An Algorithm for Adaptive Assessment in Time Series Seasonal Oscillations and Its Testing Based on the Example of Variations in the CO2 Concentration in the Atmosphere

Deshcherevskii, A.V.; Sidorin, A.Y.

Авторы: Deshcherevskii A.V., Sidorin A.Ya
Журнал: Izvestiya - Atmospheric and Oceanic Physics
Том: 58
Номер: 7
Год издания: 2022
Издательство: Pleiades Publishing, Ltd
Местоположение издательства: Road Town, United Kingdom
Первая страница: 681
Последняя страница: 707
Оригинальные версии: Алгоритм адаптивной оценки сезонных колебаний временных рядов и его тестирование на примере вариаций концентрации СО2 в атмосфере , Алгоритм адаптивной оценки сезонных колебаний временных рядов и его тестирование на примере вариаций концентрации СО2 в атмосфере
DOI: 10.1134/s0001433822070027
Аннотация: An adaptive model is proposed to describe time-varying seasonal effects. The seasonal averagefunction is constructed using an iterative algorithm that provides a neat decomposition of the signal into ageneralized trend, seasonal and residual components. By a trend, we mean long-term evolutionary changesin the average signal level, both unidirectional and chaotic, in the form of a slow random drift. This algorithmallows one to obtain unbiased estimates for each of the signal components, even in the presence of a significant number of missing observations. The series length is not required to be a multiple of an integer numberof years. In contrast to the usual “Climate Normals” (CN) model, the considered adaptive model of seasonaleffects assumes a continuous slow change in the properties of the seasonal component over time. The degreeof allowable variability in seasonal effects from year to year is entered as a tunable parameter of the model. Inparticular, this allows one to show the dynamics of the growth of the amplitude of seasonal fluctuations intime in the form of a continuous (smooth) function without necessarily linking these changes to predetermined calendar epochs. The algorithm was tested on the atmospheric CO2 concentration monitoring seriesat Barrow, Mauna Loa, Tutuila, and South Pole stations located at different latitudes. The form of the seasonal variation was estimated, and the average amplitude of the seasonal variation and the rate of its changeat each station were calculated. Noticeable differences in the dynamics of the studied parameters between stations are demonstrated. Mean amplitude of seasonal variation in CO2 concentration at Barrow, Mauna Loa,Tutuila, and South Pole stations in the epoch 2010–2019 was estimated as 18.15, 7.08, 1.30, and 1.26 ppm,respectively, and the average rate of increase in the amplitude of the seasonal variation in the increase in CO2concentration in the interval 1976–2019 is 0.085, 0.0100, 0.0165, and 0.0031 ppm/year. In relative terms, theincrease is 0.57 ± 0.03, 0.11 ± 0.02, 2.24 ± 0.24, and 0.27 ± 0.04% per year.
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An Algorithm for Adaptive Assessment in Time Series Seasonal Oscillations and Its Testing Based on the Example of Variations in the CO2 Concentration in the Atmosphereстатья

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