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.).
Highlights
There are several important reasons to study ethane time series
The main sources of ethane are located in the Northern Hemisphere, and the dominating emissions are associated to production and transport of natural gas (Xiao et al 2008)
We present selected methods designed for the analysis of time trends and trend reversals and apply them to our dataset
Summary
There are several important reasons to study ethane time series. First, ethane is an indirect greenhouse gas influencing the atmospheric lifetime of methane. Hausmann et al (2016) use a bootstrap method to study trends in atmospheric methane and ethane emissions measured at Zugspitze and Lauder. The latter two papers split the sample into two periods and compare the changes in trends. This issue can be resolved using data-driven methods to select the break point While trend estimates, such as slopes for linear approaches, usually come with confidence intervals, the break location is often stated without any measure of uncertainty. It provides researchers with a tool to test for the presence of a break and, if so, it gives an estimate of its location together with a reliable confidence interval With this method, it is not necessary to split the sample. In the Supplementary Appendix, we give additional technical details in part A and provide a Monte Carlo simulation study in part B
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