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Home » Archive of journals » Volume 12, No. 1, 2022 » Dynamics in the content of greenhouse gases in the surface layer of atmospheric air of the Arctic Island of Bely in the summer period 2015—2017

DYNAMICS IN THE CONTENT OF GREENHOUSE GASES IN THE SURFACE LAYER OF ATMOSPHERIC AIR OF THE ARCTIC ISLAND OF BELY IN THE SUMMER PERIOD 2015—2017

JOURNAL: Volume 12, No. 1, 2022, p. 68-76

HEADING: Research activities in the Arctic

AUTHORS: Subbotina, I.E., Baglaeva, E.M., Buevich, A.G., Sergeev, A.P., Shichkin, A.V.

ORGANIZATIONS: Institute of Industrial Ecology, Ural Branch of the RAS

DOI: 10.25283/2223-4594-2022-1-68-76

UDC: 504.3.054(985)

The article was received on: 19.07.2021

Keywords: Arctic zone of the Russian Federation, climate change, monitoring, greenhouse gases

Bibliographic description: Subbotina, I.E., Baglaeva, E.M., Buevich, A.G., Sergeev, A.P., Shichkin, A.V. Dynamics in the content of greenhouse gases in the surface layer of atmospheric air of the Arctic Island of Bely in the summer period 2015—2017. Arktika: ekologiya i ekonomika. [Arctic: Ecology and Economy], 2022, vol. 12, no. 1, pp. 68-76. DOI: 10.25283/2223-4594-2022-1-68-76. (In Russian).


Abstract:

The reports of the Intergovernmental Panel on Climate Change, define the increase in the content of greenhouse gases (GHGs) in the atmosphere as the most likely cause of warming. The consequences of global warming are most pronounced in the Arctic zone of the planet, where temperature changes are significant. To provide an information basis for taking additional measures in the field of mitigating the effects of anthropogenic impact on climate change, reliable data on changes in GHG content in the Arctic are required.
Based on monitoring data of meteorological conditions and concentrations of carbon dioxide, methane and water vapor in the surface layer of the atmosphere on the Island of Bely (Arctic zone of the Russian Federation) in the summer seasons 2015—2017 the authors investigated the cycles of GHG content variability. There was no evidence of a linear relationship between the average temperature and GHG content in the summer seasons for three years. The highest average daily temperature in the summer season corresponded to the hottest 2016. No differences in mean daily GHG concentrations exceeding the standard deviation were found for three years. For all greenhouse gases, dramatic changes can be observed from a high positive Spearman coefficient to a negative one and vice versa. To demonstrate the contribution of period-related harmonics, a Fourier analysis period gram is constructed. Analysis of time series associated with cross-correlation of GHG concentrations and temperature show that methane and water vapor concentrations contain periodic components: month, decade, week and day. The identified periodic components should be considered when constructing predictive models to describe the seasonal dynamics of fluctuations in the concentration of CH4 and H2O depending on temperature. No significant periodic delays in the CO2 time series are detected, indicating a strong noise level.
The data obtained during the monitoring is used as a basis for creating models that predict trends in the content changes of major GHGs.


Finance info: he authors are grateful to Yu. I. Markelov for his help in organizing and holding monitoring, the results of which formed the basis of the manuscript.

References:

1. Summary for Policymakers. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [eds.: H.-O. Pörtner, D. C. Roberts, V. Masson-Delmotte et al.]. IPCC, 2019. Available at: https://www.ipcc.ch/srocc/.

2. Global Warming of 1.5°C: An IPCC Special Report on the Impacts of Global Warming of 1.5L°C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty [eds.: V. Masson-Delmotte, P. Zhai, H.-O. Pörtner et al.]. IPCC, 2018. Available at: https://www.ipcc.ch/sr15/.

3. Alexander L. V., Zhang X., Peterson T. C. et al. Global Observed Changes in Daily Climate Extremes of Temperature and Precipitation. J. of Geophysical Research, 2006, vol. 111, p. D05109. Available at: http://dx.doi.org/10.1029/2005jd006290.

4. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [eds.: T. F. Stocker, D. Qin, G.-K. Plattner et al.]. IPCC. Cambridge, UK; New York, USA, Cambridge Univ. Press, 2013, 1535 p.

5. The Global Climate in 2015—2019 [eds.: P. Siegmund, J. Abermann, O. Baddour et al.]. World Meteorological Organization (WMO-¹ 1249). Geneva, Switzerland, 2020. Available at: https://library.wmo.int/doc_num.php?explnum_id=10251.

6. Lietzke B., Vogt R., Feigenwinter C., Parlow E. On the controlling factors for the variability of carbon dioxide flux in a heterogeneous urban environment. Intern. J. of Climatology, 2015, vol. 35 (13), pp. 3921—3941. Available at: https://doi.org/10.1002/joc.4255.

7. Yunqiu Gao, Xuhui Lee, Shoudong Liu et al. Spatiotemporal variability of the near-surface CO2 concentration across an industrial-urban-rural transect, Nanjing, China. Science of the Total Environment, 2018, vol. 631—632, pp. 1192—1200.

8. Hirano T., Sugawara H., Murayama S., Kondo H. Diurnal Variation of CO2 Flux in an Urban Area of Tokyo. SOLA, 2015, vol. 11, pp. 100—103. DOI: 10.2151/sola.2015-024.

