Abstract
Abstract. We present a statistical framework to identify regional signals in station-based CO2 time series with minimal local influence. A curve-fitting function is first applied to the detrended time series to derive a harmonic describing the annual CO2 cycle. We then combine a polynomial fit to the data with a short-term residual filter to estimate the smoothed cycle and define a seasonally adjusted noise component, equal to 2 standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which surpass this ±2σ threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale atmospheric transport events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts.
Highlights
Continuous measurements of long-lived atmospheric greenhouse gases (GHGs) at ground-based monitoring stations exhibit variations at multiple timescales
This period is selected since the winter of 2015–2016 is the first year in which we have CO2 data available at enough sites to accurately discern the number of localized fluctuations detected at each site, for which we require that CO2 data must be present at no fewer than five sites
We find that the algorithm captures well the signature effects of unusually strong or persistent atmospheric transport regimes in the wintertime
Summary
Continuous measurements of long-lived atmospheric greenhouse gases (GHGs) at ground-based monitoring stations exhibit variations at multiple timescales These include a wellestablished diurnal cycle and an annual pattern linked to seasonality which generally exist on top of the long-term trend of the background concentration. Other variations, related to localized surface fluxes or regional-scale atmospheric transport patterns, are observable at synoptic frequencies lasting from 1–2 d to several weeks, while others reflect longerterm meteorological occurrences such as droughts or ocean circulation anomalies. Identification of these latter components can reveal much about the intensity and geographic extent of specific atmospheric events while improving understanding of background signal evolution. These include backtrajectory analyses that categorize readings based on air provenance (e.g., Schuepbach et al, 2001; Balzani Loöv et al, 2008; Cui et al, 2011) and the application of chemical filters using markers such as 222Rn (e.g., Biraud et al, 2000; Pal et al, 2015; Chambers et al, 2016) or NOy and Published by Copernicus Publications on behalf of the European Geosciences Union
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