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A statistical analysis of time trends in atmospheric ethane

Author

Listed:
  • Marina Friedrich

    (Potsdam Institute for Climate Impact Research – Member of the Leibniz Association
    Vrije Universiteit Amsterdam)

  • Eric Beutner

    (Vrije Universiteit Amsterdam)

  • Hanno Reuvers

    (Erasmus University)

  • Stephan Smeekes

    (Maastricht University)

  • Jean-Pierre Urbain

    (Maastricht University)

  • Whitney Bader

    (Avenue Prince de Liège)

  • Bruno Franco

    (Université libre de Bruxelles (ULB))

  • Bernard Lejeune

    (University of Liège)

  • Emmanuel Mahieu

    (University of Liège)

Abstract

Ethane is the most abundant non-methane hydrocarbon in the Earth’s atmosphere and an important precursor of tropospheric ozone through various chemical pathways. Ethane is also an indirect greenhouse gas (global warming potential), influencing the atmospheric lifetime of methane through the consumption of the hydroxyl radical (OH). Understanding the development of trends and identifying trend reversals in atmospheric ethane is therefore crucial. Our dataset consists of four series of daily ethane columns. As with many other decadal time series, our data are characterized by autocorrelation, heteroskedasticity, and seasonal effects. Additionally, missing observations due to instrument failure or unfavorable measurement conditions are common in such series. The goal of this paper is therefore to analyze trends in atmospheric ethane with statistical tools that correctly address these data features. We present selected methods designed for the analysis of time trends and trend reversals. We consider bootstrap inference on broken linear trends and smoothly varying nonlinear trends. In particular, for the broken trend model, we propose a bootstrap method for inference on the break location and the corresponding changes in slope. For the smooth trend model, we construct simultaneous confidence bands around the nonparametrically estimated trend. Our autoregressive wild bootstrap approach, combined with a seasonal filter, is able to handle all issues mentioned above (we provide R code for all proposed methods on https://www.stephansmeekes.nl/code .).

Suggested Citation

  • Marina Friedrich & Eric Beutner & Hanno Reuvers & Stephan Smeekes & Jean-Pierre Urbain & Whitney Bader & Bruno Franco & Bernard Lejeune & Emmanuel Mahieu, 2020. "A statistical analysis of time trends in atmospheric ethane," Climatic Change, Springer, vol. 162(1), pages 105-125, September.
    • Handle: RePEc:spr:climat:v:162:y:2020:i:1:d:10.1007_s10584-020-02806-2
    DOI: 10.1007/s10584-020-02806-2
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    References listed on IDEAS

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    3. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.

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