Search

In this work, the annual cycle and interannual variability of the content of greenhouse gases were studied in the Arctic regions of Russia. Gases concentrations (methane, water vapor) and main meteorological parameters (temperature, atmospheric pressure) were measured on the Arctic Island Belyy, Russia in July-August, 2015-2017. The daily mean concentrations of greenhouse gases were not differed within the mean plus or minus standard deviation year to year. The autoregressive models and the spectral Fourier analysis were built for determining the pattern of the dynamic changes in the time series Spearman's rank correlation coefficients between the greenhouse gases concentrations and temperature. The analysis of the dependence of Spearman's rank correlation coefficients on the time delay of the temperature reaction to a change in the content of greenhouse gases in the air revealed four invariants: one, three, seven, and ten days. There were explanations for the daily, weekly and ten-day rhythms, but the three-day one requires further explanation. [ABSTRACT FROM AUTHOR]

The paper considered the use of one of the most accurate artificial neural networks for predicting time series - a nonlinear autoregressive neural network with external input (NARX) for predicting the dynamics of changes in the concentrations of the main greenhouse gases. The data were obtained in the course of monitoring the dynamics of changes in the main greenhouse gases on the Arctic island Belyy, Russia. The data of the surface concentration of methane, carbon dioxide, carbon monoxide and water vapor were used. A time interval of 168 hours was chosen for the study during the summer period (July-August 2016). The NARX model accurately predicted concentration changes for all greenhouse gases. [ABSTRACT FROM AUTHOR]