Articles | Volume 12, issue 6
Atmos. Meas. Tech., 12, 3383–3394, 2019
https://doi.org/10.5194/amt-12-3383-2019
Atmos. Meas. Tech., 12, 3383–3394, 2019
https://doi.org/10.5194/amt-12-3383-2019

Research article 27 Jun 2019

Research article | 27 Jun 2019

Flexible approach for quantifying average long-term changes and seasonal cycles of tropospheric trace species

David D. Parrish et al.

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Cited articles

Bowdalo, D. R., Evans, M. J., and Sofen, E. D.: Spectral analysis of atmospheric composition: application to surface ozone model–measurement comparisons, Atmos. Chem. Phys., 16, 8295–8308, https://doi.org/10.5194/acp-16-8295-2016, 2016. 
Chang, K.-L., Petropavlovskikh, I., Cooper, O. R., Schultz, M. G., and Wang, T.: Regional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia, Elem. Sci. Anth., 5, 50, https://doi.org/10.1525/elementa.243, 2017. 
Derwent, R. G., Simmonds, P. G., Seuring, S., and Dimmer, C.: Observation and interpretation of the seasonal cycles in the surface concentrations of ozone and carbon monoxide at Mace Head, Ireland from 1990 to 1994, Atmos. Environ., 32, 145–157, 1998. 
Derwent, R. G., Parrish, D. D., Galbally, I. E., Stevenson, D. S., Doherty, R. M., Young, P. J., and Shallcross, D. E.: Interhemispheric differences in seasonal cycles of tropospheric ozone in the marine boundary layer: Observation-model comparisons, J. Geophys. Res.-Atmos., 121, 11075–11085, https://doi.org/10.1002/2016JD024836, 2016. 
Derwent, R. G., Manning, A. J., Simmonds, P. G., Spain, T. G., and O'Doherty, S.: Long-term trends in ozone in baseline and European regionally-polluted air at Mace Head, Ireland over a 30-year period, Atmos. Environ., 179, 279–287, https://doi.org/10.1016/j.atmosenv.2018.02.024, 2018a. 
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Short summary
We present a flexible method that employs a power series expansion and Fourier series analysis to characterize the average long-term change and seasonal cycle, respectively, from a time series of observations of a trace atmospheric species. This approach maximizes the statistically significant information derived, including non-linear aspects of the long-term trends, without over fitting the data. Generally, a small set of parameter values (e.g., 7 or 8) provides this characterization.