitoring. Journal of Remote Sensing, 19(1): 34-45
  4. 4.
    Buchwitz M, de Beek R, Noël S, Burrows J P, Bovensmann H, Bremer H, Bergamaschi P, Körner S and Heimann M. 2005. Carbon monoxide, methane and carbon dioxide columns retrieved from SCIAMACHY by WFM-DOAS: year 2003 initial data set. Atmospheric Chemistry and Physics, 5(12): 3313-3329
  5. 5.
    Buchwitz M, Reuter M, Bovensmann H, Pillai D, Heymann J, Schneising O, Rozanov V, Krings T, Burrows J P, Boesch H, Gerbig C, Meijer Y and Löscher A. 2013. Carbon Monitoring Satellite (CarbonSat): assessment of scattering related atmospheric CO2 and CH4 retrieval errors and first results on implications for inferring city CO2 emissions. Atmospheric Measurement Techniques Discussions, 6: 4769-4850
  6. 6.
    Cogan A J, Boesch H, Parker R J, Feng L, Palmer P I, Blavier J F L, Deutscher N M, Macatangay R, Notholt J, Roehl C, Warneke T and Wunch D. 2012. Atmospheric carbon dioxide retrieved from the Greenhouse gases Observing SATellite (GOSAT): comparison with ground-based TCCON observations and GEOS-Chem model calculations. Journal of Geophysical Research: Atmospheres, 117(D21): D21301
  7. 7.
    Dai L J, Cui W H and Jiang Y B. 2012. Temporal and spatial distribution of tropospheric carbon dioxide from 2003 to 2010 in China. Ecology and Environmental Sciences, 21(7): 1266-1270
  8. 8.
    Deng A J, Guo H B, Hu J, Jiang C Z, Liu P G and Jing H F. 2020. Temporal and distribution characteristic of CO2 concentration over China based on GOSAT satellite data. Journal of Remote Sensing, 24(3): 319-325
  9. 9.
    Dettinger M D and Ghil M. 1998. Seasonal and interannual variations of atmospheric CO2 and climate. Tellus B, 50(1): 1-24
  10. 10.
    Gregg J S, Andres R J and Marland G. 2008. China: emissions pattern of the world leader in CO2 emissions from fossil fuel consumption and cement production. Geophysical Research Letters, 35(8): L08806
  11. 11.
    Guerlet S, Butz A, Schepers D, Basu S, Hasekamp O P, Kuze A, Yokota T, Blavier J F, Deutscher N M, Griffith D W T, Hase F, Kyro E, Morino I, Sherlock V, Sussmann R, Galli A and Aben I. 2013. Impact of aerosol and thin cirrus on retrieving and validating XCO2 from GOSAT shortwave infrared measurements. Journal of Geophysical Research: Atmospheres, 118(10): 4887-4905
  12. 12.
    Guo M, Wang X, Li J, Wang H. 2013. Examining the relationships between land cover and greenhouse gas concentrations using remote-sensing data in East Asia. International Journal of Remote Sensing, 34 (12): 4281-4303
  13. 13.
    Houghton R A. 2002. Magnitude, distribution and causes of terrestrial carbon sinks and some implications for policy. Climate Policy, 2(1): 71-88
  14. 14.
    IPCC. 2013. Climate Change 2013: The Physical Science Basis. Cambridge (eds Thomas. F. Stocher et al). United Kingdom and New York: Cambridge University Press
  15. 15.
    Kuze A, Suto H, Nakajima M and Hamazaki T. 2009. Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring. Applied Optics, 48(35): 6716-6733
  16. 16.
    Le Quéré C, Raupach M R, Canadell J G, Marland G, Bopp L, Ciais P, Conway T J, Doney S C, Feely R A, Foster P, Friedlingstein P, Gurney K, Houghton R A, House J I, Huntingford C, Levy P E, Lomas M R, Majkut J, Metzl N, Ometto J P, Peters G P, Prentice I C, Randerson J T, Running S W, Sarmiento J L, Schuster U, Sitch S, Takahashi T, Viovy N, van der Werf G R and Woodward F I. 2009. Trends in the sources and sinks of carbon dioxide. Nature Geoscience, 2(12): 831-836
  17. 17.
    Liu L X, Zhou L X, Zhang X C, Wen M, Zhang F, Yao B, Fang S X. 2009. The characteristics of atmospheric CO2 concentration variation of four national background stations in China. Science in China Series D: Earth Sciences, 52(11): 1857-1863
  18. 18.
    Liu R X, Zhang X Y, Liu J and Liu Y G. 2015. A spatial-temporal distribution characteristics study on the atmospheric carbon dioxide observed by GOSAT satellite remote sensing. Journal of Tropical Meteorology, 21(4): 408-416
  19. 19.