9. Silva J. M. N., Carreiras J. M. B., Rosa I., Pereira J. M. C. Greenhouse gas emissions from shifting cultivation in the tropics, including uncertainty and sensitivity analysis. J. of Geophysical Research, 2011, vol. 116, p. D20304. DOI: 10.1029/2011JD016056.

10. Chuvilin E., Grebenkin S., Zhmaev M. Gas Permeability of Sandy Sediments: Effects of Phase Changes in Pore Ice and Gas Hydrates. Energy & Fuels, 2021, vol. 35 (9), pp. 7874—7882. DOI: 10.1021/acs.energyfuels.1c00366.

11. Saunois M., Jackson R. B., Bousquet P. et al. The growing role of methane in anthropogenic climate change. Environmental Research Letters, 2016, vol. 11, p. 120207.

12. Hegglin M. I., Tegtmeier S., Anderson J. et al. SPARC Data Initiative: Comparison of water vapor climatologies from international satellite limb sounders. J. of Geophysical Research: Atmospheres, 2013, vol. 118, pp. 11824—11846. DOI: 10.1002/jgrd.50752.

13. Mateos D., Antón M., Sanchez-Lorenzo A. et al. Long-term changes in the radiative effects of aerosols and clouds in a mid-latitude region (1985—2010). Global and Planetary Change, 2013, vol. 111, pp. 288—295. Available at: https://doi.org/10.1016/j.gloplacha.2013.10.004.

14. Serreze M. C., Barry R. G. Processes and impacts of Arctic amplification: A research synthesis. Global and Planetary Change, 2011, vol. 77, ðð. 85—96.

15. Schaller C., Kittler F., Foken T., Göckede M. Characterisation of short-term extreme methane fluxes related to non-turbulent mixing above an Arctic permafrost ecosystem. Atmospheric Chemistry and Physics, 2019, vol. 19, pp. 4041—4059.

16. Methane as an Arctic climate forcer: Arctic Monitoring and Assessment Programme (AMAP). AMAP Assessment. Oslo, Norway, 2015, 139 p.

17. Fisher R. E., Sriskantharajah S., Lowry D. et al. Arctic methane sources: Isotopic evidence for atmospheric inputs. Geophysical Research Letters, 2011, vol. 38 (21), p. L21803.

18. Tan Z., Zhuang Q. Arctic lakes are continuous methane sources to the atmosphere under warming conditions. Environmental Research Letters, 2015, vol. 10, p. 054016.

19. Zona D., Gioli B., Commane R. et al. Cold season emissions dominate the Arctic tundra methane budget. Proceedings of the National Academy of Sciences USA, 2016, vol. 113, pp. 40—45.

20. Baglaeva E. M., Sergeev A. P., Shichkin A. V. et al. Particulate matter size distribution in air surface layer of middle Ural and Arctic territories. Atmospheric Pollution Research, 2019, vol. 10 (4), pp. 1220—1226.

21. Makosko A. A., Matesheva A. V. Assessment of the long-range pollution trends of the atmosphere in the Arctic zone of Russia in 1980—2050 considering climate change scenarios. Arktika: ekologiya i ekonomika. [Arctic: Ecology and Economy], 2020, no. 1 (37), pp. 45—52. DOI: 10.25283/2223-4594-2020-1-45-52. (In Russian).

22. First Global Assessment of Air Pollution Legislation. Report of UNEP (02 September 2021). Available at: https://www.unep.org/resources/report/first-global-assessment-air-pollution-legislation.

23. Edelgeriev R. S. Kh., Romanovskaya A. A. New approaches to adaptation to climate change on the example of the Arctic zone of the Russian Federation. Meteorologiya i gidrologiya, 2020, no. 5, pp. 12—28. (In Russian).

24. Slagoda Ye. A., Leybman M. O., Khomutov A. V., Orekhov P. T. Cryolithological structure of the first terrace of Bely Island in the Kara Sea (part 1). Kriosfera Zemli, 2013, no. 17 (4), pp. 11—21. (In Russian).

25. Trofimova I. E., Balybina A. S. Climate classification and climatic regionalization of the West Siberian Plain. Geografiya i Prirod. Resursy, 2014, no. 2, pp. 11—21. (In Russian).

26. Abakumov E., Shamilishviliy G., Yurtaev A. Soil polychemical contamination on Beliy Island as key background and reference plot for Yamal region. Polish Polar Research, 2017, vol. 38 (3), pp. 313—332.

27. Cooley J. W., Tukey J. W. An Algorithm for the Machine Calculation of Complex Fourier Series. Mathematics of Computation, 1965, vol. 19, pp. 297—301. Available at: https://doi.org/10.2307/2003354.

28. Sedehi O., Katafygiotis L. S., Papadimitriou C. Hierarchical Bayesian operational modal analysis: Theory and computations. Mechanical Systems and Signal Processing, 2020, vol. 140, p. 106663.

29. Poddubny V. A., Nagovitsyna E. S., Markelov Yu. I. et al. Assessment of the spatial distribution of methane concentration in the Barents and Kara Seas in the summer 2016—2017. Meteorologiya i gidrologiya, 2020, no. 3, pp. 77—86. (In Russian).

30. Subbotina I. E., Buevich A. G., Sergeev A. P. et al. A two-step combined algorithm for improving the accuracy of forecasting the concentration of methane in the atmospheric air based on the NARX neural network and subsequent prediction of residuals. Arktika: ekologiya i ekonomika. [Arctic: Ecology and Economy], 2020, no. 2 (38), pp. 59—66. DOI: 10.25283/2223-4594-2020-2-59-66. (In Russian).


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