  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 obtained from ground-based FTIR
measurements. As 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.
