1. A statistical analysis of time trends in atmospheric ethane
- Author
-
Friedrich, Marina, Beutner, Eric, Reuvers, Hanno, Smeekes, Stephan, Urbain, Jean-Pierre, Bader, Whitney, Franco, Bruno, Lejeune, Bernard, Mahieu, Emmanuel, Department of Econometrics and Data Science, Vrije Universiteit Amsterdam, 1081HV Amsterdam, The Netherlands, Department of Econometrics, Erasmus University, 3062PA Rotterdam, The Netherlands, Department of Quantitative Economics, Maastricht University, 6200MD Maastricht, The Netherlands, Agence Wallone de l’Air et du Climat, Avenue Prince de Liège, Jambes, Belgium, Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Université libre de Bruxelles (ULB), Brussels, Belgium, Institute of Astrophysics and Geophysics, University of Liège, Liège, Belgium, Econometrics, Econometrics and Data Science, QE Econometrics, RS: GSBE Theme Data-Driven Decision-Making, and RS: FSE DACS Mathematics Centre Maastricht
- Subjects
FOS: Computer and information sciences ,Atmospheric Science ,Heteroscedasticity ,010504 meteorology & atmospheric sciences ,Econometrics (econ.EM) ,BANDWIDTH ,Atmospheric sciences ,Statistics - Applications ,01 natural sciences ,INCREASE ,TROPOSPHERE ,FOS: Economics and business ,Environnement et pollution ,Troposphere ,010104 statistics & probability ,chemistry.chemical_compound ,Break point estimation ,Trend analysis ,BOOTSTRAP ,Phénomènes atmosphériques ,REGRESSION ,SDG 13 - Climate Action ,SPECTRA ,Applications (stat.AP) ,Tropospheric ozone ,0101 mathematics ,Economics - Econometrics ,0105 earth and related environmental sciences ,Global and Planetary Change ,SPECTROSCOPY ,Autocorrelation ,SERIES ,Atmospheric ethane ,ddc:551.5 ,Bootstrapping (electronics) ,Autoregressive model ,chemistry ,13. Climate action ,Greenhouse gas ,TESTS ,Environmental science ,Bootstrapping ,BURDEN - 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.)., Horizon 2020 https://doi.org/10.13039/501100007601, Fonds De La Recherche Scientifique - FNRS https://doi.org/10.13039/501100002661
- Published
- 2020
- Full Text
- View/download PDF