TYPOLOGY OF CALENDAR YEARS AND THERMAL REGIME DYNAMICS UNDER GLOBAL WARMING: RISK ASSESSMENT FOR ALMATY
DOI:
https://doi.org/10.54668/2789-6323-2026-123-3-67-78Keywords:
Almaty climate, multivariate statistical analysis, factorization, clustering, year typology, recurrence dynamics, change point, Pettitt test, Mann–Kendall test, climate risks, adaptation.Abstract
The aim of this work was to develop a statistically substantiated typology of thermal conditions of calendar years for Almaty and a quantitative analysis of its transformation against the background of ongoing climate change. A two-stage approach was used for the study. The first stage: dimensionality reduction using principal component analysis (PCA) followed by k-means clustering to identify integral year types. The second stage: analysis of the recurrence dynamics of the obtained types using nonparametric statistics (Pettitt test, Mann–Kendall test, Theil–Sen estimator). The PCA results (KMO=0.764) revealed four latent factors (61.6% of variance) characterizing the main seasonal regimes. Cluster analysis based on these factors allowed the identification of 5 statistically stable types (classes) of calendar years with a classification accuracy of 95.8% as confirmed by discriminant analysis (LDA). Dynamic analysis recorded a statistically significant change point in the climate regime in 1993 (Pettitt test, p<0.001). Exceptionally high rates of transformation were established: the frequency of the warmest type (mild winter, very warm summer) increases by +8.4% per decade, while the frequency of the coldest type decreases by –7.1% per decade. In the modern period (2001...2025), a new climatic norm has formed, with the warmest type accounting for 68% of years. The resulting typology and quantitative estimates of its dynamics serve as a practical tool for detailed climate diagnostics, assessment of complex risks (heat waves, air quality deterioration, infrastructure load), and the development of targeted adaptation strategies for a metropolis in a global warming hotspot region.
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