Динамика содержания парниковых газов в приземном слое атмосферного воздуха арк­тического острова Белый в летний период 2015—2017 гг.

Abstract

The reports of the Intergovernmental Panel on Climate Change, define the increase in the content of greenhouse gases (GHGs) in the atmosphere as the most likely cause of warming. The consequences of global warming are most pronounced in the Arctic zone of the planet, where temperature changes are significant. To provide an information basis for taking additional measures in the field of mitigating the effects of anthropogenic impact on climate change, reliable data on changes in GHG content in the Arctic are required. Based on monitoring data of meteorological conditions and concentrations of carbon dioxide, methane and water vapor in the surface layer of the atmosphere on the Island of Bely (Arctic zone of the Russian Federation) in the summer seasons 2015—2017 the authors investigated the cycles of GHG content variability. There was no evidence of a linear relationship between the average temperature and GHG content in the summer seasons for three years. The highest average daily temperature in the summer season corresponded to the hottest 2016. No differences in mean daily GHG concentrations exceeding the standard deviation were found for three years. For all greenhouse gases, dramatic changes can be observed from a high positive Spearman coefficient to a negative one and vice versa. To demonstrate the contribution of period-related harmonics, a Fourier analysis period gram is constructed. Analysis of time series associated with cross-correlation of GHG concentrations and temperature show that methane and water vapor concentrations contain periodic components: month, decade, week and day. The identified periodic components should be considered when constructing predictive models to describe the seasonal dynamics of fluctuations in the concentration of CH4 and H2O depending on temperature. No significant periodic delays in the CO2 time series are detected, indicating a strong noise level. The data obtained during the monitoring is used as a basis for creating models that predict trends in the content changes of major GHGs.