Robust extraction of baseline signal of atmospheric trace species using local regression

Abstract

Abstract. The identification of atmospheric trace species measurements that are representative of well-mixed background air masses is required for monitoring atmospheric composition change at background sites. We present a statistical method based on robust local regression that is well suited for the selection of background measurements and the estimation of associated baseline curves. The bootstrap technique is applied to calculate the uncertainty in the resulting baseline curve. The non-parametric nature of the proposed approach makes it a very flexible data filtering method. The determined average annual change of CO at Jungfraujoch for the 1996 to 2009 period as estimated from filtered annual mean CO concentrations is −2.2 ± 1.1 ppb yr −1 . For comparison, the linear trend of unfiltered CO measurements at Jungfraujoch for this time period is −2.9 ± 1.3 ppb yr −1 .

Recent increases in the atmospheric growth rate and emissions of HFC-23 (CHF<sub>3</sub>) and the link to HCFC-22 (CHClF<sub>2</sub>) production

“…Here, the inversion relies on the continuous observations from the Jungfraujoch and Mace Head and requires a priori estimates of the emissions distribution. The observations are split into a baseline concentration and above-baseline excursions of the signal that are attributed to recent emissions using the method of Ruckstuhl et al (2012). The inversion estimates spatially distributed, annual mean emissions and a 2-weekly concentration baseline.…”

Continuous CO<sub>2</sub>/CH<sub>4</sub>/CO measurements (2012–2014) at Beromünster tall tower station in Switzerland

“…As an independent approach to our elimination of local events, we have also conducted the seasonality analysis by using the so-called robust extraction of baseline signal (REBS) technique (Ruckstuhl et al, 2012). The resulting baseline is indicated by the green curve in Fig.…”

“…In addition to calculating a background based on the iterative approach described above, we have also applied the robust extraction of baseline signal (REBS) technique (Ruckstuhl et al, 2012) for comparison. This baseline estimate is a statistical method using non-parametric local regression with robustness weights.…”

Atmospheric histories and emissions of chlorofluorocarbons CFC-13 (CClF<sub>3</sub>), ΣCFC-114 (C<sub>2</sub>Cl<sub>2</sub>F<sub>4</sub>), and CFC-115 (C<sub>2</sub>ClF<sub>5</sub>)

“…We calculated qualitative emission distributions by combining model-derived source sensitivities with the baseline observations from Gosan above. A smooth statistical baseline fit (Ruckstuhl et al, 2012) was subtracted from the observational data. Surface source sensitivities were computed with the Lagrangian particle dispersion model FLEXPART (Stohl et al, 2005) driven by operational analysis and fore- casts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) modeling system.…”