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Annual and seasonal cycles of CO2 and CH4 in a Mediterranean Spanish environment using different kernel functions

Fernández-Duque, Beatriz, Pérez, Isidro A., García, M. Ángeles, Pardo, Nuria, Sánchez, M. Luisa
Stochastic environmental research and risk assessment 2019 v.33 no.3 pp. 915-930
atmospheric circulation, autumn, biosphere, carbon dioxide, data collection, gases, methane, rain, seasonal variation, seeds, spring, summer, winter
This paper is based on CO₂ and CH₄ semi-hourly mole fraction measurements obtained at the Low Atmosphere Research Centre (CIB) between 2010 and 2016 using a Picarro G1301 analyser. The main aims of the study were to examine the temporal variation of CO₂ and CH₄ by using six different kernel functions, and to study the suitability of these functions to the dataset. The method used for the current study was based on experimental contour plots of R² values in order to simultaneously determine the bandwidths of kernel functions for the long-term and short-term. An Epanechnikov, a Gaussian, a biweight, a triangular, a tricubic and a rectangular kernel function were applied to extract the salient features of both the long-term (trend) and the short-term (seasonality). The average linear increase growth rates found were mainly attributed to the terrestrial biosphere cycle and changes in the atmospheric circulation regime. The seasonal cycle exhibited a cyclical variation, revealing summer minima for both gases, which may be explained by a biological minimum. Kernel analysis showed two nocturnal CO₂ maxima, in spring and autumn, linked to an increase in rainfall. For CO₂ daytime records, only the spring peak was detected. As regards CH₄, the maximum was located in winter. The best fit for the trend was obtained by the biweight kernel. In contrast, the best adjustment for seasonality was achieved from the Gaussian and the triangular kernel. To sum up, optimal bandwidth selection is important when kernel regression functions are employed. Since no important differences were found between the kernels employed, those which involve least computational effort are recommended.