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Trends in atmospheric ethane

Author

Listed:
  • Federico Maddanu

    (CY Cergy Paris Université)

  • Tommaso Proietti

    (Università di Roma “Tor Vergata”)

Abstract

Understanding the dynamics of the underlying ethane (C2H6) trends has great significance in the context of climate change. The paper focuses on the time series of Fourier Transform Infrared (FTIR) solar spectra ethane column measurements conducted from the ground and recorded at 15 stations in the Northern and Southern Hemispheres. In particular, it deals with assessing time trends in the presence of a strong and persistent annual seasonal component and a very large proportion of missing observations. Our approach proposes a structural model such that seasonality and trend evolve stochastically according to possibly nonstationary long memory models and can be estimated by linear state space methods. The results suggest the existence of a common pattern in the dynamics of ethane trends in both the Northern and Southern Hemispheres. In particular, we found that atmospheric ethane at the Northern Hempisphere stations increased on average by 2.7%yr $$^{-1}$$ - 1 in the period 2009-2015. On the other hand, we estimated an average increase of 1.4%yr $$^{-1}$$ - 1 at the Southern Hempisphere stations.

Suggested Citation

  • Federico Maddanu & Tommaso Proietti, 2023. "Trends in atmospheric ethane," Climatic Change, Springer, vol. 176(5), pages 1-23, May.
  • Handle: RePEc:spr:climat:v:176:y:2023:i:5:d:10.1007_s10584-023-03508-1
    DOI: 10.1007/s10584-023-03508-1
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    References listed on IDEAS

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    1. Isobel J. Simpson & Mads P. Sulbaek Andersen & Simone Meinardi & Lori Bruhwiler & Nicola J. Blake & Detlev Helmig & F. Sherwood Rowland & Donald R. Blake, 2012. "Long-term decline of global atmospheric ethane concentrations and implications for methane," Nature, Nature, vol. 488(7412), pages 490-494, August.
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    4. Tobias Hartl & Roland Jucknewitz, 2022. "Approximate state space modelling of unobserved fractional components," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 75-98, January.
    5. Dissanayake, G.S. & Peiris, M.S. & Proietti, T., 2016. "State space modeling of Gegenbauer processes with long memory," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 115-130.
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