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

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 more flexible than other commonly used statistical data filtering methods. Application to carbon monoxide (CO) measured from 1996 to 2009 at the high alpine site Jungfraujoch (Switzerland, 3580 m a.s.l.) demonstrates the feasibility and usefulness of the proposed approach. The determined average annual change for the 1996 to 2009 period as estimated from filtered annual mean CO concentrations is −2.1 ± 1.3 ppb/yr. For comparison, the linear trend of unfiltered CO measurements at Jungfraujoch for this time period is −2.9 ± 1.5 ppb/yr.