    Mousavi S M, Falahatkar S and Farajzadeh M. 2017. Assessment of seasonal variations of carbon dioxide concentration in Iran using GOSAT data. Natural Resources Forum, 41(2): 83-91
  20. 20.
    Patra P K, Crisp D, Kaiser J W, Wunch D, Saeki T, Ichii K, Sekiya T, Wennberg P O, Feist D G, Pollard D F, Griffith D W T, Velazco V A, De Maziere M, Sha M K, Roehl C, Chatterjee A and Ishijima K. 2017. The Orbiting Carbon Observatory (OCO-2) tracks 2-3 peta-gram increase in carbon release to the atmosphere during the 2014—2016 El Niño. Scientific Reports, 7(1): 13567
  21. 21.
    Piao S L, Zhang X P, Chen A P, Liu Q, Lian X, Wang X H, Peng S S and Wu X C. 2019. The impacts of climate extremes on the terrestrial carbon cycle: a review. Science China: Earth Sciences, 62(10): 1551-1563
  22. 22.
    Reichstein M, Bahn M, Ciais P, Frank D, Mahecha M D, Seneviratne S I, Zscheischler J, Beer C, Buchmann N, Frank D C, Papale D, Rammig A, Smith P, Thonicke K, van der Velde M, Vicca S, Walz A and Wattenbach M. 2013. Climate extremes and the carbon cycle. Nature, 500(7462): 287-295
  23. 23.
    Reuter M, Bovensmann H, Buchwitz M, Burrows J P, Connor B J, Deutscher N M, Griffith D W T, Heymann J, Keppel-Aleks G, Messerschmidt J, Notholt J, Petri C, Robinson J, Schneising O, Sherlock V, Velazco V, Warneke T, Wennberg P O and Wunch D. 2011. Retrieval of atmospheric CO2 with enhanced accuracy and precision from SCIAMACHY: validation with FTS measurements and comparison with model results. Journal of Geophysical Research: Atmospheres, 116(D4): D04301
  24. 24.
    Wang K, Jiang H, Zhang X Y and Zhou G M. 2011. Analysis of spatial and temporal variations of carbon dioxide over China using SCIAMACHY satellite observations during 2003—2005. International Journal of Remote Sensing, 32(3): 815-832
  25. 25.
    Wang S P, Zhang X Y, Wang W H and Fang L. 2015. Global and China-based regional near-surface distributions of CO2 concentrations based on GOSAT L4B data. Science and Technology Review, 33(17): 63-68
  26. 26.
    Weatherhead E C, Reinsel G C, Tiao G C, Meng X L, Choi D, Cheang W K, Keller T, DeLuisi J, Wuebbles D J, Kerr J B, Miller A J, Oltmans S J and Frederick J E. 1998. Factors affecting the detection of trends: statistical considerations and applications to environmental data. Journal of Geophysical Research: Atmospheres, 103(D14): 17149-17161
  27. 27.
    Xia L J, Liu L X, Li B Z and Zhou L X. 2018. Spatial and temporal distribution characteristics of atmospheric CO2 in Central China. China Environmental Science, 38(8): 2811-2819
  28. 28.
    Yang D X, Liu Y, Cai Z N, Deng J B, Wang J and Chen X. 2015. An advanced carbon dioxide retrieval algorithm for satellite measurements and its application to GOSAT observations. Science Bulletin, 60(23): 2063-2066
  29. 29.
    Yoshida Y, Kikuchi N, Morino I, Uchino O, Oshchepkov S, Bril A, Saeki T, Schutgens N, Toon G C, Wunch D, Roehl C M, Wennberg P O, Griffith D W T, Deutscher N M, Warneke T, Notholt J, Robinson J, Sherlock V, Connor B, Rettinger M, Sussmann R, Ahonen P, Heikkinen P, Kyrö E and Yokota T. 2013. Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data. Atmospheric Measurement Techniques Discussions, 6: 949-988
  30. 30.
    Zhang X Y, Zhang P, Fang Z Y, Qiu H, Li X J and Zhang Y. 2007. The progress in trace gas remote sensing study based on the satellite monitoring. Meteorological Monthly, 33(7): 3-14
  31. 31.
    Zhang Y J, Niu Z, Wang L and Chang C Y. 2008. Study on tropospheric NO2 change trend in cities using OMI satellite data. Geography and Geo-information Science, 24(3): 96-99
  32. 32.
    Zhao M W, Li C C, Zhang X Y, Wang C, Jiang L, Sun J L. 2017. Study on the Spatial-Temporal Change Characteristics of Global CO2 Concentration Based on SCIAMACHY Retrievals. Climate Change Research, 13(2): 95-102

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