Local regressions for decomposing CO2 and CH4 time-series in a semi-arid ecosystem

Abstract

Local regressions have been widely employed for decomposing atmospheric data series. However, the use of local quadratic regressions is less extended. The current paper is grounded on the hypothesis that local linear regressions are able to capture CO2 and CH4 temporal evolution equally as well as quadratic linear regressions. Thus, the current paper pursues the following goals: (1) to quantify the temporal variability of both gases by using the local linear and local quadratic regression method; (2) to analyse the main statistics of the detrended series over time; (3) to compare results between the local linear and local quadratic regression method. Minimum mixing ratios in late summer and maximum in winter were detected for both gases. Atmospheric increases of an average of 1.98 ppm year−1 for CO2 and 11 ppb year−1 for CH4 were found when applying the local linear regression method. Alternatively, an increase of 2.24 ppm year−1 for CO2 and around 10.34 ppb year−1 for CH4 was obtained when the local quadratic regression method was applied. The Pearson correlation coefficients (0.21–0.40) showed acceptable values due to the large amount of available data. Statistically significant differences for the initial and the smoothed trend, as well as statistically significant differences for the seasonal component, were reported when comparing the local linear with the local quadratic method. Overall, both methods proved easy to apply and both provided acceptable data accuracy.