A statistical analysis of time trends in atmospheric ethane

Marina Friedrich; Eric Beutner; Hanno Reuvers; Stephan Smeekes; Jean Pierre Urbain; Whitney Bader; Bruno Franco; Bernard Lejeune; Emmanuel Mahieu

doi:10.1007/s10584-020-02806-2

A statistical analysis of time trends in atmospheric ethane

Marina Friedrich^*, Eric Beutner, Hanno Reuvers, Stephan Smeekes, Jean Pierre Urbain, Whitney Bader, Bruno Franco, Bernard Lejeune, Emmanuel Mahieu

^*Corresponding author for this work

Econometrics and Data Science

Research output: Contribution to Journal › Article › Academic › peer-review

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.).

Original language	English
Pages (from-to)	105-125
Number of pages	21
Journal	Climatic Change
Volume	162
Issue number	1
Early online date	27 Aug 2020
DOIs	https://doi.org/10.1007/s10584-020-02806-2
Publication status	Published - 1 Sept 2020

Funding

Support to the Liège team has been primarily provided by the F.R.S. - FNRS (Brussels) under Grant J.0147.18. Emmanuel Mahieu is a Research Associate with F.R.S. - FNRS. The vital support from the GAW-CH programme of MeteoSwiss is acknowledged. Mission expenses at the Jungfraujoch station were funded by the Fédération Wallonie-Bruxelles. We thank the International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG, Bern) for supporting the facilities needed to perform the observations. W. Bader has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 704951, and from the University of Toronto through a Faculty of Arts & Science Postdoctoral Fellowship Award. We gratefully acknowledge D. Smale (National Institute of Water and Atmospheric research, NIWA, Lauder, NZ), J.W. Hannigan (National Center for Atmospheric Research, NCAR, Boulder, CO, USA) and K. Strong (University of Toronto, Toronto, ON, Canada) for providing FTIR data for the Lauder, Thule and Toronto stations, respectively. Network for the Detection of Atmospheric Composition Change data are publicly available at http://www.ndacc.org . The NIWA FTIR program is funded through the New Zealand government’s core research grant framework from the Ministry of Business, Innovation and Employment. NCAR is sponsored by the National Science Foundation. The NCAR FTS observation program at Thule is supported under contract by the National Aeronautics and Space Administration (NASA). The Thule work is further supported by the NSF Office of Polar Programs (OPP). Measurements at Toronto were made at the University of Toronto Atmospheric Observatory, funded by CFCAS, ABB Bomem, CFI, CSA, ECCC, NSERC, ORDCF, PREA, and the University of Toronto.

Funders	Funder number
ABB Bomem
MeteoSwiss
National Science Foundation
Office of Polar Programs
National Aeronautics and Space Administration
Horizon 2020 Framework Programme
Canadian Space Agency
Natural Sciences and Engineering Research Council of Canada
Canada Foundation for Innovation
Canadian Foundation for Climate and Atmospheric Sciences
Fonds De La Recherche Scientifique - FNRS	J.0147.18
Fédération Wallonie-Bruxelles
Ministry of Business, Innovation and Employment
University of Toronto
Horizon 2020	704951
Environment and Climate Change Canada

Keywords

Atmospheric ethane
Bootstrapping
Break point estimation
Trend analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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abstract = "Ethane is the most abundant non-methane hydrocarbon in the Earth{\textquoteright}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.).",

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AU - Friedrich, Marina

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AU - Smeekes, Stephan

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AU - Bader, Whitney

AU - Franco, Bruno

AU - Lejeune, Bernard

AU - Mahieu, Emmanuel

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A statistical analysis of time trends in atmospheric ethane

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Keywords

UN SDGs

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