FEATURES OF TEMPORAL VARIATIONS IN AVERAGE MONTHLY AIR TEMPERATURE VALUES ACCORDING TO DATA FROM THE ZUGSPITZE WEATHER STATION

Abstract

This study analyzes the temporal dynamics of average monthly and average annual air temperature values based on instrumental observations of temperature variations at the Zugspitze high-altitude weather station from August, 1900 to July, 2025. Several methods were used to isolate the trend component and locate the change point of temperature variations: the Mann–Kendall test, the Theil–Sen estimator, the singular spectrum analysis method, the CUSUM algorithm, and the segmented regression analysis method. It was found that air temperature changes tend to increase over time. Identification of the trend component using the singular spectrum analysis method demonstrated an increase in the rate of temperature increase starting around 1969 is confirmed by the results obtained using the segmented regression analysis method and the CUSUM algorithm. The two-segment regression provides a good approximation of temperature variations over the period from 1901 to 2024 (the coefficient of determination is 0.61).